diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..d748b27
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,2 @@
+* text=auto eol=lf
+*.ipynb filter=nbstripout
\ No newline at end of file
diff --git a/README.md b/README.md
index 583486b..1048e8f 100644
--- a/README.md
+++ b/README.md
@@ -1,95 +1,85 @@
-# 昇思MindSpore技术公开课
+
-- ***探究前沿***：解读技术热点，解构热点模型
-- ***应用实践***：理论实践相结合，手把手指导开发
-- ***专家解读***：多领域专家，多元解读
-- ***开源共享***：课程免费，课件代码开源
-- ***大赛赋能***：ICT大赛赋能课程（大模型专题第一、二期）
-- ***系列课程***：大模型专题课程开展中，其他专题课程敬请期待
+Brief introduction to repository.
-## 报名方式
+## 📢 News
-报名链接：https://xihe.mindspore.cn/course/foundation-model-v2/introduction
+- **2025-10-21 [Course Update]**：The XXX course has been updated with a new chapter on XXX, including complete videos, slides, and code examples. ([*View details*](xxx))
+- **2025-10-18 [Feature Optimization]**：Repository refactored for clearer course resource navigation; added CI pipeline for more standardized contributions. ([*View details*](xxx))
+- **2025-10-10 [Bug Fix]**：Fixed the xxx issue — thanks to @username for the PR contribution. ([View details](pr_link))
-（注：参与免费课程必须报名哦！同步添加[QQ群](./assets/groupchat_qq.png)，后续课程事宜将在群内通知！）
+## Prerequisites
-## 大模型专题第一期(已完结)&第二期（进行中）
+Before starting this course, you should be familiar with:
-第二期课程10月14日起每双周六14：00-15：00在[b站](https://live.bilibili.com/22127570?broadcast_type=0&is_room_feed=1&spm_id_from=333.999.to_liveroom.0.click&live_from=86002)进行直播。
+- Basic Python programming
+- Basic Linux commands
+- Using Jupyter Notebook
+- Using Docker images
-每节课程的ppt和代码会随授课逐步上传至[github](https://github.com/mindspore-courses/step_into_llm)，系列视频回放归档至[b站](https://space.bilibili.com/526894060/channel/seriesdetail?sid=3293489)，大家可以在[昇思MindSpore公众号](./assets/wechat_official_account.png)中获取每节课的知识点回顾与下节课的课程预告，同时欢迎大家在[MindSpore社区](https://gitee.com/mindspore/community/issues)领取大模型系列任务进行挑战。
+You can take the Prerequisite Test (*Coming Soon*) to assess your readiness.
-> 因为课程周期较长，课节安排可能会在中途出现微调，以最终通知为准，感谢理解！
+## Environment Setup
-> 热烈欢迎小伙伴们参与到课程的建设中来，基于课程的趣味开发可以提交至[昇思MindSpore大模型平台](https://xihe.mindspore.cn/)
+To ensure all example code runs smoothly, set up your environment using one of the following methods. For details, see [Set Up Development Environment](https://github.com/mindspore-courses/step_into_llm/wiki/Set-Up-Development-Environment) in Wiki.
-> 如果在学习过程中发现任何课件及代码方面的问题，希望我们讲解哪方面的内容，或是对课程有什么建议，都可以直接在本仓库中创建issue
+### Install Dependencies
+Confirm your Python version meets the course requirements, then run:
-### 教研团队
+```bash
+pip install -r requirements.txt
+```
-
-
-
+**Guidelines**: [Issue & PR Submission](https://github.com/mindspore-courses/step_into_llm/wiki/Contributing-Guidelines)
+
+## Contributors
+
+Special thanks to all contributors for improving this project!
+
+
diff --git a/README_ZH.md b/README_ZH.md
new file mode 100644
index 0000000..18d29b3
--- /dev/null
+++ b/README_ZH.md
@@ -0,0 +1,81 @@
+
+
+（1-2句话点名项目核心价值）项目仓介绍。
+
+## 📢 最新消息
+
+- 2025-10-21 「课程更新」：新增XXX课程，包含完整视频、课件及代码案例。（[查看详情](xxxx)）
+- 2025-10-18 「功能优化」：项目仓完成重构，查找课程资源更清晰，新增PR检查门禁，合入内容更规范。（[查看详情](xxx)）
+- 2025-10-10 「Bug修复」：修复xxxxxx问题，感谢@username的PR贡献。（[查看详情](xxxx)）
+
+## 前置知识
+
+在学习本门课程之前，您需要掌握：
+
+- Python基础
+- Linux命令基础
+- Jupyter基础
+- Docker镜像使用
+
+您可以通过前置学习考试（*待上线*）进行自检。
+
+## 环境准备
+
+为确保项目仓中实践代码可正常运行，推荐以下环境准备方式。更详细的环境准备指导详见[Wiki](https://github.com/mindspore-courses/step_into_llm/wiki/Set-Up-Development-Environment)。
+
+### 直接安装依赖
+
+请先确保 Python 版本符合[课程要求](#版本维护)后，进入仓库根目录，执行：
+
+```bash
+pip install requirements.txt
+```
+
+### 使用Docker镜像（*待发布*）
+
+为方便开发者更加便捷地进行代码实践，节约环境准备的时间，我们提供了预装好的基础Dockerfile文件。课程的所有镜像可从[dockerfile](./dockerfile/)获取。本课程镜像文件信息如下，开发者可根据实际需求进行拉取：
+
+镜像基础使用教程详见环境准备Wiki中的[Docker镜像使用](https://github.com/mindspore-courses/step_into_llm/wiki/Set-Up-Development-Environment)部分。
+
+## 课程内容
+
+| 序号 | 课节 | 简介 | 课程资源 | 能力认证入口 |
+| :-- | :------ | :--------------- | :----------------------- | :---------- |
+| 1 | xxx | xxx | [PPT](跳转链接) · [代码](跳转链接) · [视频](跳转链接) · [云沙箱实验](跳转链接) · [学习路径](跳转链接) | |
+| 2 | xxx | xxx | [PPT](跳转链接) · [代码](跳转链接) · [视频](跳转链接) · [云沙箱实验](跳转链接) · [学习路径](跳转链接) | [初级认证入口](xxxx) |
+| 3 | xxx | xxx | [PPT](跳转链接) · [代码](跳转链接) · [视频](跳转链接) · [云沙箱实验](跳转链接) · [学习路径](跳转链接) | |
+| 4 | xxx | xxx | [PPT](跳转链接) · [代码](跳转链接) · [视频](跳转链接) · [云沙箱实验](跳转链接) · [学习路径](跳转链接) | [中级认证入口](xxxx) |
+
+## 版本维护
+
+项目随昇思MindSpore及昇思MindSpore NLP套件迭代同步发布版本，本项目仓每**半年**进行版本发布。
+
+| 版本名 | Python | MindSpore | MindSpore NLP |
+| :----- | :----- |:------ |:------ |
+| master | xxx | xxx | xxx |
+| r1.0 | xxx | xxx | xxx |
+
+## 常见问题（FAQ）
+
+详见Wiki中[FAQ](https://github.com/mindspore-courses/step_into_llm/wiki/Developer-FAQ)。
+
+## 贡献与反馈
+
+欢迎各位开发者通过 [Issue](https://github.com/mindspore-courses/step_into_llm/issues) 提交建议或 bug 反馈，也可直接发起 [PR](https://github.com/mindspore-courses/step_into_llm/pulls) 进行Bug修复或代码贡献（提交前请参考提交规范，由Committer @username 完成评审合入），你的每一份参与都能让本项目更加完善。
+
+### 提交规范
+
+详见WIKI：[Issue与PR提交规范](https://github.com/mindspore-courses/step_into_llm/wiki/Contributing-Guidelines)
+
+### 贡献者展示
+
+向本项目的贡献者们致以最诚挚的感谢！
+
+
diff --git a/Season1.step_into_chatgpt/0.Course-Review/3-GPT.md b/Season1.step_into_chatgpt/0.Course-Review/3-GPT.md
index 8237000..5840162 100644
--- a/Season1.step_into_chatgpt/0.Course-Review/3-GPT.md
+++ b/Season1.step_into_chatgpt/0.Course-Review/3-GPT.md
@@ -9,7 +9,7 @@ GPT-1是更早于BERT提出了预训练语言模型（Pre-train+Fine-tune）的
## 1. 课程回顾
- Semi-Supervised Learning
-- Unsupervised Pretraining
+- Unsupervised Pretraining
- 模型预训练优化目标
- 模型结构
- Supervised Fine-tuning
diff --git a/Season1.step_into_chatgpt/0.Course-Review/5-Parallel.md b/Season1.step_into_chatgpt/0.Course-Review/5-Parallel.md
index a6898e6..61e4f0f 100644
--- a/Season1.step_into_chatgpt/0.Course-Review/5-Parallel.md
+++ b/Season1.step_into_chatgpt/0.Course-Review/5-Parallel.md
@@ -50,13 +50,13 @@
- 内存优化
1. 重计算
-
+
type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import mindspore\n",
"from mindspore import nn\n",
@@ -353,14 +237,14 @@
"\n",
" embed_size = query.shape[-1]\n",
" scaling_factor = self.sqrt(Tensor(embed_size, mstype.float32))\n",
- " \n",
+ "\n",
"\n",
" attn = ops.matmul(query, key.swapaxes(-2, -1) / scaling_factor)\n",
"\n",
"\n",
" if attn_mask is not None:\n",
" attn = attn.masked_fill(attn_mask, -1e9)\n",
- " \n",
+ "\n",
" attn = self.softmax(attn)\n",
"\n",
" attn = self.dropout(attn)\n",
@@ -372,24 +256,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] CORE(7763,ffff8a17c0b0,python):2024-12-03-14:45:24.134.177 [mindspore/core/utils/ms_context.cc:530] GetJitLevel] Set jit level to O2 for rank table startup method.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(128, 8, 32, 64) (128, 8, 32, 32)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"attention = ScaledDotProductAttention()\n",
"q_s = k_s = v_s = ops.ones((128, 8, 32, 64), mindspore.float32)\n",
@@ -416,7 +285,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -446,25 +315,13 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[[False False True True]\n",
- " [False False True True]\n",
- " [False False True True]\n",
- " [False False True True]]]\n",
- "(1, 4) (1, 4, 4)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"q = k = Tensor([[1, 1, 0, 0]], mstype.float32)\n",
"pad_idx = 0\n",
@@ -553,7 +410,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
@@ -604,21 +461,13 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(1, 2, 10) (1, 5, 2, 2)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"dmodel, dk, nheads = 10, 2, 5\n",
"q = k = v = ops.ones((1, 2, 10), mstype.float32)\n",
@@ -701,7 +550,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
@@ -738,22 +587,13 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[[0. 1. 0. 1. ]\n",
- " [0.84147096 0.54030234 0.00999983 0.99995005]]]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"x = ops.Zeros()((1, 2, 4), mstype.float32)\n",
"pe = PositionalEncoding(4)\n",
@@ -798,7 +638,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -827,21 +667,13 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(1, 2, 4)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"x = ops.ones((1, 2, 4), mstype.float32)\n",
"ffn = PoswiseFeedForward(16, 4)\n",
@@ -868,7 +700,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -881,29 +713,20 @@
" super().__init__()\n",
" self.layer_norm = nn.LayerNorm((d_model, ), epsilon=1e-5)\n",
" self.dropout = nn.Dropout(p=dropout_p)\n",
- " \n",
+ "\n",
" def construct(self, x, residual):\n",
" return self.layer_norm(self.dropout(x) + residual)"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(1, 2, 4)\n",
- "-\r"
- ]
- }
- ],
+ "outputs": [],
"source": [
"x = ops.ones((1, 2, 4), mstype.float32)\n",
"residual = ops.ones((1, 2, 4), mstype.float32)\n",
@@ -926,7 +749,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -944,7 +767,7 @@
" self.pos_ffn = PoswiseFeedForward(d_ff, d_model, dropout_p)\n",
" self.add_norm1 = AddNorm(d_model, dropout_p)\n",
" self.add_norm2 = AddNorm(d_model, dropout_p)\n",
- " \n",
+ "\n",
" def construct(self, enc_inputs, enc_self_attn_mask):\n",
" \"\"\"\n",
" enc_inputs: [batch_size, src_len, d_model]\n",
@@ -966,21 +789,13 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(1, 2, 8) (1, 4, 2, 2)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"x = ops.ones((1, 2, 8), mstype.float32)\n",
"mask = Tensor([False]).broadcast_to((1, 2, 2))\n",
@@ -1004,7 +819,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -1020,7 +835,7 @@
" self.layers = nn.CellList([EncoderLayer(d_model, n_heads, d_ff, dropout_p) for _ in range(n_layers)])\n",
" self.scaling_factor = ops.Sqrt()(Tensor(d_model, mstype.float32))\n",
"\n",
- " \n",
+ "\n",
" def construct(self, enc_inputs, src_pad_idx):\n",
" \"\"\"enc_inputs : [batch_size, src_len]\n",
" \"\"\"\n",
@@ -1098,7 +913,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
@@ -1108,7 +923,7 @@
"source": [
"def get_attn_subsequent_mask(seq_q, seq_k):\n",
" \"\"\"生成时间掩码，使decoder在第t时刻只能看到序列的前t-1个元素\n",
- " \n",
+ "\n",
" Args:\n",
" seq_q (Tensor): query序列，shape = [batch size, len_q]\n",
" seq_k (Tensor): key序列，shape = [batch size, len_k]\n",
@@ -1123,24 +938,13 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[[0. 1. 1. 1.]\n",
- " [0. 0. 1. 1.]\n",
- " [0. 0. 0. 1.]\n",
- " [0. 0. 0. 0.]]]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"q = k = ops.ones((1, 4), mstype.float32)\n",
"mask = get_attn_subsequent_mask(q, k)\n",
@@ -1162,7 +966,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -1182,7 +986,7 @@
" self.add_norm1 = AddNorm(d_model, dropout_p)\n",
" self.add_norm2 = AddNorm(d_model, dropout_p)\n",
" self.add_norm3 = AddNorm(d_model, dropout_p)\n",
- " \n",
+ "\n",
" def construct(self, dec_inputs, enc_outputs, dec_self_attn_mask, dec_enc_attn_mask):\n",
" \"\"\"\n",
" dec_inputs: [batch_size, trg_len, d_model]\n",
@@ -1196,7 +1000,7 @@
"\n",
" dec_outputs = self.add_norm1(dec_outputs, residual)\n",
" residual = dec_outputs\n",
- " \n",
+ "\n",
" dec_outputs, dec_enc_attn = self.dec_enc_attn(dec_outputs, enc_outputs, enc_outputs, dec_enc_attn_mask)\n",
"\n",
" dec_outputs = self.add_norm2(dec_outputs, residual)\n",
@@ -1211,21 +1015,13 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(1, 2, 4) (1, 1, 2, 2) (1, 1, 2, 2)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"x = y = ops.ones((1, 2, 4), mstype.float32)\n",
"mask1 = mask2 = Tensor([False]).broadcast_to((1, 2, 2))\n",
@@ -1251,7 +1047,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -1266,8 +1062,8 @@
" self.pos_emb = PositionalEncoding(d_model, dropout_p)\n",
" self.layers = nn.CellList([DecoderLayer(d_model, n_heads, d_ff) for _ in range(n_layers)])\n",
" self.projection = nn.Dense(d_model, trg_vocab_size)\n",
- " self.scaling_factor = ops.Sqrt()(Tensor(d_model, mstype.float32)) \n",
- " \n",
+ " self.scaling_factor = ops.Sqrt()(Tensor(d_model, mstype.float32))\n",
+ "\n",
" def construct(self, dec_inputs, enc_inputs, enc_outputs, src_pad_idx, trg_pad_idx):\n",
" \"\"\"\n",
" dec_inputs: [batch_size, trg_len]\n",
@@ -1308,7 +1104,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -1321,7 +1117,7 @@
" super().__init__()\n",
" self.encoder = encoder\n",
" self.decoder = decoder\n",
- " \n",
+ "\n",
" def construct(self, enc_inputs, dec_inputs, src_pad_idx, trg_pad_idx):\n",
" \"\"\"\n",
" enc_inputs: [batch_size, src_len]\n",
@@ -1333,8 +1129,7 @@
"\n",
" dec_logits = dec_outputs.view((-1, dec_outputs.shape[-1]))\n",
"\n",
- " return dec_logits, enc_self_attns, dec_self_attns, dec_enc_attns\n",
- " "
+ " return dec_logits, enc_self_attns, dec_self_attns, dec_enc_attns\n"
]
},
{
@@ -1395,37 +1190,13 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Downloading data from https://modelscope.cn/api/v1/datasets/SelinaRR/Multi30K/repo?Revision=master&FilePath=Multi30K.zip (1 byte)\n",
- "\n",
- "file_sizes: 1.37MB [00:00, 5.29MB/s] \n",
- "Extracting zip file...\n",
- "Successfully downloaded / unzipped to ./\n",
- "========================================datasets in ./datasets/train/train.de========================================\n",
- "0 Zwei junge weiße Männer sind im Freien in der Nähe vieler Büsche.\n",
- "1 Mehrere Männer mit Schutzhelmen bedienen ein Antriebsradsystem.\n",
- "2 Ein kleines Mädchen klettert in ein Spielhaus aus Holz.\n",
- "3 Ein Mann in einem blauen Hemd steht auf einer Leiter und putzt ein Fenster.\n",
- "4 Zwei Männer stehen am Herd und bereiten Essen zu.\n",
- "========================================datasets in ./datasets/train/train.en========================================\n",
- "0 Two young, White males are outside near many bushes.\n",
- "1 Several men in hard hats are operating a giant pulley system.\n",
- "2 A little girl climbing into a wooden playhouse.\n",
- "3 A man in a blue shirt is standing on a ladder cleaning a window.\n",
- "4 Two men are at the stove preparing food.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from download import download\n",
"import re\n",
@@ -1489,7 +1260,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
@@ -1501,18 +1272,18 @@
"\n",
"class Multi30K():\n",
" \"\"\"Multi30K数据集加载器\n",
- " \n",
+ "\n",
" 加载Multi30K数据集并处理为一个Python迭代对象。\n",
- " \n",
+ "\n",
" \"\"\"\n",
" def __init__(self, path):\n",
" self.data = self._load(path)\n",
- " \n",
+ "\n",
" def _load(self, path):\n",
" def tokenize(text):\n",
" text = text.rstrip()\n",
" return [tok.lower() for tok in re.findall(r'\\w+|[^\\w\\s]', text)]\n",
- " \n",
+ "\n",
" members = {i.split('.')[-1]: i for i in os.listdir(path)}\n",
" de_path = os.path.join(path, members['de'])\n",
" en_path = os.path.join(path, members['en'])\n",
@@ -1524,17 +1295,17 @@
" en = [tokenize(i) for i in en]\n",
"\n",
" return list(zip(de, en))\n",
- " \n",
+ "\n",
" def __getitem__(self, idx):\n",
" return self.data[idx]\n",
- " \n",
+ "\n",
" def __len__(self):\n",
" return len(self.data)"
]
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -1558,22 +1329,13 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "de = ['ein', 'mann', 'mit', 'einem', 'orangefarbenen', 'hut', ',', 'der', 'etwas', 'anstarrt', '.']\n",
- "en = ['a', 'man', 'in', 'an', 'orange', 'hat', 'starring', 'at', 'something', '.']\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"for de, en in test_dataset:\n",
" print(f'de = {de}')\n",
@@ -1611,7 +1373,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
@@ -1684,24 +1446,13 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "7"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"word_count = {'a':20, 'b':10, 'c':1, 'd':2}\n",
"\n",
@@ -1724,7 +1475,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -1748,21 +1499,13 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Unique tokens in de vocabulary: 7882\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"de_vocab, en_vocab = build_vocab(train_dataset)\n",
"print('Unique tokens in de vocabulary:', len(de_vocab))"
@@ -1802,7 +1545,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
@@ -1878,43 +1621,13 @@
},
{
"cell_type": "code",
- "execution_count": 33,
- "metadata": {
- "slideshow": {
- "slide_type": "slide"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "src_idx.shape:(128, 32)\n",
- "[[ 2 5 13 ... 1 1 1]\n",
- " [ 2 5 13 ... 1 1 1]\n",
- " [ 2 5 13 ... 1 1 1]\n",
- " ...\n",
- " [ 2 5 52 ... 1 1 1]\n",
- " [ 2 8 37 ... 1 1 1]\n",
- " [ 2 5 33 ... 1 1 1]]\n",
- "src_len.shape:(128,)\n",
- "[27 25 24 24 23 23 23 23 22 22 22 21 21 21 21 21 20 20 20 20 20 19 19 19\n",
- " 18 18 18 18 18 18 18 18 17 17 17 17 17 17 17 17 17 17 16 16 16 16 16 16\n",
- " 16 16 16 16 15 15 15 15 15 15 15 15 15 15 15 14 14 14 14 14 14 14 14 14\n",
- " 14 14 14 14 13 13 13 13 13 13 13 13 13 12 12 12 12 12 12 12 12 12 12 12\n",
- " 12 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 10 10 10 10 10 10\n",
- " 10 9 9 9 9 9 9 8]\n",
- "trg_idx.shape:(128, 32)\n",
- "[[ 2 4 2243 ... 1 1 1]\n",
- " [ 2 4 9 ... 1 1 1]\n",
- " [ 2 4 9 ... 1 1 1]\n",
- " ...\n",
- " [ 2 4 55 ... 1 1 1]\n",
- " [ 2 4 38 ... 1 1 1]\n",
- " [ 2 4 35 ... 1 1 1]]\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ }
+ },
+ "outputs": [],
"source": [
"train_iterator = Iterator(train_dataset, de_vocab, en_vocab, batch_size=128, max_len=32, drop_reminder=True)\n",
"valid_iterator = Iterator(valid_dataset, de_vocab, en_vocab, batch_size=128, max_len=32, drop_reminder=False)\n",
@@ -1941,7 +1654,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -1991,7 +1704,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -2055,7 +1768,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
@@ -2110,7 +1823,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -2134,7 +1847,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -2163,7 +1876,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -2207,7 +1920,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
@@ -2248,54 +1961,13 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch: 0: 0%| | 0/226 [00:00 max_len - 2:\n",
" src_len = max_len\n",
@@ -2405,24 +2066,24 @@
" else:\n",
" src_len = len(tokens) + 2\n",
" tokens = [''] + tokens + [''] + [''] * (max_len - src_len)\n",
- " \n",
+ "\n",
" # 将德语单词转换为数字索引，并进一步转换为tensor\n",
" # enc_inputs: [1, src_len]\n",
" indexes = de_vocab.encode(tokens)\n",
" enc_inputs = Tensor(indexes, mstype.float32).expand_dims(0)\n",
- " \n",
+ "\n",
" # 将输入送入encoder，获取信息\n",
" enc_outputs, _ = new_model.encoder(enc_inputs, src_pad_idx)\n",
"\n",
" dec_inputs = Tensor([[en_vocab.bos_idx]], mstype.float32)\n",
- " \n",
+ "\n",
" # 初始化decoder输入，此时仅有句首占位符\n",
" # dec_inputs: [1, 1]\n",
" max_len = enc_inputs.shape[1]\n",
" for _ in range(max_len):\n",
" dec_outputs, _, _ = new_model.decoder(dec_inputs, enc_inputs, enc_outputs, src_pad_idx, trg_pad_idx)\n",
" dec_logits = dec_outputs.view((-1, dec_outputs.shape[-1]))\n",
- " \n",
+ "\n",
" # 找到下一个词的概率分布，并输出预测\n",
" dec_logits = dec_logits[-1, :]\n",
" pred = dec_logits.argmax(axis=0).expand_dims(0).expand_dims(0)\n",
@@ -2453,23 +2114,13 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "src = ['ein', 'mann', 'mit', 'einem', 'orangefarbenen', 'hut', ',', 'der', 'etwas', 'anstarrt', '.']\n",
- "trg = ['a', 'man', 'in', 'an', 'orange', 'hat', 'starring', 'at', 'something', '.']\n",
- "predicted trg = ['a', 'man', 'in', 'an', 'orange', 'hat', 'is', '', 'something', '.']\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"example_idx = 0\n",
"\n",
@@ -2511,37 +2162,29 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "BLEU score = 44.92\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from nltk.translate.bleu_score import corpus_bleu\n",
"\n",
"def calculate_bleu(dataset, max_len=50):\n",
" trgs = []\n",
" pred_trgs = []\n",
- " \n",
+ "\n",
" for data in dataset[:10]:\n",
- " \n",
+ "\n",
" src = data[0]\n",
" trg = data[1]\n",
"\n",
" pred_trg = inference(src, max_len)\n",
" pred_trgs.append(pred_trg)\n",
" trgs.append([trg])\n",
- " \n",
+ "\n",
" return corpus_bleu(trgs, pred_trgs)\n",
"\n",
"bleu_score = calculate_bleu(test_dataset)\n",
diff --git a/Season1.step_into_chatgpt/2.BERT/bert_emotect_finetune.ipynb b/Season1.step_into_chatgpt/2.BERT/bert_emotect_finetune.ipynb
index 6ceed46..74760b1 100644
--- a/Season1.step_into_chatgpt/2.BERT/bert_emotect_finetune.ipynb
+++ b/Season1.step_into_chatgpt/2.BERT/bert_emotect_finetune.ipynb
@@ -16,109 +16,22 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
- "Collecting mindspore==2.5.0\n",
- " Using cached https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.5.0/MindSpore/unified/aarch64/mindspore-2.5.0-cp39-cp39-linux_aarch64.whl (345.0 MB)\n",
- "Requirement already satisfied: pip in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (25.1)\n",
- "\u001b[31mERROR: Could not find a version that satisfies the requirement install (from versions: none)\u001b[0m\u001b[31m\n",
- "\u001b[0m\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
- "\u001b[31mERROR: No matching distribution found for install\u001b[0m\u001b[31m\n",
- "\u001b[0m"
- ]
- }
- ],
+ "outputs": [],
"source": [
"!pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.5.0/MindSpore/unified/aarch64/mindspore-2.5.0-cp39-cp39-linux_aarch64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: https://repo.huaweicloud.com/repository/pypi/simple/\n",
- "Requirement already satisfied: mindnlp==0.4.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (0.4.0)\n",
- "Requirement already satisfied: mindspore>=2.2.14 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.5.0)\n",
- "Requirement already satisfied: tqdm in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (4.67.1)\n",
- "Requirement already satisfied: requests in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.32.3)\n",
- "Requirement already satisfied: datasets in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (3.6.0)\n",
- "Requirement already satisfied: evaluate in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.4.3)\n",
- "Requirement already satisfied: tokenizers==0.19.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.19.1)\n",
- "Requirement already satisfied: safetensors in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.3)\n",
- "Requirement already satisfied: sentencepiece in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.2.0)\n",
- "Requirement already satisfied: regex in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2024.11.6)\n",
- "Requirement already satisfied: addict in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.4.0)\n",
- "Requirement already satisfied: ml-dtypes in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.1)\n",
- "Requirement already satisfied: pyctcdecode in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.0)\n",
- "Requirement already satisfied: jieba in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.42.1)\n",
- "Requirement already satisfied: pytest==7.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (7.2.0)\n",
- "Requirement already satisfied: pillow>=10.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (11.2.1)\n",
- "Requirement already satisfied: attrs>=19.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (25.3.0)\n",
- "Requirement already satisfied: iniconfig in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.1.0)\n",
- "Requirement already satisfied: packaging in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (24.2)\n",
- "Requirement already satisfied: pluggy<2.0,>=0.12 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.5.0)\n",
- "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.2.0)\n",
- "Requirement already satisfied: tomli>=1.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.2.1)\n",
- "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from tokenizers==0.19.1->mindnlp==0.4.0) (0.32.3)\n",
- "Requirement already satisfied: filelock in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (3.18.0)\n",
- "Requirement already satisfied: fsspec>=2023.5.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (2025.3.0)\n",
- "Requirement already satisfied: pyyaml>=5.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (6.0.2)\n",
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (4.12.2)\n",
- "Requirement already satisfied: hf-xet<2.0.0,>=1.1.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (1.1.2)\n",
- "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.26.4)\n",
- "Requirement already satisfied: protobuf>=3.13.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (6.30.2)\n",
- "Requirement already satisfied: asttokens>=2.0.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (3.0.0)\n",
- "Requirement already satisfied: scipy>=1.5.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.13.1)\n",
- "Requirement already satisfied: psutil>=5.6.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (5.9.0)\n",
- "Requirement already satisfied: astunparse>=1.6.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.6.3)\n",
- "Requirement already satisfied: dill>=0.3.7 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (0.3.8)\n",
- "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (0.45.1)\n",
- "Requirement already satisfied: six<2.0,>=1.6.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (1.17.0)\n",
- "Requirement already satisfied: pyarrow>=15.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (20.0.0)\n",
- "Requirement already satisfied: pandas in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (2.2.3)\n",
- "Requirement already satisfied: xxhash in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.5.0)\n",
- "Requirement already satisfied: multiprocess<0.70.17 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (0.70.16)\n",
- "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (3.12.7)\n",
- "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (2.6.1)\n",
- "Requirement already satisfied: aiosignal>=1.1.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.3.2)\n",
- "Requirement already satisfied: async-timeout<6.0,>=4.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (5.0.1)\n",
- "Requirement already satisfied: frozenlist>=1.1.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.6.0)\n",
- "Requirement already satisfied: multidict<7.0,>=4.5 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (6.4.4)\n",
- "Requirement already satisfied: propcache>=0.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (0.3.1)\n",
- "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.20.0)\n",
- "Requirement already satisfied: idna>=2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from yarl<2.0,>=1.17.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (3.10)\n",
- "Requirement already satisfied: charset-normalizer<4,>=2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (3.4.1)\n",
- "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2.4.0)\n",
- "Requirement already satisfied: certifi>=2017.4.17 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2025.4.26)\n",
- "Requirement already satisfied: python-dateutil>=2.8.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2.9.0.post0)\n",
- "Requirement already satisfied: pytz>=2020.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
- "Requirement already satisfied: tzdata>=2022.7 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
- "Requirement already satisfied: pygtrie<3.0,>=2.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (2.5.0)\n",
- "Requirement already satisfied: hypothesis<7,>=6.14 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (6.133.2)\n",
- "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.0) (2.4.0)\n",
- "\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"!pip install mindnlp==0.4.0"
]
@@ -156,30 +69,11 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "Building prefix dict from the default dictionary ...\n",
- "Loading model from cache /tmp/jieba.cache\n",
- "Loading model cost 0.908 seconds.\n",
- "Prefix dict has been built successfully.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import os\n",
"\n",
@@ -192,7 +86,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -244,35 +138,11 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "--2025-06-03 16:26:40-- https://baidu-nlp.bj.bcebos.com/emotion_detection-dataset-1.0.0.tar.gz\n",
- "正在解析主机 baidu-nlp.bj.bcebos.com (baidu-nlp.bj.bcebos.com)... 36.110.192.178, 2409:8c04:1001:1203:0:ff:b0bb:4f27\n",
- "正在连接 baidu-nlp.bj.bcebos.com (baidu-nlp.bj.bcebos.com)|36.110.192.178|:443... 已连接。\n",
- "已发出 HTTP 请求，正在等待回应... 200 OK\n",
- "长度：1710581 (1.6M) [application/x-gzip]\n",
- "正在保存至: “emotion_detection.tar.gz”\n",
- "\n",
- "emotion_detection.t 100%[===================>] 1.63M 7.02MB/s 用时 0.2s \n",
- "\n",
- "2025-06-03 16:26:41 (7.02 MB/s) - 已保存 “emotion_detection.tar.gz” [1710581/1710581])\n",
- "\n",
- "data/\n",
- "data/test.tsv\n",
- "data/infer.tsv\n",
- "data/dev.tsv\n",
- "data/train.tsv\n",
- "data/vocab.txt\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# download dataset\n",
"!wget https://baidu-nlp.bj.bcebos.com/emotion_detection-dataset-1.0.0.tar.gz -O emotion_detection.tar.gz\n",
@@ -290,7 +160,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -302,7 +172,7 @@
" is_ascend = mindspore.get_context('device_target') == 'Ascend'\n",
"\n",
" column_names = [\"label\", \"text_a\"]\n",
- " \n",
+ "\n",
" dataset = GeneratorDataset(source, column_names=column_names, shuffle=shuffle)\n",
" # transforms\n",
" type_cast_op = transforms.TypeCast(mindspore.int32)\n",
@@ -334,76 +204,11 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "70d7820ca2334d3ba52d2b57e7a23918",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0.00/49.0 [00:00 1:\n",
" predictions = np.argmax(predictions, axis=-1)\n",
"\n",
" accuracy = (predictions == labels).mean()\n",
- " \n",
+ "\n",
" return {\"accuracy\": float(accuracy)}"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -624,156 +351,11 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "a557eb2f81dd4b7893e2173ae25c116b",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0/1510 [00:00 type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "Building prefix dict from the default dictionary ...\n",
- "Loading model from cache /tmp/jieba.cache\n",
- "Loading model cost 1.042 seconds.\n",
- "Prefix dict has been built successfully.\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "6421e22f124345fe924aa445c22403a2",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "0.00B [00:00, ?B/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "30735811eedd4f2ea525d38c702c177f",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "0.00B [00:00, ?B/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "2e931b32674045c599aeb89a2088f180",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0.00/334 [00:00 \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
- "\u001b[31mERROR: No matching distribution found for install\u001b[0m\u001b[31m\n",
- "\u001b[0m"
- ]
- }
- ],
+ "outputs": [],
"source": [
"!pip install pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.5.0/MindSpore/unified/aarch64/mindspore-2.5.0-cp39-cp39-linux_aarch64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: https://repo.huaweicloud.com/repository/pypi/simple/\n",
- "Requirement already satisfied: mindnlp==0.4.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (0.4.0)\n",
- "Requirement already satisfied: mindspore>=2.2.14 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.4.1)\n",
- "Requirement already satisfied: tqdm in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (4.67.1)\n",
- "Requirement already satisfied: requests in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.32.3)\n",
- "Requirement already satisfied: datasets in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (3.6.0)\n",
- "Requirement already satisfied: evaluate in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.4.3)\n",
- "Requirement already satisfied: tokenizers==0.19.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.19.1)\n",
- "Requirement already satisfied: safetensors in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.3)\n",
- "Requirement already satisfied: sentencepiece in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.2.0)\n",
- "Requirement already satisfied: regex in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2024.11.6)\n",
- "Requirement already satisfied: addict in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.4.0)\n",
- "Requirement already satisfied: ml-dtypes in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.1)\n",
- "Requirement already satisfied: pyctcdecode in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.0)\n",
- "Requirement already satisfied: jieba in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.42.1)\n",
- "Requirement already satisfied: pytest==7.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (7.2.0)\n",
- "Requirement already satisfied: pillow>=10.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (11.1.0)\n",
- "Requirement already satisfied: attrs>=19.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (25.3.0)\n",
- "Requirement already satisfied: iniconfig in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.1.0)\n",
- "Requirement already satisfied: packaging in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (24.2)\n",
- "Requirement already satisfied: pluggy<2.0,>=0.12 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.5.0)\n",
- "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.2.0)\n",
- "Requirement already satisfied: tomli>=1.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.2.1)\n",
- "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from tokenizers==0.19.1->mindnlp==0.4.0) (0.32.3)\n",
- "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.26.4)\n",
- "Requirement already satisfied: protobuf>=3.13.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (6.30.2)\n",
- "Requirement already satisfied: asttokens>=2.0.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (2.0.5)\n",
- "Requirement already satisfied: scipy>=1.5.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.13.1)\n",
- "Requirement already satisfied: psutil>=5.6.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (5.9.0)\n",
- "Requirement already satisfied: astunparse>=1.6.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.6.3)\n",
- "Requirement already satisfied: filelock in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.18.0)\n",
- "Requirement already satisfied: pyarrow>=15.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (20.0.0)\n",
- "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (0.3.8)\n",
- "Requirement already satisfied: pandas in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (2.2.3)\n",
- "Requirement already satisfied: xxhash in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.5.0)\n",
- "Requirement already satisfied: multiprocess<0.70.17 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (0.70.16)\n",
- "Requirement already satisfied: fsspec<=2025.3.0,>=2023.1.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (2025.3.0)\n",
- "Requirement already satisfied: pyyaml>=5.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (6.0.2)\n",
- "Requirement already satisfied: charset-normalizer<4,>=2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (3.4.1)\n",
- "Requirement already satisfied: idna<4,>=2.5 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (3.10)\n",
- "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2.3.0)\n",
- "Requirement already satisfied: certifi>=2017.4.17 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2025.1.31)\n",
- "Requirement already satisfied: pygtrie<3.0,>=2.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (2.5.0)\n",
- "Requirement already satisfied: hypothesis<7,>=6.14 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (6.133.2)\n",
- "Requirement already satisfied: six in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from asttokens>=2.0.4->mindspore>=2.2.14->mindnlp==0.4.0) (1.16.0)\n",
- "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (0.45.1)\n",
- "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (3.12.7)\n",
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (4.12.2)\n",
- "Requirement already satisfied: hf-xet<2.0.0,>=1.1.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (1.1.2)\n",
- "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.0) (2.4.0)\n",
- "Requirement already satisfied: python-dateutil>=2.8.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2.9.0.post0)\n",
- "Requirement already satisfied: pytz>=2020.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
- "Requirement already satisfied: tzdata>=2022.7 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
- "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (2.6.1)\n",
- "Requirement already satisfied: aiosignal>=1.1.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.3.2)\n",
- "Requirement already satisfied: async-timeout<6.0,>=4.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (5.0.1)\n",
- "Requirement already satisfied: frozenlist>=1.1.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.6.0)\n",
- "Requirement already satisfied: multidict<7.0,>=4.5 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (6.4.4)\n",
- "Requirement already satisfied: propcache>=0.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (0.3.1)\n",
- "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.20.0)\n",
- "\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"!pip install mindnlp==0.4.0"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: https://repo.huaweicloud.com/repository/pypi/simple/\n",
- "Requirement already satisfied: jieba in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (0.42.1)\n",
- "\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"!pip install jieba"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "env: HF_ENDPOINT=https://hf-mirror.com\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%env HF_ENDPOINT=https://hf-mirror.com"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:02.546.745 [mindspore/run_check/_check_version.py:329] MindSpore version 2.4.1 and Ascend AI software package (Ascend Data Center Solution)version 7.6 does not match, the version of software package expect one of ['7.3', '7.5']. Please refer to the match info on: https://www.mindspore.cn/install\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:04.810.368 [mindspore/run_check/_check_version.py:347] MindSpore version 2.4.1 and \"te\" wheel package version 7.6 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:04.812.087 [mindspore/run_check/_check_version.py:354] MindSpore version 2.4.1 and \"hccl\" wheel package version 7.6 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:04.812.731 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 3\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:05.814.363 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 2\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:06.816.061 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 1\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:09.473.235 [mindspore/run_check/_check_version.py:329] MindSpore version 2.4.1 and Ascend AI software package (Ascend Data Center Solution)version 7.6 does not match, the version of software package expect one of ['7.3', '7.5']. Please refer to the match info on: https://www.mindspore.cn/install\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:09.474.964 [mindspore/run_check/_check_version.py:347] MindSpore version 2.4.1 and \"te\" wheel package version 7.6 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:09.475.567 [mindspore/run_check/_check_version.py:354] MindSpore version 2.4.1 and \"hccl\" wheel package version 7.6 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:09.476.234 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 3\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:10.477.850 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 2\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:11.478.869 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 1\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:12.480.761 [mindspore/run_check/_check_version.py:329] MindSpore version 2.4.1 and Ascend AI software package (Ascend Data Center Solution)version 7.6 does not match, the version of software package expect one of ['7.3', '7.5']. Please refer to the match info on: https://www.mindspore.cn/install\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:12.482.175 [mindspore/run_check/_check_version.py:347] MindSpore version 2.4.1 and \"te\" wheel package version 7.6 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:12.482.802 [mindspore/run_check/_check_version.py:354] MindSpore version 2.4.1 and \"hccl\" wheel package version 7.6 does not match. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:12.483.400 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 3\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:13.485.045 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 2\n",
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:14.486.640 [mindspore/run_check/_check_version.py:368] Please pay attention to the above warning, countdown: 1\n",
- "Building prefix dict from the default dictionary ...\n",
- "Loading model from cache /tmp/jieba.cache\n",
- "Loading model cost 1.075 seconds.\n",
- "Prefix dict has been built successfully.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import os\n",
"\n",
@@ -218,7 +73,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -234,29 +89,18 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "2500"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"imdb_train.get_dataset_size()"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -291,21 +135,11 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
- " warnings.warn(\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from mindnlp.transformers import OpenAIGPTTokenizer\n",
"# tokenizer\n",
@@ -322,19 +156,11 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] ME(250:281472944873504,MainProcess):2025-06-03-14:44:51.758.363 [mindspore/dataset/engine/datasets.py:2534] Dataset is shuffled before split.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# split train dataset into train and valid datasets\n",
"imdb_train, imdb_val = imdb_train.split([0.7, 0.3])"
@@ -342,7 +168,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -355,39 +181,18 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[Tensor(shape=[4, 512], dtype=Int64, value=\n",
- " [[ 500, 246, 1322 ... 40480, 40480, 40480],\n",
- " [ 1473, 980, 246 ... 40480, 40480, 40480],\n",
- " [39516, 498, 481 ... 40480, 40480, 40480],\n",
- " [ 616, 544, 808 ... 40480, 40480, 40480]]),\n",
- " Tensor(shape=[4, 512], dtype=Int64, value=\n",
- " [[1, 1, 1 ... 0, 0, 0],\n",
- " [1, 1, 1 ... 0, 0, 0],\n",
- " [1, 1, 1 ... 0, 0, 0],\n",
- " [1, 1, 1 ... 0, 0, 0]]),\n",
- " Tensor(shape=[4], dtype=Int32, value= [1, 1, 0, 0])]"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"next(dataset_train.create_tuple_iterator())"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -407,41 +212,11 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "94d1b9d8276a4040a27030d34c8d44e2",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0.00/457M [00:00 type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n",
- "Building prefix dict from the default dictionary ...\n",
- "Loading model from cache /tmp/jieba.cache\n",
- "Loading model cost 1.298 seconds.\n",
- "Prefix dict has been built successfully.\n"
- ]
- }
- ],
+ "execution_count": null,
+ "id": "8",
+ "metadata": {},
+ "outputs": [],
"source": [
"from mindnlp.utils import http_get\n",
"\n",
@@ -254,21 +123,10 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "id": "9b5868b6-7a52-4f97-b934-4d3632a978a2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "50000"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "id": "9",
+ "metadata": {},
+ "outputs": [],
"source": [
"from mindspore.dataset import TextFileDataset\n",
"\n",
@@ -279,7 +137,7 @@
},
{
"cell_type": "markdown",
- "id": "f2697c68-be45-44c1-bc26-7c04424ebf0c",
+ "id": "10",
"metadata": {},
"source": [
"**本案例默认在GPU P100上运行，因中文文本，tokenizer使用的是bert tokenizer而非gpt tokenizer等原因，全量数据训练1个epoch的时间约为80分钟。**\n",
@@ -289,8 +147,8 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "id": "9bf79231-864c-4e11-9409-995c95cdb30f",
+ "execution_count": null,
+ "id": "11",
"metadata": {},
"outputs": [],
"source": [
@@ -301,7 +159,7 @@
},
{
"cell_type": "markdown",
- "id": "a1f0d574-53a0-4bac-9303-6eb769418c04",
+ "id": "12",
"metadata": {},
"source": [
"2. 数据预处理\n",
@@ -320,8 +178,8 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "f1ee1961-0658-4e70-95c2-81fefd83a40b",
+ "execution_count": null,
+ "id": "13",
"metadata": {},
"outputs": [],
"source": [
@@ -340,7 +198,7 @@
" tokenized = tokenizer(text=article, text_pair=summary,\n",
" padding='max_length', truncation='only_first', max_length=max_seq_len)\n",
" return tokenized['input_ids'], tokenized['input_ids']\n",
- " \n",
+ "\n",
" dataset = dataset.map(read_map, 'text', ['article', 'summary'])\n",
" # change column names to input_ids and labels for the following training\n",
" dataset = dataset.map(merge_and_pad, ['article', 'summary'], ['input_ids', 'labels'])\n",
@@ -354,7 +212,7 @@
},
{
"cell_type": "markdown",
- "id": "e0ce3dab-9486-4365-be7c-34bd5a761080",
+ "id": "14",
"metadata": {},
"source": [
"因GPT2无中文的tokenizer，我们使用BertTokenizer替代。"
@@ -362,29 +220,10 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "id": "e3cd8e57-72bc-4d2e-b38d-38b24efadd49",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "21128"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "id": "15",
+ "metadata": {},
+ "outputs": [],
"source": [
"from mindnlp.transformers import BertTokenizer\n",
"\n",
@@ -395,8 +234,8 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "id": "0e89c26b-4970-449a-a0c4-b6c61845e336",
+ "execution_count": null,
+ "id": "16",
"metadata": {},
"outputs": [],
"source": [
@@ -405,31 +244,17 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "id": "1b65cc13-0a52-4bae-ab5f-ebb813a4d3ab",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[Tensor(shape=[1, 1024], dtype=Int64, value=\n",
- " [[ 101, 1724, 3862 ... 0, 0, 0]]),\n",
- " Tensor(shape=[1, 1024], dtype=Int64, value=\n",
- " [[ 101, 1724, 3862 ... 0, 0, 0]])]"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "id": "17",
+ "metadata": {},
+ "outputs": [],
