Merge pull request #65 from ayasyrev/pydantic

Pydantic

Author: ayasyrev

Nbs/00_ModelConstructor.ipynb

@@ -28,22 +28,7 @@
    "source": [
     "#hide\n",
     "import torch\n",
-    "import torch.nn as nn\n",
-    "\n",
-    "from nbdev.showdoc import show_doc\n",
-    "from IPython.display import Markdown, display"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# hide\n",
-    "def print_doc(func_name):\n",
-    "    doc = show_doc(func_name, title_level=4, disp=False)\n",
-    "    display(Markdown(doc))"
+    "import torch.nn as nn"
    ]
   },
   {
@@ -59,20 +44,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#hide\n",
     "from model_constructor.yaresnet import YaResBlock"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#hide_input\n",
-    "# print_doc(YaResBlock)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -341,9 +315,9 @@
        "    (se): SEModule(\n",
        "      (squeeze): AdaptiveAvgPool2d(output_size=1)\n",
        "      (excitation): Sequential(\n",
-       "        (fc_reduce): Linear(in_features=512, out_features=32, bias=True)\n",
+       "        (reduce): Linear(in_features=512, out_features=32, bias=True)\n",
        "        (se_act): ReLU(inplace=True)\n",
-       "        (fc_expand): Linear(in_features=32, out_features=512, bias=True)\n",
+       "        (expand): Linear(in_features=32, out_features=512, bias=True)\n",
        "        (se_gate): Sigmoid()\n",
        "      )\n",
        "    )\n",
@@ -443,9 +417,9 @@
        "    (se): SEModule(\n",
        "      (squeeze): AdaptiveAvgPool2d(output_size=1)\n",
        "      (excitation): Sequential(\n",
-       "        (fc_reduce): Linear(in_features=512, out_features=32, bias=True)\n",
+       "        (reduce): Linear(in_features=512, out_features=32, bias=True)\n",
        "        (se_act): ReLU(inplace=True)\n",
-       "        (fc_expand): Linear(in_features=32, out_features=512, bias=True)\n",
+       "        (expand): Linear(in_features=32, out_features=512, bias=True)\n",
        "        (se_gate): Sigmoid()\n",
        "      )\n",
        "    )\n",
@@ -468,8 +442,14 @@
    ],
    "source": [
     "#collapse_output\n",
-    "bl = YaResBlock(4, 64, 128, stride=2, pool=pool, act_fn=nn.LeakyReLU(), dw=True,\n",
-    "                se=SEModule, sa=SimpleSelfAttention)\n",
+    "bl = YaResBlock(\n",
+    "    4, 64, 128,\n",
+    "    stride=2,\n",
+    "    pool=pool,\n",
+    "    act_fn=nn.LeakyReLU(),\n",
+    "    dw=True,\n",
+    "    se=SEModule,\n",
+    "    sa=SimpleSelfAttention)\n",
     "bl"
    ]
   },
@@ -528,7 +508,7 @@
     {
      "data": {
       "text/plain": [
-       "([64, 64, 128, 256, 512], [2, 2, 2, 2])"
+       "([64, 128, 256, 512], [2, 2, 2, 2])"
       ]
      },
      "execution_count": null,
@@ -982,9 +962,9 @@
        "    (se): SEModule(\n",
        "      (squeeze): AdaptiveAvgPool2d(output_size=1)\n",
        "      (excitation): Sequential(\n",
-       "        (fc_reduce): Linear(in_features=64, out_features=4, bias=True)\n",
+       "        (reduce): Linear(in_features=64, out_features=4, bias=True)\n",
        "        (se_act): ReLU(inplace=True)\n",
-       "        (fc_expand): Linear(in_features=4, out_features=64, bias=True)\n",
+       "        (expand): Linear(in_features=4, out_features=64, bias=True)\n",
        "        (se_gate): Sigmoid()\n",
        "      )\n",
        "    )\n",
@@ -1003,6 +983,76 @@
     "yaresnet.body.l_0.bl_0"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# YaResnet34, YaResnet50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from model_constructor.yaresnet import YaResNet34, YaResNet50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YaResnet34 constructor\n",
+       "  in_chans: 3, num_classes: 1000\n",
+       "  expansion: 1, groups: 1, dw: False, div_groups: None\n",
+       "  sa: False, se: False\n",
+       "  stem sizes: [3, 32, 32, 64], stride on 0\n",
+       "  body sizes [64, 128, 256, 512]\n",
+       "  layers: [3, 4, 6, 3]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yaresnet34 = YaResNet34()\n",
+    "yaresnet34"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "YaResnet50 constructor\n",
+       "  in_chans: 3, num_classes: 1000\n",
+       "  expansion: 4, groups: 1, dw: False, div_groups: None\n",
+       "  sa: False, se: False\n",
+       "  stem sizes: [3, 32, 32, 64], stride on 0\n",
+       "  body sizes [64, 128, 256, 512]\n",
+       "  layers: [3, 4, 6, 3]"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yaresnet50 = YaResNet50()\n",
+    "yaresnet50"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},