W8-9/정리_Data_Augmentation.ipynb

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f9ccc9b442087beb5cd8b198543":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}}}}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"bk0eYbJB2aLR"},"outputs":[],"source":["import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","from torchvision import transforms, utils\n","import itertools\n","import matplotlib.pyplot as plt\n","import os\n","import numpy as np\n","import pandas as pd\n","import random"]},{"cell_type":"code","source":["import albumentations\n","import albumentations.pytorch"],"metadata":{"id":"4T6z8K7bs0xN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["albumentations.__version__"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"RR3hufwtpp21","executionInfo":{"status":"ok","timestamp":1647153073866,"user_tz":-540,"elapsed":13,"user":{"displayName":"Jaehoon Kim","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GizEXeQ6HMz4cgKYwF05OOke2Q8RdtVQvsKnyotMg=s64","userId":"03064955329912050209"}},"outputId":"e03238ce-740a-43b8-a37e-eb966472fb4c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'0.1.12'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["!pip3 install quickdraw"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2oc3C05b9e7h","executionInfo":{"status":"ok","timestamp":1647153078510,"user_tz":-540,"elapsed":4655,"user":{"displayName":"Jaehoon Kim","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GizEXeQ6HMz4cgKYwF05OOke2Q8RdtVQvsKnyotMg=s64","userId":"03064955329912050209"}},"outputId":"2a62e079-3814-45a8-881a-115ad535210e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting quickdraw\n","  Downloading quickdraw-0.2.0-py3-none-any.whl (10 kB)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from quickdraw) (2.23.0)\n","Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from quickdraw) (7.1.2)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->quickdraw) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->quickdraw) (3.0.4)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->quickdraw) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->quickdraw) (2021.10.8)\n","Installing collected packages: quickdraw\n","Successfully installed quickdraw-0.2.0\n"]}]},{"cell_type":"code","source":["from quickdraw import QuickDrawData, QuickDrawDataGroup"],"metadata":{"id":"nNL0MBc695ZD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["seed = 111\n","\n","torch.manual_seed(seed)\n","torch.cuda.manual_seed(seed)\n","torch.cuda.manual_seed_all(seed)\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","np.random.seed(seed)\n","random.seed(seed)"],"metadata":{"id":"uwablAmt9g3N"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["num_img_per_class = 3000\n","\n","qd = QuickDrawData(max_drawings=num_img_per_class)\n","\n","class_list = ['apple', \n","              'wine bottle', \n","              'spoon', \n","              'rainbow', \n","              'panda', \n","              'hospital', \n","              'scissors', \n","              'toothpaste', \n","              'baseball', \n","              'hourglass']\n","\n","class_dict = {'apple' : 0, \n","              'wine bottle' : 1, \n","              'spoon' : 2, \n","              'rainbow' : 3, \n","              'panda': 4, \n","              'hospital' : 5, \n","              'scissors' : 6, \n","              'toothpaste' : 7, \n","              'baseball' : 8, \n","              'hourglass' : 9}"],"metadata":{"id":"qxd6ztDW9x6J"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["qd.load_drawings(class_list)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CJaImocj9zqI","executionInfo":{"status":"ok","timestamp":1647153086474,"user_tz":-540,"elapsed":7986,"user":{"displayName":"Jaehoon Kim","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GizEXeQ6HMz4cgKYwF05OOke2Q8RdtVQvsKnyotMg=s64","userId":"03064955329912050209"}},"outputId":"85fb1041-2eb8-4187-b66c-6528ca8e219e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["downloading apple from https://storage.googleapis.com/quickdraw_dataset/full/binary/apple.bin\n","download complete\n","loading apple drawings\n","load complete\n","downloading wine bottle from https://storage.googleapis.com/quickdraw_dataset/full/binary/wine bottle.bin\n","download complete\n","loading wine bottle drawings\n","load complete\n","downloading spoon from https://storage.googleapis.com/quickdraw_dataset/full/binary/spoon.bin\n","download complete\n","loading spoon drawings\n","load complete\n","downloading rainbow from https://storage.googleapis.com/quickdraw_dataset/full/binary/rainbow.bin\n","download complete\n","loading rainbow drawings\n","load complete\n","downloading panda from https://storage.googleapis.com/quickdraw_dataset/full/binary/panda.bin\n","download complete\n","loading panda drawings\n","load complete\n","downloading hospital from https://storage.googleapis.com/quickdraw_dataset/full/binary/hospital.bin\n","download complete\n","loading hospital drawings\n","load complete\n","downloading scissors from https://storage.googleapis.com/quickdraw_dataset/full/binary/scissors.bin\n","download complete\n","loading scissors drawings\n","load complete\n","downloading toothpaste from https://storage.googleapis.com/quickdraw_dataset/full/binary/toothpaste.bin\n","download complete\n","loading toothpaste drawings\n","load complete\n","downloading baseball from https://storage.googleapis.com/quickdraw_dataset/full/binary/baseball.bin\n","download complete\n","loading baseball drawings\n","load complete\n","downloading hourglass from https://storage.googleapis.com/quickdraw_dataset/full/binary/hourglass.bin\n","download complete\n","loading hourglass drawings\n","load complete\n"]}]},{"cell_type":"code","source":["train_data = []\n","val_data = []\n","\n","train_label = []\n","val_label = []\n","\n","for class_name in class_list:\n","    qdgroup = QuickDrawDataGroup(class_name,\n","                                 max_drawings=num_img_per_class)\n","    \n","    for i, img in enumerate(qdgroup.drawings):\n","        if i<int(0.9*num_img_per_class):\n","            train_data.append(img.get_image())\n","            train_label.append(class_dict[class_name])\n","        else:\n","            val_data.append(img.get_image())\n","            val_label.append(class_dict[class_name])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IPz2nLBU-Iq0","executionInfo":{"status":"ok","timestamp":1647153095126,"user_tz":-540,"elapsed":8671,"user":{"displayName":"Jaehoon Kim","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GizEXeQ6HMz4cgKYwF05OOke2Q8RdtVQvsKnyotMg=s64","userId":"03064955329912050209"}},"outputId":"923840f6-dfae-4fbf-a3d9-31fcd086e144"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["loading apple drawings\n","load complete\n","loading wine bottle drawings\n","load complete\n","loading spoon drawings\n","load complete\n","loading rainbow drawings\n","load complete\n","loading panda drawings\n","load complete\n","loading hospital drawings\n","load complete\n","loading scissors drawings\n","load complete\n","loading toothpaste drawings\n","load complete\n","loading baseball drawings\n","load complete\n","loading hourglass drawings\n","load complete\n"]}]},{"cell_type":"code","source":["class QuickDrawDataset(Dataset):\n","\n","    def __init__(self, data, labels, transform=None):\n","        self.data = data\n","        self.labels = labels\n","        self.transform = transform\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, idx):\n","\n","        img = self.data[idx]\n","        \n","        label = self.labels[idx]\n","        \n","        if self.transform:\n","            img = self.transform(img)\n","        \n","        return img, label"],"metadata":{"id":"Nl1AHPlX-Xhj"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# torchvision Transform"],"metadata":{"id":"3vw8pDzdtwwT"}},{"cell_type":"code","source":["transform = transforms.Compose([\n","    transforms.RandomAdjustSharpness(sharpness_factor=0),\n","    transforms.RandomVerticalFlip(),\n","    transforms.GaussianBlur(kernel_size=5),\n","    transforms.RandomCrop(230), # 255 -> 230\n","    transforms.ToTensor(),\n","    