"source": [
"next(train_dataset.create_tuple_iterator())"
]
},
{
"cell_type": "markdown",
- "id": "7e1497ee-2ad1-4da8-b659-9c7f2d45fccc",
+ "id": "18",
"metadata": {},
"source": [
"### 模型构建\n",
@@ -439,8 +264,8 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "id": "2f295944-ea2e-41e1-8301-472e09223792",
+ "execution_count": null,
+ "id": "19",
"metadata": {},
"outputs": [],
"source": [
@@ -481,7 +306,7 @@
},
{
"cell_type": "markdown",
- "id": "0f6af843-64d7-49a3-875f-605d6b2e74b2",
+ "id": "20",
"metadata": {},
"source": [
"2. 动态学习率"
@@ -489,8 +314,8 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "id": "73c7be3d-44dc-49d4-abd8-c41f316a28d9",
+ "execution_count": null,
+ "id": "21",
"metadata": {},
"outputs": [],
"source": [
@@ -519,7 +344,7 @@
},
{
"cell_type": "markdown",
- "id": "c45e9db2-11df-4cc4-8bef-87d473d99e5a",
+ "id": "22",
"metadata": {},
"source": [
"### 模型训练"
@@ -527,8 +352,8 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "id": "1a655320-2d05-4c93-bc8b-f1b4f45f809f",
+ "execution_count": null,
+ "id": "23",
"metadata": {},
"outputs": [],
"source": [
@@ -541,21 +366,10 @@
},
{
"cell_type": "code",
- "execution_count": 11,
- "id": "81ac9003-2dcf-42f8-b42d-3a788f172d98",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "GPT2LMHeadModel has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`.`PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
- " - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
- " - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n",
- "[WARNING] DEVICE(12319,ffffaba360b0,python):2024-12-03-21:03:34.197.849 [mindspore/ccsrc/plugin/device/ascend/hal/device/ascend_vmm_adapter.h:188] CheckVmmDriverVersion] Driver version is less than 24.0.0, vmm is disabled by default, drvier_version: 23.0.6\n"
- ]
- }
- ],
+ "execution_count": null,
+ "id": "24",
+ "metadata": {},
+ "outputs": [],
"source": [
"from mindspore import nn\n",
"from mindnlp.transformers import GPT2Config, GPT2LMHeadModel\n",
@@ -570,18 +384,10 @@
},
{
"cell_type": "code",
- "execution_count": 12,
- "id": "2803c71c-3591-48cf-a6a9-6b840af749bf",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "number of model parameters: 102068736\n"
- ]
- }
- ],
+ "execution_count": null,
+ "id": "25",
+ "metadata": {},
+ "outputs": [],
"source": [
"# 记录模型参数数量\n",
"print('number of model parameters: {}'.format(model.num_parameters()))"
@@ -589,8 +395,8 @@
},
{
"cell_type": "code",
- "execution_count": 13,
- "id": "1492649c-dfdb-4cfd-85bb-aef478aff5d2",
+ "execution_count": null,
+ "id": "26",
"metadata": {},
"outputs": [],
"source": [
@@ -608,8 +414,8 @@
},
{
"cell_type": "code",
- "execution_count": 14,
- "id": "88259c93-5366-4406-a417-396808ec767c",
+ "execution_count": null,
+ "id": "27",
"metadata": {},
"outputs": [],
"source": [
@@ -624,7 +430,7 @@
" learning_rate=learning_rate,\n",
" max_grad_norm=max_grad_norm,\n",
" warmup_steps=warmup_steps\n",
- " \n",
+ "\n",
")\n",
"\n",
"from mindnlp.engine import Trainer\n",
@@ -638,56 +444,10 @@
},
{
"cell_type": "code",
- "execution_count": 15,
- "id": "ebf47838-460a-49e4-8850-f10fe7b5ff2b",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 0%| | 0/45 [00:00 黄 传 庆 ） 练 车 间 隙 ， 发 现 训 练 场 附 近 一 根 电 线 杆 上 有 一 个 鸟 窝 ， 21 岁 的 刁 某 立 即 爬 上 去 捉 鸟 ， 不 想 就 此 命 丧 黄 泉 。 日 前 发 生 在 博 白 县 文 地 镇 一 驾 校 训 练 场 的 意 外 事 故 ， 令 人 唏 嘘 。 今 年 21 岁 的 刁 某 是 博 白 县 宁 潭 镇 新 荣 村 人 ， 不 久 前 到 其 堂 叔 刁 某 某 任 教 练 的 宁 潭 镇 某 驾 校 参 加 汽 车 驾 驶 员 培 训 。 25 日 中 午 ， 刁 某 随 堂 叔 刁 某 某 及 另 外 几 名 学 员 开 车 到 文 地 镇 ， 借 用 文 地 镇 钛 白 粉 厂 附 近 的 训 练 场 练 车 。 14 时 许 ， 训 练 间 隙 ， 刁 某 发 现 训 练 场 附 近 一 根 电 线 杆 上 有 一 个 鸟 窝 ， 一 只 [UNK] 八 哥 [UNK] 正 叼 着 虫 子 飞 回 鸟 窝 。 平 时 刁 某 偶 尔 会 捕 鸟 出 卖 ， 知 道 这 种 鸟 价 值 100 多 元 。 他 跟 身 边 学 员 打 了 个 招 呼 ， 便 飞 奔 过 去 ， 欲 爬 上 电 线 杆 捉 鸟 。 刁 某 某 发 现 后 追 过 去 欲 阻 止 ， 但 为 时 已 晚 ， 等 刁 某 某 赶 到 时 ， 刁 某 已 爬 到 电 线 杆 顶 ， 伸 手 捉 鸟 时 不 慎 触 碰 到 头 顶 的 高 压 电 线 ， 当 即 身 亡 。 其 手 掌 被 外 露 的 钢 枝 刺 穿 ， 尸 体 悬 挂 在 电 线 杆 上 。 26 日 下 午 ， 在 文 地 、 宁 潭 镇 政 府 及 相 关 部 门 协 调 下 ， 相 关 责 任 方 与 死 者 家 属 达 成 赔 偿 协 议 ， 死 者 家 属 同 意 将 死 者 尸 体 取 下 搬 走 ， 相 关 责 任 方 共 赔 偿 死 者 家 属 15. 8 万 元 ， 其 中 宁 潭 镇 某 驾 校 赔 付 7. 3 万 元 ， 死 者 堂 叔 刁 某 某 （ 驾 校 教 练 ） 赔 付 5. 3 万 元 ， 文 地 供 电 所 本 来 没 有 直 接 责 任 ， 但 出 于 人 道 主 义 赔 付 3. 2 万 元 。 （ 原 标 题 ： 为 捉 一 只 鸟 < [UNK] > 赔 上 一 条 命 博 白 一 男 子 爬 电 线 杆 捉 鸟 ， 不 慎 触 电 身 亡 ） [SEP] ， 。 ， ， 的 ， [UNK] 的 的 。 。 [UNK] ， 了 ， 大 的 了 。 的 [UNK] 。 一 的 一 ， 出 ， 上 ， 人 的 大 ， 和 ， 子 ， 到 ， 市 ， 有 ， 行 ， 也 ， < ，\n"
- ]
- }
- ],
+ "execution_count": null,
+ "id": "34",
+ "metadata": {},
+ "outputs": [],
"source": [
"model.set_train(False)\n",
"model.config.eos_token_id = model.config.sep_token_id\n",
@@ -854,7 +540,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "8654a5d5-d94b-4906-92bf-52d2d85685a7",
+ "id": "35",
"metadata": {},
"outputs": [],
"source": []
@@ -862,7 +548,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "9613743c-3655-45e2-a720-c311a5854c94",
+ "id": "36",
"metadata": {},
"outputs": [],
"source": []
diff --git a/Season1.step_into_chatgpt/7.Prompt/roberta_sequence_classification.ipynb b/Season1.step_into_chatgpt/7.Prompt/roberta_sequence_classification.ipynb
index 75c4c9e..25e1ce7 100644
--- a/Season1.step_into_chatgpt/7.Prompt/roberta_sequence_classification.ipynb
+++ b/Season1.step_into_chatgpt/7.Prompt/roberta_sequence_classification.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "7a2ac91c",
+ "id": "0",
"metadata": {},
"source": [
"# 基于MindNLP的Roberta模型Prompt Tuning"
@@ -10,7 +10,7 @@
},
{
"cell_type": "markdown",
- "id": "324424c6",
+ "id": "1",
"metadata": {},
"source": [
"安装mindspore, mindnlp及其他依赖"
@@ -18,66 +18,27 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "id": "cd3f2df1-da30-4009-8b33-80df52be80c7",
+ "execution_count": null,
+ "id": "2",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
- "Collecting mindspore==2.4.1\n",
- " Downloading https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.4.1/MindSpore/unified/aarch64/mindspore-2.4.1-cp39-cp39-linux_aarch64.whl (335.5 MB)\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m335.5/335.5 MB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
- "\u001b[?25hRequirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (1.26.1)\n",
- "Requirement already satisfied: protobuf>=3.13.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (3.20.3)\n",
- "Requirement already satisfied: asttokens>=2.0.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (2.4.1)\n",
- "Requirement already satisfied: pillow>=6.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (9.0.1)\n",
- "Requirement already satisfied: scipy>=1.5.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (1.11.3)\n",
- "Requirement already satisfied: packaging>=20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (23.2)\n",
- "Requirement already satisfied: psutil>=5.6.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (5.9.5)\n",
- "Requirement already satisfied: astunparse>=1.6.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.1) (1.6.3)\n",
- "Collecting safetensors>=0.4.0 (from mindspore==2.4.1)\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/08/94/7760694760f1e5001bd62c93155b8b7ccb652d1f4d0161d1e72b5bf9581a/safetensors-0.4.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (442 kB)\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m442.4/442.4 kB\u001b[0m \u001b[31m39.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25hRequirement already satisfied: six>=1.12.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from asttokens>=2.0.4->mindspore==2.4.1) (1.16.0)\n",
- "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore==2.4.1) (0.41.2)\n",
- "\u001b[33mDEPRECATION: moxing-framework 2.1.16.2ae09d45 has a non-standard version number. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of moxing-framework or contact the author to suggest that they release a version with a conforming version number. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
- "\u001b[0mInstalling collected packages: safetensors, mindspore\n",
- " Attempting uninstall: mindspore\n",
- " Found existing installation: mindspore 2.3.0\n",
- " Uninstalling mindspore-2.3.0:\n",
- " Successfully uninstalled mindspore-2.3.0\n",
- "Successfully installed mindspore-2.4.1 safetensors-0.4.5\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"!pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.4.1/MindSpore/unified/aarch64/mindspore-2.4.1-cp39-cp39-linux_aarch64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple"
]
},
{
"cell_type": "code",
- "execution_count": 14,
- "id": "d8b0ba09",
+ "execution_count": null,
+ "id": "3",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "env: HF_ENDPOINT=https://hf-mirror.com\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%env HF_ENDPOINT=https://hf-mirror.com"
]
},
{
"cell_type": "markdown",
- "id": "5b0e977f",
+ "id": "4",
"metadata": {},
"source": [
"## 模型与数据集加载\n",
@@ -87,8 +48,8 @@
},
{
"cell_type": "code",
- "execution_count": 15,
- "id": "ef577ba3",
+ "execution_count": null,
+ "id": "5",
"metadata": {},
"outputs": [],
"source": [
@@ -115,8 +76,8 @@
},
{
"cell_type": "code",
- "execution_count": 16,
- "id": "af061f0b",
+ "execution_count": null,
+ "id": "6",
"metadata": {},
"outputs": [],
"source": [
@@ -130,7 +91,7 @@
},
{
"cell_type": "markdown",
- "id": "f949e9cb",
+ "id": "7",
"metadata": {},
"source": [
"prompt tuning配置，任务类型选为\"SEQ_CLS\", 即序列分类。"
@@ -138,8 +99,8 @@
},
{
"cell_type": "code",
- "execution_count": 17,
- "id": "4e9663be",
+ "execution_count": null,
+ "id": "8",
"metadata": {},
"outputs": [],
"source": [
@@ -151,7 +112,7 @@
},
{
"cell_type": "markdown",
- "id": "3dc55fc7",
+ "id": "9",
"metadata": {},
"source": [
"加载tokenizer。如模型为GPT、OPT或BLOOM类模型，从序列左侧添加padding，其他情况下从序列右侧添加padding。"
@@ -159,19 +120,10 @@
},
{
"cell_type": "code",
- "execution_count": 18,
- "id": "871ebbae",
+ "execution_count": null,
+ "id": "10",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
- " warnings.warn(\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# load tokenizer\n",
"if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
@@ -186,18 +138,10 @@
},
{
"cell_type": "code",
- "execution_count": 19,
- "id": "79ef5257",
+ "execution_count": null,
+ "id": "11",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'sentence1': Tensor(shape=[], dtype=String, value= 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .'), 'sentence2': Tensor(shape=[], dtype=String, value= 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'), 'label': Tensor(shape=[], dtype=Int64, value= 1), 'idx': Tensor(shape=[], dtype=Int64, value= 0)}\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"datasets = load_dataset(\"glue\", task)\n",
"print(next(datasets['train'].create_dict_iterator()))"
@@ -205,8 +149,8 @@
},
{
"cell_type": "code",
- "execution_count": 20,
- "id": "151943cb",
+ "execution_count": null,
+ "id": "12",
"metadata": {},
"outputs": [],
"source": [
@@ -233,57 +177,19 @@
},
{
"cell_type": "code",
- "execution_count": 21,
- "id": "a99c4ab6",
+ "execution_count": null,
+ "id": "13",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
- "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
- "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
- "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
- "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
- "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
- "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'input_ids': Tensor(shape=[32, 70], dtype=Int64, value=\n",
- "[[ 0, 10127, 1001 ... 1, 1, 1],\n",
- " [ 0, 975, 26802 ... 1, 1, 1],\n",
- " [ 0, 1213, 56 ... 1, 1, 1],\n",
- " ...\n",
- " [ 0, 133, 1154 ... 1, 1, 1],\n",
- " [ 0, 12667, 8423 ... 1, 1, 1],\n",
- " [ 0, 32478, 1033 ... 1, 1, 1]]), 'attention_mask': Tensor(shape=[32, 70], dtype=Int64, value=\n",
- "[[1, 1, 1 ... 0, 0, 0],\n",
- " [1, 1, 1 ... 0, 0, 0],\n",
- " [1, 1, 1 ... 0, 0, 0],\n",
- " ...\n",
- " [1, 1, 1 ... 0, 0, 0],\n",
- " [1, 1, 1 ... 0, 0, 0],\n",
- " [1, 1, 1 ... 0, 0, 0]]), 'labels': Tensor(shape=[32], dtype=Int64, value= [1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, \n",
- " 1, 1, 0, 0, 1, 1, 1, 0])}\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print(next(train_dataset.create_dict_iterator()))"
]
},
{
"cell_type": "code",
- "execution_count": 22,
- "id": "9dc17398",
- "metadata": {
- "scrolled": true
- },
+ "execution_count": null,
+ "id": "14",
+ "metadata": {},
"outputs": [],
"source": [
"metric = evaluate.load(\"glue\", task)"
@@ -291,7 +197,7 @@
},
{
"cell_type": "markdown",
- "id": "9034b5b2",
+ "id": "15",
"metadata": {},
"source": [
"加载模型并打印微调参数量，可以看到仅有不到0.6%的参数参与了微调。\n",
@@ -308,26 +214,10 @@
},
{
"cell_type": "code",
- "execution_count": 23,
- "id": "f929a616",
+ "execution_count": null,
+ "id": "16",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at AI-ModelScope/roberta-large and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
- "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "trainable params: 1,061,890 || all params: 356,423,684 || trainable%: 0.2979291353713745\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# load model\n",
"model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True, mirror=\"modelscope\")\n",
@@ -338,7 +228,7 @@
},
{
"cell_type": "markdown",
- "id": "6fe629f6",
+ "id": "17",
"metadata": {},
"source": [
"## 模型微调（prompt tuning）"
@@ -346,7 +236,7 @@
},
{
"cell_type": "markdown",
- "id": "855ae5a5",
+ "id": "18",
"metadata": {},
"source": [
"指定优化器和学习率调整策略"
@@ -354,8 +244,8 @@
},
{
"cell_type": "code",
- "execution_count": 24,
- "id": "3c7ee704",
+ "execution_count": null,
+ "id": "19",
"metadata": {},
"outputs": [],
"source": [
@@ -371,7 +261,7 @@
},
{
"cell_type": "markdown",
- "id": "c4f5b68a",
+ "id": "20",
"metadata": {},
"source": [
"打印参与微调的模型参数"
@@ -379,41 +269,10 @@
},
{
"cell_type": "code",
- "execution_count": 25,
- "id": "a0d2bff6",
+ "execution_count": null,
+ "id": "21",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(Tensor(shape=[1024, 1024], dtype=Float32, value=\n",
- " [[-1.36615150e-02, 4.08777148e-02, 2.55590724e-03 ... 3.47721018e-02, 9.83245391e-03, 3.02866008e-02],\n",
- " [-1.82124749e-02, -1.49800153e-02, -7.02886097e-03 ... 2.07055025e-02, 3.45048914e-03, -3.01328991e-02],\n",
- " [-6.06489694e-03, 6.34483900e-03, 1.55880465e-03 ... 3.41698825e-02, -7.40761030e-03, 3.69770750e-02],\n",
- " ...\n",
- " [-4.91964221e-02, 1.94903351e-02, 2.51724524e-03 ... 3.08064763e-02, -7.55657675e-04, -8.02899338e-03],\n",
- " [-2.02472787e-03, -2.46642623e-02, -7.02362158e-04 ... 2.86021479e-03, 8.27849377e-03, 9.28967725e-03],\n",
- " [-2.06481982e-02, 2.20393538e-02, 3.17191752e-03 ... -2.68367468e-03, -4.67487238e-02, 9.09192720e-04]]),\n",
- " Tensor(shape=[1024], dtype=Float32, value= [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00 ... 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]),\n",
- " Tensor(shape=[2, 1024], dtype=Float32, value=\n",
- " [[ 8.87530856e-03, 2.81313114e-04, 3.74777764e-02 ... -2.02168617e-02, 4.23110556e-03, -3.84111144e-02],\n",
- " [ 3.84113006e-03, -1.38288038e-02, 1.98907983e-02 ... -3.23316827e-02, -3.48059200e-02, 7.11114611e-04]]),\n",
- " Tensor(shape=[2], dtype=Float32, value= [ 0.00000000e+00, 0.00000000e+00]),\n",
- " Tensor(shape=[10, 1024], dtype=Float32, value=\n",
- " [[-1.75136819e-01, 6.45715892e-02, 1.14947283e+00 ... 8.42640877e-01, 6.34459913e-01, 9.26455021e-01],\n",
- " [ 7.65107423e-02, 5.32130003e-01, -2.12189722e+00 ... 1.34316778e+00, 4.83163930e-02, -2.11086214e-01],\n",
- " [-7.30758488e-01, -8.77783835e-01, -5.94429135e-01 ... -2.58468151e-01, -2.85294857e-02, -2.18536639e+00],\n",
- " ...\n",
- " [ 4.13678169e-01, -1.15315497e+00, 8.49422574e-01 ... 2.54201055e-01, -1.30300558e+00, 2.13208008e+00],\n",
- " [ 5.60092032e-01, -8.55898261e-01, -7.30682373e-01 ... -1.04416716e+00, -1.10600793e+00, 4.29843873e-01],\n",
- " [-1.94377673e+00, 4.45314497e-02, -4.56895113e-01 ... 1.88079858e+00, -6.05825901e-01, -3.19380850e-01]]))"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# print name of trainable parameters\n",
"model.trainable_params()"
@@ -421,7 +280,7 @@
},
{
"cell_type": "markdown",
- "id": "b61576ae",
+ "id": "22",
"metadata": {},
"source": [
"按照如下步骤定义训练逻辑：\n",
@@ -434,93 +293,10 @@
},
{
"cell_type": "code",
- "execution_count": 26,
- "id": "0667ebea",
+ "execution_count": null,
+ "id": "23",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 115/115 [00:26<00:00, 4.38it/s]\n",
- "100%|██████████| 13/13 [00:01<00:00, 7.83it/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "epoch 0: {'accuracy': 0.6985294117647058, 'f1': 0.8183161004431314}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 115/115 [00:26<00:00, 4.42it/s]\n",
- "100%|██████████| 13/13 [00:01<00:00, 7.78it/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "epoch 1: {'accuracy': 0.7009803921568627, 'f1': 0.8195266272189349}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 115/115 [00:26<00:00, 4.38it/s]\n",
- "100%|██████████| 13/13 [00:01<00:00, 7.76it/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "epoch 2: {'accuracy': 0.7083333333333334, 'f1': 0.8231797919762258}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 115/115 [00:26<00:00, 4.39it/s]\n",
- "100%|██████████| 13/13 [00:01<00:00, 8.15it/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "epoch 3: {'accuracy': 0.7009803921568627, 'f1': 0.8195266272189349}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 115/115 [00:27<00:00, 4.21it/s]\n",
- "100%|██████████| 13/13 [00:01<00:00, 8.02it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "epoch 4: {'accuracy': 0.7009803921568627, 'f1': 0.8195266272189349}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from mindnlp.core import value_and_grad\n",
"def forward_fn(**batch):\n",
@@ -557,7 +333,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "4de28f75",
+ "id": "24",
"metadata": {},
"outputs": [],
"source": []
@@ -565,7 +341,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "7cb41077-b027-4c0f-87ed-380cd816d2f4",
+ "id": "25",
"metadata": {},
"outputs": [],
"source": []
diff --git a/Season2.step_into_llm/01.ChatGLM/chatglm4_simple_inference.ipynb b/Season2.step_into_llm/01.ChatGLM/chatglm4_simple_inference.ipynb
index 5483ffc..569a14f 100644
--- a/Season2.step_into_llm/01.ChatGLM/chatglm4_simple_inference.ipynb
+++ b/Season2.step_into_llm/01.ChatGLM/chatglm4_simple_inference.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "d73b7cdc",
+ "id": "0",
"metadata": {},
"source": [
"# ChatGLM4聊天机器人"
@@ -10,7 +10,7 @@
},
{
"cell_type": "markdown",
- "id": "80128802-8a28-45e1-a728-4e673abfdb3e",
+ "id": "1",
"metadata": {},
"source": [
"## 环境配置\n",
@@ -28,7 +28,7 @@
},
{
"cell_type": "markdown",
- "id": "265300fd-3bf5-4df6-9248-1b27cd4f570f",
+ "id": "2",
"metadata": {},
"source": [
"## 代码开发"
@@ -36,294 +36,12 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "897d4ee9-b2b2-4de5-9f4f-7be4bc49b67a",
+ "execution_count": null,
+ "id": "3",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "Building prefix dict from the default dictionary ...\n",
- "Dumping model to file cache /tmp/jieba.cache\n",
- "Loading model cost 1.022 seconds.\n",
- "Prefix dict has been built successfully.\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "ef6c147f60104856b73d7926b515b038",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0.00/6.01k [00:00=1.3\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a4/ed/1f1afb2e9e7f38a545d628f864d562a5ae64fe6f7a10e28ffb9b185b4e89/importlib_resources-6.5.2-py3-none-any.whl (37 kB)\n",
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- "Requirement already satisfied: semantic-version~=2.0 in /home/ma-user/modelarts-dev/modelarts-sdk (from gradio) (2.10.0)\n",
- "Collecting pydub\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a6/53/d78dc063216e62fc55f6b2eebb447f6a4b0a59f55c8406376f76bf959b08/pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
- "Requirement already satisfied: packaging in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from gradio) (24.1)\n",
- "Collecting urllib3~=2.0\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c8/19/4ec628951a74043532ca2cf5d97b7b14863931476d117c471e8e2b1eb39f/urllib3-2.3.0-py3-none-any.whl (128 kB)\n",
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- "\u001b[?25hRequirement already satisfied: huggingface-hub>=0.19.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from gradio) (0.24.2)\n",
- "Requirement already satisfied: pillow<11.0,>=8.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from gradio) (10.0.1)\n",
- "Requirement already satisfied: matplotlib~=3.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from gradio) (3.5.1)\n",
- "Collecting websockets<13.0,>=10.0\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/25/a9/a3e03f9f3c4425a914e5875dd09f2c2559d61b44edd52cf1e6b73f938898/websockets-12.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (130 kB)\n",
- "\u001b[K |████████████████████████████████| 130 kB 51.0 MB/s eta 0:00:01\n",
- "\u001b[?25hRequirement already satisfied: fsspec in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from gradio-client==1.3.0->gradio) (2024.6.1)\n",
- "Requirement already satisfied: idna>=2.8 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from anyio<5.0,>=3.0->gradio) (2.10)\n",
- "Collecting sniffio>=1.1\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl (10 kB)\n",
- "Requirement already satisfied: exceptiongroup>=1.0.2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from anyio<5.0,>=3.0->gradio) (1.2.2)\n",
- "Collecting starlette<0.42.0,>=0.40.0\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/96/00/2b325970b3060c7cecebab6d295afe763365822b1306a12eeab198f74323/starlette-0.41.3-py3-none-any.whl (73 kB)\n",
- "\u001b[K |████████████████████████████████| 73 kB 7.2 MB/s eta 0:00:01\n",
- "\u001b[?25hCollecting httpcore==1.*\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/87/f5/72347bc88306acb359581ac4d52f23c0ef445b57157adedb9aee0cd689d2/httpcore-1.0.7-py3-none-any.whl (78 kB)\n",
- "\u001b[K |████████████████████████████████| 78 kB 20.3 MB/s eta 0:00:01\n",
- "\u001b[?25hRequirement already satisfied: certifi in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from httpx>=0.24.1->gradio) (2024.7.4)\n",
- "Collecting h11<0.15,>=0.13\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/95/04/ff642e65ad6b90db43e668d70ffb6736436c7ce41fcc549f4e9472234127/h11-0.14.0-py3-none-any.whl (58 kB)\n",
- "\u001b[K |████████████████████████████████| 58 kB 19.4 MB/s eta 0:00:01\n",
- "\u001b[?25hRequirement already satisfied: filelock in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub>=0.19.3->gradio) (3.15.4)\n",
- "Requirement already satisfied: requests in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub>=0.19.3->gradio) (2.32.3)\n",
- "Requirement already satisfied: tqdm>=4.42.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub>=0.19.3->gradio) (4.66.4)\n",
- "Requirement already satisfied: zipp>=3.1.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from importlib-resources<7.0,>=1.3->gradio) (3.19.2)\n",
- "Requirement already satisfied: python-dateutil>=2.7 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from matplotlib~=3.0->gradio) (2.9.0.post0)\n",
- "Requirement already satisfied: fonttools>=4.22.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from matplotlib~=3.0->gradio) (4.53.1)\n",
- "Requirement already satisfied: pyparsing>=2.2.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from matplotlib~=3.0->gradio) (3.1.2)\n",
- "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from matplotlib~=3.0->gradio) (1.4.5)\n",
- "Requirement already satisfied: cycler>=0.10 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from matplotlib~=3.0->gradio) (0.12.1)\n",
- "Requirement already satisfied: pytz>=2017.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas<3.0,>=1.0->gradio) (2024.1)\n",
- "Collecting pydantic-core==2.27.2\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/8a/9e/e44b8cb0edf04a2f0a1f6425a65ee089c1d6f9c4c2dcab0209127b6fdfc2/pydantic_core-2.27.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.8 MB)\n",
- "\u001b[K |████████████████████████████████| 1.8 MB 44.3 MB/s eta 0:00:01\n",
- "\u001b[?25hCollecting annotated-types>=0.6.0\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl (13 kB)\n",
- "Requirement already satisfied: six>=1.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib~=3.0->gradio) (1.16.0)\n",
- "Requirement already satisfied: rich>=10.11.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from typer<1.0,>=0.12->gradio) (13.7.1)\n",
- "Requirement already satisfied: click>=8.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from typer<1.0,>=0.12->gradio) (8.1.7)\n",
- "Collecting shellingham>=1.3.0\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e0/f9/0595336914c5619e5f28a1fb793285925a8cd4b432c9da0a987836c7f822/shellingham-1.5.4-py2.py3-none-any.whl (9.8 kB)\n",
- "Requirement already satisfied: markdown-it-py>=2.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (3.0.0)\n",
- "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (2.18.0)\n",
- "Requirement already satisfied: mdurl~=0.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0,>=0.12->gradio) (0.1.2)\n",
- "Requirement already satisfied: latex2mathml in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mdtex2html) (3.77.0)\n",
- "Requirement already satisfied: markdown in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mdtex2html) (3.6)\n",
- "Requirement already satisfied: importlib-metadata>=4.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from markdown->mdtex2html) (8.2.0)\n",
- "Requirement already satisfied: charset-normalizer<4,>=2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->huggingface-hub>=0.19.3->gradio) (2.0.12)\n",
- "Installing collected packages: urllib3, sniffio, h11, pydantic-core, httpcore, anyio, annotated-types, websockets, starlette, shellingham, pydantic, httpx, uvicorn, typer, tomlkit, ruff, python-multipart, pydub, orjson, importlib-resources, gradio-client, ffmpy, fastapi, aiofiles, gradio\n",
- " Attempting uninstall: urllib3\n",
- " Found existing installation: urllib3 1.26.7\n",
- " Uninstalling urllib3-1.26.7:\n",
- " Successfully uninstalled urllib3-1.26.7\n",
- " Attempting uninstall: tomlkit\n",
- " Found existing installation: tomlkit 0.13.0\n",
- " Uninstalling tomlkit-0.13.0:\n",
- " Successfully uninstalled tomlkit-0.13.0\n",
- "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
- "botocore 1.15.49 requires urllib3<1.26,>=1.20; python_version != \"3.4\", but you have urllib3 2.3.0 which is incompatible.\n",
- "modelarts 1.4.28 requires lxml==5.1.0, but you have lxml 4.9.3 which is incompatible.\n",
- "modelarts 1.4.28 requires matplotlib==3.5.2, but you have matplotlib 3.5.1 which is incompatible.\n",
- "modelarts 1.4.28 requires prettytable<=3.7.0, but you have prettytable 3.10.2 which is incompatible.\n",
- "modelarts 1.4.28 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
- "modelarts 1.4.28 requires tqdm<=4.66.1, but you have tqdm 4.66.4 which is incompatible.\n",
- "modelarts 1.4.28 requires typing-extensions==4.7.1, but you have typing-extensions 4.12.2 which is incompatible.\n",
- "modelarts 1.4.28 requires urllib3==1.26.18, but you have urllib3 2.3.0 which is incompatible.\u001b[0m\n",
- "Successfully installed aiofiles-23.2.1 annotated-types-0.7.0 anyio-4.7.0 fastapi-0.115.6 ffmpy-0.5.0 gradio-4.44.1 gradio-client-1.3.0 h11-0.14.0 httpcore-1.0.7 httpx-0.28.1 importlib-resources-6.5.2 orjson-3.10.13 pydantic-2.10.4 pydantic-core-2.27.2 pydub-0.25.1 python-multipart-0.0.20 ruff-0.8.6 shellingham-1.5.4 sniffio-1.3.1 starlette-0.41.3 tomlkit-0.12.0 typer-0.15.1 urllib3-2.3.0 uvicorn-0.34.0 websockets-12.0\n",
- "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
- "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n",
- "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
- "Collecting ipywidgets\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/22/2d/9c0b76f2f9cc0ebede1b9371b6f317243028ed60b90705863d493bae622e/ipywidgets-8.1.5-py3-none-any.whl (139 kB)\n",
- "\u001b[K |████████████████████████████████| 139 kB 1.6 MB/s eta 0:00:01\n",
- "\u001b[?25hCollecting jupyterlab-widgets~=3.0.12\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a9/93/858e87edc634d628e5d752ba944c2833133a28fa87bb093e6832ced36a3e/jupyterlab_widgets-3.0.13-py3-none-any.whl (214 kB)\n",
- "\u001b[K |████████████████████████████████| 214 kB 6.4 MB/s eta 0:00:01 |██████████████████████████████▋ | 204 kB 6.4 MB/s eta 0:00:01\n",
- "\u001b[?25hRequirement already satisfied: traitlets>=4.3.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipywidgets) (5.14.3)\n",
- "Collecting comm>=0.1.3\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e6/75/49e5bfe642f71f272236b5b2d2691cf915a7283cc0ceda56357b61daa538/comm-0.2.2-py3-none-any.whl (7.2 kB)\n",
- "Requirement already satisfied: ipython>=6.1.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipywidgets) (8.18.1)\n",
- "Collecting widgetsnbextension~=4.0.12\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/21/02/88b65cc394961a60c43c70517066b6b679738caf78506a5da7b88ffcb643/widgetsnbextension-4.0.13-py3-none-any.whl (2.3 MB)\n",
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- "\u001b[?25hRequirement already satisfied: decorator in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (4.4.1)\n",
- "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.47)\n",
- "Requirement already satisfied: typing-extensions in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (4.12.2)\n",
- "Requirement already satisfied: pygments>=2.4.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (2.18.0)\n",
- "Requirement already satisfied: exceptiongroup in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (1.2.2)\n",
- "Requirement already satisfied: jedi>=0.16 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n",
- "Requirement already satisfied: matplotlib-inline in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.7)\n",
- "Requirement already satisfied: stack-data in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n",
- "Requirement already satisfied: pexpect>4.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n",
- "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.4)\n",
- "Requirement already satisfied: ptyprocess>=0.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n",
- "Requirement already satisfied: wcwidth in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.13)\n",
- "Requirement already satisfied: executing>=1.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n",
- "Requirement already satisfied: asttokens>=2.1.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n",
- "Requirement already satisfied: pure-eval in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.3)\n",
- "Requirement already satisfied: six>=1.12.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n",
- "Installing collected packages: widgetsnbextension, jupyterlab-widgets, comm, ipywidgets\n",
- "Successfully installed comm-0.2.2 ipywidgets-8.1.5 jupyterlab-widgets-3.0.13 widgetsnbextension-4.0.13\n",
- "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
- "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"!pip install gradio mdtex2html -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
"!pip install ipywidgets -i https://pypi.tuna.tsinghua.edu.cn/simple"
@@ -308,30 +143,10 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "id": "6d43d04e",
+ "execution_count": null,
+ "id": "10",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "--2025-01-05 02:06:29-- https://openi.pcl.ac.cn/lvyufeng/frpc-gradio/raw/branch/master/frpc_linux_amd64\n",
- "Resolving proxy-notebook.modelarts.com (proxy-notebook.modelarts.com)... 192.168.0.33\n",
- "Connecting to proxy-notebook.modelarts.com (proxy-notebook.modelarts.com)|192.168.0.33|:8083... connected.\n",
- "Proxy request sent, awaiting response... 200 OK\n",
- "Length: unspecified [application/octet-stream]\n",
- "Saving to: ‘/home/ma-user/work/frpc_linux_amd64’\n",
- "\n",
- "frpc_linux_amd64 [ <=> ] 10.85M 23.3MB/s in 0.5s \n",
- "\n",
- "2025-01-05 02:06:30 (23.3 MB/s) - ‘/home/ma-user/work/frpc_linux_amd64’ saved [11374592]\n",
- "\n",
- "cp: cannot create regular file '/home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages/gradio/frpc_linux_amd64_v0.2': No such file or directory\n",
- "chmod: cannot access '/home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages/gradio/frpc_linux_amd64_v0.2': No such file or directory\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# %%capture captured_output\n",
"!wget -P /home/ma-user/work https://openi.pcl.ac.cn/lvyufeng/frpc-gradio/raw/branch/master/frpc_linux_amd64\n",
@@ -341,7 +156,7 @@
},
{
"cell_type": "markdown",
- "id": "f6703d3e-8451-47e7-bac8-401cdb039be7",
+ "id": "11",
"metadata": {},
"source": [
"## 3. 代码开发"
@@ -349,259 +164,10 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "id": "9b8ee640",
+ "execution_count": null,
+ "id": "12",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.331.962 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleGetModelId failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleGetModelId\n",
- "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.332.029 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleLoadFromMem failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleLoadFromMem\n",
- "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.332.048 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleUnload failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleUnload\n",
- "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.332.232 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtGetMemUceInfo failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtGetMemUceInfo\n",
- "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.332.249 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtDeviceTaskAbort failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtDeviceTaskAbort\n",
- "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.332.265 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtMemUceRepair failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtMemUceRepair\n",
- "[WARNING] GE_ADPT(6632,ffff8654b0b0,python):2025-01-05-02:06:54.334.715 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol acltdtCleanChannel failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libacl_tdt_channel.so: undefined symbol: acltdtCleanChannel\n",
- "[WARNING] ME(6632:281472935440560,MainProcess):2025-01-05-02:06:54.576.575 [mindspore/run_check/_check_version.py:398] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "cannot found `mindformers.experimental`, please install dev version by\n",
- "`pip install git+https://gitee.com/mindspore/mindformers` \n",
- "or remove mindformers by \n",
- "`pip uninstall mindformers`\n",
- "Building prefix dict from the default dictionary ...\n",
- "Dumping model to file cache /tmp/jieba.cache\n",
- "Loading model cost 1.327 seconds.\n",
- "Prefix dict has been built successfully.\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/Cython/Compiler/Main.py:384: FutureWarning: Cython directive 'language_level' not set, using '3str' for now (Py3). This has changed from earlier releases! File: /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/mindnlp/transformers/models/graphormer/algos_graphormer.pyx\n",
- " tree = Parsing.p_module(s, pxd, full_module_name)\n",
- "[WARNING] ME(6632:281472935440560,MainProcess):2025-01-05-02:07:20.833.731 [mindspore/run_check/_check_version.py:398] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(6632:281472935440560,MainProcess):2025-01-05-02:07:20.836.993 [mindspore/run_check/_check_version.py:398] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "f26abf33eb1c47ed8105f3a2788d3e5c",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0.00/773 [00:00"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/plain": []
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"#运行Gradio界面，运行成功后点击“Running on public URL”后的网页链接即可体验\n",
"import gradio as gr\n",
@@ -814,7 +319,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "356a6c98-d42a-4470-a1e9-40714a17642e",
+ "id": "18",
"metadata": {},
"outputs": [],
"source": []
diff --git "a/Season2.step_into_llm/03.Decoding/\346\226\207\346\234\254\350\247\243\347\240\201.ipynb" "b/Season2.step_into_llm/03.Decoding/\346\226\207\346\234\254\350\247\243\347\240\201.ipynb"
index 22921f5..bcf33e0 100644
--- "a/Season2.step_into_llm/03.Decoding/\346\226\207\346\234\254\350\247\243\347\240\201.ipynb"
+++ "b/Season2.step_into_llm/03.Decoding/\346\226\207\346\234\254\350\247\243\347\240\201.ipynb"
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "70943fc4",
+ "id": "0",
"metadata": {},
"source": [
"## __文本解码原理\\-\\-以MindNLP为例__\n",
@@ -26,7 +26,7 @@
},
{
"cell_type": "markdown",
- "id": "39212dbd",
+ "id": "1",
"metadata": {},
"source": [
"__Greedy search__\n",
@@ -43,7 +43,7 @@
},
{
"cell_type": "markdown",
- "id": "f1b3ea92",
+ "id": "2",
"metadata": {},
"source": [
"__环境准备__"
@@ -52,7 +52,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "5c56827b",
+ "id": "3",
"metadata": {},
"outputs": [],
"source": [
@@ -64,8 +64,8 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "id": "c40cdb84",
+ "execution_count": null,
+ "id": "4",
"metadata": {},
"outputs": [],
"source": [
@@ -96,118 +96,10 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "id": "5593606f",
+ "execution_count": null,
+ "id": "5",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33mWARNING: Skipping mindspore-gpu as it is not installed.\u001b[0m\n",
- "\u001b[33mWARNING: Skipping mindvision as it is not installed.\u001b[0m\n",
- "\u001b[33mWARNING: Skipping mindinsight as it is not installed.\u001b[0m\n",
- "Looking in indexes: http://pip.modelarts.private.com:8888/repository/pypi/simple\n",
- "Processing ./mindnlp-0.4.1-py3-none-any.whl\n",
- "Requirement already satisfied: pytest==7.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (7.2.0)\n",
- "Requirement already satisfied: evaluate in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.4.3)\n",
- "Requirement already satisfied: regex in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2024.7.24)\n",
- "Requirement already satisfied: safetensors in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.4.5)\n",
- "Requirement already satisfied: pyctcdecode in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.5.0)\n",
- "Requirement already satisfied: tokenizers==0.19.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.19.1)\n",
- "Requirement already satisfied: datasets in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (3.1.0)\n",
- "Requirement already satisfied: sentencepiece in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.2.0)\n",
- "Requirement already satisfied: mindspore>=2.2.14 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2.4.0)\n",
- "Requirement already satisfied: requests in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2.32.3)\n",
- "Requirement already satisfied: pillow>=10.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (10.0.1)\n",
- "Requirement already satisfied: tqdm in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (4.65.0)\n",
- "Requirement already satisfied: addict in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2.4.0)\n",
- "Requirement already satisfied: ml-dtypes in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.4.0)\n",
- "Requirement already satisfied: tomli>=1.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (2.0.1)\n",
- "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (1.2.2)\n",
- "Requirement already satisfied: iniconfig in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (2.0.0)\n",
- "Requirement already satisfied: pluggy<2.0,>=0.12 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (1.5.0)\n",
- "Requirement already satisfied: attrs>=19.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (23.2.0)\n",
- "Requirement already satisfied: packaging in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (24.1)\n",
- "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from tokenizers==0.19.1->mindnlp==0.4.1) (0.24.2)\n",
- "Requirement already satisfied: pyyaml>=5.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (6.0.1)\n",
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (4.12.2)\n",
- "Requirement already satisfied: filelock in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (3.15.4)\n",
- "Requirement already satisfied: fsspec>=2023.5.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (2024.6.1)\n",
- "Requirement already satisfied: asttokens>=2.0.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (2.4.1)\n",
- "Requirement already satisfied: psutil>=5.6.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (5.9.5)\n",
- "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.22.0)\n",
- "Requirement already satisfied: scipy>=1.5.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.10.1)\n",
- "Requirement already satisfied: protobuf>=3.13.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (3.20.2)\n",
- "Requirement already satisfied: astunparse>=1.6.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.6.3)\n",
- "Requirement already satisfied: six>=1.12.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from asttokens>=2.0.4->mindspore>=2.2.14->mindnlp==0.4.1) (1.16.0)\n",
- "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.1) (0.38.4)\n",
- "Requirement already satisfied: multiprocess<0.70.17 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (0.70.16)\n",
- "Collecting tqdm\n",
- " Downloading http://pip.modelarts.private.com:8888/repository/pypi/packages/tqdm/4.67.1/tqdm-4.67.1-py3-none-any.whl (78 kB)\n",
- "\u001b[K |████████████████████████████████| 78 kB 39.9 MB/s eta 0:00:01\n",
- "\u001b[?25hRequirement already satisfied: pandas in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (1.3.5)\n",
- "Requirement already satisfied: xxhash in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (3.5.0)\n",
- "Requirement already satisfied: aiohttp in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (3.11.9)\n",
- "Requirement already satisfied: pyarrow>=15.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (18.1.0)\n",
- "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (0.3.8)\n",
- "Requirement already satisfied: async-timeout<6.0,>=4.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (5.0.1)\n",
- "Requirement already satisfied: propcache>=0.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (0.2.1)\n",
- "Requirement already satisfied: aiosignal>=1.1.2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (1.3.1)\n",
- "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (1.18.3)\n",
- "Requirement already satisfied: multidict<7.0,>=4.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (6.1.0)\n",
- "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (2.4.4)\n",
- "Requirement already satisfied: frozenlist>=1.1.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (1.5.0)\n",
- "Requirement already satisfied: idna<4,>=2.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2.10)\n",
- "Requirement already satisfied: charset-normalizer<4,>=2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2.0.12)\n",
- "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (1.26.7)\n",
- "Requirement already satisfied: certifi>=2017.4.17 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2024.7.4)\n",
- "Requirement already satisfied: python-dateutil>=2.7.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.1) (2.9.0.post0)\n",
- "Requirement already satisfied: pytz>=2017.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.1) (2024.1)\n",
- "Requirement already satisfied: pygtrie<3.0,>=2.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.1) (2.5.0)\n",
- "Requirement already satisfied: hypothesis<7,>=6.14 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.1) (6.122.1)\n",
- "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.1) (2.4.0)\n",
- "Installing collected packages: tqdm, mindnlp\n",
- " Attempting uninstall: tqdm\n",
- " Found existing installation: tqdm 4.65.0\n",
- " Uninstalling tqdm-4.65.0:\n",
- " Successfully uninstalled tqdm-4.65.0\n",
- " Attempting uninstall: mindnlp\n",
- " Found existing installation: mindnlp 0.3.0\n",
- " Uninstalling mindnlp-0.3.0:\n",
- " Successfully uninstalled mindnlp-0.3.0\n",
- "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
- "modelarts 1.4.28 requires lxml==5.1.0, but you have lxml 4.9.3 which is incompatible.\n",
- "modelarts 1.4.28 requires matplotlib==3.5.2, but you have matplotlib 3.5.1 which is incompatible.\n",
- "modelarts 1.4.28 requires prettytable<=3.7.0, but you have prettytable 3.10.2 which is incompatible.\n",
- "modelarts 1.4.28 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
- "modelarts 1.4.28 requires tqdm<=4.66.1, but you have tqdm 4.67.1 which is incompatible.\n",
- "modelarts 1.4.28 requires typing-extensions==4.7.1, but you have typing-extensions 4.12.2 which is incompatible.\n",
- "modelarts 1.4.28 requires urllib3==1.26.18, but you have urllib3 1.26.7 which is incompatible.\u001b[0m\n",
- "Successfully installed mindnlp-0.4.1 tqdm-4.67.1\n",
- "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
- "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n",
- "env: no_proxy='a.test.com,127.0.0.1,2.2.2.2'\n",
- "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
- "Collecting mindspore==2.4.0\n",
- " Using cached https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.4.0/MindSpore/unified/aarch64/mindspore-2.4.0-cp39-cp39-linux_aarch64.whl (333.7 MB)\n",
- "Requirement already satisfied: protobuf>=3.13.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (3.20.2)\n",
- "Requirement already satisfied: psutil>=5.6.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (5.9.5)\n",
- "Requirement already satisfied: astunparse>=1.6.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (1.6.3)\n",
- "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (1.22.0)\n",
- "Requirement already satisfied: scipy>=1.5.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (1.10.1)\n",
- "Requirement already satisfied: pillow>=6.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (10.0.1)\n",
