1 files changed, 1106 insertions, 0 deletions
diff --git a/pretrained/swin/603_timm_swin.ipynb b/pretrained/swin/603_timm_swin.ipynb
new file mode 100644
index 0000000..5e4ef1c
--- /dev/null
+++ b/pretrained/swin/603_timm_swin.ipynb
@@ -0,0 +1,1106 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:13:01.793980Z",
+     "start_time": "2022-06-03T03:12:58.523232Z"
+    }
+   },
+   "source": [
+    "\n",
+    "### 1. 导入模块"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:23:13.466151Z",
+     "start_time": "2022-06-03T03:23:13.462758Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import timm\n",
+    "import torch\n",
+    "import torchvision\n",
+    "from PIL import Image\n",
+    "from torchvision import transforms as T\n",
+    "import json\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. 输入EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:23:32.646262Z",
+     "start_time": "2022-06-03T03:23:32.642829Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "image = Image.open('./house_finch.jpg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:23:33.718387Z",
+     "start_time": "2022-06-03T03:23:33.714618Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'PIL.JpegImagePlugin.JpegImageFile'> RGB JPEG (480, 360) ('R', 'G', 'B')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(image), image.mode, image.format, image.size, image.getbands())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:23:36.983614Z",
+     "start_time": "2022-06-03T03:23:36.929060Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=480x360 at 0x1586D7198>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. 输入预处理"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:40:24.751142Z",
+     "start_time": "2022-06-03T03:40:24.723186Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=224x224 at 0x16D750588>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "T.Compose([T.Resize(256), T.CenterCrop(224)])(image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:42:57.682222Z",
+     "start_time": "2022-06-03T03:42:57.679214Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.485, 0.456, 0.406) (0.229, 0.224, 0.225)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(timm.data.IMAGENET_DEFAULT_MEAN, timm.data.IMAGENET_DEFAULT_STD)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:43:13.189027Z",
+     "start_time": "2022-06-03T03:43:13.185780Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "trans_ = T.Compose([T.Resize(256), \n",
+    "                    T.CenterCrop(224), \n",
+    "                    T.ToTensor(), \n",
+    "                    T.Normalize(timm.data.IMAGENET_DEFAULT_MEAN, timm.data.IMAGENET_DEFAULT_STD)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:45:41.612711Z",
+     "start_time": "2022-06-03T03:45:41.606902Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "transed_input = trans_(image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:43:48.754468Z",
+     "start_time": "2022-06-03T03:43:48.750842Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'torch.Tensor'> torch.Size([3, 224, 224])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(transed_input), transed_input.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:45:44.445842Z",
+     "start_time": "2022-06-03T03:45:44.443234Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "batch_input = transed_input.unsqueeze(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:45:45.541216Z",
+     "start_time": "2022-06-03T03:45:45.537164Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 3, 224, 224])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch_input.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. 模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:24:02.898656Z",
+     "start_time": "2022-06-03T03:24:02.893917Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['swin_base_patch4_window7_224',\n",
+       " 'swin_base_patch4_window7_224_in22k',\n",
+       " 'swin_base_patch4_window12_384',\n",
+       " 'swin_base_patch4_window12_384_in22k',\n",
+       " 'swin_large_patch4_window7_224',\n",
+       " 'swin_large_patch4_window7_224_in22k',\n",
+       " 'swin_large_patch4_window12_384',\n",
+       " 'swin_large_patch4_window12_384_in22k',\n",
+       " 'swin_small_patch4_window7_224',\n",
+       " 'swin_tiny_patch4_window7_224']"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "timm.list_models('swin*')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:24:13.279315Z",
+     "start_time": "2022-06-03T03:24:10.504147Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/chunhuizhang/anaconda3/lib/python3.6/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)\n",
+      "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = timm.create_model('swin_base_patch4_window7_224', pretrained=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:33:39.593106Z",
+     "start_time": "2022-06-03T03:33:39.583112Z"
+    },
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SwinTransformer(\n",
+       "  (patch_embed): PatchEmbed(\n",
+       "    (proj): Conv2d(3, 128, kernel_size=(4, 4), stride=(4, 4))\n",
+       "    (norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "  )\n",
+       "  (pos_drop): Dropout(p=0.0, inplace=False)\n",
+       "  (layers): Sequential(\n",
+       "    (0): BasicLayer(\n",
+       "      dim=128, input_resolution=(56, 56), depth=2\n",
+       "      (blocks): ModuleList(\n",
+       "        (0): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=128, out_features=384, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=128, out_features=128, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): Identity()\n",
+       "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=128, out_features=512, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=512, out_features=128, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (1): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=128, out_features=384, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=128, out_features=128, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=128, out_features=512, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=512, out_features=128, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "      (downsample): PatchMerging(\n",
+       "        input_resolution=(56, 56), dim=128\n",
+       "        (reduction): Linear(in_features=512, out_features=256, bias=False)\n",
+       "        (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (1): BasicLayer(\n",
+       "      dim=256, input_resolution=(28, 28), depth=2\n",
+       "      (blocks): ModuleList(\n",
