diff --git a/05 Transformer/代码/HW03.pdf b/05 Transformer/代码/HW03.pdf new file mode 100644 index 0000000..d33a258 Binary files /dev/null and b/05 Transformer/代码/HW03.pdf differ diff --git a/05 Transformer/代码/HW3_CNN.ipynb b/05 Transformer/代码/HW3_CNN.ipynb new file mode 100644 index 0000000..b1bc0cd --- /dev/null +++ b/05 Transformer/代码/HW3_CNN.ipynb @@ -0,0 +1,529 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "HW3-CNN.ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "D_a2USyd4giE" + }, + "source": [ + "# **Homework 3 - Convolutional Neural Network**\n", + "\n", + "This is the example code of homework 3 of the machine learning course by Prof. Hung-yi Lee.\n", + "\n", + "In this homework, you are required to build a convolutional neural network for image classification, possibly with some advanced training tips.\n", + "\n", + "\n", + "There are three levels here:\n", + "\n", + "**Easy**: Build a simple convolutional neural network as the baseline. (2 pts)\n", + "\n", + "**Medium**: Design a better architecture or adopt different data augmentations to improve the performance. (2 pts)\n", + "\n", + "**Hard**: Utilize provided unlabeled data to obtain better results. (2 pts)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VHpJocsDr6iA" + }, + "source": [ + "## **About the Dataset**\n", + "\n", + "The dataset used here is food-11, a collection of food images in 11 classes.\n", + "\n", + "For the requirement in the homework, TAs slightly modified the data.\n", + "Please DO NOT access the original fully-labeled training data or testing labels.\n", + "\n", + "Also, the modified dataset is for this course only, and any further distribution or commercial use is forbidden." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zhzdomRTOKoJ" + }, + "source": [ + "# Download the dataset\n", + "# You may choose where to download the data.\n", + "\n", + "# Google Drive\n", + "# !gdown --id '1awF7pZ9Dz7X1jn1_QAiKN-_v56veCEKy' --output food-11.zip\n", + "\n", + "# Dropbox\n", + "!wget https://www.dropbox.com/s/m9q6273jl3djall/food-11.zip -O food-11.zip\n", + "\n", + "# Unzip the dataset.\n", + "# This may take some time.\n", + "!unzip -q food-11.zip" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BBVSCWWhp6uq" + }, + "source": [ + "## **Import Packages**\n", + "\n", + "First, we need to import packages that will be used later.\n", + "\n", + "In this homework, we highly rely on **torchvision**, a library of PyTorch." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9sVrKci4PUFW" + }, + "source": [ + "# Import necessary packages.\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", + "# \"ConcatDataset\" and \"Subset\" are possibly useful when doing semi-supervised learning.\n", + "from torch.utils.data import ConcatDataset, DataLoader, Subset\n", + "from torchvision.datasets import DatasetFolder\n", + "\n", + "# This is for the progress bar.\n", + "from tqdm.auto import tqdm" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F0i9ZCPrOVN_" + }, + "source": [ + "## **Dataset, Data Loader, and Transforms**\n", + "\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](https://pytorch.org/vision/stable/transforms.html) for details about different transforms." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gKd2abixQghI" + }, + "source": [ + "# It is important to do data augmentation in training.\n", + "# However, not every augmentation is useful.\n", + "# Please think about what kind of augmentation is helpful for food recognition.\n", + "train_tfm = transforms.Compose([\n", + " # Resize the image into a fixed shape (height = width = 128)\n", + " transforms.Resize((128, 128)),\n", + " # You may add some transforms here.\n", + " # ToTensor() should be the last one of the transforms.\n", + " transforms.ToTensor(),\n", + "])\n", + "\n", + "# We don't need augmentations in testing and validation.\n", + "# All we need here is to resize the PIL image and transform it into Tensor.\n", + "test_tfm = transforms.Compose([\n", + " transforms.Resize((128, 128)),\n", + " transforms.ToTensor(),\n", + "])\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "qz6jeMnkQl0_" + }, + "source": [ + "# Batch size for training, validation, and testing.\n", + "# A greater batch size usually gives a more stable gradient.\n", + "# But the GPU memory is limited, so please adjust it carefully.\n", + "batch_size = 128\n", + "\n", + "# Construct datasets.\n", + "# The argument \"loader\" tells how torchvision reads the data.