transforms.Resize((224,224)),\n","    transforms.Normalize(mean=[0.485, 0.456, 0.406],\n","                         std=[0.229, 0.224, 0.225]) \n","    # ImageNet의 mean과 std로, 통상적으로 normalize 수행할 때 사용함\n","])\n","\n","transform_val = transforms.Compose([\n","    transforms.ToTensor(),\n","    transforms.Resize((224,224)),\n","    transforms.Normalize(mean=[0.485, 0.456, 0.406],\n","                         std=[0.229, 0.224, 0.225]) \n","])"],"metadata":{"id":"BMY4epFl-aS9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["image = next(iter(train_data))\n","print(type(image))\n","\n","tensor = transforms.ToTensor()(image)\n","print(tensor.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f3H2Us97RYb7","executionInfo":{"status":"ok","timestamp":1647153095772,"user_tz":-540,"elapsed":649,"user":{"displayName":"Jaehoon Kim","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GizEXeQ6HMz4cgKYwF05OOke2Q8RdtVQvsKnyotMg=s64","userId":"03064955329912050209"}},"outputId":"294db314-243a-429f-dd6e-698eaa968a8e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'PIL.Image.Image'>\n","torch.Size([3, 255, 255])\n"]}]},{"cell_type":"code","source":["image = transform(image)\n","plt.imshow(image.permute(1, 2, 0))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":324},"id":"Ci8jdWu0tWdF","executionInfo":{"status":"ok","timestamp":1647153095773,"user_tz":-540,"elapsed":20,"user":{"displayName":"Jaehoon Kim","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GizEXeQ6HMz4cgKYwF05OOke2Q8RdtVQvsKnyotMg=s64","userId":"03064955329912050209"}},"outputId":"117e7091-97bb-41bb-f581-f7279611de13"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f72353bfc90>"]},"metadata":{},"execution_count":13},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# albumentations Transform"],"metadata":{"id":"PrscdwdTttfk"}},{"cell_type":"code","source":["albumentations_transform = albumentations.Compose([\n","    albumentations.RandomCrop(230,230),\n","    albumentations.Resize(224, 224), \n","    albumentations.HorizontalFlip(),\n","    albumentations.Blur(p=1.0),\n","    albumentations.pytorch.transforms.ToTensor()\n","])"],"metadata":{"id":"e-UBKcyJsA1i"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["image = next(iter(train_data))\n","print(type(image))\n","\n","# tensor = albumentations.pytorch.transforms.ToTensor()(image=np.array(image))\n","# print(tensor.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VFbYwoxvt-vo","executionInfo":{"status":"ok","timestamp":1647153095774,"user_tz":-540,"elapsed":13,"user":{"displayName":"Jaehoon Kim","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GizEXeQ6HMz4cgKYwF05OOke2Q8RdtVQvsKnyotMg=s64","userId":"03064955329912050209"}},"outputId":"8a413f50-2744-4088-9ccf-8159c2f71ad9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'PIL.Image.Image'>\n"]}]},{"cell_type":"code","source":["# image = albumentations_transform(image=np.array(image))\n","# plt.imshow(image)"],"metadata":{"id":"cJLxgpiXt_2p"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Dataset, DataLoader"],"metadata":{"id":"G5Wij1U27wVE"}},{"cell_type":"code","source":["qd_train_dataset = QuickDrawDataset(train_data, train_label, transform)\n","qd_val_dataset = QuickDrawDataset(val_data, val_label, transform_val)\n","\n","qd_train_dataloader = DataLoader(qd_train_dataset, batch_size=64, shuffle=True)\n","qd_val_dataloader = DataLoader(qd_val_dataset, batch_size=64, shuffle=True)"],"metadata":{"id":"AP_g0Zcewl9j"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Visualizing augmented images\n","- Channel order:\n","  - cv2: BGR\n","  - torch: RGB\n","- Dimension:\n","  - cv2: (height, width, channel)\n","  - torch conv2d layer: (batch_size, channel, height, width)"],"metadata":{"id":"IcsOW1yo1DiL"}},{"cell_type":"code","source":["img, label = next(iter(qd_train_dataloader))\n","\n","plt.imshow(img[0].permute(1, 2, 0)) # torch.permute: dimension swapping\n","plt.show()\n","print(img[0].shape)\n","print(label[0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":342},"id":"golm5LKfx-ft","executionInfo":{"status":"ok","timestamp":1647153096368,"user_tz":-540,"elapsed":603,"user":{"displayName":"Jaehoon