- "Requirement already satisfied: asttokens>=2.0.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (2.4.1)\n",
- "Requirement already satisfied: safetensors>=0.4.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (0.4.5)\n",
- "Requirement already satisfied: packaging>=20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (24.1)\n",
- "Requirement already satisfied: six>=1.12.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from asttokens>=2.0.4->mindspore==2.4.0) (1.16.0)\n",
- "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore==2.4.0) (0.38.4)\n",
- "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
- "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"#安装mindspore2.4\n",
"!pip uninstall mindspore-gpu -y\n",
@@ -220,95 +112,10 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "id": "54e990b4",
+ "execution_count": null,
+ "id": "6",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.447.323 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleGetModelId failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleGetModelId\n",
- "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.447.394 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleLoadFromMem failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleLoadFromMem\n",
- "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.447.413 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleUnload failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleUnload\n",
- "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.447.594 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtGetMemUceInfo failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtGetMemUceInfo\n",
- "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.447.611 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtDeviceTaskAbort failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtDeviceTaskAbort\n",
- "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.447.627 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtMemUceRepair failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtMemUceRepair\n",
- "[WARNING] GE_ADPT(36162,ffff9d7f40b0,python):2024-12-03-21:20:47.449.575 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol acltdtCleanChannel failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libacl_tdt_channel.so: undefined symbol: acltdtCleanChannel\n",
- "[WARNING] ME(36162:281473324105904,MainProcess):2024-12-03-21:20:47.589.499 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "Building prefix dict from the default dictionary ...\n",
- "Loading model from cache /tmp/jieba.cache\n",
- "Loading model cost 1.288 seconds.\n",
- "Prefix dict has been built successfully.\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/mindnlp/transformers/tokenization_utils_base.py:1526: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted, and will be then set to `False` by default. \n",
- " warnings.warn(\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "6716298ddb8449b38bfaef38c9ca1860",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 1%|1 | 7.61M/523M [00:00=1.20.0 (from mindspore==2.5.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/54/30/c2a907b9443cf42b90c17ad10c1e8fa801975f01cb9764f3f8eb8aea638b/numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.2/18.2 MB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
- "\u001b[?25hCollecting protobuf>=3.13.0 (from mindspore==2.5.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/28/50/1925de813499546bc8ab3ae857e3ec84efe7d2f19b34529d0c7c3d02d11d/protobuf-6.30.2-cp39-abi3-manylinux2014_x86_64.whl (316 kB)\n",
- "Requirement already satisfied: asttokens>=2.0.4 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore==2.5.0) (3.0.0)\n",
- "Collecting pillow>=6.2.0 (from mindspore==2.5.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/f6/46/0bd0ca03d9d1164a7fa33d285ef6d1c438e963d0c8770e4c5b3737ef5abe/pillow-11.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.4 MB)\n",
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- "\u001b[?25hCollecting scipy>=1.5.4 (from mindspore==2.5.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/35/f5/d0ad1a96f80962ba65e2ce1de6a1e59edecd1f0a7b55990ed208848012e0/scipy-1.13.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.6 MB)\n",
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- "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore==2.5.0) (24.2)\n",
- "Requirement already satisfied: psutil>=5.6.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore==2.5.0) (5.9.1)\n",
- "Collecting astunparse>=1.6.3 (from mindspore==2.5.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/2b/03/13dde6512ad7b4557eb792fbcf0c653af6076b81e5941d36ec61f7ce6028/astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\n",
- "Collecting safetensors>=0.4.0 (from mindspore==2.5.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/a6/f8/dae3421624fcc87a89d42e1898a798bc7ff72c61f38973a65d60df8f124c/safetensors-0.5.3-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (471 kB)\n",
- "Collecting dill>=0.3.7 (from mindspore==2.5.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/46/d1/e73b6ad76f0b1fb7f23c35c6d95dbc506a9c8804f43dda8cb5b0fa6331fd/dill-0.3.9-py3-none-any.whl (119 kB)\n",
- "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore==2.5.0) (0.45.1)\n",
- "Requirement already satisfied: six<2.0,>=1.6.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore==2.5.0) (1.17.0)\n",
- "Installing collected packages: safetensors, protobuf, pillow, numpy, dill, astunparse, scipy, mindspore\n",
- "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
- "auto-tune 0.1.0 requires te, which is not installed.\n",
- "schedule-search 0.0.1 requires absl-py, which is not installed.\u001b[0m\u001b[31m\n",
- "\u001b[0mSuccessfully installed astunparse-1.6.3 dill-0.3.9 mindspore-2.5.0 numpy-1.26.4 pillow-11.1.0 protobuf-6.30.2 safetensors-0.5.3 scipy-1.13.1\n",
- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.5.0/MindSpore/unified/x86_64/mindspore-2.5.0-cp39-cp39-linux_x86_64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple"
]
@@ -154,88 +115,9 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: https://mirrors.aliyun.com/pypi/simple\n",
- "Collecting mindnlp==0.4.0\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/0f/a8/5a072852d28a51417b5e330b32e6ae5f26b491ef01a15ba968e77f785e69/mindnlp-0.4.0-py3-none-any.whl (8.4 MB)\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m0m\n",
- "\u001b[?25hRequirement already satisfied: mindspore>=2.2.14 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.5.0)\n",
- "Requirement already satisfied: tqdm in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (4.67.1)\n",
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- "Requirement already satisfied: datasets in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (3.5.0)\n",
- "Requirement already satisfied: evaluate in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.4.3)\n",
- "Requirement already satisfied: tokenizers==0.19.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.19.1)\n",
- "Requirement already satisfied: safetensors in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.3)\n",
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- "Collecting jieba (from mindnlp==0.4.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/c6/cb/18eeb235f833b726522d7ebed54f2278ce28ba9438e3135ab0278d9792a2/jieba-0.42.1.tar.gz (19.2 MB)\n",
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- "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n",
- "\u001b[?25hRequirement already satisfied: pytest==7.2.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (7.2.0)\n",
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- "Requirement already satisfied: attrs>=19.2.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (24.3.0)\n",
- "Requirement already satisfied: iniconfig in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.1.0)\n",
- "Requirement already satisfied: packaging in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (24.2)\n",
- "Requirement already satisfied: pluggy<2.0,>=0.12 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.5.0)\n",
- "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.2.2)\n",
- "Requirement already satisfied: tomli>=1.0.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.0.1)\n",
- "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from tokenizers==0.19.1->mindnlp==0.4.0) (0.30.2)\n",
- "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.26.4)\n",
- "Requirement already satisfied: protobuf>=3.13.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (6.30.2)\n",
- "Requirement already satisfied: asttokens>=2.0.4 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (3.0.0)\n",
- "Requirement already satisfied: scipy>=1.5.4 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.13.1)\n",
- "Requirement already satisfied: psutil>=5.6.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (5.9.1)\n",
- "Requirement already satisfied: astunparse>=1.6.3 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.6.3)\n",
- "Requirement already satisfied: dill>=0.3.7 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (0.3.8)\n",
- "Requirement already satisfied: filelock in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.18.0)\n",
- "Requirement already satisfied: pyarrow>=15.0.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (19.0.1)\n",
- "Requirement already satisfied: pandas in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (2.2.3)\n",
- "Requirement already satisfied: xxhash in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.5.0)\n",
- "Requirement already satisfied: multiprocess<0.70.17 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (0.70.16)\n",
- "Requirement already satisfied: fsspec<=2024.12.0,>=2023.1.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from fsspec[http]<=2024.12.0,>=2023.1.0->datasets->mindnlp==0.4.0) (2024.12.0)\n",
- "Requirement already satisfied: aiohttp in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.11.16)\n",
- "Requirement already satisfied: pyyaml>=5.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (6.0.2)\n",
- "Requirement already satisfied: charset-normalizer<4,>=2 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (3.3.2)\n",
- "Requirement already satisfied: idna<4,>=2.5 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (3.7)\n",
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- "Requirement already satisfied: certifi>=2017.4.17 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2025.1.31)\n",
- "Requirement already satisfied: pygtrie<3.0,>=2.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (2.5.0)\n",
- "Requirement already satisfied: hypothesis<7,>=6.14 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (6.130.13)\n",
- "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (0.45.1)\n",
- "Requirement already satisfied: six<2.0,>=1.6.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (1.17.0)\n",
- "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (2.6.1)\n",
- "Requirement already satisfied: aiosignal>=1.1.2 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (1.3.2)\n",
- "Requirement already satisfied: async-timeout<6.0,>=4.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (5.0.1)\n",
- "Requirement already satisfied: frozenlist>=1.1.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (1.5.0)\n",
- "Requirement already satisfied: multidict<7.0,>=4.5 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (6.4.2)\n",
- "Requirement already satisfied: propcache>=0.2.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (0.3.1)\n",
- "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (1.19.0)\n",
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (4.13.1)\n",
- "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.0) (2.4.0)\n",
- "Requirement already satisfied: python-dateutil>=2.8.2 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2.9.0.post0)\n",
- "Requirement already satisfied: pytz>=2020.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
- "Requirement already satisfied: tzdata>=2022.7 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
- "Building wheels for collected packages: jieba\n",
- " Building wheel for jieba (setup.py) ... \u001b[?25ldone\n",
- "\u001b[?25h Created wheel for jieba: filename=jieba-0.42.1-py3-none-any.whl size=19314508 sha256=30064bba508d12a9c2c545bdec7e271f61d5a83e9fdd53298a82e74659e1fd26\n",
- " Stored in directory: /home/jiangna1/.cache/pip/wheels/95/ef/7c/d8b3108835edfa15487417c5bddff166482b195d8090117ac5\n",
- "Successfully built jieba\n",
- "Installing collected packages: jieba, mindnlp\n",
- "Successfully installed jieba-0.42.1 mindnlp-0.4.0\n",
- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%pip install mindnlp==0.4.0 -i https://mirrors.aliyun.com/pypi/simple\n"
]
@@ -272,7 +154,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -284,17 +166,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] ME(52617:140460371482432,MainProcess):2025-04-11-09:21:14.572.566 [mindspore/context.py:1335] For 'context.set_context', the parameter 'device_target' will be deprecated and removed in a future version. Please use the api mindspore.set_device() instead.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"#将模式设置为动态图模式（PYNATIVE_MODE），并指定设备目标为Ascend芯片\n",
"ms.set_context(mode=ms.PYNATIVE_MODE, device_target=\"Ascend\")\n"
@@ -302,7 +176,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -366,7 +240,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -404,7 +278,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -423,17 +297,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[{'instruction': '只剩一个心脏了还能活吗？', 'output': '能，人本来就只有一个心脏。'}, {'instruction': '爸爸再婚，我是不是就有了个新娘？', 'output': '不是的，你有了一个继母。\"新娘\"是指新婚的女方，而你爸爸再婚，他的新婚妻子对你来说是继母。'}, {'instruction': '樟脑丸是我吃过最难吃的硬糖有奇怪的味道怎么还有人买', 'output': '樟脑丸并不是硬糖，而是一种常见的驱虫药，不能食用。虽然它的味道可能不太好，但是由于其有效的驱虫效果，所以仍然有很多人会购买。'}]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"with open(data_path, 'r', encoding='utf-8') as f:\n",
" data = json.load(f)\n",
@@ -449,20 +315,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[1, 29871, 2056]"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"from mindnlp.transformers import AutoTokenizer\n",
"\n",
@@ -482,7 +337,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -515,32 +370,9 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[MS_ALLOC_CONF]Runtime config: enable_vmm:True vmm_align_size:2MB\n",
- "Sample 0: Input IDs: [ 1 4911 29901 29871 47133 32002 37755 30743 33302 31704]\n",
- "Sample 0: Labels: [ 1 4911 29901 29871 47133 32002 37755 30743 33302 31704]\n",
- "\n",
- "Sample 1: Input IDs: [ 1 4911 29901 29871 33594 31733 33364 30214 30672 32308]\n",
- "Sample 1: Labels: [ 1 4911 29901 29871 33594 31733 33364 30214 30672 32308]\n",
- "\n",
- "Sample 2: Input IDs: [ 1 4911 29901 29871 47019 33027 31818 34030 39950 44345]\n",
- "Sample 2: Labels: [ 1 4911 29901 29871 47019 33027 31818 34030 39950 44345]\n",
- "\n",
- "Sample 3: Input IDs: [ 1 4911 29901 29871 34214 30698 30429 36310 32658 30743]\n",
- "Sample 3: Labels: [ 1 4911 29901 29871 34214 30698 30429 36310 32658 30743]\n",
- "\n",
- "Sample 4: Input IDs: [ 1 4911 29901 32581 34822 31639 2882 6530 30883 30210]\n",
- "Sample 4: Labels: [ 1 4911 29901 32581 34822 31639 2882 6530 30883 30210]\n",
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"for i, sample in enumerate(train_dataset.create_dict_iterator()):\n",
" if i >= 5:\n",
@@ -565,7 +397,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -584,33 +416,9 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "LlamaForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`.`PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
- " - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
- " - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/jiangna1/miniconda3/envs/ms39/lib/python3.9/site-packages/mindnlp/transformers/generation/configuration_utils.py:557: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.2` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
- " warnings.warn(\n",
- "/home/jiangna1/miniconda3/envs/ms39/lib/python3.9/site-packages/mindnlp/transformers/generation/configuration_utils.py:562: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
- " warnings.warn(\n",
- "/home/jiangna1/miniconda3/envs/ms39/lib/python3.9/site-packages/mindnlp/transformers/generation/configuration_utils.py:557: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.2` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
- " warnings.warn(\n",
- "/home/jiangna1/miniconda3/envs/ms39/lib/python3.9/site-packages/mindnlp/transformers/generation/configuration_utils.py:562: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
- " warnings.warn(\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from mindnlp.transformers import AutoModelForCausalLM, GenerationConfig\n",
"\n",
@@ -621,7 +429,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {},
"outputs": [],
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+ "execution_count": null,
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"tags": []
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{
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"outputs": [],
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"outputs": [],
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- " 3%|▎ | 10/350 [02:02<1:04:58, 11.47s/it]We detected that you are passing `past_key_values` as a tuple and this is deprecated. Please use an appropriate `Cache` class\n",
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- "{'eval_loss': 3.0746374130249023, 'eval_runtime': 2.405, 'eval_samples_per_second': 2.079, 'eval_steps_per_second': 0.416, 'epoch': 41.41}\n"
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- "{'eval_loss': 3.060976266860962, 'eval_runtime': 2.3947, 'eval_samples_per_second': 2.088, 'eval_steps_per_second': 0.418, 'epoch': 45.18}\n"
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- "{'eval_loss': 3.049464702606201, 'eval_runtime': 2.3926, 'eval_samples_per_second': 2.09, 'eval_steps_per_second': 0.418, 'epoch': 48.94}\n"
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- "{'eval_loss': 3.047043561935425, 'eval_runtime': 2.3877, 'eval_samples_per_second': 2.094, 'eval_steps_per_second': 0.419, 'epoch': 50.82}\n"
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- "{'eval_loss': 3.0422093868255615, 'eval_runtime': 2.3938, 'eval_samples_per_second': 2.089, 'eval_steps_per_second': 0.418, 'epoch': 54.59}\n"
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- "{'eval_loss': 3.0407938957214355, 'eval_runtime': 2.3837, 'eval_samples_per_second': 2.098, 'eval_steps_per_second': 0.42, 'epoch': 56.47}\n"
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- "{'eval_loss': 3.0404062271118164, 'eval_runtime': 2.3948, 'eval_samples_per_second': 2.088, 'eval_steps_per_second': 0.418, 'epoch': 58.35}\n"
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- "{'eval_loss': 3.0399832725524902, 'eval_runtime': 2.3929, 'eval_samples_per_second': 2.089, 'eval_steps_per_second': 0.418, 'epoch': 60.24}\n"
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- "{'eval_loss': 3.039731740951538, 'eval_runtime': 2.3855, 'eval_samples_per_second': 2.096, 'eval_steps_per_second': 0.419, 'epoch': 62.12}\n"
- ]
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- "{'eval_loss': 3.0396060943603516, 'eval_runtime': 2.3904, 'eval_samples_per_second': 2.092, 'eval_steps_per_second': 0.418, 'epoch': 64.0}\n"
- ]
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- "output_type": "stream",
- "text": [
- "{'eval_loss': 3.039623260498047, 'eval_runtime': 2.3877, 'eval_samples_per_second': 2.094, 'eval_steps_per_second': 0.419, 'epoch': 65.88}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "The intermediate checkpoints of PEFT may not be saved correctly, consider using a custom callback to save adapter_model.bin in corresponding saving folders. Check some examples here: https://github.com/huggingface/peft/issues/96\n",
- "100%|██████████| 350/350 [1:08:12<00:00, 11.69s/it]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'train_runtime': 4092.112, 'train_samples_per_second': 23.264, 'train_steps_per_second': 0.086, 'train_loss': 3.2515819876534597, 'epoch': 65.88}\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"trainer.train()\n",
"\n",
@@ -1415,109 +627,9 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "model merge succeeded\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "LlamaForCausalLM(\n",
- " (model): LlamaModel(\n",
- " (embed_tokens): Embedding(55296, 4096, padding_idx=0)\n",
- " (layers): ModuleList(\n",
- " (0-3): 4 x LlamaDecoderLayer(\n",
- " (self_attn): LlamaAttention(\n",
- " (q_proj): lora.Linear(\n",
- " (base_layer): Linear (4096 -> 4096)\n",
- " (lora_dropout): ModuleDict(\n",
- " (default): Dropout(p=0.1, inplace=False)\n",
- " )\n",
- " (lora_A): ModuleDict(\n",
- " (default): Linear (4096 -> 8)\n",
- " )\n",
- " (lora_B): ModuleDict(\n",
- " (default): Linear (8 -> 4096)\n",
- " )\n",
- " (lora_embedding_A): ParameterDict()\n",
- " (lora_embedding_B): ParameterDict()\n",
- " (lora_magnitude_vector): ModuleDict()\n",
- " )\n",
- " (k_proj): lora.Linear(\n",
- " (base_layer): Linear (4096 -> 4096)\n",
- " (lora_dropout): ModuleDict(\n",
- " (default): Dropout(p=0.1, inplace=False)\n",
- " )\n",
- " (lora_A): ModuleDict(\n",
- " (default): Linear (4096 -> 8)\n",
- " )\n",
- " (lora_B): ModuleDict(\n",
- " (default): Linear (8 -> 4096)\n",
- " )\n",
- " (lora_embedding_A): ParameterDict()\n",
- " (lora_embedding_B): ParameterDict()\n",
- " (lora_magnitude_vector): ModuleDict()\n",
- " )\n",
- " (v_proj): lora.Linear(\n",
- " (base_layer): Linear (4096 -> 4096)\n",
- " (lora_dropout): ModuleDict(\n",
- " (default): Dropout(p=0.1, inplace=False)\n",
- " )\n",
- " (lora_A): ModuleDict(\n",
- " (default): Linear (4096 -> 8)\n",
- " )\n",
- " (lora_B): ModuleDict(\n",
- " (default): Linear (8 -> 4096)\n",
- " )\n",
- " (lora_embedding_A): ParameterDict()\n",
- " (lora_embedding_B): ParameterDict()\n",
- " (lora_magnitude_vector): ModuleDict()\n",
- " )\n",
- " (o_proj): lora.Linear(\n",
- " (base_layer): Linear (4096 -> 4096)\n",
- " (lora_dropout): ModuleDict(\n",
- " (default): Dropout(p=0.1, inplace=False)\n",
- " )\n",
- " (lora_A): ModuleDict(\n",
- " (default): Linear (4096 -> 8)\n",
- " )\n",
- " (lora_B): ModuleDict(\n",
- " (default): Linear (8 -> 4096)\n",
- " )\n",
- " (lora_embedding_A): ParameterDict()\n",
- " (lora_embedding_B): ParameterDict()\n",
- " (lora_magnitude_vector): ModuleDict()\n",
- " )\n",
- " (rotary_emb): LlamaRotaryEmbedding()\n",
- " )\n",
- " (mlp): LlamaMLP(\n",
- " (gate_proj): Linear (4096 -> 11008)\n",
- " (up_proj): Linear (4096 -> 11008)\n",
- " (down_proj): Linear (11008 -> 4096)\n",
- " (act_fn): SiLU()\n",
- " )\n",
- " (input_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n",
- " (post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n",
- " )\n",
- " )\n",
- " (norm): LlamaRMSNorm((4096,), eps=1e-05)\n",
- " (rotary_emb): LlamaRotaryEmbedding()\n",
- " )\n",
- " (lm_head): Linear (4096 -> 55296)\n",
- ")"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"#将 LoRA微调后的参数加载到预训练模型中\n",
"from mindnlp.peft import PeftModel\n",
@@ -1536,7 +648,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1570,30 +682,9 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/jiangna1/miniconda3/envs/ms39/lib/python3.9/site-packages/mindnlp/transformers/generation/configuration_utils.py:557: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.2` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
- " warnings.warn(\n",
- "/home/jiangna1/miniconda3/envs/ms39/lib/python3.9/site-packages/mindnlp/transformers/generation/configuration_utils.py:562: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
- " warnings.warn(\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "User: 如何保持清醒?\n",
- "LLAMA: 以下是用户和助手之间的问答。\n",
- "问：如何保持清醒?\n",
- "答：在你睡觉的时候，你的大脑会一直处于兴奋状态中;当你醒来时，它就会继续工作了。所以如果你的睡眠时间很短的话，你就不会感到太疲劳或昏沉。你可以通过使用一些药物来帮助恢复精力、提高警觉度以及降低血压等方法使自己进入深度睡眠的状态。此外，你还可以通过服用维生素B6片剂或者吃富含蛋白质的食物等方式让自己重新振作起来。\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"question = \"如何保持清醒?\"\n",
"response = generate_response(question, model, tokenizer)\n",
diff --git a/Season2.step_into_llm/09.PEFT/PEFT_exampleWith_mrpcDataset.ipynb b/Season2.step_into_llm/09.PEFT/PEFT_exampleWith_mrpcDataset.ipynb
index 7bf5450..9f70250 100644
--- a/Season2.step_into_llm/09.PEFT/PEFT_exampleWith_mrpcDataset.ipynb
+++ b/Season2.step_into_llm/09.PEFT/PEFT_exampleWith_mrpcDataset.ipynb
@@ -10,7 +10,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -21,7 +21,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -80,178 +80,20 @@
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{
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- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
- "Collecting mindspore==2.3.1\n",
- " Downloading https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.3.1/MindSpore/unified/x86_64/mindspore-2.3.1-cp39-cp39-linux_x86_64.whl (946.9 MB)\n",
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- "\u001b[?25hCollecting numpy<2.0.0,>=1.20.0 (from mindspore==2.3.1)\n",
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- "Requirement already satisfied: asttokens>=2.0.4 in /home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages (from mindspore==2.3.1) (2.4.1)\n",
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- "Collecting astunparse>=1.6.3 (from mindspore==2.3.1)\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/2b/03/13dde6512ad7b4557eb792fbcf0c653af6076b81e5941d36ec61f7ce6028/astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\n",
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- "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore==2.3.1) (0.44.0)\n",
- "Installing collected packages: protobuf, pillow, numpy, astunparse, scipy, mindspore\n",
- "Successfully installed astunparse-1.6.3 mindspore-2.3.1 numpy-1.26.4 pillow-10.4.0 protobuf-5.28.2 scipy-1.13.1\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"!pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.3.1/MindSpore/unified/x86_64/mindspore-2.3.1-cp39-cp39-linux_x86_64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {
- "scrolled": true,
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: http://repo.myhuaweicloud.com/repository/pypi/simple\n",
- "Collecting mindnlp==0.4.0\n",
- " Downloading https://repo.mindspore.cn/mindspore-lab/mindnlp/newest/any/mindnlp-0.4.0-py3-none-any.whl (8.2 MB)\n",
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- " Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/48/5d/acf5905c36149bbaec41ccf7f2b68814647347b72075ac0b1fe3022fdc73/tqdm-4.66.5-py3-none-any.whl (78 kB)\n",
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- "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n",
- "\u001b[?25hCollecting pytest==7.2.0 (from mindnlp==0.4.0)\n",
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- "Requirement already satisfied: six>=1.12.0 in /home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages (from asttokens>=2.0.4->mindspore>=2.2.14->mindnlp==0.4.0) (1.16.0)\n",
- "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (0.44.0)\n",
- "Collecting aiohappyeyeballs>=2.3.0 (from aiohttp->datasets->mindnlp==0.4.0)\n",
- " Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/18/b6/58ea188899950d759a837f9a58b2aee1d1a380ea4d6211ce9b1823748851/aiohappyeyeballs-2.4.0-py3-none-any.whl (12 kB)\n",
- "Collecting aiosignal>=1.1.2 (from aiohttp->datasets->mindnlp==0.4.0)\n",
- " Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/76/ac/a7305707cb852b7e16ff80eaf5692309bde30e2b1100a1fcacdc8f731d97/aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
- "Collecting frozenlist>=1.1.1 (from aiohttp->datasets->mindnlp==0.4.0)\n",
- " Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/70/b0/6f1ebdabfb604e39a0f84428986b89ab55f246b64cddaa495f2c953e1f6b/frozenlist-1.4.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (240 kB)\n",
- "Collecting multidict<7.0,>=4.5 (from aiohttp->datasets->mindnlp==0.4.0)\n",
- " Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/5e/41/0d0fb18c1ad574f807196f5f3d99164edf9de3e169a58c6dc2d6ed5742b9/multidict-6.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (124 kB)\n",
- "Collecting yarl<2.0,>=1.0 (from aiohttp->datasets->mindnlp==0.4.0)\n",
- " Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/ff/be/78953a3d5154b974af49ce367f1a8d4751ababdf26a66ae607b4ae625d99/yarl-1.11.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (453 kB)\n",
- "Collecting async-timeout<5.0,>=4.0 (from aiohttp->datasets->mindnlp==0.4.0)\n",
- " Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/a7/fa/e01228c2938de91d47b307831c62ab9e4001e747789d0b05baf779a6488c/async_timeout-4.0.3-py3-none-any.whl (5.7 kB)\n",
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (4.12.2)\n",
- "Collecting sortedcontainers<3.0.0,>=2.1.0 (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.0)\n",
- " Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/32/46/9cb0e58b2deb7f82b84065f37f3bffeb12413f947f9388e4cac22c4621ce/sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB)\n",
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- "Collecting pytz>=2020.1 (from pandas->datasets->mindnlp==0.4.0)\n",
- " Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/11/c3/005fcca25ce078d2cc29fd559379817424e94885510568bc1bc53d7d5846/pytz-2024.2-py2.py3-none-any.whl (508 kB)\n",
- "Collecting tzdata>=2022.7 (from pandas->datasets->mindnlp==0.4.0)\n",
- " Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/65/58/f9c9e6be752e9fcb8b6a0ee9fb87e6e7a1f6bcab2cdc73f02bb7ba91ada0/tzdata-2024.1-py2.py3-none-any.whl (345 kB)\n",
- "Building wheels for collected packages: jieba\n",
- " Building wheel for jieba (setup.py) ... \u001b[?25ldone\n",
- "\u001b[?25h Created wheel for jieba: filename=jieba-0.42.1-py3-none-any.whl size=19314458 sha256=de190811901ea689a37a0ecc8e410ef914b6b76740894df9b47c2bdbfd51decc\n",
- " Stored in directory: /home/ma-user/.cache/pip/wheels/2d/22/9e/9af7e8c2773513ac75905acfb75073922bcc1aa176f730a0c9\n",
- "Successfully built jieba\n",
- "Installing collected packages: sortedcontainers, sentencepiece, pytz, pygtrie, jieba, addict, xxhash, urllib3, tzdata, tqdm, tomli, safetensors, regex, pyyaml, pyarrow, pluggy, multidict, ml-dtypes, iniconfig, idna, fsspec, frozenlist, filelock, dill, charset-normalizer, certifi, attrs, async-timeout, aiohappyeyeballs, yarl, requests, pytest, pandas, multiprocess, hypothesis, aiosignal, pyctcdecode, huggingface-hub, aiohttp, tokenizers, datasets, evaluate, mindnlp\n",
- "Successfully installed addict-2.4.0 aiohappyeyeballs-2.4.0 aiohttp-3.10.5 aiosignal-1.3.1 async-timeout-4.0.3 attrs-24.2.0 certifi-2024.8.30 charset-normalizer-3.3.2 datasets-3.0.0 dill-0.3.8 evaluate-0.4.3 filelock-3.16.1 frozenlist-1.4.1 fsspec-2024.6.1 huggingface-hub-0.25.0 hypothesis-6.112.1 idna-3.10 iniconfig-2.0.0 jieba-0.42.1 mindnlp-0.4.0 ml-dtypes-0.5.0 multidict-6.1.0 multiprocess-0.70.16 pandas-2.2.2 pluggy-1.5.0 pyarrow-17.0.0 pyctcdecode-0.5.0 pygtrie-2.5.0 pytest-7.2.0 pytz-2024.2 pyyaml-6.0.2 regex-2024.9.11 requests-2.32.3 safetensors-0.4.5 sentencepiece-0.2.0 sortedcontainers-2.4.0 tokenizers-0.19.1 tomli-2.0.1 tqdm-4.66.5 tzdata-2024.1 urllib3-2.2.3 xxhash-3.5.0 yarl-1.11.1\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"#安装mindnlp的daily包，待正式发布后可改为直接安装mindnlp包\n",
"!pip install https://repo.mindspore.cn/mindspore-lab/mindnlp/newest/any/mindnlp-0.4.0-py3-none-any.whl\n",
@@ -260,52 +102,18 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Name: mindspore\n",
- "Version: 2.3.1\n",
- "Summary: MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.\n",
- "Home-page: https://www.mindspore.cn\n",
- "Author: The MindSpore Authors\n",
- "Author-email: contact@mindspore.cn\n",
- "License: Apache 2.0\n",
- "Location: /home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages\n",
- "Requires: asttokens, astunparse, numpy, packaging, pillow, protobuf, psutil, scipy\n",
- "Required-by: mindnlp\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"!pip show mindspore"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Name: mindnlp\n",
- "Version: 0.4.0\n",
- "Summary: An open source natural language processing research tool box. Git version: [sha1]:2fb76bf, [branch]: (HEAD, origin/master, origin/HEAD, master)\n",
- "Home-page: https://github.com/mindlab-ai/mindnlp/tree/master/\n",
- "Author: MindSpore Team\n",
- "Author-email: \n",
- "License: Apache 2.0\n",
- "Location: /home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages\n",
- "Requires: addict, datasets, evaluate, jieba, mindspore, ml-dtypes, pyctcdecode, pytest, regex, requests, safetensors, sentencepiece, tokenizers, tqdm\n",
- "Required-by: \n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"!pip show mindnlp"
]
@@ -330,45 +138,18 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n",
- "Building prefix dict from the default dictionary ...\n",
- "Loading model from cache /tmp/jieba.cache\n",
- "Loading model cost 0.753 seconds.\n",
- "Prefix dict has been built successfully.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from mindnlp.dataset import load_dataset"
]
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Repo card metadata block was not found. Setting CardData to empty.\n",
- "Downloading data: 100%|██████████| 1.14M/1.14M [00:00<00:00, 1.45MB/s]\n",
- "Downloading data: 100%|██████████| 127k/127k [00:00<00:00, 131kB/s] \n",
- "Downloading data: 100%|██████████| 533k/533k [00:00<00:00, 666kB/s] \n",
- "Generating train split: 3668 examples [00:00, 176571.87 examples/s]\n",
- "Generating validation split: 408 examples [00:00, 48980.37 examples/s]\n",
- "Generating test split: 1725 examples [00:00, 153982.47 examples/s]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"mrpc_dict = load_dataset(\"SetFit/mrpc\") # 如果本地未下载会先下载，若已下载则会直接加载\n",
"mrpc_train = mrpc_dict['train']\n",
@@ -378,19 +159,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "train: 3668 samples\n",
- "validation: 408 samples\n",
- "test: 1725 samples\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# 打印每个数据集的样本数量\n",
"for k,v in mrpc_dict.items():\n",
@@ -399,21 +170,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "text1: Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .\n",
- "text2: Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .\n",
- "label: 1\n",
- "idx: 0\n",
- "label_text: equivalent\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# 打印原数据集的样本格式及其内容\n",
"for dataDict in mrpc_train.create_dict_iterator():\n",
@@ -424,7 +183,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -483,7 +242,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -522,7 +281,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -606,7 +365,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -649,27 +408,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 26.0/26.0 [00:00<00:00, 134kB/s]\n",
- "0.99MB [00:00, 3.22MB/s]\n",
- "446kB [00:00, 1.77MB/s]\n",
- "1.29MB [00:00, 4.27MB/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "3\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from mindnlp.transformers import GPT2Tokenizer\n",
"\n",
@@ -686,7 +427,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -706,7 +447,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -718,7 +459,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -730,20 +471,9 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['input_ids', 'attention_mask', 'token_type_ids', 'lens', 'labels']"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"train_dataloader.get_col_names() # 数据集样本的列名"
]
@@ -764,7 +494,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -787,40 +517,18 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 523M/523M [00:32<00:00, 16.7MB/s] \n",
- "100%|██████████| 124/124 [00:00<00:00, 365kB/s]\n",
- "The following parameters in models are missing parameter:\n",
- "['score.weight']\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"model = GPT2ForSequenceClassification.from_pretrained(\"gpt2\", num_labels = 2)"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Embedding"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"model.config.pad_token_id = tokenizer.pad_token_id\n",
"model.resize_token_embeddings(model.config.vocab_size + num_added_toks)"
@@ -835,7 +543,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -845,17 +553,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "trainable params: 296,448 || all params: 124,737,792 || trainable%: 0.23765692437461133\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"if args.is_lora:\n",
" # build peft model\n",
@@ -867,7 +567,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -878,7 +578,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -901,107 +601,18 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The train will start from the checkpoint saved in '.mindnlp/peft_model/mrpc_IA3'.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 0: 100%|██████████| 459/459 [03:05<00:00, 2.47it/s, loss=0.6872016] \n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Checkpoint: 'gpt2_mrpc_finetune_epoch_0.ckpt' has been saved in epoch: 0.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Evaluate: 100%|██████████| 51/51 [00:07<00:00, 7.18it/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Evaluate Score: {'Accuracy': 0.6838235294117647}\n",
- "---------------Best Model: 'gpt2_mrpc_finetune_best.ckpt' has been saved in epoch: 0.---------------\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Epoch 1: 100%|██████████| 459/459 [03:04<00:00, 2.49it/s, loss=0.6677042] \n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Checkpoint: 'gpt2_mrpc_finetune_epoch_1.ckpt' has been saved in epoch: 1.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Evaluate: 100%|██████████| 51/51 [00:07<00:00, 7.21it/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Evaluate Score: {'Accuracy': 0.6838235294117647}\n",
- "Loading best model from '.mindnlp/peft_model/mrpc_IA3' with '['Accuracy']': [0.6838235294117647]...\n",
- "---------------The model is already load the best model from 'gpt2_mrpc_finetune_best.ckpt'.---------------\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"trainer.run(tgt_columns=\"labels\")"
]
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Evaluate: 100%|██████████| 216/216 [00:30<00:00, 7.13it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Evaluate Score: {'Accuracy': 0.664927536231884}\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"evaluator = Evaluator(network=model, eval_dataset=test_dataloader, metrics=metric)\n",
"evaluator.run(tgt_columns=\"labels\")"
diff --git a/Season2.step_into_llm/13.musicgen/run_musicgen.ipynb b/Season2.step_into_llm/13.musicgen/run_musicgen.ipynb
index e04f60f..5b49f96 100644
--- a/Season2.step_into_llm/13.musicgen/run_musicgen.ipynb
+++ b/Season2.step_into_llm/13.musicgen/run_musicgen.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "70300319-d206-43ce-b3bf-3da6b079f20f",
+ "id": "0",
"metadata": {
"id": "70300319-d206-43ce-b3bf-3da6b079f20f"
},
@@ -31,7 +31,7 @@
},
{
"cell_type": "markdown",
- "id": "640da8c2",
+ "id": "1",
"metadata": {},
"source": [
"## Environment Setup\n",
@@ -50,7 +50,7 @@
},
{
"cell_type": "markdown",
- "id": "77ee39cc-654b-4f0e-b601-013e484c16f0",
+ "id": "2",
"metadata": {
"id": "77ee39cc-654b-4f0e-b601-013e484c16f0"
},
@@ -62,89 +62,12 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "b0d87424-9f38-4658-ba47-2a465d52ad77",
+ "execution_count": null,
+ "id": "3",
"metadata": {
"id": "b0d87424-9f38-4658-ba47-2a465d52ad77"
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "Building prefix dict from the default dictionary ...\n",
- "Dumping model to file cache /tmp/jieba.cache\n",
- "Loading model cost 1.012 seconds.\n",
- "Prefix dict has been built successfully.\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "75a95ec53fc947d5988d6827e7d5053c",
- "version_major": 2,
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- },
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- " "
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"from IPython.display import Audio\n",
"\n",
@@ -238,7 +134,7 @@
},
{
"cell_type": "markdown",
- "id": "6de58334-40f7-4924-addb-2d6ff34c0590",
+ "id": "8",
"metadata": {
"id": "6de58334-40f7-4924-addb-2d6ff34c0590"
},
@@ -248,8 +144,8 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "id": "04291f52-0a75-4ddb-9eff-e853d0f17288",
+ "execution_count": null,
+ "id": "9",
"metadata": {
"id": "04291f52-0a75-4ddb-9eff-e853d0f17288"
},
@@ -262,7 +158,7 @@
},
{
"cell_type": "markdown",
- "id": "e52ff5b2-c170-4079-93a4-a02acbdaeb39",
+ "id": "10",
"metadata": {
"id": "e52ff5b2-c170-4079-93a4-a02acbdaeb39"
},
@@ -272,23 +168,12 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "id": "d75ad107-e19b-47f3-9cf1-5102ab4ae74a",
+ "execution_count": null,
+ "id": "11",
"metadata": {
"id": "d75ad107-e19b-47f3-9cf1-5102ab4ae74a"
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "5.12"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"audio_length_in_s = 256 / model.config.audio_encoder.frame_rate\n",
"\n",
@@ -297,7 +182,7 @@
},
{
"cell_type": "markdown",
- "id": "9a0e999b-2595-4090-8e1a-acfaa42d2581",
+ "id": "12",
"metadata": {
"id": "9a0e999b-2595-4090-8e1a-acfaa42d2581"
},
@@ -311,108 +196,12 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "id": "5fba4154-13f6-403a-958b-101d6eacfb6e",
+ "execution_count": null,
+ "id": "13",
"metadata": {
"id": "5fba4154-13f6-403a-958b-101d6eacfb6e"
},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "58ec6de737fd4523ae119fb576f5d490",
- "version_major": 2,
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- " "
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"from mindnlp.transformers import AutoProcessor\n",
"\n",
@@ -431,7 +220,7 @@
},
{
"cell_type": "markdown",
- "id": "4851a94c-ae02-41c9-b1dd-c1422ba34dc0",
+ "id": "14",
"metadata": {
"id": "4851a94c-ae02-41c9-b1dd-c1422ba34dc0"
},
@@ -445,7 +234,7 @@
},
{
"cell_type": "markdown",
- "id": "d391b2a1-6376-4b69-b562-4388b731cf60",
+ "id": "15",
"metadata": {
"id": "d391b2a1-6376-4b69-b562-4388b731cf60"
},
@@ -459,58 +248,12 @@
},
{
"cell_type": "code",
- "execution_count": 12,
- "id": "56a5c28a-f6c1-4ac8-ae08-6776a2b2c5b8",
+ "execution_count": null,
+ "id": "16",
"metadata": {
"id": "56a5c28a-f6c1-4ac8-ae08-6776a2b2c5b8"
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
- "Requirement already satisfied: soundfile in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (0.12.1)\n",
- "Collecting librosa\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/8c/8a/2d231b35456506b7c98b3ab9bbf07917b205fed8615d2e59e976ab497fff/librosa-0.10.2.post1-py3-none-any.whl (260 kB)\n",
- "Requirement already satisfied: cffi>=1.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from soundfile) (1.17.0)\n",
- "Collecting audioread>=2.1.9 (from librosa)\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/57/8d/30aa32745af16af0a9a650115fbe81bde7c610ed5c21b381fca0196f3a7f/audioread-3.0.1-py3-none-any.whl (23 kB)\n",
- "Requirement already satisfied: numpy!=1.22.0,!=1.22.1,!=1.22.2,>=1.20.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from librosa) (1.26.4)\n",
- "Requirement already satisfied: scipy>=1.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from librosa) (1.13.1)\n",
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- "Requirement already satisfied: joblib>=0.14 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from librosa) (1.4.2)\n",
- "Requirement already satisfied: decorator>=4.3.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from librosa) (5.1.1)\n",
- "Collecting numba>=0.51.0 (from librosa)\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/3b/bd/f1985719ff34e37e07bb18f9d3acd17e5a21da255f550c8eae031e2ddf5f/numba-0.60.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl (3.4 MB)\n",
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- "\u001b[?25hCollecting pooch>=1.1 (from librosa)\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a8/87/77cc11c7a9ea9fd05503def69e3d18605852cd0d4b0d3b8f15bbeb3ef1d1/pooch-1.8.2-py3-none-any.whl (64 kB)\n",
- "Collecting soxr>=0.3.2 (from librosa)\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c3/c6/f3d54e8c579aa5f192d62c87cd86b88e0b1d6fcab7b541663a3816f1eb06/soxr-0.4.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.2 MB)\n",
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- "\u001b[?25hRequirement already satisfied: typing-extensions>=4.1.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from librosa) (4.11.0)\n",
- "Collecting lazy-loader>=0.1 (from librosa)\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl (12 kB)\n",
- "Collecting msgpack>=1.0 (from librosa)\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ad/61/225d64e983e51f960cac41fd1084188764fcc7430e75f609ad9d86e47839/msgpack-1.0.8-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (375 kB)\n",
- "Requirement already satisfied: pycparser in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from cffi>=1.0->soundfile) (2.22)\n",
- "Requirement already satisfied: packaging in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from lazy-loader>=0.1->librosa) (24.1)\n",
- "Collecting llvmlite<0.44,>=0.43.0dev0 (from numba>=0.51.0->librosa)\n",
- " Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c8/c6/9324eb5de2ba9d99cbed853d85ba7a318652a48e077797bec27cf40f911d/llvmlite-0.43.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (42.9 MB)\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.9/42.9 MB\u001b[0m \u001b[31m95.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
- "\u001b[?25hRequirement already satisfied: platformdirs>=2.5.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pooch>=1.1->librosa) (4.2.2)\n",
- "Requirement already satisfied: requests>=2.19.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pooch>=1.1->librosa) (2.32.3)\n",
- "Requirement already satisfied: threadpoolctl>=3.1.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from scikit-learn>=0.20.0->librosa) (3.5.0)\n",
- "Requirement already satisfied: charset-normalizer<4,>=2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests>=2.19.0->pooch>=1.1->librosa) (3.3.2)\n",
- "Requirement already satisfied: idna<4,>=2.5 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests>=2.19.0->pooch>=1.1->librosa) (3.7)\n",
- "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests>=2.19.0->pooch>=1.1->librosa) (2.2.2)\n",
- "Requirement already satisfied: certifi>=2017.4.17 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests>=2.19.0->pooch>=1.1->librosa) (2024.7.4)\n",
- "Installing collected packages: soxr, msgpack, llvmlite, lazy-loader, audioread, pooch, numba, librosa\n",
- "Successfully installed audioread-3.0.1 lazy-loader-0.4 librosa-0.10.2.post1 llvmlite-0.43.0 msgpack-1.0.8 numba-0.60.0 pooch-1.8.2 soxr-0.4.0\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from mindnlp.dataset import load_dataset\n",
"\n",
@@ -520,8 +263,8 @@
},
{
"cell_type": "code",
- "execution_count": 13,
- "id": "79fd7ab3-4d1f-4838-aff8-13d6fa568b3c",
+ "execution_count": null,
+ "id": "17",
"metadata": {},
"outputs": [],
"source": [
@@ -539,36 +282,10 @@
},
{
"cell_type": "code",
- "execution_count": 14,
- "id": "3787d4e6-6d1c-479b-8c92-c8a58d176144",
+ "execution_count": null,
+ "id": "18",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "/\r"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " \n",
- " "
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=256)\n",
"\n",
@@ -577,7 +294,7 @@
},
{
"cell_type": "markdown",
- "id": "77518aa4-1b9b-4af6-b5ac-8ecdcb79b4cc",
+ "id": "19",
"metadata": {
"id": "77518aa4-1b9b-4af6-b5ac-8ecdcb79b4cc"
},
@@ -590,31 +307,12 @@
},
{
"cell_type": "code",
- "execution_count": 15,
- "id": "5495f568-51ca-439d-b47b-8b52e89b78f1",
+ "execution_count": null,
+ "id": "20",
"metadata": {
"id": "5495f568-51ca-439d-b47b-8b52e89b78f1"
},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " \n",
- " "
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"sample = next(iter(dataset.create_dict_iterator(output_numpy=True)))[\"audio\"]\n",
"\n",
@@ -642,7 +340,7 @@
},
{
"cell_type": "markdown",
- "id": "viwTDmzl8ZDN",
+ "id": "21",
"metadata": {
"id": "viwTDmzl8ZDN"
},
@@ -654,37 +352,19 @@
},
{
"cell_type": "code",
- "execution_count": 16,
- "id": "0zM4notb8Y1g",
+ "execution_count": null,
+ "id": "22",
"metadata": {
"id": "0zM4notb8Y1g"
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "GenerationConfig {\n",
- " \"bos_token_id\": 2048,\n",
- " \"decoder_start_token_id\": 2048,\n",
- " \"do_sample\": true,\n",
- " \"guidance_scale\": 3.0,\n",
- " \"max_length\": 1500,\n",
- " \"pad_token_id\": 2048\n",
- "}"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"model.generation_config"
]
},
{
"cell_type": "markdown",
- "id": "DLSnSwau8jyW",
+ "id": "23",
"metadata": {
"id": "DLSnSwau8jyW"
},
@@ -694,8 +374,8 @@
},
{
"cell_type": "code",
- "execution_count": 17,
- "id": "ensSj1IB81dA",
+ "execution_count": null,
+ "id": "24",
"metadata": {
"id": "ensSj1IB81dA"
},
@@ -713,7 +393,7 @@
},
{
"cell_type": "markdown",
- "id": "UjqGnfc-9ZFJ",
+ "id": "25",
"metadata": {
"id": "UjqGnfc-9ZFJ"
},
@@ -723,8 +403,8 @@
},
{
"cell_type": "code",
- "execution_count": 18,
- "id": "KAExrhDl9YvS",
+ "execution_count": null,
+ "id": "26",
"metadata": {
"id": "KAExrhDl9YvS"
},
@@ -735,7 +415,7 @@
},
{
"cell_type": "markdown",
- "id": "HdGdoGAs84hS",
+ "id": "27",
"metadata": {
"id": "HdGdoGAs84hS"
},
diff --git a/Season2.step_into_llm/16.Practical-cases/difussion/mindspore_diffusion.ipynb b/Season2.step_into_llm/16.Practical-cases/difussion/mindspore_diffusion.ipynb
index 5787de7..b8e0074 100644
--- a/Season2.step_into_llm/16.Practical-cases/difussion/mindspore_diffusion.ipynb
+++ b/Season2.step_into_llm/16.Practical-cases/difussion/mindspore_diffusion.ipynb
@@ -51,28 +51,13 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n",
- "[WARNING] GE_ADPT(2087,ffffa9645010,python):2024-11-22-08:06:47.434.450 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleGetModelId failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleGetModelId\n",
- "[WARNING] GE_ADPT(2087,ffffa9645010,python):2024-11-22-08:06:47.434.505 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleLoadFromMem failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleLoadFromMem\n",
- "[WARNING] GE_ADPT(2087,ffffa9645010,python):2024-11-22-08:06:47.434.522 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleUnload failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleUnload\n",
- "[WARNING] ME(2087:281473523666960,MainProcess):2024-11-22-08:06:47.587.306 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(2087:281473523666960,MainProcess):2024-11-22-08:06:52.295.753 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(2087:281473523666960,MainProcess):2024-11-22-08:06:52.299.553 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import math\n",
"from functools import partial\n",
@@ -301,7 +286,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -371,7 +356,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -403,7 +388,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -443,7 +428,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -518,7 +503,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -618,7 +603,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -669,7 +654,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -806,7 +791,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -833,7 +818,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -881,25 +866,13 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/image_cat.zip (170 kB)\n",
- "\n",
- "file_sizes: 100%|████████████████████████████| 174k/174k [00:00<00:00, 2.34MB/s]\n",
- "Extracting zip file...\n",
- "Successfully downloaded / unzipped to ./\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# 下载猫猫图像\n",
"url = 'https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/image_cat.zip'\n",
@@ -908,24 +881,13 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"from PIL import Image\n",
"\n",
@@ -952,28 +914,13 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(1, 3, 128, 128)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] MD(2087,fffb11ffb0e0,python):2024-11-22-08:07:31.342.048 [mindspore/ccsrc/minddata/dataset/engine/datasetops/source/image_folder_op.cc:192] PrescanWorkerEntry] ImageFolder operator unsupported file found: ./image_cat/jpg/.ipynb_checkpoints, extension: .ipynb_checkpoints.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from mindspore.dataset import ImageFolderDataset\n",
"\n",
@@ -1011,7 +958,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1048,31 +995,13 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] CORE(2087,ffffa9645010,python):2024-11-22-08:07:34.817.459 [mindspore/core/utils/ms_context.cc:530] GetJitLevel] Set jit level to O2 for rank table startup method.\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"reverse_image = compose(reverse_transform, x_start[0])\n",
"reverse_image.show()"
@@ -1091,7 +1020,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1119,7 +1048,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1139,31 +1068,13 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# 设置 time step\n",
"t = Tensor([40])\n",
@@ -1185,7 +1096,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1221,31 +1132,13 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\\\r"
- ]
- },
- {
- "data": {
- "image/png": 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AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAED/NNINAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACgfxrpBgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA0D+NdAMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAOifRroBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD0z/8DTbR3cU1TivcAAAAASUVORK5CYII=",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"plot([get_noisy_image(x_start, Tensor([t])) for t in [0, 50, 100, 150, 199]])"
]
@@ -1263,7 +1156,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1313,25 +1206,13 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/dataset.zip (29.4 MB)\n",
- "\n",
- "file_sizes: 100%|██████████████████████████| 30.9M/30.9M [00:03<00:00, 9.62MB/s]\n",
- "Extracting zip file...\n",
- "Successfully downloaded / unzipped to ./\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# 下载MNIST数据集\n",
"url = 'https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/dataset.zip'\n",
@@ -1340,7 +1221,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1371,7 +1252,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1394,21 +1275,13 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dict_keys(['image'])\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"x = next(dataset.create_dict_iterator())\n",
"print(x.keys())"
@@ -1438,7 +1311,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1510,7 +1383,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1557,293 +1430,13 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] ME(2087:281473523666960,MainProcess):2024-11-22-08:09:24.234.888 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(2087:281473523666960,MainProcess):2024-11-22-08:09:24.237.027 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " epoch: 0 step: 0 Loss: 0.48542005\n",
- " epoch: 0 step: 500 Loss: 0.11173138\n",
- " epoch: 0 step: 1000 Loss: 0.13944255\n",
- " epoch: 0 step: 1500 Loss: 0.10376401\n",
- " epoch: 0 step: 2000 Loss: 0.089299366\n",
- " epoch: 0 step: 2500 Loss: 0.07341044\n",
- " epoch: 0 step: 3000 Loss: 0.09959525\n",
- " epoch: 0 step: 3500 Loss: 0.07543949\n",
- "training time: 3413.4763474464417 s\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 7%|▋ | 14/200 [00:06<01:12, 2.57it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 20%|██ | 40/200 [00:16<01:01, 2.59it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\\\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 33%|███▎ | 66/200 [00:25<00:50, 2.68it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "|\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 46%|████▋ | 93/200 [00:36<00:40, 2.66it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "/\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 60%|██████ | 120/200 [00:46<00:30, 2.66it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 73%|███████▎ | 146/200 [00:55<00:19, 2.71it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\\\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 85%|████████▌ | 170/200 [01:06<00:13, 2.24it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "|\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 96%|█████████▌| 192/200 [01:16<00:03, 2.30it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "/\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 100%|██████████| 200/200 [01:19<00:00, 2.53it/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " epoch: 1 step: 0 Loss: 0.055218957\n",
- " epoch: 1 step: 500 Loss: 0.06055673\n",
- " epoch: 1 step: 1000 Loss: 0.04366886\n",
- " epoch: 1 step: 1500 Loss: 0.07368293\n",
- " epoch: 1 step: 2000 Loss: 0.046694543\n",
- " epoch: 1 step: 2500 Loss: 0.0933092\n",
- " epoch: 1 step: 3000 Loss: 0.07171022\n",
- " epoch: 1 step: 3500 Loss: 0.046739772\n",
- "training time: 3252.585412979126 s\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 6%|▌ | 12/200 [00:04<01:06, 2.85it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\\\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 20%|██ | 41/200 [00:14<00:54, 2.94it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "|\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 35%|███▌ | 70/200 [00:24<00:44, 2.93it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "/\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 50%|████▉ | 99/200 [00:34<00:35, 2.85it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 62%|██████▎ | 125/200 [00:43<00:36, 2.04it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\\\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 74%|███████▎ | 147/200 [00:54<00:19, 2.66it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "|\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 88%|████████▊ | 175/200 [01:04<00:08, 2.88it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "/\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 100%|██████████| 200/200 [01:12<00:00, 2.76it/s]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Training Success!\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"import mindspore\n",
"import time\n",
@@ -1889,126 +1482,13 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 12%|█▎ | 25/200 [00:09<01:04, 2.72it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\\\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 25%|██▌ | 50/200 [00:19<00:58, 2.56it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "|\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 38%|███▊ | 76/200 [00:29<00:47, 2.62it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "/\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 52%|█████▏ | 103/200 [00:39<00:32, 2.94it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "-\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 66%|██████▋ | 133/200 [00:49<00:22, 2.93it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\\\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 80%|████████ | 160/200 [00:59<00:15, 2.55it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "|\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 92%|█████████▏| 184/200 [01:09<00:06, 2.37it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "/\r"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "sampling loop time step: 100%|██████████| 200/200 [01:15<00:00, 2.64it/s]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# 采样64个图片\n",
"unet_model.set_train(False)\n",
@@ -2017,34 +1497,13 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# 展示一个随机效果\n",
"random_index = 5\n",
@@ -2068,31 +1527,13 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "MovieWriter ffmpeg unavailable; using Pillow instead.\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"import matplotlib.animation as animation\n",
"\n",
diff --git a/Season2.step_into_llm/16.Practical-cases/gan/mindspore_gan.ipynb b/Season2.step_into_llm/16.Practical-cases/gan/mindspore_gan.ipynb
index b4d1563..5faafc3 100644
--- a/Season2.step_into_llm/16.Practical-cases/gan/mindspore_gan.ipynb
+++ b/Season2.step_into_llm/16.Practical-cases/gan/mindspore_gan.ipynb
@@ -78,36 +78,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-09T09:45:06.383718Z",
- "start_time": "2023-02-09T09:44:40.500860Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip (10.3 MB)\n",
- "\n",
- "file_sizes: 100%|██████████████████████████| 10.8M/10.8M [00:00<00:00, 14.3MB/s]\n",
- "Extracting zip file...\n",
- "Successfully downloaded / unzipped to .\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'.'"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"# 数据下载\n",
"from download import download\n",
@@ -127,34 +100,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-09T09:45:20.017779Z",
- "start_time": "2023-02-09T09:45:06.385713Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] GE_ADPT(89941,ffff9151c010,python):2024-11-21-11:25:34.848.224 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleGetModelId failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleGetModelId\n",
- "[WARNING] GE_ADPT(89941,ffff9151c010,python):2024-11-21-11:25:34.848.282 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleLoadFromMem failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleLoadFromMem\n",
- "[WARNING] GE_ADPT(89941,ffff9151c010,python):2024-11-21-11:25:34.848.301 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleUnload failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleUnload\n",
- "[WARNING] ME(89941:281473119797264,MainProcess):2024-11-21-11:25:34.978.156 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(89941:281473119797264,MainProcess):2024-11-21-11:25:39.614.403 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(89941:281473119797264,MainProcess):2024-11-21-11:25:39.616.933 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Iter size: 468\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"import numpy as np\n",
"import mindspore.dataset as ds\n",
@@ -197,25 +145,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-09T09:45:21.094899Z",
- "start_time": "2023-02-09T09:45:20.018778Z"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
@@ -241,22 +173,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-09T09:45:21.152791Z",
- "start_time": "2023-02-09T09:45:21.096895Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] CORE(89941,ffff9151c010,python):2024-11-21-11:25:45.211.882 [mindspore/core/utils/ms_context.cc:530] GetJitLevel] Set jit level to O2 for rank table startup method.\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"import random\n",
"import numpy as np\n",
@@ -288,13 +207,8 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-09T09:45:21.190676Z",
- "start_time": "2023-02-09T09:45:21.153742Z"
- }
- },
+ "execution_count": null,
+ "metadata": {},
"outputs": [],
"source": [
"from mindspore import nn\n",
@@ -347,13 +261,8 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-09T09:45:21.207645Z",
- "start_time": "2023-02-09T09:45:21.191642Z"
- }
- },
+ "execution_count": null,
+ "metadata": {},
"outputs": [],
"source": [
" # 判别器\n",
@@ -390,13 +299,8 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-09T09:45:21.267439Z",
- "start_time": "2023-02-09T09:45:21.208597Z"
- }
- },
+ "execution_count": null,
+ "metadata": {},
"outputs": [],
"source": [
"lr = 0.0002 # 学习率\n",
@@ -428,13 +332,8 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-09T09:45:21.273427Z",
- "start_time": "2023-02-09T09:45:21.268436Z"
- }
- },
+ "execution_count": null,
+ "metadata": {},
"outputs": [],
"source": [
"import os\n",
@@ -457,1239 +356,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-09T13:36:50.742960Z",
- "start_time": "2023-02-09T09:45:21.274421Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] ME(89941:281473119797264,MainProcess):2024-11-21-11:25:57.508.263 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(89941:281473119797264,MainProcess):2024-11-21-11:25:57.510.581 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch:[ 0/200], step:[ 0/ 468], loss_d:1.320055 , loss_g:0.719802 , time:19.905008s, lr:0.000200\n",
- "Epoch:[ 0/200], step:[ 100/ 468], loss_d:1.163385 , loss_g:1.108864 , time:0.041823s, lr:0.000200\n",
- "Epoch:[ 0/200], step:[ 200/ 468], loss_d:1.350701 , loss_g:0.743335 , time:0.042872s, lr:0.000200\n",
- "Epoch:[ 0/200], step:[ 300/ 468], loss_d:1.167697 , loss_g:0.798472 , time:0.039293s, lr:0.000200\n",
- "Epoch:[ 0/200], step:[ 400/ 468], loss_d:1.238361 , loss_g:0.894295 , time:0.043511s, lr:0.000200\n",
- "time of epoch 1 is 49.98s\n",
- "Epoch:[ 1/200], step:[ 0/ 468], loss_d:1.078835 , loss_g:1.547477 , time:0.040593s, lr:0.000200\n",
- "Epoch:[ 1/200], step:[ 100/ 468], loss_d:1.147045 , loss_g:1.294871 , time:0.039692s, lr:0.000200\n",
- "Epoch:[ 1/200], step:[ 200/ 468], loss_d:0.900437 , loss_g:1.353014 , time:0.040931s, lr:0.000200\n",
- "Epoch:[ 1/200], step:[ 300/ 468], loss_d:1.205142 , loss_g:1.071688 , time:0.051307s, lr:0.000200\n",
- "Epoch:[ 1/200], step:[ 400/ 468], loss_d:1.007885 , loss_g:1.318245 , time:0.042542s, lr:0.000200\n",
- "time of epoch 2 is 33.42s\n",
- "Epoch:[ 2/200], step:[ 0/ 468], loss_d:1.242887 , loss_g:1.014036 , time:0.043069s, lr:0.000200\n",
- "Epoch:[ 2/200], step:[ 100/ 468], loss_d:1.413479 , loss_g:1.971671 , time:0.041541s, lr:0.000200\n",
- "Epoch:[ 2/200], step:[ 200/ 468], loss_d:1.120526 , loss_g:1.033336 , time:0.036249s, lr:0.000200\n",
- "Epoch:[ 2/200], step:[ 300/ 468], loss_d:1.235674 , loss_g:1.074993 , time:0.039372s, lr:0.000200\n",
- "Epoch:[ 2/200], step:[ 400/ 468], loss_d:1.221805 , loss_g:0.840315 , time:0.039893s, lr:0.000200\n",
- "time of epoch 3 is 31.02s\n",
- "Epoch:[ 3/200], step:[ 0/ 468], loss_d:1.117647 , loss_g:1.612250 , time:0.046237s, lr:0.000200\n",
- "Epoch:[ 3/200], step:[ 100/ 468], loss_d:1.265316 , loss_g:0.903599 , time:0.036853s, lr:0.000200\n",
- "Epoch:[ 3/200], step:[ 200/ 468], loss_d:1.226729 , loss_g:0.980157 , time:0.038633s, lr:0.000200\n",
- "Epoch:[ 3/200], step:[ 300/ 468], loss_d:1.200382 , loss_g:1.071837 , time:0.036601s, lr:0.000200\n",
- "Epoch:[ 3/200], step:[ 400/ 468], loss_d:1.513936 , loss_g:1.807340 , time:0.040931s, lr:0.000200\n",
- "time of epoch 4 is 31.60s\n",
- "Epoch:[ 4/200], step:[ 0/ 468], loss_d:1.126245 , loss_g:1.145799 , time:0.040272s, lr:0.000200\n",
- "Epoch:[ 4/200], step:[ 100/ 468], loss_d:1.167360 , loss_g:0.821916 , time:0.041112s, lr:0.000200\n",
- "Epoch:[ 4/200], step:[ 200/ 468], loss_d:1.376115 , loss_g:1.165061 , time:0.039760s, lr:0.000200\n",
- "Epoch:[ 4/200], step:[ 300/ 468], loss_d:1.219422 , loss_g:1.012858 , time:0.036109s, lr:0.000200\n",
- "Epoch:[ 4/200], step:[ 400/ 468], loss_d:1.218934 , loss_g:0.849428 , time:0.041807s, lr:0.000200\n",
- "time of epoch 5 is 32.15s\n",
- "Epoch:[ 5/200], step:[ 0/ 468], loss_d:1.050424 , loss_g:1.264432 , time:0.042454s, lr:0.000200\n",
- "Epoch:[ 5/200], step:[ 100/ 468], loss_d:1.178966 , loss_g:1.074042 , time:0.048522s, lr:0.000200\n",
- "Epoch:[ 5/200], step:[ 200/ 468], loss_d:1.221158 , loss_g:1.131485 , time:0.045912s, lr:0.000200\n",
- "Epoch:[ 5/200], step:[ 300/ 468], loss_d:1.155866 , loss_g:0.830786 , time:0.040529s, lr:0.000200\n",
- "Epoch:[ 5/200], step:[ 400/ 468], loss_d:1.127836 , loss_g:0.964429 , time:0.043036s, lr:0.000200\n",
- "time of epoch 6 is 36.01s\n",
- "Epoch:[ 6/200], step:[ 0/ 468], loss_d:1.285615 , loss_g:0.762968 , time:0.048970s, lr:0.000200\n",
- "Epoch:[ 6/200], step:[ 100/ 468], loss_d:1.235404 , loss_g:0.955353 , time:0.054592s, lr:0.000200\n",
- "Epoch:[ 6/200], step:[ 200/ 468], loss_d:1.069780 , loss_g:1.035738 , time:0.046011s, lr:0.000200\n",
- "Epoch:[ 6/200], step:[ 300/ 468], loss_d:1.252335 , loss_g:1.127682 , time:0.048059s, lr:0.000200\n",
- "Epoch:[ 6/200], step:[ 400/ 468], loss_d:1.130203 , loss_g:0.855021 , time:0.046108s, lr:0.000200\n",
- "time of epoch 7 is 35.53s\n",
- "Epoch:[ 7/200], step:[ 0/ 468], loss_d:1.193702 , loss_g:0.957167 , time:0.045807s, lr:0.000200\n",
- "Epoch:[ 7/200], step:[ 100/ 468], loss_d:1.342039 , loss_g:0.788124 , time:0.046072s, lr:0.000200\n",
- "Epoch:[ 7/200], step:[ 200/ 468], loss_d:1.210948 , loss_g:0.975252 , time:0.045730s, lr:0.000200\n",
- "Epoch:[ 7/200], step:[ 300/ 468], loss_d:1.169287 , loss_g:1.272509 , time:0.046294s, lr:0.000200\n",
- "Epoch:[ 7/200], step:[ 400/ 468], loss_d:1.419244 , loss_g:1.419567 , time:0.049928s, lr:0.000200\n",
- "time of epoch 8 is 35.55s\n",
- "Epoch:[ 8/200], step:[ 0/ 468], loss_d:1.169651 , loss_g:0.964933 , time:0.044559s, lr:0.000200\n",
- "Epoch:[ 8/200], step:[ 100/ 468], loss_d:1.184227 , loss_g:0.818912 , time:0.042548s, lr:0.000200\n",
- "Epoch:[ 8/200], step:[ 200/ 468], loss_d:1.213391 , loss_g:0.982263 , time:0.043411s, lr:0.000200\n",
- "Epoch:[ 8/200], step:[ 300/ 468], loss_d:1.315528 , loss_g:0.855187 , time:0.045020s, lr:0.000200\n",
- "Epoch:[ 8/200], step:[ 400/ 468], loss_d:1.512848 , loss_g:2.308600 , time:0.041002s, lr:0.000200\n",
- "time of epoch 9 is 32.37s\n",
- "Epoch:[ 9/200], step:[ 0/ 468], loss_d:1.265780 , loss_g:1.055720 , time:0.053221s, lr:0.000200\n",
- "Epoch:[ 9/200], step:[ 100/ 468], loss_d:1.251823 , loss_g:0.990450 , time:0.049277s, lr:0.000200\n",
- "Epoch:[ 9/200], step:[ 200/ 468], loss_d:1.296410 , loss_g:0.843512 , time:0.050128s, lr:0.000200\n",
- "Epoch:[ 9/200], step:[ 300/ 468], loss_d:1.161609 , loss_g:1.045182 , time:0.042616s, lr:0.000200\n",
- "Epoch:[ 9/200], step:[ 400/ 468], loss_d:1.479120 , loss_g:1.678396 , time:0.047976s, lr:0.000200\n",
- "time of epoch 10 is 35.10s\n",
- "Epoch:[ 10/200], step:[ 0/ 468], loss_d:1.189949 , loss_g:0.991716 , time:0.048863s, lr:0.000200\n",
- "Epoch:[ 10/200], step:[ 100/ 468], loss_d:1.175365 , loss_g:0.987654 , time:0.045419s, lr:0.000200\n",
- "Epoch:[ 10/200], step:[ 200/ 468], loss_d:1.191629 , loss_g:1.138480 , time:0.047600s, lr:0.000200\n",
- "Epoch:[ 10/200], step:[ 300/ 468], loss_d:1.199500 , loss_g:0.890635 , time:0.042973s, lr:0.000200\n",
- "Epoch:[ 10/200], step:[ 400/ 468], loss_d:1.197489 , loss_g:1.243246 , time:0.041783s, lr:0.000200\n",
- "time of epoch 11 is 34.87s\n",
- "Epoch:[ 11/200], step:[ 0/ 468], loss_d:1.220011 , loss_g:0.887339 , time:0.049956s, lr:0.000200\n",
- "Epoch:[ 11/200], step:[ 100/ 468], loss_d:1.292171 , loss_g:1.429485 , time:0.046158s, lr:0.000200\n",
- "Epoch:[ 11/200], step:[ 200/ 468], loss_d:1.266596 , loss_g:1.168179 , time:0.042513s, lr:0.000200\n",
- "Epoch:[ 11/200], step:[ 300/ 468], loss_d:1.211829 , loss_g:0.902453 , time:0.044381s, lr:0.000200\n",
- "Epoch:[ 11/200], step:[ 400/ 468], loss_d:1.225353 , loss_g:1.143901 , time:0.046554s, lr:0.000200\n",
- "time of epoch 12 is 36.42s\n",
- "Epoch:[ 12/200], step:[ 0/ 468], loss_d:1.251894 , loss_g:0.852054 , time:0.051266s, lr:0.000200\n",
- "Epoch:[ 12/200], step:[ 100/ 468], loss_d:1.463628 , loss_g:1.512013 , time:0.045766s, lr:0.000200\n",
- "Epoch:[ 12/200], step:[ 200/ 468], loss_d:1.232771 , loss_g:0.834363 , time:0.049550s, lr:0.000200\n",
- "Epoch:[ 12/200], step:[ 300/ 468], loss_d:1.232190 , loss_g:0.870226 , time:0.045891s, lr:0.000200\n",
- "Epoch:[ 12/200], step:[ 400/ 468], loss_d:1.340999 , loss_g:1.031653 , time:0.045531s, lr:0.000200\n",
- "time of epoch 13 is 35.49s\n",
- "Epoch:[ 13/200], step:[ 0/ 468], loss_d:1.268104 , loss_g:0.748836 , time:0.052089s, lr:0.000200\n",
- "Epoch:[ 13/200], step:[ 100/ 468], loss_d:1.159301 , loss_g:0.993767 , time:0.039213s, lr:0.000200\n",
- "Epoch:[ 13/200], step:[ 200/ 468], loss_d:1.278076 , loss_g:1.458778 , time:0.043160s, lr:0.000200\n",
- "Epoch:[ 13/200], step:[ 300/ 468], loss_d:1.215638 , loss_g:0.828333 , time:0.052066s, lr:0.000200\n",
- "Epoch:[ 13/200], step:[ 400/ 468], loss_d:1.183518 , loss_g:0.977532 , time:0.044924s, lr:0.000200\n",
- "time of epoch 14 is 35.07s\n",
- "Epoch:[ 14/200], step:[ 0/ 468], loss_d:1.290870 , loss_g:1.256727 , time:0.046731s, lr:0.000200\n",
- "Epoch:[ 14/200], step:[ 100/ 468], loss_d:1.252140 , loss_g:1.025967 , time:0.051552s, lr:0.000200\n",
- "Epoch:[ 14/200], step:[ 200/ 468], loss_d:1.221723 , loss_g:0.829824 , time:0.049074s, lr:0.000200\n",
- "Epoch:[ 14/200], step:[ 300/ 468], loss_d:1.319794 , loss_g:0.717890 , time:0.050208s, lr:0.000200\n",
- "Epoch:[ 14/200], step:[ 400/ 468], loss_d:1.243484 , loss_g:0.951304 , time:0.044178s, lr:0.000200\n",
- "time of epoch 15 is 36.31s\n",
- "Epoch:[ 15/200], step:[ 0/ 468], loss_d:1.242785 , loss_g:0.920318 , time:0.050593s, lr:0.000200\n",
- "Epoch:[ 15/200], step:[ 100/ 468], loss_d:1.327693 , loss_g:0.678757 , time:0.051874s, lr:0.000200\n",
- "Epoch:[ 15/200], step:[ 200/ 468], loss_d:1.243325 , loss_g:1.076648 , time:0.048385s, lr:0.000200\n",
- "Epoch:[ 15/200], step:[ 300/ 468], loss_d:1.348646 , loss_g:0.664726 , time:0.053389s, lr:0.000200\n",
- "Epoch:[ 15/200], step:[ 400/ 468], loss_d:1.203607 , loss_g:0.997613 , time:0.046669s, lr:0.000200\n",
- "time of epoch 16 is 37.13s\n",
- "Epoch:[ 16/200], step:[ 0/ 468], loss_d:1.383142 , loss_g:1.444427 , time:0.050258s, lr:0.000200\n",
- "Epoch:[ 16/200], step:[ 100/ 468], loss_d:1.298983 , loss_g:0.897634 , time:0.044567s, lr:0.000200\n",
- "Epoch:[ 16/200], step:[ 200/ 468], loss_d:1.300359 , loss_g:0.790605 , time:0.050700s, lr:0.000200\n",
- "Epoch:[ 16/200], step:[ 300/ 468], loss_d:1.251686 , loss_g:0.923806 , time:0.042916s, lr:0.000200\n",
- "Epoch:[ 16/200], step:[ 400/ 468], loss_d:1.231390 , loss_g:1.050423 , time:0.050157s, lr:0.000200\n",
- "time of epoch 17 is 35.96s\n",
- "Epoch:[ 17/200], step:[ 0/ 468], loss_d:1.445552 , loss_g:1.408975 , time:0.044960s, lr:0.000200\n",
- "Epoch:[ 17/200], step:[ 100/ 468], loss_d:1.225799 , loss_g:0.813605 , time:0.045700s, lr:0.000200\n",
- "Epoch:[ 17/200], step:[ 200/ 468], loss_d:1.261435 , loss_g:1.052767 , time:0.044289s, lr:0.000200\n",
- "Epoch:[ 17/200], step:[ 300/ 468], loss_d:1.345048 , loss_g:0.727858 , time:0.046592s, lr:0.000200\n",
- "Epoch:[ 17/200], step:[ 400/ 468], loss_d:1.332736 , loss_g:0.774781 , time:0.047509s, lr:0.000200\n",
- "time of epoch 18 is 34.87s\n",
- "Epoch:[ 18/200], step:[ 0/ 468], loss_d:1.280479 , loss_g:0.849144 , time:0.048033s, lr:0.000200\n",
- "Epoch:[ 18/200], step:[ 100/ 468], loss_d:1.314199 , loss_g:0.715470 , time:0.046265s, lr:0.000200\n",
- "Epoch:[ 18/200], step:[ 200/ 468], loss_d:1.287218 , loss_g:0.827181 , time:0.056520s, lr:0.000200\n",
- "Epoch:[ 18/200], step:[ 300/ 468], loss_d:1.284780 , loss_g:0.911753 , time:0.051426s, lr:0.000200\n",
- "Epoch:[ 18/200], step:[ 400/ 468], loss_d:1.277134 , loss_g:1.080672 , time:0.053081s, lr:0.000200\n",
- "time of epoch 19 is 35.96s\n",
- "Epoch:[ 19/200], step:[ 0/ 468], loss_d:1.280129 , loss_g:0.966483 , time:0.049744s, lr:0.000200\n",
- "Epoch:[ 19/200], step:[ 100/ 468], loss_d:1.289833 , loss_g:0.954576 , time:0.047417s, lr:0.000200\n",
- "Epoch:[ 19/200], step:[ 200/ 468], loss_d:1.300976 , loss_g:0.766563 , time:0.048097s, lr:0.000200\n",
- "Epoch:[ 19/200], step:[ 300/ 468], loss_d:1.331150 , loss_g:0.678930 , time:0.044687s, lr:0.000200\n",
- "Epoch:[ 19/200], step:[ 400/ 468], loss_d:1.301981 , loss_g:1.000206 , time:0.044564s, lr:0.000200\n",
- "time of epoch 20 is 34.50s\n",
- "Epoch:[ 20/200], step:[ 0/ 468], loss_d:1.279692 , loss_g:0.822624 , time:0.052456s, lr:0.000200\n",
- "Epoch:[ 20/200], step:[ 100/ 468], loss_d:1.257872 , loss_g:0.904037 , time:0.046702s, lr:0.000200\n",
- "Epoch:[ 20/200], step:[ 200/ 468], loss_d:1.353937 , loss_g:0.690819 , time:0.046989s, lr:0.000200\n",
- "Epoch:[ 20/200], step:[ 300/ 468], loss_d:1.334056 , loss_g:0.850024 , time:0.042930s, lr:0.000200\n",
- "Epoch:[ 20/200], step:[ 400/ 468], loss_d:1.279646 , loss_g:0.982189 , time:0.048367s, lr:0.000200\n",
- "time of epoch 21 is 36.19s\n",
- "Epoch:[ 21/200], step:[ 0/ 468], loss_d:1.297226 , loss_g:0.781633 , time:0.055503s, lr:0.000200\n",
- "Epoch:[ 21/200], step:[ 100/ 468], loss_d:1.298951 , loss_g:0.683510 , time:0.043011s, lr:0.000200\n",
- "Epoch:[ 21/200], step:[ 200/ 468], loss_d:1.386955 , loss_g:1.018742 , time:0.042411s, lr:0.000200\n",
- "Epoch:[ 21/200], step:[ 300/ 468], loss_d:1.211753 , loss_g:1.122474 , time:0.045273s, lr:0.000200\n",
- "Epoch:[ 21/200], step:[ 400/ 468], loss_d:1.276753 , loss_g:0.905103 , time:0.042710s, lr:0.000200\n",
- "time of epoch 22 is 37.28s\n",
- "Epoch:[ 22/200], step:[ 0/ 468], loss_d:1.229076 , loss_g:0.929315 , time:0.050070s, lr:0.000200\n",
- "Epoch:[ 22/200], step:[ 100/ 468], loss_d:1.202423 , loss_g:0.868061 , time:0.041032s, lr:0.000200\n",
- "Epoch:[ 22/200], step:[ 200/ 468], loss_d:1.320514 , loss_g:0.770018 , time:0.041260s, lr:0.000200\n",
- "Epoch:[ 22/200], step:[ 300/ 468], loss_d:1.279201 , loss_g:0.929916 , time:0.043524s, lr:0.000200\n",
- "Epoch:[ 22/200], step:[ 400/ 468], loss_d:1.241618 , loss_g:0.857822 , time:0.039553s, lr:0.000200\n",
- "time of epoch 23 is 33.17s\n",
- "Epoch:[ 23/200], step:[ 0/ 468], loss_d:1.320108 , loss_g:0.766503 , time:0.052546s, lr:0.000200\n",
- "Epoch:[ 23/200], step:[ 100/ 468], loss_d:1.256048 , loss_g:0.833245 , time:0.052305s, lr:0.000200\n",
- "Epoch:[ 23/200], step:[ 200/ 468], loss_d:1.267036 , loss_g:0.895304 , time:0.048953s, lr:0.000200\n",
- "Epoch:[ 23/200], step:[ 300/ 468], loss_d:1.267351 , loss_g:0.981992 , time:0.045972s, lr:0.000200\n",
- "Epoch:[ 23/200], step:[ 400/ 468], loss_d:1.315502 , loss_g:0.875669 , time:0.048048s, lr:0.000200\n",
- "time of epoch 24 is 36.83s\n",
- "Epoch:[ 24/200], step:[ 0/ 468], loss_d:1.264999 , loss_g:0.887498 , time:0.049255s, lr:0.000200\n",
- "Epoch:[ 24/200], step:[ 100/ 468], loss_d:1.246589 , loss_g:0.848617 , time:0.041775s, lr:0.000200\n",
- "Epoch:[ 24/200], step:[ 200/ 468], loss_d:1.282457 , loss_g:0.865989 , time:0.049172s, lr:0.000200\n",
- "Epoch:[ 24/200], step:[ 300/ 468], loss_d:1.280339 , loss_g:0.966150 , time:0.051222s, lr:0.000200\n",
- "Epoch:[ 24/200], step:[ 400/ 468], loss_d:1.246938 , loss_g:1.136630 , time:0.041883s, lr:0.000200\n",
- "time of epoch 25 is 35.77s\n",
- "Epoch:[ 25/200], step:[ 0/ 468], loss_d:1.262290 , loss_g:0.937077 , time:0.051020s, lr:0.000200\n",
- "Epoch:[ 25/200], step:[ 100/ 468], loss_d:1.342394 , loss_g:0.832883 , time:0.045452s, lr:0.000200\n",
- "Epoch:[ 25/200], step:[ 200/ 468], loss_d:1.320593 , loss_g:0.776873 , time:0.044877s, lr:0.000200\n",
- "Epoch:[ 25/200], step:[ 300/ 468], loss_d:1.248505 , loss_g:0.845244 , time:0.050501s, lr:0.000200\n",
- "Epoch:[ 25/200], step:[ 400/ 468], loss_d:1.285810 , loss_g:0.814478 , time:0.044672s, lr:0.000200\n",
- "time of epoch 26 is 35.27s\n",
- "Epoch:[ 26/200], step:[ 0/ 468], loss_d:1.303516 , loss_g:0.854692 , time:0.051517s, lr:0.000200\n",
- "Epoch:[ 26/200], step:[ 100/ 468], loss_d:1.308690 , loss_g:0.954799 , time:0.047952s, lr:0.000200\n",
- "Epoch:[ 26/200], step:[ 200/ 468], loss_d:1.334051 , loss_g:0.871949 , time:0.045283s, lr:0.000200\n",
- "Epoch:[ 26/200], step:[ 300/ 468], loss_d:1.292772 , loss_g:0.960908 , time:0.048186s, lr:0.000200\n",
- "Epoch:[ 26/200], step:[ 400/ 468], loss_d:1.302948 , loss_g:0.911412 , time:0.047252s, lr:0.000200\n",
- "time of epoch 27 is 36.79s\n",
- "Epoch:[ 27/200], step:[ 0/ 468], loss_d:1.339568 , loss_g:0.687065 , time:0.050902s, lr:0.000200\n",
- "Epoch:[ 27/200], step:[ 100/ 468], loss_d:1.323819 , loss_g:0.694374 , time:0.044282s, lr:0.000200\n",
- "Epoch:[ 27/200], step:[ 200/ 468], loss_d:1.294970 , loss_g:0.952515 , time:0.045241s, lr:0.000200\n",
- "Epoch:[ 27/200], step:[ 300/ 468], loss_d:1.274956 , loss_g:1.008439 , time:0.043347s, lr:0.000200\n",
- "Epoch:[ 27/200], step:[ 400/ 468], loss_d:1.315406 , loss_g:0.854019 , time:0.046216s, lr:0.000200\n",
- "time of epoch 28 is 36.43s\n",
- "Epoch:[ 28/200], step:[ 0/ 468], loss_d:1.307089 , loss_g:0.788577 , time:0.047084s, lr:0.000200\n",
- "Epoch:[ 28/200], step:[ 100/ 468], loss_d:1.281474 , loss_g:0.872102 , time:0.049869s, lr:0.000200\n",
- "Epoch:[ 28/200], step:[ 200/ 468], loss_d:1.305486 , loss_g:0.732561 , time:0.052928s, lr:0.000200\n",
- "Epoch:[ 28/200], step:[ 300/ 468], loss_d:1.301066 , loss_g:0.899581 , time:0.046291s, lr:0.000200\n",
- "Epoch:[ 28/200], step:[ 400/ 468], loss_d:1.352162 , loss_g:1.095462 , time:0.045346s, lr:0.000200\n",
- "time of epoch 29 is 34.94s\n",
- "Epoch:[ 29/200], step:[ 0/ 468], loss_d:1.312884 , loss_g:0.911392 , time:0.049603s, lr:0.000200\n",
- "Epoch:[ 29/200], step:[ 100/ 468], loss_d:1.359673 , loss_g:0.787973 , time:0.048052s, lr:0.000200\n",
- "Epoch:[ 29/200], step:[ 200/ 468], loss_d:1.268647 , loss_g:0.724316 , time:0.050181s, lr:0.000200\n",
- "Epoch:[ 29/200], step:[ 300/ 468], loss_d:1.345999 , loss_g:0.772939 , time:0.048231s, lr:0.000200\n",
- "Epoch:[ 29/200], step:[ 400/ 468], loss_d:1.319518 , loss_g:0.864878 , time:0.051538s, lr:0.000200\n",
- "time of epoch 30 is 35.46s\n",
- "Epoch:[ 30/200], step:[ 0/ 468], loss_d:1.280796 , loss_g:1.165704 , time:0.051660s, lr:0.000200\n",
- "Epoch:[ 30/200], step:[ 100/ 468], loss_d:1.297693 , loss_g:0.886735 , time:0.045506s, lr:0.000200\n",
- "Epoch:[ 30/200], step:[ 200/ 468], loss_d:1.319736 , loss_g:0.950605 , time:0.045071s, lr:0.000200\n",
- "Epoch:[ 30/200], step:[ 300/ 468], loss_d:1.305269 , loss_g:0.844066 , time:0.042378s, lr:0.000200\n",
- "Epoch:[ 30/200], step:[ 400/ 468], loss_d:1.361835 , loss_g:0.951016 , time:0.041587s, lr:0.000200\n",
- "time of epoch 31 is 32.28s\n",
- "Epoch:[ 31/200], step:[ 0/ 468], loss_d:1.369297 , loss_g:0.613835 , time:0.051512s, lr:0.000200\n",
- "Epoch:[ 31/200], step:[ 100/ 468], loss_d:1.351585 , loss_g:0.751359 , time:0.046028s, lr:0.000200\n",
- "Epoch:[ 31/200], step:[ 200/ 468], loss_d:1.309000 , loss_g:0.693362 , time:0.048977s, lr:0.000200\n",
- "Epoch:[ 31/200], step:[ 300/ 468], loss_d:1.288536 , loss_g:0.805936 , time:0.041386s, lr:0.000200\n",
- "Epoch:[ 31/200], step:[ 400/ 468], loss_d:1.348979 , loss_g:0.866533 , time:0.045714s, lr:0.000200\n",
- "time of epoch 32 is 36.20s\n",
- "Epoch:[ 32/200], step:[ 0/ 468], loss_d:1.365918 , loss_g:1.034280 , time:0.047943s, lr:0.000200\n",
- "Epoch:[ 32/200], step:[ 100/ 468], loss_d:1.304678 , loss_g:0.696834 , time:0.041659s, lr:0.000200\n",
- "Epoch:[ 32/200], step:[ 200/ 468], loss_d:1.260548 , loss_g:0.977799 , time:0.042688s, lr:0.000200\n",
- "Epoch:[ 32/200], step:[ 300/ 468], loss_d:1.348652 , loss_g:0.722059 , time:0.043951s, lr:0.000200\n",
- "Epoch:[ 32/200], step:[ 400/ 468], loss_d:1.266247 , loss_g:0.784706 , time:0.043008s, lr:0.000200\n",
- "time of epoch 33 is 32.44s\n",
- "Epoch:[ 33/200], step:[ 0/ 468], loss_d:1.269314 , loss_g:0.882053 , time:0.049339s, lr:0.000200\n",
- "Epoch:[ 33/200], step:[ 100/ 468], loss_d:1.290730 , loss_g:0.853020 , time:0.045531s, lr:0.000200\n",
- "Epoch:[ 33/200], step:[ 200/ 468], loss_d:1.281943 , loss_g:0.942438 , time:0.044451s, lr:0.000200\n",
- "Epoch:[ 33/200], step:[ 300/ 468], loss_d:1.284876 , loss_g:0.919751 , time:0.045468s, lr:0.000200\n",
- "Epoch:[ 33/200], step:[ 400/ 468], loss_d:1.262706 , loss_g:1.000260 , time:0.044615s, lr:0.000200\n",
- "time of epoch 34 is 36.71s\n",
- "Epoch:[ 34/200], step:[ 0/ 468], loss_d:1.227641 , loss_g:0.965049 , time:0.045004s, lr:0.000200\n",
- "Epoch:[ 34/200], step:[ 100/ 468], loss_d:1.291415 , loss_g:0.879837 , time:0.044621s, lr:0.000200\n",
- "Epoch:[ 34/200], step:[ 200/ 468], loss_d:1.332647 , loss_g:0.777545 , time:0.043519s, lr:0.000200\n",
- "Epoch:[ 34/200], step:[ 300/ 468], loss_d:1.329867 , loss_g:0.719222 , time:0.045459s, lr:0.000200\n",
- "Epoch:[ 34/200], step:[ 400/ 468], loss_d:1.312113 , loss_g:0.690303 , time:0.045122s, lr:0.000200\n",
- "time of epoch 35 is 34.18s\n",
- "Epoch:[ 35/200], step:[ 0/ 468], loss_d:1.287444 , loss_g:0.821160 , time:0.053471s, lr:0.000200\n",
- "Epoch:[ 35/200], step:[ 100/ 468], loss_d:1.337309 , loss_g:0.676506 , time:0.048249s, lr:0.000200\n",
- "Epoch:[ 35/200], step:[ 200/ 468], loss_d:1.356080 , loss_g:0.799282 , time:0.047357s, lr:0.000200\n",
- "Epoch:[ 35/200], step:[ 300/ 468], loss_d:1.296711 , loss_g:0.983517 , time:0.041711s, lr:0.000200\n",
- "Epoch:[ 35/200], step:[ 400/ 468], loss_d:1.301166 , loss_g:0.874214 , time:0.051219s, lr:0.000200\n",
- "time of epoch 36 is 36.05s\n",
- "Epoch:[ 36/200], step:[ 0/ 468], loss_d:1.327729 , loss_g:0.861126 , time:0.048495s, lr:0.000200\n",
- "Epoch:[ 36/200], step:[ 100/ 468], loss_d:1.227875 , loss_g:0.866475 , time:0.039400s, lr:0.000200\n",
- "Epoch:[ 36/200], step:[ 200/ 468], loss_d:1.365551 , loss_g:0.775811 , time:0.049545s, lr:0.000200\n",
- "Epoch:[ 36/200], step:[ 300/ 468], loss_d:1.313647 , loss_g:0.858610 , time:0.049676s, lr:0.000200\n",
- "Epoch:[ 36/200], step:[ 400/ 468], loss_d:1.316855 , loss_g:0.801388 , time:0.046284s, lr:0.000200\n",
- "time of epoch 37 is 35.66s\n",
- "Epoch:[ 37/200], step:[ 0/ 468], loss_d:1.261399 , loss_g:0.832099 , time:0.046235s, lr:0.000200\n",
- "Epoch:[ 37/200], step:[ 100/ 468], loss_d:1.237418 , loss_g:0.872276 , time:0.044756s, lr:0.000200\n",
- "Epoch:[ 37/200], step:[ 200/ 468], loss_d:1.369729 , loss_g:0.720704 , time:0.044346s, lr:0.000200\n",
- "Epoch:[ 37/200], step:[ 300/ 468], loss_d:1.417087 , loss_g:0.652918 , time:0.044417s, lr:0.000200\n",
- "Epoch:[ 37/200], step:[ 400/ 468], loss_d:1.348940 , loss_g:0.737759 , time:0.045472s, lr:0.000200\n",
- "time of epoch 38 is 35.36s\n",
- "Epoch:[ 38/200], step:[ 0/ 468], loss_d:1.287829 , loss_g:0.849732 , time:0.043812s, lr:0.000200\n",
- "Epoch:[ 38/200], step:[ 100/ 468], loss_d:1.283490 , loss_g:0.982167 , time:0.044198s, lr:0.000200\n",
- "Epoch:[ 38/200], step:[ 200/ 468], loss_d:1.327147 , loss_g:0.944892 , time:0.041505s, lr:0.000200\n",
- "Epoch:[ 38/200], step:[ 300/ 468], loss_d:1.304804 , loss_g:0.893149 , time:0.039486s, lr:0.000200\n",
- "Epoch:[ 38/200], step:[ 400/ 468], loss_d:1.345741 , loss_g:0.745120 , time:0.043633s, lr:0.000200\n",
- "time of epoch 39 is 31.42s\n",
- "Epoch:[ 39/200], step:[ 0/ 468], loss_d:1.288782 , loss_g:0.964864 , time:0.047678s, lr:0.000200\n",
- "Epoch:[ 39/200], step:[ 100/ 468], loss_d:1.375822 , loss_g:0.827069 , time:0.043002s, lr:0.000200\n",
- "Epoch:[ 39/200], step:[ 200/ 468], loss_d:1.287768 , loss_g:0.920949 , time:0.047505s, lr:0.000200\n",
- "Epoch:[ 39/200], step:[ 300/ 468], loss_d:1.334036 , loss_g:0.895171 , time:0.048463s, lr:0.000200\n",
- "Epoch:[ 39/200], step:[ 400/ 468], loss_d:1.302328 , loss_g:0.926613 , time:0.049276s, lr:0.000200\n",
- "time of epoch 40 is 36.63s\n",
- "Epoch:[ 40/200], step:[ 0/ 468], loss_d:1.380958 , loss_g:0.599270 , time:0.047673s, lr:0.000200\n",
- "Epoch:[ 40/200], step:[ 100/ 468], loss_d:1.307715 , loss_g:0.816424 , time:0.045313s, lr:0.000200\n",
- "Epoch:[ 40/200], step:[ 200/ 468], loss_d:1.321781 , loss_g:0.989347 , time:0.044734s, lr:0.000200\n",
- "Epoch:[ 40/200], step:[ 300/ 468], loss_d:1.250957 , loss_g:1.019878 , time:0.040730s, lr:0.000200\n",
- "Epoch:[ 40/200], step:[ 400/ 468], loss_d:1.290118 , loss_g:0.961664 , time:0.046052s, lr:0.000200\n",
- "time of epoch 41 is 35.40s\n",
- "Epoch:[ 41/200], step:[ 0/ 468], loss_d:1.367668 , loss_g:0.810686 , time:0.050680s, lr:0.000200\n",
- "Epoch:[ 41/200], step:[ 100/ 468], loss_d:1.315075 , loss_g:1.090730 , time:0.042978s, lr:0.000200\n",
- "Epoch:[ 41/200], step:[ 200/ 468], loss_d:1.248324 , loss_g:0.828350 , time:0.044426s, lr:0.000200\n",
- "Epoch:[ 41/200], step:[ 300/ 468], loss_d:1.346446 , loss_g:0.688865 , time:0.042962s, lr:0.000200\n",
- "Epoch:[ 41/200], step:[ 400/ 468], loss_d:1.288957 , loss_g:0.926077 , time:0.041530s, lr:0.000200\n",
- "time of epoch 42 is 33.80s\n",
- "Epoch:[ 42/200], step:[ 0/ 468], loss_d:1.310691 , loss_g:0.795922 , time:0.047856s, lr:0.000200\n",
- "Epoch:[ 42/200], step:[ 100/ 468], loss_d:1.244992 , loss_g:0.954601 , time:0.044255s, lr:0.000200\n",
- "Epoch:[ 42/200], step:[ 200/ 468], loss_d:1.272303 , loss_g:0.819405 , time:0.046012s, lr:0.000200\n",
- "Epoch:[ 42/200], step:[ 300/ 468], loss_d:1.286385 , loss_g:0.807411 , time:0.041375s, lr:0.000200\n",
- "Epoch:[ 42/200], step:[ 400/ 468], loss_d:1.266324 , loss_g:0.903966 , time:0.038501s, lr:0.000200\n",
- "time of epoch 43 is 33.83s\n",
- "Epoch:[ 43/200], step:[ 0/ 468], loss_d:1.288128 , loss_g:0.778919 , time:0.049178s, lr:0.000200\n",
- "Epoch:[ 43/200], step:[ 100/ 468], loss_d:1.347371 , loss_g:0.902382 , time:0.044103s, lr:0.000200\n",
- "Epoch:[ 43/200], step:[ 200/ 468], loss_d:1.257854 , loss_g:0.908624 , time:0.045427s, lr:0.000200\n",
- "Epoch:[ 43/200], step:[ 300/ 468], loss_d:1.337106 , loss_g:0.802611 , time:0.048371s, lr:0.000200\n",
- "Epoch:[ 43/200], step:[ 400/ 468], loss_d:1.232496 , loss_g:0.991593 , time:0.046462s, lr:0.000200\n",
- "time of epoch 44 is 33.09s\n",
- "Epoch:[ 44/200], step:[ 0/ 468], loss_d:1.312129 , loss_g:0.638454 , time:0.046639s, lr:0.000200\n",
- "Epoch:[ 44/200], step:[ 100/ 468], loss_d:1.292330 , loss_g:0.922597 , time:0.045461s, lr:0.000200\n",
- "Epoch:[ 44/200], step:[ 200/ 468], loss_d:1.394777 , loss_g:0.824005 , time:0.046561s, lr:0.000200\n",
- "Epoch:[ 44/200], step:[ 300/ 468], loss_d:1.245670 , loss_g:0.809577 , time:0.038827s, lr:0.000200\n",
- "Epoch:[ 44/200], step:[ 400/ 468], loss_d:1.293214 , loss_g:0.867745 , time:0.047072s, lr:0.000200\n",
- "time of epoch 45 is 35.05s\n",
- "Epoch:[ 45/200], step:[ 0/ 468], loss_d:1.279787 , loss_g:0.962956 , time:0.050580s, lr:0.000200\n",
- "Epoch:[ 45/200], step:[ 100/ 468], loss_d:1.317848 , loss_g:0.918102 , time:0.046428s, lr:0.000200\n",
- "Epoch:[ 45/200], step:[ 200/ 468], loss_d:1.260370 , loss_g:0.781658 , time:0.045924s, lr:0.000200\n",
- "Epoch:[ 45/200], step:[ 300/ 468], loss_d:1.311301 , loss_g:0.899292 , time:0.040322s, lr:0.000200\n",
- "Epoch:[ 45/200], step:[ 400/ 468], loss_d:1.357540 , loss_g:1.075780 , time:0.054156s, lr:0.000200\n",
- "time of epoch 46 is 35.65s\n",
- "Epoch:[ 46/200], step:[ 0/ 468], loss_d:1.325554 , loss_g:0.820363 , time:0.045199s, lr:0.000200\n",
- "Epoch:[ 46/200], step:[ 100/ 468], loss_d:1.318613 , loss_g:0.880800 , time:0.047017s, lr:0.000200\n",
- "Epoch:[ 46/200], step:[ 200/ 468], loss_d:1.294578 , loss_g:0.823434 , time:0.042264s, lr:0.000200\n",
- "Epoch:[ 46/200], step:[ 300/ 468], loss_d:1.300950 , loss_g:0.817897 , time:0.042405s, lr:0.000200\n",
- "Epoch:[ 46/200], step:[ 400/ 468], loss_d:1.272316 , loss_g:1.051758 , time:0.046407s, lr:0.000200\n",
- "time of epoch 47 is 33.12s\n",
- "Epoch:[ 47/200], step:[ 0/ 468], loss_d:1.315550 , loss_g:0.802643 , time:0.052024s, lr:0.000200\n",
- "Epoch:[ 47/200], step:[ 100/ 468], loss_d:1.262430 , loss_g:0.985606 , time:0.052467s, lr:0.000200\n",
- "Epoch:[ 47/200], step:[ 200/ 468], loss_d:1.302797 , loss_g:0.768079 , time:0.045465s, lr:0.000200\n",
- "Epoch:[ 47/200], step:[ 300/ 468], loss_d:1.303171 , loss_g:0.803109 , time:0.048615s, lr:0.000200\n",
- "Epoch:[ 47/200], step:[ 400/ 468], loss_d:1.310919 , loss_g:0.912796 , time:0.045622s, lr:0.000200\n",
- "time of epoch 48 is 37.42s\n",
- "Epoch:[ 48/200], step:[ 0/ 468], loss_d:1.218272 , loss_g:0.818076 , time:0.046897s, lr:0.000200\n",
- "Epoch:[ 48/200], step:[ 100/ 468], loss_d:1.316115 , loss_g:0.756210 , time:0.044725s, lr:0.000200\n",
- "Epoch:[ 48/200], step:[ 200/ 468], loss_d:1.270249 , loss_g:0.903219 , time:0.045236s, lr:0.000200\n",
- "Epoch:[ 48/200], step:[ 300/ 468], loss_d:1.289837 , loss_g:0.797583 , time:0.041352s, lr:0.000200\n",
- "Epoch:[ 48/200], step:[ 400/ 468], loss_d:1.303824 , loss_g:0.925728 , time:0.041929s, lr:0.000200\n",
- "time of epoch 49 is 34.82s\n",
- "Epoch:[ 49/200], step:[ 0/ 468], loss_d:1.292621 , loss_g:1.012322 , time:0.053586s, lr:0.000200\n",
- "Epoch:[ 49/200], step:[ 100/ 468], loss_d:1.263226 , loss_g:0.931532 , time:0.049332s, lr:0.000200\n",
- "Epoch:[ 49/200], step:[ 200/ 468], loss_d:1.287496 , loss_g:0.872472 , time:0.040813s, lr:0.000200\n",
- "Epoch:[ 49/200], step:[ 300/ 468], loss_d:1.295976 , loss_g:0.868473 , time:0.047594s, lr:0.000200\n",
- "Epoch:[ 49/200], step:[ 400/ 468], loss_d:1.320219 , loss_g:0.866667 , time:0.040644s, lr:0.000200\n",
- "time of epoch 50 is 37.00s\n",
- "Epoch:[ 50/200], step:[ 0/ 468], loss_d:1.318443 , loss_g:0.721376 , time:0.044799s, lr:0.000200\n",
