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Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,321 @@ { "cells": [ { "cell_type": "code", "execution_count": null, "id": "electric-prime", "metadata": {}, "outputs": [], "source": [ "import sys\n", "!{sys.executable} -m pip install -U torch numpy matplotlib torchvision ipywidgets jupyter widgetsnbextension" ] }, { "cell_type": "code", "execution_count": null, "id": "compressed-procedure", "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function, division\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "from torch.optim import lr_scheduler\n", "import torch.backends.cudnn as cudnn\n", "import numpy as np\n", "import torchvision\n", "from torchvision import datasets, models, transforms\n", "import matplotlib.pyplot as plt\n", "import time\n", "import os\n", "import copy\n", "\n", "cudnn.benchmark = True\n", "plt.ion() # interactive mode" ] }, { "cell_type": "code", "execution_count": null, "id": "periodic-barrel", "metadata": {}, "outputs": [], "source": [ "import urllib.request\n", "import zipfile\n", "filename, _ = urllib.request.urlretrieve(\"https://download.pytorch.org/tutorial/hymenoptera_data.zip\", \"data.zip\")\n", "zipfile.ZipFile(filename).extractall()" ] }, { "cell_type": "code", "execution_count": null, "id": "opponent-trust", "metadata": {}, "outputs": [], "source": [ "data_transforms = {\n", " 'train': transforms.Compose([\n", " transforms.RandomResizedCrop(224),\n", " transforms.RandomHorizontalFlip(),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", " ]),\n", " 'val': transforms.Compose([\n", " transforms.Resize(256),\n", " transforms.CenterCrop(224),\n", " transforms.ToTensor(),\n", " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", " ]),\n", "}\n", "\n", "data_dir = 'hymenoptera_data'\n", "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n", " data_transforms[x])\n", " for x in ['train', 'val']}\n", "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n", " shuffle=True, num_workers=4)\n", " for x in ['train', 'val']}\n", "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n", "class_names = image_datasets['train'].classes\n", "\n", "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")" ] }, { "cell_type": "code", "execution_count": null, "id": "threaded-johnson", "metadata": {}, "outputs": [], "source": [ "def imshow(inp, title=None):\n", " \"\"\"Imshow for Tensor.\"\"\"\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", " inp = np.clip(inp, 0, 1)\n", " plt.imshow(inp)\n", " if title is not None:\n", " plt.title(title)\n", " plt.pause(0.001) # pause a bit so that plots are updated\n", "\n", "\n", "# Get a batch of training data\n", "inputs, classes = next(iter(dataloaders['train']))\n", "\n", "# Make a grid from batch\n", "out = torchvision.utils.make_grid(inputs)\n", "\n", "imshow(out, title=[class_names[x] for x in classes])\n" ] }, { "cell_type": "code", "execution_count": null, "id": "speaking-rescue", "metadata": {}, "outputs": [], "source": [ "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n", " since = time.time()\n", "\n", " best_model_wts = copy.deepcopy(model.state_dict())\n", " best_acc = 0.0\n", "\n", " for epoch in range(num_epochs):\n", " print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n", " print('-' * 10)\n", "\n", " # Each epoch has a training and validation phase\n", " for phase in ['train', 'val']:\n", " if phase == 'train':\n", " model.train() # Set model to training mode\n", " else:\n", " model.eval() # Set model to evaluate mode\n", "\n", " running_loss = 0.0\n", " running_corrects = 0\n", "\n", " # Iterate over data.