diff --git a/12-architectures/TL_AE_HW.ipynb b/12-architectures/TL_AE_HW.ipynb
index df85859..c1c628a 100644
--- a/12-architectures/TL_AE_HW.ipynb
+++ b/12-architectures/TL_AE_HW.ipynb
@@ -1,531 +1,8195 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-11-20T12:19:29.305598Z",
- "iopub.status.busy": "2022-11-20T12:19:29.304997Z",
- "iopub.status.idle": "2022-11-20T12:19:31.757563Z",
- "shell.execute_reply": "2022-11-20T12:19:31.756141Z",
- "shell.execute_reply.started": "2022-11-20T12:19:29.305481Z"
- },
- "id": "z84mnv1t_425"
- },
- "outputs": [],
- "source": [
- "%matplotlib inline\n",
- "import time\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "import datetime as dt\n",
- "import matplotlib.pyplot as plt\n",
- "from mpl_toolkits.axes_grid1 import ImageGrid\n",
- "from os import listdir, makedirs, getcwd, remove\n",
- "from os.path import isfile, join, abspath, exists, isdir, expanduser\n",
- "from PIL import Image\n",
- "import torch\n",
- "from torch.optim import lr_scheduler\n",
- "from torch.autograd import Variable\n",
- "from torch.utils.data import Dataset, DataLoader\n",
- "import torchvision\n",
- "from torchvision import transforms, datasets, models"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-11-20T12:19:31.760714Z",
- "iopub.status.busy": "2022-11-20T12:19:31.759296Z",
- "iopub.status.idle": "2022-11-20T12:19:31.766464Z",
- "shell.execute_reply": "2022-11-20T12:19:31.764974Z",
- "shell.execute_reply.started": "2022-11-20T12:19:31.760670Z"
- },
- "id": "fKajd0ZU_426"
- },
- "outputs": [],
- "source": [
- "np.random.seed(1337)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "_cell_guid": "f5af2eca-5b02-4d89-a36d-8ca330851ac3",
- "_uuid": "11db94b40434802ac10d5ed76e21d33b9ff6befe",
- "id": "FwTpmkJO_422"
- },
- "source": [
- "# Dog Breed Identification\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "IykQuyPyDlXs"
- },
- "source": [
- "Download the dataset from [Dog Breed Identification\n",
- " Competition](https://www.kaggle.com/competitions/dog-breed-identification/data)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "43tf4uPHAa2O"
- },
- "outputs": [],
- "source": [
- "!mkdir ~/.kaggle\n",
- "!cp kaggle.json ~/.kaggle/\n",
- "!chmod 600 ~/.kaggle/kaggle.json"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "QCEyHF13AMzx"
- },
- "outputs": [],
- "source": [
- "!kaggle competitions download -c dog-breed-identification"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Q5gUVsn-A-6U"
- },
- "outputs": [],
- "source": [
- "!unzip dog-breed-identification.zip -d data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "MIsz6XZv_429"
- },
- "source": [
- "Let's use 64 most frequent breeds to simplify the task."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-11-20T12:19:35.403850Z",
- "iopub.status.busy": "2022-11-20T12:19:35.403431Z",
- "iopub.status.idle": "2022-11-20T12:19:36.485851Z",
- "shell.execute_reply": "2022-11-20T12:19:36.484579Z",
- "shell.execute_reply.started": "2022-11-20T12:19:35.403818Z"
- },
- "id": "owX4xvVg_429"
- },
- "outputs": [],
- "source": [
- "INPUT_SIZE = 224\n",
- "NUM_CLASSES = 64\n",
- "data_dir = '/content/data/'\n",
- "labels = pd.read_csv(join(data_dir, 'labels.csv'))\n",
- "sample_submission = pd.read_csv(join(data_dir, 'sample_submission.csv'))\n",
- "print(len(listdir(join(data_dir, 'train'))), len(labels))\n",
- "print(len(listdir(join(data_dir, 'test'))), len(sample_submission))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-11-20T12:19:38.418926Z",