- "Epoch:[ 50/200], step:[ 100/ 468], loss_d:1.285122 , loss_g:0.957997 , time:0.043847s, lr:0.000200\n",
- "Epoch:[ 50/200], step:[ 200/ 468], loss_d:1.240816 , loss_g:0.899173 , time:0.038968s, lr:0.000200\n",
- "Epoch:[ 50/200], step:[ 300/ 468], loss_d:1.304349 , loss_g:0.765403 , time:0.043167s, lr:0.000200\n",
- "Epoch:[ 50/200], step:[ 400/ 468], loss_d:1.309040 , loss_g:0.988446 , time:0.040379s, lr:0.000200\n",
- "time of epoch 51 is 35.06s\n",
- "Epoch:[ 51/200], step:[ 0/ 468], loss_d:1.308657 , loss_g:0.793816 , time:0.045506s, lr:0.000200\n",
- "Epoch:[ 51/200], step:[ 100/ 468], loss_d:1.238881 , loss_g:0.987974 , time:0.044426s, lr:0.000200\n",
- "Epoch:[ 51/200], step:[ 200/ 468], loss_d:1.315138 , loss_g:0.843769 , time:0.042363s, lr:0.000200\n",
- "Epoch:[ 51/200], step:[ 300/ 468], loss_d:1.287251 , loss_g:0.984575 , time:0.045839s, lr:0.000200\n",
- "Epoch:[ 51/200], step:[ 400/ 468], loss_d:1.295429 , loss_g:0.723810 , time:0.045896s, lr:0.000200\n",
- "time of epoch 52 is 32.82s\n",
- "Epoch:[ 52/200], step:[ 0/ 468], loss_d:1.300465 , loss_g:0.876405 , time:0.051698s, lr:0.000200\n",
- "Epoch:[ 52/200], step:[ 100/ 468], loss_d:1.326642 , loss_g:1.024687 , time:0.046578s, lr:0.000200\n",
- "Epoch:[ 52/200], step:[ 200/ 468], loss_d:1.357387 , loss_g:0.874437 , time:0.049810s, lr:0.000200\n",
- "Epoch:[ 52/200], step:[ 300/ 468], loss_d:1.330598 , loss_g:0.878415 , time:0.047935s, lr:0.000200\n",
- "Epoch:[ 52/200], step:[ 400/ 468], loss_d:1.277013 , loss_g:0.807051 , time:0.051237s, lr:0.000200\n",
- "time of epoch 53 is 37.07s\n",
- "Epoch:[ 53/200], step:[ 0/ 468], loss_d:1.255883 , loss_g:0.910230 , time:0.049790s, lr:0.000200\n",
- "Epoch:[ 53/200], step:[ 100/ 468], loss_d:1.252091 , loss_g:0.901952 , time:0.040147s, lr:0.000200\n",
- "Epoch:[ 53/200], step:[ 200/ 468], loss_d:1.292533 , loss_g:0.860166 , time:0.048048s, lr:0.000200\n",
- "Epoch:[ 53/200], step:[ 300/ 468], loss_d:1.287165 , loss_g:0.948555 , time:0.047112s, lr:0.000200\n",
- "Epoch:[ 53/200], step:[ 400/ 468], loss_d:1.259679 , loss_g:0.778806 , time:0.045686s, lr:0.000200\n",
- "time of epoch 54 is 35.52s\n",
- "Epoch:[ 54/200], step:[ 0/ 468], loss_d:1.364654 , loss_g:0.995422 , time:0.051511s, lr:0.000200\n",
- "Epoch:[ 54/200], step:[ 100/ 468], loss_d:1.291315 , loss_g:0.943074 , time:0.043797s, lr:0.000200\n",
- "Epoch:[ 54/200], step:[ 200/ 468], loss_d:1.271781 , loss_g:0.816772 , time:0.044844s, lr:0.000200\n",
- "Epoch:[ 54/200], step:[ 300/ 468], loss_d:1.301126 , loss_g:0.994051 , time:0.044531s, lr:0.000200\n",
- "Epoch:[ 54/200], step:[ 400/ 468], loss_d:1.231170 , loss_g:1.125083 , time:0.043720s, lr:0.000200\n",
- "time of epoch 55 is 32.21s\n",
- "Epoch:[ 55/200], step:[ 0/ 468], loss_d:1.300256 , loss_g:0.862063 , time:0.052109s, lr:0.000200\n",
- "Epoch:[ 55/200], step:[ 100/ 468], loss_d:1.291621 , loss_g:0.766734 , time:0.046120s, lr:0.000200\n",
- "Epoch:[ 55/200], step:[ 200/ 468], loss_d:1.316266 , loss_g:0.954601 , time:0.040339s, lr:0.000200\n",
- "Epoch:[ 55/200], step:[ 300/ 468], loss_d:1.312849 , loss_g:0.913029 , time:0.046576s, lr:0.000200\n",
- "Epoch:[ 55/200], step:[ 400/ 468], loss_d:1.274158 , loss_g:0.842703 , time:0.047056s, lr:0.000200\n",
- "time of epoch 56 is 34.05s\n",
- "Epoch:[ 56/200], step:[ 0/ 468], loss_d:1.260212 , loss_g:1.015531 , time:0.048740s, lr:0.000200\n",
- "Epoch:[ 56/200], step:[ 100/ 468], loss_d:1.252423 , loss_g:0.882333 , time:0.046604s, lr:0.000200\n",
- "Epoch:[ 56/200], step:[ 200/ 468], loss_d:1.218026 , loss_g:1.092749 , time:0.045265s, lr:0.000200\n",
- "Epoch:[ 56/200], step:[ 300/ 468], loss_d:1.354401 , loss_g:0.711898 , time:0.044151s, lr:0.000200\n",
- "Epoch:[ 56/200], step:[ 400/ 468], loss_d:1.273095 , loss_g:0.790462 , time:0.041293s, lr:0.000200\n",
- "time of epoch 57 is 33.36s\n",
- "Epoch:[ 57/200], step:[ 0/ 468], loss_d:1.259933 , loss_g:0.885635 , time:0.046207s, lr:0.000200\n",
- "Epoch:[ 57/200], step:[ 100/ 468], loss_d:1.305125 , loss_g:0.837233 , time:0.042881s, lr:0.000200\n",
- "Epoch:[ 57/200], step:[ 200/ 468], loss_d:1.277225 , loss_g:0.849327 , time:0.044298s, lr:0.000200\n",
- "Epoch:[ 57/200], step:[ 300/ 468], loss_d:1.372616 , loss_g:0.731302 , time:0.044944s, lr:0.000200\n",
- "Epoch:[ 57/200], step:[ 400/ 468], loss_d:1.345253 , loss_g:0.900683 , time:0.051527s, lr:0.000200\n",
- "time of epoch 58 is 33.53s\n",
- "Epoch:[ 58/200], step:[ 0/ 468], loss_d:1.229842 , loss_g:1.125973 , time:0.048882s, lr:0.000200\n",
- "Epoch:[ 58/200], step:[ 100/ 468], loss_d:1.293970 , loss_g:0.938224 , time:0.048591s, lr:0.000200\n",
- "Epoch:[ 58/200], step:[ 200/ 468], loss_d:1.294961 , loss_g:0.931782 , time:0.041631s, lr:0.000200\n",
- "Epoch:[ 58/200], step:[ 300/ 468], loss_d:1.272239 , loss_g:0.878815 , time:0.042164s, lr:0.000200\n",
- "Epoch:[ 58/200], step:[ 400/ 468], loss_d:1.347243 , loss_g:0.815062 , time:0.042747s, lr:0.000200\n",
- "time of epoch 59 is 35.48s\n",
- "Epoch:[ 59/200], step:[ 0/ 468], loss_d:1.286474 , loss_g:1.023923 , time:0.047378s, lr:0.000200\n",
- "Epoch:[ 59/200], step:[ 100/ 468], loss_d:1.244428 , loss_g:0.825548 , time:0.045329s, lr:0.000200\n",
- "Epoch:[ 59/200], step:[ 200/ 468], loss_d:1.290440 , loss_g:0.880932 , time:0.048620s, lr:0.000200\n",
- "Epoch:[ 59/200], step:[ 300/ 468], loss_d:1.289803 , loss_g:1.063917 , time:0.044954s, lr:0.000200\n",
- "Epoch:[ 59/200], step:[ 400/ 468], loss_d:1.280102 , loss_g:0.882475 , time:0.046160s, lr:0.000200\n",
- "time of epoch 60 is 34.21s\n",
- "Epoch:[ 60/200], step:[ 0/ 468], loss_d:1.255521 , loss_g:0.853849 , time:0.045967s, lr:0.000200\n",
- "Epoch:[ 60/200], step:[ 100/ 468], loss_d:1.318899 , loss_g:0.614634 , time:0.042422s, lr:0.000200\n",
- "Epoch:[ 60/200], step:[ 200/ 468], loss_d:1.281980 , loss_g:0.890118 , time:0.043733s, lr:0.000200\n",
- "Epoch:[ 60/200], step:[ 300/ 468], loss_d:1.345798 , loss_g:0.827052 , time:0.045998s, lr:0.000200\n",
- "Epoch:[ 60/200], step:[ 400/ 468], loss_d:1.331466 , loss_g:0.800259 , time:0.042480s, lr:0.000200\n",
- "time of epoch 61 is 31.84s\n",
- "Epoch:[ 61/200], step:[ 0/ 468], loss_d:1.302438 , loss_g:0.785887 , time:0.050745s, lr:0.000200\n",
- "Epoch:[ 61/200], step:[ 100/ 468], loss_d:1.287652 , loss_g:0.954560 , time:0.047067s, lr:0.000200\n",
- "Epoch:[ 61/200], step:[ 200/ 468], loss_d:1.289292 , loss_g:0.669971 , time:0.049213s, lr:0.000200\n",
- "Epoch:[ 61/200], step:[ 300/ 468], loss_d:1.288193 , loss_g:0.740092 , time:0.048287s, lr:0.000200\n",
- "Epoch:[ 61/200], step:[ 400/ 468], loss_d:1.285052 , loss_g:0.855835 , time:0.050388s, lr:0.000200\n",
- "time of epoch 62 is 34.51s\n",
- "Epoch:[ 62/200], step:[ 0/ 468], loss_d:1.292556 , loss_g:1.079693 , time:0.052391s, lr:0.000200\n",
- "Epoch:[ 62/200], step:[ 100/ 468], loss_d:1.265508 , loss_g:0.928496 , time:0.042626s, lr:0.000200\n",
- "Epoch:[ 62/200], step:[ 200/ 468], loss_d:1.334546 , loss_g:0.702883 , time:0.048831s, lr:0.000200\n",
- "Epoch:[ 62/200], step:[ 300/ 468], loss_d:1.358715 , loss_g:0.631464 , time:0.039810s, lr:0.000200\n",
- "Epoch:[ 62/200], step:[ 400/ 468], loss_d:1.262371 , loss_g:0.928034 , time:0.046339s, lr:0.000200\n",
- "time of epoch 63 is 35.50s\n",
- "Epoch:[ 63/200], step:[ 0/ 468], loss_d:1.309542 , loss_g:0.804688 , time:0.050791s, lr:0.000200\n",
- "Epoch:[ 63/200], step:[ 100/ 468], loss_d:1.214239 , loss_g:0.957408 , time:0.043333s, lr:0.000200\n",
- "Epoch:[ 63/200], step:[ 200/ 468], loss_d:1.251779 , loss_g:1.007886 , time:0.043063s, lr:0.000200\n",
- "Epoch:[ 63/200], step:[ 300/ 468], loss_d:1.284945 , loss_g:0.883139 , time:0.042834s, lr:0.000200\n",
- "Epoch:[ 63/200], step:[ 400/ 468], loss_d:1.265973 , loss_g:0.959387 , time:0.042672s, lr:0.000200\n",
- "time of epoch 64 is 32.52s\n",
- "Epoch:[ 64/200], step:[ 0/ 468], loss_d:1.277903 , loss_g:0.884865 , time:0.048743s, lr:0.000200\n",
- "Epoch:[ 64/200], step:[ 100/ 468], loss_d:1.286232 , loss_g:0.835865 , time:0.046963s, lr:0.000200\n",
- "Epoch:[ 64/200], step:[ 200/ 468], loss_d:1.245985 , loss_g:0.907815 , time:0.044935s, lr:0.000200\n",
- "Epoch:[ 64/200], step:[ 300/ 468], loss_d:1.253784 , loss_g:0.788492 , time:0.042575s, lr:0.000200\n",
- "Epoch:[ 64/200], step:[ 400/ 468], loss_d:1.268703 , loss_g:0.990364 , time:0.044148s, lr:0.000200\n",
- "time of epoch 65 is 32.35s\n",
- "Epoch:[ 65/200], step:[ 0/ 468], loss_d:1.296594 , loss_g:0.913499 , time:0.047911s, lr:0.000200\n",
- "Epoch:[ 65/200], step:[ 100/ 468], loss_d:1.309034 , loss_g:0.872568 , time:0.050152s, lr:0.000200\n",
- "Epoch:[ 65/200], step:[ 200/ 468], loss_d:1.247439 , loss_g:1.056649 , time:0.044123s, lr:0.000200\n",
- "Epoch:[ 65/200], step:[ 300/ 468], loss_d:1.278377 , loss_g:1.034659 , time:0.053725s, lr:0.000200\n",
- "Epoch:[ 65/200], step:[ 400/ 468], loss_d:1.297263 , loss_g:0.915143 , time:0.047854s, lr:0.000200\n",
- "time of epoch 66 is 36.04s\n",
- "Epoch:[ 66/200], step:[ 0/ 468], loss_d:1.369360 , loss_g:0.765675 , time:0.050224s, lr:0.000200\n",
- "Epoch:[ 66/200], step:[ 100/ 468], loss_d:1.327502 , loss_g:0.717607 , time:0.042028s, lr:0.000200\n",
- "Epoch:[ 66/200], step:[ 200/ 468], loss_d:1.241568 , loss_g:0.933459 , time:0.046437s, lr:0.000200\n",
- "Epoch:[ 66/200], step:[ 300/ 468], loss_d:1.304989 , loss_g:0.916806 , time:0.042258s, lr:0.000200\n",
- "Epoch:[ 66/200], step:[ 400/ 468], loss_d:1.276990 , loss_g:0.747656 , time:0.045870s, lr:0.000200\n",
- "time of epoch 67 is 36.07s\n",
- "Epoch:[ 67/200], step:[ 0/ 468], loss_d:1.283766 , loss_g:0.867362 , time:0.051041s, lr:0.000200\n",
- "Epoch:[ 67/200], step:[ 100/ 468], loss_d:1.235177 , loss_g:1.072618 , time:0.044535s, lr:0.000200\n",
- "Epoch:[ 67/200], step:[ 200/ 468], loss_d:1.307476 , loss_g:0.916048 , time:0.041543s, lr:0.000200\n",
- "Epoch:[ 67/200], step:[ 300/ 468], loss_d:1.271056 , loss_g:0.798044 , time:0.043018s, lr:0.000200\n",
- "Epoch:[ 67/200], step:[ 400/ 468], loss_d:1.257254 , loss_g:0.779152 , time:0.041918s, lr:0.000200\n",
- "time of epoch 68 is 34.94s\n",
- "Epoch:[ 68/200], step:[ 0/ 468], loss_d:1.296121 , loss_g:0.800338 , time:0.047953s, lr:0.000200\n",
- "Epoch:[ 68/200], step:[ 100/ 468], loss_d:1.291616 , loss_g:0.780140 , time:0.049308s, lr:0.000200\n",
- "Epoch:[ 68/200], step:[ 200/ 468], loss_d:1.340003 , loss_g:0.802455 , time:0.046625s, lr:0.000200\n",
- "Epoch:[ 68/200], step:[ 300/ 468], loss_d:1.242630 , loss_g:0.926233 , time:0.045050s, lr:0.000200\n",
- "Epoch:[ 68/200], step:[ 400/ 468], loss_d:1.314312 , loss_g:0.861805 , time:0.038497s, lr:0.000200\n",
- "time of epoch 69 is 35.27s\n",
- "Epoch:[ 69/200], step:[ 0/ 468], loss_d:1.324181 , loss_g:1.164202 , time:0.048760s, lr:0.000200\n",
- "Epoch:[ 69/200], step:[ 100/ 468], loss_d:1.243429 , loss_g:0.919210 , time:0.047086s, lr:0.000200\n",
- "Epoch:[ 69/200], step:[ 200/ 468], loss_d:1.286552 , loss_g:0.829626 , time:0.049871s, lr:0.000200\n",
- "Epoch:[ 69/200], step:[ 300/ 468], loss_d:1.307333 , loss_g:0.892695 , time:0.049534s, lr:0.000200\n",
- "Epoch:[ 69/200], step:[ 400/ 468], loss_d:1.322739 , loss_g:0.872402 , time:0.046559s, lr:0.000200\n",
- "time of epoch 70 is 38.50s\n",
- "Epoch:[ 70/200], step:[ 0/ 468], loss_d:1.368777 , loss_g:0.708430 , time:0.047241s, lr:0.000200\n",
- "Epoch:[ 70/200], step:[ 100/ 468], loss_d:1.225775 , loss_g:0.873233 , time:0.046533s, lr:0.000200\n",
- "Epoch:[ 70/200], step:[ 200/ 468], loss_d:1.270439 , loss_g:0.965898 , time:0.051210s, lr:0.000200\n",
- "Epoch:[ 70/200], step:[ 300/ 468], loss_d:1.238077 , loss_g:0.899558 , time:0.041717s, lr:0.000200\n",
- "Epoch:[ 70/200], step:[ 400/ 468], loss_d:1.247410 , loss_g:0.835551 , time:0.044206s, lr:0.000200\n",
- "time of epoch 71 is 37.44s\n",
- "Epoch:[ 71/200], step:[ 0/ 468], loss_d:1.342798 , loss_g:0.672657 , time:0.054056s, lr:0.000200\n",
- "Epoch:[ 71/200], step:[ 100/ 468], loss_d:1.274170 , loss_g:0.745518 , time:0.047717s, lr:0.000200\n",
- "Epoch:[ 71/200], step:[ 200/ 468], loss_d:1.282508 , loss_g:0.873342 , time:0.045828s, lr:0.000200\n",
- "Epoch:[ 71/200], step:[ 300/ 468], loss_d:1.335411 , loss_g:1.079905 , time:0.045776s, lr:0.000200\n",
- "Epoch:[ 71/200], step:[ 400/ 468], loss_d:1.261366 , loss_g:0.911983 , time:0.045209s, lr:0.000200\n",
- "time of epoch 72 is 36.27s\n",
- "Epoch:[ 72/200], step:[ 0/ 468], loss_d:1.315783 , loss_g:0.764974 , time:0.050861s, lr:0.000200\n",
- "Epoch:[ 72/200], step:[ 100/ 468], loss_d:1.273635 , loss_g:1.171997 , time:0.046914s, lr:0.000200\n",
- "Epoch:[ 72/200], step:[ 200/ 468], loss_d:1.268991 , loss_g:0.939548 , time:0.047256s, lr:0.000200\n",
- "Epoch:[ 72/200], step:[ 300/ 468], loss_d:1.327120 , loss_g:1.111541 , time:0.048827s, lr:0.000200\n",
- "Epoch:[ 72/200], step:[ 400/ 468], loss_d:1.275147 , loss_g:1.082589 , time:0.046197s, lr:0.000200\n",
- "time of epoch 73 is 36.95s\n",
- "Epoch:[ 73/200], step:[ 0/ 468], loss_d:1.275911 , loss_g:1.046679 , time:0.054416s, lr:0.000200\n",
- "Epoch:[ 73/200], step:[ 100/ 468], loss_d:1.272111 , loss_g:0.853113 , time:0.043983s, lr:0.000200\n",
- "Epoch:[ 73/200], step:[ 200/ 468], loss_d:1.263924 , loss_g:0.852001 , time:0.045799s, lr:0.000200\n",
- "Epoch:[ 73/200], step:[ 300/ 468], loss_d:1.319084 , loss_g:0.855385 , time:0.047494s, lr:0.000200\n",
- "Epoch:[ 73/200], step:[ 400/ 468], loss_d:1.268350 , loss_g:0.885188 , time:0.048661s, lr:0.000200\n",
- "time of epoch 74 is 37.11s\n",
- "Epoch:[ 74/200], step:[ 0/ 468], loss_d:1.293881 , loss_g:0.754639 , time:0.049930s, lr:0.000200\n",
- "Epoch:[ 74/200], step:[ 100/ 468], loss_d:1.264755 , loss_g:0.938970 , time:0.043838s, lr:0.000200\n",
- "Epoch:[ 74/200], step:[ 200/ 468], loss_d:1.333288 , loss_g:0.853924 , time:0.045347s, lr:0.000200\n",
- "Epoch:[ 74/200], step:[ 300/ 468], loss_d:1.245891 , loss_g:1.094631 , time:0.049545s, lr:0.000200\n",
- "Epoch:[ 74/200], step:[ 400/ 468], loss_d:1.226994 , loss_g:0.995898 , time:0.043600s, lr:0.000200\n",
- "time of epoch 75 is 37.10s\n",
- "Epoch:[ 75/200], step:[ 0/ 468], loss_d:1.258013 , loss_g:0.854201 , time:0.051806s, lr:0.000200\n",
- "Epoch:[ 75/200], step:[ 100/ 468], loss_d:1.232367 , loss_g:1.068004 , time:0.043721s, lr:0.000200\n",
- "Epoch:[ 75/200], step:[ 200/ 468], loss_d:1.214932 , loss_g:0.988707 , time:0.044482s, lr:0.000200\n",
- "Epoch:[ 75/200], step:[ 300/ 468], loss_d:1.257394 , loss_g:0.798832 , time:0.044866s, lr:0.000200\n",
- "Epoch:[ 75/200], step:[ 400/ 468], loss_d:1.266918 , loss_g:0.972293 , time:0.046030s, lr:0.000200\n",
- "time of epoch 76 is 34.09s\n",
- "Epoch:[ 76/200], step:[ 0/ 468], loss_d:1.265741 , loss_g:0.851110 , time:0.047946s, lr:0.000200\n",
- "Epoch:[ 76/200], step:[ 100/ 468], loss_d:1.325474 , loss_g:0.923661 , time:0.047422s, lr:0.000200\n",
- "Epoch:[ 76/200], step:[ 200/ 468], loss_d:1.246524 , loss_g:1.174241 , time:0.044890s, lr:0.000200\n",
- "Epoch:[ 76/200], step:[ 300/ 468], loss_d:1.278205 , loss_g:0.878475 , time:0.045019s, lr:0.000200\n",
- "Epoch:[ 76/200], step:[ 400/ 468], loss_d:1.289550 , loss_g:0.792831 , time:0.042174s, lr:0.000200\n",
- "time of epoch 77 is 34.52s\n",
- "Epoch:[ 77/200], step:[ 0/ 468], loss_d:1.301568 , loss_g:1.052245 , time:0.049309s, lr:0.000200\n",
- "Epoch:[ 77/200], step:[ 100/ 468], loss_d:1.238838 , loss_g:0.964254 , time:0.044177s, lr:0.000200\n",
- "Epoch:[ 77/200], step:[ 200/ 468], loss_d:1.231171 , loss_g:0.900018 , time:0.043667s, lr:0.000200\n",
- "Epoch:[ 77/200], step:[ 300/ 468], loss_d:1.324486 , loss_g:1.060407 , time:0.038931s, lr:0.000200\n",
- "Epoch:[ 77/200], step:[ 400/ 468], loss_d:1.247087 , loss_g:1.089732 , time:0.041284s, lr:0.000200\n",
- "time of epoch 78 is 33.08s\n",
- "Epoch:[ 78/200], step:[ 0/ 468], loss_d:1.237552 , loss_g:0.895267 , time:0.049780s, lr:0.000200\n",
- "Epoch:[ 78/200], step:[ 100/ 468], loss_d:1.246083 , loss_g:1.006943 , time:0.042779s, lr:0.000200\n",
- "Epoch:[ 78/200], step:[ 200/ 468], loss_d:1.260101 , loss_g:0.838199 , time:0.044947s, lr:0.000200\n",
- "Epoch:[ 78/200], step:[ 300/ 468], loss_d:1.251656 , loss_g:0.881454 , time:0.049420s, lr:0.000200\n",
- "Epoch:[ 78/200], step:[ 400/ 468], loss_d:1.314604 , loss_g:0.898436 , time:0.046385s, lr:0.000200\n",
- "time of epoch 79 is 34.12s\n",
- "Epoch:[ 79/200], step:[ 0/ 468], loss_d:1.328437 , loss_g:0.616518 , time:0.047537s, lr:0.000200\n",
- "Epoch:[ 79/200], step:[ 100/ 468], loss_d:1.283821 , loss_g:0.894089 , time:0.043893s, lr:0.000200\n",
- "Epoch:[ 79/200], step:[ 200/ 468], loss_d:1.304629 , loss_g:0.759582 , time:0.046498s, lr:0.000200\n",
- "Epoch:[ 79/200], step:[ 300/ 468], loss_d:1.228148 , loss_g:0.812896 , time:0.045336s, lr:0.000200\n",
- "Epoch:[ 79/200], step:[ 400/ 468], loss_d:1.275430 , loss_g:0.910555 , time:0.048991s, lr:0.000200\n",
- "time of epoch 80 is 37.10s\n",
- "Epoch:[ 80/200], step:[ 0/ 468], loss_d:1.239350 , loss_g:0.873942 , time:0.047822s, lr:0.000200\n",
- "Epoch:[ 80/200], step:[ 100/ 468], loss_d:1.259196 , loss_g:0.805264 , time:0.042970s, lr:0.000200\n",
- "Epoch:[ 80/200], step:[ 200/ 468], loss_d:1.257325 , loss_g:0.998146 , time:0.042103s, lr:0.000200\n",
- "Epoch:[ 80/200], step:[ 300/ 468], loss_d:1.249333 , loss_g:0.925574 , time:0.041429s, lr:0.000200\n",
- "Epoch:[ 80/200], step:[ 400/ 468], loss_d:1.286314 , loss_g:0.786037 , time:0.043013s, lr:0.000200\n",
- "time of epoch 81 is 35.81s\n",
- "Epoch:[ 81/200], step:[ 0/ 468], loss_d:1.223749 , loss_g:1.089445 , time:0.048441s, lr:0.000200\n",
- "Epoch:[ 81/200], step:[ 100/ 468], loss_d:1.262170 , loss_g:0.779977 , time:0.042042s, lr:0.000200\n",
- "Epoch:[ 81/200], step:[ 200/ 468], loss_d:1.240216 , loss_g:1.296717 , time:0.046043s, lr:0.000200\n",
- "Epoch:[ 81/200], step:[ 300/ 468], loss_d:1.286365 , loss_g:0.926722 , time:0.046888s, lr:0.000200\n",
- "Epoch:[ 81/200], step:[ 400/ 468], loss_d:1.286471 , loss_g:0.785367 , time:0.041220s, lr:0.000200\n",
- "time of epoch 82 is 31.51s\n",
- "Epoch:[ 82/200], step:[ 0/ 468], loss_d:1.286746 , loss_g:0.874846 , time:0.048254s, lr:0.000200\n",
- "Epoch:[ 82/200], step:[ 100/ 468], loss_d:1.255161 , loss_g:1.081918 , time:0.046894s, lr:0.000200\n",
- "Epoch:[ 82/200], step:[ 200/ 468], loss_d:1.234870 , loss_g:0.839434 , time:0.043743s, lr:0.000200\n",
- "Epoch:[ 82/200], step:[ 300/ 468], loss_d:1.252229 , loss_g:0.926773 , time:0.043304s, lr:0.000200\n",
- "Epoch:[ 82/200], step:[ 400/ 468], loss_d:1.274880 , loss_g:0.833000 , time:0.038898s, lr:0.000200\n",
- "time of epoch 83 is 33.15s\n",
- "Epoch:[ 83/200], step:[ 0/ 468], loss_d:1.264673 , loss_g:1.038509 , time:0.051706s, lr:0.000200\n",
- "Epoch:[ 83/200], step:[ 100/ 468], loss_d:1.345782 , loss_g:1.087709 , time:0.049118s, lr:0.000200\n",
- "Epoch:[ 83/200], step:[ 200/ 468], loss_d:1.226377 , loss_g:0.878318 , time:0.048773s, lr:0.000200\n",
- "Epoch:[ 83/200], step:[ 300/ 468], loss_d:1.305567 , loss_g:0.957153 , time:0.043780s, lr:0.000200\n",
- "Epoch:[ 83/200], step:[ 400/ 468], loss_d:1.215334 , loss_g:0.924216 , time:0.043612s, lr:0.000200\n",
- "time of epoch 84 is 35.28s\n",
- "Epoch:[ 84/200], step:[ 0/ 468], loss_d:1.269334 , loss_g:1.009192 , time:0.049968s, lr:0.000200\n",
- "Epoch:[ 84/200], step:[ 100/ 468], loss_d:1.231154 , loss_g:0.925275 , time:0.049879s, lr:0.000200\n",
- "Epoch:[ 84/200], step:[ 200/ 468], loss_d:1.243465 , loss_g:0.766953 , time:0.045686s, lr:0.000200\n",
- "Epoch:[ 84/200], step:[ 300/ 468], loss_d:1.232594 , loss_g:1.027395 , time:0.047827s, lr:0.000200\n",
- "Epoch:[ 84/200], step:[ 400/ 468], loss_d:1.323746 , loss_g:0.785085 , time:0.047624s, lr:0.000200\n",
- "time of epoch 85 is 36.25s\n",
- "Epoch:[ 85/200], step:[ 0/ 468], loss_d:1.318126 , loss_g:0.844004 , time:0.051803s, lr:0.000200\n",
- "Epoch:[ 85/200], step:[ 100/ 468], loss_d:1.311495 , loss_g:0.666721 , time:0.040497s, lr:0.000200\n",
- "Epoch:[ 85/200], step:[ 200/ 468], loss_d:1.388227 , loss_g:1.277321 , time:0.038909s, lr:0.000200\n",
- "Epoch:[ 85/200], step:[ 300/ 468], loss_d:1.275890 , loss_g:0.752525 , time:0.040990s, lr:0.000200\n",
- "Epoch:[ 85/200], step:[ 400/ 468], loss_d:1.235005 , loss_g:1.103637 , time:0.039921s, lr:0.000200\n",
- "time of epoch 86 is 33.69s\n",
- "Epoch:[ 86/200], step:[ 0/ 468], loss_d:1.260627 , loss_g:0.981582 , time:0.048254s, lr:0.000200\n",
- "Epoch:[ 86/200], step:[ 100/ 468], loss_d:1.321540 , loss_g:0.812446 , time:0.048524s, lr:0.000200\n",
- "Epoch:[ 86/200], step:[ 200/ 468], loss_d:1.332921 , loss_g:0.592381 , time:0.045508s, lr:0.000200\n",
- "Epoch:[ 86/200], step:[ 300/ 468], loss_d:1.210480 , loss_g:0.950624 , time:0.045275s, lr:0.000200\n",
- "Epoch:[ 86/200], step:[ 400/ 468], loss_d:1.245843 , loss_g:1.062170 , time:0.051763s, lr:0.000200\n",
- "time of epoch 87 is 34.86s\n",
- "Epoch:[ 87/200], step:[ 0/ 468], loss_d:1.177821 , loss_g:0.966781 , time:0.046404s, lr:0.000200\n",
- "Epoch:[ 87/200], step:[ 100/ 468], loss_d:1.218120 , loss_g:1.019230 , time:0.045331s, lr:0.000200\n",
- "Epoch:[ 87/200], step:[ 200/ 468], loss_d:1.331244 , loss_g:0.876511 , time:0.048616s, lr:0.000200\n",
- "Epoch:[ 87/200], step:[ 300/ 468], loss_d:1.292220 , loss_g:0.946034 , time:0.051211s, lr:0.000200\n",
- "Epoch:[ 87/200], step:[ 400/ 468], loss_d:1.234660 , loss_g:1.019270 , time:0.045827s, lr:0.000200\n",
- "time of epoch 88 is 36.76s\n",
- "Epoch:[ 88/200], step:[ 0/ 468], loss_d:1.277607 , loss_g:0.857668 , time:0.044126s, lr:0.000200\n",
- "Epoch:[ 88/200], step:[ 100/ 468], loss_d:1.263812 , loss_g:0.791105 , time:0.044314s, lr:0.000200\n",
- "Epoch:[ 88/200], step:[ 200/ 468], loss_d:1.256955 , loss_g:0.856700 , time:0.051137s, lr:0.000200\n",
- "Epoch:[ 88/200], step:[ 300/ 468], loss_d:1.279761 , loss_g:0.956007 , time:0.049695s, lr:0.000200\n",
- "Epoch:[ 88/200], step:[ 400/ 468], loss_d:1.280383 , loss_g:1.067522 , time:0.046208s, lr:0.000200\n",
- "time of epoch 89 is 34.59s\n",
- "Epoch:[ 89/200], step:[ 0/ 468], loss_d:1.261170 , loss_g:0.952920 , time:0.050930s, lr:0.000200\n",
- "Epoch:[ 89/200], step:[ 100/ 468], loss_d:1.310192 , loss_g:0.947633 , time:0.047690s, lr:0.000200\n",
- "Epoch:[ 89/200], step:[ 200/ 468], loss_d:1.229793 , loss_g:0.857427 , time:0.051690s, lr:0.000200\n",
- "Epoch:[ 89/200], step:[ 300/ 468], loss_d:1.231180 , loss_g:1.018060 , time:0.045959s, lr:0.000200\n",
- "Epoch:[ 89/200], step:[ 400/ 468], loss_d:1.289767 , loss_g:0.893649 , time:0.045805s, lr:0.000200\n",
- "time of epoch 90 is 37.15s\n",
- "Epoch:[ 90/200], step:[ 0/ 468], loss_d:1.266213 , loss_g:0.824098 , time:0.041780s, lr:0.000200\n",
- "Epoch:[ 90/200], step:[ 100/ 468], loss_d:1.265238 , loss_g:0.941994 , time:0.046977s, lr:0.000200\n",
- "Epoch:[ 90/200], step:[ 200/ 468], loss_d:1.343167 , loss_g:0.738443 , time:0.044112s, lr:0.000200\n",
- "Epoch:[ 90/200], step:[ 300/ 468], loss_d:1.244670 , loss_g:0.962614 , time:0.044930s, lr:0.000200\n",
- "Epoch:[ 90/200], step:[ 400/ 468], loss_d:1.246223 , loss_g:0.898849 , time:0.043061s, lr:0.000200\n",
- "time of epoch 91 is 34.73s\n",
- "Epoch:[ 91/200], step:[ 0/ 468], loss_d:1.177226 , loss_g:1.023588 , time:0.044062s, lr:0.000200\n",
- "Epoch:[ 91/200], step:[ 100/ 468], loss_d:1.280674 , loss_g:0.722278 , time:0.039358s, lr:0.000200\n",
- "Epoch:[ 91/200], step:[ 200/ 468], loss_d:1.301066 , loss_g:0.952798 , time:0.041793s, lr:0.000200\n",
- "Epoch:[ 91/200], step:[ 300/ 468], loss_d:1.241785 , loss_g:0.855447 , time:0.047965s, lr:0.000200\n",
- "Epoch:[ 91/200], step:[ 400/ 468], loss_d:1.284462 , loss_g:0.905266 , time:0.041410s, lr:0.000200\n",
- "time of epoch 92 is 34.28s\n",
- "Epoch:[ 92/200], step:[ 0/ 468], loss_d:1.274682 , loss_g:1.157544 , time:0.052458s, lr:0.000200\n",
- "Epoch:[ 92/200], step:[ 100/ 468], loss_d:1.208257 , loss_g:1.005727 , time:0.039868s, lr:0.000200\n",
- "Epoch:[ 92/200], step:[ 200/ 468], loss_d:1.262125 , loss_g:0.836799 , time:0.041194s, lr:0.000200\n",
- "Epoch:[ 92/200], step:[ 300/ 468], loss_d:1.274592 , loss_g:0.932075 , time:0.041309s, lr:0.000200\n",
- "Epoch:[ 92/200], step:[ 400/ 468], loss_d:1.286282 , loss_g:0.731010 , time:0.040235s, lr:0.000200\n",
- "time of epoch 93 is 32.12s\n",
- "Epoch:[ 93/200], step:[ 0/ 468], loss_d:1.285608 , loss_g:0.937456 , time:0.045049s, lr:0.000200\n",
- "Epoch:[ 93/200], step:[ 100/ 468], loss_d:1.194318 , loss_g:0.829386 , time:0.047853s, lr:0.000200\n",
- "Epoch:[ 93/200], step:[ 200/ 468], loss_d:1.198999 , loss_g:0.976121 , time:0.046536s, lr:0.000200\n",
- "Epoch:[ 93/200], step:[ 300/ 468], loss_d:1.215620 , loss_g:0.983590 , time:0.050829s, lr:0.000200\n",
- "Epoch:[ 93/200], step:[ 400/ 468], loss_d:1.232252 , loss_g:0.860814 , time:0.038771s, lr:0.000200\n",
- "time of epoch 94 is 33.80s\n",
- "Epoch:[ 94/200], step:[ 0/ 468], loss_d:1.222739 , loss_g:1.027187 , time:0.047755s, lr:0.000200\n",
- "Epoch:[ 94/200], step:[ 100/ 468], loss_d:1.243713 , loss_g:0.990652 , time:0.044362s, lr:0.000200\n",
- "Epoch:[ 94/200], step:[ 200/ 468], loss_d:1.265000 , loss_g:0.901564 , time:0.045394s, lr:0.000200\n",
- "Epoch:[ 94/200], step:[ 300/ 468], loss_d:1.253571 , loss_g:0.734750 , time:0.041754s, lr:0.000200\n",
- "Epoch:[ 94/200], step:[ 400/ 468], loss_d:1.269130 , loss_g:0.960593 , time:0.039069s, lr:0.000200\n",
- "time of epoch 95 is 33.98s\n",
- "Epoch:[ 95/200], step:[ 0/ 468], loss_d:1.272148 , loss_g:0.842761 , time:0.049536s, lr:0.000200\n",
- "Epoch:[ 95/200], step:[ 100/ 468], loss_d:1.255142 , loss_g:0.959849 , time:0.041048s, lr:0.000200\n",
- "Epoch:[ 95/200], step:[ 200/ 468], loss_d:1.203595 , loss_g:0.905932 , time:0.054551s, lr:0.000200\n",
- "Epoch:[ 95/200], step:[ 300/ 468], loss_d:1.240350 , loss_g:0.987186 , time:0.048185s, lr:0.000200\n",
- "Epoch:[ 95/200], step:[ 400/ 468], loss_d:1.218228 , loss_g:0.794264 , time:0.039880s, lr:0.000200\n",
- "time of epoch 96 is 36.20s\n",
- "Epoch:[ 96/200], step:[ 0/ 468], loss_d:1.251075 , loss_g:1.063750 , time:0.050336s, lr:0.000200\n",
- "Epoch:[ 96/200], step:[ 100/ 468], loss_d:1.302071 , loss_g:0.925102 , time:0.044689s, lr:0.000200\n",
- "Epoch:[ 96/200], step:[ 200/ 468], loss_d:1.252329 , loss_g:1.020200 , time:0.052826s, lr:0.000200\n",
- "Epoch:[ 96/200], step:[ 300/ 468], loss_d:1.203419 , loss_g:0.876964 , time:0.048047s, lr:0.000200\n",
- "Epoch:[ 96/200], step:[ 400/ 468], loss_d:1.268883 , loss_g:0.825612 , time:0.044607s, lr:0.000200\n",
- "time of epoch 97 is 34.58s\n",
- "Epoch:[ 97/200], step:[ 0/ 468], loss_d:1.266016 , loss_g:0.912950 , time:0.048479s, lr:0.000200\n",
- "Epoch:[ 97/200], step:[ 100/ 468], loss_d:1.239652 , loss_g:1.135652 , time:0.044792s, lr:0.000200\n",
- "Epoch:[ 97/200], step:[ 200/ 468], loss_d:1.306474 , loss_g:0.930136 , time:0.045076s, lr:0.000200\n",
- "Epoch:[ 97/200], step:[ 300/ 468], loss_d:1.265135 , loss_g:0.839461 , time:0.051776s, lr:0.000200\n",
- "Epoch:[ 97/200], step:[ 400/ 468], loss_d:1.268726 , loss_g:0.893282 , time:0.046870s, lr:0.000200\n",
- "time of epoch 98 is 35.25s\n",
- "Epoch:[ 98/200], step:[ 0/ 468], loss_d:1.262786 , loss_g:1.122138 , time:0.051108s, lr:0.000200\n",
- "Epoch:[ 98/200], step:[ 100/ 468], loss_d:1.264205 , loss_g:1.072651 , time:0.045852s, lr:0.000200\n",
- "Epoch:[ 98/200], step:[ 200/ 468], loss_d:1.287501 , loss_g:0.959713 , time:0.047680s, lr:0.000200\n",
- "Epoch:[ 98/200], step:[ 300/ 468], loss_d:1.263124 , loss_g:1.040372 , time:0.051059s, lr:0.000200\n",
- "Epoch:[ 98/200], step:[ 400/ 468], loss_d:1.271025 , loss_g:0.951961 , time:0.041565s, lr:0.000200\n",
- "time of epoch 99 is 35.92s\n",
- "Epoch:[ 99/200], step:[ 0/ 468], loss_d:1.263868 , loss_g:0.863676 , time:0.051904s, lr:0.000200\n",
- "Epoch:[ 99/200], step:[ 100/ 468], loss_d:1.240167 , loss_g:0.968805 , time:0.044200s, lr:0.000200\n",
- "Epoch:[ 99/200], step:[ 200/ 468], loss_d:1.230386 , loss_g:0.856215 , time:0.047688s, lr:0.000200\n",
- "Epoch:[ 99/200], step:[ 300/ 468], loss_d:1.276353 , loss_g:0.886626 , time:0.050296s, lr:0.000200\n",
- "Epoch:[ 99/200], step:[ 400/ 468], loss_d:1.311608 , loss_g:1.052250 , time:0.047153s, lr:0.000200\n",
- "time of epoch 100 is 36.35s\n",
- "Epoch:[100/200], step:[ 0/ 468], loss_d:1.195933 , loss_g:1.159281 , time:0.045802s, lr:0.000200\n",
- "Epoch:[100/200], step:[ 100/ 468], loss_d:1.322469 , loss_g:0.918175 , time:0.043946s, lr:0.000200\n",
- "Epoch:[100/200], step:[ 200/ 468], loss_d:1.315774 , loss_g:1.067750 , time:0.045911s, lr:0.000200\n",
- "Epoch:[100/200], step:[ 300/ 468], loss_d:1.220087 , loss_g:0.760998 , time:0.048778s, lr:0.000200\n",
- "Epoch:[100/200], step:[ 400/ 468], loss_d:1.262182 , loss_g:0.922417 , time:0.047162s, lr:0.000200\n",
- "time of epoch 101 is 33.53s\n",
- "Epoch:[101/200], step:[ 0/ 468], loss_d:1.229516 , loss_g:0.991987 , time:0.046079s, lr:0.000200\n",
- "Epoch:[101/200], step:[ 100/ 468], loss_d:1.223289 , loss_g:0.995996 , time:0.048062s, lr:0.000200\n",
- "Epoch:[101/200], step:[ 200/ 468], loss_d:1.308380 , loss_g:0.973232 , time:0.042402s, lr:0.000200\n",
- "Epoch:[101/200], step:[ 300/ 468], loss_d:1.332341 , loss_g:1.187854 , time:0.043210s, lr:0.000200\n",
- "Epoch:[101/200], step:[ 400/ 468], loss_d:1.263397 , loss_g:1.013150 , time:0.047899s, lr:0.000200\n",
- "time of epoch 102 is 35.28s\n",
- "Epoch:[102/200], step:[ 0/ 468], loss_d:1.268744 , loss_g:1.035616 , time:0.046276s, lr:0.000200\n",
- "Epoch:[102/200], step:[ 100/ 468], loss_d:1.174612 , loss_g:1.105113 , time:0.053629s, lr:0.000200\n",
- "Epoch:[102/200], step:[ 200/ 468], loss_d:1.268577 , loss_g:0.820688 , time:0.048571s, lr:0.000200\n",
- "Epoch:[102/200], step:[ 300/ 468], loss_d:1.291828 , loss_g:0.884673 , time:0.040891s, lr:0.000200\n",
- "Epoch:[102/200], step:[ 400/ 468], loss_d:1.186356 , loss_g:1.160244 , time:0.039562s, lr:0.000200\n",
- "time of epoch 103 is 35.42s\n",
- "Epoch:[103/200], step:[ 0/ 468], loss_d:1.202083 , loss_g:0.936231 , time:0.045745s, lr:0.000200\n",
- "Epoch:[103/200], step:[ 100/ 468], loss_d:1.216438 , loss_g:0.908985 , time:0.048187s, lr:0.000200\n",
- "Epoch:[103/200], step:[ 200/ 468], loss_d:1.215895 , loss_g:1.025792 , time:0.042521s, lr:0.000200\n",
- "Epoch:[103/200], step:[ 300/ 468], loss_d:1.210754 , loss_g:0.895619 , time:0.043612s, lr:0.000200\n",
- "Epoch:[103/200], step:[ 400/ 468], loss_d:1.236582 , loss_g:1.168654 , time:0.041896s, lr:0.000200\n",
- "time of epoch 104 is 35.07s\n",
- "Epoch:[104/200], step:[ 0/ 468], loss_d:1.204531 , loss_g:1.008565 , time:0.051636s, lr:0.000200\n",
- "Epoch:[104/200], step:[ 100/ 468], loss_d:1.238383 , loss_g:0.973770 , time:0.044151s, lr:0.000200\n",
- "Epoch:[104/200], step:[ 200/ 468], loss_d:1.317574 , loss_g:1.126151 , time:0.043505s, lr:0.000200\n",
- "Epoch:[104/200], step:[ 300/ 468], loss_d:1.260759 , loss_g:0.824432 , time:0.042852s, lr:0.000200\n",
- "Epoch:[104/200], step:[ 400/ 468], loss_d:1.311132 , loss_g:0.922607 , time:0.043626s, lr:0.000200\n",
- "time of epoch 105 is 34.50s\n",
- "Epoch:[105/200], step:[ 0/ 468], loss_d:1.278582 , loss_g:1.162642 , time:0.049615s, lr:0.000200\n",
- "Epoch:[105/200], step:[ 100/ 468], loss_d:1.276598 , loss_g:0.807140 , time:0.040067s, lr:0.000200\n",
- "Epoch:[105/200], step:[ 200/ 468], loss_d:1.300205 , loss_g:0.863271 , time:0.045648s, lr:0.000200\n",
- "Epoch:[105/200], step:[ 300/ 468], loss_d:1.210071 , loss_g:0.872550 , time:0.040214s, lr:0.000200\n",
- "Epoch:[105/200], step:[ 400/ 468], loss_d:1.216179 , loss_g:1.026746 , time:0.044720s, lr:0.000200\n",
- "time of epoch 106 is 35.20s\n",
- "Epoch:[106/200], step:[ 0/ 468], loss_d:1.264625 , loss_g:1.014118 , time:0.042536s, lr:0.000200\n",
- "Epoch:[106/200], step:[ 100/ 468], loss_d:1.320001 , loss_g:0.981012 , time:0.042151s, lr:0.000200\n",
- "Epoch:[106/200], step:[ 200/ 468], loss_d:1.337474 , loss_g:1.028437 , time:0.048315s, lr:0.000200\n",
- "Epoch:[106/200], step:[ 300/ 468], loss_d:1.255221 , loss_g:0.765179 , time:0.046887s, lr:0.000200\n",
- "Epoch:[106/200], step:[ 400/ 468], loss_d:1.302818 , loss_g:0.784797 , time:0.046842s, lr:0.000200\n",
- "time of epoch 107 is 36.22s\n",
- "Epoch:[107/200], step:[ 0/ 468], loss_d:1.204974 , loss_g:0.981459 , time:0.047930s, lr:0.000200\n",
- "Epoch:[107/200], step:[ 100/ 468], loss_d:1.271407 , loss_g:0.854389 , time:0.049189s, lr:0.000200\n",
- "Epoch:[107/200], step:[ 200/ 468], loss_d:1.358407 , loss_g:1.301154 , time:0.041494s, lr:0.000200\n",
- "Epoch:[107/200], step:[ 300/ 468], loss_d:1.237980 , loss_g:0.869114 , time:0.041105s, lr:0.000200\n",
- "Epoch:[107/200], step:[ 400/ 468], loss_d:1.251710 , loss_g:0.961053 , time:0.043255s, lr:0.000200\n",
- "time of epoch 108 is 34.39s\n",
- "Epoch:[108/200], step:[ 0/ 468], loss_d:1.316208 , loss_g:0.752527 , time:0.047863s, lr:0.000200\n",
- "Epoch:[108/200], step:[ 100/ 468], loss_d:1.226642 , loss_g:1.014658 , time:0.044958s, lr:0.000200\n",
- "Epoch:[108/200], step:[ 200/ 468], loss_d:1.242489 , loss_g:0.805440 , time:0.048612s, lr:0.000200\n",
- "Epoch:[108/200], step:[ 300/ 468], loss_d:1.336364 , loss_g:0.950849 , time:0.047765s, lr:0.000200\n",
- "Epoch:[108/200], step:[ 400/ 468], loss_d:1.250432 , loss_g:0.867144 , time:0.047037s, lr:0.000200\n",
- "time of epoch 109 is 36.11s\n",
- "Epoch:[109/200], step:[ 0/ 468], loss_d:1.271340 , loss_g:0.811424 , time:0.041381s, lr:0.000200\n",
- "Epoch:[109/200], step:[ 100/ 468], loss_d:1.277861 , loss_g:0.777123 , time:0.040948s, lr:0.000200\n",
- "Epoch:[109/200], step:[ 200/ 468], loss_d:1.262773 , loss_g:1.134534 , time:0.040416s, lr:0.000200\n",
- "Epoch:[109/200], step:[ 300/ 468], loss_d:1.217336 , loss_g:0.876227 , time:0.042833s, lr:0.000200\n",
- "Epoch:[109/200], step:[ 400/ 468], loss_d:1.187468 , loss_g:1.047299 , time:0.039426s, lr:0.000200\n",
- "time of epoch 110 is 35.33s\n",
- "Epoch:[110/200], step:[ 0/ 468], loss_d:1.290276 , loss_g:0.999078 , time:0.051047s, lr:0.000200\n",
- "Epoch:[110/200], step:[ 100/ 468], loss_d:1.314519 , loss_g:0.766162 , time:0.040582s, lr:0.000200\n",
- "Epoch:[110/200], step:[ 200/ 468], loss_d:1.277430 , loss_g:0.976806 , time:0.045778s, lr:0.000200\n",
- "Epoch:[110/200], step:[ 300/ 468], loss_d:1.238655 , loss_g:0.868288 , time:0.050655s, lr:0.000200\n",
- "Epoch:[110/200], step:[ 400/ 468], loss_d:1.351409 , loss_g:0.888745 , time:0.041035s, lr:0.000200\n",
- "time of epoch 111 is 34.84s\n",
- "Epoch:[111/200], step:[ 0/ 468], loss_d:1.253341 , loss_g:0.940134 , time:0.047712s, lr:0.000200\n",
- "Epoch:[111/200], step:[ 100/ 468], loss_d:1.264957 , loss_g:0.803884 , time:0.041883s, lr:0.000200\n",
- "Epoch:[111/200], step:[ 200/ 468], loss_d:1.268405 , loss_g:1.134383 , time:0.038082s, lr:0.000200\n",
- "Epoch:[111/200], step:[ 300/ 468], loss_d:1.228103 , loss_g:0.894812 , time:0.047263s, lr:0.000200\n",
- "Epoch:[111/200], step:[ 400/ 468], loss_d:1.261829 , loss_g:0.993884 , time:0.047271s, lr:0.000200\n",
- "time of epoch 112 is 35.59s\n",
- "Epoch:[112/200], step:[ 0/ 468], loss_d:1.258753 , loss_g:1.191665 , time:0.046999s, lr:0.000200\n",
- "Epoch:[112/200], step:[ 100/ 468], loss_d:1.259707 , loss_g:0.870625 , time:0.052085s, lr:0.000200\n",
- "Epoch:[112/200], step:[ 200/ 468], loss_d:1.278076 , loss_g:1.036802 , time:0.043790s, lr:0.000200\n",
- "Epoch:[112/200], step:[ 300/ 468], loss_d:1.267510 , loss_g:0.793068 , time:0.044864s, lr:0.000200\n",
- "Epoch:[112/200], step:[ 400/ 468], loss_d:1.214011 , loss_g:1.011071 , time:0.048509s, lr:0.000200\n",
- "time of epoch 113 is 36.19s\n",
- "Epoch:[113/200], step:[ 0/ 468], loss_d:1.192810 , loss_g:0.947201 , time:0.047911s, lr:0.000200\n",
- "Epoch:[113/200], step:[ 100/ 468], loss_d:1.239804 , loss_g:1.054534 , time:0.042661s, lr:0.000200\n",
- "Epoch:[113/200], step:[ 200/ 468], loss_d:1.174106 , loss_g:0.880735 , time:0.044992s, lr:0.000200\n",
- "Epoch:[113/200], step:[ 300/ 468], loss_d:1.314139 , loss_g:0.851185 , time:0.045122s, lr:0.000200\n",
- "Epoch:[113/200], step:[ 400/ 468], loss_d:1.247335 , loss_g:0.754230 , time:0.041527s, lr:0.000200\n",
- "time of epoch 114 is 33.72s\n",
- "Epoch:[114/200], step:[ 0/ 468], loss_d:1.266369 , loss_g:1.062516 , time:0.047467s, lr:0.000200\n",
- "Epoch:[114/200], step:[ 100/ 468], loss_d:1.208506 , loss_g:1.067186 , time:0.046478s, lr:0.000200\n",
- "Epoch:[114/200], step:[ 200/ 468], loss_d:1.210452 , loss_g:1.011056 , time:0.042296s, lr:0.000200\n",
- "Epoch:[114/200], step:[ 300/ 468], loss_d:1.297954 , loss_g:1.119763 , time:0.046112s, lr:0.000200\n",
- "Epoch:[114/200], step:[ 400/ 468], loss_d:1.276475 , loss_g:1.105644 , time:0.044821s, lr:0.000200\n",
- "time of epoch 115 is 32.94s\n",
- "Epoch:[115/200], step:[ 0/ 468], loss_d:1.211765 , loss_g:1.040868 , time:0.049438s, lr:0.000200\n",
- "Epoch:[115/200], step:[ 100/ 468], loss_d:1.248928 , loss_g:1.385214 , time:0.045408s, lr:0.000200\n",
- "Epoch:[115/200], step:[ 200/ 468], loss_d:1.260017 , loss_g:1.123079 , time:0.040535s, lr:0.000200\n",
- "Epoch:[115/200], step:[ 300/ 468], loss_d:1.254612 , loss_g:1.095119 , time:0.042827s, lr:0.000200\n",
- "Epoch:[115/200], step:[ 400/ 468], loss_d:1.249005 , loss_g:1.181086 , time:0.042072s, lr:0.000200\n",
- "time of epoch 116 is 34.16s\n",
- "Epoch:[116/200], step:[ 0/ 468], loss_d:1.213979 , loss_g:1.108207 , time:0.047231s, lr:0.000200\n",
- "Epoch:[116/200], step:[ 100/ 468], loss_d:1.256017 , loss_g:0.858797 , time:0.044244s, lr:0.000200\n",
- "Epoch:[116/200], step:[ 200/ 468], loss_d:1.318390 , loss_g:0.773079 , time:0.041134s, lr:0.000200\n",
- "Epoch:[116/200], step:[ 300/ 468], loss_d:1.250701 , loss_g:0.912923 , time:0.047869s, lr:0.000200\n",
- "Epoch:[116/200], step:[ 400/ 468], loss_d:1.231393 , loss_g:1.022859 , time:0.043941s, lr:0.000200\n",
- "time of epoch 117 is 33.48s\n",
- "Epoch:[117/200], step:[ 0/ 468], loss_d:1.200470 , loss_g:0.837901 , time:0.047325s, lr:0.000200\n",
- "Epoch:[117/200], step:[ 100/ 468], loss_d:1.364929 , loss_g:1.018399 , time:0.042544s, lr:0.000200\n",
- "Epoch:[117/200], step:[ 200/ 468], loss_d:1.245403 , loss_g:0.955839 , time:0.042407s, lr:0.000200\n",
- "Epoch:[117/200], step:[ 300/ 468], loss_d:1.291929 , loss_g:1.063396 , time:0.040782s, lr:0.000200\n",
- "Epoch:[117/200], step:[ 400/ 468], loss_d:1.371064 , loss_g:0.708585 , time:0.041774s, lr:0.000200\n",
- "time of epoch 118 is 35.10s\n",
- "Epoch:[118/200], step:[ 0/ 468], loss_d:1.251089 , loss_g:0.932023 , time:0.049962s, lr:0.000200\n",
- "Epoch:[118/200], step:[ 100/ 468], loss_d:1.271200 , loss_g:0.865552 , time:0.045633s, lr:0.000200\n",
- "Epoch:[118/200], step:[ 200/ 468], loss_d:1.183690 , loss_g:0.924572 , time:0.043103s, lr:0.000200\n",
- "Epoch:[118/200], step:[ 300/ 468], loss_d:1.229748 , loss_g:1.135687 , time:0.040202s, lr:0.000200\n",
- "Epoch:[118/200], step:[ 400/ 468], loss_d:1.269796 , loss_g:0.878133 , time:0.042011s, lr:0.000200\n",
- "time of epoch 119 is 32.63s\n",
- "Epoch:[119/200], step:[ 0/ 468], loss_d:1.249897 , loss_g:0.994552 , time:0.053744s, lr:0.000200\n",
- "Epoch:[119/200], step:[ 100/ 468], loss_d:1.277300 , loss_g:0.709877 , time:0.050448s, lr:0.000200\n",
- "Epoch:[119/200], step:[ 200/ 468], loss_d:1.325880 , loss_g:1.005621 , time:0.044147s, lr:0.000200\n",
- "Epoch:[119/200], step:[ 300/ 468], loss_d:1.326895 , loss_g:0.782711 , time:0.048421s, lr:0.000200\n",
- "Epoch:[119/200], step:[ 400/ 468], loss_d:1.222522 , loss_g:0.880688 , time:0.050931s, lr:0.000200\n",
- "time of epoch 120 is 36.55s\n",
- "Epoch:[120/200], step:[ 0/ 468], loss_d:1.228207 , loss_g:1.091192 , time:0.050076s, lr:0.000200\n",
- "Epoch:[120/200], step:[ 100/ 468], loss_d:1.224199 , loss_g:0.950746 , time:0.041954s, lr:0.000200\n",
- "Epoch:[120/200], step:[ 200/ 468], loss_d:1.240260 , loss_g:0.855890 , time:0.051784s, lr:0.000200\n",
- "Epoch:[120/200], step:[ 300/ 468], loss_d:1.266151 , loss_g:1.146879 , time:0.042392s, lr:0.000200\n",
- "Epoch:[120/200], step:[ 400/ 468], loss_d:1.257645 , loss_g:0.852158 , time:0.048375s, lr:0.000200\n",
- "time of epoch 121 is 36.18s\n",
- "Epoch:[121/200], step:[ 0/ 468], loss_d:1.219151 , loss_g:0.966171 , time:0.053296s, lr:0.000200\n",
- "Epoch:[121/200], step:[ 100/ 468], loss_d:1.321041 , loss_g:0.920692 , time:0.040022s, lr:0.000200\n",
- "Epoch:[121/200], step:[ 200/ 468], loss_d:1.247602 , loss_g:0.717073 , time:0.039237s, lr:0.000200\n",
- "Epoch:[121/200], step:[ 300/ 468], loss_d:1.248384 , loss_g:0.937853 , time:0.037790s, lr:0.000200\n",
- "Epoch:[121/200], step:[ 400/ 468], loss_d:1.209570 , loss_g:0.791589 , time:0.036499s, lr:0.000200\n",
- "time of epoch 122 is 34.59s\n",
- "Epoch:[122/200], step:[ 0/ 468], loss_d:1.236038 , loss_g:0.961080 , time:0.043517s, lr:0.000200\n",
- "Epoch:[122/200], step:[ 100/ 468], loss_d:1.239962 , loss_g:0.975202 , time:0.051728s, lr:0.000200\n",
- "Epoch:[122/200], step:[ 200/ 468], loss_d:1.227556 , loss_g:1.078973 , time:0.042627s, lr:0.000200\n",
- "Epoch:[122/200], step:[ 300/ 468], loss_d:1.315636 , loss_g:1.082853 , time:0.041076s, lr:0.000200\n",
- "Epoch:[122/200], step:[ 400/ 468], loss_d:1.246736 , loss_g:0.929578 , time:0.043762s, lr:0.000200\n",
- "time of epoch 123 is 35.11s\n",
- "Epoch:[123/200], step:[ 0/ 468], loss_d:1.231609 , loss_g:0.876290 , time:0.044724s, lr:0.000200\n",
- "Epoch:[123/200], step:[ 100/ 468], loss_d:1.168045 , loss_g:0.968915 , time:0.038062s, lr:0.000200\n",
- "Epoch:[123/200], step:[ 200/ 468], loss_d:1.207285 , loss_g:0.952756 , time:0.041762s, lr:0.000200\n",
- "Epoch:[123/200], step:[ 300/ 468], loss_d:1.297474 , loss_g:0.813684 , time:0.040283s, lr:0.000200\n",
- "Epoch:[123/200], step:[ 400/ 468], loss_d:1.200598 , loss_g:0.855109 , time:0.039354s, lr:0.000200\n",
- "time of epoch 124 is 36.65s\n",
- "Epoch:[124/200], step:[ 0/ 468], loss_d:1.233270 , loss_g:1.012869 , time:0.047932s, lr:0.000200\n",
- "Epoch:[124/200], step:[ 100/ 468], loss_d:1.231152 , loss_g:0.935236 , time:0.042354s, lr:0.000200\n",
- "Epoch:[124/200], step:[ 200/ 468], loss_d:1.324848 , loss_g:1.103574 , time:0.046100s, lr:0.000200\n",
- "Epoch:[124/200], step:[ 300/ 468], loss_d:1.243928 , loss_g:0.928588 , time:0.045043s, lr:0.000200\n",
- "Epoch:[124/200], step:[ 400/ 468], loss_d:1.297712 , loss_g:0.812811 , time:0.040345s, lr:0.000200\n",
- "time of epoch 125 is 34.17s\n",
- "Epoch:[125/200], step:[ 0/ 468], loss_d:1.298354 , loss_g:1.074371 , time:0.047265s, lr:0.000200\n",
- "Epoch:[125/200], step:[ 100/ 468], loss_d:1.192448 , loss_g:0.843773 , time:0.041822s, lr:0.000200\n",
- "Epoch:[125/200], step:[ 200/ 468], loss_d:1.378511 , loss_g:1.214076 , time:0.045200s, lr:0.000200\n",
- "Epoch:[125/200], step:[ 300/ 468], loss_d:1.280184 , loss_g:1.001797 , time:0.047696s, lr:0.000200\n",
- "Epoch:[125/200], step:[ 400/ 468], loss_d:1.369594 , loss_g:1.003022 , time:0.044084s, lr:0.000200\n",
- "time of epoch 126 is 35.73s\n",
- "Epoch:[126/200], step:[ 0/ 468], loss_d:1.249043 , loss_g:1.100413 , time:0.052885s, lr:0.000200\n",
- "Epoch:[126/200], step:[ 100/ 468], loss_d:1.290439 , loss_g:0.771846 , time:0.043131s, lr:0.000200\n",
- "Epoch:[126/200], step:[ 200/ 468], loss_d:1.303779 , loss_g:1.080373 , time:0.039379s, lr:0.000200\n",
- "Epoch:[126/200], step:[ 300/ 468], loss_d:1.259372 , loss_g:0.811913 , time:0.047549s, lr:0.000200\n",
- "Epoch:[126/200], step:[ 400/ 468], loss_d:1.259651 , loss_g:0.752175 , time:0.042367s, lr:0.000200\n",
- "time of epoch 127 is 34.70s\n",
- "Epoch:[127/200], step:[ 0/ 468], loss_d:1.216092 , loss_g:1.120060 , time:0.055629s, lr:0.000200\n",
- "Epoch:[127/200], step:[ 100/ 468], loss_d:1.270169 , loss_g:0.868980 , time:0.049590s, lr:0.000200\n",
- "Epoch:[127/200], step:[ 200/ 468], loss_d:1.237363 , loss_g:0.881967 , time:0.050152s, lr:0.000200\n",
- "Epoch:[127/200], step:[ 300/ 468], loss_d:1.188738 , loss_g:1.040956 , time:0.045134s, lr:0.000200\n",
- "Epoch:[127/200], step:[ 400/ 468], loss_d:1.239505 , loss_g:0.915287 , time:0.050848s, lr:0.000200\n",
- "time of epoch 128 is 37.49s\n",
- "Epoch:[128/200], step:[ 0/ 468], loss_d:1.251801 , loss_g:0.771124 , time:0.047905s, lr:0.000200\n",
- "Epoch:[128/200], step:[ 100/ 468], loss_d:1.278723 , loss_g:1.058657 , time:0.042116s, lr:0.000200\n",
- "Epoch:[128/200], step:[ 200/ 468], loss_d:1.285703 , loss_g:0.774826 , time:0.058672s, lr:0.000200\n",
- "Epoch:[128/200], step:[ 300/ 468], loss_d:1.237582 , loss_g:1.111778 , time:0.050733s, lr:0.000200\n",
- "Epoch:[128/200], step:[ 400/ 468], loss_d:1.190418 , loss_g:0.802273 , time:0.047387s, lr:0.000200\n",
- "time of epoch 129 is 37.73s\n",
- "Epoch:[129/200], step:[ 0/ 468], loss_d:1.230130 , loss_g:0.910067 , time:0.051656s, lr:0.000200\n",
- "Epoch:[129/200], step:[ 100/ 468], loss_d:1.250949 , loss_g:1.108891 , time:0.044065s, lr:0.000200\n",
- "Epoch:[129/200], step:[ 200/ 468], loss_d:1.180374 , loss_g:0.962172 , time:0.048027s, lr:0.000200\n",
- "Epoch:[129/200], step:[ 300/ 468], loss_d:1.166507 , loss_g:0.972092 , time:0.043529s, lr:0.000200\n",
- "Epoch:[129/200], step:[ 400/ 468], loss_d:1.241718 , loss_g:0.973474 , time:0.039280s, lr:0.000200\n",
- "time of epoch 130 is 35.39s\n",
- "Epoch:[130/200], step:[ 0/ 468], loss_d:1.237576 , loss_g:0.957496 , time:0.050475s, lr:0.000200\n",
- "Epoch:[130/200], step:[ 100/ 468], loss_d:1.272929 , loss_g:1.078616 , time:0.047593s, lr:0.000200\n",
- "Epoch:[130/200], step:[ 200/ 468], loss_d:1.279699 , loss_g:1.126004 , time:0.038760s, lr:0.000200\n",
- "Epoch:[130/200], step:[ 300/ 468], loss_d:1.253120 , loss_g:0.756736 , time:0.044957s, lr:0.000200\n",
- "Epoch:[130/200], step:[ 400/ 468], loss_d:1.265954 , loss_g:0.835062 , time:0.052487s, lr:0.000200\n",
- "time of epoch 131 is 35.92s\n",
- "Epoch:[131/200], step:[ 0/ 468], loss_d:1.199909 , loss_g:1.096966 , time:0.053551s, lr:0.000200\n",
- "Epoch:[131/200], step:[ 100/ 468], loss_d:1.227247 , loss_g:1.041866 , time:0.045922s, lr:0.000200\n",
- "Epoch:[131/200], step:[ 200/ 468], loss_d:1.313592 , loss_g:0.666782 , time:0.044803s, lr:0.000200\n",
- "Epoch:[131/200], step:[ 300/ 468], loss_d:1.248281 , loss_g:1.027408 , time:0.046066s, lr:0.000200\n",
- "Epoch:[131/200], step:[ 400/ 468], loss_d:1.236027 , loss_g:1.039880 , time:0.050180s, lr:0.000200\n",
- "time of epoch 132 is 37.00s\n",
- "Epoch:[132/200], step:[ 0/ 468], loss_d:1.219428 , loss_g:0.830245 , time:0.061098s, lr:0.000200\n",
- "Epoch:[132/200], step:[ 100/ 468], loss_d:1.240666 , loss_g:0.861235 , time:0.042396s, lr:0.000200\n",
- "Epoch:[132/200], step:[ 200/ 468], loss_d:1.249452 , loss_g:1.034500 , time:0.046122s, lr:0.000200\n",
- "Epoch:[132/200], step:[ 300/ 468], loss_d:1.219478 , loss_g:0.899734 , time:0.043537s, lr:0.000200\n",
- "Epoch:[132/200], step:[ 400/ 468], loss_d:1.326759 , loss_g:1.027639 , time:0.043309s, lr:0.000200\n",
- "time of epoch 133 is 35.64s\n",
- "Epoch:[133/200], step:[ 0/ 468], loss_d:1.288635 , loss_g:1.069440 , time:0.047987s, lr:0.000200\n",
- "Epoch:[133/200], step:[ 100/ 468], loss_d:1.238994 , loss_g:1.017625 , time:0.040841s, lr:0.000200\n",
- "Epoch:[133/200], step:[ 200/ 468], loss_d:1.225071 , loss_g:0.850641 , time:0.048242s, lr:0.000200\n",
- "Epoch:[133/200], step:[ 300/ 468], loss_d:1.238514 , loss_g:0.906290 , time:0.044202s, lr:0.000200\n",
- "Epoch:[133/200], step:[ 400/ 468], loss_d:1.174123 , loss_g:1.038534 , time:0.048240s, lr:0.000200\n",
- "time of epoch 134 is 34.53s\n",
- "Epoch:[134/200], step:[ 0/ 468], loss_d:1.215767 , loss_g:0.971510 , time:0.051183s, lr:0.000200\n",
- "Epoch:[134/200], step:[ 100/ 468], loss_d:1.287062 , loss_g:0.828384 , time:0.042149s, lr:0.000200\n",
- "Epoch:[134/200], step:[ 200/ 468], loss_d:1.250480 , loss_g:0.946067 , time:0.044558s, lr:0.000200\n",
- "Epoch:[134/200], step:[ 300/ 468], loss_d:1.271723 , loss_g:1.133352 , time:0.048002s, lr:0.000200\n",
- "Epoch:[134/200], step:[ 400/ 468], loss_d:1.240910 , loss_g:0.865673 , time:0.052469s, lr:0.000200\n",
- "time of epoch 135 is 36.09s\n",
- "Epoch:[135/200], step:[ 0/ 468], loss_d:1.186677 , loss_g:0.959959 , time:0.049916s, lr:0.000200\n",
- "Epoch:[135/200], step:[ 100/ 468], loss_d:1.239182 , loss_g:0.857179 , time:0.043276s, lr:0.000200\n",
- "Epoch:[135/200], step:[ 200/ 468], loss_d:1.223235 , loss_g:1.018876 , time:0.043041s, lr:0.000200\n",
- "Epoch:[135/200], step:[ 300/ 468], loss_d:1.239283 , loss_g:1.011850 , time:0.041442s, lr:0.000200\n",
- "Epoch:[135/200], step:[ 400/ 468], loss_d:1.236282 , loss_g:0.854093 , time:0.040757s, lr:0.000200\n",
- "time of epoch 136 is 32.01s\n",
- "Epoch:[136/200], step:[ 0/ 468], loss_d:1.204626 , loss_g:0.894870 , time:0.056207s, lr:0.000200\n",
- "Epoch:[136/200], step:[ 100/ 468], loss_d:1.216238 , loss_g:1.085996 , time:0.048250s, lr:0.000200\n",
- "Epoch:[136/200], step:[ 200/ 468], loss_d:1.199035 , loss_g:1.021832 , time:0.047195s, lr:0.000200\n",
- "Epoch:[136/200], step:[ 300/ 468], loss_d:1.266105 , loss_g:0.928631 , time:0.043134s, lr:0.000200\n",
- "Epoch:[136/200], step:[ 400/ 468], loss_d:1.202520 , loss_g:0.806209 , time:0.045631s, lr:0.000200\n",
- "time of epoch 137 is 35.41s\n",
- "Epoch:[137/200], step:[ 0/ 468], loss_d:1.342219 , loss_g:1.287664 , time:0.051517s, lr:0.000200\n",
- "Epoch:[137/200], step:[ 100/ 468], loss_d:1.265011 , loss_g:1.242670 , time:0.041192s, lr:0.000200\n",
- "Epoch:[137/200], step:[ 200/ 468], loss_d:1.227713 , loss_g:0.975574 , time:0.045488s, lr:0.000200\n",
- "Epoch:[137/200], step:[ 300/ 468], loss_d:1.221781 , loss_g:0.949308 , time:0.047062s, lr:0.000200\n",
- "Epoch:[137/200], step:[ 400/ 468], loss_d:1.289430 , loss_g:0.855573 , time:0.042515s, lr:0.000200\n",
- "time of epoch 138 is 34.25s\n",
- "Epoch:[138/200], step:[ 0/ 468], loss_d:1.252146 , loss_g:1.092927 , time:0.048329s, lr:0.000200\n",
- "Epoch:[138/200], step:[ 100/ 468], loss_d:1.324106 , loss_g:0.924327 , time:0.043616s, lr:0.000200\n",
- "Epoch:[138/200], step:[ 200/ 468], loss_d:1.193763 , loss_g:0.978442 , time:0.041951s, lr:0.000200\n",
- "Epoch:[138/200], step:[ 300/ 468], loss_d:1.218822 , loss_g:0.810233 , time:0.043603s, lr:0.000200\n",
- "Epoch:[138/200], step:[ 400/ 468], loss_d:1.273668 , loss_g:1.156853 , time:0.043699s, lr:0.000200\n",
- "time of epoch 139 is 34.37s\n",
- "Epoch:[139/200], step:[ 0/ 468], loss_d:1.289938 , loss_g:0.901528 , time:0.048920s, lr:0.000200\n",
- "Epoch:[139/200], step:[ 100/ 468], loss_d:1.245776 , loss_g:1.229541 , time:0.050046s, lr:0.000200\n",
- "Epoch:[139/200], step:[ 200/ 468], loss_d:1.231757 , loss_g:1.043111 , time:0.043995s, lr:0.000200\n",
- "Epoch:[139/200], step:[ 300/ 468], loss_d:1.254862 , loss_g:0.781832 , time:0.038852s, lr:0.000200\n",
- "Epoch:[139/200], step:[ 400/ 468], loss_d:1.282014 , loss_g:0.805559 , time:0.044610s, lr:0.000200\n",
- "time of epoch 140 is 35.74s\n",
- "Epoch:[140/200], step:[ 0/ 468], loss_d:1.228223 , loss_g:0.999424 , time:0.050335s, lr:0.000200\n",
- "Epoch:[140/200], step:[ 100/ 468], loss_d:1.230281 , loss_g:0.809730 , time:0.050109s, lr:0.000200\n",
- "Epoch:[140/200], step:[ 200/ 468], loss_d:1.317479 , loss_g:0.581595 , time:0.046570s, lr:0.000200\n",
- "Epoch:[140/200], step:[ 300/ 468], loss_d:1.263096 , loss_g:0.923749 , time:0.038997s, lr:0.000200\n",
- "Epoch:[140/200], step:[ 400/ 468], loss_d:1.231823 , loss_g:0.942184 , time:0.043436s, lr:0.000200\n",
- "time of epoch 141 is 35.80s\n",
- "Epoch:[141/200], step:[ 0/ 468], loss_d:1.312307 , loss_g:1.246901 , time:0.048033s, lr:0.000200\n",
- "Epoch:[141/200], step:[ 100/ 468], loss_d:1.280633 , loss_g:0.998498 , time:0.047358s, lr:0.000200\n",
- "Epoch:[141/200], step:[ 200/ 468], loss_d:1.251682 , loss_g:0.931941 , time:0.042737s, lr:0.000200\n",
- "Epoch:[141/200], step:[ 300/ 468], loss_d:1.207261 , loss_g:0.982106 , time:0.049536s, lr:0.000200\n",
- "Epoch:[141/200], step:[ 400/ 468], loss_d:1.264504 , loss_g:1.248677 , time:0.042446s, lr:0.000200\n",
- "time of epoch 142 is 34.89s\n",
- "Epoch:[142/200], step:[ 0/ 468], loss_d:1.231033 , loss_g:1.339029 , time:0.044511s, lr:0.000200\n",
- "Epoch:[142/200], step:[ 100/ 468], loss_d:1.230095 , loss_g:0.904688 , time:0.043046s, lr:0.000200\n",
- "Epoch:[142/200], step:[ 200/ 468], loss_d:1.276222 , loss_g:0.864175 , time:0.041712s, lr:0.000200\n",
- "Epoch:[142/200], step:[ 300/ 468], loss_d:1.233822 , loss_g:0.967283 , time:0.038140s, lr:0.000200\n",
- "Epoch:[142/200], step:[ 400/ 468], loss_d:1.283755 , loss_g:0.782747 , time:0.047657s, lr:0.000200\n",
- "time of epoch 143 is 33.33s\n",
- "Epoch:[143/200], step:[ 0/ 468], loss_d:1.236485 , loss_g:0.938999 , time:0.053652s, lr:0.000200\n",
- "Epoch:[143/200], step:[ 100/ 468], loss_d:1.214107 , loss_g:1.218673 , time:0.046750s, lr:0.000200\n",
- "Epoch:[143/200], step:[ 200/ 468], loss_d:1.231279 , loss_g:0.850708 , time:0.051629s, lr:0.000200\n",
- "Epoch:[143/200], step:[ 300/ 468], loss_d:1.258048 , loss_g:1.013894 , time:0.048475s, lr:0.000200\n",
- "Epoch:[143/200], step:[ 400/ 468], loss_d:1.260021 , loss_g:0.728290 , time:0.043300s, lr:0.000200\n",
- "time of epoch 144 is 35.32s\n",
- "Epoch:[144/200], step:[ 0/ 468], loss_d:1.214455 , loss_g:1.170789 , time:0.049644s, lr:0.000200\n",
- "Epoch:[144/200], step:[ 100/ 468], loss_d:1.238240 , loss_g:0.881131 , time:0.051033s, lr:0.000200\n",
- "Epoch:[144/200], step:[ 200/ 468], loss_d:1.245214 , loss_g:0.827520 , time:0.052569s, lr:0.000200\n",
- "Epoch:[144/200], step:[ 300/ 468], loss_d:1.157818 , loss_g:1.211782 , time:0.042830s, lr:0.000200\n",
- "Epoch:[144/200], step:[ 400/ 468], loss_d:1.237574 , loss_g:0.956799 , time:0.043697s, lr:0.000200\n",
- "time of epoch 145 is 36.59s\n",
- "Epoch:[145/200], step:[ 0/ 468], loss_d:1.214580 , loss_g:1.078360 , time:0.049105s, lr:0.000200\n",
- "Epoch:[145/200], step:[ 100/ 468], loss_d:1.290705 , loss_g:1.140330 , time:0.048227s, lr:0.000200\n",
- "Epoch:[145/200], step:[ 200/ 468], loss_d:1.267706 , loss_g:1.195359 , time:0.052533s, lr:0.000200\n",
- "Epoch:[145/200], step:[ 300/ 468], loss_d:1.193339 , loss_g:0.985734 , time:0.046947s, lr:0.000200\n",
- "Epoch:[145/200], step:[ 400/ 468], loss_d:1.246293 , loss_g:0.992945 , time:0.048728s, lr:0.000200\n",
- "time of epoch 146 is 35.62s\n",
- "Epoch:[146/200], step:[ 0/ 468], loss_d:1.278365 , loss_g:1.151565 , time:0.050347s, lr:0.000200\n",
- "Epoch:[146/200], step:[ 100/ 468], loss_d:1.220055 , loss_g:0.934415 , time:0.048027s, lr:0.000200\n",
- "Epoch:[146/200], step:[ 200/ 468], loss_d:1.221439 , loss_g:1.111594 , time:0.041240s, lr:0.000200\n",
- "Epoch:[146/200], step:[ 300/ 468], loss_d:1.243252 , loss_g:0.885589 , time:0.041939s, lr:0.000200\n",
- "Epoch:[146/200], step:[ 400/ 468], loss_d:1.217750 , loss_g:0.941551 , time:0.042511s, lr:0.000200\n",
- "time of epoch 147 is 33.15s\n",
- "Epoch:[147/200], step:[ 0/ 468], loss_d:1.253747 , loss_g:0.979363 , time:0.049524s, lr:0.000200\n",
- "Epoch:[147/200], step:[ 100/ 468], loss_d:1.270115 , loss_g:0.886575 , time:0.041592s, lr:0.000200\n",
- "Epoch:[147/200], step:[ 200/ 468], loss_d:1.334394 , loss_g:1.370667 , time:0.044621s, lr:0.000200\n",
- "Epoch:[147/200], step:[ 300/ 468], loss_d:1.202730 , loss_g:0.900760 , time:0.043717s, lr:0.000200\n",
- "Epoch:[147/200], step:[ 400/ 468], loss_d:1.177239 , loss_g:1.229463 , time:0.042015s, lr:0.000200\n",
- "time of epoch 148 is 32.63s\n",
- "Epoch:[148/200], step:[ 0/ 468], loss_d:1.191921 , loss_g:1.024941 , time:0.044980s, lr:0.000200\n",
- "Epoch:[148/200], step:[ 100/ 468], loss_d:1.240819 , loss_g:1.084795 , time:0.047200s, lr:0.000200\n",
- "Epoch:[148/200], step:[ 200/ 468], loss_d:1.133924 , loss_g:1.059991 , time:0.048905s, lr:0.000200\n",
- "Epoch:[148/200], step:[ 300/ 468], loss_d:1.248736 , loss_g:1.160723 , time:0.045889s, lr:0.000200\n",
- "Epoch:[148/200], step:[ 400/ 468], loss_d:1.207293 , loss_g:0.920350 , time:0.041910s, lr:0.000200\n",
- "time of epoch 149 is 35.40s\n",
- "Epoch:[149/200], step:[ 0/ 468], loss_d:1.178374 , loss_g:1.090297 , time:0.059093s, lr:0.000200\n",
- "Epoch:[149/200], step:[ 100/ 468], loss_d:1.174750 , loss_g:1.050161 , time:0.044784s, lr:0.000200\n",
- "Epoch:[149/200], step:[ 200/ 468], loss_d:1.239224 , loss_g:0.984491 , time:0.044765s, lr:0.000200\n",
- "Epoch:[149/200], step:[ 300/ 468], loss_d:1.260408 , loss_g:0.937319 , time:0.042257s, lr:0.000200\n",
- "Epoch:[149/200], step:[ 400/ 468], loss_d:1.269423 , loss_g:1.052568 , time:0.044614s, lr:0.000200\n",
- "time of epoch 150 is 33.14s\n",
- "Epoch:[150/200], step:[ 0/ 468], loss_d:1.170594 , loss_g:0.991195 , time:0.048889s, lr:0.000200\n",
- "Epoch:[150/200], step:[ 100/ 468], loss_d:1.178068 , loss_g:0.941558 , time:0.041894s, lr:0.000200\n",
- "Epoch:[150/200], step:[ 200/ 468], loss_d:1.158231 , loss_g:1.014776 , time:0.044062s, lr:0.000200\n",
- "Epoch:[150/200], step:[ 300/ 468], loss_d:1.232818 , loss_g:1.034113 , time:0.048266s, lr:0.000200\n",
- "Epoch:[150/200], step:[ 400/ 468], loss_d:1.153843 , loss_g:0.956440 , time:0.049545s, lr:0.000200\n",
- "time of epoch 151 is 35.61s\n",
- "Epoch:[151/200], step:[ 0/ 468], loss_d:1.280484 , loss_g:1.074107 , time:0.049894s, lr:0.000200\n",
- "Epoch:[151/200], step:[ 100/ 468], loss_d:1.233203 , loss_g:0.978087 , time:0.040923s, lr:0.000200\n",
- "Epoch:[151/200], step:[ 200/ 468], loss_d:1.219635 , loss_g:0.792682 , time:0.041144s, lr:0.000200\n",
- "Epoch:[151/200], step:[ 300/ 468], loss_d:1.348707 , loss_g:1.156421 , time:0.043480s, lr:0.000200\n",
- "Epoch:[151/200], step:[ 400/ 468], loss_d:1.190515 , loss_g:0.937349 , time:0.041330s, lr:0.000200\n",
- "time of epoch 152 is 33.80s\n",
- "Epoch:[152/200], step:[ 0/ 468], loss_d:1.229221 , loss_g:1.012020 , time:0.050176s, lr:0.000200\n",
- "Epoch:[152/200], step:[ 100/ 468], loss_d:1.206731 , loss_g:0.860064 , time:0.051631s, lr:0.000200\n",
- "Epoch:[152/200], step:[ 200/ 468], loss_d:1.240012 , loss_g:1.180171 , time:0.044624s, lr:0.000200\n",
- "Epoch:[152/200], step:[ 300/ 468], loss_d:1.175616 , loss_g:1.050436 , time:0.043550s, lr:0.000200\n",
- "Epoch:[152/200], step:[ 400/ 468], loss_d:1.279641 , loss_g:0.961793 , time:0.045515s, lr:0.000200\n",
- "time of epoch 153 is 37.29s\n",
- "Epoch:[153/200], step:[ 0/ 468], loss_d:1.273757 , loss_g:0.892181 , time:0.045317s, lr:0.000200\n",
- "Epoch:[153/200], step:[ 100/ 468], loss_d:1.273622 , loss_g:0.938109 , time:0.045328s, lr:0.000200\n",
- "Epoch:[153/200], step:[ 200/ 468], loss_d:1.200373 , loss_g:0.951342 , time:0.045191s, lr:0.000200\n",
- "Epoch:[153/200], step:[ 300/ 468], loss_d:1.260965 , loss_g:1.044254 , time:0.043867s, lr:0.000200\n",
- "Epoch:[153/200], step:[ 400/ 468], loss_d:1.221386 , loss_g:1.017935 , time:0.044915s, lr:0.000200\n",
- "time of epoch 154 is 34.92s\n",
- "Epoch:[154/200], step:[ 0/ 468], loss_d:1.236784 , loss_g:0.841637 , time:0.046248s, lr:0.000200\n",
- "Epoch:[154/200], step:[ 100/ 468], loss_d:1.296975 , loss_g:1.137346 , time:0.042837s, lr:0.000200\n",
- "Epoch:[154/200], step:[ 200/ 468], loss_d:1.236956 , loss_g:1.016962 , time:0.047586s, lr:0.000200\n",
- "Epoch:[154/200], step:[ 300/ 468], loss_d:1.432515 , loss_g:0.676165 , time:0.049176s, lr:0.000200\n",
- "Epoch:[154/200], step:[ 400/ 468], loss_d:1.208143 , loss_g:1.026819 , time:0.049749s, lr:0.000200\n",
- "time of epoch 155 is 36.94s\n",
- "Epoch:[155/200], step:[ 0/ 468], loss_d:1.252760 , loss_g:0.912173 , time:0.058003s, lr:0.000200\n",
- "Epoch:[155/200], step:[ 100/ 468], loss_d:1.227688 , loss_g:0.998305 , time:0.050207s, lr:0.000200\n",
- "Epoch:[155/200], step:[ 200/ 468], loss_d:1.204697 , loss_g:0.871638 , time:0.046391s, lr:0.000200\n",
- "Epoch:[155/200], step:[ 300/ 468], loss_d:1.271309 , loss_g:0.929498 , time:0.047021s, lr:0.000200\n",
- "Epoch:[155/200], step:[ 400/ 468], loss_d:1.198491 , loss_g:0.910898 , time:0.044628s, lr:0.000200\n",
- "time of epoch 156 is 34.98s\n",
- "Epoch:[156/200], step:[ 0/ 468], loss_d:1.249652 , loss_g:0.935166 , time:0.052474s, lr:0.000200\n",
- "Epoch:[156/200], step:[ 100/ 468], loss_d:1.215906 , loss_g:1.001854 , time:0.038426s, lr:0.000200\n",
- "Epoch:[156/200], step:[ 200/ 468], loss_d:1.202658 , loss_g:0.850606 , time:0.047502s, lr:0.000200\n",
- "Epoch:[156/200], step:[ 300/ 468], loss_d:1.252039 , loss_g:0.752556 , time:0.045426s, lr:0.000200\n",
- "Epoch:[156/200], step:[ 400/ 468], loss_d:1.266144 , loss_g:0.874625 , time:0.045419s, lr:0.000200\n",
- "time of epoch 157 is 34.95s\n",
- "Epoch:[157/200], step:[ 0/ 468], loss_d:1.202284 , loss_g:0.941207 , time:0.047721s, lr:0.000200\n",
- "Epoch:[157/200], step:[ 100/ 468], loss_d:1.249295 , loss_g:1.107733 , time:0.044802s, lr:0.000200\n",
- "Epoch:[157/200], step:[ 200/ 468], loss_d:1.277378 , loss_g:1.010626 , time:0.044204s, lr:0.000200\n",
- "Epoch:[157/200], step:[ 300/ 468], loss_d:1.238174 , loss_g:1.010879 , time:0.048519s, lr:0.000200\n",
- "Epoch:[157/200], step:[ 400/ 468], loss_d:1.217827 , loss_g:1.080772 , time:0.050322s, lr:0.000200\n",
- "time of epoch 158 is 36.80s\n",
- "Epoch:[158/200], step:[ 0/ 468], loss_d:1.196756 , loss_g:1.392768 , time:0.060238s, lr:0.000200\n",
- "Epoch:[158/200], step:[ 100/ 468], loss_d:1.217244 , loss_g:1.058053 , time:0.048495s, lr:0.000200\n",
- "Epoch:[158/200], step:[ 200/ 468], loss_d:1.195158 , loss_g:1.239378 , time:0.047403s, lr:0.000200\n",
- "Epoch:[158/200], step:[ 300/ 468], loss_d:1.136801 , loss_g:0.934092 , time:0.050702s, lr:0.000200\n",
- "Epoch:[158/200], step:[ 400/ 468], loss_d:1.250435 , loss_g:1.012433 , time:0.048983s, lr:0.000200\n",
- "time of epoch 159 is 36.98s\n",
- "Epoch:[159/200], step:[ 0/ 468], loss_d:1.340272 , loss_g:0.802437 , time:0.043710s, lr:0.000200\n",
- "Epoch:[159/200], step:[ 100/ 468], loss_d:1.267144 , loss_g:0.959948 , time:0.044165s, lr:0.000200\n",
- "Epoch:[159/200], step:[ 200/ 468], loss_d:1.213295 , loss_g:1.074334 , time:0.040443s, lr:0.000200\n",
- "Epoch:[159/200], step:[ 300/ 468], loss_d:1.207185 , loss_g:0.931791 , time:0.043606s, lr:0.000200\n",
- "Epoch:[159/200], step:[ 400/ 468], loss_d:1.250235 , loss_g:0.929704 , time:0.045086s, lr:0.000200\n",
- "time of epoch 160 is 34.73s\n",
- "Epoch:[160/200], step:[ 0/ 468], loss_d:1.267033 , loss_g:1.028349 , time:0.055836s, lr:0.000200\n",
- "Epoch:[160/200], step:[ 100/ 468], loss_d:1.306480 , loss_g:0.963357 , time:0.047053s, lr:0.000200\n",
- "Epoch:[160/200], step:[ 200/ 468], loss_d:1.188892 , loss_g:0.955224 , time:0.038865s, lr:0.000200\n",
- "Epoch:[160/200], step:[ 300/ 468], loss_d:1.236253 , loss_g:0.878152 , time:0.047959s, lr:0.000200\n",
- "Epoch:[160/200], step:[ 400/ 468], loss_d:1.239311 , loss_g:1.179810 , time:0.044868s, lr:0.000200\n",
- "time of epoch 161 is 34.68s\n",
- "Epoch:[161/200], step:[ 0/ 468], loss_d:1.244338 , loss_g:0.965392 , time:0.052398s, lr:0.000200\n",
- "Epoch:[161/200], step:[ 100/ 468], loss_d:1.191032 , loss_g:0.994752 , time:0.048003s, lr:0.000200\n",
- "Epoch:[161/200], step:[ 200/ 468], loss_d:1.183452 , loss_g:1.096201 , time:0.047343s, lr:0.000200\n",
- "Epoch:[161/200], step:[ 300/ 468], loss_d:1.222740 , loss_g:0.860494 , time:0.045638s, lr:0.000200\n",
- "Epoch:[161/200], step:[ 400/ 468], loss_d:1.238803 , loss_g:1.017025 , time:0.052061s, lr:0.000200\n",
- "time of epoch 162 is 35.54s\n",
- "Epoch:[162/200], step:[ 0/ 468], loss_d:1.306711 , loss_g:0.693623 , time:0.048806s, lr:0.000200\n",
- "Epoch:[162/200], step:[ 100/ 468], loss_d:1.378708 , loss_g:0.732352 , time:0.038485s, lr:0.000200\n",
- "Epoch:[162/200], step:[ 200/ 468], loss_d:1.297013 , loss_g:1.237053 , time:0.049946s, lr:0.000200\n",
- "Epoch:[162/200], step:[ 300/ 468], loss_d:1.185789 , loss_g:0.883647 , time:0.045509s, lr:0.000200\n",
- "Epoch:[162/200], step:[ 400/ 468], loss_d:1.325860 , loss_g:0.777724 , time:0.044852s, lr:0.000200\n",
- "time of epoch 163 is 33.93s\n",
- "Epoch:[163/200], step:[ 0/ 468], loss_d:1.261062 , loss_g:0.933405 , time:0.047442s, lr:0.000200\n",
- "Epoch:[163/200], step:[ 100/ 468], loss_d:1.243260 , loss_g:1.197348 , time:0.039491s, lr:0.000200\n",
- "Epoch:[163/200], step:[ 200/ 468], loss_d:1.319026 , loss_g:1.180923 , time:0.041142s, lr:0.000200\n",
- "Epoch:[163/200], step:[ 300/ 468], loss_d:1.199208 , loss_g:0.940118 , time:0.048506s, lr:0.000200\n",
- "Epoch:[163/200], step:[ 400/ 468], loss_d:1.193522 , loss_g:1.162683 , time:0.042281s, lr:0.000200\n",
- "time of epoch 164 is 35.18s\n",
- "Epoch:[164/200], step:[ 0/ 468], loss_d:1.193270 , loss_g:1.048745 , time:0.050211s, lr:0.000200\n",
- "Epoch:[164/200], step:[ 100/ 468], loss_d:1.203523 , loss_g:0.989984 , time:0.045682s, lr:0.000200\n",
- "Epoch:[164/200], step:[ 200/ 468], loss_d:1.228045 , loss_g:1.060421 , time:0.042259s, lr:0.000200\n",
- "Epoch:[164/200], step:[ 300/ 468], loss_d:1.230802 , loss_g:1.032586 , time:0.046835s, lr:0.000200\n",
- "Epoch:[164/200], step:[ 400/ 468], loss_d:1.220490 , loss_g:1.023786 , time:0.050582s, lr:0.000200\n",
- "time of epoch 165 is 33.19s\n",
- "Epoch:[165/200], step:[ 0/ 468], loss_d:1.197300 , loss_g:0.819975 , time:0.049417s, lr:0.000200\n",
- "Epoch:[165/200], step:[ 100/ 468], loss_d:1.141800 , loss_g:1.130530 , time:0.046407s, lr:0.000200\n",
- "Epoch:[165/200], step:[ 200/ 468], loss_d:1.236614 , loss_g:1.113174 , time:0.048641s, lr:0.000200\n",
- "Epoch:[165/200], step:[ 300/ 468], loss_d:1.201457 , loss_g:0.878331 , time:0.044142s, lr:0.000200\n",
- "Epoch:[165/200], step:[ 400/ 468], loss_d:1.331488 , loss_g:0.792865 , time:0.043573s, lr:0.000200\n",
- "time of epoch 166 is 33.31s\n",
- "Epoch:[166/200], step:[ 0/ 468], loss_d:1.306885 , loss_g:0.819407 , time:0.054159s, lr:0.000200\n",
- "Epoch:[166/200], step:[ 100/ 468], loss_d:1.289713 , loss_g:0.991035 , time:0.045716s, lr:0.000200\n",
- "Epoch:[166/200], step:[ 200/ 468], loss_d:1.272629 , loss_g:1.228504 , time:0.046092s, lr:0.000200\n",
- "Epoch:[166/200], step:[ 300/ 468], loss_d:1.208199 , loss_g:1.228082 , time:0.047422s, lr:0.000200\n",
- "Epoch:[166/200], step:[ 400/ 468], loss_d:1.172051 , loss_g:1.045154 , time:0.046528s, lr:0.000200\n",
- "time of epoch 167 is 35.10s\n",
- "Epoch:[167/200], step:[ 0/ 468], loss_d:1.317217 , loss_g:0.852527 , time:0.047978s, lr:0.000200\n",
- "Epoch:[167/200], step:[ 100/ 468], loss_d:1.249088 , loss_g:0.826664 , time:0.041497s, lr:0.000200\n",
- "Epoch:[167/200], step:[ 200/ 468], loss_d:1.216322 , loss_g:1.087818 , time:0.044692s, lr:0.000200\n",
- "Epoch:[167/200], step:[ 300/ 468], loss_d:1.241948 , loss_g:0.954411 , time:0.046008s, lr:0.000200\n",
- "Epoch:[167/200], step:[ 400/ 468], loss_d:1.318762 , loss_g:0.829006 , time:0.051752s, lr:0.000200\n",
- "time of epoch 168 is 35.92s\n",
- "Epoch:[168/200], step:[ 0/ 468], loss_d:1.194308 , loss_g:0.783405 , time:0.052012s, lr:0.000200\n",
- "Epoch:[168/200], step:[ 100/ 468], loss_d:1.210498 , loss_g:1.081323 , time:0.042148s, lr:0.000200\n",
- "Epoch:[168/200], step:[ 200/ 468], loss_d:1.369564 , loss_g:1.191396 , time:0.040777s, lr:0.000200\n",
- "Epoch:[168/200], step:[ 300/ 468], loss_d:1.204381 , loss_g:0.978527 , time:0.043683s, lr:0.000200\n",
- "Epoch:[168/200], step:[ 400/ 468], loss_d:1.292288 , loss_g:1.043221 , time:0.043641s, lr:0.000200\n",
- "time of epoch 169 is 32.17s\n",
- "Epoch:[169/200], step:[ 0/ 468], loss_d:1.168257 , loss_g:1.011050 , time:0.046923s, lr:0.000200\n",
- "Epoch:[169/200], step:[ 100/ 468], loss_d:1.225904 , loss_g:1.143820 , time:0.046160s, lr:0.000200\n",
- "Epoch:[169/200], step:[ 200/ 468], loss_d:1.255177 , loss_g:0.849501 , time:0.045283s, lr:0.000200\n",
- "Epoch:[169/200], step:[ 300/ 468], loss_d:1.220416 , loss_g:0.875058 , time:0.044675s, lr:0.000200\n",
- "Epoch:[169/200], step:[ 400/ 468], loss_d:1.218172 , loss_g:0.919142 , time:0.043473s, lr:0.000200\n",
- "time of epoch 170 is 34.88s\n",
- "Epoch:[170/200], step:[ 0/ 468], loss_d:1.241298 , loss_g:1.284683 , time:0.050715s, lr:0.000200\n",
- "Epoch:[170/200], step:[ 100/ 468], loss_d:1.251287 , loss_g:0.799930 , time:0.044601s, lr:0.000200\n",
- "Epoch:[170/200], step:[ 200/ 468], loss_d:1.226526 , loss_g:1.038939 , time:0.042885s, lr:0.000200\n",
- "Epoch:[170/200], step:[ 300/ 468], loss_d:1.189696 , loss_g:0.976870 , time:0.040921s, lr:0.000200\n",
- "Epoch:[170/200], step:[ 400/ 468], loss_d:1.246282 , loss_g:1.104314 , time:0.044547s, lr:0.000200\n",
- "time of epoch 171 is 34.16s\n",
- "Epoch:[171/200], step:[ 0/ 468], loss_d:1.224246 , loss_g:0.951312 , time:0.051148s, lr:0.000200\n",
- "Epoch:[171/200], step:[ 100/ 468], loss_d:1.258456 , loss_g:1.062141 , time:0.048251s, lr:0.000200\n",
- "Epoch:[171/200], step:[ 200/ 468], loss_d:1.219401 , loss_g:0.781191 , time:0.044288s, lr:0.000200\n",
- "Epoch:[171/200], step:[ 300/ 468], loss_d:1.261454 , loss_g:1.051230 , time:0.042094s, lr:0.000200\n",
- "Epoch:[171/200], step:[ 400/ 468], loss_d:1.244421 , loss_g:1.064613 , time:0.044860s, lr:0.000200\n",
- "time of epoch 172 is 33.27s\n",
- "Epoch:[172/200], step:[ 0/ 468], loss_d:1.278136 , loss_g:1.060717 , time:0.051893s, lr:0.000200\n",
- "Epoch:[172/200], step:[ 100/ 468], loss_d:1.144892 , loss_g:0.936683 , time:0.042638s, lr:0.000200\n",
- "Epoch:[172/200], step:[ 200/ 468], loss_d:1.156648 , loss_g:0.966154 , time:0.042105s, lr:0.000200\n",
- "Epoch:[172/200], step:[ 300/ 468], loss_d:1.200349 , loss_g:1.036019 , time:0.046842s, lr:0.000200\n",
- "Epoch:[172/200], step:[ 400/ 468], loss_d:1.211750 , loss_g:0.924513 , time:0.046779s, lr:0.000200\n",
- "time of epoch 173 is 34.42s\n",
- "Epoch:[173/200], step:[ 0/ 468], loss_d:1.177241 , loss_g:0.980363 , time:0.054743s, lr:0.000200\n",
- "Epoch:[173/200], step:[ 100/ 468], loss_d:1.215636 , loss_g:1.240232 , time:0.046565s, lr:0.000200\n",
- "Epoch:[173/200], step:[ 200/ 468], loss_d:1.164805 , loss_g:1.097504 , time:0.042699s, lr:0.000200\n",
- "Epoch:[173/200], step:[ 300/ 468], loss_d:1.261997 , loss_g:0.857288 , time:0.046725s, lr:0.000200\n",
- "Epoch:[173/200], step:[ 400/ 468], loss_d:1.307989 , loss_g:0.924246 , time:0.042768s, lr:0.000200\n",
- "time of epoch 174 is 34.43s\n",
- "Epoch:[174/200], step:[ 0/ 468], loss_d:1.168073 , loss_g:1.153193 , time:0.048002s, lr:0.000200\n",
- "Epoch:[174/200], step:[ 100/ 468], loss_d:1.234723 , loss_g:1.106092 , time:0.048643s, lr:0.000200\n",
- "Epoch:[174/200], step:[ 200/ 468], loss_d:1.218813 , loss_g:1.282322 , time:0.048120s, lr:0.000200\n",
- "Epoch:[174/200], step:[ 300/ 468], loss_d:1.239144 , loss_g:0.861288 , time:0.048877s, lr:0.000200\n",
- "Epoch:[174/200], step:[ 400/ 468], loss_d:1.164380 , loss_g:1.004864 , time:0.050008s, lr:0.000200\n",
- "time of epoch 175 is 36.72s\n",
- "Epoch:[175/200], step:[ 0/ 468], loss_d:1.259525 , loss_g:0.953751 , time:0.049259s, lr:0.000200\n",
- "Epoch:[175/200], step:[ 100/ 468], loss_d:1.179477 , loss_g:1.022547 , time:0.045414s, lr:0.000200\n",
- "Epoch:[175/200], step:[ 200/ 468], loss_d:1.263935 , loss_g:1.076475 , time:0.046725s, lr:0.000200\n",
- "Epoch:[175/200], step:[ 300/ 468], loss_d:1.260028 , loss_g:0.869681 , time:0.043782s, lr:0.000200\n",
- "Epoch:[175/200], step:[ 400/ 468], loss_d:1.286478 , loss_g:1.306736 , time:0.042404s, lr:0.000200\n",
- "time of epoch 176 is 35.29s\n",
- "Epoch:[176/200], step:[ 0/ 468], loss_d:1.303428 , loss_g:0.855975 , time:0.053799s, lr:0.000200\n",
- "Epoch:[176/200], step:[ 100/ 468], loss_d:1.211591 , loss_g:0.857514 , time:0.046750s, lr:0.000200\n",
- "Epoch:[176/200], step:[ 200/ 468], loss_d:1.303570 , loss_g:1.119506 , time:0.044973s, lr:0.000200\n",
- "Epoch:[176/200], step:[ 300/ 468], loss_d:1.285334 , loss_g:1.069412 , time:0.045181s, lr:0.000200\n",
- "Epoch:[176/200], step:[ 400/ 468], loss_d:1.217211 , loss_g:1.000850 , time:0.048740s, lr:0.000200\n",
- "time of epoch 177 is 35.01s\n",
- "Epoch:[177/200], step:[ 0/ 468], loss_d:1.163360 , loss_g:1.038829 , time:0.049220s, lr:0.000200\n",
- "Epoch:[177/200], step:[ 100/ 468], loss_d:1.263352 , loss_g:1.033413 , time:0.050307s, lr:0.000200\n",
- "Epoch:[177/200], step:[ 200/ 468], loss_d:1.191785 , loss_g:1.079380 , time:0.049384s, lr:0.000200\n",
- "Epoch:[177/200], step:[ 300/ 468], loss_d:1.249770 , loss_g:1.088413 , time:0.050026s, lr:0.000200\n",
- "Epoch:[177/200], step:[ 400/ 468], loss_d:1.220351 , loss_g:0.886867 , time:0.042614s, lr:0.000200\n",
- "time of epoch 178 is 35.48s\n",
- "Epoch:[178/200], step:[ 0/ 468], loss_d:1.195096 , loss_g:0.987785 , time:0.051629s, lr:0.000200\n",
- "Epoch:[178/200], step:[ 100/ 468], loss_d:1.266780 , loss_g:1.301589 , time:0.043558s, lr:0.000200\n",
- "Epoch:[178/200], step:[ 200/ 468], loss_d:1.261156 , loss_g:0.924103 , time:0.044296s, lr:0.000200\n",
- "Epoch:[178/200], step:[ 300/ 468], loss_d:1.300958 , loss_g:1.146890 , time:0.052032s, lr:0.000200\n",
- "Epoch:[178/200], step:[ 400/ 468], loss_d:1.156795 , loss_g:0.903449 , time:0.046894s, lr:0.000200\n",
- "time of epoch 179 is 37.72s\n",
- "Epoch:[179/200], step:[ 0/ 468], loss_d:1.098413 , loss_g:1.013364 , time:0.051289s, lr:0.000200\n",
- "Epoch:[179/200], step:[ 100/ 468], loss_d:1.276092 , loss_g:0.827745 , time:0.052877s, lr:0.000200\n",
- "Epoch:[179/200], step:[ 200/ 468], loss_d:1.234274 , loss_g:0.820050 , time:0.042670s, lr:0.000200\n",
- "Epoch:[179/200], step:[ 300/ 468], loss_d:1.245669 , loss_g:0.817511 , time:0.044519s, lr:0.000200\n",
- "Epoch:[179/200], step:[ 400/ 468], loss_d:1.211035 , loss_g:0.997647 , time:0.041989s, lr:0.000200\n",
- "time of epoch 180 is 32.41s\n",
- "Epoch:[180/200], step:[ 0/ 468], loss_d:1.252230 , loss_g:0.906906 , time:0.055448s, lr:0.000200\n",
- "Epoch:[180/200], step:[ 100/ 468], loss_d:1.203182 , loss_g:1.064631 , time:0.051734s, lr:0.000200\n",
- "Epoch:[180/200], step:[ 200/ 468], loss_d:1.268211 , loss_g:0.928888 , time:0.050325s, lr:0.000200\n",
- "Epoch:[180/200], step:[ 300/ 468], loss_d:1.216875 , loss_g:0.972128 , time:0.040000s, lr:0.000200\n",
- "Epoch:[180/200], step:[ 400/ 468], loss_d:1.138947 , loss_g:1.017994 , time:0.042363s, lr:0.000200\n",
- "time of epoch 181 is 35.05s\n",
- "Epoch:[181/200], step:[ 0/ 468], loss_d:1.201890 , loss_g:1.253512 , time:0.048703s, lr:0.000200\n",
- "Epoch:[181/200], step:[ 100/ 468], loss_d:1.221572 , loss_g:1.084337 , time:0.048046s, lr:0.000200\n",
- "Epoch:[181/200], step:[ 200/ 468], loss_d:1.278651 , loss_g:1.096883 , time:0.051671s, lr:0.000200\n",
- "Epoch:[181/200], step:[ 300/ 468], loss_d:1.250518 , loss_g:0.937740 , time:0.041773s, lr:0.000200\n",
- "Epoch:[181/200], step:[ 400/ 468], loss_d:1.182371 , loss_g:1.078604 , time:0.038210s, lr:0.000200\n",
- "time of epoch 182 is 33.72s\n",
- "Epoch:[182/200], step:[ 0/ 468], loss_d:1.220909 , loss_g:1.277611 , time:0.044699s, lr:0.000200\n",
- "Epoch:[182/200], step:[ 100/ 468], loss_d:1.226709 , loss_g:0.913745 , time:0.044920s, lr:0.000200\n",
- "Epoch:[182/200], step:[ 200/ 468], loss_d:1.199324 , loss_g:0.951796 , time:0.046564s, lr:0.000200\n",
- "Epoch:[182/200], step:[ 300/ 468], loss_d:1.181819 , loss_g:1.012426 , time:0.045454s, lr:0.000200\n",
- "Epoch:[182/200], step:[ 400/ 468], loss_d:1.226393 , loss_g:1.050092 , time:0.045428s, lr:0.000200\n",
- "time of epoch 183 is 35.29s\n",
- "Epoch:[183/200], step:[ 0/ 468], loss_d:1.205263 , loss_g:1.052972 , time:0.058037s, lr:0.000200\n",
- "Epoch:[183/200], step:[ 100/ 468], loss_d:1.168257 , loss_g:1.063699 , time:0.044908s, lr:0.000200\n",
- "Epoch:[183/200], step:[ 200/ 468], loss_d:1.225259 , loss_g:1.049176 , time:0.043375s, lr:0.000200\n",
- "Epoch:[183/200], step:[ 300/ 468], loss_d:1.213476 , loss_g:0.993739 , time:0.049268s, lr:0.000200\n",
- "Epoch:[183/200], step:[ 400/ 468], loss_d:1.224397 , loss_g:1.123203 , time:0.050683s, lr:0.000200\n",
- "time of epoch 184 is 35.30s\n",
- "Epoch:[184/200], step:[ 0/ 468], loss_d:1.235125 , loss_g:1.059956 , time:0.048866s, lr:0.000200\n",
- "Epoch:[184/200], step:[ 100/ 468], loss_d:1.166338 , loss_g:0.822342 , time:0.046082s, lr:0.000200\n",
- "Epoch:[184/200], step:[ 200/ 468], loss_d:1.272878 , loss_g:1.047076 , time:0.043800s, lr:0.000200\n",
- "Epoch:[184/200], step:[ 300/ 468], loss_d:1.147182 , loss_g:1.057525 , time:0.045788s, lr:0.000200\n",
- "Epoch:[184/200], step:[ 400/ 468], loss_d:1.206162 , loss_g:0.868631 , time:0.044592s, lr:0.000200\n",
- "time of epoch 185 is 36.81s\n",
- "Epoch:[185/200], step:[ 0/ 468], loss_d:1.223336 , loss_g:0.858114 , time:0.050555s, lr:0.000200\n",
- "Epoch:[185/200], step:[ 100/ 468], loss_d:1.222463 , loss_g:1.204376 , time:0.040848s, lr:0.000200\n",
- "Epoch:[185/200], step:[ 200/ 468], loss_d:1.205641 , loss_g:1.048867 , time:0.040236s, lr:0.000200\n",
- "Epoch:[185/200], step:[ 300/ 468], loss_d:1.187565 , loss_g:1.099094 , time:0.045253s, lr:0.000200\n",
- "Epoch:[185/200], step:[ 400/ 468], loss_d:1.225134 , loss_g:0.900765 , time:0.045837s, lr:0.000200\n",
- "time of epoch 186 is 33.74s\n",
- "Epoch:[186/200], step:[ 0/ 468], loss_d:1.269069 , loss_g:1.000440 , time:0.047070s, lr:0.000200\n",
- "Epoch:[186/200], step:[ 100/ 468], loss_d:1.211031 , loss_g:1.093462 , time:0.048214s, lr:0.000200\n",
- "Epoch:[186/200], step:[ 200/ 468], loss_d:1.246028 , loss_g:0.738132 , time:0.049433s, lr:0.000200\n",
- "Epoch:[186/200], step:[ 300/ 468], loss_d:1.226768 , loss_g:1.017653 , time:0.043042s, lr:0.000200\n",
- "Epoch:[186/200], step:[ 400/ 468], loss_d:1.222387 , loss_g:0.956919 , time:0.043321s, lr:0.000200\n",
- "time of epoch 187 is 34.91s\n",
- "Epoch:[187/200], step:[ 0/ 468], loss_d:1.202455 , loss_g:0.817922 , time:0.049112s, lr:0.000200\n",
- "Epoch:[187/200], step:[ 100/ 468], loss_d:1.241711 , loss_g:0.849983 , time:0.052105s, lr:0.000200\n",
- "Epoch:[187/200], step:[ 200/ 468], loss_d:1.190964 , loss_g:0.977885 , time:0.052275s, lr:0.000200\n",
- "Epoch:[187/200], step:[ 300/ 468], loss_d:1.257135 , loss_g:0.940552 , time:0.049508s, lr:0.000200\n",
- "Epoch:[187/200], step:[ 400/ 468], loss_d:1.235184 , loss_g:0.883774 , time:0.045740s, lr:0.000200\n",
- "time of epoch 188 is 36.85s\n",
- "Epoch:[188/200], step:[ 0/ 468], loss_d:1.218155 , loss_g:1.063594 , time:0.044652s, lr:0.000200\n",
- "Epoch:[188/200], step:[ 100/ 468], loss_d:1.160277 , loss_g:1.041409 , time:0.043465s, lr:0.000200\n",
- "Epoch:[188/200], step:[ 200/ 468], loss_d:1.287502 , loss_g:0.807326 , time:0.041938s, lr:0.000200\n",
- "Epoch:[188/200], step:[ 300/ 468], loss_d:1.230783 , loss_g:1.153967 , time:0.041447s, lr:0.000200\n",
- "Epoch:[188/200], step:[ 400/ 468], loss_d:1.197217 , loss_g:1.034803 , time:0.042261s, lr:0.000200\n",
- "time of epoch 189 is 33.66s\n",
- "Epoch:[189/200], step:[ 0/ 468], loss_d:1.225700 , loss_g:1.117663 , time:0.050365s, lr:0.000200\n",
- "Epoch:[189/200], step:[ 100/ 468], loss_d:1.196917 , loss_g:1.073184 , time:0.044238s, lr:0.000200\n",
- "Epoch:[189/200], step:[ 200/ 468], loss_d:1.129148 , loss_g:1.021484 , time:0.040764s, lr:0.000200\n",
- "Epoch:[189/200], step:[ 300/ 468], loss_d:1.190587 , loss_g:1.327877 , time:0.045524s, lr:0.000200\n",
- "Epoch:[189/200], step:[ 400/ 468], loss_d:1.234074 , loss_g:1.060046 , time:0.038496s, lr:0.000200\n",
- "time of epoch 190 is 34.80s\n",
- "Epoch:[190/200], step:[ 0/ 468], loss_d:1.174294 , loss_g:0.960412 , time:0.048246s, lr:0.000200\n",
- "Epoch:[190/200], step:[ 100/ 468], loss_d:1.270005 , loss_g:0.972594 , time:0.045788s, lr:0.000200\n",
- "Epoch:[190/200], step:[ 200/ 468], loss_d:1.237765 , loss_g:1.176997 , time:0.035734s, lr:0.000200\n",
- "Epoch:[190/200], step:[ 300/ 468], loss_d:1.229265 , loss_g:0.806409 , time:0.038106s, lr:0.000200\n",
- "Epoch:[190/200], step:[ 400/ 468], loss_d:1.258551 , loss_g:1.079994 , time:0.038538s, lr:0.000200\n",
- "time of epoch 191 is 35.85s\n",
- "Epoch:[191/200], step:[ 0/ 468], loss_d:1.275414 , loss_g:0.969427 , time:0.045643s, lr:0.000200\n",
- "Epoch:[191/200], step:[ 100/ 468], loss_d:1.211061 , loss_g:1.370910 , time:0.041514s, lr:0.000200\n",
- "Epoch:[191/200], step:[ 200/ 468], loss_d:1.221945 , loss_g:1.107085 , time:0.046227s, lr:0.000200\n",
- "Epoch:[191/200], step:[ 300/ 468], loss_d:1.143856 , loss_g:0.913824 , time:0.042443s, lr:0.000200\n",
- "Epoch:[191/200], step:[ 400/ 468], loss_d:1.419553 , loss_g:1.258330 , time:0.049940s, lr:0.000200\n",
- "time of epoch 192 is 35.05s\n",
- "Epoch:[192/200], step:[ 0/ 468], loss_d:1.191133 , loss_g:0.980667 , time:0.046218s, lr:0.000200\n",
- "Epoch:[192/200], step:[ 100/ 468], loss_d:1.167464 , loss_g:1.023220 , time:0.048479s, lr:0.000200\n",
- "Epoch:[192/200], step:[ 200/ 468], loss_d:1.172068 , loss_g:1.092904 , time:0.037380s, lr:0.000200\n",
- "Epoch:[192/200], step:[ 300/ 468], loss_d:1.232565 , loss_g:1.228527 , time:0.038632s, lr:0.000200\n",
- "Epoch:[192/200], step:[ 400/ 468], loss_d:1.243355 , loss_g:0.987800 , time:0.043290s, lr:0.000200\n",
- "time of epoch 193 is 33.33s\n",
- "Epoch:[193/200], step:[ 0/ 468], loss_d:1.166546 , loss_g:1.206838 , time:0.051881s, lr:0.000200\n",
- "Epoch:[193/200], step:[ 100/ 468], loss_d:1.148963 , loss_g:1.054476 , time:0.041737s, lr:0.000200\n",
- "Epoch:[193/200], step:[ 200/ 468], loss_d:1.096771 , loss_g:1.062835 , time:0.040473s, lr:0.000200\n",
- "Epoch:[193/200], step:[ 300/ 468], loss_d:1.153534 , loss_g:1.175065 , time:0.039009s, lr:0.000200\n",
- "Epoch:[193/200], step:[ 400/ 468], loss_d:1.149676 , loss_g:1.271051 , time:0.040645s, lr:0.000200\n",
- "time of epoch 194 is 34.69s\n",
- "Epoch:[194/200], step:[ 0/ 468], loss_d:1.163631 , loss_g:1.246624 , time:0.044640s, lr:0.000200\n",
- "Epoch:[194/200], step:[ 100/ 468], loss_d:1.226489 , loss_g:1.216786 , time:0.037571s, lr:0.000200\n",
- "Epoch:[194/200], step:[ 200/ 468], loss_d:1.210007 , loss_g:1.041248 , time:0.040890s, lr:0.000200\n",
- "Epoch:[194/200], step:[ 300/ 468], loss_d:1.239979 , loss_g:0.876833 , time:0.036461s, lr:0.000200\n",
- "Epoch:[194/200], step:[ 400/ 468], loss_d:1.231214 , loss_g:1.015513 , time:0.039640s, lr:0.000200\n",
- "time of epoch 195 is 33.30s\n",
- "Epoch:[195/200], step:[ 0/ 468], loss_d:1.132347 , loss_g:0.993191 , time:0.051763s, lr:0.000200\n",
- "Epoch:[195/200], step:[ 100/ 468], loss_d:1.246632 , loss_g:0.923422 , time:0.038327s, lr:0.000200\n",
- "Epoch:[195/200], step:[ 200/ 468], loss_d:1.209304 , loss_g:1.295300 , time:0.042164s, lr:0.000200\n",
- "Epoch:[195/200], step:[ 300/ 468], loss_d:1.156869 , loss_g:1.063914 , time:0.038578s, lr:0.000200\n",
- "Epoch:[195/200], step:[ 400/ 468], loss_d:1.203755 , loss_g:0.958413 , time:0.043637s, lr:0.000200\n",
- "time of epoch 196 is 33.91s\n",
- "Epoch:[196/200], step:[ 0/ 468], loss_d:1.194354 , loss_g:1.017212 , time:0.048939s, lr:0.000200\n",
- "Epoch:[196/200], step:[ 100/ 468], loss_d:1.284311 , loss_g:1.383054 , time:0.045014s, lr:0.000200\n",
- "Epoch:[196/200], step:[ 200/ 468], loss_d:1.136481 , loss_g:1.056764 , time:0.040642s, lr:0.000200\n",
- "Epoch:[196/200], step:[ 300/ 468], loss_d:1.283196 , loss_g:0.949850 , time:0.042258s, lr:0.000200\n",
- "Epoch:[196/200], step:[ 400/ 468], loss_d:1.160837 , loss_g:1.011806 , time:0.042898s, lr:0.000200\n",
- "time of epoch 197 is 34.83s\n",
- "Epoch:[197/200], step:[ 0/ 468], loss_d:1.197892 , loss_g:1.009276 , time:0.049630s, lr:0.000200\n",
- "Epoch:[197/200], step:[ 100/ 468], loss_d:1.168854 , loss_g:1.164440 , time:0.042325s, lr:0.000200\n",
- "Epoch:[197/200], step:[ 200/ 468], loss_d:1.190279 , loss_g:1.018515 , time:0.036608s, lr:0.000200\n",
- "Epoch:[197/200], step:[ 300/ 468], loss_d:1.196159 , loss_g:1.074415 , time:0.043475s, lr:0.000200\n",
- "Epoch:[197/200], step:[ 400/ 468], loss_d:1.166519 , loss_g:1.054058 , time:0.037495s, lr:0.000200\n",
- "time of epoch 198 is 34.83s\n",
- "Epoch:[198/200], step:[ 0/ 468], loss_d:1.265847 , loss_g:1.432787 , time:0.050606s, lr:0.000200\n",
- "Epoch:[198/200], step:[ 100/ 468], loss_d:1.191604 , loss_g:1.128230 , time:0.041880s, lr:0.000200\n",
- "Epoch:[198/200], step:[ 200/ 468], loss_d:1.238599 , loss_g:1.050956 , time:0.050597s, lr:0.000200\n",
- "Epoch:[198/200], step:[ 300/ 468], loss_d:1.194071 , loss_g:1.239539 , time:0.045487s, lr:0.000200\n",
- "Epoch:[198/200], step:[ 400/ 468], loss_d:1.359404 , loss_g:0.890722 , time:0.045265s, lr:0.000200\n",
- "time of epoch 199 is 32.91s\n",
- "Epoch:[199/200], step:[ 0/ 468], loss_d:1.205836 , loss_g:1.123699 , time:0.049476s, lr:0.000200\n",
- "Epoch:[199/200], step:[ 100/ 468], loss_d:1.275838 , loss_g:1.206755 , time:0.038420s, lr:0.000200\n",
- "Epoch:[199/200], step:[ 200/ 468], loss_d:1.154675 , loss_g:1.040136 , time:0.039236s, lr:0.000200\n",
- "Epoch:[199/200], step:[ 300/ 468], loss_d:1.226662 , loss_g:0.902854 , time:0.042469s, lr:0.000200\n",
- "Epoch:[199/200], step:[ 400/ 468], loss_d:1.299620 , loss_g:1.181718 , time:0.044500s, lr:0.000200\n",
- "time of epoch 200 is 35.76s\n"
- ]
- },
- {
- "data": {
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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"import mindspore\n",
"mindspore.set_context(device_target=\"Ascend\")\n",
@@ -1787,25 +456,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-10T01:39:37.298891Z",
- "start_time": "2023-02-10T01:39:37.177217Z"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"plt.figure(figsize=(6, 4))\n",
"plt.title(\"Generator and Discriminator Loss During Training\")\n",
@@ -1821,37 +474,16 @@
},
{
"cell_type": "markdown",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-07T08:15:38.144519Z",
- "start_time": "2023-02-07T08:15:38.137537Z"
- }
- },
+ "metadata": {},
"source": [
"可视化训练过程中通过隐向量生成的图像。"
]
},
{
"cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-09T13:36:54.052385Z",
- "start_time": "2023-02-09T13:36:50.876603Z"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": 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4PLS3tzMwMIDP5yMjI4MNGzZgMpmIRqMMDg5y4MCBhMxQJgiCHGGm0WhwuVzyIupyuea7eZ8hHA7j8/kIh8PXXOQlUXa5XLS0tNDV1UVHRweTk5PARQ+UjIyMeQ8znys0Gg1r167lq1/9KhUVFYiiyOjoKGfPnqW1tZV4PC6bEhO9r1fijhdmyYXqRz/6ER6Ph/LycqLRKAMDAzQ1NTE6OkpBQQFarZbKyko5nHdgYGDeghNminTJ8v777+N0OhkZGcFms8mTu7u7m2g0yurVq1myZAl2ux2v1ysfFxPNTU5KVpSXl0daWhrBYJDe3l5+/etfX+IaqCR8Pt91d3uxWIyxsTH6+/tJT09neHgYi8WC1WqVw7CDwSCdnZ2Mjo7i8/lmLFZKFDVBECgpKeFzn/scDz30EHq9nkAgwKlTp2TbspRCwOfzyS51SuzLbLnjhRku7kb6+/t5/vnnL0mBGYvFEAQBv9+Pw+EALpoHLly4IJsAEgGPx8OBAwf45JNPAGR3uFgsRnJyMhkZGbIHxujoaMLk/5iORqPBbDazbNkyFi9eTGpqKmNjY5w4cYKzZ88qcrc8U2KxGOfOnePo0aPU19eTlZXF5s2bycrKor29nfHxccLhMC6XC5/Pp8gF6EYwGo08+OCDbNmyRY7+8/l8HDp0SN4tS77N4XA44TYQM0EV5t8g+U9ejiiKmEwmjEYjsViMYDDIwMCAvGonCpKz/uUIgiAnlYlGo3K4a6IsOhJGo5Hy8nL+/M//nPr6egA6Ozt55ZVXGBkZuWGxUtLYxmIxWlpaKCkpYdOmTWzatAmv18u6detwOp10dnZy+PBhPvroo4QU5emJiQRBICkpiVWrVlFRUUEsFsPtdtPe3k5fXx9ut1u+f5Bc6hYiqjBfB8nxXRRFWbx7enpwOp2K9mGeKVLVZMl1bmJigv7+frxeb8IcDQ0GA6WlpXzpS1+itrYWs9nM6Ogozc3NtLa2zsrn9XIXrtvBtY7jPp+PyclJAoEAZrMZnU4n514uKysjPT2d8+fPMz4+fsO/c76Z3mez2cymTZvIzs4mFArR3d3Nf/7nf3LkyBHOnDlzyeZiIe6UJVRhvg56vZ6SkhKysrLkHL6tra3z7k42F0i32lVVVQiCwPj4OF1dXXR3dyvSrexKSGHny5cv55577iE5ORmXy8XHH3/Mm2++icPhmPU4KWlhisfjcjGD1tZW/H4/OTk55OfnYzQacbvds0qqpaQ+arVaUlJSKC8vx2g00tPTw6FDh3jjjTfo6upK+BS0N4IqzNdBSjk4NTVFd3c3x44dY2hoaEH4UUq7Ja/Xy/j4uJxj2uVyJcxtt8FgYNGiRdx9992UlJSg1+vp7+/n+PHjHDt2LKEm8/X+3hMTE5w8eZLk5GTMZrOcl1mv12M2my9JRJ+ISHmbKysrSUlJobu7m4aGhjtOlEEV5usSCoU4ceIEWVlZVFZW8tFHHzE1NUU0Gk0I4boW0mXfvn37yMrKoq2tjZaWFsXm9b0cjUZDeno6q1ev5u6778ZkMhGJRGhvb6ejowO/35/wYyQRj8fp6elh165dFBQUUFVVRW1tLX6/XzZBSbmKExWpEEBubi6iKNLd3U1bW9uCMBneKKowXwPJ5jc1NSUfH0dGRhgbG1swkz4UCjE0NMTx48eZmJhgdHQ0YeznoihSXl7OihUryMrKIh6PMzQ0xM6dOzl48OCC22VNTU3J/ucdHR1y7giTycSuXbtkn+ZEJRaLMT4+zuHDh4lEIhw4cIBTp04l9GIzW1RhvgoajQadTkdSUhKFhYXU1taSkZFBc3Mzdrsdl8uV0BNf8jbJzMykvLycZcuWcfToUfR6PTabLSEmeTwelyt6xGIxBgcH2b1794LdZUmeCFJQyn/+539y4sQJ7HY7DQ0NOJ3O+W7irDEYDOTn57Nq1Sra29tpbm6mo6MjoefYzaAK81WQdsM6nY60tDRMJhMOh4OBgQH5djyRkVyTzGYzWVlZBINBRkZGGBgYUFQhz2shXYh5vV7GxsY4c+YM77//Pm1tbQvCn/dqSAItvYs6nW7GSf+lDYcSMgdOx2w2k5KSIi+oU1NTCy7M+kZQhfk6SEnxx8fHaWlpYXR0FK/Xm/CTXnIHM5vNWK1WGhoa6OjoYGJiQvEeGZKJKR6P09vby549e2hqauLChQucOHGC0dHRhEqCP1usVqtclPVGRFZJgiwRi8XQ6/VUV1dz4cKFBe2jPBNUYb4GsViMaDSKx+Ohv79fTp6/EF4YKfovFAoxNjbG4cOHGRkZSQgTwHRh7ujooKurC/g0WlOJwjPXaDQaLBYLpaWl6HQ6GhoaZuR7Lp0ylIY0r1atWkVvb69c/ftORRXmayDV+Xv33XflKsQLadJ7PB7OnTtHS0tLQu1QprdzLurdSSlGldb/ayXPBxgbG6OyspJly5bR29tLb2/vZ0T3es9QAlJ+81AoxOHDh/H7/XJVk4U0324EVZivgxSGvRCRyv0oeZd8Oyan0ib/9HwtV/u5xWIhFApx/Phxzp8/j8vl+owoK3GxkZg+rnq9nsWLF1NTU8MvfvELJicnL7kjSITFZa7RxJX2Vs4h0WiUAwcOKEJYi4uLqaysnNNn9vX10dTUNKfPnA2CILBp0yaMRuOcPbOnp4eWlpY5e95sEUWRTZs2YTAY5uyZsViMAwcO3NQFslarnZPTQkFBATU1NTf1jOnMpm+iKMr1Jvv7+z9jwpjt4lxYWEh1dfUNf08JLGhhVlFRUUlEEiuFmIqKisodgCrMKioqKgpDFWYVFRUVhaEKs4qKiorCUIVZRUVFRWEsaD/mYDDI17/+dSYmJmb1fVEUsdls+P3+m3a5e+yxx/jGN75xU8+4nHfeeYfnn39+Tp85GwwGAz/84Q9JT0+fs2f++te/5t/+7d9m/X0p85rb7b4plzKj0ciLL75IamrqrJ9xOeFwmN/93d9lZGRkzp45W3bs2MG3vvWtOXteKBTiG9/4BmNjY7P6vjTnAoHATeejefjhh/n93//9m3rGfLGghTkajbJ3716GhoZm9X0pl0QwGLzhIAypiq/kk1lVVTWrNlyL3t5e3n333Tl/7o1iNBrx+/1z+syurq6b6pter0er1c4qEY5U7zAWi2EymeY8YVUsFmPfvn309vbO6XNnQ1FR0Zw+LxqNsm/fvlkX9J3LOVdWVjarNiiBBS3MN0s8Hsfr9c76+1LEkuoqfvsJhUKzTsYkCII6ZvPEzc45URQXxJxThfk6SHkURFFEEATC4fCMjsZKTRZzJyHtoLRarVwlfCYTVskh6ncCNzPnlJ4ZcaaownwNNBoNJpMJk8lEVlYWZrOZ/v5+PB4Pfr//js5+pXR0Oh0mkwmbzUZBQQHj4+M4HA58Pt+CqNe4ULnSnBsYGMDtdt9Rc27BC7NkL5wNGo0Gu93OI488Qn19PcnJyTgcDo4ePcp7771HT0+Puru6RUi7ptkKqE6no6qqiqeeeopFixbJtQA//vhjDhw4MKMUmSo3jjRuN/N9m80mz7nU1FQmJiZoaGhgz549dHd33xFzbsEL82wnn0ajISkpic2bN/Pggw9SWVlJKBSSV/KCggKmpqaYmJi44zJf3S5mO3ZarZaKigoefPBBtm3bhtFoZHJyErfbTXZ2Nvn5+XR3d6s7Z4UhbYQ2b97Mjh07qK6uJhQKYTQa6enpkefc+Pj4gp9zqjBfhaSkJDZu3Mhjjz1GZWUlw8PDnD59GpfLxfDwMCkpKWRlZREIBBKuookgCFitVjIzM8nMzESv1xOJRAiFQrhcLsbHx3E6nfMqWjezoFZWVvLAAw+wfft2zGYzhw8fZnh4mPHxcSKRCDk5OUxOTiZEtZbpaDQa9Ho9ycnJ5OXlYbVa0Wg0cg3A8fFxxsbG5jWbolTAYDbY7XZ5zlVVVTEyMsLp06dxOp0MDw+TnJxMZmYmfr8/4ebcjbLghXk2xyqNRkNeXh5f+cpXWLlyJbFYjMOHD/P6669js9nkXLharRaTyZQwVU0k+11ubi6VlZWsWbOG5cuXY7fbCYfDOJ1OOjo6OHbsGIcOHZrX08D0cbsRk4ZWq2X79u08+uij5OTk0NPTwyuvvMLU1BRWqxWtVks4HMZkMimy9t3V0Gq1pKSkUFRUxLJly9i0aRN5eXlotVo8Hg+jo6OcPn2aDz/8kAsXLsy5++KtRqPRkJOTw1e+8hVWrVpFLBbjyJEjvPrqqwk952bLghdmqbadKIrAzG7cBUHAZrNRVlaGzWajubmZ3t5exsbG5LI34XBYTjKfKBcSZrOZ2tpafvu3f5uNGzeSm5uLyWSS3cOkW+36+noMBgPvvPPOTbku3QySWEo38zOtHqPT6SgoKCA/P59gMEh7ezsTExP09fXJ4zZ97BJBlAVBICcnh23btvHUU09RW1tLUlISWq1WXrSi0SgPPPAAeXl5/OxnP+PcuXPz2ubZzDm73b7g5txsWfDCPP3FvRG0Wi02mw2TyYTVaiUpKQmTyYTT6cTr9Sbci6HX6ykoKOBzn/scmzdvpri4GIPBILuRiaKIVqvFaDSSlZVFUVEROp1u3tor7Zij0egN+aVKpwKr1Yooitjtdux2OwBerzfh7MqS3XX9+vXs2LGDVatWkZSUhEajkQVPGjetVktxcfGcRmDOltnMOWlDJHnT2O12TCYTLpcLj8eTcHPuZljwwixNwhuZjCaTidTUVCwWC3q9Xg7vTeSAkfLycp5++mnuv/9+cnNzCYfDnDp1igMHDjA4OIgoijz22GMsW7YMjUaD0Wi8qdv1uWSmf29BEEhOTiYpKQmDwUAkEsFkMqHVauXis4k2dkajkQcffJBHHnmEVatWYTKZGB0d5YMPPqC5uRmPx0NJSQlPP/00NpsNvV4/rwvqdG7kb200GklPT8dkMslzzmg0JvScuxkWvDDPhvT0dKqqqjAajcRiMSYmJnC5XPLuLdFISkpi1apVPProoxQUFOB0Ojl+/Di7du3io48+YnBwUDYBFBcXYzKZyMvLm9cJPpuJqNPpqK6uJjMzE0EQCAQCDA8PEwwGE3JyS2Pyuc99jo0bN6LVamlqauLDDz/krbfeoqWlBbfbzapVq6ivr6eiooKsrCxSUlLmu+k3TEZGBlVVVZjN5kvm3FyUz0pEVGG+DEEQKCkpYd26dWi1WiYnJzlz5gxdXV0zjhxTEnq9nrq6Ou655x7Ky8sJBAIcPHiQl156iaNHj+L3+2X78tTUFIFAgJycHGpqajCbzQlVqdhoNFJfX09BQQHBYJD+/n6OHz+Oz+dLmD5ICIJAWloa27dvZ8WKFaSlpdHc3Mxrr73GT3/6U0ZHR2VThs/nY3JyEkEQKCsrIy8vD1EUE+boLwgCRUVFrF+/Hp1Ox9TUFGfPnqWzszMhF9S5QE37eRlGo5GKigrWrVuHRqOhtbWVkydP0t/fjyAI6PV6xRzxr4cgCOTn5/PUU0/x+OOPo9FoGBgY4Je//CUnTpz4jGBJx0edTofFYpHttImAIAikpKTIwjwxMcGpU6dobGwkEAig1Wrly7JEwGKxsHz5cv7oj/6I4uJiPB4PR48e5fXXX79ElOGijdlsNqPT6TAajVit1jktjHurMRgMlJeXXzLnjh8/Tl9fHxqNBp1OlzDjNleowjwNQRAoKCigrKyMlJQUotEoTU1NjI2NYbfbqa6uZsmSJbL/qNIxGAzs2LGDdevWYbFY6O/v5+///u85duwYbrf7ks/qdDqysrJk1zmv10sgEEiY3YrFYqG6upq8vDyMRiPDw8O0tLQQiUQoLCykrq5Odi9LBBYtWsQTTzxBbm4uAG+++SY//vGP6e3tvUSUJZ/0nJwcRFHE7/fj9/sTJjpOo9FQUFBAeXk5KSkpxGIxmpqaGB8fx263U1VVJc+5m4niTTQS4y2dI6SENlcLKhBFkY0bN8r2vKmpKUZGRiguLqa0tBSbzcYnn3yCy+Wit7dX8d4ZoihSW1srT+7JyUkaGhpwuVyXhDybTCZWrlxJYWEhJpOJWCyGTqdTlH1Pas/V/t4ZGRk8/vjjZGVlEYvFcDqdBINB7rrrLpYsWUJ7ezuxWIxAIMDo6Kji/ZfT09NZunSp7DnT1tZGa2srkUhEdm+UTkTLly8nOTkZURTlE51S3ktpzl3t7y2KIvX19WzcuBGdTofH42FkZISioiJKSkpISkpKqDk3V9xRwgyf+jVf6SURBIHCwkLy8/MBZNeqtLQ0bDabfPmXnJwMwPDwMA6HQzHidTkajUY+wsOnR8a8vDySk5PRaDQEg0GSk5NZv349ixYtkhcupV10Xk9EzWazfHkk+btKrn9Sjl69Xk9mZiYGg4H+/v55jZC7HoIgXHKEz8zMZMWKFRiNRpKSkvB6veh0OsrLy7n77rtls5O0mCpp0blWW6RTakFBAfDpnEtNTcVut8vunNKcGxkZuSPSINwxwqzT6dDpdEQikWteaEkXYeFwGI/HQygUYnx8nO7ubqampvB4PJjNZsxmM36/H4fDcZt7MnOi0Sjt7e0MDAxgt9tJS0vjySefxGw2k5+fj0ajYWpqCrvdTmFhIUlJSQiCgNvtpq2tjVAoNO8TXHLdi8fj1x07uNjnYDAoh+22tLTQ0NDA5OQkkUiE9PR0zGaz7K0xk98/HzgcDpqbmyksLMRsNrNmzRpyc3PJzMwkOzsbh8OBKIpkZmaSk5Mj76y7u7sZHh6e93GDmc85+DRNrtvtJhgMMj4+Tk9Pz2fmXCAQmHVFokTijhFmu92OzWbD7XbjdDqv+rloNEooFMLn89HT00NzczPnzp3D5XIRj8fJyMhg5cqVdHd3Mzk5qYgJcDWCwSA7d+4kIyMDi8VCVlYW9913n+z8HwgEsFgsctUIyd+3q6uL119/HZfLNd9dQBRFsrKy5Dwe16pIEgqFiEQijI2N0dHRwcmTJxkaGsLr9WI2m6muriYlJSUhMpR1dnbyyiuvkJ2dzeLFi1m6dCk1NTWygBkMBsLhMBaLRb7oC4VCvPvuuzQ0NChiRykFibjd7mu+S+FwWJ5zvb29tLS00NjYyOTkJNFolMzMTHnOSfNwoXPHCLPL5WJqauqadtN4PE4gEMDj8RAOh2lsbGRsbEz+rhRm2trayuDgoOKFWdox/+AHP2BgYIAvf/nLJCcnMz4+ztmzZ2lubsbv91NdXc1TTz0lB2WMjo5y7tw5RRz1I5EIAwMD140ki0QieL1ePB4P7e3tXLhwAZfLhdPpJBwOE41G6e/vZ2xsjMHBQcUnL3K5XOzfvx+Xy8Uf/MEfsGTJEmKxGD09PZw4cYK+vj7S0tLYsmULmzdvBi66zbW2tjIwMDC/jf8NUka/a90NAPKci0aj8pybnJxkcnISjUaDw+FImDk3V9wxwizZqq5FLBajtbWVs2fPUlJSgiiKLF26FFEU6erqwufzodPpGBkZweVyJUSFklAoRE9PD6+//jqnT59Gp9Ph9/txuVxMTk5iMBjw+/08/PDDxONxJiYmZPurUibATP7Obrebo0ePkpKSgs/nIycnh5UrV6LX6xkdHUUURXw+H06nU/bdngnz9TeIxWJMTk5y/Phx/u///b+kpKQQj8dxu91MTEzg9/spLS1l0aJFco6Tnp4enE6nYi7HZjLnotEoHR0dnDlzhkWLFiGKIsuWLUMQhISdc3PBHSPMMyEajXLu3DkWLVpEYWEhS5cupbi4mOrqavr6+ujv76e3t5f29nbF77imEw6H6evro6+v7zM/S0tLIxQKySeJiYkJhoeHE8IdcDoul4uDBw+yZMkSsrOzsdlsVFRUsHjxYnncurq6FCVc1yMej+PxeDhx4sRnfibZl6VETKFQiL6+PgKBQEK5lcViMc6fP09paSlFRUUsWbKE4uJiqqqq5He2t7eXjo6OO0aUQRXmS4jH4/T399Pd3Q1ATU0Nfr+fiooK/H4/w8PDHDx4cNYVgJWIKIpYLBY5J4HT6WRkZCRhxEvC5/PR0tKCw+Ggrq4Oi8WCx+NhyZIleDweGhsbefvtt2/4mK/kBUrK4SIJ88DAAB6PRzEnnZkQj8fp6+ujq6sLuDjnAoEA5eXl+P1+RkZGOHjwoGzOulO4I4VZEqErEYlE8Pv9uN1uAoGAfEmUnZ0t55F47733GBoaup1NvmXodDpsNhsajYZQKMTo6Cg9PT2KDGOWRPJK7YrH4wSDQbkeo+T3bDAYKCgoICcnh+7ubo4ePZpQYeZXQwosMRqNsn29q6uLiYkJRe4srzXnpItoj8dDIBCQ25+VlUVxcTFms5k9e/YwODh4O5s8r9yRwnytG2tpgg8PD8u5I5YvX05KSgo6nY709HQ5U1miI4oiaWlp1NbWyjuX5uZm+vr6FJmr+HrtkfJ9nDt3jmg0itVqpb6+HpvNJoct32i+E6X9DSQMBgNlZWXk5ubicrlob2+ntbWVqakpRbZ5JnNuaGhInnN1dXWkpKRgMBjkNKZK7Net4o4U5uvR2dnJ22+/TV5eHkVFRUSjUQRBkJO2BwKBhDvqXwnpsu/AgQPk5eXR3t7OuXPnZlwuXmkEAgE+/PBDuru7yc3NZcmSJWg0GnnsIpGIIjxN5gLJg6GsrAyPx8OHH36I0+lUXHDJTOnq6vrMnJPGTppzSndxnEtUYb6MeDzOyMgIR48eJSMjg/7+fnJycsjIyECn03Hq1CncbndCvvyXE4/H5ex5+fn5DA4OyoE0iTgJwuEw58+fZ2BggOzsbNlWqdfraW9vp7u7W5HH/NkQDodl04zL5eL8+fOMjY0lVH4TiXg8zujoKMeOHSM9PZ3+/n6ys7PJyMhAr9dz6tSphLOd3yyqMF+BQCBAMBiUi0BqtVpcLhcGg4Fdu3YxOTk53028aaSsXVLlC8k+K50GEnESxGIxXC4XbrebsbEx3G43er2e5cuXc/z4cU6dOpWQJ4HLEUURg8GATqcjHo/j9Xrx+XzySScRx276nBsZGUGn0zE5OYnJZGLXrl2KCHa6najCfBWkm+6JiQneffddzp49i8lkor29fcbHYSVfMgmCgMlkIj09nfLycqqqqhgaGiIej2MwGBTtjXA9otGoHI49ODhIaWmpHFwyk/G41iXjfCPlP7Hb7RQVFVFVVUVqaipHjhxBr9cnTJrWK3G1OdfR0UEgEJjRM5Q8524EVZivgxQCK7lZzVSUpdzNSgrUmI6UE8RgMJCcnExPTw8dHR10dnbi9/sXhA09Fovh8Xhobm6Wq5jMBKlyixJ91aVx02g0JCcnEw6HaW1tlX20F4KpZvqc02g0MxZlpc+5G0EV5usglVVPTk7G5/PR3d09owkei8UUkQToasTjcURRxGq1YrFY2Lt3r7wzWQiiDBczzhUUFKDX6+np6ZmxCUrp4haPx9Hr9aSmpjI4OMiRI0eYnJxUfCrTmaLRaMjOziYlJQW/309XV9eCmHM3girMXP/44/F4WLx4MTk5OQQCgSsGYFzpGUq2Z0q2yQsXLuB2u+nu7sbr9Sq6zZdzPZNDKBQiFAqxdetWzGYz586dw+PxXPe5Sp/Y0WiUiYkJjhw5QiAQYHBwMOFE+XoZHqU5l5ubK9duTPQ5dyPc8cJ8PVuqlLx7aGgIrVa7YHaTcFG4hoeHGR4enu+m3DDXGzdRFNFoNIyNjTEwMHDVgJlEtEnGYjHcbjctLS3z3ZRZcb2x0+v1eL1eBgYG0Ol0C2rOzZQFLcyCIFBTU0NGRsZVfw6f2u2u9HOLxUIwGCQSichuPFlZWfJnZnpRJFURmUvS0tJYunTpnD/3RtHr9ej1+jl9ZkZGxlX7Nr36Clz5by95m0jmJ6lQ6fTPzmTsTCbTnFcL12g01NTUyMnf55O8vLw5fZ4gCFRXV5OamnrVn8ONzbmsrCyysrLkz0vjf73d8Vz37XaiiSfaduEGUUr3bpWXw0Lun9q3W4/aN2Wy4IVZRUVFJdFInPyAKioqKncIqjCrqKioKAxVmFVUVFQUhirMKioqKgpjQbvLxeNx2traFBHJlZ6eTnZ29pw+0+FwKCJ5uCAIlJeXz6lb2cTEhCKKEQiCQEVFBVrt3E2VeDyumPJkaWlp5OTkzNnzlDTn5rpvt5X4Asbr9cbz8vLigiDM+7/vfOc7c96/5557bt77JYpi3Gw2x3t7e+e0b//8z/88730TBCFusVjiAwMDc9q3QCAQLy4unve+CYIQ/+Y3vzmnffP7/fGCgoJ5fycFQYg/++yzc9q328mC3jEDcpHRhYoS+nYr2hBXSJWYW9UGpbyX8Tn2lo3/JnBkIfbtdqLamO9wEtkJX2Vhor6TC9zGfLNICcmnh+5Go1H5XyKvyBILoQ8qn6LT6eQ8IcJvCqDeSMpTJaC+k6owXxGdTofVaqWkpITf+73fIzs7Wy7D1NLSwtmzZzlz5gwjIyOKuOS4nLlIzDN916JOlMTAYDBw//33s2TJEvLy8sjKyqK/v5/nnnuOtra2eR3HuX4nYWG/l6owX4bBYGD16tXs2LGD4uJi7rvvPlJSUojH4/h8Pvr7+2lra+Ptt99m3759dHV1Ke4FmU17NBoNZrOZ9PR0SktLL0my8+GHH3L06NE7MstXomAwGFiyZAmPPvoo69evJz09neTkZMbGxvD5fFy4cIHW1lYOHjw4L7vn2c4RnU6HzWaTi+vm5+djMpkYGRnhF7/4xYItOaUK82/QarXk5+dTW1vLY489xhNPPIHNZpNXaY1Gg9VqpbKykqKiIiwWCxkZGezdu5cTJ04kvGgJgkBSUhLLli3jvvvuY+vWraSlpTE5OcnY2BjHjx9P+D4uVNLT01m/fj2bN29m7dq1pKenEwwGmZiYIDU1lS9+8Yu0trby6quvcvjw4YQxa2g0GgwGA7m5uWzfvp3Pfe5zVFZWotVqOXHihFx/U2kbo7lAFWY+Fd3Nmzfz1a9+lVWrVmE2m+XyNl6vVy7no9VqsVqt1NXVsXjxYsrKyvibv/kbOVl5oiIIAikpKSxZsoQ1a9aQnp5OPB6nt7eXiYkJRbz8M033eCeRmZnJ9u3b+fa3v01eXh4TExN89NFHDA4OkpSUxMaNG8nOzuajjz7i/PnzCfW3k4S5oKCATZs2UVpaitlsZmxsjAsXLijCD/xWoQozF3fL6enpFBcXy3lk3W43/f39/M//+T85c+YMfr8fvV5PVlYWq1evZsuWLRQXF1NaWspXv/pVXn75ZXp7exPyZREEAbvdTk1NDStXrmTRokUYDAZcLhcDAwM4HA5FTGij0YjBYCAWi8kXXPHfFPBcSCWxZorVauXJJ5/kz/7sz0hNTWVqaooXX3xRriqdnp7O7/zO7/DEE0/Q0dFBW1ubIsZxpuj1enJycli9ejVLliwhIyODcDgsv5eJvBG6HqowczGh9p/+6Z9SX19PTk4OwWCQtrY2/uM//oODBw8yOjpKJBJBEAQGBgbo7Oxk//79FBcXs3LlSu6++27q6ur4m7/5GxobGxPq5YeLE6CsrIx7772X5cuXYzabCQQCdHd3s3//fi5cuDDvfdJoNNhsNurq6ti2bRtLlizBaDQSiURoaGjgF7/4Ba2trZcUy51+WSSJuLTzF0UxoT1rzGYz3/nOd/jqV79KXl4eoVCIsbEx3G43Xq+XiYkJzGYzWVlZeDwe4vE4FotFMaef66HRaEhPT2f16tU88MADZGVlIQgCTqeT06dPs3//fvkkuxC544VZFEXS09NZs2YNixYtIhKJ0NjYyBtvvMH777/P+Pg4kUgEuBgU4Pf78fv9slivWLFCDkfesGEDo6OjigglvhGMRiPLli1j8eLFpKenIwgCU1NT7N+/n6NHjzI6OjrvE0Cn03H33XfzxBNPUFJSQm1tLUajkVgsRmlpKaWlpXR1dbFz504ikQiFhYUUFRXJJhlJiNvb23n//fdxu934fL6ELN6ZlJTEt771LZ555hnKysoIhUL09fXx5ptvcv78eXw+HzqdjtTUVEpLS4nH44yPjzM1NZUwfdVoNOTl5bFixQpKS0vlRbipqYl9+/bR1taWkKfTmXLHC7MgCAiCIB+DNRoNQ0NDHDt2jNHR0asel6LRKOFwGI1Gg8lkwu/3Y7fbMZlMt7P5N43kjbF48WKysrLkemvSBOjt7Z1x+fhbTSgUor+/n4mJCTIyMsjOzkav11NcXEx+fj6Tk5NyGbGSkhJKS0svEeZIJEJrayv5+fmMjo5y8uRJ2tracDqd89yzmSOKIjk5OTzxxBNUVlYSDofp6Ojg5Zdf5s0336Snpwefz4fNZqOwsJDc3FwMBgPhcBi32z3fzZ8xWq2W3NxcampqMJvNAPT393PkyBE++eQTXC5Xwiwys+GOF+ZoNMrk5CTnz5+nsLAQu90u17C7XuIaj8cjl433eDxMTU0lnN3LaDSSn59PZWUlycnJ8oXfnj17OHv2rGImcygUYufOnXzwwQcUFBQQCoWora2luLiYrKwszGYzNpuNJ554Qh6/K10W1tXVUV1djcvl4l/+5V8YHx9PGGHW6/VkZ2ezfv16bDYbHo+H7u5u3nzzTb73ve/h8XjkvmZmZlJfX09qaio2mw2bzSbX21M6Go2GtLQ0ysrKKC4uRqvV4vf7OXToEB9//DH9/f3zblq71dzxwhyLxRgeHuaNN94gLy+PyspKLBYL5eXldHV1AeDz+a5YOj0/P5/S0lJ0Oh3JyclUVFSQkpJCX1/ffHRlVmRkZHDvvfdSWVmJ1WrF4XBw6tQpdu3ahdPpVNSuJBKJEIlEaG9v56//+q/ly68vfOELFBcXy/cAOp1OtinHYjFCoZDseWOxWDAajRiNRtxut2JOA9fDZDJRUlLCtm3b+L3f+z1SUlJobGzkJz/5CS+//DJut/uSsaqurua3f/u3SUpKAj494SUCWq2W1atXU19fLwd39fT0sHv3bs6ePSubFhcyd7www0UPjI8++ojx8XE5oCQ9PZ1vfOMbtLS0cODAAfr7+2WXOYPBINs8t2/fLl+qHDt2jP7+/tvefin09kYRRZHMzExWrVpFUlIS4XCYo0eP8sYbb9DX1zejCTAfeQ2kU47b7Wbnzp2cPn0auBjEYDQaAQgEApfYkC0WC08++SSf//znsdls8me1Wu2cRKXdSkwmEw8//DCPP/44y5cvx2az0dLSwgsvvMC7777L5OTkJZ+3WCykpqZit9vRaDR4vd7bbksXBOGqlbCvh9FopLa2lkWLFqHVahkZGeHll1/m+PHjTE1NXff7Sh/PmaAKMxd3zS6Xi1OnTuHxeEhNTSUrK4unnnqK1atXU1tby8DAAMFgEI/Hw4oVK7DZbCxbtoy8vDyi0SgejweHwzEvO7DZRvpVV1fzyCOPyJ4Y4+PjnDp1imPHjuH3+2/Z754rYrEYPT09DAwMyB4WkvkpEokQDoflBSspKYmenh7cbjcGg0H2hVXaqeByrFYrX/va13jkkUeora3FZrPR1dXFf/zHf/DBBx8wOjr6me8sW7aMu+66i3g8jtfr5bvf/S779u27re2erSjbbDYefPBBtmzZQmFhIaFQiJ6eHtk3eyYbECWP50xRhXkaPp+PlpYW2V85JyeHJ598knvvvVdOBuPz+WSPAL1eTyAQoLm5md27d9Pd3T0vx8XZvIh2u10OPZcS+Dc3N3Pu3DnGxsYS5uUOBoOXuMhN/98SoihSUlJCQUEBOp0On8/H8PAwg4ODirGhX4mkpCS2b9/OF77wBcrLywkEArS1tfH+++/z3nvvMTg4+JlxEgSBpUuXsnHjRsLhMM3Nzbzzzju0tbXd1rbP5v3R6XTk5eXx4IMPsnTpUqxWK4ODg5w4cYKurq4ZbxYWAqowX4Y00T0eDz/72c8wGo185Stfoby8HEDO2iVlmhsaGuLdd9/l+9///rz4iM7m2KbRaCgpKWH16tVUVFQgiiJut5sjR45w7ty5BReoYTKZ2LJlC/X19SQlJdHf38/p06flm31RFBFFUVHuV0lJSaxbt45vfOMbFBcXMzAwwJkzZzh8+DBvv/024+PjV9w9iqJIXl4excXFjI6O8s4779xW903pwnU2WK1Wli9fzsqVK0lNTSUSidDX18fevXtnZMJYSCTGNe08EI/HcTgcvPzyy7S3tyMIAlqtFlEUgYsvYDAYpKuri6NHj8pO/PPRzhtFEARWrlzJihUrMBqNhEIhuru7OXr0qCKTMt0Mer2eiooK7r//fpYtW4ZOp2NoaIif//zn9Pf3o9FoMBqNJCcny2OrBGpra/nWt77FmjVrEEWRAwcO8O///u/88pe/ZGxs7IqLp0ajITMzk+TkZDQaDWNjY/zyl79kYGDgtrV7tiYMuFgKatu2bWRmZqLRaJiYmODcuXMcPXr0jtotg7pjviZ6vZ7FixdjtVrxer3yTkCv18uT2OFwXLfGmeQrrRS0Wi2ZmZmkpaURjUbly5Vz584patd4swiCQG5uLv/f//f/sXbtWvR6PUNDQ5w7d47BwUFCoRCCIBCJRBRl0jAajRQVFbFy5UosFguTk5M0NTVx9uzZq24ANBoNSUlJfO1rX2Pbtm2yucbtdieEa5kgCFgsFvLy8tDpdPj9fj755BPeeOMNXC5XQvRhLlGF+TeIoiiv9tKLHw6HaWlp4V//9V9JTU3FaDRSWVnJY489RnZ2NkajkZycHBYtWsTw8PBVTQBKKSMEF/uZnZ3NokWLSE9Px+/3097ezscff8zIyMiC2i3n5eXx1FNPsXz5cux2O/F4nP7+ft577z05Ck66LAyFQooYI0EQeOyxx/iDP/gDbDYbwWCQzs5OBgYG8Pl8Vx2f5ORkvvSlL8k+2idPnuSFF15IGB9tm81GUVERJSUl6HQ6+vv7OXHiBKdOnUoYN7+5RBVmLtogCwoKmJycRKvVEgqFmJiYIBqN0tPTw+joKIIgyIEM8Oku2Gw2Y7FYrmpXm60r261ACj9/8sknWbNmDWazmQsXLrBnzx66u7sTxqf3aky3t4uiSGFhIQ899BDp6eloNBo8Hg+9vb20tLQQDAZlP2eNRqMYu7rU7lgsxo9+9CNEUaShoYHm5uarttFqtVJdXc2GDRtoaWnhvffeo7u7m4MHDyqmX9fCZDKxYsUKnnjiCXJycohEInzyySd88sknCZPbY66544XZZrNRXV1NfX093d3duFwuhoaGcLlcsstVOBxGEATy8/OpqKjAarXKpolIJCJP8ishXRbON6IokpKSwrp16/jiF79IdXU1gUCACxcu8P7778/KbexmLnpuNampqVRUVLBo0SI0Gg3t7e00Nzdz8OBBXC6XLFhKEi5RFFm5ciXFxcV0d3fz6quv4nA46O7uxuPxXPE7BoOB4uJi7r//fgoLC3n55Zd5//33EyYHiMFgoKKigh07dvDwww9jMpkYGBjg0KFDNDY2ziqYREmbodlyxwtzaWkpX/7yl9myZQvt7e3s27dPdheTXmydTkdSUhLV1dWsXr0ak8lELBbD4XAwODiI3++/6iSIRqNotdrrhnffakwmE5WVlXzpS1+ioKCAeDxOX18fJ06coK+vb1a2ZaVNfKk9Wq2WiooKVq9ejc/nIxAI8NOf/pS3336b9vZ2vF7vZ76jBPR6Pc888wyPPPIIvb295OXlyb71lyOFLefm5lJfX8+jjz6Kz+fD4/Fc0WVQiYiiSEZGBvfddx/33HMPRqMRr9fLqVOnaG5uxuFwzGp8lDSms+WOF+acnBzWrVtHYWEh+fn56HQ6JiYm6OvrIxwOo9fryc3NZdWqVTz44IMsWrQIURRxOBy89NJL/Md//Af9/f3XFDYplHi+kMww69atY82aNVitVkZGRti1axc/+9nPbirrmBIngdVqJScnh8zMTDweD2fOnOG9996jqalJ0ZeboVCIX//61xQVFWG1WhkeHpbbK/nNSxgMBr72ta+xevVq9Ho94+Pj/O///b85ceLEfDX/hjEajSxZsoQNGzZQVlZGMBikqamJF154gTNnzsx6zijxnbxR7nhhPnPmDD/84Q/5H//jf8jipdfrqa6uJhQKUVpais1mIz8/n7KyMsxmM8FgkNdee4033niDCxcuKP5ywmq1snTpUnbs2EFaWhqCIDAyMsKFCxcYHR1N+GPf5ZjNZsrKyli2bBmRSITm5mZGRkYULcrwqWlIyvkhXTibTCaeeeYZtm7dKmeKm5qaoqysjEgkwsGDB9m5cydnzpy55DSgZLRaLVlZWTzwwAMsXrwYg8HA1NQUFy5coK+vD5/PN99NnFfueGEeGRnhww8/ZN26dTz66KOkpaWxdu1aSktLiUajZGRkIIoiBoMBo9GIz+fj+PHj7Nq1i6amJsWLsiAIFBcXs2bNGmpqatDpdHg8Hg4ePEhDQwOBQGBB7DCm4/P5mJiYoLOzk56eHg4cOMD4+Ph8N+u6SCHmQ0NDlJeX8+STT7J+/Xr0ej319fXU1tbKwUBHjx7lgw8+oK2tjZaWFk6fPp1QyX2sVisrVqxg5cqVZGZmEo1G6e3tZdeuXdf0cLpTuOOFWcou9+qrr7Jy5UrsdjtJSUlyVi64GA3ocrk4f/48TU1NfPjhh5w8eTIhopEEQSA7O5uSkhIsFgs+n4/z589z+PBh2tvbFb+w3CgGg4Hk5GQcDge7d++msbGRM2fOXPXyTEnEYjE6Ozv54IMPgIsBF1/60pewWq309/dz8OBBfD6fHKX53nvv0dnZqSh3zJliMpkoKyuT82cPDAzQ0NBAQ0MDU1NTCdefueaOF2a4mFf52LFjHDhwALfbjdFolL0N4vG4fMQ6fPgwH3zwAePj4wlzwSJlxBMEAb/fT29vLzt37qSzs3O+mzbnSAVlFy9ejM1m4+zZsxw/fjyhjsXhcFjOO11UVMRf/uVfkpaWxr59+zh06BATExOEw2H8fj9OpzOhdsnTkULhY7EY4+PjHD16lL179ya8y+ZcoQrzb3A6nfz1X/+1nMt3uhtYLBYjHA4TDAYJBAIJtZrHYjG8Xi/Dw8N0dHRw/Phx9u7dS2tr64KrmSYIAsnJyUQiEQKBgOwJk2h9DIVChEIhWlpaePbZZxEEgUAgQDAYJBaLyR5DifQeXk44HJYv2aempvj44485fPgwY2NjC+4UNxtUYf4N8Xj8lkVJ6XQ6dDrdLXn21ZB8OePxOI2NjQwPD8vhvaOjo9d08UtUJBfGL33pS5jNZl577bWEtqFHIhEmJibmuxlzxvQczVNTU7z99tscPHiQSCSCw+HA4XAk7AlgrlGF+TYQjUZv+2XGdDGanJz8TDL12aD0BOQajQa/38/U1BQ6ne4S9zKV+efydAeDg4M3nWBJ6e/kbFGFeQ7R6XREo9HPHDHn43LmVrysSp4EUlHZ2tpa2traCAQCC9IVMJG5/N2Zi3dp+l3QQmJBC7MgCFRVVZGWlnZbfpfJZCIcDl/RXzYnJ2fOf2daWhqLFy9GFMXbsiO/Wqir0Wicc1ON1LeZtker1ZKWlsaOHTs4efIkPT09GAwGKisrgU+TVN2oUN+Kvmk0GqqqqrDb7XP63NmQl5c3p8+bPuduhx1cSndwpd+Tm5t7S3/3rUQTX2hLzWUopXu3KqfEQu7fbEtmzfXfRCl9uxWofVMmC16YVVRUVBKN+U97pqKioqJyCaowq6ioqCgMVZhVVFRUFIYqzCoqKioKY0G7y8Xjcdrb2+c1mkjyEkhLSyMrK2tOn+10Om9rafqrodFoKC8vn9NiAA6Hg+Hh4Tl73myQQvPLysrmtG/xeJyOjg5FhB6npKTMqSvnXPdNGoMrucNdzwMnNTWV7OzsOWnHbSe+gPF6vfG8vLy4IAi35Z8oinFRFC/5b1qtNi6KYvw73/nOnPfv+eefv219u9Y/s9kc7+3tndO+/fM///Nt7cPl4yaKYlyr1catVmt8YGBgTvsWCATixcXF8z5ugiDEv/nNb85p3/x+f7ywsHBOxkAQhLhOp4tbrda4Vqv9zGf1ev1n/vv0f88+++yc9u12sqB3zDD/Fapv5e+OKySRza1og1L6dqsCd+b7vZSIz7G3rDRuc9W3WCxGNBq94q5ZGpurRf/Ndd9uJ6qN+TokspP6nY46dspmpuNzPaGPT8vBsVBQhfk6LLQBv5NQx27hcKeN5YI3ZaioLBQ0Gg1arRar1Up1dTVmsxm4mL95cnJSTp3p8/kUKWR6vZ5YLCZfxiuxjUrhjhTm2eZT0Gg0cv0/i8WCwWBAEARCoRCjo6N3fJ2y28Fsx04QBHQ6HSaTCYvFglarJR6P43K5EqJEmEajISUlhcLCQiorK/mv//W/UlxcTCwWw+VycfbsWY4dO8aHH35IS0uL4vqk0WiwWq2EQqFLynxJhSmm25KlpFwztVMvxAxzd6Qwz1aUjUYjWVlZ1NXVcf/991NTU4PZbKa1tZU/+ZM/YWRkZEG9HEpkNn9fURRJTk6mrKyM9evXc88995CdnU0kEuG5557jl7/8pSJc166GTqcjOTmZhx56iD/8wz8kKyuLzMxMOd90SkoK2dnZrFq1ivz8fF5//XWOHz+umDqHkthOr+UnCAJms5mioiK5Qvbk5CRms5m0tDTGx8cZGxu7ZqEDKWOgVApOqSeF2XBHCvNs0el05ObmsmXLFtavX09ubi4+n4/m5mb1oknh2Gw2lixZwj333MPy5csxGo309PQowjPiWphMJtatW8eOHTvYunUrNTU1l5gEJCEyGAxkZWXx0EMPUVNTw65du/j+978/7/UOJVG+/AIvPT2dJ554gk2bNhGPxxkZGSEYDFJVVYXNZsPpdPLuu+/yxhtv4HQ6rzhO0gk1GAzKJykl5wy/EVRhniHSrquyspJVq1aRm5uLTqdjamqK/v5+Re+4boSFdiyUTjqFhYUsW7aM2tpa7HY7gUCAwcFBXC6XYvuq0WiwWCxUVVWxbt06ysvL5RJoO3fupKenh1AohFarxWazUVJSQkVFBVVVVQQCAVpbWzl48CCTk5Pz1kfJLi6JJ1y0Nefm5rJx40Y2bNiAKIo4HA7C4TBlZWWYTCYCgQBms5lQKMSHH37I0NDQVQPFppsQlTqWN4oqzDPEarWyaNEiNm7cSGlpKVarFbfbTX9/P83NzQSDQcW/FFqtVq4ibbFYiEaj+P1+hoeH6e3tBS4m9E9JSSEQCJCUlIROp6Ozs5O+vj7F9+9KiKJIRkYGK1eupK6ujszMTABcLhfNzc2MjIwodtdstVpZt24dq1evJjMzk2g0isPhoKGhgeeff57z588TCATQ6XSkpqZSW1vLmjVrWLRoEVarlUcffRSbzca+ffsYGRmZlz5IkXvTT5SCIBCJROju7qaqqgpRFOnv72dqagpBECgsLJTHrby8nBMnTsxb++cLVZhngEajITs7m3Xr1rFlyxaSk5MBGBwc5JNPPuHQoUP4/f75beRVEEURs9lMUlISWVlZLFmyhD/6oz8iLy8Pj8fDhQsXePPNN3nrrbew2Wzcd999LF68mPHxcdasWYPdbue1117jl7/8JSMjI4rt59UwGAzU1taybds22QwwNTXF+fPn+fDDD+nq6lKkMGs0GnJzc/mjP/ojVq5cicFgwOl00tDQwL/8y7/Q1NSEx+MhHo8TDofx+/2MjIxw5MgRCgsLue+++3jmmWfYuHEjU1NT7Nu3b17GTjK5TDc1hEIh2traeP7554lEIvh8Pj755BPGxsa455572Lx5M0lJSZw6dYp9+/Zdc7e8UFGFeQaIokhhYSErVqwgJSUFnU6Hx+OhoaGB999/n/7+fkW+OBqNhtTUVDZu3MgjjzzC3XffjclkwmazEQ6HGR0d5fz58+zdu5dgMMj69evJz88nEolgMpnIzc0lNTWVb3/729TV1fFP//RPXLhwAZfLlTAeKGazmZUrV1JYWIjFYiEejzM6Osrbb7/N2bNnFee9ICGKIjabjezsbCwWC+FwmHPnzvHzn/+cM2fOyKIsIQm09B6mp6eTm5uLKIqsXbuWjo4OWltbb3s/4vH4Jd4WUmRgOBxmZGSEf/3Xf5XFORKJ0NfXx5tvvklOTg5er5e+vj7cbvc1F8+FZn4DVZhnhF6vJy8vj0WLFmEwGAiHw5w6dYr9+/dz/vz5K9b4UwparZaqqirWrl1LQUEBfr+f5uZmXn/9dQ4fPszAwAD9/f2YTCaam5u5cOEC4XCYaDTKm2++SX5+Pl/4whcwGo3k5ubyW7/1W3zve9+bl0l+o0jiVlVVRXp6OoIgMDw8zEcffcThw4cZHx9X7AITj8cJBAKMjIxQXFyMwWAgEokwMTFBKBS6qghJNmjJfXNycpKRkZF5uwTUaDQIgiC7v01vdyQSYXR0VP5vGo2GYDBIOBzGZrMRjUYJBALXPdFIphKlhPHPBaowXwdRFKmurmbFihXk5+cjCAIOh4MPPviAo0ePMjU1pdiVWqvVYrFYKCgoIDc3l97eXn7wgx/Q1tbGqVOn6Ovrk3czgiAwODgoH4tjsRitra3Y7XYGBwcxm814vV7WrFlDSkrKfHdtRiQnJ7N27Vq58GkwGKS1tZWdO3fS39+v6AU1FovhcDjYu3cvBQUF5OfnY7VaycvLk23LV3vvTCYTVqsVjUaDXq8nOzsbm812m3vwKZcL8vT/Pp3U1FQ2bNhAYWEheXl5NDQ00NjYeN3nS89W6jycDaowXwetVktdXR11dXUkJSXh9Xr55JNP+Oijj+jv71fsy2C329m6dSsVFRWUl5fT0dHBm2++yb/9278xOTl5yWcl53+tVovP5yMYDMqTaXJykg8//BCAsrIyent7ueuuu3A4HLS1td3yftyM+1NGRgYbN24kLy8PrVZLa2srH3/8McePH1e8rTwej+NwONi9ezd2u521a9fi8/koLCxk/fr1dHd309vbK5tipMVV+E2V6pUrV8r+vVLwxnz141rjp9FoMJvNFBcXs379enbs2EFWVhZTU1N0dnbKVbCv9zum/9+FwB0hzIIgzCrRiVarJT8/n2XLllFUVEQ8HmdoaIhXX32VlpYWAoHAdZ8xH36VgiBQUFDAf//v/51FixbR1NTEiy++yEsvvUQwGPzM50VRlHfBsViMQCBwRZu5x+Ph/PnzfO1rX2NsbEzRwpyUlERFRQXLly/HbDbj9/s5cuQIu3btYmJiYkZH3vn2TQ8EApw7d47R0VHOnTtHUVER0WiUJ598ksHBQfbt20dnZ6csvOnp6RiNRu699142bdqEyWTC7XZz+vRpBgYGbkubp3thXGvcBEFAFEVycnIoLCxk3bp1bN26lbKyMrxeLx0dHYyPj894DBaSKMMdIsyzsTtpNBrS0tL48pe/zLZt28jOzsbr9dLe3k5DQwMOh2NGz5mPF8Zut1NTU0NaWhrxeJzXX3+dn/70p1cUZYBwOMyFCxeAa2fyMhqN1NTUkJSUhCiKt6z905nN2Ol0OtauXctXvvIVFi9ejNFopKOjg9OnT9PU1DTji1olTPZoNMro6CjvvfceNpuNwsJCVq5cySOPPMLy5csZHR0lGAwyMTHB3XffTUpKCikpKVgsFjweD/39/Xg8ntu2Y5Y2QNcTZZ1Oh91u5+mnn6a8vJyenh5eeOEFiouLaWtro7m5mdHRUUWbm24lC16YZ7vrMZlMlJeXs3XrVgoKCmQb7N69e2e845ovli1bxn/7b/+N5ORkjh07RmNj43XDc68nVoIgYDQaMRgMhEKhW+6FMpNd19XIyspizZo1bNiwAbPZTDAY5JNPPqGxsfGqi5OSiUajOJ1OJicnmZiY4J/+6Z/4L//lv7BmzRpWrFgh75hTUlIQBAGNRsP4+Dh79+7lBz/4AefOnVNUv6cLd2dnJ83NzbS3tzMyMoLBYJDNaYDcHyUskreTBS/Msx3Q1NRU7rnnHkpKSjCZTPj9fjo7O/noo4/wer1z3Mq5xW63U1ZWJrtQzQU5OTksW7aM7u5uUlNTb/kt/2xz7IqiSF1dHWvWrCE9PV2+RDt8+DBtbW2KXlCvhXSScbvdnDlzhhdeeIGMjAy2bNlySQBHPB4nGAzS19fHRx99pDiXQKmt0WgUj8fDkSNHCAQCuN1uQqGQfN+xfPlyUlJScDqdDA8PMzAwsGCia2fCghfm2aDRaLDb7SxbtgybzUYkEqGtrY2PP/6Yzs5OxR+v4vG4nEfh3LlzN2VfFASBpUuXcu+997Jq1Srcbje7d+9WrLucIAiUl5dTUlKCIAhMTk5y8OBBTp06xcTExHw3b06IRCKMjY3JF5jSjlJayCKRCIODg5w+ffqa9yCXR+TdDqQ2xmIx/H7/Z95NURTJy8tjyZIlaLVazGYzJSUlDA0NodFoGB0dxe/3k5KS8pmTwPTMdIm+w1aF+QrodDq5SKUoikxOTnL48GH27Nmj+Nt8uJif1+12Y7PZaGtrm3XBVq1Wy7Jly/jt3/5ttm/fTjgcZv/+/fzqV79SpDBLN/xZWVkkJycTCoXo6+vjjTfeoLu7W7E+yzeKVqslJyeH4eFhDh8+LOdoLiwsxGw2y5fdlwehXM58XW5ez/5sMBhwuVxMTk4yNTXF8uXLSU5OJikpSfbJNplMXLhwQfbplnJymEwmvF5vwo+1KsyXIQgCWVlZLF26lNLSUkRRpLOzk5MnT9LR0ZEQAz45OUlLSwtr167lscceo6+vjz179tzwMd5ut/NXf/VXbN26ld7eXl555RVeeuklxeaeNhgMVFdXU1NTQ3p6Oi6XizNnznDy5EmcTud8N++GEQQBk8kk2/OliLlQKMTRo0e5cOECer0eq9XK+vXrefbZZykuLsZsNpOfn8/ixYvp6+u7qgngav7F80k4HKapqYn29na5fS0tLZjNZsxmM6tWrSIpKYlDhw7Jfx9RFOVFRvLAUeL7eSOowjwNQRBISUlhx44dPPvss6SmpjI1NcXu3bs5fPjwjNzjLmc+Li5aW1t54YUXWLx4MevXr+fIkSOcOnWK4eFh4FNXpSv5t4qiKPuOpqamotfr8Xq9NDQ08NOf/lSxSX8MBgOlpaX82Z/9GRs3bkQQBDo7O/nFL37ByMjIDU/U+XaV02g0JCUlUVdXx8jIiOxj3tPTQyQSwePx4PP50Gg0FBQUkJqaKvuiA9eNmpPGX2lI9yLTFxPpBCgIAm63m5SUFLRaLbm5uUQiESorK4nH45w6dUoxOahvFlWYf4MgCNhsNjZv3sz9999PcXExAH19fZw9e5b+/n5FvshXYmpqira2NoLBIFlZWXzxi18kGAzy3HPPAbBixQo2bNhAR0cHb7zxhixaer1e9iXds2cPTqeTf/mXf8FisdDR0cHAwIAi/wZ6vZ6ioiKeeOIJVq5cSVJSEhMTE7S2tnL+/PlZXVRKx+P5Ii8vj+3bt3PvvffS1tZGY2Mj586dk9smhR9rtVrS09NZtWoVFotF/r6Uo+JqxGIxRFG8bW6PN4PUl2g0itFoJC8vj+TkZCwWC0ePHiUrKwuTycSJEyfwer2KzFtzo6jC/Bu0Wi3FxcVs3ryZ5cuXE4/HZfvkbCc3zI8vbCwWY3BwkO9///vcc8891NTU8Pjjj8v2x/LycsrLy+nu7gZg9+7deL1eYrEYTqeToaEhvF4vLpeLjz76iEgkotgLT41GQ3JyMsuWLeO+++4jNTUVt9vNJ598ws6dOxkbG5v1sXY+j/mFhYU89thjLF++nOrqaux2OxMTE7K/uSAI2O12iouL2bJlC0uWLMFgMBAMBmloaOBXv/oVXV1dVxWpRMwrodFo5Nwn+fn5nD17lsnJSc6dO4fBYGBycpJwOKw488xsUIWZT1/y9evXs3LlSrKysggEArS0tLBr1y56enoSbrDHx8f53ve+R3NzMxs3bqSgoIAlS5aQmpqK3W7H4XAQCAQoKSlBp9MBF2/zjx07xvHjx+UJrXQXJZ1OR3FxMXfddRfl5eUYDAa6u7s5dOgQ+/fvn5X5SQlEIhECgQBWq5WsrCzgYuSl3+8nHA6TnJxMZmYmy5Yt46677iInJwdBEGhsbORnP/sZv/zlL5mamrrurllpttjpkYM2mw2TyYTL5ZL/m9VqxW63YzKZOH/+PC6Xi+PHjwModvMwG1Rh5qJ9sri4mPvvv59Fixah0WiYnJykqakJh8ORsEejQCDA7t272bt3r1xjraKigtraWoaHhzl//jyDg4P4/f5L3JgSZSclRWeuXr2abdu2YTabiUajdHV10dXVdUm/Eo0zZ87wj//4j5SVlVFbW0ttbS02m41ly5YRCoUoLy/HaDRit9tJT09Ho9EwMTHBK6+8wp49exRdmeVqSEmXMjIy5HQIqamptLW1yalB9Xo9Y2NjNDU10dPTkxAFKmaDKsxczKuwfv16ysvLsdls+Hw+zp8/zyuvvMLQ0JDidhU3wvSLFK/Xy+TkJGfPnpXNE4m66MDFC6yKigpWrVpFTk6O7Of6zjvv8PHHHyfsbhku1rG7cOECP/rRj/j2t79NSUkJhYWFZGdnE4vFMBgMwKc+61LCo3379jEwMKBYsdLpdBgMBnQ63SV5WURRxGg0kpmZyTPPPIPFYmFoaAiHw8GqVasYGRmhsbGRxsZGOjo65GrbSu3nzaIKMxdNGUlJSRgMBjweD01NTezfvx+n05nQonw5UlSYksJzbxaz2SyntBweHmbfvn20tbVdMy1mouB0OtmzZw8PPfSQnKBIMjsBuN1uenp6aGho4IMPPqC5uZnu7m7Fmp80Gg0bN27kc5/7nFy/8Be/+AXHjx9Hp9NRV1fH8uXLKS8vx+fzkZ6ezsTEBIcOHaKlpUU+vUqpdhPlZDcbVGHmU/9Qn8+H0+nkyJEjfPzxxwtOmBcaUtmiYDCIw+Hg3LlzvPfee7IwJzqRSISenh6+//3v884776DT6S4JvQ4EAoyPj9PV1UVLS4viAyvi8ThjY2OEw2E2bNiA0WjEbDazZcsWdDodWVlZGI1GwuEw7e3ttLW10dvbS2trK8PDw4pdcG4FqjADPp+Pc+fO8fbbb+N2uzl69CjNzc14PJ4FvSonOvF4nMHBQQ4cOMDAwADNzc0cO3aMwcHBBXMRFAgEeOutt4DP+lZPz0M8m9OB5M9+O+nu7mbv3r3k5uZSWlqKw+GQvYUGBgYYGRnB7XZz9uxZzp49e0m9P7vdLhdyWOjcscI8PUez2+3mgw8+YN++fbLNLpFtrwsdQRDkqLDW1lYuXLggJ8aJRCILbjG9Vf2ZHkx0uzAYDJw/f56/+qu/YvXq1TQ0NMhJwaSIPclvWcr3AhfHXEqe5ff7F3zGuTtWmKe/7JLt9WaZbUJ+lRtj+tjNxSIquWItNEGXuJqITS/eertwu914PB653t/0u4ArtVFKTBSJROSKQYIgoNVqF8yp6ErcEcJ8u1ZXVZTnntsxdgt9zIxGI5FI5Io22tvdd6kN8Xh8RkFb0yMYpy8iC93evKCFWa/X893vfle2Sd3ql/BaIlJbWzvnv++ee+7hpZdemvPn3ijTS1PNFdu3b+ell166bYvq1X6PVqslOTl5Tn+XTqfj7//+729LXgcp69rVgkmqqqrm9Pfp9Xr+4R/+4Yo5y2cyltf6zI2+CzU1NTP+rNLQxBf6dkFFRUUlwbi9ln8VFRUVleuiCrOKioqKwlCFWUVFRUVhqMKsoqKiojAWtFdGPB7nwoULiggWSU1NJTMzc06f6XQ6GRkZmdNnzgaNRsOiRYvk6hlzwVz3bXoo841+b677Fo/H6ezsVITLV3JyMtnZ2XP2PCXNuZSUFDllasIRX8B4vd54fn5+XBCEef/3J3/yJ3Pev+eff37e+yUIQtxiscR7e3vntG/f+973PvN7RFGMi6J41XaIohjXarWf+YwoinGLxRI3mUyX/EwUxbhOp4vr9Xr52Zd/12q1xgcGBua0b4FAIF5cXDzv4yYIQvyb3/zmnPbN7/fHCwsL571fgiDEn3322Tnt2+1kQe+Y4WLtMyVEdN2KNsQVkmHrViTOmeu80FJ9POnZEtPbfiU/2VuVFEgpea8v7+9cPG+h9u12suCFWSUxmWm9vZmaKOJqVKZikRIpKTkz3u1GFWaVKyL8puJJcXExbreb0dHRq4b1zidzKbaqcKsoBVWYr4AUxmqz2aioqJCrDweDQVwuF06nE6fTmdCli66G2WzGbreTnJxMdXU169evZ2xsjCNHjnDmzJlLMn7dSq72O4TfJLBZKEU3VT67UzaZTKSlpZGZmUlSUpKcAS8ajdLd3U1vb68iTCW3ElWYL0MQBFJSUigoKKCqqoo///M/p6CggHg8jsPh4OzZsxw9epT9+/fT2tqK2+2e7ybfNKIokp2djdFopLCwkKqqKmpqanjggQfIzMwkFArx2muvcebMmXkXQ71ej81mw+FwyBNaEuv4bxLexONxRFFEo9Hc0A5f+E06UZXbj9FoxGq1YjQaycvLY8OGDWzbto26ujqMRiNw8Z7gRz/6ET//+c9xOByMj48v2PFShXkaOp2O5ORkHnnkEX7/93+fzMxMsrOz5XI+KSkp5OTksGrVKvLy8nj99dc5derUFRO2JAqCIFBUVMQ///M/U1FRIddjg4siCDAxMaGIChKiKBKNRi8RZb1eT3p6OtnZ2XIqSbjoBqbVaunv78ftdl/VfintxiTTjVSFWqloNBp5IZLaLtnPo9GovDAlEqIosmLFCh5//HHy8vJIT0/HYDCQlZVFSkoKJpMJuDj/vvnNb/Lggw9y5MgR/s//+T8MDAwsSHFWhfk3mM1m1q5dy4MPPsg999xDbW2tXDByuk+m0WgkJyeHRx55hMWLF/PrX/+aF198MWGrKpSUlPD7v//7rF27loyMDAC6urp49913CYVCbNmyBVEU5cT084V0QTTdlCKKIvX19ezYsYOysjLGx8cZGhoiLy+PvLw8ANrb2/nBD35AR0fHFctNxWIxOcm+3+8nEokoNgm7ZF4rLS1l1apVZGVlodVq8fl8jI2N0draKld2TxREUSQvL4/6+noef/xx7HY7er2eSCSCVqtFp9NdcnGbmpqKzWYjKSkJt9vND3/4Qzo7OxU5XjeDKsxc3C1ZLBYWL15MfX29XCjS4XCwc+dOent75RfFZrNRUlJCRUUFNTU1+Hw+Ojo6OHToEG63O6FekMzMTOrr63nooYcuSW05MjLCrl278Hq9lJWVsXjxYrKystDpdPNWyHX64iB5YtjtdpYsWcLdd99NSUkJU1NTjI2NkZubKwcWVFVV4XK5eOedd2hubmZqauozz5bGTMk7ZZvNRmVlJfX19Sxbtozq6mpSUlIQRZFAIIDb7aarq4u33nqLAwcOMDQ0NN9Nvi46nY7ly5ezfft2duzYQUFBgRzIEwwGCYfDTExM0N/fT39/vxyklZ6eTm5uLg8//DCTk5O8/vrrtLW1zXNv5hZVmAGr1cq6detYsWIFaWlpRKNRJiYmaGho4Pnnn6epqYlgMIhOpyMtLY3a2lrWrVtHaWkpZrOZxx57DJvNxr59+xgfH5/v7swIjUZDbW0tDz/8MCUlJZdUX4aLFy1dXV20tbVRU1PD4sWLufvuu9m3b9+8nA6kI7xUbUS6oJUmbmpqqlyY1OfzEY/HSU1NxWAwUFVVxYkTJ+jq6rqiMCsdvV5PeXk5Dz74IF/4whcoLS3FYDDIP5cWqmXLluH1eunu7k4IYTaZTNxzzz08/fTT1NTUXFLmKhaL0d3dzfHjxzl9+jRtbW0UFBRQX19PfX09ycnJZGVlUVxcjN1un8de3BrueGHWaDTk5+fz7LPPsnz5cvR6PU6nk6NHj/Lcc8/R1NSEx+MhHo8TCoXw+XwMDQ1x6NAhiouLuf/++/niF7/Ixo0bcblcfPzxxwlRoVmn07F06VIeeOAB+eg4Pj6O1+slEomwfv16Ojo6OHjwIPn5+WzYsIE/+7M/4/Tp0wwMDNz29ko24un+zQ6Hg7feeovJyUnuu+8+GhoaOHbsGOXl5WzdupUlS5bg8XjYuXMn586dS0hRFkWRwsJCtm7dyrZt2yguLkYQBLloaTgcxmq1kp2djUajkY/6iYBer6ekpIS0tLRL7OUej4exsTF2797Nj370I/r6+ohEIiQlJZGVlcWWLVsIh8OMj4/zxhtvcP78+XnuydxzxwuzVqvFbreTnZ2N1WolGAxy5swZXn75Zc6cOYPX673EPBGPxy+plZaRkUFubi4Aa9eupbOzk46Ojnnpy40i7UBDoRC9vb18//vf58SJE2RnZ7NkyRLMZjPHjh3D6XTS0tJCTk7OvC060Wj0kl2zFF3m9/v5+OOPOXHiBF6vF5/PR29vL8eOHSMtLY2UlBS6urqYmJi4bo04pXllaDQa7HY7v/Vbv8WOHTsoKioiFArR0tLCj3/8YxoaGpiYmGD58uX8j//xP8jJyUGv13/m9KNUgsEgvb29DA8Pyxd8Pp+PAwcO8NZbb3HixAnGx8florEWi4XU1FQsFgtTU1N0dXURi8XQ6XQJe8dzNe54YY7H4wQCAUZGRiguLkav18urcSgUuqrNOB6P43Q6GRsbIxqNMjk5ydjYWMK8IJFIhKmpKUZHRwmFQrz88st8/PHHpKamsnHjRurr6ykrK+Pv/u7vOHnyJF1dXfKEmA+m+7LCp3bhWCyGx+P5jH0/EAjIO8i2trYZ+T1LhT/n+6JTwmQysXTpUu6++27Ky8txOp0cPHiQn/3sZ5w5c4aJiQnC4TDJyckMDw+TnZ1NZWUlBQUF8930GeHz+fj5z3/O8ePHSUpKIhgM4vV6GR4eZmhoCLfbTUpKCmvWrGHdunUUFBRQV1eHVqtlYGCAPXv2MDw8rKjFdK6444U5Go0yPj7Ohx9+SGFhIdnZ2dhsNnJzc2Xb8tUmqcFgwGw2o9Fo0Ov1ZGVlYbVab3MPZkcsFqOzs5OdO3cSDAZ5//336e/vZ+nSpaxbt46ysjKys7P51a9+RUdHB729vcD8RMdJXhJXCqu+/L8JgsDSpUupra0lNzcXo9HIqVOnZvR7rvY75gOdTkdpaSm/8zu/Q3V1NQaDgdbWVn7xi19w8OBB2bwGn+bd0Gq18uWYwWCYt4vamSIFjIyMjMiVsMPh8CWujdFoFJPJxIYNG6isrCQ5OZlAIEBPTw+HDx+mr68vIUyHN8odL8zxeJzx8XHeffdd7HY7q1evxufzUVRUxNq1a+nt7aW3t1cOJJGO04IgUFtby4oVKzAajfIuK5Hi/VtbW3n55ZeJxWK0tbURDAbx+/2Mj4+TmZmJyWRi5cqVtLa20tjYeFsF6/JcGdfbFWm1WlJTU1m6dCk7duxg2bJl6HQ62tvbL7kouxZKEWW46DGzefNmHnjgAVJSUmhsbGTPnj0cOnToElGGi7balJQUtFotkUgEQRBk98JE4FqnzEgkQigUIjk5Wb7M7e/v5/Dhw1y4cAGXy6WYMZtL7nhhhovH3rNnzzI8PMzWrVvJy8tDo9HwxS9+kf7+fvbu3UtPT4/sQ5uWlobRaOS+++6jvr4eo9GI0+nk5MmTCXEbLjE4OIjL5SInJ4f8/HyMRiMOh4Nf/epX9Pf3s2TJEpYtW8bx48dpamq67Tl2p++Ur/ZzjUaDzWYjPz+fiooKHnzwQVasWIHVamVgYIC+vj7ZPn09lDTBi4uL2bFjBzabjWg0yrvvvssbb7yB0+m8pJ0mk4nMzEwyMzMRRZFQKEQgEFBEPuS5wGq1smjRIjlgCKCnp4d3332XyclJRY3ZXKIK82+IRCKMjIywe/duLBYLJSUlrFy5kscff5zly5czPj6O3+/H6XSyefNmkpKSSEtLw2Kx4Ha7GRgYwOv1KsLeJQna9QIlBEGgpKSEP/zDP2Tz5s1Eo1Fee+01+bj80EMPUVBQQDAYlD03bhcz2b1qtVq0Wi11dXV84QtfQKfT8cEHH3D8+HEikQiNjY10dHQoZlxuBJvNRlFRETqdjnA4jNPpxOPxYLPZ5EtKQRCorKzkrrvuwm63o9VqMRqNcoRkomOxWFi1ahV/+Id/SF5eHqIo4vf7GR0dZXh4eEH08WqowjwNyX/Z5XLhcDj47ne/yx//8R+zatUqVq1aJed2TklJkc0Zo6OjfPDBB/zwhz+ksbFxXu16oihiNBoRBIFQKITRaCQYDBIKhT4jTIIgkJOTQ11dHaWlpUSjUfr6+mhra6Onp4doNMrLL79MTk4Oo6OjirRXSuLkdrtpaGigs7OT9vZ2WdR9Ph/hcFgeq0SayMFgkKmpKaLRKKIosnz5cuLxOBkZGeTk5OBwODAYDBQVFVFVVYXFYkGj0eD1eheEzdVkMvGFL3yB73znO+Tn56PRaAgEArz11lu88MILOByOhFtsbwRVmC9DukgJh8OcPn2aF154gczMTDZt2iQfneHiji4YDNLT08P+/fs5ffo0Ho9n3tptMBj40pe+RDgcZmRkhPr6eqxWKwaDgQMHDrB//34cDgcajQar1YrNZmPDhg2sWLGCN998U/YqaWtrIxAIEI/HGRwclHebShM26TQQCoXo7u5mbGwMh8MhB5fodDpycnKora0lEAgwNjbGwMAATqdzvps+I/r6+ti9eze5ublkZmaybt06qqursdls2O12Of+H3W4nLS0NURQJh8McPHiQxsbGhBYtg8HAjh07ePrpp6mtrcXn8/H+++9z9uxZPvroI06cOHFd18dERxXmayAlxfH7/bIoTz9iRyIRBgYGOHPmzDV3lDNN+j4X1NXVkZeXx8aNGzGZTFgsFmpra1m0aBEXLlxgZGSE1NRUKisr2bBhA0ajkffff5/Dhw9/xuXMaDSydetWdDodTqcTr9fLiRMnFOMSKC2iDofjM/kh7HY7ixYtYunSpXR3d5OdnU1xcTEulwuA7u5uMjMzGR0dZWBg4JJ+KyFx+8DAAG+99RYWi4UHHniAvLw8kpKScLlctLS0MDExgVarZdGiRaSnp8sbhSNHjtDc3JyQtldBEEhLS+Ohhx7iySefZM2aNQQCAU6fPs3Pf/5zGhoaZPfOhY4qzNdAq9WSk5PD8PAwn3zyiezkXlhYiNlslm19lwehXI7kyXEriUajNDU1sW3bNp544olLbuU3btzI4sWL6enpoampieTkZJYvX05ubi79/f3cc889DA8P09raSjAYJCkpiaKiIgoLC/njP/5j9Ho9AwMDjIyM0NXVxdDQ0LzvyK4nPHq9HlEU6e/vp7W1lcrKSoqLi+XEOBkZGWRmZnLixAmGhoYuSSEqednM5wLk9/s5d+4czz//PG63m5qaGmKxGH19fZw5c4bBwUFyc3N5/PHHKSsrk/3q+/r6FH8qMBqNpKenk5qaytTUFC6XC4vFQllZGRUVFfzFX/wFRUVFxONxWlpaePXVVzl06NCCTvN5Oaowc3Eymkwm2etCyigXCoU4cuQIHR0dGAwGrFYrGzZs4Nlnn8VisWAymSgoKKCmpkZOdHQlbkcNNIPBwDPPPMPWrVs/4yoViURkv9ji4mLZnCGKIgUFBXz1q19FEAR+8pOfMDIywtq1a/nmN79JXV0dKSkphMNhcnNzOX36NEaj8baeAGbL6OgoDocDrVZLKBSio6MDk8mE2WwmOzubDRs2cPbsWUZGRjCbzUSjUfmC02QyEQ6H5/1kEIlE6O3t5R//8R8v2cVLGfAqKirYuHGjHI3a2dmJx+O55ZuAm0Gv11NWVsZDDz1EfX09LS0tHD58mMrKSr7+9a+Tnp4up18dGxvj4MGDvPLKKwvepnw5d7wwazQakpKSqKurY2hoCL1ej9frlS/APB4PPp8PQRAoLCyUQ0KnTxTJJnslpEiyW43f7+df//VfMRgMfP3rX79kchqNRvR6PfF4HK/Xy5EjR1i0aBFlZWVyYYC77rqL4eFhvF4vGzduZM2aNXI0Vjwex+VycfLkSZxOp6JszVdDyk0smZgikQg+nw+n08n4+Djj4+MYjUaSkpKw2WwYjUaWLFnCqVOn5NzTSliApBPZ5UgXvUajUTZjDAwMEAwGFSvMVquV2tpaHn30Ub74xS+SmprKmjVrePzxx2W3P0EQ8Pv9NDU18dprr/H6669f4qssXeROD8tPRLPN9bjjhTk/P59t27axbds2OZCiqanpEnvy9KiqlStXYjab5e9fz60rFoshiuItd/iPxWL09vYyMjJySRUP+NSU0t3dzXPPPcfJkyfZsmULjz32GLW1tUQiEfx+P1u3bsVqtZKRkUEsFmNoaIh3332XxsZGent7aW1tTchEQMAlYyllCczIyCA5OZnx8XFGRkaora2ltbWVQCBwzcVWKWi1WkRRlC9Be3t7cTgcivRhNplMfOc732Hz5s2UlJRQUFCAIAjYbDYyMzMv+axeryc7O1tOlA/IHiqVlZWsXr2atLQ0Tp48yYkTJxgZGVFkn2+GO16YS0pKePzxx6mrq5NvvcfGxujs7AQurtBJSUmUlJRw9913s3jxYjnc9dixY7z++ut0dXVd9cWQxOB2TPJIJMLhw4f57ne/S3JyMk899RRJSUlEo1EaGxv52c9+xssvv8z4+DhOp5PJyUmWLFmCz+ejvb0do9GIyWSSd4qBQIAPP/yQtrY2JicnFZ2v+EbQ6XTk5uayYsUKnE6nbDc/duwYw8PD+P3+K2azUxI6nY78/HwyMjIIBAKMjo7S1tamuBONRqMhPT2dL33pS3z5y1+moqLiut8RBIGsrCy2bdtGeno6ExMTjI+P4/F4qKmpYd26dSQlJbF8+XLC4TCHDx9mcnLyNvTm9nHHC7NkS7bZbGRlZRGPx+V0iuFwmJSUFDIzM6mrq+Puu+8mJycHgPPnz/OTn/yE119/XV7Nr8btsDFLfdm/fz+HDh2S83YsXboUh8PBG2+8wb//+7/L4bxNTU2Mjo6yZ88eQqGQHI4tia+0E5uamkpIQZ5egiktLQ2v10soFEKj0WAymbBaraSlpdHe3k5HRwcTExNy3pDpY6VUYdZqtRgMBqLRKP39/Zw4cYK+vj7FFQgWRZGcnByefvppCgsLr/nZyclJzp49i8/nIysri/z8fIqKimhra6OtrY3k5GQWL15MUVGRnKDq7bfflkugLSTueGE+ceIE3/3udykrK6O6upolS5Zgt9tZuXIl4XCY8vJyDAYDycnJpKWlodFoGB8f52c/+xnvv/++4mL1pWO43+/nL//yL9m+fTudnZ0cPXoUn88nfy4cDjM6Oion9p9eumn67l9JfZspoihit9tJSkrCaDRSW1tLX1/fZ475x44d49SpU3INwStd9in1wikUCtHQ0EB6ejrj4+Ps3bsXl8ulOJur9F41NzdTUlIiF1YNhUKMjIzIC2EsFqOlpYW//du/ZXx8nE2bNvHAAw+QmprKm2++yf79+3n00Ufl0GytVsvk5CROp3NBus/d8cIcDAZpb2/nRz/6Ed/+9rcpLCykuLiY3NxcYrHYJQlwpGTyu3fv5qOPPvqM/6uSiEQi9Pf38+qrr8r5E6YTj8cvEanpgTNKR6PRyBeaUiVs6ZJSyvi3ceNG7r77blwuF/39/VitVjmHb0dHB3v37gUu2i4T0T4Zi8VwOp00NTXh9/sZHBxkdHQUn8+nqMUkFAoxNDREQ0MDNTU1mEwmBEGgtbWV//W//pdcizEWixEIBBgfHycajfLmm2/ywQcfIIoibrcbr9fLiy++yP79+1m0aBF2u13OMqfUU83NcMcLM1yshLFnzx4efvhhOYOVdDySjvM9PT0cO3aMffv20dzcfE27slKIxWIzvqxLBEGWSEtL4ytf+QqrVq0iNTWV8+fP89prr9Hf3091dTXLly+ntraW1NRUJiYmyM/P5/Tp0zQ3N9Pd3Y3P55N3x0oSsZmi1+ux2Wzk5ORQXl5OXl4eLS0t2Gw2XC6XokKy4/E4k5OTfPjhh4yOjrJ69WrC4TB79uyhsbERj8dzRTOg2+2WMzpKSHZm6T7EYrEQCoUUPw9ngyrMXDzWd3V18dxzz5Gfn49Op7tkBymlwuzo6KC1tTUhk+IsJILBIP39/Xzxi19k6dKlVFRUkJubi8vlIjs7m6SkJARBYGxsjPPnzzM0NERLSwudnZ1y5F+io9fryczMlPMTDw8PMzk5qcicJuFwmO7ubkZHR2ltbSUSidDW1jarOSSZ6qTYg9udXOt2oQrzb/D7/fzqV7+64rFI2k3ONl+v5HupMjf4fD6OHDnCzp07mZyclPN4pKenEwgE6O/vx+l0Mjw8zNGjR2lra5OP+DqdTq7EksiLq1arJSUlhampKTo7O+WKH0rsk1QlKBAIyFGJN3tCk/y7L0+sv1BQhXkat+qlvh1+zLcSpdmfpRpwzz//PB9//DGiKHLixAmMRqOcUW56BOf0iWsymUhPT5fD6JXSpxtBek+lAgfSxW4i9GWu27gQL/5AFeY55Wr5jyXXu0RFp9MhCIJibJfhcJixsTFCoRCHDx+WU0JeK/+05P7ndrvx+/1EIhG0Wi2xWCzhdlyRSITBwUF27dolX5olgiirzBxVmOcQKcdCIovwlVBKePJ0JDGaLqrXEqfpn5PGR8qNkohEo9ErhmqrLAwWtDDr9Xr+4R/+4RL/3VuFRqO55g5s8eLFc/47t2zZwosvvjjnz71RRFGUQ2fniq1bt161b9erzDKXaLVakpOT5/SZOp2O//f//t+85u+WqK6untPn6fV6/u7v/k4Ri0ZNTc18N2HWaOKJumVQUVFRWaCorgIqKioqCkMVZhUVFRWFoQqzioqKisJQhVlFRUVFYajCrKKioqIwVGFWUVFRURiqMKuoqKgoDFWYVVRUVBSGKswqKioqCkMVZhUVFRWFoQqzioqKisJQhVlFRUVFYajCrKKioqIwVGFWUVFRURiqMKuoqKgoDFWYVVRUVBSGKswqKioqCkMVZhUVFRWFoQqzioqKisJQhVlFRUVFYajCrKKioqIwVGFWUVFRURiqMKuoqKgoDFWYVVRUVBSGKswqKioqCkMVZhUVFRWFoQqzioqKisJQhVlFRUVFYajCrKKioqIwVGFWUVFRURiqMKuoqKgoDFWYVVRUVBSGKswqKioqCkMVZhUVFRWF8f8D1INbZj7laWIAAAAASUVORK5CYII=",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"import cv2\n",
"import matplotlib.animation as animation\n",
@@ -1890,25 +522,9 @@
},
{
"cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-09T13:36:54.707633Z",
- "start_time": "2023-02-09T13:36:54.053382Z"
- }
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"import mindspore as ms\n",
"\n",
diff --git a/Season2.step_into_llm/16.Practical-cases/rnn/mindspore_sentiment_analysis.ipynb b/Season2.step_into_llm/16.Practical-cases/rnn/mindspore_sentiment_analysis.ipynb
index 35e1b79..5bebb46 100644
--- a/Season2.step_into_llm/16.Practical-cases/rnn/mindspore_sentiment_analysis.ipynb
+++ b/Season2.step_into_llm/16.Practical-cases/rnn/mindspore_sentiment_analysis.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "ace41c03-dfa3-4cb6-88bc-bcaa72cfdc85",
+ "id": "0",
"metadata": {},
"source": [
"[](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.4.0/tutorials/zh_cn/nlp/mindspore_sentiment_analysis.ipynb) [](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.4.0/tutorials/zh_cn/nlp/mindspore_sentiment_analysis.py) [](https://gitee.com/mindspore/docs/blob/r2.4.0/tutorials/source_zh_cn/nlp/sentiment_analysis.ipynb)\n",
@@ -12,7 +12,7 @@
},
{
"cell_type": "markdown",
- "id": "1823275c-96a6-4c12-839c-623ac2662c6c",
+ "id": "1",
"metadata": {
"jp-MarkdownHeadingCollapsed": true,
"tags": []
@@ -35,7 +35,7 @@
},
{
"cell_type": "markdown",
- "id": "1bd4ac0d-886b-44a0-b794-ef7c4d867a3e",
+ "id": "2",
"metadata": {},
"source": [
"## 数据准备"
@@ -43,7 +43,7 @@
},
{
"cell_type": "markdown",
- "id": "9db27766-c50a-4142-b107-8f96c877c7db",
+ "id": "3",
"metadata": {
"tags": []
},
@@ -60,7 +60,7 @@
},
{
"cell_type": "markdown",
- "id": "a5a74272-f34c-485c-8785-c220c9e6bc01",
+ "id": "4",
"metadata": {},
"source": [
"### 数据下载模块\n",
@@ -72,8 +72,8 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "id": "572e506f-169a-4a07-95a8-40d89fc39104",
+ "execution_count": null,
+ "id": "5",
"metadata": {},
"outputs": [],
"source": [
@@ -118,7 +118,7 @@
},
{
"cell_type": "markdown",
- "id": "ba2340db-925a-4cc8-8d3d-545b13bda228",
+ "id": "6",
"metadata": {},
"source": [
"完成数据下载模块后，下载IMDB数据集进行测试(此处使用华为云的镜像用于提升下载速度)。下载过程及保存的路径如下："
@@ -126,28 +126,10 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "id": "bb0df701-e3b5-46e3-968d-9d44a2796eec",
+ "execution_count": null,
+ "id": "7",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 84125825/84125825 [00:05<00:00, 14175797.70B/s]\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'/home/ma-user/.mindspore_examples/aclImdb_v1.tar.gz'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"imdb_path = download('aclImdb_v1.tar.gz', 'https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/aclImdb_v1.tar.gz')\n",
"imdb_path"
@@ -155,7 +137,7 @@
},
{
"cell_type": "markdown",
- "id": "b146320f-206d-4ce3-92b8-19c71d23e3d9",
+ "id": "8",
"metadata": {},
"source": [
"### 加载IMDB数据集\n",
@@ -179,8 +161,8 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "id": "0f260663-5525-4831-ad5b-b24cdc2ee07b",
+ "execution_count": null,
+ "id": "9",
"metadata": {},
"outputs": [],
"source": [
@@ -229,7 +211,7 @@
},
{
"cell_type": "markdown",
- "id": "78e5d172-f64d-4bb7-8cf2-d8e07d251c59",
+ "id": "10",
"metadata": {},
"source": [
"完成IMDB数据加载器后，加载训练数据集进行测试，输出数据集数量："
@@ -237,21 +219,10 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "e08908b9-4997-4c93-a1e1-d1573991d729",
+ "execution_count": null,
+ "id": "11",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "25000"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"imdb_train = IMDBData(imdb_path, 'train')\n",
"len(imdb_train)"
@@ -259,7 +230,7 @@
},
{
"cell_type": "markdown",
- "id": "f6a05899-85f4-4e84-803a-e6afb3e784a7",
+ "id": "12",
"metadata": {},
"source": [
"将IMDB数据集加载至内存并构造为迭代对象后，可以使用`mindspore.dataset`提供的`Generatordataset`接口加载数据集迭代对象，并进行下一步的数据处理，下面封装一个函数将train和test分别使用`Generatordataset`进行加载，并指定数据集中文本和标签的`column_name`分别为`text`和`label`:"
@@ -267,21 +238,10 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "id": "cbf93849-0061-41e3-b49e-0f6475c84f00",
+ "execution_count": null,
+ "id": "13",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] GE_ADPT(4694,ffff9b460010,python):2024-11-22-16:14:29.588.533 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleGetModelId failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleGetModelId\n",
- "[WARNING] GE_ADPT(4694,ffff9b460010,python):2024-11-22-16:14:29.588.591 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleLoadFromMem failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleLoadFromMem\n",
- "[WARNING] GE_ADPT(4694,ffff9b460010,python):2024-11-22-16:14:29.588.627 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleUnload failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleUnload\n",
- "[WARNING] ME(4694:281473286799376,MainProcess):2024-11-22-16:14:29.737.886 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import mindspore.dataset as ds\n",
"\n",
@@ -293,7 +253,7 @@
},
{
"cell_type": "markdown",
- "id": "145600b6-a525-46dd-952c-8f83db5c7eae",
+ "id": "14",
"metadata": {},
"source": [
"加载IMDB数据集，可以看到`imdb_train`是一个GeneratorDataset对象。"
@@ -301,21 +261,10 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "id": "2471d850-6770-4308-bcbe-40b5bbd919db",
+ "execution_count": null,
+ "id": "15",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"imdb_train, imdb_test = load_imdb(imdb_path)\n",
"imdb_train"
@@ -323,7 +272,7 @@
},
{
"cell_type": "markdown",
- "id": "45ee5b20-7547-4ca1-83fa-6ee083cc65a4",
+ "id": "16",
"metadata": {
"tags": []
},
@@ -344,8 +293,8 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "id": "519a863f-4053-4c20-93e2-b0acac829372",
+ "execution_count": null,
+ "id": "17",
"metadata": {},
"outputs": [],
"source": [
@@ -376,7 +325,7 @@
},
{
"cell_type": "markdown",
- "id": "7fa216e1-db92-4c74-868a-b0a1889810f3",
+ "id": "18",
"metadata": {},
"source": [
"由于数据集中可能存在词表没有覆盖的单词，因此需要加入``标记符；同时由于输入长度的不一致，在打包为一个batch时需要将短的文本进行填充，因此需要加入``标记符。完成后的词表长度为原词表长度+2。\n",
@@ -386,28 +335,10 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "id": "34ca0aee-7deb-45ea-8231-2be5f9c2e632",
+ "execution_count": null,
+ "id": "19",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 862182613/862182613 [01:03<00:00, 13613579.64B/s]\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "400002"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"glove_path = download('glove.6B.zip', 'https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/glove.6B.zip')\n",
"vocab, embeddings = load_glove(glove_path)\n",
@@ -416,7 +347,7 @@
},
{
"cell_type": "markdown",
- "id": "0f199e78-e711-4801-87f0-a099289bdfc1",
+ "id": "20",
"metadata": {},
"source": [
"使用词表将`the`转换为index id，并查询词向量矩阵对应的词向量："
@@ -424,38 +355,10 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "id": "b12e8554-496c-4f35-a0bc-67a3f60ff488",
+ "execution_count": null,
+ "id": "21",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(0,\n",
- " array([-0.038194, -0.24487 , 0.72812 , -0.39961 , 0.083172, 0.043953,\n",
- " -0.39141 , 0.3344 , -0.57545 , 0.087459, 0.28787 , -0.06731 ,\n",
- " 0.30906 , -0.26384 , -0.13231 , -0.20757 , 0.33395 , -0.33848 ,\n",
- " -0.31743 , -0.48336 , 0.1464 , -0.37304 , 0.34577 , 0.052041,\n",
- " 0.44946 , -0.46971 , 0.02628 , -0.54155 , -0.15518 , -0.14107 ,\n",
- " -0.039722, 0.28277 , 0.14393 , 0.23464 , -0.31021 , 0.086173,\n",
- " 0.20397 , 0.52624 , 0.17164 , -0.082378, -0.71787 , -0.41531 ,\n",
- " 0.20335 , -0.12763 , 0.41367 , 0.55187 , 0.57908 , -0.33477 ,\n",
- " -0.36559 , -0.54857 , -0.062892, 0.26584 , 0.30205 , 0.99775 ,\n",
- " -0.80481 , -3.0243 , 0.01254 , -0.36942 , 2.2167 , 0.72201 ,\n",
- " -0.24978 , 0.92136 , 0.034514, 0.46745 , 1.1079 , -0.19358 ,\n",
- " -0.074575, 0.23353 , -0.052062, -0.22044 , 0.057162, -0.15806 ,\n",
- " -0.30798 , -0.41625 , 0.37972 , 0.15006 , -0.53212 , -0.2055 ,\n",
- " -1.2526 , 0.071624, 0.70565 , 0.49744 , -0.42063 , 0.26148 ,\n",
- " -1.538 , -0.30223 , -0.073438, -0.28312 , 0.37104 , -0.25217 ,\n",
- " 0.016215, -0.017099, -0.38984 , 0.87424 , -0.72569 , -0.51058 ,\n",
- " -0.52028 , -0.1459 , 0.8278 , 0.27062 ], dtype=float32))"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"idx = vocab.tokens_to_ids('the')\n",
"embedding = embeddings[idx]\n",
@@ -464,7 +367,7 @@
},
{
"cell_type": "markdown",
- "id": "186eba95-4501-4427-9079-f5202f06e989",
+ "id": "22",
"metadata": {},
"source": [
"## 数据集预处理\n",
@@ -482,19 +385,10 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "id": "6a9451b7-e30b-4039-9955-40daac18b6e8",
+ "execution_count": null,
+ "id": "23",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] ME(4694:281473286799376,MainProcess):2024-11-22-16:17:01.292.785 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(4694:281473286799376,MainProcess):2024-11-22-16:17:01.295.116 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import mindspore as ms\n",
"ms.set_context(device_target=\"Ascend\")\n",
@@ -506,7 +400,7 @@
},
{
"cell_type": "markdown",
- "id": "35e08575-85cf-4c50-8654-52bae0c3b413",
+ "id": "24",
"metadata": {},
"source": [
"完成预处理操作后，需将其加入到数据集处理流水线中，使用`map`接口对指定的column添加操作。"
@@ -514,8 +408,8 @@
},
{
"cell_type": "code",
- "execution_count": 11,
- "id": "4f8cdc63-8460-4432-baa7-297acb17dd80",
+ "execution_count": null,
+ "id": "25",
"metadata": {},
"outputs": [],
"source": [
@@ -528,7 +422,7 @@
},
{
"cell_type": "markdown",
- "id": "3efc8699-25b3-4d04-9f0c-f77c7a99703f",
+ "id": "26",
"metadata": {},
"source": [
"由于IMDB数据集本身不包含验证集，我们手动将其分割为训练和验证两部分，比例取0.7, 0.3。"
@@ -536,25 +430,17 @@
},
{
"cell_type": "code",
- "execution_count": 12,
- "id": "c8a62480-abb5-4a11-b83b-bef585e69066",
+ "execution_count": null,
+ "id": "27",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] ME(4694:281473286799376,MainProcess):2024-11-22-16:17:03.701.435 [mindspore/dataset/engine/datasets.py:1231] Dataset is shuffled before split.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"imdb_train, imdb_valid = imdb_train.split([0.7, 0.3])"
]
},
{
"cell_type": "markdown",
- "id": "ec8a81cf-2376-4840-9bda-84338897f8af",
+ "id": "28",
"metadata": {},
"source": [
"最后指定数据集的batch大小，通过`batch`接口指定，并设置是否丢弃无法被batch size整除的剩余数据。\n",
@@ -564,8 +450,8 @@
},
{
"cell_type": "code",
- "execution_count": 13,
- "id": "8b9988cf-0dfb-424d-930a-1dc2f1ff9176",
+ "execution_count": null,
+ "id": "29",
"metadata": {},
"outputs": [],
"source": [
@@ -575,7 +461,7 @@
},
{
"cell_type": "markdown",
- "id": "bfab4316-c35d-430d-bfaa-7af9b4f5b736",
+ "id": "30",
"metadata": {},
"source": [
"## 模型构建\n",
@@ -591,7 +477,7 @@
},
{
"cell_type": "markdown",
- "id": "f64cdfd1-4fef-4394-a4ea-e5e7b509a9cc",
+ "id": "31",
"metadata": {},
"source": [
"### Embedding\n",
@@ -637,19 +523,10 @@
},
{
"cell_type": "code",
- "execution_count": 14,
- "id": "c0f1e26d-1b7c-4d50-947d-4fdda527b651",
+ "execution_count": null,
+ "id": "32",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] ME(4694:281473286799376,MainProcess):2024-11-22-16:17:09.229.763 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(4694:281473286799376,MainProcess):2024-11-22-16:17:09.231.974 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import math\n",
"import mindspore as ms\n",
@@ -683,7 +560,7 @@
},
{
"cell_type": "markdown",
- "id": "8e703dd4-aa69-4b16-a2c6-95219d7a95f5",
+ "id": "33",
"metadata": {},
"source": [
"### 损失函数与优化器\n",
@@ -693,18 +570,10 @@
},
{
"cell_type": "code",
- "execution_count": 15,
- "id": "591b1301-e9e3-44eb-a88b-09d2e807f9f9",
+ "execution_count": null,
+ "id": "34",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] CORE(4694,ffff9b460010,python):2024-11-22-16:17:10.940.604 [mindspore/core/utils/ms_context.cc:530] GetJitLevel] Set jit level to O2 for rank table startup method.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"hidden_size = 256\n",
"output_size = 1\n",
@@ -720,7 +589,7 @@
},
{
"cell_type": "markdown",
- "id": "db8ee0e4-2381-4c4c-b493-60c40396165d",
+ "id": "35",
"metadata": {},
"source": [
"### 训练逻辑\n",
@@ -736,8 +605,8 @@
},
{
"cell_type": "code",
- "execution_count": 16,
- "id": "f5479830-8370-484d-a6ae-2a5985ab43b3",
+ "execution_count": null,
+ "id": "36",
"metadata": {},
"outputs": [],
"source": [
@@ -770,7 +639,7 @@
},
{
"cell_type": "markdown",
- "id": "95ffc4b0-2779-46a8-a952-7d4f404943f6",
+ "id": "37",
"metadata": {},
"source": [
"### 评估指标和逻辑\n",
@@ -780,8 +649,8 @@
},
{
"cell_type": "code",
- "execution_count": 17,
- "id": "028a0b84-deee-4d1b-a1ad-a6ad515fd22f",
+ "execution_count": null,
+ "id": "38",
"metadata": {},
"outputs": [],
"source": [
@@ -799,7 +668,7 @@
},
{
"cell_type": "markdown",
- "id": "db89c215-0152-4bc1-9ae5-f82a6d4a3261",
+ "id": "39",
"metadata": {},
"source": [
"有了准确率计算函数后，类似于训练逻辑，对评估逻辑进行设计, 分别为以下步骤：\n",
@@ -816,8 +685,8 @@
},
{
"cell_type": "code",
- "execution_count": 18,
- "id": "78bfafbc-3a10-4aab-b9bb-3189f84bc81a",
+ "execution_count": null,
+ "id": "40",
"metadata": {},
"outputs": [],
"source": [
@@ -847,7 +716,7 @@
},
{
"cell_type": "markdown",
- "id": "126a6e44-3401-4b5d-b169-6f33584670a0",
+ "id": "41",
"metadata": {},
"source": [
"## 模型训练与保存\n",
@@ -857,43 +726,10 @@
},
{
"cell_type": "code",
- "execution_count": 19,
- "id": "851ec476-ca3a-439c-95db-55312dbda7c2",
+ "execution_count": null,
+ "id": "42",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] ME(4694:281473286799376,MainProcess):2024-11-22-16:17:22.830.118 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "[WARNING] ME(4694:281473286799376,MainProcess):2024-11-22-16:17:22.833.006 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "Epoch 0: 0%| | 0/273 [00:00"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"import mindspore\n",
"import matplotlib.pyplot as plt\n",
diff --git a/Season2.step_into_llm/17.Qwen/qwen2_finetune_inference.ipynb b/Season2.step_into_llm/17.Qwen/qwen2_finetune_inference.ipynb
index 9138541..693486d 100644
--- a/Season2.step_into_llm/17.Qwen/qwen2_finetune_inference.ipynb
+++ b/Season2.step_into_llm/17.Qwen/qwen2_finetune_inference.ipynb
@@ -100,134 +100,16 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: https://mirrors.aliyun.com/pypi/simple/\n",
- "Collecting mindspore==2.5.0\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/23/22/dff0f1bef6c0846a97271ae5d39ca187914f39562f9e3f6787041dea1a97/mindspore-2.5.0-cp39-cp39-manylinux1_x86_64.whl (958.4 MB)\n",
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- "\u001b[?25hCollecting numpy<2.0.0,>=1.20.0 (from mindspore==2.5.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/54/30/c2a907b9443cf42b90c17ad10c1e8fa801975f01cb9764f3f8eb8aea638b/numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n",
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- "\u001b[?25hCollecting protobuf>=3.13.0 (from mindspore==2.5.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/28/50/1925de813499546bc8ab3ae857e3ec84efe7d2f19b34529d0c7c3d02d11d/protobuf-6.30.2-cp39-abi3-manylinux2014_x86_64.whl (316 kB)\n",
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- " Downloading https://mirrors.aliyun.com/pypi/packages/f6/46/0bd0ca03d9d1164a7fa33d285ef6d1c438e963d0c8770e4c5b3737ef5abe/pillow-11.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.4 MB)\n",
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- "\u001b[?25hCollecting scipy>=1.5.4 (from mindspore==2.5.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/35/f5/d0ad1a96f80962ba65e2ce1de6a1e59edecd1f0a7b55990ed208848012e0/scipy-1.13.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (38.6 MB)\n",
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- "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore==2.5.0) (24.2)\n",
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- "Collecting astunparse>=1.6.3 (from mindspore==2.5.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/2b/03/13dde6512ad7b4557eb792fbcf0c653af6076b81e5941d36ec61f7ce6028/astunparse-1.6.3-py2.py3-none-any.whl (12 kB)\n",
- "Collecting safetensors>=0.4.0 (from mindspore==2.5.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/a6/f8/dae3421624fcc87a89d42e1898a798bc7ff72c61f38973a65d60df8f124c/safetensors-0.5.3-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (471 kB)\n",
- "Collecting dill>=0.3.7 (from mindspore==2.5.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/46/d1/e73b6ad76f0b1fb7f23c35c6d95dbc506a9c8804f43dda8cb5b0fa6331fd/dill-0.3.9-py3-none-any.whl (119 kB)\n",
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- "Requirement already satisfied: six<2.0,>=1.6.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore==2.5.0) (1.17.0)\n",
- "Installing collected packages: safetensors, protobuf, pillow, numpy, dill, astunparse, scipy, mindspore\n",
- "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
- "auto-tune 0.1.0 requires te, which is not installed.\n",
- "schedule-search 0.0.1 requires absl-py, which is not installed.\u001b[0m\u001b[31m\n",
- "\u001b[0mSuccessfully installed astunparse-1.6.3 dill-0.3.9 mindspore-2.5.0 numpy-1.26.4 pillow-11.1.0 protobuf-6.30.2 safetensors-0.5.3 scipy-1.13.1\n",
- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.5.0/MindSpore/unified/x86_64/mindspore-2.5.0-cp39-cp39-linux_x86_64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: https://mirrors.aliyun.com/pypi/simple\n",
- "Collecting mindnlp==0.4.0\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/0f/a8/5a072852d28a51417b5e330b32e6ae5f26b491ef01a15ba968e77f785e69/mindnlp-0.4.0-py3-none-any.whl (8.4 MB)\n",
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- "\u001b[?25hRequirement already satisfied: mindspore>=2.2.14 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.5.0)\n",
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- "Requirement already satisfied: safetensors in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.3)\n",
- "Requirement already satisfied: sentencepiece in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.2.0)\n",
- "Requirement already satisfied: regex in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (2024.11.6)\n",
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- "Requirement already satisfied: ml-dtypes in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.1)\n",
- "Requirement already satisfied: pyctcdecode in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.0)\n",
- "Collecting jieba (from mindnlp==0.4.0)\n",
- " Downloading https://mirrors.aliyun.com/pypi/packages/c6/cb/18eeb235f833b726522d7ebed54f2278ce28ba9438e3135ab0278d9792a2/jieba-0.42.1.tar.gz (19.2 MB)\n",
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- "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n",
- "\u001b[?25hRequirement already satisfied: pytest==7.2.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindnlp==0.4.0) (7.2.0)\n",
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- "Requirement already satisfied: attrs>=19.2.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (24.3.0)\n",
- "Requirement already satisfied: iniconfig in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.1.0)\n",
