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check notebooks, first version of docs
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‎Nbs/00_ModelConstructor.ipynb‎

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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The autoreload extension is already loaded. To reload it, use:\n",
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" %reload_ext autoreload\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"#hide\n",
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"%load_ext autoreload\n",
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"source": [
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"#hide\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"\n",
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"from nbdev.showdoc import show_doc\n",
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"from IPython.display import Markdown, display"
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"import torch.nn as nn"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"<h4 id=\"ResBlock\" class=\"doc_header\"><code>class</code> <code>ResBlock</code><a href=\"model_constructor/model_constructor.py#L26\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n",
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"\n",
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"> <code>ResBlock</code>(**`expansion`**, **`in_channels`**, **`mid_channels`**, **`stride`**=*`1`*, **`conv_layer`**=*`ConvBnAct`*, **`act_fn`**=*`ReLU(inplace=True)`*, **`zero_bn`**=*`True`*, **`bn_1st`**=*`True`*, **`groups`**=*`1`*, **`dw`**=*`False`*, **`div_groups`**=*`None`*, **`pool`**=*`None`*, **`se`**=*`None`*, **`sa`**=*`None`*) :: `Module`\n",
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"\n",
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"Resnet block"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"outputs": [],
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"source": [
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"#hide_input\n",
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"print_doc(ResBlock)"
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"# print_doc(ResBlock)"
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]
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},
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{
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"block"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Stem, Body, Head"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#hide\n",
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"from model_constructor.model_constructor import _make_body, _make_head, _make_layer, _make_stem"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"<h4 id=\"_make_layer\" class=\"doc_header\"><code>_make_layer</code><a href=\"model_constructor/model_constructor.py#L88\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n",
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"\n",
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"> <code>_make_layer</code>(**`expansion`**, **`in_channels`**, **`out_channels`**, **`blocks`**, **`stride`**, **`sa`**)\n",
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"\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"#hide_input\n",
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"print_doc(_make_layer)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"<h4 id=\"_make_stem\" class=\"doc_header\"><code>_make_stem</code><a href=\"model_constructor/model_constructor.py#L75\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n",
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"\n",
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"> <code>_make_stem</code>()\n",
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"\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"#hide_input\n",
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"print_doc(_make_stem)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"<h4 id=\"_make_body\" class=\"doc_header\"><code>_make_body</code><a href=\"model_constructor/model_constructor.py#L99\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n",
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"\n",
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"> <code>_make_body</code>()\n",
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"\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"#hide_input\n",
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"print_doc(_make_body)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"<h4 id=\"_make_head\" class=\"doc_header\"><code>_make_head</code><a href=\"model_constructor/model_constructor.py#L109\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n",
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"\n",
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"> <code>_make_head</code>()\n",
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"\n"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"#hide_input\n",
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"print_doc(_make_head)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"<h4 id=\"ModelConstructor\" class=\"doc_header\"><code>class</code> <code>ModelConstructor</code><a href=\"model_constructor/model_constructor.py#L117\" class=\"source_link\" style=\"float:right\">[source]</a></h4>\n",
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"\n",
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"> <code>ModelConstructor</code>(**`name`**=*`'MC'`*, **`in_chans`**=*`3`*, **`num_classes`**=*`1000`*, **`block`**=*`ResBlock`*, **`conv_layer`**=*`ConvBnAct`*, **`block_sizes`**=*`[64, 128, 256, 512]`*, **`layers`**=*`[2, 2, 2, 2]`*, **`norm`**=*`BatchNorm2d`*, **`act_fn`**=*`ReLU(inplace=True)`*, **`pool`**=*`AvgPool2d(kernel_size=2, stride=2, padding=0)`*, **`expansion`**=*`1`*, **`groups`**=*`1`*, **`dw`**=*`False`*, **`div_groups`**=*`None`*, **`sa`**:`Union`\\[`bool`, `int`, `typing.Callable`\\]=*`False`*, **`se`**:`Union`\\[`bool`, `int`, `typing.Callable`\\]=*`False`*, **`se_module`**=*`None`*, **`se_reduction`**=*`None`*, **`bn_1st`**=*`True`*, **`zero_bn`**=*`True`*, **`stem_stride_on`**=*`0`*, **`stem_sizes`**=*`[32, 32, 64]`*, **`stem_pool`**=*`MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)`*, **`stem_bn_end`**=*`False`*, **`_init_cnn`**=*`init_cnn`*, **`_make_stem`**=*`_make_stem`*, **`_make_layer`**=*`_make_layer`*, **`_make_body`**=*`_make_body`*, **`_make_head`**=*`_make_head`*)\n",
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"\n",
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"Model constructor. As default - xresnet18"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"outputs": [],
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"source": [
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"#hide_input\n",
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"print_doc(ModelConstructor)"
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"# print_doc(ModelConstructor)"
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]
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},
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{
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"mc"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[64, 128, 256, 512]"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mc._block_sizes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[64, 64, 128, 256, 512]"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mc.block_sizes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"MC constructor\n",
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" in_chans: 3, num_classes: 1000\n",
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" expansion: 1, groups: 1, dw: False, div_groups: None\n",
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" sa: False, se: False\n",
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" stem sizes: [3, 32, 32, 64], stride on 0\n",
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" body sizes [128, 256, 512, 1024]\n",
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" layers: [2, 2, 2, 2]"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mc._block_sizes = [128, 256, 512, 1024]\n",
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"mc"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[64, 128, 256, 512, 1024]"
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]
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},
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"mc.block_sizes"
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]
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},
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"mc.act_fn =nn.LeakyReLU(inplace=True)"
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"mc.act_fn = nn.LeakyReLU(inplace=True)"
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]
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{
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" (se): SEModule(\n",
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" (squeeze): AdaptiveAvgPool2d(output_size=1)\n",
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" (excitation): Sequential(\n",
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" (fc_reduce): Linear(in_features=64, out_features=4, bias=True)\n",
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" (reduce): Linear(in_features=64, out_features=4, bias=True)\n",
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" (se_act): ReLU(inplace=True)\n",
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" (fc_expand): Linear(in_features=4, out_features=64, bias=True)\n",
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" (expand): Linear(in_features=4, out_features=64, bias=True)\n",
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" (se_gate): Sigmoid()\n",
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" )\n",
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" )\n",
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" (se): SEModule(\n",
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" (squeeze): AdaptiveAvgPool2d(output_size=1)\n",
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" (excitation): Sequential(\n",
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" (fc_reduce): Linear(in_features=64, out_features=4, bias=True)\n",
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" (reduce): Linear(in_features=64, out_features=4, bias=True)\n",
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" (se_act): ReLU(inplace=True)\n",
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" (fc_expand): Linear(in_features=4, out_features=64, bias=True)\n",
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" (expand): Linear(in_features=4, out_features=64, bias=True)\n",
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" (se_gate): Sigmoid()\n",
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" )\n",
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" )\n",

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