{
"cells": [
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"id": "8ce61861"
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"source": [
"# NoteBook for HW3\n",
"\n",
"Please read `README.md` for instructions on how to set up the python environment."
]
},
{
"cell_type": "markdown",
"id": "90dcc260",
"metadata": {
"id": "90dcc260"
},
"source": [
"## Import packages you need"
]
},
{
"cell_type": "code",
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"id": "6844d531",
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"source": [
"# Import necessary packages.\n",
"import random\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn as nn\n",
"import torchvision.transforms as transforms\n",
"from PIL import Image\n",
"from torch.utils.data import ConcatDataset, DataLoader, Dataset\n",
"from torchvision.datasets import DatasetFolder\n",
"from tqdm import tqdm\n",
"from PIL import ImageFile\n",
"ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
"random.seed(1234)\n",
"np.random.seed(1234)\n",
"torch.manual_seed(1234)"
]
},
{
"cell_type": "markdown",
"id": "a92d5876",
"metadata": {
"id": "a92d5876"
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"source": [
"## Dataset, Data Loader, and Transforms\n",
"Torchvision provides lots of useful utilities for image preprocessing, data wrapping as well as data augmentation.\n",
"\n",
"Here, since our data are stored in folders by class labels, we can directly apply torchvision.datasets.DatasetFolder for wrapping data without much effort.\n",
"\n",
"Please refer to PyTorch official website for details about different transforms."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dd6625ab",
"metadata": {
"execution": {
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"source": [
"folder = './datasets'\n",
"NUM_CLASSES = 14"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "6bb0cb3c-3a3e-42f8-943e-10a01e19bef1",
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"execution": {
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"source": [
"train_tfm = transforms.Compose(\n",
" [\n",
" # Resize the image to a fixed shape (height = width = 256)\n",
" transforms.Resize((256, 256)),\n",
" transforms.Lambda(lambda x: x.convert(\"RGB\")),\n",
" \n",
" # Random horizontal flip to increase robustness to object orientation\n",
" transforms.RandomHorizontalFlip(),\n",
" \n",
" # Random rotation, a common transformation to handle rotated images\n",
" transforms.RandomRotation(20), # Rotate image by a random angle between -20 and 20 degrees\n",
" \n",
" # Random cropping to simulate random scene zoom\n",
" transforms.RandomResizedCrop(224, scale=(0.8, 1.0)), # Crop and resize to 224x224\n",
" \n",
" # Random color jitter to make the model robust to lighting changes\n",
" transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2),\n",
" \n",
" # Random affine transformation (translation, scaling, rotation)\n",
" transforms.RandomAffine(degrees=10, translate=(0.1, 0.1), scale=(0.8, 1.2)),\n",
" \n",
" # Convert to tensor\n",
" transforms.ToTensor(),\n",
" \n",
" # Normalize the image with mean and standard deviation for better convergence\n",
" transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
" ]\n",
")\n",
"\n",
"test_tfm = transforms.Compose(\n",
" [\n",
" # Resize to the fixed size\n",
" transforms.Resize((256, 256)),\n",
" transforms.Lambda(lambda x: x.convert(\"RGB\")),\n",
" \n",
" # Convert to tensor\n",
" transforms.ToTensor(),\n",
" \n",
" # Normalize the image with mean and standard deviation (same as in training)\n",
" transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
" ]\n",
")\n",
"\n",
"def get_dataset():\n",
" train_set = DatasetFolder(\n",
" folder + \"/train/labeled\",\n",
" loader=lambda x: Image.open(x),\n",
" extensions=\"jpg\",\n",
" # transform=train_tfm,\n",
" )\n",
" valid_set = DatasetFolder(\n",
" folder + \"/val\",\n",
" loader=lambda x: Image.open(x),\n",
" extensions=\"jpg\",\n",
" transform=test_tfm,\n",
" )\n",
" unlabeled_set = DatasetFolder(\n",
" folder + \"/train/unlabeled\",\n",
" loader=lambda x: Image.open(x),\n",
" extensions=\"jpg\",\n",
" # transform=train_tfm,\n",
" )\n",
" test_set = DatasetFolder(\n",
" folder + \"/test\",\n",
" loader=lambda x: Image.open(x),\n",
" extensions=\"jpg\",\n",
" transform=test_tfm,\n",
" )\n",
" return train_set, valid_set, unlabeled_set, test_set\n",
"\n",
"def train_collate_fn(batch):\n",
" data, labels = zip(*batch)\n",