- "Requirement already satisfied: packaging in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (24.2)\n",
- "Requirement already satisfied: pluggy<2.0,>=0.12 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.5.0)\n",
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- "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from tokenizers==0.19.1->mindnlp==0.4.0) (0.30.2)\n",
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- "Requirement already satisfied: asttokens>=2.0.4 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (3.0.0)\n",
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- "Requirement already satisfied: psutil>=5.6.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (5.9.1)\n",
- "Requirement already satisfied: astunparse>=1.6.3 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.6.3)\n",
- "Requirement already satisfied: dill>=0.3.7 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (0.3.8)\n",
- "Requirement already satisfied: filelock in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.18.0)\n",
- "Requirement already satisfied: pyarrow>=15.0.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (19.0.1)\n",
- "Requirement already satisfied: pandas in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (2.2.3)\n",
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- "Requirement already satisfied: aiohttp in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.11.16)\n",
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- "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (0.45.1)\n",
- "Requirement already satisfied: six<2.0,>=1.6.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (1.17.0)\n",
- "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (2.6.1)\n",
- "Requirement already satisfied: aiosignal>=1.1.2 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (1.3.2)\n",
- "Requirement already satisfied: async-timeout<6.0,>=4.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (5.0.1)\n",
- "Requirement already satisfied: frozenlist>=1.1.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (1.5.0)\n",
- "Requirement already satisfied: multidict<7.0,>=4.5 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (6.4.2)\n",
- "Requirement already satisfied: propcache>=0.2.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (0.3.1)\n",
- "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.0) (1.19.0)\n",
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (4.13.1)\n",
- "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.0) (2.4.0)\n",
- "Requirement already satisfied: python-dateutil>=2.8.2 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2.9.0.post0)\n",
- "Requirement already satisfied: pytz>=2020.1 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
- "Requirement already satisfied: tzdata>=2022.7 in /home/jiangna1/miniconda3/envs/llama39/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
- "Building wheels for collected packages: jieba\n",
- " Building wheel for jieba (setup.py) ... \u001b[?25ldone\n",
- "\u001b[?25h Created wheel for jieba: filename=jieba-0.42.1-py3-none-any.whl size=19314508 sha256=30064bba508d12a9c2c545bdec7e271f61d5a83e9fdd53298a82e74659e1fd26\n",
- " Stored in directory: /home/jiangna1/.cache/pip/wheels/95/ef/7c/d8b3108835edfa15487417c5bddff166482b195d8090117ac5\n",
- "Successfully built jieba\n",
- "Installing collected packages: jieba, mindnlp\n",
- "Successfully installed jieba-0.42.1 mindnlp-0.4.0\n",
- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%pip install mindnlp==0.4.0 -i https://mirrors.aliyun.com/pypi/simple\n"
]
@@ -262,24 +144,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/liangdeqi/anaconda3/envs/py39/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/liangdeqi/anaconda3/envs/py39/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n",
- "/home/liangdeqi/anaconda3/envs/py39/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " setattr(self, word, getattr(machar, word).flat[0])\n",
- "/home/liangdeqi/anaconda3/envs/py39/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n",
- " return self._float_to_str(self.smallest_subnormal)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import json\n",
"import numpy as np\n",
@@ -289,18 +156,9 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] ME(3117682:281473615360032,MainProcess):2025-04-22-05:47:07.212.180 [mindspore/context.py:1335] For 'context.set_context', the parameter 'device_target' will be deprecated and removed in a future version. Please use the api mindspore.set_device() instead.\n",
- "[WARNING] ME(3117682:281473615360032,MainProcess):2025-04-22-05:47:07.214.143 [mindspore/context.py:1335] For 'context.set_context', the parameter 'device_id' will be deprecated and removed in a future version. Please use the api mindspore.set_device() instead.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"#将模式设置为动态图模式（PYNATIVE_MODE），并指定设备目标为Ascend芯片\n",
"ms.set_context(mode=ms.PYNATIVE_MODE, device_target=\"Ascend\",device_id=5)\n"
@@ -308,7 +166,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -372,7 +230,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -410,7 +268,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -429,17 +287,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[{'instruction': '只剩一个心脏了还能活吗？', 'output': '能，人本来就只有一个心脏。'}, {'instruction': '爸爸再婚，我是不是就有了个新娘？', 'output': '不是的，你有了一个继母。\"新娘\"是指新婚的女方，而你爸爸再婚，他的新婚妻子对你来说是继母。'}, {'instruction': '樟脑丸是我吃过最难吃的硬糖有奇怪的味道怎么还有人买', 'output': '樟脑丸并不是硬糖，而是一种常见的驱虫药，不能食用。虽然它的味道可能不太好，但是由于其有效的驱虫效果，所以仍然有很多人会购买。'}]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"with open(data_path, 'r', encoding='utf-8') as f:\n",
" data = json.load(f)\n",
@@ -455,37 +305,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] ME(3117682:281473615360032,MainProcess):2025-04-22-05:47:28.427.424 [mindspore/context.py:1335] For 'context.set_context', the parameter 'ascend_config' will be deprecated and removed in a future version. Please use the api mindspore.device_context.ascend.op_precision.precision_mode(),\n",
- " mindspore.device_context.ascend.op_precision.op_precision_mode(),\n",
- " mindspore.device_context.ascend.op_precision.matmul_allow_hf32(),\n",
- " mindspore.device_context.ascend.op_precision.conv_allow_hf32(),\n",
- " mindspore.device_context.ascend.op_tuning.op_compile() instead.\n",
- "/home/liangdeqi/anaconda3/envs/py39/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n",
- "Building prefix dict from the default dictionary ...\n",
- "Loading model from cache /tmp/jieba.cache\n",
- "Loading model cost 0.937 seconds.\n",
- "Prefix dict has been built successfully.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[2587]"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"from mindnlp.transformers import AutoTokenizer\n",
"\n",
@@ -505,7 +327,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -538,32 +360,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[MS_ALLOC_CONF]Runtime config: enable_vmm:True vmm_align_size:2MB\n",
- "Sample 0: Input IDs: [ 1474 25 26853 103 100124 46944 103023 34187 104246 75606]\n",
- "Sample 0: Labels: [ 1474 25 26853 103 100124 46944 103023 34187 104246 75606]\n",
- "\n",
- "Sample 1: Input IDs: [ 1474 25 10236 230 116 99962 87256 99838 3837 35946]\n",
- "Sample 1: Labels: [ 1474 25 10236 230 116 99962 87256 99838 3837 35946]\n",
- "\n",
- "Sample 2: Input IDs: [ 1474 25 6567 101 253 99931 106256 104927 111505 116080]\n",
- "Sample 2: Labels: [ 1474 25 6567 101 253 99931 106256 104927 111505 116080]\n",
- "\n",
- "Sample 3: Input IDs: [ 1474 25 18137 102 105 17447 30534 107118 103009 99504]\n",
- "Sample 3: Labels: [ 1474 25 18137 102 105 17447 30534 107118 103009 99504]\n",
- "\n",
- "Sample 4: Input IDs: [ 1474 25 220 100678 106727 36587 1867 6484 24300 9370]\n",
- "Sample 4: Labels: [ 1474 25 220 100678 106727 36587 1867 6484 24300 9370]\n",
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"for i, sample in enumerate(train_dataset.create_dict_iterator()):\n",
" if i >= 5:\n",
@@ -588,7 +387,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -607,34 +406,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Qwen2ForCausalLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`.`PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
- " - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
- " - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n",
- "Sliding Window Attention is enabled but not implemented for `eager`; unexpected results may be encountered.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[MS_ALLOC_CONF]Runtime config: enable_vmm:True vmm_align_size:2MB\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Loading checkpoint shards: 100%|██████████| 2/2 [00:17<00:00, 8.75s/it]\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from mindnlp.transformers import AutoModelForCausalLM, GenerationConfig\n",
"\n",
@@ -645,7 +419,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -663,7 +437,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {
"tags": []
},
@@ -692,7 +466,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -716,7 +490,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -780,7 +554,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -801,703 +575,9 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 0%| | 0/420 [00:00 2048)\n",
- " (lora_dropout): ModuleDict(\n",
- " (default): Dropout(p=0.1, inplace=False)\n",
- " )\n",
- " (lora_A): ModuleDict(\n",
- " (default): Linear (2048 -> 8)\n",
- " )\n",
- " (lora_B): ModuleDict(\n",
- " (default): Linear (8 -> 2048)\n",
- " )\n",
- " (lora_embedding_A): ParameterDict()\n",
- " (lora_embedding_B): ParameterDict()\n",
- " (lora_magnitude_vector): ModuleDict()\n",
- " )\n",
- " (k_proj): lora.Linear(\n",
- " (base_layer): Linear (2048 -> 256)\n",
- " (lora_dropout): ModuleDict(\n",
- " (default): Dropout(p=0.1, inplace=False)\n",
- " )\n",
- " (lora_A): ModuleDict(\n",
- " (default): Linear (2048 -> 8)\n",
- " )\n",
- " (lora_B): ModuleDict(\n",
- " (default): Linear (8 -> 256)\n",
- " )\n",
- " (lora_embedding_A): ParameterDict()\n",
- " (lora_embedding_B): ParameterDict()\n",
- " (lora_magnitude_vector): ModuleDict()\n",
- " )\n",
- " (v_proj): lora.Linear(\n",
- " (base_layer): Linear (2048 -> 256)\n",
- " (lora_dropout): ModuleDict(\n",
- " (default): Dropout(p=0.1, inplace=False)\n",
- " )\n",
- " (lora_A): ModuleDict(\n",
- " (default): Linear (2048 -> 8)\n",
- " )\n",
- " (lora_B): ModuleDict(\n",
- " (default): Linear (8 -> 256)\n",
- " )\n",
- " (lora_embedding_A): ParameterDict()\n",
- " (lora_embedding_B): ParameterDict()\n",
- " (lora_magnitude_vector): ModuleDict()\n",
- " )\n",
- " (o_proj): lora.Linear(\n",
- " (base_layer): Linear (2048 -> 2048)\n",
- " (lora_dropout): ModuleDict(\n",
- " (default): Dropout(p=0.1, inplace=False)\n",
- " )\n",
- " (lora_A): ModuleDict(\n",
- " (default): Linear (2048 -> 8)\n",
- " )\n",
- " (lora_B): ModuleDict(\n",
- " (default): Linear (8 -> 2048)\n",
- " )\n",
- " (lora_embedding_A): ParameterDict()\n",
- " (lora_embedding_B): ParameterDict()\n",
- " (lora_magnitude_vector): ModuleDict()\n",
- " )\n",
- " (rotary_emb): Qwen2RotaryEmbedding()\n",
- " )\n",
- " (mlp): Qwen2MLP(\n",
- " (gate_proj): Linear (2048 -> 11008)\n",
- " (up_proj): Linear (2048 -> 11008)\n",
- " (down_proj): Linear (11008 -> 2048)\n",
- " (act_fn): SiLU()\n",
- " )\n",
- " (input_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)\n",
- " (post_attention_layernorm): Qwen2RMSNorm((2048,), eps=1e-06)\n",
- " )\n",
- " )\n",
- " (norm): Qwen2RMSNorm((2048,), eps=1e-06)\n",
- " )\n",
- " (lm_head): Linear (2048 -> 151936)\n",
- ")"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"#将 LoRA微调后的参数加载到预训练模型中\n",
"from mindnlp.peft import PeftModel\n",
@@ -1665,7 +646,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1699,27 +680,9 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Both `max_new_tokens` (=2048) and `max_length`(=256) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "User: 如何保持清醒?\n",
- "LLAMA: 以下是用户和助手之间的问答。\n",
- "问：如何保持清醒?\n",
- "答：喝咖啡或茶，吃一些富含蛋白质的食物。\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"question = \"如何保持清醒?\"\n",
"response = generate_response(question, model, tokenizer)\n",
![](\"./assets/bert-base-bert-large-encoders.png\")
"
@@ -165,7 +165,7 @@
},
{
"cell_type": "markdown",
- "id": "e42d1ce8",
+ "id": "9",
"metadata": {},
"source": [
"接受输入序列后，BERT会输出每个位置对应的向量（长度等于hidden size），在后续下游任务中，我们会选取与任务相关的位置的向量，输入到最终输出层中得到结果。\n",
@@ -177,7 +177,7 @@
},
{
"cell_type": "markdown",
- "id": "a27de31d",
+ "id": "10",
"metadata": {
"slideshow": {
"slide_type": "slide"
@@ -198,87 +198,10 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "id": "d6c39086",
+ "execution_count": null,
+ "id": "11",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: https://repo.huaweicloud.com/repository/pypi/simple/\n",
- "Requirement already satisfied: mindnlp==0.4.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (0.4.0)\n",
- "Requirement already satisfied: mindspore>=2.2.14 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.4.1)\n",
- "Requirement already satisfied: tqdm in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (4.67.1)\n",
- "Requirement already satisfied: requests in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.32.3)\n",
- "Requirement already satisfied: datasets in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (3.6.0)\n",
- "Requirement already satisfied: evaluate in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.4.3)\n",
- "Requirement already satisfied: tokenizers==0.19.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.19.1)\n",
- "Requirement already satisfied: safetensors in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.3)\n",
- "Requirement already satisfied: sentencepiece in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.2.0)\n",
- "Requirement already satisfied: regex in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2024.11.6)\n",
- "Requirement already satisfied: addict in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (2.4.0)\n",
- "Requirement already satisfied: ml-dtypes in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.1)\n",
- "Requirement already satisfied: pyctcdecode in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.5.0)\n",
- "Requirement already satisfied: jieba in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (0.42.1)\n",
- "Requirement already satisfied: pytest==7.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (7.2.0)\n",
- "Requirement already satisfied: pillow>=10.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp==0.4.0) (11.2.1)\n",
- "Requirement already satisfied: attrs>=19.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (25.3.0)\n",
- "Requirement already satisfied: iniconfig in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.1.0)\n",
- "Requirement already satisfied: packaging in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (24.2)\n",
- "Requirement already satisfied: pluggy<2.0,>=0.12 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.5.0)\n",
- "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (1.2.0)\n",
- "Requirement already satisfied: tomli>=1.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.0) (2.2.1)\n",
- "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from tokenizers==0.19.1->mindnlp==0.4.0) (0.31.1)\n",
- "Requirement already satisfied: filelock in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (3.18.0)\n",
- "Requirement already satisfied: fsspec>=2023.5.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (2025.3.0)\n",
- "Requirement already satisfied: pyyaml>=5.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (6.0.2)\n",
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (4.12.2)\n",
- "Requirement already satisfied: hf-xet<2.0.0,>=1.1.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.0) (1.1.0)\n",
- "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.26.4)\n",
- "Requirement already satisfied: protobuf>=3.13.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (6.30.2)\n",
- "Requirement already satisfied: asttokens>=2.0.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (3.0.0)\n",
- "Requirement already satisfied: scipy>=1.5.4 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.13.1)\n",
- "Requirement already satisfied: psutil>=5.6.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (5.9.0)\n",
- "Requirement already satisfied: astunparse>=1.6.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.0) (1.6.3)\n",
- "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (0.45.1)\n",
- "Requirement already satisfied: six<2.0,>=1.6.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.0) (1.17.0)\n",
- "Requirement already satisfied: pyarrow>=15.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (20.0.0)\n",
- "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (0.3.8)\n",
- "Requirement already satisfied: pandas in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (2.2.3)\n",
- "Requirement already satisfied: xxhash in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (3.5.0)\n",
- "Requirement already satisfied: multiprocess<0.70.17 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets->mindnlp==0.4.0) (0.70.16)\n",
- "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (3.11.18)\n",
- "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (2.6.1)\n",
- "Requirement already satisfied: aiosignal>=1.1.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.3.2)\n",
- "Requirement already satisfied: async-timeout<6.0,>=4.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (5.0.1)\n",
- "Requirement already satisfied: frozenlist>=1.1.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.6.0)\n",
- "Requirement already satisfied: multidict<7.0,>=4.5 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (6.4.3)\n",
- "Requirement already satisfied: propcache>=0.2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (0.3.1)\n",
- "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (1.20.0)\n",
- "Requirement already satisfied: idna>=2.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from yarl<2.0,>=1.17.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2025.3.0,>=2023.1.0->datasets->mindnlp==0.4.0) (3.10)\n",
- "Requirement already satisfied: charset-normalizer<4,>=2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (3.4.1)\n",
- "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2.4.0)\n",
- "Requirement already satisfied: certifi>=2017.4.17 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from requests->mindnlp==0.4.0) (2025.4.26)\n",
- "Requirement already satisfied: python-dateutil>=2.8.2 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2.9.0.post0)\n",
- "Requirement already satisfied: pytz>=2020.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
- "Requirement already satisfied: tzdata>=2022.7 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.0) (2025.2)\n",
- "Requirement already satisfied: pygtrie<3.0,>=2.1 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (2.5.0)\n",
- "Requirement already satisfied: hypothesis<7,>=6.14 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.0) (6.131.15)\n",
- "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.0) (2.4.0)\n",
- "\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
- "Looking in indexes: https://repo.huaweicloud.com/repository/pypi/simple/\n",
- "Requirement already satisfied: pytesseract in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (0.3.13)\n",
- "Requirement already satisfied: packaging>=21.3 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytesseract) (24.2)\n",
- "Requirement already satisfied: Pillow>=8.0.0 in /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytesseract) (11.2.1)\n",
- "\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.1.1\u001b[0m\n",
- "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# install mindnlp\n",
"!pip install mindnlp==0.4.0\n",
@@ -287,94 +210,10 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "id": "0ad0c6f0",
+ "execution_count": null,
+ "id": "12",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.673.160 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleGetModelId failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleGetModelId\n",
- "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.673.218 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleLoadFromMem failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleLoadFromMem\n",
- "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.673.244 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleUnload failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleUnload\n",
- "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.673.383 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtGetMemUceInfo failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtGetMemUceInfo\n",
- "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.673.408 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtDeviceTaskAbort failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtDeviceTaskAbort\n",
- "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.673.431 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtMemUceRepair failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtMemUceRepair\n",
- "[WARNING] GE_ADPT(6108,ffffab724020,python):2025-05-13-12:28:08.674.937 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol acltdtCleanChannel failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libacl_tdt_channel.so: undefined symbol: acltdtCleanChannel\n",
- "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for ![](\"https://mindspore-demo.obs.cn-north-4.myhuaweicloud.com/imgs/ai-gallery/change-kernel.PNG\")
"
@@ -92,7 +92,7 @@
},
{
"cell_type": "markdown",
- "id": "d2fe8b0f-8ab4-4658-b64f-5eefb64a25ec",
+ "id": "7",
"metadata": {},
"source": [
"2. 安装mindspore2.2.12、indNLP及相关依赖，MindNLP官方仓详见：MindNLP"
@@ -100,8 +100,8 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "id": "52f84d8f-746d-4a87-af68-2dadf693f002",
+ "execution_count": null,
+ "id": "8",
"metadata": {},
"outputs": [],
"source": [
@@ -114,7 +114,7 @@
},
{
"cell_type": "markdown",
- "id": "d965c42b-d37a-42e4-aac9-4d25a4fb33be",
+ "id": "9",
"metadata": {},
"source": [
"***注：执行如上命令完成安装后，请点击上方的restart kernel图标重启kernel，再进行实验***"
@@ -122,8 +122,8 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "id": "074df3ae-4bfc-4655-be9f-8041fc211f96",
+ "execution_count": null,
+ "id": "10",
"metadata": {
"tags": []
},
@@ -134,8 +134,8 @@
},
{
"cell_type": "code",
- "execution_count": 3,
- "id": "9a161bfb-a15e-4a07-9bb0-688b76f87de3",
+ "execution_count": null,
+ "id": "11",
"metadata": {
"tags": []
},
@@ -148,7 +148,7 @@
},
{
"cell_type": "markdown",
- "id": "37ab3a91-292b-420d-9b61-c85280dd8dee",
+ "id": "12",
"metadata": {},
"source": [
"## GPT-2 Self-attention: 1- Creating queries, keys, and values"
@@ -156,7 +156,7 @@
},
{
"cell_type": "markdown",
- "id": "22664691-6db2-4d62-a76a-a4a8a6050199",
+ "id": "13",
"metadata": {},
"source": [
""
@@ -164,8 +164,8 @@
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "76d2591f-26c2-4961-bca3-be30c4352aef",
+ "execution_count": null,
+ "id": "14",
"metadata": {
"tags": []
},
@@ -181,38 +181,18 @@
},
{
"cell_type": "code",
- "execution_count": 5,
- "id": "2d315a4e-5663-404e-b93d-efb1cf354414",
+ "execution_count": null,
+ "id": "15",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/ma-user/anaconda3/envs/python-3.9.0/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- "\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "((1, 10, 768), (1, 10, 768), (1, 10, 768))"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"from mindnlp._legacy.functional import split\n",
"from mindnlp.transformers.ms_utils import Conv1D\n",
"\n",
"# query = Wq * X, key = Wk * X, value = Wv * X\n",
- "# c_attn: (1, 10, 768*3) --> query, key, value: (1, 10, 768), (1, 10, 768), (1, 10, 768) \n",
+ "# c_attn: (1, 10, 768*3) --> query, key, value: (1, 10, 768), (1, 10, 768), (1, 10, 768)\n",
"c_attn = Conv1D(3 * embed_dim, embed_dim)\n",
"query, key, value = split(c_attn(x), embed_dim, axis=2)\n",
"query.shape, key.shape, value.shape"
@@ -220,7 +200,7 @@
},
{
"cell_type": "markdown",
- "id": "d2c7757e-16e4-4ff9-8a63-3e19767588db",
+ "id": "16",
"metadata": {},
"source": [
"\n",
@@ -230,8 +210,8 @@
},
{
"cell_type": "code",
- "execution_count": 6,
- "id": "abb7ccac-7cfe-401a-ab32-763de70b4669",
+ "execution_count": null,
+ "id": "17",
"metadata": {
"tags": []
},
@@ -245,28 +225,17 @@
" new_shape = tensor.shape[:-1] + (num_heads, attn_head_size)\n",
" tensor = tensor.view(new_shape)\n",
" # (batch_size, seq_len, num_heads, attn_head_size) --> (batch_size, num_heads, seq_len, attn_head_size)\n",
- " return ops.transpose(tensor, (0, 2, 1, 3)) "
+ " return ops.transpose(tensor, (0, 2, 1, 3))"
]
},
{
"cell_type": "code",
- "execution_count": 7,
- "id": "72abe0fe-5225-425b-9bda-0723f3fb27cf",
+ "execution_count": null,
+ "id": "18",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "((1, 12, 10, 64), (1, 12, 10, 64), (1, 12, 10, 64))"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"num_heads = 12\n",
"head_dim = embed_dim // num_heads\n",
@@ -281,7 +250,7 @@
},
{
"cell_type": "markdown",
- "id": "fa0f65b2-b291-4ad1-b3ea-8e77e6a254d3",
+ "id": "19",
"metadata": {},
"source": [
"## GPT-2 Self-attention: 2- Scoring\n",
@@ -293,23 +262,12 @@
},
{
"cell_type": "code",
- "execution_count": 8,
- "id": "9f952236-de74-4419-9469-7e78d3b7c3e4",
+ "execution_count": null,
+ "id": "20",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1, 12, 10, 10)"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# qk点积\n",
"# q: (1, 12, 10, 64), k^T: (1, 12, 64, 10)\n",
@@ -321,7 +279,7 @@
},
{
"cell_type": "markdown",
- "id": "501d6de9-cdb7-40cd-aed1-e4fe059054b5",
+ "id": "21",
"metadata": {
"tags": []
},
@@ -331,30 +289,12 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "id": "0ff22248-deff-4962-afae-55772f63f142",
+ "execution_count": null,
+ "id": "22",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Tensor(shape=[1, 1, 10, 10], dtype=Bool, value=\n",
- "[[[[ True, False, False ... False, False, False],\n",
- " [ True, True, False ... False, False, False],\n",
- " [ True, True, True ... False, False, False],\n",
- " ...\n",
- " [ True, True, True ... True, False, False],\n",
- " [ True, True, True ... True, True, False],\n",
- " [ True, True, True ... True, True, True]]]])"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# diagonal matrix to implement masked multi-head attention\n",
"# To ensure not to attend to future information\n",
@@ -367,7 +307,7 @@
},
{
"cell_type": "markdown",
- "id": "a783a2bc-01dd-4496-a018-ac01e643cd89",
+ "id": "23",
"metadata": {},
"source": [
"\n",
@@ -377,8 +317,8 @@
},
{
"cell_type": "code",
- "execution_count": 10,
- "id": "d957ce17-6df6-4f5e-a262-24ff3a8ce0d1",
+ "execution_count": null,
+ "id": "24",
"metadata": {
"tags": []
},
@@ -395,58 +335,29 @@
},
{
"cell_type": "code",
- "execution_count": 11,
- "id": "dee63bfd-f394-4558-9e9f-e102a2fd283c",
+ "execution_count": null,
+ "id": "25",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "-3.4028235e+38"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"np.finfo(np.float32).min"
]
},
{
"cell_type": "code",
- "execution_count": 12,
- "id": "d2faad14-9a3d-4495-8bcc-d7ac2695e83d",
+ "execution_count": null,
+ "id": "26",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Tensor(shape=[10, 10], dtype=Float32, value=\n",
- "[[-3.72267663e-01, -3.40282347e+38, -3.40282347e+38 ... -3.40282347e+38, -3.40282347e+38, -3.40282347e+38],\n",
- " [ 4.12474960e-01, -6.20999515e-01, -3.40282347e+38 ... -3.40282347e+38, -3.40282347e+38, -3.40282347e+38],\n",
- " [ 1.29110947e-01, 2.28423685e-01, -1.90024704e-01 ... -3.40282347e+38, -3.40282347e+38, -3.40282347e+38],\n",
- " ...\n",
- " [ 2.14589074e-01, 1.79385528e-01, 2.11229175e-01 ... -8.21841732e-02, -3.40282347e+38, -3.40282347e+38],\n",
- " [-3.86964470e-01, 1.50564313e-03, -7.81135634e-02 ... -8.60612690e-02, -3.31553906e-01, -3.40282347e+38],\n",
- " [ 1.89703301e-01, -7.32186437e-02, -2.44263425e-01 ... 4.69686151e-01, -6.34481907e-01, 6.83065802e-02]])"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"attn_weights[0, 0]"
]
},
{
"cell_type": "markdown",
- "id": "54f61883-a535-4135-851c-c41e9c227e18",
+ "id": "27",
"metadata": {},
"source": [
""
@@ -454,23 +365,12 @@
},
{
"cell_type": "code",
- "execution_count": 13,
- "id": "df9cdaae-ac5a-4bc0-9e59-403d176c0d3b",
+ "execution_count": null,
+ "id": "28",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1, 12, 10, 10)"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"attn_weights = softmax(attn_weights, axis=-1)\n",
"attn_weights.shape"
@@ -478,37 +378,19 @@
},
{
"cell_type": "code",
- "execution_count": 14,
- "id": "5771f68c-8b35-4b1a-83d1-287b2ce7a47e",
+ "execution_count": null,
+ "id": "29",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Tensor(shape=[10, 10], dtype=Float32, value=\n",
- "[[ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00 ... 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
- " [ 7.37588942e-01, 2.62411058e-01, 0.00000000e+00 ... 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
- " [ 3.53208542e-01, 3.90087605e-01, 2.56703824e-01 ... 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
- " ...\n",
- " [ 1.25348046e-01, 1.21012121e-01, 1.24927595e-01 ... 9.31602344e-02, 0.00000000e+00, 0.00000000e+00],\n",
- " [ 8.72338116e-02, 1.28645703e-01, 1.18800178e-01 ... 1.17859736e-01, 9.22039151e-02, 0.00000000e+00],\n",
- " [ 1.08949542e-01, 8.37606117e-02, 7.05920979e-02 ... 1.44151926e-01, 4.77844179e-02, 9.64947045e-02]])"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"attn_weights[0, 0]"
]
},
{
"cell_type": "markdown",
- "id": "4a376e6b-0cd8-434a-aa5b-c647251200fa",
+ "id": "30",
"metadata": {},
"source": [
""
@@ -516,23 +398,12 @@
},
{
"cell_type": "code",
- "execution_count": 15,
- "id": "0ba1e0ff-5627-4b70-8911-4ffa7383e29d",
+ "execution_count": null,
+ "id": "31",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1, 12, 10, 64)"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"attn_output = ops.matmul(attn_weights, value)\n",
"\n",
@@ -541,7 +412,7 @@
},
{
"cell_type": "markdown",
- "id": "5952ef91-b1e1-4d5b-9b42-a29f56f8f430",
+ "id": "32",
"metadata": {},
"source": [
"## GPT-2 Self-attention: 3.5- Merge attention heads\n",
@@ -551,8 +422,8 @@
},
{
"cell_type": "code",
- "execution_count": 16,
- "id": "80e44dd1-4013-4d01-b267-92463b296e5b",
+ "execution_count": null,
+ "id": "33",
"metadata": {
"tags": []
},
@@ -570,23 +441,12 @@
},
{
"cell_type": "code",
- "execution_count": 17,
- "id": "5b35f8ee-70b4-4cb4-ad9b-d0b685482b59",
+ "execution_count": null,
+ "id": "34",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1, 10, 768)"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# (1, 12, 10, 64) --> (1, 10, 12, 64) --> (1, 10, 768)\n",
"attn_output = merge_heads(attn_output, num_heads, head_dim)\n",
@@ -596,7 +456,7 @@
},
{
"cell_type": "markdown",
- "id": "de14b271-4432-44a0-b1f9-d2632ed2cd5b",
+ "id": "35",
"metadata": {},
"source": [
"## GPT-2 Self-attention: 4- Projecting\n",
@@ -606,8 +466,8 @@
},
{
"cell_type": "code",
- "execution_count": 18,
- "id": "ff788df6-a6a7-4b43-9a76-95eaef4918c7",
+ "execution_count": null,
+ "id": "36",
"metadata": {
"tags": []
},
@@ -618,23 +478,12 @@
},
{
"cell_type": "code",
- "execution_count": 19,
- "id": "0c7d4c1f-4ddc-4605-acba-f6e17cbfe2d5",
+ "execution_count": null,
+ "id": "37",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1, 10, 768)"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"attn_output = c_proj(attn_output)\n",
"attn_output.shape"
@@ -643,7 +492,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "9300497c-e27a-4fad-b02e-fe2b6a38aec2",
+ "id": "38",
"metadata": {},
"outputs": [],
"source": []
diff --git a/Season1.step_into_chatgpt/4.GPT2/gpt2_summarization.ipynb b/Season1.step_into_chatgpt/4.GPT2/gpt2_summarization.ipynb
index 7daef40..c56a189 100644
--- a/Season1.step_into_chatgpt/4.GPT2/gpt2_summarization.ipynb
+++ b/Season1.step_into_chatgpt/4.GPT2/gpt2_summarization.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "cb430c0e-fd70-46e2-91e2-b14cf782bd06",
+ "id": "0",
"metadata": {},
"source": [
"# 基于MindSpore的GPT2文本摘要\n",
@@ -14,8 +14,8 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "id": "f9f085c7-b2b3-4b18-95f9-13b298b10d58",
+ "execution_count": null,
+ "id": "1",
"metadata": {},
"outputs": [],
"source": [
@@ -27,7 +27,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "d9436813-c813-425e-b1ed-af0534ef862c",
+ "id": "2",
"metadata": {},
"outputs": [],
"source": [
@@ -58,7 +58,7 @@
},
{
"cell_type": "markdown",
- "id": "0bdfae31-df62-4746-bd3e-1c556413ffd2",
+ "id": "3",
"metadata": {},
"source": [
"***注：以上代码执行完成后，需点击左上角或右上角将kernel更换为python-3.9.0***"
@@ -66,7 +66,7 @@
},
{
"cell_type": "markdown",
- "id": "757cb29c-48f4-4b6b-9b71-6c008e629eb8",
+ "id": "4",
"metadata": {},
"source": [
"2. 安装mindspore2.2.12，安装指南详见：[MindSpore安装](https://www.mindspore.cn/install)\n",
@@ -75,112 +75,10 @@
},
{
"cell_type": "code",
- "execution_count": 21,
- "id": "6491560b-1ec6-4ca1-88cc-c7f9c1297725",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Looking in indexes: http://pip.modelarts.private.com:8888/repository/pypi/simple\n",
- "Processing ./mindnlp-0.4.1-py3-none-any.whl\n",
- "Requirement already satisfied: addict in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2.4.0)\n",
- "Requirement already satisfied: pytest==7.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (7.2.0)\n",
- "Requirement already satisfied: tqdm in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (4.66.4)\n",
- "Requirement already satisfied: regex in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2024.7.24)\n",
- "Requirement already satisfied: evaluate in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.4.3)\n",
- "Requirement already satisfied: pyctcdecode in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.5.0)\n",
- "Requirement already satisfied: ml-dtypes in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.4.0)\n",
- "Requirement already satisfied: sentencepiece in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.2.0)\n",
- "Requirement already satisfied: tokenizers==0.19.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.19.1)\n",
- "Requirement already satisfied: datasets in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (3.1.0)\n",
- "Requirement already satisfied: safetensors in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (0.4.5)\n",
- "Requirement already satisfied: pillow>=10.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (10.0.1)\n",
- "Requirement already satisfied: mindspore>=2.2.14 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2.4.0)\n",
- "Requirement already satisfied: requests in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindnlp==0.4.1) (2.32.3)\n",
- "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (1.2.2)\n",
- "Requirement already satisfied: pluggy<2.0,>=0.12 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (1.5.0)\n",
- "Requirement already satisfied: attrs>=19.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (23.2.0)\n",
- "Requirement already satisfied: packaging in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (24.1)\n",
- "Requirement already satisfied: iniconfig in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (2.0.0)\n",
- "Requirement already satisfied: tomli>=1.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pytest==7.2.0->mindnlp==0.4.1) (2.0.1)\n",
- "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from tokenizers==0.19.1->mindnlp==0.4.1) (0.24.2)\n",
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (4.12.2)\n",
- "Requirement already satisfied: fsspec>=2023.5.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (2024.6.1)\n",
- "Requirement already satisfied: filelock in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (3.15.4)\n",
- "Requirement already satisfied: pyyaml>=5.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1->mindnlp==0.4.1) (6.0.1)\n",
- "Requirement already satisfied: psutil>=5.6.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (5.9.5)\n",
- "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.22.0)\n",
- "Requirement already satisfied: astunparse>=1.6.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.6.3)\n",
- "Requirement already satisfied: asttokens>=2.0.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (2.4.1)\n",
- "Requirement already satisfied: scipy>=1.5.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (1.10.1)\n",
- "Requirement already satisfied: protobuf>=3.13.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore>=2.2.14->mindnlp==0.4.1) (3.20.2)\n",
- "Requirement already satisfied: six>=1.12.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from asttokens>=2.0.4->mindspore>=2.2.14->mindnlp==0.4.1) (1.16.0)\n",
- "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore>=2.2.14->mindnlp==0.4.1) (0.38.4)\n",
- "Requirement already satisfied: pandas in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (1.3.5)\n",
- "Requirement already satisfied: multiprocess<0.70.17 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (0.70.16)\n",
- "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (0.3.8)\n",
- "Requirement already satisfied: aiohttp in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (3.11.9)\n",
- "Requirement already satisfied: pyarrow>=15.0.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (18.1.0)\n",
- "Requirement already satisfied: xxhash in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from datasets->mindnlp==0.4.1) (3.5.0)\n",
- "Requirement already satisfied: async-timeout<6.0,>=4.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (5.0.1)\n",
- "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (2.4.4)\n",
- "Requirement already satisfied: multidict<7.0,>=4.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (6.1.0)\n",
- "Requirement already satisfied: aiosignal>=1.1.2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (1.3.1)\n",
- "Requirement already satisfied: yarl<2.0,>=1.17.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (1.18.3)\n",
- "Requirement already satisfied: propcache>=0.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (0.2.1)\n",
- "Requirement already satisfied: frozenlist>=1.1.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from aiohttp->datasets->mindnlp==0.4.1) (1.5.0)\n",
- "Requirement already satisfied: charset-normalizer<4,>=2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2.0.12)\n",
- "Requirement already satisfied: certifi>=2017.4.17 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2024.7.4)\n",
- "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (1.26.7)\n",
- "Requirement already satisfied: idna<4,>=2.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->mindnlp==0.4.1) (2.10)\n",
- "Requirement already satisfied: pytz>=2017.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.1) (2024.1)\n",
- "Requirement already satisfied: python-dateutil>=2.7.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pandas->datasets->mindnlp==0.4.1) (2.9.0.post0)\n",
- "Requirement already satisfied: hypothesis<7,>=6.14 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.1) (6.122.1)\n",
- "Requirement already satisfied: pygtrie<3.0,>=2.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from pyctcdecode->mindnlp==0.4.1) (2.5.0)\n",
- "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from hypothesis<7,>=6.14->pyctcdecode->mindnlp==0.4.1) (2.4.0)\n",
- "mindnlp is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
- "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
- "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n",
- "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
- "Requirement already satisfied: tokenizers==0.19.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (0.19.1)\n",
- "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from tokenizers==0.19.1) (0.24.2)\n",
- "Requirement already satisfied: packaging>=20.9 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (24.1)\n",
- "Requirement already satisfied: fsspec>=2023.5.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (2024.6.1)\n",
- "Requirement already satisfied: pyyaml>=5.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (6.0.1)\n",
- "Requirement already satisfied: tqdm>=4.42.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (4.66.4)\n",
- "Requirement already satisfied: requests in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (2.32.3)\n",
- "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (4.12.2)\n",
- "Requirement already satisfied: filelock in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (3.15.4)\n",
- "Requirement already satisfied: charset-normalizer<4,>=2 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (2.0.12)\n",
- "Requirement already satisfied: idna<4,>=2.5 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (2.10)\n",
- "Requirement already satisfied: certifi>=2017.4.17 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (2024.7.4)\n",
- "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from requests->huggingface-hub<1.0,>=0.16.4->tokenizers==0.19.1) (1.26.7)\n",
- "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
- "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n",
- "env: no_proxy='a.test.com,127.0.0.1,2.2.2.2'\n",
- "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
- "Collecting mindspore==2.4.0\n",
- " Using cached https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.4.0/MindSpore/unified/aarch64/mindspore-2.4.0-cp39-cp39-linux_aarch64.whl (333.7 MB)\n",
- "Requirement already satisfied: protobuf>=3.13.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (3.20.2)\n",
- "Requirement already satisfied: psutil>=5.6.1 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (5.9.5)\n",
- "Requirement already satisfied: asttokens>=2.0.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (2.4.1)\n",
- "Requirement already satisfied: packaging>=20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (24.1)\n",
- "Requirement already satisfied: numpy<2.0.0,>=1.20.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (1.22.0)\n",
- "Requirement already satisfied: safetensors>=0.4.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (0.4.5)\n",
- "Requirement already satisfied: scipy>=1.5.4 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (1.10.1)\n",
- "Requirement already satisfied: pillow>=6.2.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (10.0.1)\n",
- "Requirement already satisfied: astunparse>=1.6.3 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from mindspore==2.4.0) (1.6.3)\n",
- "Requirement already satisfied: six>=1.12.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from asttokens>=2.0.4->mindspore==2.4.0) (1.16.0)\n",
- "Requirement already satisfied: wheel<1.0,>=0.23.0 in /home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages (from astunparse>=1.6.3->mindspore==2.4.0) (0.38.4)\n",
- "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 24.3.1 is available.\n",
- "You should consider upgrading via the '/home/ma-user/anaconda3/envs/MindSpore/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
- "Note: you may need to restart the kernel to use updated packages.\n",
- "\u001b[33mWARNING: Skipping mindformers as it is not installed.\u001b[0m\n"
- ]
- }
- ],
+ "execution_count": null,
+ "id": "5",
+ "metadata": {},
+ "outputs": [],
"source": [
"!pip install mindnlp-0.4.1-py3-none-any.whl # 将安装mindnlp版本更换为mindnlp-0.4.0-py3-none-any.whl（daily版本）\n",
"!pip install tokenizers==0.19.1 -i https://pypi.tuna.tsinghua.edu.cn/simple # 修改tokenizers版本为0.19.1\n",
@@ -191,7 +89,7 @@
},
{
"cell_type": "markdown",
- "id": "0faae5c6-8397-4574-9e9c-f86230bb071a",
+ "id": "6",
"metadata": {},
"source": [
"***注：执行如上命令完成安装后，请点击上方的restart kernel图标重启kernel，再进行实验***"
@@ -199,7 +97,7 @@
},
{
"cell_type": "markdown",
- "id": "bb699e4a-a2dc-44f2-b3cb-6b86fa9b24f6",
+ "id": "7",
"metadata": {},
"source": [
"### 数据集加载与处理\n",
@@ -211,39 +109,10 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "id": "27bf5931-7b09-4984-841c-fbea311d3955",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.821.904 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleGetModelId failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleGetModelId\n",
- "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.821.956 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleLoadFromMem failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleLoadFromMem\n",
- "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.821.975 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclmdlBundleUnload failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclmdlBundleUnload\n",
- "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.822.162 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtGetMemUceInfo failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtGetMemUceInfo\n",
- "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.822.179 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtDeviceTaskAbort failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtDeviceTaskAbort\n",
- "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.822.193 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol aclrtMemUceRepair failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libascendcl.so: undefined symbol: aclrtMemUceRepair\n",
- "[WARNING] GE_ADPT(12319,ffffaba360b0,python):2024-12-03-21:03:05.823.653 [mindspore/ccsrc/utils/dlopen_macro.h:163] DlsymAscend] Dynamically load symbol acltdtCleanChannel failed, result = /usr/local/Ascend/ascend-toolkit/latest/lib64/libacl_tdt_channel.so: undefined symbol: acltdtCleanChannel\n",
- "[WARNING] ME(12319:281473561354416,MainProcess):2024-12-03-21:03:05.955.541 [mindspore/run_check/_check_version.py:396] Can not find the tbe operator implementation(need by mindspore-ascend). Please check whether the Environment Variable PYTHONPATH is set. For details, refer to the installation guidelines: https://www.mindspore.cn/install\n",
- "/home/ma-user/anaconda3/envs/MindSpore/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for