\n", + "train_set = DatasetFolder(\"food-11/training/labeled\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=train_tfm)\n", + "valid_set = DatasetFolder(\"food-11/validation\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=test_tfm)\n", + "unlabeled_set = DatasetFolder(\"food-11/training/unlabeled\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=train_tfm)\n", + "test_set = DatasetFolder(\"food-11/testing\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=test_tfm)\n", + "\n", + "# Construct data loaders.\n", + "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)\n", + "valid_loader = DataLoader(valid_set, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)\n", + "test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j9YhZo7POPYG" + }, + "source": [ + "## **Model**\n", + "\n", + "The basic model here is simply a stack of convolutional layers followed by some fully-connected layers.\n", + "\n", + "Since there are three channels for a color image (RGB), the input channels of the network must be three.\n", + "In each convolutional layer, typically the channels of inputs grow, while the height and width shrink (or remain unchanged, according to some hyperparameters like stride and padding).\n", + "\n", + "Before fed into fully-connected layers, the feature map must be flattened into a single one-dimensional vector (for each image).\n", + "These features are then transformed by the fully-connected layers, and finally, we obtain the \"logits\" for each class.\n", + "\n", + "### **WARNING -- You Must Know**\n", + "You are free to modify the model architecture here for further improvement.\n", + "However, if you want to use some well-known architectures such as ResNet50, please make sure **NOT** to load the pre-trained weights.\n", + "Using such pre-trained models is considered cheating and therefore you will be punished.\n", + "Similarly, it is your responsibility to make sure no pre-trained weights are used if you use **torch.hub** to load any modules.\n", + "\n", + "For example, if you use ResNet-18 as your model:\n", + "\n", + "model = torchvision.models.resnet18(pretrained=**False**) → This is fine.\n", + "\n", + "model = torchvision.models.resnet18(pretrained=**True**) → This is **NOT** allowed." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Y1c-GwrMQqMl" + }, + "source": [ + "class Classifier(nn.Module):\n", + " def __init__(self):\n", + " super(Classifier, self).__init__()\n", + " # The arguments for commonly used modules:\n", + " # torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)\n", + " # torch.nn.MaxPool2d(kernel_size, stride, padding)\n", + "\n", + " # input image size: [3, 128, 128]\n", + " self.cnn_layers = nn.Sequential(\n", + " nn.Conv2d(3, 64, 3, 1, 1),\n", + " nn.BatchNorm2d(64),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2, 2, 0),\n", + "\n", + " nn.Conv2d(64, 128, 3, 1, 1),\n", + " nn.BatchNorm2d(128),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2, 2, 0),\n", + "\n", + " nn.Conv2d(128, 256, 3, 1, 1),\n", + " nn.BatchNorm2d(256),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(4, 4, 0),\n", + " )\n", + " self.fc_layers = nn.Sequential(\n", + " nn.Linear(256 * 8 * 8, 256),\n", + " nn.ReLU(),\n", + " nn.Linear(256, 256),\n", + " nn.ReLU(),\n", + " nn.Linear(256, 11)\n", + " )\n", + "\n", + " def forward(self, x):\n", + " # input (x): [batch_size, 3, 128, 128]\n", + " # output: [batch_size, 11]\n", + "\n", + " # Extract features by convolutional layers.\n", + " x = self.cnn_layers(x)\n", + "\n", + " # The extracted feature map must be flatten before going to fully-connected layers.\n", + " x = x.flatten(1)\n", + "\n", + " # The features are transformed by fully-connected layers to obtain the final logits.\n", + " x = self.fc_layers(x)\n", + " return x" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aEnGbriXORN3" + }, + "source": [ + "## **Training**\n", + "\n", + "You can finish supervised learning by simply running the provided code without any modification.\n", + "\n", + "The function \"get_pseudo_labels\" is used for semi-supervised learning.\n", + "It is expected to get better performance if you use unlabeled data for semi-supervised learning.\n", + "However, you have to implement the function on your own and need to adjust several hyperparameters manually.\n", + "\n", + "For more details about semi-supervised learning, please refer to [Prof. Lee's slides](https://speech.ee.ntu.edu.tw/~tlkagk/courses/ML_2016/Lecture/semi%20(v3).pdf).\n", + "\n", + "Again, please notice that utilizing external data (or pre-trained model) for training is **prohibited**." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "swlf5EwA-hxA" + }, + "source": [ + "def get_pseudo_labels(dataset, model, threshold=0.65):\n", + " # This functions generates pseudo-labels of a dataset using given model.\n", + " # It returns an instance of DatasetFolder containing images whose prediction confidences exceed a given threshold.\n", + " # You are NOT allowed to use any models trained on external data for pseudo-labeling.\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", + " # Iterate over the dataset by batches.\n", + " for batch in tqdm(dataloader):\n", + " img, _ = batch\n", + "\n", + " # Forward the data\n", + " # Using torch.no_grad() accelerates the forward process.\n", + " with torch.no_grad():\n", + " logits = model(img.to(device))\n", + "\n", + " # Obtain the probability distributions by applying softmax on logits.\n", + " probs = softmax(logits)\n", + "\n", + " # ---------- TODO ----------\n", + " # Filter the data and construct a new dataset.\n", + "\n", + " # # Turn off the eval mode.\n", + " model.train()\n", + " return dataset" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "PHaFE-8oQtkC" + }, + "source": [ + "# \"cuda\" only when GPUs are available.\n", + "device = \"cuda\" 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.device = 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 = torch.optim.Adam(model.parameters(), lr=0.0003, weight_decay=1e-5)\n", + "\n", + "# The number of training epochs.\n", + "n_epochs = 80\n", + "\n", + "# Whether to do semi-supervised learning.\n", + "do_semi = False\n", + "\n", + "for epoch in range(n_epochs):\n", + " # ---------- TODO ----------\n", + " # In each epoch, relabel the unlabeled dataset for semi-supervised learning.\n", + " # Then you can combine the labeled dataset and pseudo-labeled dataset for the training.\n", + " if do_semi:\n", + " # Obtain pseudo-labels for unlabeled data using trained model.\n", + " pseudo_set = get_pseudo_labels(unlabeled_set, model)\n", + "\n", + " # Construct a new dataset and a data loader for training.\n", + " # This is used in semi-supervised learning only.\n", + " concat_dataset = ConcatDataset([train_set, pseudo_set])\n", + " train_loader = DataLoader(concat_dataset, batch_size=batch_size, shuffle=True, num_workers=8, pin_memory=True)\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", + "\n", + " # A batch consists of image data and corresponding labels.\n", + " imgs, labels = batch\n", + "\n", + " # Forward the data. (Make sure data and model are on the same device.)\n", + " logits = model(imgs.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(f\"[ Train | {epoch + 1:03d}/{n_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", + "\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(f\"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2o1oCMXy61_3" + }, + "source": [ + "## **Testing**\n", + "\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).\n", + "\n", + "Last but not least, don't forget to save the predictions into a single CSV file.\n", + "The format of CSV file should follow the rules mentioned in the slides.\n", + "\n", + "### **WARNING -- Keep in Mind**\n", + "\n", + "Cheating includes but not limited to:\n", + "1. using testing labels,\n", + "2. submitting results to previous Kaggle competitions,\n", + "3. sharing predictions with others,\n", + "4. copying codes from any creatures on Earth,\n", + "5. asking other people to do it for you.\n", + "\n", + "Any violations bring you punishments from getting a discount on the final grade to failing the course.\n", + "\n", + "It is your responsibility to check whether your code violates the rules.\n", + "When citing codes from the Internet, you should know what these codes exactly do.\n", + "You will **NOT** be tolerated if you break the rule and claim you don't know what these codes do.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4HznI9_-ocrq" + }, + "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.eval()\n", + "\n", + "# Initialize a list to store the predictions.\n", + "predictions = []\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())" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "3t2q2Th85ZUE" + }, + "source": [ + "# 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\")" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/05 Transformer/范例/HW03.ipynb b/05 Transformer/范例/HW03.ipynb new file mode 100644 index 0000000..51c48d3 --- 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(2 pts)\n", + "\n", + "**Medium**: Design a better architecture or adopt different data augmentations to improve the performance. (2 pts)\n", + "\n", + "**Hard**: Utilize provided unlabeled data to obtain better results. (2 pts)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VHpJocsDr6iA" + }, + "source": [ + "## **About the Dataset**\n", + "\n", + "The dataset used here is food-11, a collection of food images in 11 classes.\n", + "\n", + "For the requirement in the homework, TAs slightly modified the data.\n", + "Please DO NOT access the original fully-labeled training data or testing labels.\n", + "\n", + "Also, the modified dataset is for this course only, and any further distribution or commercial use is forbidden." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zhzdomRTOKoJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "ecf29947-bb69-4f3a-d9bb-0acc1acd724e" + }, + "source": [ + "# Download the dataset\n", + "# You may choose where to download the data.\n", + "\n", + "# Google Drive\n", + "# !gdown --id '1awF7pZ9Dz7X1jn1_QAiKN-_v56veCEKy' --output food-11.zip\n", + "\n", + "# Dropbox\n", + "!wget https://www.dropbox.com/s/m9q6273jl3djall/food-11.zip -O food-11.zip\n", + "\n", + "# Unzip the dataset.\n", + "# This may take some time.\n", + "!unzip -q food-11.zip" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BBVSCWWhp6uq" + }, + "source": [ + "## **Import Packages**\n", + "\n", + "First, we need to import packages that will be used later.\n", + "\n", + "In this homework, we highly rely on **torchvision**, a library of PyTorch." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9sVrKci4PUFW" + }, + "source": [ + "# Import necessary packages.\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", + "# \"ConcatDataset\" and \"Subset\" are possibly useful when doing semi-supervised learning.\n", + "from torch.utils.data import ConcatDataset, DataLoader, Subset\n", + "from torchvision.datasets import DatasetFolder\n", + "\n", + "# This is for the progress bar.\n", + "from tqdm.auto import tqdm" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F0i9ZCPrOVN_" + }, + "source": [ + "## **Dataset, Data Loader, and Transforms**\n", + "\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](https://pytorch.org/vision/stable/transforms.html) for details about different transforms." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gKd2abixQghI" + }, + "source": [ + "# It is important to do data augmentation in training.\n", + "# However, not every augmentation is useful.\n", + "# Please think about what kind of augmentation is helpful for food recognition.\n", + "train_tfm = transforms.Compose([\n", + " # Resize the image into a fixed shape (height = width = 128)\n", + " transforms.Resize((128, 128)),\n", + " # You may add some transforms here.\n", + " # ToTensor() should be the last one of the transforms.\n", + " transforms.ToTensor(),\n", + "])\n", + "\n", + "# We don't need augmentations in testing and validation.\n", + "# All we need here is to resize the PIL image and transform it into Tensor.\n", + "test_tfm = transforms.Compose([\n", + " transforms.Resize((128, 128)),\n", + " transforms.ToTensor(),\n", + "])\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "qz6jeMnkQl0_" + }, + "source": [ + "# Batch size for training, validation, and testing.\n", + "# A greater batch size usually gives a more stable gradient.\n", + "# But the GPU memory is limited, so please adjust it carefully.\n", + "batch_size = 128\n", + "\n", + "# Construct datasets.\n", + "# The argument \"loader\" tells how torchvision reads the data.\n", + "train_set = DatasetFolder(\"food-11/training/labeled\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=train_tfm)\n", + "valid_set = DatasetFolder(\"food-11/validation\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=test_tfm)\n", + "unlabeled_set = DatasetFolder(\"food-11/training/unlabeled\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=train_tfm)\n", + "test_set = DatasetFolder(\"food-11/testing\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=test_tfm)\n", + "\n", + "# Construct data loaders.\n", + "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)\n", + "valid_loader = DataLoader(valid_set, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)\n", + "test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j9YhZo7POPYG" + }, + "source": [ + "## **Model**\n", + "\n", + "The basic model here is simply a stack of convolutional layers followed by some fully-connected layers.\n", + "\n", + "Since there are three channels for a color image (RGB), the input channels of the network must be three.\n", + "In each convolutional layer, typically the channels of inputs grow, while the height and width shrink (or remain unchanged, according to some hyperparameters like stride and padding).\n", + "\n", + "Before fed into fully-connected layers, the feature map must be flattened into a single one-dimensional vector (for each image).\n", + "These features are then transformed by the fully-connected layers, and finally, we obtain the \"logits\" for each class.\n", + "\n", + "### **WARNING -- You Must Know**\n", + "You are free to modify the model architecture here for further improvement.\n", + "However, if you want to use some well-known architectures such as ResNet50, please make sure **NOT** to load the pre-trained weights.\n", + "Using such pre-trained models is considered cheating and therefore you will be punished.\n", + "Similarly, it is your responsibility to make sure no pre-trained weights are used if you use **torch.hub** to load any modules.\n", + "\n", + "For example, if you use ResNet-18 as your model:\n", + "\n", + "model = torchvision.models.resnet18(pretrained=**False**) → This is fine.\n", + "\n", + "model = torchvision.models.resnet18(pretrained=**True**) → This is **NOT** allowed." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Y1c-GwrMQqMl" + }, + "source": [ + "class Classifier(nn.Module):\n", + " def __init__(self):\n", + " super(Classifier, self).__init__()\n", + " # The arguments for commonly used modules:\n", + " # torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)\n", + " # torch.nn.MaxPool2d(kernel_size, stride, padding)\n", + "\n", + " # input image size: [3, 128, 128]\n", + " self.cnn_layers = nn.Sequential(\n", + " nn.Conv2d(3, 64, 3, 1, 1),\n", + " nn.BatchNorm2d(64),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2, 2, 0),\n", + "\n", + " nn.Conv2d(64, 128, 3, 1, 1),\n", + " nn.BatchNorm2d(128),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2, 2, 0),\n", + "\n", + " nn.Conv2d(128, 256, 3, 1, 1),\n", + " nn.BatchNorm2d(256),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(4, 4, 0),\n", + " )\n", + " self.fc_layers = nn.Sequential(\n", + " nn.Linear(256 * 8 * 8, 256),\n", + " nn.ReLU(),\n", + " nn.Linear(256, 256),\n", + " nn.ReLU(),\n", + " nn.Linear(256, 11)\n", + " )\n", + "\n", + " def forward(self, x):\n", + " # input (x): [batch_size, 3, 128, 128]\n", + " # output: [batch_size, 11]\n", + "\n", + " # Extract features by convolutional layers.\n", + " x = self.cnn_layers(x)\n", + "\n", + " # The extracted feature map must be flatten before going to fully-connected layers.\n", + " x = x.flatten(1)\n", + "\n", + " # The features are transformed by fully-connected layers to obtain the final logits.\n", + " x = self.fc_layers(x)\n", + " return x" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aEnGbriXORN3" + }, + "source": [ + "## **Training**\n", + "\n", + "You can finish supervised learning by simply running the provided code without any modification.\n", + "\n", + "The function \"get_pseudo_labels\" is used for semi-supervised learning.\n", + "It is expected to get better performance if you use unlabeled data for semi-supervised learning.\n", + "However, you have to implement the function on your own and need to adjust several hyperparameters manually.\n", + "\n", + "For more details about semi-supervised learning, please refer to [Prof. Lee's slides](https://speech.ee.ntu.edu.tw/~tlkagk/courses/ML_2016/Lecture/semi%20(v3).pdf).\n", + "\n", + "Again, please notice that utilizing external data (or pre-trained model) for training is **prohibited**." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "swlf5EwA-hxA" + }, + "source": [ + "def get_pseudo_labels(dataset, model, threshold=0.65):\n", + " # This functions generates pseudo-labels of a dataset using given model.\n", + " # It returns an instance of DatasetFolder containing images whose prediction confidences exceed a given threshold.\n", + " # You are NOT allowed to use any models trained on external data for pseudo-labeling.\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", + " # Iterate over the dataset by batches.\n", + " for batch in tqdm(dataloader):\n", + " img, _ = batch\n", + "\n", + " # Forward the data\n", + " # Using torch.no_grad() accelerates the forward process.\n", + " with torch.no_grad():\n", + " logits = model(img.to(device))\n", + "\n", + " # Obtain the probability distributions by applying softmax on logits.\n", + " probs = softmax(logits)\n", + "\n", + " # ---------- TODO ----------\n", + " # Filter the data and construct a new dataset.\n", + "\n", + " # # Turn off the eval mode.\n", + " model.train()\n", + " return dataset" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "PHaFE-8oQtkC", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "7bbaa77bbe394c739fd5d82af852ec92", + "a13a265c154b4a8bae9a738122adbaba", + "33d009a524c741a78854283c21b0bc8c", + "6d4a24fd00204a69bde40d798cb8c308", + "13fd634be4894532bb197357ceb7bcff", + "ea2c4c662f1941e9a4bd30b54456338c", + "e551b43494844eaf89062660a5642eb9", + "b49d0f5525f94d57a8949692df43ac51", + "e41b713e089d4cc1b86d6132e9f344a9", + "2cbd990fd5134b13b1bd937cebe36d5e", + "1643ae0fe8844c87ad78f47165a7f656", + "e42cee09c92f479fb05b4b015b0a24d9", + "70d55a554b9449d292a2fee28f0cad4f", + "88af3d5de4ac471fbefa54642fe10fb3", 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"model.device = 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 = torch.optim.Adam(model.parameters(), lr=0.0003, weight_decay=1e-5)\n", + "\n", + "# The number of training epochs.\n", + "n_epochs = 80\n", + "\n", + "# Whether to do semi-supervised learning.\n", + "do_semi = False\n", + "\n", + "for epoch in range(n_epochs):\n", + " # ---------- TODO ----------\n", + " # In each epoch, relabel the unlabeled dataset for semi-supervised learning.\n", + " # Then you can combine the labeled dataset and pseudo-labeled dataset for the training.\n", + " if do_semi:\n", + " # Obtain pseudo-labels for unlabeled data using trained model.\n", + " pseudo_set = get_pseudo_labels(unlabeled_set, model)\n", + "\n", + " # Construct a new dataset and a data loader for training.\n", + " # This is used in semi-supervised learning only.\n", + " concat_dataset = ConcatDataset([train_set, pseudo_set])\n", + " train_loader = DataLoader(concat_dataset, batch_size=batch_size, shuffle=True, num_workers=8, pin_memory=True)\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", + "\n", + " # A batch consists of image data and corresponding labels.\n", + " imgs, labels = batch\n", + "\n", + " # Forward the data. (Make sure data and model are on the same device.)\n", + " logits = model(imgs.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(f\"[ Train | {epoch + 1:03d}/{n_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", + "\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(f\"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2o1oCMXy61_3" + }, + "source": [ + "## **Testing**\n", + "\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).\n", + "\n", + "Last but not least, don't forget to save the predictions into a single CSV file.\n", + "The format of CSV file should follow the rules mentioned in the slides.\n", + "\n", + "### **WARNING -- Keep in Mind**\n", + "\n", + "Cheating includes but not limited to:\n", + "1. using testing labels,\n", + "2. submitting results to previous Kaggle competitions,\n", + "3. sharing predictions with others,\n", + "4. copying codes from any creatures on Earth,\n", + "5. asking other people to do it for you.\n", + "\n", + "Any violations bring you punishments from getting a discount on the final grade to failing the course.\n", + "\n", + "It is your responsibility to check whether your code violates the rules.\n", + "When citing codes from the Internet, you should know what these codes exactly do.\n", + "You will **NOT** be tolerated if you break the rule and claim you don't know what these codes do.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4HznI9_-ocrq" + }, + "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.eval()\n", + "\n", + "# Initialize a list to store the predictions.\n", + "predictions = []\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())" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "3t2q2Th85ZUE" + }, + "source": [ + "# 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\")" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/05 Transformer/范例/HW03.pdf b/05 Transformer/范例/HW03.pdf new file mode 100644 index 0000000..52a6a8b Binary files /dev/null and b/05 Transformer/范例/HW03.pdf differ