" # data = torch.stack(data)\n",
" labels = torch.tensor(labels)\n",
" return data, labels\n",
"\n",
"def test_collate_fn(batch):\n",
" data, labels = zip(*batch)\n",
" data = torch.stack(data)\n",
" labels = torch.tensor(labels)\n",
" return data, labels"
]
},
{
"cell_type": "markdown",
"id": "470dfe9a",
"metadata": {
"id": "470dfe9a"
},
"source": [
"## Training\n",
"* You can finish supervised learning by simply running the provided code without any modification.\n",
"* The function \"get_pseudo_labels\" is used for semi-supervised learning. It is expected to get better performance if you use unlabeled data for semi-supervised learning. However, you have to implement the function on your own and need to adjust several hyperparameters manually.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
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"source": [
"from utils import CustomDataset\n",
"\n",
"\n",
"def update_dataset(\n",
" train_set, unlabeled_set, model, threshold, batch_size=128, num_workers=8\n",
") -> Dataset:\n",
" \"\"\"\n",
" This function generates pseudo-labels for a dataset using the given model.\n",
" It returns an instance of DatasetFolder containing images whose prediction confidences exceed a given threshold.\n",
" train_set, unlabeled_set = update_dataset(train_set, unlabeled_set, model)\n",
" \"\"\"\n",
" device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"\n",
"\n",
" # Make sure the model is in eval mode.\n",
" model.eval()\n",
" # Define softmax function.\n",
" softmax = nn.Softmax(dim=-1)\n",
"\n",
" # Create a dataloader for the unlabeled data\n",
" unlabeled_loader = DataLoader(\n",
" unlabeled_set,\n",
" batch_size=batch_size,\n",
" shuffle=False,\n",
" num_workers=num_workers,\n",
" pin_memory=True,\n",
" collate_fn=train_collate_fn,\n",
" )\n",
"\n",
" # List to store the most confident predictions\n",
" confident_samples = []\n",
" confident_labels = []\n",
"\n",
" with torch.no_grad():\n",
" for batch_idx, (images, _) in enumerate(\n",
" tqdm(unlabeled_loader, desc=\"Generating pseudo-labels\")\n",
" ):\n",
" # Apply test transform to each image in the batch\n",
" new_images = torch.stack([test_tfm(img) for img in images])\n",
"\n",
" # Forward pass through the model\n",
" outputs = model(new_images.to(device))\n",
"\n",
" # Apply softmax to get probabilities\n",
" probabilities = softmax(outputs)\n",
"\n",
" # Get the maximum probability and corresponding class for each sample\n",
" max_probs, pseudo_labels = torch.max(probabilities, dim=1)\n",
"\n",
" # For each sample in the batch, check confidence threshold\n",
" for i, (prob, label) in enumerate(zip(max_probs, pseudo_labels)):\n",
" # If the prediction is confident enough, add to confident set\n",
" if prob.item() > threshold:\n",
" # Get the actual index in the unlabeled_set\n",
" idx = batch_idx * batch_size + i\n",
" if idx < len(unlabeled_set):\n",
" img, _ = unlabeled_set[idx]\n",
" confident_samples.append(img)\n",
" confident_labels.append(int(label.cpu()))\n",
"\n",
" # Create new dataset from the confident predictions\n",
" if confident_samples:\n",
" pseudo_set = CustomDataset(images=confident_samples, labels=confident_labels)\n",
"\n",
" # Combine with existing labeled data\n",
" new_set = ConcatDataset([train_set, pseudo_set])\n",
"\n",
" print(f\"Added {len(confident_samples)} pseudo-labeled samples to training set\")\n",
" else:\n",
" print(\"No confident pseudo-labels found.\")\n",
" new_set = train_set\n",
" return new_set\n"
]
},
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ubuntu/SDSC6001/SDSC6001_HW3/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
" warnings.warn(\n",
"/home/ubuntu/SDSC6001/SDSC6001_HW3/.venv/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
" warnings.warn(msg)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model loaded successfully.\n"
]
}
],
"source": [
"from models import VGG16Classifier, ResNet50Classifier,ResNet101Classifier\n",
"from torch import optim\n",
"\n",
"# \"cuda\" only when GPUs are available.\n",
"device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
"\n",
"# Initialize a model, and put it on the device specified.\n",
"# model = Classifier().to(device)\n",
"# model = VGG16Classifier(num_classes=NUM_CLASSES).to(device)\n",
"# model = ResNet50Classifier(num_classes=NUM_CLASSES).to(device)\n",
"model = ResNet101Classifier(num_classes=NUM_CLASSES).to(device)\n",
"\n",
"# For the classification task, we use cross-entropy as the measurement of performance.\n",
"criterion = nn.CrossEntropyLoss()\n",
"\n",
"# Initialize optimizer, you may fine-tune some hyperparameters such as learning rate on your own.\n",
"optimizer = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "814308e4",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Generating pseudo-labels: 0%| | 0/22 [00:00 epochs // 4 and epoch % 2 == 0:\n",
" new_set = update_dataset(\n",
" train_set=train_set,\n",
" unlabeled_set=unlabeled_set,\n",
" model=model,\n",
" threshold=threshold,\n",
" batch_size=batch_size,\n",
" num_workers=num_workers,\n",
" )\n",
" train_loader = DataLoader(\n",
" dataset=new_set,\n",
" batch_size=batch_size,\n",
" shuffle=True,\n",
" num_workers=num_workers,\n",
" pin_memory=True,\n",
" collate_fn=train_collate_fn,\n",
" )\n",
"\n",
" # ---------- Training ----------\n",
" # Make sure the model is in train mode before training.\n",
" model.train()\n",
"\n",
" # These are used to record information in training.\n",
" train_loss = []\n",
" train_accs = []\n",
"\n",
" # Iterate the training set by batches.\n",
" for batch in tqdm(train_loader):\n",
" # A batch consists of image data and corresponding labels.\n",
" imgs, labels = batch\n",
"\n",
" new_images = torch.stack([train_tfm(img) for img in imgs])\n",
"\n",
" # Forward the data. (Make sure data and model are on the same device.)\n",
" logits = model(new_images.to(device))\n",
"\n",
" # Calculate the cross-entropy loss.\n",
" # We don't need to apply softmax before computing cross-entropy as it is done automatically.\n",
" loss = criterion(logits, labels.to(device))\n",
"\n",
" # Gradients stored in the parameters in the previous step should be cleared out first.\n",
" optimizer.zero_grad()\n",
"\n",
" # Compute the gradients for parameters.\n",
" loss.backward()\n",
"\n",
" # Clip the gradient norms for stable training.\n",
" # grad_norm = nn.utils.clip_grad_norm_(model.parameters(), max_norm=10)\n",
"\n",
" # Update the parameters with computed gradients.\n",
" optimizer.step()\n",
"\n",
" # Compute the accuracy for current batch.\n",
" acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()\n",
"\n",
" # Record the loss and accuracy.\n",
" train_loss.append(loss.item())\n",
" train_accs.append(acc)\n",
"\n",
" # The average loss and accuracy of the training set is the average of the recorded values.\n",
" train_loss = sum(train_loss) / len(train_loss)\n",
" train_acc = sum(train_accs) / len(train_accs)\n",
"\n",
" # Print the information.\n",
" print(\n",
" f\"[ Train | {epoch + 1:03d}/{epochs:03d} ] loss = {train_loss:.5f}, acc = {train_acc:.5f}\"\n",
" )\n",
" # ---------- Validation ----------\n",
" # Make sure the model is in eval mode so that some modules like dropout are disabled and work normally.\n",
" model.eval()\n",
"\n",
" # These are used to record information in validation.\n",
" valid_loss = []\n",
" valid_accs = []\n",
"\n",
" # Iterate the validation set by batches.\n",
" for batch in tqdm(valid_loader):\n",
" # A batch consists of image data and corresponding labels.\n",
" imgs, labels = batch\n",
"\n",
" # We don't need gradient in validation.\n",
" # Using torch.no_grad() accelerates the forward process.\n",
" with torch.no_grad():\n",
" logits = model(imgs.to(device))\n",
"\n",
" # We can still compute the loss (but not the gradient).\n",
" loss = criterion(logits, labels.to(device))\n",
"\n",
" # Compute the accuracy for current batch.\n",
" acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()\n",
"\n",
" # Record the loss and accuracy.\n",
" valid_loss.append(loss.item())\n",
" valid_accs.append(acc)\n",
"\n",
" # The average loss and accuracy for entire validation set is the average of the recorded values.\n",
" valid_loss = sum(valid_loss) / len(valid_loss)\n",
" valid_acc = sum(valid_accs) / len(valid_accs)\n",
"\n",
" # Print the information.\n",
" print(\n",
" f\"[ Valid | {epoch + 1:03d}/{epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}\"\n",
" )\n",
"\n",
" if valid_loss < best_valid_loss:\n",
" best_valid_loss = valid_loss\n",
" torch.save(model.state_dict(), \"best_model.pth\")\n",
" print(f\"Model saved with loss: {valid_loss:.5f}, acc: {valid_acc:.5f}\")\n",
" elif early_stop:\n",
" if epoch > epochs // 2 and valid_loss > best_valid_loss * 1.2:\n",
" print(\"Early stopping\")\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b890f42b",
"metadata": {},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mnotebook controller is DISPOSED. \n",
"\u001b[1;31m有关更多详细信息，请查看 Jupyter log。"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mnotebook controller is DISPOSED. \n",
"\u001b[1;31m有关更多详细信息，请查看 Jupyter log。"
]
}
],
"source": [
"# torch.save(model.state_dict(), \"best_model.pth\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7999c1c7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ResNet101Classifier(\n",
" (resnet): ResNet(\n",
" (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
" (layer1): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (layer2): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (3): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (layer3): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (3): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (4): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (5): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (6): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (7): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (8): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (9): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (10): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (11): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (12): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (13): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (14): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (15): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (16): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (17): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (18): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (19): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (20): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (21): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (22): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (layer4): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
" (fc): Linear(in_features=2048, out_features=14, bias=True)\n",
" )\n",
")"
]
},
"execution_count": 8,
"metadata": {},
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}
],
"source": [
"# LOAD BEST MODEL\n",
"model.load_state_dict(torch.load(\"best_model.pth\"))\n",
"model.eval()"
]
},
{
"cell_type": "markdown",
"id": "c6e73214",
"metadata": {
"id": "c6e73214"
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"source": [
"## Testing\n",
"For inference, we need to make sure the model is in eval mode, and the order of the dataset should not be shuffled (\"shuffle=False\" in test_loader)."
]
},
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"text": [
"100%|██████████| 33/33 [00:10<00:00, 3.27it/s]\n"
]
}
],
"source": [
"# Make sure the model is in eval mode.\n",
"# Some modules like Dropout or BatchNorm affect if the model is in training mode.\n",
"# model.load_state_dict(torch.load(\"model.ckpt\", map_location=lambda storage, loc: storage))\n",
"# model.device = device\n",
"model.eval()\n",
"\n",
"# Initialize a list to store the predictions.\n",
"predictions = []\n",
"\n",
"\n",
"# Iterate the testing set by batches.\n",
"for batch in tqdm(test_loader):\n",
" # A batch consists of image data and corresponding labels.\n",
" # But here the variable \"labels\" is useless since we do not have the ground-truth.\n",
" # If printing out the labels, you will find that it is always 0.\n",
" # This is because the wrapper (DatasetFolder) returns images and labels for each batch,\n",
" # so we have to create fake labels to make it work normally.\n",
" imgs, labels = batch\n",
"\n",
" # We don't need gradient in testing, and we don't even have labels to compute loss.\n",
" # Using torch.no_grad() accelerates the forward process.\n",
" with torch.no_grad():\n",
" logits = model(imgs.to(device))\n",
"\n",
" # Take the class with greatest logit as prediction and record it.\n",
" predictions.extend(logits.argmax(dim=-1).cpu().numpy().tolist())"
]
},
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"# Save predictions into the file.\n",
"with open(\"predict.csv\", \"w\") as f:\n",
"\n",
" # The first row must be \"Id, Category\"\n",
" f.write(\"Id,Category\\n\")\n",
"\n",
" # For the rest of the rows, each image id corresponds to a predicted class.\n",
" for i, pred in enumerate(predictions):\n",
" f.write(f\"{i},{pred}\\n\")"
]
}
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