Kim","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GizEXeQ6HMz4cgKYwF05OOke2Q8RdtVQvsKnyotMg=s64","userId":"03064955329912050209"}},"outputId":"c30f8463-b533-4fbe-e924-4eb882b8c03e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["torch.Size([3, 224, 224])\n","tensor(6)\n"]}]},{"cell_type":"code","source":["val_img, val_label = next(iter(qd_val_dataloader))\n","\n","plt.imshow(val_img[0].permute(1, 2, 0))\n","plt.show()\n","print(val_img[0].shape)\n","print(val_label[0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":342},"id":"tofdJaY12YL2","executionInfo":{"status":"ok","timestamp":1647153096923,"user_tz":-540,"elapsed":565,"user":{"displayName":"Jaehoon Kim","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GizEXeQ6HMz4cgKYwF05OOke2Q8RdtVQvsKnyotMg=s64","userId":"03064955329912050209"}},"outputId":"1ec32b93-5420-4d66-a993-c1c7252dc8b0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["torch.Size([3, 224, 224])\n","tensor(9)\n"]}]},{"cell_type":"markdown","source":["# Training VGG-11 with augmented images"],"metadata":{"id":"9wH3zZ5-7q3O"}},{"cell_type":"code","source":["from torchvision.models import vgg11\n","\n","pretrained=True\n","\n","model = vgg11(pretrained)\n","\n","model"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":712,"referenced_widgets":["3b88d65ed5e04bc88690bf853b8a79cb","2a8df2d035e24434bec129b6404f61a3","c2994263883d43199f816aecb701c2fd","df67dfdda3504c388317c4729e6c8056","a63d252d436a4974ba12e16071efb202","eda2bcb18ed74d91a7543e8462c2ea93","f8f5dec50d4c46f9b881d08ef1b65c77","60f0dd7e2f6a48fdb3646a7ebcf68125","fb58ddee18c445b8a25716071023a1d0","c23e0d217ad5465d9b5ca6d86b2d7030","be007f9ccc9b442087beb5cd8b198543"]},"id":"w2qoOOUg5iQS","executionInfo":{"status":"ok","timestamp":1647153108286,"user_tz":-540,"elapsed":11368,"user":{"displayName":"Jaehoon Kim","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GizEXeQ6HMz4cgKYwF05OOke2Q8RdtVQvsKnyotMg=s64","userId":"03064955329912050209"}},"outputId":"cc1063f4-a37f-4d5c-9275-3671d9bba966"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Downloading: \"https://download.pytorch.org/models/vgg11-8a719046.pth\" to /root/.cache/torch/hub/checkpoints/vgg11-8a719046.pth\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0.00/507M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3b88d65ed5e04bc88690bf853b8a79cb"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["VGG(\n","  (features): Sequential(\n","    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (1): ReLU(inplace=True)\n","    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (4): ReLU(inplace=True)\n","    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (6): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (7): ReLU(inplace=True)\n","    (8): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (9): ReLU(inplace=True)\n","    (10): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (11): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (12): ReLU(inplace=True)\n","    (13): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (14): ReLU(inplace=True)\n","    (15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (17): ReLU(inplace=True)\n","    (18): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (19): ReLU(inplace=True)\n","    (20): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n","  (classifier): Sequential(\n","    (0): Linear(in_features=25088, out_features=4096, bias=True)\n","    (1): ReLU(inplace=True)\n","    (2): Dropout(p=0.5, inplace=False)\n","    (3): Linear(in_features=4096, out_features=4096, bias=True)\n","    (4): ReLU(inplace=True)\n","    (5): Dropout(p=0.5, inplace=False)\n","    (6): Linear(in_features=4096, out_features=1000, bias=True)\n","  )\n",")"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["model.classifier[6] = nn.Linear(in_features=4096, out_features=10, bias=True)\n","model.cuda()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PJWTZ6Pc8JME","executionInfo":{"status":"ok","timestamp":1647153116762,"user_tz":-540,"elapsed":8488,"user":{"displayName":"Jaehoon