- "iopub.status.busy": "2022-11-20T12:19:38.418491Z",
- "iopub.status.idle": "2022-11-20T12:19:38.480493Z",
- "shell.execute_reply": "2022-11-20T12:19:38.479321Z",
- "shell.execute_reply.started": "2022-11-20T12:19:38.418889Z"
- },
- "id": "nmDLzy33_429"
- },
- "outputs": [],
- "source": [
- "selected_breed_list = list(labels.groupby('breed').count().sort_values(by='id', ascending=False).head(NUM_CLASSES).index)\n",
- "labels = labels[labels['breed'].isin(selected_breed_list)]\n",
- "labels['target'] = 1\n",
- "labels['rank'] = labels.groupby('breed').rank()['id']\n",
- "labels_pivot = labels.pivot('id', 'breed', 'target').reset_index().fillna(0)\n",
- "\n",
- "train = labels_pivot.sample(frac=0.8)\n",
- "valid = labels_pivot[~labels_pivot['id'].isin(train['id'])]\n",
- "print(train.shape, valid.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "AbjdVxCND9Cu"
- },
- "source": [
- "Custom dataset is going to be useful for our needs."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-11-20T12:21:25.372206Z",
- "iopub.status.busy": "2022-11-20T12:21:25.371623Z",
- "iopub.status.idle": "2022-11-20T12:21:25.385456Z",
- "shell.execute_reply": "2022-11-20T12:21:25.384555Z",
- "shell.execute_reply.started": "2022-11-20T12:21:25.372151Z"
- },
- "id": "YeBIQ3Y6_42-"
- },
- "outputs": [],
- "source": [
- "class DogsDataset(Dataset):\n",
- " def __init__(self, labels, root_dir, subset=False, transform=None):\n",
- " self.labels = labels\n",
- " self.root_dir = root_dir\n",
- " self.transform = transform\n",
- " \n",
- " def __len__(self):\n",
- " return len(self.labels)\n",
- " \n",
- " def __getitem__(self, idx):\n",
- " img_name = '{}.jpg'.format(self.labels.iloc[idx, 0])\n",
- " fullname = join(self.root_dir, img_name)\n",
- " image = Image.open(fullname)\n",
- " labels = self.labels.iloc[idx, 1:].to_numpy().astype('float')\n",
- " labels = np.argmax(labels)\n",
- " if self.transform:\n",
- " image = self.transform(image)\n",
- " return [image, labels]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-11-20T12:21:25.639926Z",
- "iopub.status.busy": "2022-11-20T12:21:25.639454Z",
- "iopub.status.idle": "2022-11-20T12:21:25.648535Z",
- "shell.execute_reply": "2022-11-20T12:21:25.647318Z",
- "shell.execute_reply.started": "2022-11-20T12:21:25.639888Z"
- },
- "id": "Pi1WBa83_42-"
- },
- "outputs": [],
- "source": [
- "normalize = transforms.Normalize(\n",
- " mean=[0.485, 0.456, 0.406],\n",
- " std=[0.229, 0.224, 0.225]\n",
- ")\n",
- "ds_trans = transforms.Compose([transforms.Resize(224),\n",
- " transforms.CenterCrop(224),\n",
- " transforms.ToTensor(),\n",
- " normalize])\n",
- "train_ds = DogsDataset(train, data_dir + 'train/', transform=ds_trans)\n",
- "valid_ds = DogsDataset(valid, data_dir + 'train/', transform=ds_trans)\n",
- "\n",
- "train_dl = DataLoader(train_ds, batch_size=4, shuffle=True, num_workers=4)\n",
- "valid_dl = DataLoader(valid_ds, batch_size=4, shuffle=True, num_workers=4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-11-20T12:21:25.847758Z",
- "iopub.status.busy": "2022-11-20T12:21:25.847349Z",
- "iopub.status.idle": "2022-11-20T12:21:25.854367Z",
- "shell.execute_reply": "2022-11-20T12:21:25.853367Z",
- "shell.execute_reply.started": "2022-11-20T12:21:25.847725Z"
- },
- "id": "rsZF3ehw_42-"
- },
- "outputs": [],
- "source": [
- "def imshow(axis, inp):\n",
- " \"\"\"Denormalize and show\"\"\"\n",
- " inp = inp.numpy().transpose((1, 2, 0))\n",