+       "        (0): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=256, out_features=768, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=256, out_features=256, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=256, out_features=1024, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=1024, out_features=256, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (1): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=256, out_features=768, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=256, out_features=256, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=256, out_features=1024, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=1024, out_features=256, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "      (downsample): PatchMerging(\n",
+       "        input_resolution=(28, 28), dim=256\n",
+       "        (reduction): Linear(in_features=1024, out_features=512, bias=False)\n",
+       "        (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (2): BasicLayer(\n",
+       "      dim=512, input_resolution=(14, 14), depth=18\n",
+       "      (blocks): ModuleList(\n",
+       "        (0): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (1): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (2): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (3): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (4): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (5): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (6): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (7): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (8): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (9): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (10): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (11): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (12): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (13): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (14): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (15): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (16): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (17): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=512, out_features=1536, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "      (downsample): PatchMerging(\n",
+       "        input_resolution=(14, 14), dim=512\n",
+       "        (reduction): Linear(in_features=2048, out_features=1024, bias=False)\n",
+       "        (norm): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
+       "      )\n",
+       "    )\n",
+       "    (3): BasicLayer(\n",
+       "      dim=1024, input_resolution=(7, 7), depth=2\n",
+       "      (blocks): ModuleList(\n",
+       "        (0): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "        (1): SwinTransformerBlock(\n",
+       "          (norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+       "          (attn): WindowAttention(\n",
+       "            (qkv): Linear(in_features=1024, out_features=3072, bias=True)\n",
+       "            (attn_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (proj): Linear(in_features=1024, out_features=1024, bias=True)\n",
+       "            (proj_drop): Dropout(p=0.0, inplace=False)\n",
+       "            (softmax): Softmax(dim=-1)\n",
+       "          )\n",
+       "          (drop_path): DropPath()\n",
+       "          (norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+       "          (mlp): Mlp(\n",
+       "            (fc1): Linear(in_features=1024, out_features=4096, bias=True)\n",
+       "            (act): GELU()\n",
+       "            (drop1): Dropout(p=0.0, inplace=False)\n",
+       "            (fc2): Linear(in_features=4096, out_features=1024, bias=True)\n",
+       "            (drop2): Dropout(p=0.0, inplace=False)\n",
+       "          )\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (norm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
+       "  (avgpool): AdaptiveAvgPool1d(output_size=1)\n",
+       "  (head): Linear(in_features=1024, out_features=1000, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:40:47.294006Z",
+     "start_time": "2022-06-03T03:40:47.291281Z"
+    }
+   },
+   "source": [
+    "### 5. 推理及预测过程"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:46:21.205781Z",
+     "start_time": "2022-06-03T03:46:20.631104Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with torch.no_grad():\n",
+    "    output = model(batch_input)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:46:31.283442Z",
+     "start_time": "2022-06-03T03:46:31.279123Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 1000])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:46:53.948492Z",
+     "start_time": "2022-06-03T03:46:53.943626Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([27.4846])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output.sum(dim=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:47:11.182283Z",
+     "start_time": "2022-06-03T03:47:11.176758Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(12)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output.argmax()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:48:49.479846Z",
+     "start_time": "2022-06-03T03:48:49.477181Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from torch import nn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:49:07.905849Z",
+     "start_time": "2022-06-03T03:49:07.900794Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(0.8581)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nn.Softmax(dim=1)(output).max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:50:17.663756Z",
+     "start_time": "2022-06-03T03:50:17.656105Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.return_types.topk(\n",
+       "values=tensor([[0.8581, 0.0190, 0.0013]]),\n",
+       "indices=tensor([[12, 10, 86]]))"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.topk(nn.Softmax(dim=1)(output), 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. 输出解析"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:47:47.766473Z",
+     "start_time": "2022-06-03T03:47:47.762380Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with open('./imagenet_labels.json') as f:\n",
+    "    labels = json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:48:00.486899Z",
+     "start_time": "2022-06-03T03:48:00.482913Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'house finch'"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels[output.argmax()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-06-03T03:50:48.617413Z",
+     "start_time": "2022-06-03T03:50:48.613544Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'partridge'"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels[86]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}