Kim","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GizEXeQ6HMz4cgKYwF05OOke2Q8RdtVQvsKnyotMg=s64","userId":"03064955329912050209"}},"outputId":"6eee4d46-2955-4d3d-b8b3-a99fb112b7f9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["VGG(\n","  (features): Sequential(\n","    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (1): ReLU(inplace=True)\n","    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (4): ReLU(inplace=True)\n","    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (6): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (7): ReLU(inplace=True)\n","    (8): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (9): ReLU(inplace=True)\n","    (10): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (11): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (12): ReLU(inplace=True)\n","    (13): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (14): ReLU(inplace=True)\n","    (15): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (16): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (17): ReLU(inplace=True)\n","    (18): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (19): ReLU(inplace=True)\n","    (20): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n","  (classifier): Sequential(\n","    (0): Linear(in_features=25088, out_features=4096, bias=True)\n","    (1): ReLU(inplace=True)\n","    (2): Dropout(p=0.5, inplace=False)\n","    (3): Linear(in_features=4096, out_features=4096, bias=True)\n","    (4): ReLU(inplace=True)\n","    (5): Dropout(p=0.5, inplace=False)\n","    (6): Linear(in_features=4096, out_features=10, bias=True)\n","  )\n",")"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["class AverageMeter(object):\n","\n","    def __init__(self):\n","        self.reset()\n","\n","    def reset(self):\n","        self.val = 0\n","        self.avg = 0\n","        self.sum = 0\n","        self.cnt = 0\n","\n","    def update(self, val, n=1):\n","        self.val = val\n","        self.sum += val * n\n","        self.cnt += n\n","        self.avg = self.sum / self.cnt"],"metadata":{"id":"hL5ZIg3HAPUY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from torch.optim import Adam\n","\n","criterion = nn.CrossEntropyLoss()\n","optimizer_ft = Adam(model.parameters(), lr=1e-4)"],"metadata":{"id":"KN-aoq6f9V86"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["log_dir = './log'\n","\n","os.makedirs(log_dir, exist_ok=True)\n","\n","with open(os.path.join(log_dir, 'augmentation_train_log.csv'), 'w') as log:\n","\n","    model.train()\n","\n","    for iteration, (img, label) in enumerate(qd_train_dataloader):\n","\n","        optimizer_ft.zero_grad()\n","\n","        img, label = img.float().cuda(), label.long().cuda()\n","\n","        pred_logit = model(img)\n","\n","        loss = criterion(pred_logit, label)\n","\n","        loss.backward()\n","        optimizer_ft.step()\n","\n","        pred_label = torch.argmax(pred_logit, 1)\n","        acc = (pred_label==label).sum().item() / len(img)\n","\n","        train_loss = loss.item()\n","        train_acc = acc\n","\n","        if (iteration%20==0) or (iteration==len(qd_train_dataloader)-1):\n","            \n","            model.eval()\n","\n","            valid_loss, valid_acc = AverageMeter(), AverageMeter()\n","\n","            for img, label in qd_val_dataloader:\n","\n","                img, label = img.float().cuda(), label.long().cuda()\n","\n","                with torch.no_grad():\n","                    pred_logit = model(img)\n","\n","                loss = criterion(pred_logit, label)\n","\n","                pred_label = torch.argmax(pred_logit, 1)\n","                acc = (pred_label==label).sum().item() / len(img)\n","\n","                valid_loss.update(loss.item(), len(img))\n","                valid_acc.update(acc, len(img))\n","\n","            valid_loss = valid_loss.avg\n","            valid_acc = valid_acc.avg\n","\n","            print(\"Iter [%3d/%3d] | Train Loss %.4f | Train Acc %.4f | Valid Loss %.4f | Valid Acc %.4f\" %\n","            (iteration, len(qd_train_dataloader), train_loss, train_acc, valid_loss, valid_acc))\n","      \n","            log.write('%d,%.4f,%.4f,%.4f,%.4f\\n'%(iteration, train_loss, train_acc, valid_loss, valid_acc))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gs_YP5wV9KSl","executionInfo":{"status":"ok","timestamp":1647153765458,"user_tz":-540,"elapsed":648699,"user":{"displayName":"Jaehoon Kim","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GizEXeQ6HMz4cgKYwF05OOke2Q8RdtVQvsKnyotMg=s64","userId":"03064955329912050209"}},"outputId":"e5162f1a-eb7d-4195-8689-cd3ecbb2839c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Iter [  0/422] | Train Loss 2.3328 | Train Acc 0.1406 | Valid Loss 2.2639 | Valid Acc 0.1503\n","Iter [ 20/422] | Train Loss 0.5087 | Train Acc 0.8750 | Valid Loss 0.5578 | Valid Acc 0.8360\n","Iter [ 40/422] | Train Loss 0.5005 | Train Acc 0.8438 | Valid Loss 0.4673 | Valid Acc 0.8590\n","Iter [ 60/422] | Train Loss 0.3800 | Train Acc 0.9062 | Valid Loss 0.4160 | Valid Acc 0.8787\n","Iter [ 80/422] | Train Loss 0.3486 | Train Acc 0.9062 | Valid Loss 0.3540 | Valid Acc 0.8940\n","Iter [100/422] | Train Loss 0.3505 | Train Acc 0.8906 | Valid Loss 0.3233 | Valid Acc 0.9037\n","Iter [120/422] | Train Loss 0.3216 | Train Acc 0.9219 | Valid Loss 0.3060 | Valid Acc 0.9053\n","Iter [140/422] | Train Loss 0.4730 | Train Acc 0.9062 | Valid Loss 0.3439 | Valid Acc 0.8993\n","Iter [160/422] | Train Loss 0.1895 | Train Acc 0.9531 | Valid Loss 0.3240 | Valid Acc 0.9003\n","Iter [180/422] | Train Loss 0.3763 | Train Acc 0.8750 | Valid Loss 0.3359 | Valid Acc 0.8933\n","Iter [200/422] | Train Loss 0.2793 | Train Acc 0.9062 | Valid Loss 0.3238 | Valid Acc 0.9003\n","Iter [220/422] | Train Loss 0.2782 | Train Acc 0.9219 | Valid Loss 0.3752 | Valid Acc 0.9030\n","Iter [240/422] | Train Loss 0.2911 | Train Acc 0.9531 | Valid Loss 0.2877 | Valid Acc 0.9100\n","Iter [260/422] | Train Loss 0.2465 | Train Acc 0.9219 | Valid Loss 0.2848 | Valid Acc 0.9190\n","Iter [280/422] | Train Loss 0.2120 | Train Acc 0.9219 | Valid Loss 0.2494 | Valid Acc 0.9223\n","Iter [300/422] | Train Loss 0.2835 | Train Acc 0.9062 | Valid Loss 0.2576 | Valid Acc 0.9237\n","Iter [320/422] | Train Loss 0.1298 | Train Acc 0.9844 | Valid Loss 0.2946 | Valid Acc 0.9133\n","Iter [340/422] | Train Loss 0.2702 | Train Acc 0.9531 | Valid Loss 0.2854 | Valid Acc 0.9120\n","Iter [360/422] | Train Loss 0.1960 | Train Acc 0.9375 | Valid Loss 0.2970 | Valid Acc 0.9090\n","Iter [380/422] | Train Loss 0.3492 | Train Acc 0.8594 | Valid Loss 0.2570 | Valid Acc 0.9207\n","Iter [400/422] | Train Loss 0.2423 | Train Acc 0.9062 | Valid Loss 0.2687 | Valid Acc 0.9177\n","Iter [420/422] | Train Loss 0.2176 | Train Acc 0.9219 | Valid Loss 0.2898 | Valid Acc 0.9110\n","Iter [421/422] | Train Loss 0.2266 | Train Acc 0.9286 | Valid Loss 0.2968 | Valid Acc 0.9067\n"]}]},{"cell_type":"code","source":["augmentation_train_log = pd.read_csv(os.path.join(log_dir, 'augmentation_train_log.csv'), index_col=0, header=None)"],"metadata":{"id":"StvIbBaZACLX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,8))\n","\n","ax1.plot(augmentation_train_log.iloc[:,0], label='Training with augmentation')\n","ax1.set_title('Loss Graph', fontsize=15)\n","ax1.set_xlabel('Iteration', fontsize=15)\n","ax1.set_ylabel('Loss', fontsize=15)\n","\n","fig.legend(fontsize=15)\n","\n","ax2.plot(augmentation_train_log.iloc[:,1], label='Training with augmentation')\n","ax2.set_title('Accuracy Graph', fontsize=15)\n","ax2.set_xlabel('Iteration', fontsize=15)\n","ax2.set_ylabel('Accuracy', fontsize=15)\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"Zrxtud2DIbcf","executionInfo":{"status":"ok","timestamp":1647153765458,"user_tz":-540,"elapsed":19,"user":{"displayName":"Jaehoon Kim","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GizEXeQ6HMz4cgKYwF05OOke2Q8RdtVQvsKnyotMg=s64","userId":"03064955329912050209"}},"outputId":"82c09fe9-c9d9-4668-8592-3d6f60bf3175"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x576 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":[""],"metadata":{"id":"sok-ZENhXOTs"},"execution_count":null,"outputs":[]}]}