- " mean = np.array([0.485, 0.456, 0.406])\n",
- " std = np.array([0.229, 0.224, 0.225])\n",
- " inp = std * inp + mean\n",
- " axis.imshow(inp)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-11-20T12:21:27.116968Z",
- "iopub.status.busy": "2022-11-20T12:21:27.116556Z",
- "iopub.status.idle": "2022-11-20T12:21:48.607590Z",
- "shell.execute_reply": "2022-11-20T12:21:48.606428Z",
- "shell.execute_reply.started": "2022-11-20T12:21:27.116931Z"
- },
- "id": "YaOJt29S_42_"
- },
- "outputs": [],
- "source": [
- "img, label = next(iter(train_dl))\n",
- "print(img.size(), label.size())\n",
- "fig = plt.figure(1, figsize=(16, 4))\n",
- "grid = ImageGrid(fig, 111, nrows_ncols=(1, 4), axes_pad=0.05) \n",
- "for i in range(img.size()[0]):\n",
- " ax = grid[i]\n",
- " imshow(ax, img[i])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "fyQlkHyl_42_"
- },
- "source": [
- "# Task 1: Transfer Learning (2 points)\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "icwJfGhzEWGV"
- },
- "source": [
- "Pick up some pretrained model, e.g. resnet 50 and tune it for our needs."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2022-11-20T12:22:42.969917Z",
- "iopub.status.busy": "2022-11-20T12:22:42.969487Z",
- "iopub.status.idle": "2022-11-20T12:23:03.494546Z",
- "shell.execute_reply": "2022-11-20T12:23:03.492219Z",
- "shell.execute_reply.started": "2022-11-20T12:22:42.969876Z"
- },
- "id": "D9giVv4z_42_"
- },
- "outputs": [],
- "source": [
- "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
- "resnet = models.resnet50(pretrained=True)\n",
- "inputs, labels = next(iter(train_dl))\n",
- "resnet = resnet.to(device)\n",
- "inputs, labels = inputs.to(device), labels.to(device)\n",
- "outputs = resnet(inputs)\n",
- "outputs.size()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "nPkcuNZ7_42_"
- },
- "source": [
- "This models provides us with 1000 values, representing the classes which ResNet was trained on. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "m596WCT2_42_"
- },
- "source": [
- "Replace last layer with one that predicts the 64 classes. \n",
- "The network weights should be fixed expected for the last layer that is trained."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true,
- "id": "7alchbxh_43A",
- "jupyter": {
- "outputs_hidden": true
- }
- },
- "outputs": [],
- "source": [
- "def train_model(dataloders,\n",
- " model,\n",
- " criterion,\n",
- " optimizer,\n",
- " scheduler,\n",
- " num_epochs=1):\n",
- " # Train the model and evaluate train and test accuracy\n",
- " # YOUR CODE\n",
- " \n",
- " return model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true,
- "id": "vzD9iRef_43A",
- "jupyter": {
- "outputs_hidden": true
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "q3lWPR2dcfUF"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-11-20T12:19:29.305598Z",
+ "iopub.status.busy": "2022-11-20T12:19:29.304997Z",
+ "iopub.status.idle": "2022-11-20T12:19:31.757563Z",
+ "shell.execute_reply": "2022-11-20T12:19:31.756141Z",
+ "shell.execute_reply.started": "2022-11-20T12:19:29.305481Z"
+ },
+ "id": "z84mnv1t_425"
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "import time\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import datetime as dt\n",
+ "import matplotlib.pyplot as plt\n",
+ "from mpl_toolkits.axes_grid1 import ImageGrid\n",
+ "from os import listdir, makedirs, getcwd, remove\n",
+ "from os.path import isfile, join, abspath, exists, isdir, expanduser\n",
+ "from PIL import Image\n",