\n", " for inputs, labels in dataloaders[phase]:\n", " inputs = inputs.to(device)\n", " labels = labels.to(device)\n", "\n", " # zero the parameter gradients\n", " optimizer.zero_grad()\n", "\n", " # forward\n", " # track history if only in train\n", " with torch.set_grad_enabled(phase == 'train'):\n", " outputs = model(inputs)\n", " _, preds = torch.max(outputs, 1)\n", " loss = criterion(outputs, labels)\n", "\n", " # backward + optimize only if in training phase\n", " if phase == 'train':\n", " loss.backward()\n", " optimizer.step()\n", "\n", " # statistics\n", " running_loss += loss.item() * inputs.size(0)\n", " running_corrects += torch.sum(preds == labels.data)\n", " if phase == 'train':\n", " scheduler.step()\n", "\n", " epoch_loss = running_loss / dataset_sizes[phase]\n", " epoch_acc = running_corrects.double() / dataset_sizes[phase]\n", "\n", " print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n", " phase, epoch_loss, epoch_acc))\n", "\n", " # deep copy the model\n", " if phase == 'val' and epoch_acc > best_acc:\n", " best_acc = epoch_acc\n", " best_model_wts = copy.deepcopy(model.state_dict())\n", "\n", " print()\n", "\n", " time_elapsed = time.time() - since\n", " print('Training complete in {:.0f}m {:.0f}s'.format(\n", " time_elapsed // 60, time_elapsed % 60))\n", " print('Best val Acc: {:4f}'.format(best_acc))\n", "\n", " # load best model weights\n", " model.load_state_dict(best_model_wts)\n", " return model" ] }, { "cell_type": "code", "execution_count": null, "id": "acting-brick", "metadata": {}, "outputs": [], "source": [ "def visualize_model(model, num_images=6):\n", " was_training = model.training\n", " model.eval()\n", " images_so_far = 0\n", " fig = plt.figure()\n", "\n", " with torch.no_grad():\n", " for i, (inputs, labels) in enumerate(dataloaders['val']):\n", " inputs = inputs.to(device)\n", " labels = labels.to(device)\n", "\n", " outputs = model(inputs)\n", " _, preds = torch.max(outputs, 1)\n", "\n", " for j in range(inputs.size()[0]):\n", " images_so_far += 1\n", " ax = plt.subplot(num_images//2, 2, images_so_far)\n", " ax.axis('off')\n", " ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n", " imshow(inputs.cpu().data[j])\n", "\n", " if images_so_far == num_images:\n", " model.train(mode=was_training)\n", " return\n", " model.train(mode=was_training)" ] }, { "cell_type": "code", "execution_count": null, "id": "surprising-allowance", "metadata": {}, "outputs": [], "source": [ "!jupyter nbextension enable --py widgetsnbextension" ] }, { "cell_type": "code", "execution_count": null, "id": "phantom-snowboard", "metadata": {}, "outputs": [], "source": [ "model_ft = models.resnet18(pretrained=True)\n", "num_ftrs = model_ft.fc.in_features\n", "# Here the size of each output sample is set to 2.\n", "# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).\n", "model_ft.fc = nn.Linear(num_ftrs, 2)\n", "\n", "model_ft = model_ft.to(device)\n", "\n", "criterion = nn.CrossEntropyLoss()\n", "\n", "# Observe that all parameters are being optimized\n", "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n", "\n", "# Decay LR by a factor of 0.1 every 7 epochs\n", "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)" ] }, { "cell_type": "code", "execution_count": null, "id": "enabling-manor", "metadata": {}, "outputs": [], "source": [ "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n", " num_epochs=25)" ] }, { "cell_type": "code", "execution_count": null, "id": "existing-shark", "metadata": {}, "outputs": [], "source": [ "device = torch.device(\"cpu\")\n", "model_ft = models.resnet18(pretrained=True)\n", "num_ftrs = model_ft.fc.in_features\n", "# Here the size of each output sample is set to 2.\n", "# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).\n", "model_ft.fc = nn.Linear(num_ftrs, 2)\n", "\n", "model_ft = model_ft.to(device)\n", "\n", "criterion = nn.CrossEntropyLoss()\n", "\n", "# Observe that all parameters are being optimized\n", "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n", "\n", "# Decay LR by a factor of 0.1 every 7 epochs\n", "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)\n", "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n", " num_epochs=25)" ] } ], "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }