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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn as nn\n",
"import torchvision.transforms as transforms\n",
"from torch.optim.lr_scheduler import ReduceLROnPlateau\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",
"\n",
"ImageFile.LOAD_TRUNCATED_IMAGES = True\n",
"random.seed(1234)\n",
"np.random.seed(1234)\n",
"torch.manual_seed(1234)\n",
"\n",
"folder = \"./datasets\"\n",
"NUM_CLASSES = 14\n",
"\n",
"\n",
"train_tfm = transforms.Compose(\n",
" [\n",
" # Resize the image to a fixed shape (height = width = 256)\n",
" transforms.Resize((224, 224)),\n",
" transforms.Lambda(lambda x: x.convert(\"RGB\")),\n",
" # Random horizontal flip to increase robustness to object orientation\n",
" transforms.RandomHorizontalFlip(),\n",
" # Random rotation, a common transformation to handle rotated images\n",
" transforms.RandomRotation(\n",
" 20\n",
" ), # Rotate image by a random angle between -20 and 20 degrees\n",
" # Random cropping to simulate random scene zoom\n",
" transforms.RandomResizedCrop(\n",
" 224, scale=(0.8, 1.0)\n",
" ), # Crop and resize to 224x224\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",
" # Random affine transformation (translation, scaling, rotation)\n",
" # transforms.RandomAffine(degrees=10, translate=(0.1, 0.1), scale=(0.8, 1.2)),\n",
" # Convert to tensor\n",
" transforms.ToTensor(),\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((224, 224)),\n",
" transforms.Lambda(lambda x: x.convert(\"RGB\")),\n",
" # Convert to tensor\n",
" transforms.ToTensor(),\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",
"\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",
"\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",
"\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\n",
"\n",
"\n",
"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 is the core function to generate pseudo-labels dataets using the given model.\n",
" inputs:\n",
" - train_set: The labeled training set\n",
" - unlabeled_set: The unlabeled dataset to be pseudo-labeled\n",
" - model: The trained model to generate pseudo-labels\n",
" - threshold: Confidence threshold for pseudo-labeling\n",
" - batch_size: Batch size for DataLoader\n",
" - num_workers: Number of workers for DataLoader\n",
" outputs:\n",
" - new_set: The updated dataset with pseudo-labels\n",
" \"\"\"\n",
" device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from models import * # noqa: F403\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",
"# model = InceptionV3Classifier(num_classes=NUM_CLASSES).to(device)\n",
"# model = ViTLargeClassifier(num_classes=NUM_CLASSES).to(device)\n",
"\n",
"# !!! ONLY USE THIS FOR RESUME TRAINING !!!\n",
"model.load_state_dict(torch.load(\"best_model.pth\"))\n",
"\n",
"criterion = nn.CrossEntropyLoss()\n",
"\n",
"optimizer = optim.Adam(model.parameters(), lr=1e-5, weight_decay=1e-5)\n",
"\n",
"scheduler = ReduceLROnPlateau(\n",
" optimizer,\n",
" mode=\"min\",\n",
" factor=0.1,\n",
" patience=2,\n",
" threshold=0.001,\n",
" threshold_mode=\"rel\",\n",
" cooldown=0,\n",
" min_lr=0,\n",
" eps=1e-08,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# TRAINING\n",
"do_semi = True\n",
"batch_size = 128\n",
"num_workers = 8\n",
"train_set, valid_set, unlabeled_set, test_set = get_dataset()\n",
"\n",
"train_loader = DataLoader(\n",
" dataset=train_set,\n",
" batch_size=batch_size,\n",
" shuffle=True,\n",
" num_workers=num_workers,\n",
" collate_fn=train_collate_fn,\n",
")\n",
"valid_loader = DataLoader(\n",
" dataset=valid_set,\n",
" batch_size=batch_size,\n",
" shuffle=True,\n",
" num_workers=num_workers,\n",
" collate_fn=test_collate_fn,\n",
")\n",
"test_loader = DataLoader(\n",
" dataset=test_set,\n",
" batch_size=batch_size,\n",
" shuffle=False,\n",
" num_workers=num_workers,\n",
" collate_fn=test_collate_fn,\n",
")\n",
"best_valid_loss = float(\"inf\")\n",
"start_epoch = 100\n",
"epochs = 100\n",
"threshold = 0.8\n",
"early_stop = False\n",
"for epoch in range(start_epoch, epochs):\n",
" if do_semi and epoch > 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",
" model.train()\n",
"\n",
" train_loss = []\n",
" train_accs = []\n",
"\n",
" for batch in tqdm(train_loader):\n",
" imgs, labels = batch\n",
"\n",
" new_images = torch.stack([train_tfm(img) for img in imgs])\n",
"\n",
" logits = model(new_images.to(device))\n",
"\n",
" loss = criterion(logits, labels.to(device))\n",
"\n",
" optimizer.zero_grad()\n",
"\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
"\n",
" acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()\n",
"\n",
" train_loss.append(loss.item())\n",
" train_accs.append(acc)\n",
"\n",
" train_loss = sum(train_loss) / len(train_loss)\n",
" train_acc = sum(train_accs) / len(train_accs)\n",
"\n",
" print(\n",
" f\"[ Train | {epoch + 1:03d}/{epochs:03d} ] loss = {train_loss:.5f}, acc = {train_acc:.5f}\"\n",
" )\n",
" # ---------- Validation ----------\n",
" model.eval()\n",
"\n",
" valid_loss = []\n",
" valid_accs = []\n",
"\n",
" for batch in tqdm(valid_loader):\n",
" imgs, labels = batch\n",
"\n",
" with torch.no_grad():\n",
" logits = model(imgs.to(device))\n",
"\n",
" loss = criterion(logits, labels.to(device))\n",
"\n",
" acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()\n",
"\n",
" valid_loss.append(loss.item())\n",
" valid_accs.append(acc)\n",
"\n",
" valid_loss = sum(valid_loss) / len(valid_loss)\n",
" valid_acc = sum(valid_accs) / len(valid_accs)\n",
"\n",
" print(\n",
" f\"[ Valid | {epoch + 1:03d}/{epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}\"\n",
" )\n",
"\n",
" scheduler.step(metrics=valid_loss)\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\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# torch.save(model.state_dict(), \"best_model.pth\")\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# LOAD BEST MODEL\n",
"\n",
"model.load_state_dict(torch.load(\"best_model.pth\"))\n",
"model.eval()\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 33/33 [00:07<00:00, 4.14it/s]\n"
]
}
],
"source": [
"# TEST\n",
"\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",
" imgs, labels = batch\n",
"\n",
" with torch.no_grad():\n",
" logits = model(imgs.to(device))\n",
"\n",
" predictions.extend(logits.argmax(dim=-1).cpu().numpy().tolist())\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predictions saved to predict.csv\n"
]
}
],
"source": [
"# Save predictions into the file.\n",
"with open(\"predict.csv\", \"w\") as f:\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\")\n",
"print(\"Predictions saved to predict.csv\")\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv (3.12.9)",
"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": "3.12.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|