+ "import torch\n",
+ "from torch.optim import lr_scheduler\n",
+ "from torch.autograd import Variable\n",
+ "from torch.utils.data import Dataset, DataLoader\n",
+ "import torchvision\n",
+ "from torchvision import transforms, datasets, models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-11-20T12:19:31.760714Z",
+ "iopub.status.busy": "2022-11-20T12:19:31.759296Z",
+ "iopub.status.idle": "2022-11-20T12:19:31.766464Z",
+ "shell.execute_reply": "2022-11-20T12:19:31.764974Z",
+ "shell.execute_reply.started": "2022-11-20T12:19:31.760670Z"
+ },
+ "id": "fKajd0ZU_426"
+ },
+ "outputs": [],
+ "source": [
+ "np.random.seed(1337)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "_cell_guid": "f5af2eca-5b02-4d89-a36d-8ca330851ac3",
+ "_uuid": "11db94b40434802ac10d5ed76e21d33b9ff6befe",
+ "id": "FwTpmkJO_422"
+ },
+ "source": [
+ "# Dog Breed Identification\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IykQuyPyDlXs"
+ },
+ "source": [
+ "Download the dataset from [Dog Breed Identification\n",
+ " Competition](https://www.kaggle.com/competitions/dog-breed-identification/data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "id": "43tf4uPHAa2O",
+ "outputId": "a01f906c-07f0-4014-dd8b-2d9e54848e21",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
+ ]
+ }
+ ],
+ "source": [
+ "!mkdir ~/.kaggle\n",
+ "!cp kaggle.json ~/.kaggle/\n",
+ "!chmod 600 ~/.kaggle/kaggle.json"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "QCEyHF13AMzx",
+ "outputId": "30e9fe1b-4c26-4322-99fd-ffba598b87a1",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Downloading dog-breed-identification.zip to /content\n",
+ "100% 691M/691M [00:30<00:00, 25.7MB/s]\n",
+ "100% 691M/691M [00:30<00:00, 23.6MB/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "!kaggle competitions download -c dog-breed-identification"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "Q5gUVsn-A-6U",
+ "outputId": "18c0c599-9a5b-4cb2-86a1-e906028f807b",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[1;30;43mВыходные данные были обрезаны до нескольких последних строк (5000).\u001b[0m\n",
+ " inflating: data/train/83bc62b0fffa99a9c94ba0b67a5f7395.jpg \n",
+ " inflating: data/train/83bcff6b55ee179a7c123fa6103c377a.jpg \n",
+ " inflating: data/train/83be6d622ab74a5e7e08b53eb8fd566a.jpg \n",
+ " inflating: data/train/83c2d7419b0429b9fe953bc1b6cddbec.jpg \n",
+ " inflating: data/train/83cf7d7cd2a759a93e2ffd95bea9c6fb.jpg \n",
+ " inflating: data/train/83d405858f0931722ef21e8ac0adee4d.jpg \n",
+ " inflating: data/train/83d4125a4c3c7dc5956563276cb1cd74.jpg \n",
+ " inflating: data/train/83f0bb565b2186dbcc6a9d009cb26ff2.jpg \n",
+ " inflating: data/train/83fad0718581a696132c96c166472627.jpg \n",
+ " inflating: data/train/83fbbcc9a612e3f712b1ba199da61f20.jpg \n",
+ " inflating: data/train/8403d8936430c2f05ab7d74d23c2c0cb.jpg \n",
+ " inflating: data/train/8406d837b2d7fac1c3cd621abb4c4f9e.jpg \n",
+ " inflating: data/train/840b67d26e5e43f8eb6430f62d4ba1ac.jpg \n",
+ " inflating: data/train/840db91ba4600148f3dcb06ec419b421.jpg \n",
+ " inflating: data/train/840dbad5a691c22611d85b2488bf4cbb.jpg \n",
+ " inflating: data/train/8410ced9ebc1759a7ebce5c42bfb5222.jpg \n",
+ " inflating: data/train/841463629c4833816e216cbb041c2778.jpg \n",
+ " inflating: data/train/8429dcca4ae91c4e0345e4ba48b0d69f.jpg \n",
+ " inflating: data/train/842e3c6e44fda4102fe83d07dac72b3e.jpg \n",
+ " inflating: data/train/8431a6ce7c70e5e36698e821eedf24b5.jpg \n",
+ " inflating: data/train/8434b6c3cee87e28395197d6fc7d3489.jpg \n",
+ " inflating: data/train/8436be99589db6a99cfac1b894421ea6.jpg \n",
+ " inflating: data/train/843cbc1fc239d24534859bd272c3bc16.jpg \n",
+ " inflating: data/train/843d766d92a7b6d6a85a81e56a99c51f.jpg \n",
+ " inflating: data/train/84421c01900b34e3e1ba42f2424fbd33.jpg \n",
+ " inflating: data/train/844dde39a9e8987e510e8d46ec4da714.jpg \n",
+ " inflating: data/train/8452a26d7243a299ea782a7ba4036f1f.jpg \n",
+ " inflating: data/train/8454b5e6546f04871561de8f10d868c7.jpg \n",
+ " inflating: data/train/84564a69c0d0fa36e0810188943683a1.jpg \n",
+ " inflating: data/train/84605f5fc5ad89a66b9b277e1223e962.jpg \n",
+ " inflating: data/train/8463aa43d88bee057082434ccc806bb0.jpg \n",
+ " inflating: data/train/8467fbd75a8fe64da70df5410b6c4f09.jpg \n",
+ " inflating: data/train/846d6384787fff8dc17d488e6b86c209.jpg \n",
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+ ]
+ }
+ ],
+ "source": [
+ "!unzip dog-breed-identification.zip -d data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MIsz6XZv_429"
+ },
+ "source": [
+ "Let's use 64 most frequent breeds to simplify the task."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-11-20T12:19:35.403850Z",
+ "iopub.status.busy": "2022-11-20T12:19:35.403431Z",
+ "iopub.status.idle": "2022-11-20T12:19:36.485851Z",
+ "shell.execute_reply": "2022-11-20T12:19:36.484579Z",
+ "shell.execute_reply.started": "2022-11-20T12:19:35.403818Z"
+ },
+ "id": "owX4xvVg_429",
+ "outputId": "d37f34b6-ba78-4143-df6e-c15e0c89da58",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "10222 10222\n",
+ "10357 10357\n"
+ ]
+ }
+ ],
+ "source": [
+ "INPUT_SIZE = 224\n",
+ "NUM_CLASSES = 64\n",
+ "data_dir = '/content/data/'\n",
+ "labels = pd.read_csv(join(data_dir, 'labels.csv'))\n",
+ "sample_submission = pd.read_csv(join(data_dir, 'sample_submission.csv'))\n",
+ "print(len(listdir(join(data_dir, 'train'))), len(labels))\n",
+ "print(len(listdir(join(data_dir, 'test'))), len(sample_submission))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-11-20T12:19:38.418926Z",
+ "iopub.status.busy": "2022-11-20T12:19:38.418491Z",
+ "iopub.status.idle": "2022-11-20T12:19:38.480493Z",
+ "shell.execute_reply": "2022-11-20T12:19:38.479321Z",
+ "shell.execute_reply.started": "2022-11-20T12:19:38.418889Z"
+ },
+ "id": "nmDLzy33_429",
+ "outputId": "c77f92bb-c263-472a-a428-47a7e0ee0264",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "(4838, 65) (1210, 65)\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ ":3: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " labels['target'] = 1\n",
+ ":4: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " labels['rank'] = labels.groupby('breed').rank()['id']\n"
+ ]
+ }
+ ],
+ "source": [
+ "selected_breed_list = list(labels.groupby('breed').count().sort_values(by='id', ascending=False).head(NUM_CLASSES).index)\n",
+ "labels = labels[labels['breed'].isin(selected_breed_list)]\n",
+ "labels['target'] = 1\n",
+ "labels['rank'] = labels.groupby('breed').rank()['id']\n",
+ "labels_pivot = labels.pivot('id', 'breed', 'target').reset_index().fillna(0)\n",
+ "\n",
+ "train = labels_pivot.sample(frac=0.8)\n",
+ "valid = labels_pivot[~labels_pivot['id'].isin(train['id'])]\n",
+ "print(train.shape, valid.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "AbjdVxCND9Cu"
+ },
+ "source": [
+ "Custom dataset is going to be useful for our needs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-11-20T12:21:25.372206Z",
+ "iopub.status.busy": "2022-11-20T12:21:25.371623Z",
+ "iopub.status.idle": "2022-11-20T12:21:25.385456Z",
+ "shell.execute_reply": "2022-11-20T12:21:25.384555Z",
+ "shell.execute_reply.started": "2022-11-20T12:21:25.372151Z"
+ },
+ "id": "YeBIQ3Y6_42-"
+ },
+ "outputs": [],
+ "source": [
+ "class DogsDataset(Dataset):\n",
+ " def __init__(self, labels, root_dir, subset=False, transform=None):\n",
+ " self.labels = labels\n",
+ " self.root_dir = root_dir\n",
+ " self.transform = transform\n",
+ " \n",
+ " def __len__(self):\n",
+ " return len(self.labels)\n",
+ " \n",
+ " def __getitem__(self, idx):\n",
+ " img_name = '{}.jpg'.format(self.labels.iloc[idx, 0])\n",
+ " fullname = join(self.root_dir, img_name)\n",
+ " image = Image.open(fullname)\n",
+ " labels = self.labels.iloc[idx, 1:].to_numpy().astype('float')\n",
+ " labels = np.argmax(labels)\n",
+ " if self.transform:\n",
+ " image = self.transform(image)\n",
+ " return [image, labels]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-11-20T12:21:25.639926Z",
+ "iopub.status.busy": "2022-11-20T12:21:25.639454Z",
+ "iopub.status.idle": "2022-11-20T12:21:25.648535Z",
+ "shell.execute_reply": "2022-11-20T12:21:25.647318Z",
+ "shell.execute_reply.started": "2022-11-20T12:21:25.639888Z"
+ },
+ "id": "Pi1WBa83_42-",
+ "outputId": "1e7833eb-859b-46af-8c0d-8a54c916c04c",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py:554: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+ " warnings.warn(_create_warning_msg(\n"
+ ]
+ }
+ ],
+ "source": [
+ "normalize = transforms.Normalize(\n",
+ " mean=[0.485, 0.456, 0.406],\n",
+ " std=[0.229, 0.224, 0.225]\n",
+ ")\n",
+ "ds_trans = transforms.Compose([transforms.Resize(224),\n",
+ " transforms.CenterCrop(224),\n",
+ " transforms.ToTensor(),\n",
+ " normalize])\n",
+ "train_ds = DogsDataset(train, data_dir + 'train/', transform=ds_trans)\n",
+ "valid_ds = DogsDataset(valid, data_dir + 'train/', transform=ds_trans)\n",
+ "\n",
+ "train_dl = DataLoader(train_ds, batch_size=4, shuffle=True, num_workers=4)\n",
+ "valid_dl = DataLoader(valid_ds, batch_size=4, shuffle=True, num_workers=4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-11-20T12:21:25.847758Z",
+ "iopub.status.busy": "2022-11-20T12:21:25.847349Z",
+ "iopub.status.idle": "2022-11-20T12:21:25.854367Z",
+ "shell.execute_reply": "2022-11-20T12:21:25.853367Z",
+ "shell.execute_reply.started": "2022-11-20T12:21:25.847725Z"
+ },
+ "id": "rsZF3ehw_42-"
+ },
+ "outputs": [],
+ "source": [
+ "def imshow(axis, inp):\n",
+ " \"\"\"Denormalize and show\"\"\"\n",
+ " inp = inp.numpy().transpose((1, 2, 0))\n",
+ " mean = np.array([0.485, 0.456, 0.406])\n",
+ " std = np.array([0.229, 0.224, 0.225])\n",
+ " inp = std * inp + mean\n",
+ " axis.imshow(inp)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2022-11-20T12:21:27.116968Z",
+ "iopub.status.busy": "2022-11-20T12:21:27.116556Z",
+ "iopub.status.idle": "2022-11-20T12:21:48.607590Z",
+ "shell.execute_reply": "2022-11-20T12:21:48.606428Z",
+ "shell.execute_reply.started": "2022-11-20T12:21:27.116931Z"
+ },
+ "id": "YaOJt29S_42_",
+ "outputId": "f10cbd9f-4d9f-4dda-bed4-a19585180c60",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 360
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
+ "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
+ "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "torch.Size([4, 3, 224, 224]) torch.Size([4])\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "