diff --git a/HW01/caishaokang-HW1.ipynb b/HW01/caishaokang-HW1.ipynb new file mode 100644 index 00000000..bc83074d --- /dev/null +++ b/HW01/caishaokang-HW1.ipynb @@ -0,0 +1,7345 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "guE34D3Fj2R9" + }, + "source": [ + "# **Homework 1: COVID-19 Cases Prediction (Regression)**" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Objectives:\n", + "* Solve a regression problem with deep neural networks (DNN).\n", + "* Understand basic DNN training tips.\n", + "* Familiarize yourself with PyTorch.\n", + "\n", + "If you have any questions, please contact the TAs via TA hours, NTU COOL, or email to mlta-2022-spring@googlegroups.com" + ], + "metadata": { + "id": "V57zhcTp1Xxb" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Tm2aXcb-j9Fc" + }, + "source": [ + "# Download data\n", + "If the Google Drive links below do not work, you can download data from [Kaggle](https://www.kaggle.com/t/a3ebd5b5542f0f55e828d4f00de8e59a), and upload data manually to the workspace." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YPmfl-awlKZA", + "outputId": "0dc37f9f-4346-4d8b-e816-9a71b821bbb3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/usr/local/lib/python3.7/dist-packages/gdown/cli.py:131: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n", + " category=FutureWarning,\n", + "Downloading...\n", + "From: https://drive.google.com/uc?id=1kLSW_-cW2Huj7bh84YTdimGBOJaODiOS\n", + "To: /content/covid.train.csv\n", + "100% 2.49M/2.49M [00:00<00:00, 238MB/s]\n", + "/usr/local/lib/python3.7/dist-packages/gdown/cli.py:131: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n", + " category=FutureWarning,\n", + "Downloading...\n", + "From: https://drive.google.com/uc?id=1iiI5qROrAhZn-o4FPqsE97bMzDEFvIdg\n", + "To: /content/covid.test.csv\n", + "100% 993k/993k [00:00<00:00, 172MB/s]\n" + ] + } + ], + "source": [ + "!gdown --id '1kLSW_-cW2Huj7bh84YTdimGBOJaODiOS' --output covid.train.csv\n", + "!gdown --id '1iiI5qROrAhZn-o4FPqsE97bMzDEFvIdg' --output covid.test.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "igqIMEgu64-F" + }, + "source": [ + "# Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "xybQNYCXYu13" + }, + "outputs": [], + "source": [ + "# Numerical Operations\n", + "import math\n", + "import numpy as np\n", + "\n", + "# Reading/Writing Data\n", + "import pandas as pd\n", + "import os\n", + "import csv\n", + "\n", + "# For Progress Bar\n", + "from tqdm import tqdm\n", + "\n", + "# Pytorch\n", + "import torch \n", + "import torch.nn as nn\n", + "from torch.utils.data import Dataset, DataLoader, random_split\n", + "\n", + "# For plotting learning curve\n", + "from torch.utils.tensorboard import SummaryWriter" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Some Utility Functions\n", + "\n", + "You do not need to modify this part." + ], + "metadata": { + "id": "fTAVqRfc2KK3" + } + }, + { + "cell_type": "code", + "source": [ + "def same_seed(seed): \n", + " '''Fixes random number generator seeds for reproducibility.'''\n", + " torch.backends.cudnn.deterministic = True\n", + " torch.backends.cudnn.benchmark = False\n", + " np.random.seed(seed)\n", + " torch.manual_seed(seed)\n", + " if torch.cuda.is_available():\n", + " torch.cuda.manual_seed_all(seed)\n", + "\n", + "def train_valid_split(data_set, valid_ratio, seed):\n", + " '''Split provided training data into training set and validation set'''\n", + " valid_set_size = int(valid_ratio * len(data_set)) \n", + " train_set_size = len(data_set) - valid_set_size\n", + " train_set, valid_set = random_split(data_set, [train_set_size, valid_set_size], generator=torch.Generator().manual_seed(seed))\n", + " return np.array(train_set), np.array(valid_set)\n", + "\n", + "def predict(test_loader, model, device):\n", + " model.eval() # Set your model to evaluation mode.\n", + " preds = []\n", + " for x in tqdm(test_loader):\n", + " x = x.to(device) \n", + " with torch.no_grad(): \n", + " pred = model(x) \n", + " preds.append(pred.detach().cpu()) \n", + " preds = torch.cat(preds, dim=0).numpy() \n", + " return preds" + ], + "metadata": { + "id": "RbrcpfYN2I-H" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IqO3lTm78nNO" + }, + "source": [ + "# Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "-mjaJM0wprMs" + }, + "outputs": [], + "source": [ + "class COVID19Dataset(Dataset):\n", + " '''\n", + " x: Features.\n", + " y: Targets, if none, do prediction.\n", + " '''\n", + " def __init__(self, x, y=None):\n", + " if y is None:\n", + " self.y = y\n", + " else:\n", + " self.y = torch.FloatTensor(y)\n", + " self.x = torch.FloatTensor(x)\n", + "\n", + " def __getitem__(self, idx):\n", + " if self.y is None:\n", + " return self.x[idx]\n", + " else:\n", + " return self.x[idx], self.y[idx]\n", + "\n", + " def __len__(self):\n", + " return len(self.x)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Neural Network Model\n", + "Try out different model architectures by modifying the class below." + ], + "metadata": { + "id": "m73ooU75CL_j" + } + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "Qn97_WvvrEkG" + }, + "outputs": [], + "source": [ + "class My_Model(nn.Module):\n", + " def __init__(self, input_dim):\n", + " super(My_Model, self).__init__()\n", + " # TODO: modify model's structure, be aware of dimensions.\n", + " self.layers = nn.Sequential(\n", + " #可以先接一个batch_norm层吗?\n", + " # 不用，batchnorm只是对一个批次做normal，可以直接对所有的数据输入做归一化处理\n", + " # nn.BatchNorm1d(input_dim),\n", + " # 这里宽度是不是愈多愈好？\n", + " # 这里深度是不是愈多愈好？\n", + " # 由于数据有明显的线性关系，因此增加深度并没有什么用\n", + " nn.Linear(input_dim, 128),\n", + " nn.LeakyReLU(0.2),\n", + " nn.BatchNorm1d(128),\n", + " nn.Dropout(0.2),\n", + " nn.Linear(128, 64),\n", + " nn.LeakyReLU(0.2),\n", + " nn.BatchNorm1d(64),\n", + " nn.Dropout(0.2),\n", + " nn.Linear(64, 1)\n", + " )\n", + "\n", + " def forward(self, x):\n", + " x = self.layers(x)\n", + " x = x.squeeze(1) # (B, 1) -> (B)\n", + " return x\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x5-LKF6R8xeq" + }, + "source": [ + "# Feature Selection\n", + "Choose features you deem useful by modifying the function below." + ] + }, + { + "cell_type": "code", + "source": [ + "'''Selects useful features to perform regression'''\n", + "def select_feat(train_data, valid_data, test_data, select_all=True):\n", + " y_train, y_valid = train_data[:, -1], valid_data[:, -1]\n", + " raw_x_train, raw_x_valid, raw_x_test = train_data[:, :-1], valid_data[:, :-1], test_data\n", + "\n", + " if select_all:\n", + " feat_idx = list(range(raw_x_train.shape[1]))\n", + " else:\n", + " # feat_idx = []\n", + " # for i in range(1, 117):\n", + " # needJump = False\n", + " # for j in range(4):\n", + " # if i == (37+j*16+10): #shop\n", + " # needJump = True\n", + " # # if i == (37+j*16+9): #restaurant\n", + " # # needJump = True\n", + " # if i == (37+j*16+8): #spent_time\n", + " # needJump = True\n", + " # if needJump is True:\n", + " # continue\n", + " # feat_idx.append(i)\n", + " # print('fead_idx', feat_idx)\n", + " #TODO: Select suitable feature columns.\n", + "\n", + " # features = pd.read_csv('./covid.train.csv')\n", + " # x_data, y_data = features.iloc[:, 0:117], features.iloc[:, 117]\n", + " # k = 24\n", + " # selector = SelectKBest(score_func=f_regression, k=k)\n", + " # result = selector.fit(x_data, y_data)\n", + " # idx = np.argsort(result.scores_)[::-1]\n", + " # feat_idx = list(np.sort(idx[:k]))\n", + " train_data_df = pd.read_csv('./covid.train.csv')\n", + " # 查看各个属性与最后预测值的线性相关性\n", + " corr_df = train_data_df.corr()\n", + " corr_df = corr_df.iloc[117, :117]\n", + " corr_array = corr_df.abs().values\n", + " print(type(corr_array))\n", + " idx = np.argsort(corr_array)[::-1]\n", + " #前面37个属性相关性太低了，直接去掉。\n", + " #后面4天中各取5个最高的属性，由于前4天的预测值相关性最高，而第5天没有，因此第5天只取4个属性\n", + " feat_idx = idx.tolist()[0:24]\n", + " print(\"feat_idx\", feat_idx)\n", + "\n", + " return raw_x_train[:, feat_idx], raw_x_valid[:, feat_idx], raw_x_test[:, feat_idx], y_train, y_valid\n" + ], + "metadata": { + "id": "0FEnKRaIIeKp" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Training Loop" + ], + "metadata": { + "id": "kADIPNQ2Ih5X" + } + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "k4Rq8_TztAhq" + }, + "outputs": [], + "source": [ + "def trainer(train_loader, valid_loader, model, config, device):\n", + " criterion = nn.MSELoss(reduction='mean') # Define your loss function, do not modify this.\n", + "\n", + " # Define your optimization algorithm.\n", + " # TODO: Please check https://pytorch.org/docs/stable/optim.html to get more available algorithms.\n", + " # TODO: L2 regularization (optimizer(weight decay...) or implement by your self).\n", + " # optimizer = torch.optim.SGD(model.parameters(), lr=config['learning_rate'], momentum=0.9, weight_decay=config['L2_regularization'])\n", + " # 为什么使用Adam loss收敛在110+了呢\n", + " # ----学习率太小的了，改成10e-4\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=config['learning_rate'], weight_decay=config['L2_regularization'])\n", + " scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer,\n", + " T_0=2, T_mult=2, eta_min=config['learning_rate'])\n", + "\n", + " # 2、stackoverflow的评论，哪一个是对的\n", + " # In SGD optimizer, L2 regularization can be obtained by weight_decay. But weight_decay and L2 regularization is different for Adam optimizer. More can be read here: openreview.net/pdf?id=rk6qdGgCZ –\n", + " # @Ashish your comment is correct that weight_decay and L2 regularization is different but in the case of PyTorch's implementation of Adam, they actually implement L2 regularization instead of true weight decay. Note that the weight decay term is applied to the gradient before the optimizer step here –\n", + "\n", + " # writer = SummaryWriter() # Writer of tensoboard.\n", + "\n", + " if not os.path.isdir('./models'):\n", + " os.mkdir('./models') # Create directory of saving models.\n", + "\n", + " n_epochs, best_loss, step, early_stop_count = config['n_epochs'], math.inf, 0, 0\n", + "\n", + " for epoch in range(n_epochs):\n", + " model.train() # Set your model to train mode.\n", + " loss_record = []\n", + "\n", + " # tqdm is a package to visualize your training progress.\n", + " train_pbar = tqdm(train_loader, position=0, leave=True)\n", + "\n", + " for x, y in train_pbar:\n", + " optimizer.zero_grad() # Set gradient to zero.\n", + " x, y = x.to(device), y.to(device) # Move your data to device.\n", + " pred = model(x)\n", + " loss = criterion(pred, y)\n", + " loss.backward() # Compute gradient(backpropagation).\n", + " optimizer.step() # Update parameters.\n", + " step += 1\n", + " loss_record.append(loss.detach().item())\n", + "\n", + " # Display current epoch number and loss on tqdm progress bar.\n", + " train_pbar.set_description(f'Epoch [{epoch + 1}/{n_epochs}]')\n", + " train_pbar.set_postfix({'loss': loss.detach().item()})\n", + " scheduler.step()\n", + " mean_train_loss = sum(loss_record) / len(loss_record)\n", + " # writer.add_scalar('Loss/train', mean_train_loss, step)\n", + "\n", + " model.eval() # Set your model to evaluation mode.\n", + " loss_record = []\n", + " for x, y in valid_loader:\n", + " x, y = x.to(device), y.to(device)\n", + " with torch.no_grad():\n", + " pred = model(x)\n", + " loss = criterion(pred, y)\n", + "\n", + " loss_record.append(loss.item())\n", + "\n", + " mean_valid_loss = sum(loss_record) / len(loss_record)\n", + " print(f'Epoch [{epoch + 1}/{n_epochs}]: Train loss: {mean_train_loss:.4f}, Valid loss: {mean_valid_loss:.4f}')\n", + " # writer.add_scalar('Loss/valid', mean_valid_loss, step)\n", + "\n", + " if mean_valid_loss < best_loss:\n", + " best_loss = mean_valid_loss\n", + " torch.save(model.state_dict(), config['save_path']) # Save your best model\n", + " print('Saving model with loss {:.3f}...'.format(best_loss))\n", + " early_stop_count = 0\n", + " else:\n", + " early_stop_count += 1\n", + "\n", + " if early_stop_count >= config['early_stop']:\n", + " print('\\nModel is not improving, so we halt the training session.')\n", + " return\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0pgkOh2e9UjE" + }, + "source": [ + "# Configurations\n", + "`config` contains hyper-parameters for training and the path to save your model." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "QoWPUahCtoT6" + }, + "outputs": [], + "source": [ + "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "config = {\n", + " # 'seed': 5201314, # Your seed number, you can pick your lucky number. :)\n", + " 'seed': 5201315, # Your seed number, you can pick your lucky number. :)\n", + " 'select_all': False, # Whether to use all features.\n", + " 'valid_ratio': 0.2, # validation_size = train_size * valid_ratio\n", + " 'n_epochs': 3000, # Number of epochs.\n", + " 'batch_size': 256,\n", + " 'learning_rate': 1e-4,\n", + " 'early_stop': 400, # If model has not improved for this many consecutive epochs, stop training.\n", + " 'save_path': './models/model.ckpt', # Your model will be saved here.\n", + " 'L2_regularization': 1e-4\n", + "}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lrS-aJJh9XkW" + }, + "source": [ + "# Dataloader\n", + "Read data from files and set up training, validation, and testing sets. You do not need to modify this part." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "2jc7ZfDot2t9", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "918ca952-0d84-4ecc-c4c4-a36e267834f5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "train_data size: (2160, 118) \n", + "valid_data size: (539, 118) \n", + "test_data size: (1078, 117)\n", + "\n", + "feat_idx [101, 85, 69, 53, 104, 88, 105, 72, 89, 56, 73, 40, 57, 41, 103, 102, 87, 86, 71, 70, 55, 54, 39, 38]\n", + "number of features: 24\n" + ] + } + ], + "source": [ + "# Set seed for reproducibility\n", + "same_seed(config['seed'])\n", + "\n", + "# train_data size: 2699 x 118 (id + 37 states + 16 features x 5 days)\n", + "# test_data size: 1078 x 117 (without last day's positive rate)\n", + "train_data_df = pd.read_csv('./covid.train.csv')\n", + "test_data_df = pd.read_csv('./covid.test.csv')\n", + "train_data, test_data = train_data_df.values, test_data_df.values\n", + "\n", + "# print(\"=====shape():\", train_data.shape)\n", + "train_data, valid_data = train_valid_split(train_data, config['valid_ratio'], config['seed'])\n", + "\n", + "# Print out the data size.\n", + "print(f\"\"\"train_data size: {train_data.shape} \n", + "valid_data size: {valid_data.shape} \n", + "test_data size: {test_data.shape}\"\"\")\n", + "\n", + "# Select features\n", + "x_train, x_valid, x_test, y_train, y_valid = select_feat(train_data, valid_data, test_data, config['select_all'])\n", + "\n", + "#模仿batch_norm做归一化处理\n", + "x_min, x_max = x_train.min(axis=0), x_train.max(axis=0)\n", + "x_mean = x_train.mean(axis=0)\n", + "x_var = ((x_train - x_mean) ** 2).mean(axis=0)\n", + "x_train = (x_train - x_mean)/x_var\n", + "x_valid = (x_valid - x_mean)/x_var\n", + "x_test = (x_test - x_mean)/x_var\n", + "# Print out the number of features.\n", + "print(f'number of features: {x_train.shape[1]}')\n", + "\n", + "train_dataset, valid_dataset, test_dataset = COVID19Dataset(x_train, y_train), \\\n", + " COVID19Dataset(x_valid, y_valid), \\\n", + " COVID19Dataset(x_test)\n", + "\n", + "# Pytorch data loader loads pytorch dataset into batches.\n", + "train_loader = DataLoader(train_dataset, batch_size=config['batch_size'], shuffle=True, pin_memory=True)\n", + "valid_loader = DataLoader(valid_dataset, batch_size=config['batch_size'], shuffle=True, pin_memory=True)\n", + "test_loader = DataLoader(test_dataset, batch_size=config['batch_size'], shuffle=False, pin_memory=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0OBYgjCA-YwD" + }, + "source": [ + "# Start training!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YdttVRkAfu2t", + "outputId": "810595f7-50fa-48c6-ca2a-b65901d2d412" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Epoch [1/3000]: 100%|██████████| 9/9 [00:02<00:00, 3.22it/s, loss=102]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [1/3000]: Train loss: 126.4936, Valid loss: 156.8587\n", + "Saving model with loss 156.859...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Epoch [2/3000]: 100%|██████████| 9/9 [00:00<00:00, 96.29it/s, loss=115]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [2/3000]: Train loss: 122.9007, Valid loss: 139.5769\n", + "Saving model with loss 139.577...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Epoch [3/3000]: 100%|██████████| 9/9 [00:00<00:00, 83.08it/s, loss=91.9]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [3/3000]: Train loss: 117.3167, Valid loss: 127.0609\n", + "Saving model with loss 127.061...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Epoch [4/3000]: 100%|██████████| 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loss: 108.2629, Valid loss: 117.2315\n", + "Saving model with loss 117.231...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Epoch [8/3000]: 100%|██████████| 9/9 [00:00<00:00, 78.95it/s, loss=121]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [8/3000]: Train loss: 108.5927, Valid loss: 121.1587\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Epoch [9/3000]: 100%|██████████| 9/9 [00:00<00:00, 72.48it/s, loss=102]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [9/3000]: Train loss: 106.6397, Valid loss: 122.1461\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Epoch [10/3000]: 100%|██████████| 9/9 [00:00<00:00, 83.51it/s, loss=97.8]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch [10/3000]: Train loss: 105.6081, Valid loss: 128.2975\n" + ] + }, + { + "output_type": "stream", + "name": 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\n\nafter running the following block.\n\n> **Notes: if the links are dead, you can download the data directly from [Kaggle](https://www.kaggle.com/c/ml2022spring-hw2/data) and upload it to the workspace, or you can use [the Kaggle API](https://www.kaggle.com/general/74235) to directly download the data into colab.**\n","metadata":{"id":"KVUGfWTo7_Oj"}},{"cell_type":"markdown","source":"### Download train/test metadata","metadata":{"id":"Bj5jYXsD9Ef3"}},{"cell_type":"markdown","source":"### Preparing Data","metadata":{"id":"_L_4anls8Drv"}},{"cell_type":"code","source":"# Main link\n# !wget -O libriphone.zip \"https://github.com/xraychen/shiny-robot/releases/download/v1.0/libriphone.zip\"\n\n# Backup Link 0\n# !pip install --upgrade gdown\n# !gdown --id '1o6Ag-G3qItSmYhTheX6DYiuyNzWyHyTc' --output libriphone.zip\n\n# Backup link 1\n# !pip install --upgrade gdown\n# !gdown --id '1R1uQYi4QpX0tBfUWt2mbZcncdBsJkxeW' --output libriphone.zip\n\n# Backup link 2\n# !wget -O libriphone.zip \"https://www.dropbox.com/s/wqww8c5dbrl2ka9/libriphone.zip?dl=1\"\n\n# Backup link 3\n# !wget -O libriphone.zip \"https://www.dropbox.com/s/p2ljbtb2bam13in/libriphone.zip?dl=1\"\n\n# !unzip -q libriphone.zip\n# !ls libriphone","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Helper functions to pre-process the training data from raw MFCC features of each utterance.**\n\nA phoneme may span several frames and is dependent to past and future frames. \\\nHence we concatenate neighboring phonemes for training to achieve higher accuracy. The **concat_feat** function concatenates past and future k frames (total 2k+1 = n frames), and we predict the center frame.\n\nFeel free to modify the data preprocess functions, but **do not drop any frame** (if you modify the functions, remember to check that the number of frames are the same as mentioned in the slides)","metadata":{"id":"po4N3C-AWuWl"}},{"cell_type":"code","source":"import os\nimport random\nimport pandas as pd\nimport torch\nfrom tqdm import tqdm\n\ndef load_feat(path):\n feat = torch.load(path)\n return feat\n\ndef shift(x, n):\n if n < 0:\n left = x[0].repeat(-n, 1)\n right = x[:n]\n\n elif n > 0:\n right = x[-1].repeat(n, 1)\n left = x[n:]\n else:\n return x\n\n return torch.cat((left, right), dim=0)\n\ndef concat_feat(x, concat_n):\n assert concat_n % 2 == 1 # n must be odd\n if concat_n < 2:\n return x\n seq_len, feature_dim = x.size(0), x.size(1)\n x = x.repeat(1, concat_n) \n x = x.view(seq_len, concat_n, feature_dim).permute(1, 0, 2) # concat_n, seq_len, feature_dim\n mid = (concat_n // 2)\n for r_idx in range(1, mid+1):\n x[mid + r_idx, :] = shift(x[mid + r_idx], r_idx)\n x[mid - r_idx, :] = shift(x[mid - r_idx], -r_idx)\n\n return x.permute(1, 0, 2).view(seq_len, concat_n * feature_dim)\n\ndef preprocess_data(split, feat_dir, phone_path, concat_nframes, train_ratio=0.8, train_val_seed=1337):\n class_num = 41 # NOTE: pre-computed, should not need change\n mode = 'train' if (split == 'train' or split == 'val') else 'test'\n\n label_dict = {}\n if mode != 'test':\n phone_file = open(os.path.join(phone_path, f'{mode}_labels.txt')).readlines()\n\n for line in phone_file:\n line = line.strip('\\n').split(' ')\n label_dict[line[0]] = [int(p) for p in line[1:]]\n\n if split == 'train' or split == 'val':\n # split training and validation data\n usage_list = open(os.path.join(phone_path, 'train_split.txt')).readlines()\n random.seed(train_val_seed)\n random.shuffle(usage_list)\n percent = int(len(usage_list) * train_ratio)\n usage_list = usage_list[:percent] if split == 'train' else usage_list[percent:]\n elif split == 'test':\n usage_list = open(os.path.join(phone_path, 'test_split.txt')).readlines()\n else:\n raise ValueError('Invalid \\'split\\' argument for dataset: PhoneDataset!')\n\n usage_list = [line.strip('\\n') for line in usage_list]\n print('[Dataset] - # phone classes: ' + str(class_num) + ', number of utterances for ' + split + ': ' + str(len(usage_list)))\n\n max_len = 3000000\n X = torch.empty(max_len, 39 * concat_nframes)\n if mode != 'test':\n y = torch.empty(max_len, concat_nframes, dtype=torch.long)\n\n idx = 0\n for i, fname in tqdm(enumerate(usage_list)):\n feat = load_feat(os.path.join(feat_dir, mode, f'{fname}.pt'))\n cur_len = len(feat)\n feat = concat_feat(feat, concat_nframes)\n if mode != 'test':\n label = torch.LongTensor(label_dict[fname]).unsqueeze(1)\n label = concat_feat(label, concat_nframes)\n\n X[idx: idx + cur_len, :] = feat\n if mode != 'test':\n y[idx: idx + cur_len] = label\n\n idx += cur_len\n\n X = X[:idx, :]\n if mode != 'test':\n y = y[:idx]\n\n print(f'[INFO] {split} set')\n print(X.shape)\n if mode != 'test':\n print(y.shape)\n return X, y\n else:\n return X","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Define Dataset","metadata":{"id":"us5XW_x6udZQ"}},{"cell_type":"code","source":"import torch\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\n\nclass LibriDataset(Dataset):\n def __init__(self, X, y=None):\n self.data = X\n if y is not None:\n self.label = torch.LongTensor(y)\n else:\n self.label = None\n\n def __getitem__(self, idx):\n if self.label is not None:\n return self.data[idx].view(-1, 39), self.label[idx]\n else:\n return self.data[idx].view(-1, 39)\n\n def __len__(self):\n return len(self.data)","metadata":{"id":"Fjf5EcmJtf4e","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Define Model","metadata":{"id":"IRqKNvNZwe3V"}},{"cell_type":"code","source":"!pip install pytorch-crf","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchcrf import CRF\n\n# class BasicBlock(nn.Module):\n# def __init__(self, input_dim, output_dim):\n# super(BasicBlock, self).__init__()\n\n# self.block = nn.Sequential(\n# nn.Linear(input_dim, output_dim),\n# nn.LeakyReLU(0.1),\n# nn.BatchNorm1d(output_dim),\n# nn.Dropout(0.25)\n# )\n\n# def forward(self, x):\n# x = self.block(x)\n# return x\n\n# class Classifier(nn.Module):\n# def __init__(self, input_dim, output_dim=41, hidden_layers=1, hidden_dim=256):\n# super(Classifier, self).__init__()\n\n# self.fc = nn.Sequential(\n# BasicBlock(input_dim, hidden_dim),\n# *[BasicBlock(hidden_dim, hidden_dim) for _ in range(hidden_layers)],\n# nn.Linear(hidden_dim, output_dim)\n# )\n\n# def forward(self, x):\n# x = self.fc(x)\n# return x\n\n# class BiLSTM(nn.Module):\n# def __init__(self, class_size=41, input_dim=39, hidden_dim=192, dropout=0.5):\n# super().__init__()\n# self.input_dim = input_dim\n# self.hidden_dim = hidden_dim\n# self.class_size = class_size\n# self.lstm = nn.LSTM(input_dim, hidden_dim // 2, dropout=dropout,\n# num_layers=3, bidirectional=True, batch_first=True)\n# self.hidden2tag = nn.Sequential(\n# nn.Dropout(dropout),\n# nn.Linear(hidden_dim, class_size)\n# )\n \n# def forward(self, x):\n# feats, _ = self.lstm(x)\n# print(\"feats shape:\", feats.shape)\n# result = self.hidden2tag(feats)\n# print(\"result shape:\", result.shape)\n# return result\n \nclass Encoder(nn.Module):\n\tdef __init__(self, input_dim=39, d_model=224, output_dim=41, dropout=0.3):\n\t\tsuper().__init__()\n\t\t# Project the dimension of features from that of input into d_model.\n\t\tself.prenet = nn.Linear(input_dim, d_model)\n\t\tself.encoder_layer = nn.TransformerEncoderLayer(\n\t\t\td_model=d_model, dim_feedforward=1024, nhead=1, dropout=dropout\n\t\t)\n\t\tself.encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=6)\n\n\t\t# Project the the dimension of features from d_model into speaker nums.\n\t\tself.pred_layer = nn.Sequential(\n nn.Dropout(dropout),\n\t\t\tnn.Linear(d_model, output_dim),\n\t\t)\n \n\tdef forward(self, mels):\n\t\t\"\"\"\n\t\targs:\n\t\t\tmels: (batch size, length, 40)\n\t\treturn:\n\t\t\tout: (batch size, n_spks)\n\t\t\"\"\"\n# \t\tprint(\"input shape===========>\", mels.shape)\n\t\t# out: (batch size, length, d_model)\n\t\tout = self.prenet(mels)\n\t\t# out: (length, batch size, d_model)\n\t\tout = out.permute(1, 0, 2)\n\t\t# The encoder layer expect features in the shape of (length, batch size, d_model).\n\t\tout = self.encoder(out)\n\t\t# out: (batch size, length, d_model)\n\t\tout = out.transpose(0, 1)\n\t\t# out: (batch, n_spks)\n\t\tout = self.pred_layer(out)\n# print(\"output shape:\", out.shape)\n\t\treturn out\n\nclass Crf(nn.Module):\n def __init__(self, class_size=41):\n super().__init__()\n self.class_size = class_size\n self.crf = CRF(self.class_size, batch_first=True)\n \n def likelihood(self, x, y):\n return self.crf(x, y)\n \n def forward(self, x):\n return torch.LongTensor(self.crf.decode(x))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Hyper-parameters","metadata":{"id":"TlIq8JeqvvHC"}},{"cell_type":"code","source":"# data prarameters\nconcat_nframes = 21 # the number of frames to concat with, n must be odd (total 2k+1 = n frames)\nmid = concat_nframes//2\ntrain_ratio = 0.95 # the ratio of data used for training, the rest will be used for validation\n\n# training parameters\nseed = 6666 # random seed\nbatch_size = 2048 # batch size\nearly_stopping = 16\nnum_epoch = 50 # the number of training epoch\nlearning_rate = 0.0001 # learning rate\nmodel1_path = './model1.ckpt' # the path where the checkpoint will be saved\nmodel2_path = './model2.ckpt'\n# model parameters\ninput_dim = 39 * concat_nframes # the input dim of the model, you should not change the value\nhidden_layers = 3 # the number of hidden layers\nhidden_dim = 1024 # the hidden dim","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Prepare dataset and model","metadata":{"id":"IIUFRgG5yoDn"}},{"cell_type":"code","source":"import gc\n\n# preprocess data\ntrain_X, train_y = preprocess_data(split='train', feat_dir='../input/ml2022spring-hw2/libriphone/libriphone/feat', \n phone_path='../input/ml2022spring-hw2/libriphone/libriphone', concat_nframes=concat_nframes, train_ratio=train_ratio)\nval_X, val_y = preprocess_data(split='val', feat_dir='../input/ml2022spring-hw2/libriphone/libriphone/feat', \n phone_path='../input/ml2022spring-hw2/libriphone/libriphone', concat_nframes=concat_nframes, train_ratio=train_ratio)\n\n# get dataset\ntrain_set = LibriDataset(train_X, train_y)\nval_set = LibriDataset(val_X, val_y)\n\n# remove raw feature to save memory\ndel train_X, train_y, val_X, val_y\ngc.collect()\n\n# get dataloader\ntrain_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\nval_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)","metadata":{"id":"c1zI3v5jyrDn","outputId":"c04339e8-0d70-4816-c222-5b4fea532516","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\nprint(f'DEVICE: {device}')","metadata":{"id":"CfRUEgC0GxUV","outputId":"29ccfa55-977a-4aa6-8342-246b727b5643","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\n\n#fix seed\ndef same_seeds(seed):\n torch.manual_seed(seed)\n if torch.cuda.is_available():\n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed) \n np.random.seed(seed) \n torch.backends.cudnn.benchmark = False\n torch.backends.cudnn.deterministic = True","metadata":{"id":"88xPiUnm0tAd","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# fix random seed\nsame_seeds(seed)\n\n# create model, define a loss function, and optimizer\n#model = Classifier(input_dim=input_dim, hidden_layers=hidden_layers, hidden_dim=hidden_dim).to(device)\nencoder = Encoder().to(device)\ncrf = Crf().to(device)\noptimizer1 = torch.optim.AdamW(encoder.parameters(), lr=learning_rate*20, weight_decay=0.015)\nscheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer1, \n T_0=8, T_mult=2, eta_min=learning_rate/2)\n\noptimizer2 = torch.optim.AdamW(crf.parameters(), lr=learning_rate*500, weight_decay=1e-8)\n\ntotal_num = 0\nfor i, param in enumerate(encoder.parameters()):\n print('Layer:', i, ' parameter num:',param.numel(), ' shape:', param.shape)\n total_num += param.numel()\n\nprint(f'Total parameters num: {total_num}')\n\ntotal_num = 0\nfor i, param in enumerate(crf.parameters()):\n print('Layer:', i, ' parameter num:',param.numel(), ' shape:', param.shape)\n total_num += param.numel()\n\nprint(f'Total parameters num: {total_num}')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Training","metadata":{"id":"pwWH1KIqzxEr"}},{"cell_type":"code","source":"best_acc = 0.0\nearly_stop_count = 0\nfor epoch in range(num_epoch):\n train_acc = 0.0\n train_loss = 0.0\n val_acc = 0.0\n val_loss = 0.0\n train_item =0\n # training\n encoder.train() # set the model to training mode\n crf.train()\n pbar = tqdm(train_loader, ncols=110)\n pbar.set_description(f'T: {epoch+1}/{num_epoch}')\n samples = 0\n for i, batch in enumerate(pbar):\n features, labels = batch\n features, labels = features.to(device), labels.to(device)\n \n optimizer1.zero_grad() \n optimizer2.zero_grad()\n encoderResult = encoder(features)\n# print(\"encoder shape:\", encoderResult.shape)\n loss = -crf.likelihood(encoderResult, labels)\n loss.backward()\n grad_norm = nn.utils.clip_grad_norm_(encoder.parameters(), max_norm=50)\n optimizer1.step()\n optimizer2.step()\n \n train_loss += loss.item()\n train_item += labels.size(0)\n \n lr1 = optimizer1.param_groups[0][\"lr\"]\n lr2 = optimizer2.param_groups[0][\"lr\"]\n pbar.set_postfix({'lr1':lr1, 'lr2':lr2, 'loss':train_loss/train_item})\n scheduler.step()\n pbar.close()\n # validation\n if len(val_set) > 0:\n encoder.eval() # set the model to evaluation mode\n crf.eval()\n with torch.no_grad():\n pbar = tqdm(val_loader, ncols=110)\n pbar.set_description(f'V: {epoch+1}/{num_epoch}')\n samples = 0\n for i, batch in enumerate(pbar):\n features, labels = batch\n features, labels = features.to(device), labels.to(device)\n outputs = crf(encoder(features)) \n val_acc += (outputs[:, mid] == labels[:, mid].cpu()).sum().item()\n samples += labels.size(0)\n pbar.set_postfix({'val acc':val_acc/samples})\n pbar.close()\n # if the model improves, save a checkpoint at this epoch\n if val_acc > best_acc:\n best_acc = val_acc\n torch.save(encoder.state_dict(), model1_path)\n torch.save(crf.state_dict(), model2_path)\n print('saving model with acc {:.3f}'.format(best_acc/(len(val_set))))\n early_stop_count = 0\n else:\n early_stop_count += 1\n if early_stop_count >= early_stopping:\n print(f\"Epoch: {epoch + 1}, model not improving, early stopping.\")\n break\n ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del train_loader, val_loader\ngc.collect()","metadata":{"id":"ab33MxosWLmG","outputId":"ed4e9622-c72f-409e-97b5-9c95fe989a29","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Testing\nCreate a testing dataset, and load model from the saved checkpoint.","metadata":{"id":"1Hi7jTn3PX-m"}},{"cell_type":"code","source":"# load data\ntest_X = preprocess_data(split='test', feat_dir='../input/ml2022spring-hw2/libriphone/libriphone/feat',\n phone_path='../input/ml2022spring-hw2/libriphone/libriphone', concat_nframes=concat_nframes)\n\ntest_set = LibriDataset(test_X)\n\nimport gc\ndel test_X\ngc.collect()\n\n# get dataloader\ntest_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)","metadata":{"id":"VOG1Ou0PGrhc","outputId":"ce423526-fd4d-43a7-f837-d430b7fd9162","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# load model\n#model = Classifier(input_dim=input_dim, hidden_layers=hidden_layers, hidden_dim=hidden_dim).to(device)\nencoder = Encoder().to(device)\nencoder.load_state_dict(torch.load(model1_path))\n\ncrf = Crf().to(device)\ncrf.load_state_dict(torch.load(model2_path))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Make prediction.","metadata":{"id":"zp-DV1p4r7Nz"}},{"cell_type":"code","source":"pred = np.array([], dtype=np.int32)\n\nencoder.eval()\ncrf.eval()\nwith torch.no_grad():\n for features in tqdm(test_loader):\n features = features.to(device)\n outputs = crf(encoder(features))\n pred = np.concatenate((pred, outputs.detach().cpu()[:, mid]), axis=0)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Write prediction to a CSV file.\n\nAfter finish running this block, download the file `prediction.csv` from the files section on the left-hand side and submit it to Kaggle.","metadata":{"id":"wyZqy40Prz0v"}},{"cell_type":"code","source":"with open('prediction.csv', 'w') as f:\n f.write('Id,Class\\n')\n for i, y in enumerate(pred):\n f.write('{},{}\\n'.format(i, y))","metadata":{"id":"GuljYSPHcZir","trusted":true},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/HW02/caishaokang_submission_hw02.csv b/HW02/caishaokang_submission_hw02.csv new file mode 100644 index 00000000..f1ee9578 --- /dev/null +++ b/HW02/caishaokang_submission_hw02.csv @@ -0,0 +1,646269 @@ +Id,Class +0,0 +1,0 +2,0 +3,0 +4,0 +5,0 +6,0 +7,0 +8,0 +9,0 +10,0 +11,0 +12,0 +13,0 +14,29 +15,29 +16,29 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"completed" + }, + "tags": [] + }, + "source": [ + "# If you want to access the version you have already modified, click \"Edit\"\n", + "# If you want to access the original sample code, click \"...\", then click \"Copy & Edit Notebook\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "561b2ba4", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-29T05:18:28.827567Z", + "iopub.status.busy": "2022-10-29T05:18:28.826757Z", + "iopub.status.idle": "2022-10-29T05:18:28.830524Z", + "shell.execute_reply": "2022-10-29T05:18:28.829956Z", + "shell.execute_reply.started": "2022-10-28T14:40:57.257074Z" + }, + "papermill": { + "duration": 0.026395, + "end_time": "2022-10-29T05:18:28.830682", + "exception": false, + "start_time": "2022-10-29T05:18:28.804287", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "_exp_name = \"sample\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fd4a3e30", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-29T05:18:28.863566Z", + "iopub.status.busy": "2022-10-29T05:18:28.862967Z", + "iopub.status.idle": "2022-10-29T05:18:30.851092Z", + "shell.execute_reply": "2022-10-29T05:18:30.850156Z", + "shell.execute_reply.started": "2022-10-28T14:40:57.273369Z" + }, + "papermill": { + "duration": 2.007143, + "end_time": "2022-10-29T05:18:30.851247", + "exception": false, + "start_time": "2022-10-29T05:18:28.844104", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "import os\n", + "# Import necessary packages.\n", + "import numpy as np\n", + "import torch\n", + "import os\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, Dataset\n", + "from torchvision.datasets import DatasetFolder, VisionDataset\n", + "\n", + "# This is for the progress bar.\n", + "from tqdm.auto import tqdm\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8d53d120", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-29T05:18:30.882947Z", + "iopub.status.busy": "2022-10-29T05:18:30.882293Z", + "iopub.status.idle": "2022-10-29T05:18:30.953353Z", + "shell.execute_reply": "2022-10-29T05:18:30.952498Z", + "shell.execute_reply.started": "2022-10-28T14:40:58.034046Z" + }, + "papermill": { + "duration": 0.088874, + "end_time": "2022-10-29T05:18:30.953480", + "exception": false, + "start_time": "2022-10-29T05:18:30.864606", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "myseed = 6666 # set a random seed for reproducibility\n", + "torch.backends.cudnn.deterministic = True\n", + "torch.backends.cudnn.benchmark = False\n", + "np.random.seed(myseed)\n", + "torch.manual_seed(myseed)\n", + "if torch.cuda.is_available():\n", + " torch.cuda.manual_seed_all(myseed)" + ] + }, + { + "cell_type": "markdown", + "id": "45c6f3fd", + "metadata": { + "papermill": { + "duration": 0.013241, + "end_time": "2022-10-29T05:18:30.980473", + "exception": false, + "start_time": "2022-10-29T05:18:30.967232", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## **Transforms**\n", + "Torchvision provides lots of useful utilities for image preprocessing, data wrapping as well as data augmentation.\n", + "\n", + "Please refer to PyTorch official website for details about different transforms." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ea0b78d5", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-29T05:18:31.015452Z", + "iopub.status.busy": "2022-10-29T05:18:31.014678Z", + "iopub.status.idle": "2022-10-29T05:18:31.016705Z", + "shell.execute_reply": "2022-10-29T05:18:31.017119Z", + "shell.execute_reply.started": "2022-10-28T14:40:58.114856Z" + }, + "papermill": { + "duration": 0.023541, + "end_time": "2022-10-29T05:18:31.017252", + "exception": false, + "start_time": "2022-10-29T05:18:30.993711", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Normally, 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", + "\n", + "# However, it is also possible to use augmentation in the testing phase.\n", + "# You may use train_tfm to produce a variety of images and then test using ensemble methods\n", + "train_tfm = transforms.Compose([\n", + " # Resize the image into a fixed shape (height = width = 128)\n", + " #transforms.CenterCrop()\n", + " transforms.RandomResizedCrop((128, 128), scale=(0.7, 1.0)),\n", + " #transforms.AutoAugment(transforms.AutoAugmentPolicy.IMAGENET),\n", + " #transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),\n", + " transforms.RandomHorizontalFlip(0.5),\n", + " transforms.RandomVerticalFlip(0.5),\n", + " transforms.RandomRotation(180),\n", + " transforms.RandomAffine(30),\n", + " #transforms.RandomInvert(p=0.2),\n", + " #transforms.RandomPosterize(bits=2),\n", + " #transforms.RandomSolarize(threshold=192.0, p=0.2),\n", + " #transforms.RandomEqualize(p=0.2),\n", + " transforms.RandomGrayscale(p=0.2),\n", + " transforms.ToTensor(),\n", + " #transforms.RandomApply(torch.nn.ModuleList([]))\n", + " # You may add some transforms here.\n", + " # ToTensor() should be the last one of the transforms.\n", + "])\n" + ] + }, + { + "cell_type": "markdown", + "id": "3d35acc1", + "metadata": { + "papermill": { + "duration": 0.01353, + "end_time": "2022-10-29T05:18:31.044766", + "exception": false, + "start_time": "2022-10-29T05:18:31.031236", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## **Datasets**\n", + "The data is labelled by the name, so we load images and label while calling '__getitem__'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "83f41f80", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-29T05:18:31.080990Z", + "iopub.status.busy": "2022-10-29T05:18:31.079898Z", + "iopub.status.idle": "2022-10-29T05:18:31.082287Z", + "shell.execute_reply": "2022-10-29T05:18:31.082689Z", + "shell.execute_reply.started": "2022-10-28T14:40:58.123780Z" + }, + "papermill": { + "duration": 0.024244, + "end_time": "2022-10-29T05:18:31.082821", + "exception": false, + "start_time": "2022-10-29T05:18:31.058577", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "class FoodDataset(Dataset):\n", + "\n", + " def __init__(self, path=None, tfm=test_tfm, files=None):\n", + " super(FoodDataset).__init__()\n", + " self.path = path\n", + " if files is not None:\n", + " self.files = files\n", + " else:\n", + " self.files = sorted([os.path.join(path, x) for x in os.listdir(path) if x.endswith(\".jpg\")])\n", + "\n", + " print(f\"One sample\", self.files[0])\n", + " self.transform = tfm\n", + " \n", + " def __len__(self):\n", + " return len(self.files)\n", + "# return 128\n", + " \n", + " def __getitem__(self,idx):\n", + " fname = self.files[idx]\n", + " im = Image.open(fname)\n", + " im = self.transform(im)\n", + " #im = self.data[idx]\n", + " try:\n", + " label = int(fname.split(\"/\")[-1].split(\"_\")[0])\n", + " except:\n", + " label = -1 # test has no label\n", + " return im,label\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d1cdb2e1", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-29T05:18:31.120634Z", + "iopub.status.busy": "2022-10-29T05:18:31.119723Z", + "iopub.status.idle": "2022-10-29T05:18:31.129536Z", + "shell.execute_reply": "2022-10-29T05:18:31.129024Z", + "shell.execute_reply.started": "2022-10-28T14:40:58.135161Z" + }, + "papermill": { + "duration": 0.033122, + "end_time": "2022-10-29T05:18:31.129657", + "exception": false, + "start_time": "2022-10-29T05:18:31.096535", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "class Residual_Block(nn.Module):\n", + " def __init__(self, ic, oc, stride=1):\n", + " # torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)\n", + " # torch.nn.MaxPool2d(kernel_size, stride, padding)\n", + " super().__init__()\n", + " self.conv1 = nn.Sequential(\n", + " nn.Conv2d(ic, oc, kernel_size=3, stride=stride, padding=1),\n", + " nn.BatchNorm2d(oc),\n", + " nn.ReLU(inplace=True)\n", + " )\n", + " \n", + " self.conv2 = nn.Sequential(\n", + " nn.Conv2d(oc, oc, kernel_size=3, stride=1, padding=1),\n", + " nn.BatchNorm2d(oc),\n", + " )\n", + " \n", + " self.relu = nn.ReLU(inplace=True)\n", + " \n", + " self.downsample = None\n", + " if stride != 1 or (ic != oc):\n", + " self.downsample = nn.Sequential(\n", + " nn.Conv2d(ic, oc, kernel_size=1, stride=stride),\n", + " nn.BatchNorm2d(oc),\n", + " )\n", + " \n", + " def forward(self, x):\n", + " residual = x\n", + " out = self.conv1(x)\n", + " out = self.conv2(out)\n", + " \n", + " if self.downsample:\n", + " residual = self.downsample(x)\n", + " \n", + " out += residual\n", + " return self.relu(out)\n", + " \n", + "class Classifier(nn.Module):\n", + " def __init__(self, block, num_layers, num_classes=11):\n", + " super().__init__()\n", + " self.preconv = nn.Sequential(\n", + " nn.Conv2d(3, 32, kernel_size=7, stride=2, padding=3, bias=False),\n", + " nn.BatchNorm2d(32),\n", + " nn.ReLU(inplace=True),\n", + " )\n", + " \n", + " self.layer0 = self.make_residual(block, 32, 64, num_layers[0], stride=2)\n", + " self.layer1 = self.make_residual(block, 64, 128, num_layers[1], stride=2)\n", + " self.layer2 = self.make_residual(block, 128, 256, num_layers[2], stride=2)\n", + " self.layer3 = self.make_residual(block, 256, 512, num_layers[3], stride=2)\n", + " \n", + " #self.avgpool = nn.AvgPool2d(2)\n", + " \n", + " self.fc = nn.Sequential( \n", + " nn.Dropout(0.4),\n", + " nn.Linear(512*4*4, 512),\n", + " nn.BatchNorm1d(512),\n", + " nn.ReLU(inplace=True),\n", + " nn.Dropout(0.2),\n", + " nn.Linear(512, 11),\n", + " )\n", + " \n", + " \n", + " def make_residual(self, block, ic, oc, num_layer, stride=1):\n", + " layers = []\n", + " layers.append(block(ic, oc, stride))\n", + " for i in range(1, num_layer):\n", + " layers.append(block(oc, oc))\n", + " return nn.Sequential(*layers)\n", + " \n", + " def forward(self, x):\n", + " # [3, 128, 128]\n", + " out = self.preconv(x) # [32, 64, 64]\n", + " out = self.layer0(out) # [64, 32, 32]\n", + " out = self.layer1(out) # [128, 16, 16]\n", + " out = self.layer2(out) # [256, 8, 8]\n", + " out = self.layer3(out) # [512, 4, 4]\n", + " #out = self.avgpool(out) # [512, 2, 2]\n", + " out = self.fc(out.view(out.size(0), -1)) \n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8eccf53e", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-29T05:18:31.159260Z", + "iopub.status.busy": "2022-10-29T05:18:31.158374Z", + "iopub.status.idle": "2022-10-29T05:18:31.167503Z", + "shell.execute_reply": "2022-10-29T05:18:31.167977Z", + "shell.execute_reply.started": "2022-10-28T14:40:58.154568Z" + }, + "papermill": { + "duration": 0.02495, + "end_time": "2022-10-29T05:18:31.168101", + "exception": false, + "start_time": "2022-10-29T05:18:31.143151", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import torch.nn.functional as F\n", + "from torch.autograd import Variable\n", + "\n", + "class FocalLoss(nn.Module):\n", + " def __init__(self, class_num, alpha=None, gamma=2, size_average=True):\n", + " super().__init__()\n", + " if alpha is None:\n", + " self.alpha = Variable(torch.ones(class_num, 1))\n", + " else:\n", + " if isinstance(alpha, Variable):\n", + " self.alpha = alpha\n", + " else:\n", + " self.alpha = Variable(alpha)\n", + " self.gamma = gamma\n", + " self.class_num = class_num\n", + " self.size_average = size_average\n", + " \n", + " def forward(self, inputs, targets):\n", + " N = inputs.size(0)\n", + " C = inputs.size(1)\n", + " P = F.softmax(inputs, dim=1)\n", + " \n", + " class_mask = inputs.data.new(N, C).fill_(0)\n", + " class_mask = Variable(class_mask)\n", + " ids = targets.view(-1, 1)\n", + " class_mask.scatter_(1, ids.data, 1.)\n", + " \n", + " if inputs.is_cuda and not self.alpha.is_cuda:\n", + " self.alpha = self.alpha.cuda()\n", + " alpha = self.alpha[ids.data.view(-1)]\n", + " probs = (P*class_mask).sum(1).view(-1, 1)\n", + " \n", + " log_p = probs.log()\n", + " \n", + " batch_loss = -alpha*(torch.pow((1-probs), self.gamma))*log_p\n", + " \n", + " if self.size_average:\n", + " loss = batch_loss.mean()\n", + " else:\n", + " loss = batch_loss.sum()\n", + " \n", + " return loss" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3b579aeb", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-29T05:18:31.203623Z", + "iopub.status.busy": "2022-10-29T05:18:31.202988Z", + "iopub.status.idle": "2022-10-29T05:18:31.206455Z", + "shell.execute_reply": "2022-10-29T05:18:31.206862Z", + "shell.execute_reply.started": "2022-10-28T14:40:58.168819Z" + }, + "papermill": { + "duration": 0.025693, + "end_time": "2022-10-29T05:18:31.207051", + "exception": false, + "start_time": "2022-10-29T05:18:31.181358", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# super params\n", + "n_folds = 4\n", + "batch_size = 128\n", + "alpha = torch.Tensor([1, 2.3, 0.66, 1, 1.1, 0.75, 2.3, 3.5, 1.1, 0.66, 1.4])\n", + "# The number of training epochs and patience.\n", + "n_epochs = 200\n", + "patience = 30 # If no improvement in 'patience' epochs, early stop\n", + "num_layers = [2, 4, 3, 1] # residual number layers" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "955dab72", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-29T05:18:31.239127Z", + "iopub.status.busy": "2022-10-29T05:18:31.238445Z", + "iopub.status.idle": "2022-10-29T05:18:31.808380Z", + "shell.execute_reply": "2022-10-29T05:18:31.807563Z", + "shell.execute_reply.started": "2022-10-28T14:40:58.187515Z" + }, + "papermill": { + "duration": 0.587756, + "end_time": "2022-10-29T05:18:31.808517", + "exception": false, + "start_time": "2022-10-29T05:18:31.220761", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# data\n", + "_dataset_dir = \"../input/ml2022spring-hw3b/food11\"\n", + "train_data_path = os.path.join(_dataset_dir, \"training\")\n", + "valid_data_path = os.path.join(_dataset_dir, \"validation\")\n", + "\n", + "train_data_files = [os.path.join(train_data_path, x) for x in os.listdir(train_data_path) if x.endswith(\".jpg\")]\n", + "valid_data_files = [os.path.join(valid_data_path, x) for x in os.listdir(valid_data_path) if x.endswith(\".jpg\")]\n", + "\n", + "total_data_files = train_data_files + valid_data_files" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "99a86273", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-29T05:18:31.839454Z", + "iopub.status.busy": "2022-10-29T05:18:31.838475Z", + "iopub.status.idle": "2022-10-29T11:44:01.482826Z", + "shell.execute_reply": "2022-10-29T11:44:01.483274Z", + "shell.execute_reply.started": "2022-10-28T14:40:59.073635Z" + }, + "papermill": { + "duration": 23129.661331, + "end_time": "2022-10-29T11:44:01.483439", + "exception": false, + "start_time": "2022-10-29T05:18:31.822108", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total_num: 3324\n", + "One sample ../input/ml2022spring-hw3b/food11/training/8_248.jpg\n", + "One sample ../input/ml2022spring-hw3b/food11/training/5_58.jpg\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b71feb3b560c4a01a4ef32ea84b1ed76", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/78 [00:00 best\n", + "Best model found at epoch 0, saving model\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e6eaf8010c674bf780534ace32b396a4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/78 [00:00 best\n", + "Best model found at epoch 1, saving model\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "092db35de06d48ddb8dc630a1aa2986f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/78 [00:00 best\n", + "Best model found at epoch 3, saving model\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ed7d6d51c8434e0f8b819498fa47b95e", + "version_major": 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print(f\"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}\")\n", + "\n", + " # save models\n", + " if valid_acc > best_acc:\n", + " print(f\"Best model found at epoch {epoch}, saving model\")\n", + " torch.save(model.state_dict(),\n", + " f\"{_exp_name}_{i_fold}_best.ckpt\") # only save best to prevent output memory exceed error\n", + " best_acc = valid_acc\n", + " stale = 0\n", + " else:\n", + " stale += 1\n", + " if stale > patience:\n", + " print(f\"No improvment {patience} consecutive epochs, early stopping\")\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1d7f3678", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-29T11:44:01.993039Z", + "iopub.status.busy": "2022-10-29T11:44:01.992200Z", + "iopub.status.idle": "2022-10-29T11:44:01.994480Z", + "shell.execute_reply": "2022-10-29T11:44:01.994950Z", + "shell.execute_reply.started": "2022-10-28T14:41:15.611568Z" + }, + "papermill": { + "duration": 0.259776, + "end_time": "2022-10-29T11:44:01.995115", + "exception": false, + "start_time": "2022-10-29T11:44:01.735339", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# test_set = FoodDataset(os.path.join(_dataset_dir,\"test\"), tfm=test_tfm)\n", + "# test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2c42b8bb", + "metadata": { + "papermill": { + "duration": 0.380887, + "end_time": "2022-10-29T11:44:02.741483", + "exception": false, + "start_time": "2022-10-29T11:44:02.360596", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Testing and generate prediction CSV" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a2364827", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-29T11:44:03.271284Z", + "iopub.status.busy": "2022-10-29T11:44:03.269479Z", + "iopub.status.idle": "2022-10-29T11:44:03.271870Z", + "shell.execute_reply": "2022-10-29T11:44:03.272320Z", + "shell.execute_reply.started": "2022-10-28T14:41:16.327382Z" + }, + "papermill": { + "duration": 0.268864, + "end_time": "2022-10-29T11:44:03.272465", + "exception": false, + "start_time": "2022-10-29T11:44:03.003601", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# models = []\n", + "# for i_fold in range(n_folds):\n", + "# model_best = Classifier(Residual_Block, num_layers).to(device)\n", + "# model_best.load_state_dict(torch.load(f\"{_exp_name}_{i_fold}_best.ckpt\"))\n", + "# model_best.eval()\n", + "# models.append(model_best)\n", + "\n", + "# prediction = []\n", + "# with torch.no_grad():\n", + "# for data, _ in test_loader:\n", + "# test_preds = []\n", + "# for model_best in models:\n", + "# test_pred = model_best(data.to(device))\n", + "# test_preds.append(test_pred.cpu().data.numpy())\n", + "# test_preds = sum(test_preds)\n", + "# test_label = np.argmax(test_preds, axis=1)\n", + "# prediction += test_label.squeeze().tolist()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e742c77b", + "metadata": { + "execution": { + "iopub.execute_input": "2022-10-29T11:44:03.779017Z", + "iopub.status.busy": "2022-10-29T11:44:03.777396Z", + "iopub.status.idle": "2022-10-29T11:44:03.779605Z", + "shell.execute_reply": "2022-10-29T11:44:03.780038Z", + "shell.execute_reply.started": "2022-10-28T14:41:16.335770Z" + }, + "papermill": { + "duration": 0.256928, + "end_time": "2022-10-29T11:44:03.780186", + "exception": false, + "start_time": "2022-10-29T11:44:03.523258", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "#create test csv\n", + "# def pad4(i):\n", + "# return \"0\"*(4-len(str(i)))+str(i)\n", + "# df = pd.DataFrame()\n", + "# df[\"Id\"] = [pad4(i) for i in range(1,len(test_set)+1)]\n", + "# df[\"Category\"] = prediction\n", + "# df.to_csv(\"submission.csv\",index = False)" + ] + 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3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# If you want to access the version you have already modified, click \"Edit\"\n# If you want to access the original sample code, click \"...\", then click \"Copy & Edit Notebook\"","metadata":{}},{"cell_type":"code","source":"_exp_name = \"sample\"","metadata":{"papermill":{"duration":0.0189,"end_time":"2022-02-23T10:03:06.279758","exception":false,"start_time":"2022-02-23T10:03:06.260858","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-10-28T14:40:57.256513Z","iopub.execute_input":"2022-10-28T14:40:57.257154Z","iopub.status.idle":"2022-10-28T14:40:57.267232Z","shell.execute_reply.started":"2022-10-28T14:40:57.257074Z","shell.execute_reply":"2022-10-28T14:40:57.266496Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\n# Import necessary packages.\nimport numpy as np\nimport torch\nimport os\nimport torch.nn as nn\nimport torchvision.transforms as transforms\nfrom PIL import Image\n# \"ConcatDataset\" and \"Subset\" are possibly useful when doing semi-supervised learning.\nfrom torch.utils.data import ConcatDataset, DataLoader, Subset, Dataset\nfrom torchvision.datasets import DatasetFolder, VisionDataset\n\n# This is for the progress bar.\nfrom tqdm.auto import tqdm\nimport random","metadata":{"papermill":{"duration":1.654263,"end_time":"2022-02-23T10:03:07.947242","exception":false,"start_time":"2022-02-23T10:03:06.292979","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-10-28T14:40:57.272572Z","iopub.execute_input":"2022-10-28T14:40:57.273407Z","iopub.status.idle":"2022-10-28T14:40:58.031539Z","shell.execute_reply.started":"2022-10-28T14:40:57.273369Z","shell.execute_reply":"2022-10-28T14:40:58.030713Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"myseed = 6666 # set a random seed for reproducibility\ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\nnp.random.seed(myseed)\ntorch.manual_seed(myseed)\nif torch.cuda.is_available():\n torch.cuda.manual_seed_all(myseed)","metadata":{"papermill":{"duration":0.078771,"end_time":"2022-02-23T10:03:08.039428","exception":false,"start_time":"2022-02-23T10:03:07.960657","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-10-28T14:40:58.033006Z","iopub.execute_input":"2022-10-28T14:40:58.034081Z","iopub.status.idle":"2022-10-28T14:40:58.112032Z","shell.execute_reply.started":"2022-10-28T14:40:58.034046Z","shell.execute_reply":"2022-10-28T14:40:58.111245Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"## **Transforms**\nTorchvision provides lots of useful utilities for image preprocessing, data wrapping as well as data augmentation.\n\nPlease refer to PyTorch official website for details about different transforms.","metadata":{"papermill":{"duration":0.01289,"end_time":"2022-02-23T10:03:08.065357","exception":false,"start_time":"2022-02-23T10:03:08.052467","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Normally, 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.\ntest_tfm = transforms.Compose([\n transforms.Resize((128, 128)),\n transforms.ToTensor(),\n])\n\n# However, it is also possible to use augmentation in the testing phase.\n# You may use train_tfm to produce a variety of images and then test using ensemble methods\ntrain_tfm = transforms.Compose([\n # Resize the image into a fixed shape (height = width = 128)\n #transforms.CenterCrop()\n transforms.RandomResizedCrop((128, 128), scale=(0.7, 1.0)),\n #transforms.AutoAugment(transforms.AutoAugmentPolicy.IMAGENET),\n #transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),\n transforms.RandomHorizontalFlip(0.5),\n transforms.RandomVerticalFlip(0.5),\n transforms.RandomRotation(180),\n transforms.RandomAffine(30),\n #transforms.RandomInvert(p=0.2),\n #transforms.RandomPosterize(bits=2),\n #transforms.RandomSolarize(threshold=192.0, p=0.2),\n #transforms.RandomEqualize(p=0.2),\n transforms.RandomGrayscale(p=0.2),\n transforms.ToTensor(),\n #transforms.RandomApply(torch.nn.ModuleList([]))\n # You may add some transforms here.\n # ToTensor() should be the last one of the transforms.\n])\n","metadata":{"papermill":{"duration":0.021406,"end_time":"2022-02-23T10:03:08.099437","exception":false,"start_time":"2022-02-23T10:03:08.078031","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-10-28T14:40:58.114495Z","iopub.execute_input":"2022-10-28T14:40:58.114899Z","iopub.status.idle":"2022-10-28T14:40:58.121912Z","shell.execute_reply.started":"2022-10-28T14:40:58.114856Z","shell.execute_reply":"2022-10-28T14:40:58.121213Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"## **Datasets**\nThe data is labelled by the name, so we load images and label while calling '__getitem__'","metadata":{"papermill":{"duration":0.012739,"end_time":"2022-02-23T10:03:08.125181","exception":false,"start_time":"2022-02-23T10:03:08.112442","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class FoodDataset(Dataset):\n\n def __init__(self, path=None, tfm=test_tfm, files=None):\n super(FoodDataset).__init__()\n self.path = path\n if files is not None:\n self.files = files\n else:\n self.files = sorted([os.path.join(path, x) for x in os.listdir(path) if x.endswith(\".jpg\")])\n\n print(f\"One sample\", self.files[0])\n self.transform = tfm\n \n def __len__(self):\n return len(self.files)\n# return 128\n \n def __getitem__(self,idx):\n fname = self.files[idx]\n im = Image.open(fname)\n im = self.transform(im)\n #im = self.data[idx]\n try:\n label = int(fname.split(\"/\")[-1].split(\"_\")[0])\n except:\n label = -1 # test has no label\n return im,label\n\n","metadata":{"papermill":{"duration":0.023022,"end_time":"2022-02-23T10:03:08.160912","exception":false,"start_time":"2022-02-23T10:03:08.13789","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-10-28T14:40:58.123368Z","iopub.execute_input":"2022-10-28T14:40:58.123818Z","iopub.status.idle":"2022-10-28T14:40:58.133086Z","shell.execute_reply.started":"2022-10-28T14:40:58.123780Z","shell.execute_reply":"2022-10-28T14:40:58.132290Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"class Residual_Block(nn.Module):\n def __init__(self, ic, oc, stride=1):\n # torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)\n # torch.nn.MaxPool2d(kernel_size, stride, padding)\n super().__init__()\n self.conv1 = nn.Sequential(\n nn.Conv2d(ic, oc, kernel_size=3, stride=stride, padding=1),\n nn.BatchNorm2d(oc),\n nn.ReLU(inplace=True)\n )\n \n self.conv2 = nn.Sequential(\n nn.Conv2d(oc, oc, kernel_size=3, stride=1, padding=1),\n nn.BatchNorm2d(oc),\n )\n \n self.relu = nn.ReLU(inplace=True)\n \n self.downsample = None\n if stride != 1 or (ic != oc):\n self.downsample = nn.Sequential(\n nn.Conv2d(ic, oc, kernel_size=1, stride=stride),\n nn.BatchNorm2d(oc),\n )\n \n def forward(self, x):\n residual = x\n out = self.conv1(x)\n out = self.conv2(out)\n \n if self.downsample:\n residual = self.downsample(x)\n \n out += residual\n return self.relu(out)\n \nclass Classifier(nn.Module):\n def __init__(self, block, num_layers, num_classes=11):\n super().__init__()\n self.preconv = nn.Sequential(\n nn.Conv2d(3, 32, kernel_size=7, stride=2, padding=3, bias=False),\n nn.BatchNorm2d(32),\n nn.ReLU(inplace=True),\n )\n \n self.layer0 = self.make_residual(block, 32, 64, num_layers[0], stride=2)\n self.layer1 = self.make_residual(block, 64, 128, num_layers[1], stride=2)\n self.layer2 = self.make_residual(block, 128, 256, num_layers[2], stride=2)\n self.layer3 = self.make_residual(block, 256, 512, num_layers[3], stride=2)\n \n #self.avgpool = nn.AvgPool2d(2)\n \n self.fc = nn.Sequential( \n nn.Dropout(0.4),\n nn.Linear(512*4*4, 512),\n nn.BatchNorm1d(512),\n nn.ReLU(inplace=True),\n nn.Dropout(0.2),\n nn.Linear(512, 11),\n )\n \n \n def make_residual(self, block, ic, oc, num_layer, stride=1):\n layers = []\n layers.append(block(ic, oc, stride))\n for i in range(1, num_layer):\n layers.append(block(oc, oc))\n return nn.Sequential(*layers)\n \n def forward(self, x):\n # [3, 128, 128]\n out = self.preconv(x) # [32, 64, 64]\n out = self.layer0(out) # [64, 32, 32]\n out = self.layer1(out) # [128, 16, 16]\n out = self.layer2(out) # [256, 8, 8]\n out = self.layer3(out) # [512, 4, 4]\n #out = self.avgpool(out) # [512, 2, 2]\n out = self.fc(out.view(out.size(0), -1)) \n return out","metadata":{"papermill":{"duration":0.0258,"end_time":"2022-02-23T10:03:08.199437","exception":false,"start_time":"2022-02-23T10:03:08.173637","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-10-28T14:40:58.134736Z","iopub.execute_input":"2022-10-28T14:40:58.135197Z","iopub.status.idle":"2022-10-28T14:40:58.152758Z","shell.execute_reply.started":"2022-10-28T14:40:58.135161Z","shell.execute_reply":"2022-10-28T14:40:58.151961Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"import torch.nn.functional as F\nfrom torch.autograd import Variable\n\nclass FocalLoss(nn.Module):\n def __init__(self, class_num, alpha=None, gamma=2, size_average=True):\n super().__init__()\n if alpha is None:\n self.alpha = Variable(torch.ones(class_num, 1))\n else:\n if isinstance(alpha, Variable):\n self.alpha = alpha\n else:\n self.alpha = Variable(alpha)\n self.gamma = gamma\n self.class_num = class_num\n self.size_average = size_average\n \n def forward(self, inputs, targets):\n N = inputs.size(0)\n C = inputs.size(1)\n P = F.softmax(inputs, dim=1)\n \n class_mask = inputs.data.new(N, C).fill_(0)\n class_mask = Variable(class_mask)\n ids = targets.view(-1, 1)\n class_mask.scatter_(1, ids.data, 1.)\n \n if inputs.is_cuda and not self.alpha.is_cuda:\n self.alpha = self.alpha.cuda()\n alpha = self.alpha[ids.data.view(-1)]\n probs = (P*class_mask).sum(1).view(-1, 1)\n \n log_p = probs.log()\n \n batch_loss = -alpha*(torch.pow((1-probs), self.gamma))*log_p\n \n if self.size_average:\n loss = batch_loss.mean()\n else:\n loss = batch_loss.sum()\n \n return loss","metadata":{"execution":{"iopub.status.busy":"2022-10-28T14:40:58.154123Z","iopub.execute_input":"2022-10-28T14:40:58.154603Z","iopub.status.idle":"2022-10-28T14:40:58.167144Z","shell.execute_reply.started":"2022-10-28T14:40:58.154568Z","shell.execute_reply":"2022-10-28T14:40:58.166624Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"# super params\nn_folds = 4\nbatch_size = 128\nalpha = torch.Tensor([1, 2.3, 0.66, 1, 1.1, 0.75, 2.3, 3.5, 1.1, 0.66, 1.4])\n# The number of training epochs and patience.\nn_epochs = 200\npatience = 30 # If no improvement in 'patience' epochs, early stop\nnum_layers = [2, 4, 3, 1] # residual number layers","metadata":{"execution":{"iopub.status.busy":"2022-10-28T14:40:58.168418Z","iopub.execute_input":"2022-10-28T14:40:58.168860Z","iopub.status.idle":"2022-10-28T14:40:58.185564Z","shell.execute_reply.started":"2022-10-28T14:40:58.168819Z","shell.execute_reply":"2022-10-28T14:40:58.184810Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"# data\n_dataset_dir = \"../input/ml2022spring-hw3b/food11\"\ntrain_data_path = os.path.join(_dataset_dir, \"training\")\nvalid_data_path = os.path.join(_dataset_dir, \"validation\")\n\ntrain_data_files = [os.path.join(train_data_path, x) for x in os.listdir(train_data_path) if x.endswith(\".jpg\")]\nvalid_data_files = [os.path.join(valid_data_path, x) for x in os.listdir(valid_data_path) if x.endswith(\".jpg\")]\n\ntotal_data_files = train_data_files + valid_data_files","metadata":{"papermill":{"duration":0.054295,"end_time":"2022-02-23T10:03:08.266338","exception":false,"start_time":"2022-02-23T10:03:08.212043","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-10-28T14:40:58.187033Z","iopub.execute_input":"2022-10-28T14:40:58.187550Z","iopub.status.idle":"2022-10-28T14:40:59.071416Z","shell.execute_reply.started":"2022-10-28T14:40:58.187515Z","shell.execute_reply":"2022-10-28T14:40:59.070408Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"# \"cuda\" only when GPUs are available.\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nfor i_fold in range(n_folds):\n if i_fold == 0 or i_fold == 1 or i_fold == 3:\n continue\n # Initialize a model, and put it on the device specified.\n model = Classifier(Residual_Block, num_layers).to(device)\n\n # For the classification task, we use cross-entropy as the measurement of performance.\n criterion = FocalLoss(11, alpha=alpha)\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 scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=16, T_mult=1)\n # Initialize trackers, these are not parameters and should not be changed\n stale = 0\n best_acc = 0\n\n total_num = int(len(total_data_files)/n_folds)\n print(\"total_num:\", total_num)\n valid_data_files = total_data_files[i_fold * total_num: (i_fold + 1) * total_num]\n train_data_files = total_data_files[:i_fold * total_num] + total_data_files[(i_fold + 1) * total_num:]\n train_set = FoodDataset(tfm=train_tfm, files=train_data_files)\n train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True)\n valid_set = FoodDataset(tfm=test_tfm, files=valid_data_files)\n valid_loader = DataLoader(valid_set, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True)\n\n for epoch in range(n_epochs):\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 pbar = tqdm(train_loader)\n pbar.set_description(f'T: {epoch + 1:03d}/{n_epochs:03d}')\n # for batch in tqdm(train_loader):\n lr = optimizer.param_groups[0][\"lr\"]\n for batch in pbar:\n # A batch consists of image data and corresponding labels.\n imgs, labels = batch\n # imgs = imgs.half()\n # print(imgs.shape,labels.shape)\n\n # Forward the data. (Make sure data and model are on the same device.)\n logits = model(imgs.to(device))\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 # 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 train_loss_value = sum(train_loss) / len(train_loss)\n train_acc_value = sum(train_accs).item() / len(train_accs)\n pbar.set_postfix({'fold': i_fold, 'lr': lr, 'b_loss': loss.item(), 'b_acc': acc.item(),\n 'loss': train_loss_value, 'acc': train_acc_value})\n scheduler.step()\n # Print the information.\n # print(f\"[ Train | {epoch + 1:03d}/{n_epochs:03d} ] loss = {train_loss:.5f}, acc = {train_acc:.5f}\")\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 # These are used to record information in validation.\n valid_loss = []\n valid_accs = []\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 # imgs = imgs.half()\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 # Record the loss and accuracy.\n valid_loss.append(loss.item())\n valid_accs.append(acc)\n # break\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}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}, fold = {i_fold}\")\n # update logs\n if valid_acc > best_acc:\n with open(f\"./{_exp_name}_log.txt\", \"a\"):\n print(\n f\"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f} -> best\")\n else:\n with open(f\"./{_exp_name}_log.txt\", \"a\"):\n print(f\"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}\")\n\n # save models\n if valid_acc > best_acc:\n print(f\"Best model found at epoch {epoch}, saving model\")\n torch.save(model.state_dict(),\n f\"{_exp_name}_{i_fold}_best.ckpt\") # only save best to prevent output memory exceed error\n best_acc = valid_acc\n stale = 0\n else:\n stale += 1\n if stale > patience:\n print(f\"No improvment {patience} consecutive epochs, early stopping\")\n break","metadata":{"papermill":{"duration":32830.720158,"end_time":"2022-02-23T19:10:19.001001","exception":false,"start_time":"2022-02-23T10:03:08.280843","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-10-28T14:40:59.073132Z","iopub.execute_input":"2022-10-28T14:40:59.073674Z","iopub.status.idle":"2022-10-28T14:41:15.609658Z","shell.execute_reply.started":"2022-10-28T14:40:59.073635Z","shell.execute_reply":"2022-10-28T14:41:15.608747Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"total_num: 3324\nOne sample ../input/ml2022spring-hw3b/food11/training/2_603.jpg\nOne sample ../input/ml2022spring-hw3b/food11/training/8_248.jpg\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" 0%| | 0/1 [00:00 best\nBest model found at epoch 0, saving model\ntotal_num: 3324\nOne sample ../input/ml2022spring-hw3b/food11/training/8_248.jpg\nOne sample ../input/ml2022spring-hw3b/food11/training/2_603.jpg\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" 0%| | 0/1 [00:00 best\nBest model found at epoch 0, saving model\n","output_type":"stream"}]},{"cell_type":"code","source":"# test_set = FoodDataset(os.path.join(_dataset_dir,\"test\"), tfm=test_tfm)\n# test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True)","metadata":{"papermill":{"duration":0.493644,"end_time":"2022-02-23T19:10:19.985992","exception":false,"start_time":"2022-02-23T19:10:19.492348","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-10-28T14:41:15.611327Z","iopub.execute_input":"2022-10-28T14:41:15.611605Z","iopub.status.idle":"2022-10-28T14:41:16.325709Z","shell.execute_reply.started":"2022-10-28T14:41:15.611568Z","shell.execute_reply":"2022-10-28T14:41:16.324929Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"One sample ../input/ml2022spring-hw3b/food11/test/0001.jpg\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Testing and generate prediction CSV","metadata":{"papermill":{"duration":0.498773,"end_time":"2022-02-23T19:10:20.961802","exception":false,"start_time":"2022-02-23T19:10:20.463029","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# models = []\n# for i_fold in range(n_folds):\n# model_best = Classifier(Residual_Block, num_layers).to(device)\n# model_best.load_state_dict(torch.load(f\"{_exp_name}_{i_fold}_best.ckpt\"))\n# model_best.eval()\n# models.append(model_best)\n\n# prediction = []\n# with torch.no_grad():\n# for data, _ in test_loader:\n# test_preds = []\n# for model_best in models:\n# test_pred = model_best(data.to(device))\n# test_preds.append(test_pred.cpu().data.numpy())\n# test_preds = sum(test_preds)\n# test_label = np.argmax(test_preds, axis=1)\n# prediction += test_label.squeeze().tolist()\n","metadata":{"papermill":{"duration":49.157727,"end_time":"2022-02-23T19:11:10.61523","exception":false,"start_time":"2022-02-23T19:10:21.457503","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-10-28T14:41:16.327151Z","iopub.execute_input":"2022-10-28T14:41:16.327418Z","iopub.status.idle":"2022-10-28T14:41:16.332161Z","shell.execute_reply.started":"2022-10-28T14:41:16.327382Z","shell.execute_reply":"2022-10-28T14:41:16.331124Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"#create test csv\n# def pad4(i):\n# return \"0\"*(4-len(str(i)))+str(i)\n# df = pd.DataFrame()\n# df[\"Id\"] = [pad4(i) for i in range(1,len(test_set)+1)]\n# df[\"Category\"] = prediction\n# df.to_csv(\"submission.csv\",index = False)","metadata":{"papermill":{"duration":0.554276,"end_time":"2022-02-23T19:11:11.870035","exception":false,"start_time":"2022-02-23T19:11:11.315759","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-10-28T14:41:16.335472Z","iopub.execute_input":"2022-10-28T14:41:16.335810Z","iopub.status.idle":"2022-10-28T14:41:16.342281Z","shell.execute_reply.started":"2022-10-28T14:41:16.335770Z","shell.execute_reply":"2022-10-28T14:41:16.341303Z"},"trusted":true},"execution_count":13,"outputs":[]}]} \ No newline at end of file diff --git a/HW04/caishaokang-HW4.ipynb b/HW04/caishaokang-HW4.ipynb new file mode 100644 index 00000000..e71a642a --- /dev/null +++ b/HW04/caishaokang-HW4.ipynb @@ -0,0 +1 @@ +{"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"name":"hw04.ipynb","provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Task description\n- Classify the speakers of given features.\n- Main goal: Learn how to use transformer.\n- Baselines:\n - Easy: Run sample code and know how to use transformer.\n - Medium: Know how to adjust parameters of transformer.\n - Strong: Construct [conformer](https://arxiv.org/abs/2005.08100) which is a variety of transformer. \n - Boss: Implement [Self-Attention Pooling](https://arxiv.org/pdf/2008.01077v1.pdf) & [Additive Margin Softmax](https://arxiv.org/pdf/1801.05599.pdf) to further boost the performance.\n\n- Other links\n - Kaggle: [link](https://www.kaggle.com/t/ac77388c90204a4c8daebeddd40ff916)\n - Slide: [link](https://docs.google.com/presentation/d/1HLAj7UUIjZOycDe7DaVLSwJfXVd3bXPOyzSb6Zk3hYU/edit?usp=sharing)\n - Data: [link](https://drive.google.com/drive/folders/1vI1kuLB-q1VilIftiwnPOCAeOOFfBZge?usp=sharing)\n\n# Download dataset\n- Data is [here](https://drive.google.com/drive/folders/1vI1kuLB-q1VilIftiwnPOCAeOOFfBZge?usp=sharing)","metadata":{"id":"C_jdZ5vHJ4A9"}},{"cell_type":"code","source":"!wget https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partaa\n!wget https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partab\n!wget https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partac\n!wget https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partad\n\n!cat Dataset.tar.gz.part* > Dataset.tar.gz\n\n# unzip the file\n!tar zxvf Dataset.tar.gz","metadata":{"id":"LhLNWB-AK2Z5","outputId":"d2481dbd-f069-4562-8544-cf4871734cc1","colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.status.busy":"2022-10-12T07:24:04.933074Z","iopub.execute_input":"2022-10-12T07:24:04.933466Z","iopub.status.idle":"2022-10-12T07:24:11.200228Z","shell.execute_reply.started":"2022-10-12T07:24:04.933435Z","shell.execute_reply":"2022-10-12T07:24:11.199061Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stdout","text":"--2022-10-12 07:24:05-- https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partaa\nResolving github.com (github.com)... 140.82.112.4\nConnecting to github.com (github.com)|140.82.112.4|:443... connected.\nHTTP request sent, awaiting response... 404 Not Found\n2022-10-12 07:24:05 ERROR 404: Not Found.\n\n--2022-10-12 07:24:06-- https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partab\nResolving github.com (github.com)... 140.82.112.4\nConnecting to github.com (github.com)|140.82.112.4|:443... connected.\nHTTP request sent, awaiting response... 404 Not Found\n2022-10-12 07:24:06 ERROR 404: Not Found.\n\n--2022-10-12 07:24:07-- https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partac\nResolving github.com (github.com)... 140.82.112.4\nConnecting to github.com (github.com)|140.82.112.4|:443... connected.\nHTTP request sent, awaiting response... 404 Not Found\n2022-10-12 07:24:08 ERROR 404: Not Found.\n\n--2022-10-12 07:24:09-- https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partad\nResolving github.com (github.com)... 140.82.112.4\nConnecting to github.com (github.com)|140.82.112.4|:443... connected.\nHTTP request sent, awaiting response... 404 Not Found\n2022-10-12 07:24:09 ERROR 404: Not Found.\n\ncat: 'Dataset.tar.gz.part*': No such file or directory\n\ngzip: stdin: unexpected end of file\ntar: Child returned status 1\ntar: Error is not recoverable: exiting now\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Fix Random Seed","metadata":{"id":"ENWVAUDVJtVY"}},{"cell_type":"code","source":"import numpy as np\nimport torch\nimport random\n\ndef set_seed(seed):\n np.random.seed(seed)\n random.seed(seed)\n torch.manual_seed(seed)\n if torch.cuda.is_available():\n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n torch.backends.cudnn.benchmark = False\n torch.backends.cudnn.deterministic = True\n\nset_seed(87)","metadata":{"id":"E6burzCXIyuA","execution":{"iopub.status.busy":"2022-10-12T07:24:11.202867Z","iopub.execute_input":"2022-10-12T07:24:11.203587Z","iopub.status.idle":"2022-10-12T07:24:11.210419Z","shell.execute_reply.started":"2022-10-12T07:24:11.203543Z","shell.execute_reply":"2022-10-12T07:24:11.209362Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"markdown","source":"# Data\n\n## Dataset\n- Original dataset is [Voxceleb2](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox2.html).\n- The [license](https://creativecommons.org/licenses/by/4.0/) and [complete version](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/files/license.txt) of Voxceleb2.\n- We randomly select 600 speakers from Voxceleb2.\n- Then preprocess the raw waveforms into mel-spectrograms.\n\n- Args:\n - data_dir: The path to the data directory.\n - metadata_path: The path to the metadata.\n - segment_len: The length of audio segment for training. \n- The architecture of data directory \\\\\n - data directory \\\\\n |---- metadata.json \\\\\n |---- testdata.json \\\\\n |---- mapping.json \\\\\n |---- uttr-{random string}.pt \\\\\n\n- The information in metadata\n - \"n_mels\": The dimention of mel-spectrogram.\n - \"speakers\": A dictionary. \n - Key: speaker ids.\n - value: \"feature_path\" and \"mel_len\"\n\n\nFor efficiency, we segment the mel-spectrograms into segments in the traing step.","metadata":{"id":"k7dVbxW2LASN"}},{"cell_type":"code","source":"import os\nimport json\nimport torch\nimport random\nfrom pathlib import Path\nfrom torch.utils.data import Dataset\nfrom torch.nn.utils.rnn import pad_sequence\n \n \nclass myDataset(Dataset):\n\tdef __init__(self, data_dir, segment_len=128):\n\t\tself.data_dir = data_dir\n\t\tself.segment_len = segment_len\n\t\n\t\t# Load the mapping from speaker neme to their corresponding id. \n\t\tmapping_path = Path(data_dir) / \"mapping.json\"\n\t\tmapping = json.load(mapping_path.open())\n\t\tself.speaker2id = mapping[\"speaker2id\"]\n\t\n\t\t# Load metadata of training data.\n\t\tmetadata_path = Path(data_dir) / \"metadata.json\"\n\t\tmetadata = json.load(open(metadata_path))[\"speakers\"]\n\t\n\t\t# Get the total number of speaker.\n\t\tself.speaker_num = len(metadata.keys())\n\t\tself.data = []\n\t\tfor speaker in metadata.keys():\n\t\t\tfor utterances in metadata[speaker]:\n\t\t\t\tself.data.append([utterances[\"feature_path\"], self.speaker2id[speaker]])\n \n\tdef __len__(self):\n\t\treturn len(self.data)\n \n\tdef __getitem__(self, index):\n\t\tfeat_path, speaker = self.data[index]\n\t\t# Load preprocessed mel-spectrogram.\n\t\tmel = torch.load(os.path.join(self.data_dir, feat_path))\n\n\t\t# Segmemt mel-spectrogram into \"segment_len\" frames.\n\t\tif len(mel) > self.segment_len:\n\t\t\t# Randomly get the starting point of the segment.\n\t\t\tstart = random.randint(0, len(mel) - self.segment_len)\n\t\t\t# Get a segment with \"segment_len\" frames.\n\t\t\tmel = torch.FloatTensor(mel[start:start+self.segment_len])\n\t\telse:\n\t\t\tmel = torch.FloatTensor(mel)\n\t\t# Turn the speaker id into long for computing loss later.\n\t\tspeaker = torch.FloatTensor([speaker]).long()\n\t\treturn mel, speaker\n \n\tdef get_speaker_number(self):\n\t\treturn self.speaker_num","metadata":{"id":"KpuGxl4CI2pr","execution":{"iopub.status.busy":"2022-10-12T07:24:11.212106Z","iopub.execute_input":"2022-10-12T07:24:11.212851Z","iopub.status.idle":"2022-10-12T07:24:11.225708Z","shell.execute_reply.started":"2022-10-12T07:24:11.212811Z","shell.execute_reply":"2022-10-12T07:24:11.224731Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"markdown","source":"## Dataloader\n- Split dataset into training dataset(90%) and validation dataset(10%).\n- Create dataloader to iterate the data.","metadata":{"id":"668hverTMlGN"}},{"cell_type":"code","source":"import torch\nfrom torch.utils.data import DataLoader, random_split\nfrom torch.nn.utils.rnn import pad_sequence\n\n\ndef collate_batch(batch):\n\t# Process features within a batch.\n\t\"\"\"Collate a batch of data.\"\"\"\n\tmel, speaker = zip(*batch)\n\t# Because we train the model batch by batch, we need to pad the features in the same batch to make their lengths the same.\n\tmel = pad_sequence(mel, batch_first=True, padding_value=-20) # pad log 10^(-20) which is very small value.\n\t# mel: (batch size, length, 40)\n\treturn mel, torch.FloatTensor(speaker).long()\n\n\ndef get_dataloader(data_dir, batch_size, n_workers):\n\t\"\"\"Generate dataloader\"\"\"\n\tdataset = myDataset(data_dir)\n\tspeaker_num = dataset.get_speaker_number()\n\t# Split dataset into training dataset and validation dataset\n\ttrainlen = int(0.9 * len(dataset))\n\tlengths = [trainlen, len(dataset) - trainlen]\n\ttrainset, validset = random_split(dataset, lengths)\n\n\ttrain_loader = DataLoader(\n\t\ttrainset,\n\t\tbatch_size=batch_size,\n\t\tshuffle=True,\n\t\tdrop_last=True,\n\t\tnum_workers=n_workers,\n\t\tpin_memory=True,\n\t\tcollate_fn=collate_batch,\n\t)\n\tvalid_loader = DataLoader(\n\t\tvalidset,\n\t\tbatch_size=batch_size,\n\t\tnum_workers=n_workers,\n\t\tdrop_last=True,\n\t\tpin_memory=True,\n\t\tcollate_fn=collate_batch,\n\t)\n\n\treturn train_loader, valid_loader, speaker_num","metadata":{"id":"B7c2gZYoJDRS","execution":{"iopub.status.busy":"2022-10-12T07:24:11.228746Z","iopub.execute_input":"2022-10-12T07:24:11.229186Z","iopub.status.idle":"2022-10-12T07:24:11.241459Z","shell.execute_reply.started":"2022-10-12T07:24:11.229148Z","shell.execute_reply":"2022-10-12T07:24:11.240494Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"markdown","source":"# Model\n- TransformerEncoderLayer:\n - Base transformer encoder layer in [Attention Is All You Need](https://arxiv.org/abs/1706.03762)\n - Parameters:\n - d_model: the number of expected features of the input (required).\n\n - nhead: the number of heads of the multiheadattention models (required).\n\n - dim_feedforward: the dimension of the feedforward network model (default=2048).\n\n - dropout: the dropout value (default=0.1).\n\n - activation: the activation function of intermediate layer, relu or gelu (default=relu).\n\n- TransformerEncoder:\n - TransformerEncoder is a stack of N transformer encoder layers\n - Parameters:\n - encoder_layer: an instance of the TransformerEncoderLayer() class (required).\n\n - num_layers: the number of sub-encoder-layers in the encoder (required).\n\n - norm: the layer normalization component (optional).","metadata":{"id":"5FOSZYxrMqhc"}},{"cell_type":"code","source":"!pip install conformer","metadata":{"execution":{"iopub.status.busy":"2022-10-12T07:24:11.242629Z","iopub.execute_input":"2022-10-12T07:24:11.242919Z","iopub.status.idle":"2022-10-12T07:24:20.886804Z","shell.execute_reply.started":"2022-10-12T07:24:11.242883Z","shell.execute_reply":"2022-10-12T07:24:20.885487Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"Requirement already satisfied: conformer in /opt/conda/lib/python3.7/site-packages (0.2.5)\nRequirement already satisfied: einops in /opt/conda/lib/python3.7/site-packages (from conformer) (0.5.0)\nRequirement already satisfied: torch in /opt/conda/lib/python3.7/site-packages (from conformer) (1.11.0)\nRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch->conformer) (4.3.0)\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}]},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom conformer import ConformerBlock\n\nclass self_Attentive_pooling(nn.Module):\n def __init__(self, dim):\n super(self_Attentive_pooling, self).__init__()\n self.sap_linear = nn.Linear(dim, dim)\n self.attention = nn.Parameter(torch.FloatTensor(dim,1))\n torch.nn.init.normal_(self.attention, std=.02)\n\n def forward(self, x):\n h=torch.tanh(self.sap_linear(x))\n w=torch.matmul(h, self.attention).squeeze(dim=2)\n w=F.softmax(w, dim=1).view(x.size(0), x.size(1), 1)\n x=torch.sum(x * w, dim=1)\n return x\n\nclass AMSoftmax(nn.Module):\n def __init__(self, in_feats, n_classes, m=0.3, s=15, annealing=False):\n super(AMSoftmax, self).__init__()\n self.linear = nn.Linear(in_feats, n_classes, bias=False)\n self.m = m\n self.s = s\n\n def _am_logsumexp(self, logits):\n max_x = torch.max(logits, dim=-1)[0].unsqueeze(-1)\n term1 = (self.s*(logits - (max_x + self.m))).exp()\n term2 = (self.s * (logits - max_x)).exp().sum(-1).unsqueeze(-1) - (self.s*(logits-max_x)).exp()\n return self.s * max_x + (term2 + term1).log()\n\n def forward(self, *inputs):\n x_vector = F.normalize(inputs[0], p=2, dim=-1)\n self.linear.weight.data = F.normalize(self.linear.weight.data, p=2, dim=-1)\n logits = self.linear(x_vector)\n scaled_logits = (logits-self.m)*self.s\n return scaled_logits - self._am_logsumexp(logits)\n\nclass Classifier(nn.Module):\n\tdef __init__(self, d_model=256, n_spks=600, dropout=0.3):\n\t\tsuper().__init__()\n\t\t# Project the dimension of features from that of input into d_model.\n\t\tself.prenet = nn.Linear(40, d_model)\n\t\t# TODO:\n\t\t# Change Transformer to Conformer.\n\t\t# https://arxiv.org/abs/2005.08100\n# \t\tself.encoder_layer = nn.TransformerEncoderLayer(\n# \t\t\td_model=d_model, dim_feedforward=1024, nhead=2, dropout=dropout\n# \t\t)\n# \t\tself.encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=3)\n\t\tself.blocks = nn.ModuleList([\n ConformerBlock(\n dim=d_model,\n dim_head=64,\n heads=8,\n ff_mult=4,\n conv_expansion_factor=2,\n conv_kernel_size=15,\n attn_dropout=dropout,\n ff_dropout=dropout,\n conv_dropout=dropout\n )\n for i in range(6)])\n\t\t# Project the the dimension of features from d_model into speaker nums.\n\t\tself.sap = self_Attentive_pooling(d_model)\n\t\tself.pred_layer = AMSoftmax(d_model, n_spks)\n\n\tdef forward(self, mels):\n\t\t\"\"\"\n\t\targs:\n\t\t\tmels: (batch size, length, 40)\n\t\treturn:\n\t\t\tout: (batch size, n_spks)\n\t\t\"\"\"\n\t\t# out: (batch size, length, d_model)\n\t\tout = self.prenet(mels)\n\t\t# out: (length, batch size, d_model)\n# \t\tout = out.permute(1, 0, 2)\n\t\t# The encoder layer expect features in the shape of (length, batch size, d_model).\n# \t\tout = self.encoder(out)\n\t\t# out: (batch size, length, d_model)\n# \t\tout = out.transpose(0, 1)\n\t\t# mean pooling\n# \t\tstats = self.sap(out)\n\t\tfor blk in self.blocks:\n\t\t\tout = blk(out)\n\t\tstats = self.sap(out)\n\t\t# out: (batch, n_spks)\n\t\tout = self.pred_layer(stats)\n\t\treturn out","metadata":{"id":"iXZ5B0EKJGs8","execution":{"iopub.status.busy":"2022-10-12T07:24:20.889013Z","iopub.execute_input":"2022-10-12T07:24:20.889545Z","iopub.status.idle":"2022-10-12T07:24:20.909804Z","shell.execute_reply.started":"2022-10-12T07:24:20.889501Z","shell.execute_reply":"2022-10-12T07:24:20.908744Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"markdown","source":"# Learning rate schedule\n- For transformer architecture, the design of learning rate schedule is different from that of CNN.\n- Previous works show that the warmup of learning rate is useful for training models with transformer architectures.\n- The warmup schedule\n - Set learning rate to 0 in the beginning.\n - The learning rate increases linearly from 0 to initial learning rate during warmup period.","metadata":{"id":"W7yX8JinM5Ly"}},{"cell_type":"code","source":"import math\n\nimport torch\nfrom torch.optim import Optimizer\nfrom torch.optim.lr_scheduler import LambdaLR\n\n\ndef get_cosine_schedule_with_warmup(\n\toptimizer: Optimizer,\n\tnum_warmup_steps: int,\n\tnum_training_steps: int,\n\tnum_cycles: float = 0.5,\n\tlast_epoch: int = -1,\n):\n\t\"\"\"\n\tCreate a schedule with a learning rate that decreases following the values of the cosine function between the\n\tinitial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the\n\tinitial lr set in the optimizer.\n\n\tArgs:\n\t\toptimizer (:class:`~torch.optim.Optimizer`):\n\t\tThe optimizer for which to schedule the learning rate.\n\t\tnum_warmup_steps (:obj:`int`):\n\t\tThe number of steps for the warmup phase.\n\t\tnum_training_steps (:obj:`int`):\n\t\tThe total number of training steps.\n\t\tnum_cycles (:obj:`float`, `optional`, defaults to 0.5):\n\t\tThe number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0\n\t\tfollowing a half-cosine).\n\t\tlast_epoch (:obj:`int`, `optional`, defaults to -1):\n\t\tThe index of the last epoch when resuming training.\n\n\tReturn:\n\t\t:obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.\n\t\"\"\"\n\tdef lr_lambda(current_step):\n\t\t# Warmup\n\t\tif current_step < num_warmup_steps:\n\t\t\treturn float(current_step) / float(max(1, num_warmup_steps))\n\t\t# decadence\n\t\tprogress = float(current_step - num_warmup_steps) / float(\n\t\t\tmax(1, num_training_steps - num_warmup_steps)\n\t\t)\n\t\treturn max(\n\t\t\t0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))\n\t\t)\n\n\treturn LambdaLR(optimizer, lr_lambda, last_epoch)","metadata":{"id":"ykt0N1nVJJi2","execution":{"iopub.status.busy":"2022-10-12T07:24:20.910932Z","iopub.execute_input":"2022-10-12T07:24:20.912234Z","iopub.status.idle":"2022-10-12T07:24:20.927590Z","shell.execute_reply.started":"2022-10-12T07:24:20.912197Z","shell.execute_reply":"2022-10-12T07:24:20.926511Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"markdown","source":"# Model Function\n- Model forward function.","metadata":{"id":"-LN2XkteM_uH"}},{"cell_type":"code","source":"import torch\n\n\ndef model_fn(batch, model, criterion, device):\n\t\"\"\"Forward a batch through the model.\"\"\"\n\n\tmels, labels = batch\n\tmels = mels.to(device)\n\tlabels = labels.to(device)\n\n\touts = model(mels)\n\n\tloss = criterion(outs, labels)\n\n\t# Get the speaker id with highest probability.\n\tpreds = outs.argmax(1)\n\t# Compute accuracy.\n\taccuracy = torch.mean((preds == labels).float())\n\n\treturn loss, accuracy","metadata":{"id":"N-rr8529JMz0","execution":{"iopub.status.busy":"2022-10-12T07:24:20.929011Z","iopub.execute_input":"2022-10-12T07:24:20.929418Z","iopub.status.idle":"2022-10-12T07:24:20.942056Z","shell.execute_reply.started":"2022-10-12T07:24:20.929384Z","shell.execute_reply":"2022-10-12T07:24:20.941045Z"},"trusted":true},"execution_count":49,"outputs":[]},{"cell_type":"markdown","source":"# Validate\n- Calculate accuracy of the validation set.","metadata":{"id":"cwM_xyOtNCI2"}},{"cell_type":"code","source":"from tqdm import tqdm\nimport torch\n\n\ndef valid(dataloader, model, criterion, device): \n\t\"\"\"Validate on validation set.\"\"\"\n\n\tmodel.eval()\n\trunning_loss = 0.0\n\trunning_accuracy = 0.0\n\tpbar = tqdm(total=len(dataloader.dataset), ncols=0, desc=\"Valid\", unit=\" uttr\")\n\n\tfor i, batch in enumerate(dataloader):\n\t\twith torch.no_grad():\n\t\t\tloss, accuracy = model_fn(batch, model, criterion, device)\n\t\t\trunning_loss += loss.item()\n\t\t\trunning_accuracy += accuracy.item()\n\n\t\tpbar.update(dataloader.batch_size)\n\t\tpbar.set_postfix(\n\t\t\tloss=f\"{running_loss / (i+1):.2f}\",\n\t\t\taccuracy=f\"{running_accuracy / (i+1):.2f}\",\n\t\t)\n\n\tpbar.close()\n\tmodel.train()\n\n\treturn running_accuracy / len(dataloader)","metadata":{"id":"YAiv6kpdJRTJ","execution":{"iopub.status.busy":"2022-10-12T07:24:20.944995Z","iopub.execute_input":"2022-10-12T07:24:20.945416Z","iopub.status.idle":"2022-10-12T07:24:20.955295Z","shell.execute_reply.started":"2022-10-12T07:24:20.945390Z","shell.execute_reply":"2022-10-12T07:24:20.954374Z"},"trusted":true},"execution_count":50,"outputs":[]},{"cell_type":"markdown","source":"# Main function","metadata":{"id":"g6ne9G-eNEdG"}},{"cell_type":"code","source":"from tqdm import tqdm\n\nimport torch\nimport torch.nn as nn\nfrom torch.optim import AdamW\nfrom torch.utils.data import DataLoader, random_split\n\n\ndef parse_args():\n\t\"\"\"arguments\"\"\"\n\tconfig = {\n\t\t\"data_dir\": \"../input/ml2022spring-hw4/Dataset\",\n\t\t\"save_path\": \"model.ckpt\",\n\t\t\"batch_size\": 128,\n\t\t\"n_workers\": 8,\n\t\t\"valid_steps\": 2000,\n\t\t\"warmup_steps\": 1000,\n\t\t\"save_steps\": 10000,\n\t\t\"total_steps\": 25000,\n\t}\n\n\treturn config\n\n\ndef main(\n\tdata_dir,\n\tsave_path,\n\tbatch_size,\n\tn_workers,\n\tvalid_steps,\n\twarmup_steps,\n\ttotal_steps,\n\tsave_steps,\n):\n\t\"\"\"Main function.\"\"\"\n\tdevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\tprint(f\"[Info]: Use {device} now!\")\n\n\ttrain_loader, valid_loader, speaker_num = get_dataloader(data_dir, batch_size, n_workers)\n\ttrain_iterator = iter(train_loader)\n\tprint(f\"[Info]: Finish loading data!\",flush = True)\n\n\tmodel = Classifier(n_spks=speaker_num).to(device)\n\tcriterion = nn.CrossEntropyLoss()\n# \tcriterion = AMSoftmax()\n\toptimizer = AdamW(model.parameters(), lr=1e-3)\n\tscheduler = get_cosine_schedule_with_warmup(optimizer, warmup_steps, total_steps)\n\tprint(f\"[Info]: Finish creating model!\",flush = True)\n\n\tbest_accuracy = -1.0\n\tbest_state_dict = None\n\n\tpbar = tqdm(total=valid_steps, ncols=0, desc=\"Train\", unit=\" step\")\n\n\tfor step in range(total_steps):\n\t\t# Get data\n\t\ttry:\n\t\t\tbatch = next(train_iterator)\n\t\texcept StopIteration:\n\t\t\ttrain_iterator = iter(train_loader)\n\t\t\tbatch = next(train_iterator)\n\n\t\tloss, accuracy = model_fn(batch, model, criterion, device)\n\t\tbatch_loss = loss.item()\n\t\tbatch_accuracy = accuracy.item()\n\n\t\t# Updata model\n\t\tloss.backward()\n\t\toptimizer.step()\n\t\tscheduler.step()\n\t\toptimizer.zero_grad()\n\n\t\t# Log\n\t\tpbar.update()\n\t\tpbar.set_postfix(\n\t\t\tloss=f\"{batch_loss:.2f}\",\n\t\t\taccuracy=f\"{batch_accuracy:.2f}\",\n\t\t\tstep=step + 1,\n\t\t)\n\n\t\t# Do validation\n\t\tif (step + 1) % valid_steps == 0:\n\t\t\tpbar.close()\n\n\t\t\tvalid_accuracy = valid(valid_loader, model, criterion, device)\n\n\t\t\t# keep the best model\n\t\t\tif valid_accuracy > best_accuracy:\n\t\t\t\tbest_accuracy = valid_accuracy\n\t\t\t\tbest_state_dict = model.state_dict()\n\n\t\t\tpbar = tqdm(total=valid_steps, ncols=0, desc=\"Train\", unit=\" step\")\n\n\t\t# Save the best model so far.\n\t\tif (step + 1) % save_steps == 0 and best_state_dict is not None:\n\t\t\ttorch.save(best_state_dict, save_path)\n\t\t\tpbar.write(f\"Step {step + 1}, best model saved. (accuracy={best_accuracy:.4f})\")\n\n\tpbar.close()\n\n\nif __name__ == \"__main__\":\n\tmain(**parse_args())","metadata":{"id":"Usv9s-CuJSG7","outputId":"0e4eb5c2-901f-419e-b917-61860e81c8ae","colab":{"base_uri":"https://localhost:8080/","height":375},"execution":{"iopub.status.busy":"2022-10-12T07:24:20.959703Z","iopub.execute_input":"2022-10-12T07:24:20.960505Z","iopub.status.idle":"2022-10-12T07:24:32.460955Z","shell.execute_reply.started":"2022-10-12T07:24:20.960461Z","shell.execute_reply":"2022-10-12T07:24:32.459638Z"},"trusted":true},"execution_count":51,"outputs":[{"name":"stdout","text":"[Info]: Use cuda now!\n[Info]: Finish loading data!\n[Info]: Finish creating model!\n","output_type":"stream"},{"name":"stderr","text":"\n\nTrain: 0% 0/2000 [00:00\u001b[0;34m\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"__main__\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mparse_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/tmp/ipykernel_17/325961183.py\u001b[0m in \u001b[0;36mmain\u001b[0;34m(data_dir, save_path, batch_size, n_workers, valid_steps, warmup_steps, total_steps, save_steps)\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;31m# Updata model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 69\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0mscheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 362\u001b[0m inputs=inputs)\n\u001b[0;32m--> 363\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 173\u001b[0m Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n\u001b[1;32m 174\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m allow_unreachable=True, accumulate_grad=True) # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m 176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m def grad(\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}]},{"cell_type":"markdown","source":"# Inference\n\n## Dataset of inference","metadata":{"id":"NLatBYAhNNMx"}},{"cell_type":"code","source":"import os\nimport json\nimport torch\nfrom pathlib import Path\nfrom torch.utils.data import Dataset\n\n\nclass InferenceDataset(Dataset):\n\tdef __init__(self, data_dir):\n\t\ttestdata_path = Path(data_dir) / \"testdata.json\"\n\t\tmetadata = json.load(testdata_path.open())\n\t\tself.data_dir = data_dir\n\t\tself.data = metadata[\"utterances\"]\n\n\tdef __len__(self):\n\t\treturn len(self.data)\n\n\tdef __getitem__(self, index):\n\t\tutterance = self.data[index]\n\t\tfeat_path = utterance[\"feature_path\"]\n\t\tmel = torch.load(os.path.join(self.data_dir, feat_path))\n\n\t\treturn feat_path, mel\n\n\ndef inference_collate_batch(batch):\n\t\"\"\"Collate a batch of data.\"\"\"\n\tfeat_paths, mels = zip(*batch)\n\n\treturn feat_paths, torch.stack(mels)","metadata":{"id":"efS4pCmAJXJH","execution":{"iopub.status.busy":"2022-10-12T07:24:32.462272Z","iopub.status.idle":"2022-10-12T07:24:32.464109Z","shell.execute_reply.started":"2022-10-12T07:24:32.463836Z","shell.execute_reply":"2022-10-12T07:24:32.463865Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Main funcrion of Inference","metadata":{"id":"tl0WnYwxNK_S"}},{"cell_type":"raw","source":"","metadata":{}},{"cell_type":"code","source":"import json\nimport csv\nfrom pathlib import Path\nfrom tqdm.notebook import tqdm\n\nimport torch\nfrom torch.utils.data import DataLoader\n\ndef parse_args():\n\t\"\"\"arguments\"\"\"\n\tconfig = {\n\t\t\"data_dir\": \"../input/ml2022spring-hw4/Dataset\",\n\t\t\"model_path\": \"./model.ckpt\",\n\t\t\"output_path\": \"./output.csv\",\n\t}\n\n\treturn config\n\n\ndef main(\n\tdata_dir,\n\tmodel_path,\n\toutput_path,\n):\n\t\"\"\"Main function.\"\"\"\n\tdevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\tprint(f\"[Info]: Use {device} now!\")\n\n\tmapping_path = Path(data_dir) / \"mapping.json\"\n\tmapping = json.load(mapping_path.open())\n\n\tdataset = InferenceDataset(data_dir)\n\tdataloader = DataLoader(\n\t\tdataset,\n\t\tbatch_size=1,\n\t\tshuffle=False,\n\t\tdrop_last=False,\n\t\tnum_workers=8,\n\t\tcollate_fn=inference_collate_batch,\n\t)\n\tprint(f\"[Info]: Finish loading data!\",flush = True)\n\n\tspeaker_num = len(mapping[\"id2speaker\"])\n\tmodel = Classifier(n_spks=speaker_num).to(device)\n\tmodel.load_state_dict(torch.load(model_path))\n\tmodel.eval()\n\tprint(f\"[Info]: Finish creating model!\",flush = True)\n\n\tresults = [[\"Id\", \"Category\"]]\n\tfor feat_paths, mels in tqdm(dataloader):\n\t\twith torch.no_grad():\n\t\t\tmels = mels.to(device)\n\t\t\touts = model(mels)\n\t\t\tpreds = outs.argmax(1).cpu().numpy()\n\t\t\tfor feat_path, pred in zip(feat_paths, preds):\n\t\t\t\tresults.append([feat_path, mapping[\"id2speaker\"][str(pred)]])\n\n\twith open(output_path, 'w', newline='') as csvfile:\n\t\twriter = csv.writer(csvfile)\n\t\twriter.writerows(results)\n\n\nif __name__ == \"__main__\":\n\tmain(**parse_args())","metadata":{"id":"i8SAbuXEJb2A","execution":{"iopub.status.busy":"2022-10-12T07:24:32.465657Z","iopub.status.idle":"2022-10-12T07:24:32.466465Z","shell.execute_reply.started":"2022-10-12T07:24:32.466203Z","shell.execute_reply":"2022-10-12T07:24:32.466228Z"},"trusted":true},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/HW04/ml2022sping-hw4-score-caishaokang.png b/HW04/ml2022sping-hw4-score-caishaokang.png new file mode 100644 index 00000000..6b5da9fd Binary files /dev/null and b/HW04/ml2022sping-hw4-score-caishaokang.png differ diff --git a/HW04/ml2022spring-hw4-caishaokang.ipynb b/HW04/ml2022spring-hw4-caishaokang.ipynb new file mode 100644 index 00000000..80ed77de --- /dev/null +++ b/HW04/ml2022spring-hw4-caishaokang.ipynb @@ -0,0 +1,1653 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "907084f5", + "metadata": { + "id": "C_jdZ5vHJ4A9", + "papermill": { + "duration": 0.006218, + "end_time": "2022-11-15T11:07:44.230922", + "exception": false, + "start_time": "2022-11-15T11:07:44.224704", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Task description\n", + "- Classify the speakers of given features.\n", + "- Main goal: Learn how to use transformer.\n", + "- Baselines:\n", + " - Easy: Run sample code and know how to use transformer.\n", + " - Medium: Know how to adjust parameters of transformer.\n", + " - Strong: Construct [conformer](https://arxiv.org/abs/2005.08100) which is a variety of transformer. \n", + " - Boss: Implement [Self-Attention Pooling](https://arxiv.org/pdf/2008.01077v1.pdf) & [Additive Margin Softmax](https://arxiv.org/pdf/1801.05599.pdf) to further boost the performance.\n", + "\n", + "- Other links\n", + " - Kaggle: [link](https://www.kaggle.com/t/ac77388c90204a4c8daebeddd40ff916)\n", + " - Slide: [link](https://docs.google.com/presentation/d/1HLAj7UUIjZOycDe7DaVLSwJfXVd3bXPOyzSb6Zk3hYU/edit?usp=sharing)\n", + " - Data: [link](https://drive.google.com/drive/folders/1vI1kuLB-q1VilIftiwnPOCAeOOFfBZge?usp=sharing)\n", + "\n", + "# Download dataset\n", + "- Data is [here](https://drive.google.com/drive/folders/1vI1kuLB-q1VilIftiwnPOCAeOOFfBZge?usp=sharing)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c98a1137", + "metadata": { + "execution": { + "iopub.execute_input": "2022-11-15T11:07:44.243012Z", + "iopub.status.busy": "2022-11-15T11:07:44.242251Z", + "iopub.status.idle": "2022-11-15T11:07:52.563072Z", + "shell.execute_reply": "2022-11-15T11:07:52.561765Z" + }, + "id": "LhLNWB-AK2Z5", + "outputId": "d2481dbd-f069-4562-8544-cf4871734cc1", + "papermill": { + "duration": 8.329807, + "end_time": "2022-11-15T11:07:52.565845", + "exception": false, + "start_time": "2022-11-15T11:07:44.236038", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2022-11-15 11:07:45-- https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partaa\r\n", + "Resolving github.com (github.com)... 140.82.114.3\r\n", + "Connecting to github.com (github.com)|140.82.114.3|:443... connected.\r\n", + "HTTP request sent, awaiting response... 404 Not Found\r\n", + "2022-11-15 11:07:45 ERROR 404: Not Found.\r\n", + "\r\n", + "--2022-11-15 11:07:46-- https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partab\r\n", + "Resolving github.com (github.com)... 140.82.112.4\r\n", + "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\r\n", + "HTTP request sent, awaiting response... 404 Not Found\r\n", + "2022-11-15 11:07:47 ERROR 404: Not Found.\r\n", + "\r\n", + "--2022-11-15 11:07:48-- https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partac\r\n", + "Resolving github.com (github.com)... 140.82.112.4\r\n", + "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\r\n", + "HTTP request sent, awaiting response... 404 Not Found\r\n", + "2022-11-15 11:07:48 ERROR 404: Not Found.\r\n", + "\r\n", + "--2022-11-15 11:07:49-- https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partad\r\n", + "Resolving github.com (github.com)... 140.82.112.4\r\n", + "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\r\n", + "HTTP request sent, awaiting response... 404 Not Found\r\n", + "2022-11-15 11:07:50 ERROR 404: Not Found.\r\n", + "\r\n", + "cat: 'Dataset.tar.gz.part*': No such file or directory\r\n", + "\r\n", + "gzip: stdin: unexpected end of file\r\n", + "tar: Child returned status 1\r\n", + "tar: Error is not recoverable: exiting now\r\n" + ] + } + ], + "source": [ + "!wget https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partaa\n", + "!wget https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partab\n", + "!wget https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partac\n", + "!wget https://github.com/MachineLearningHW/ML_HW4_Dataset/releases/latest/download/Dataset.tar.gz.partad\n", + "\n", + "!cat Dataset.tar.gz.part* > Dataset.tar.gz\n", + "\n", + "# unzip the file\n", + "!tar zxvf Dataset.tar.gz" + ] + }, + { + "cell_type": "markdown", + "id": "dc53d18d", + "metadata": { + "id": "ENWVAUDVJtVY", + "papermill": { + "duration": 0.006475, + "end_time": "2022-11-15T11:07:52.578871", + "exception": false, + "start_time": "2022-11-15T11:07:52.572396", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Fix Random Seed" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2c202c40", + "metadata": { + "execution": { + "iopub.execute_input": "2022-11-15T11:07:52.593307Z", + "iopub.status.busy": "2022-11-15T11:07:52.592948Z", + "iopub.status.idle": "2022-11-15T11:07:54.404891Z", + "shell.execute_reply": "2022-11-15T11:07:54.403848Z" + }, + "id": "E6burzCXIyuA", + "papermill": { + "duration": 1.822261, + "end_time": "2022-11-15T11:07:54.407420", + "exception": false, + "start_time": "2022-11-15T11:07:52.585159", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import random\n", + "\n", + "def set_seed(seed):\n", + " np.random.seed(seed)\n", + " random.seed(seed)\n", + " torch.manual_seed(seed)\n", + " if torch.cuda.is_available():\n", + " torch.cuda.manual_seed(seed)\n", + " torch.cuda.manual_seed_all(seed)\n", + " torch.backends.cudnn.benchmark = False\n", + " torch.backends.cudnn.deterministic = True\n", + "\n", + "set_seed(87)" + ] + }, + { + "cell_type": "markdown", + "id": "2bbdb32d", + "metadata": { + "id": "k7dVbxW2LASN", + "papermill": { + "duration": 0.005872, + "end_time": "2022-11-15T11:07:54.419754", + "exception": false, + "start_time": "2022-11-15T11:07:54.413882", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Data\n", + "\n", + "## Dataset\n", + "- Original dataset is [Voxceleb2](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox2.html).\n", + "- The [license](https://creativecommons.org/licenses/by/4.0/) and [complete version](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/files/license.txt) of Voxceleb2.\n", + "- We randomly select 600 speakers from Voxceleb2.\n", + "- Then preprocess the raw waveforms into mel-spectrograms.\n", + "\n", + "- Args:\n", + " - data_dir: The path to the data directory.\n", + " - metadata_path: The path to the metadata.\n", + " - segment_len: The length of audio segment for training. \n", + "- The architecture of data directory \\\\\n", + " - data directory \\\\\n", + " |---- metadata.json \\\\\n", + " |---- testdata.json \\\\\n", + " |---- mapping.json \\\\\n", + " |---- uttr-{random string}.pt \\\\\n", + "\n", + "- The information in metadata\n", + " - \"n_mels\": The dimention of mel-spectrogram.\n", + " - \"speakers\": A dictionary. \n", + " - Key: speaker ids.\n", + " - value: \"feature_path\" and \"mel_len\"\n", + "\n", + "\n", + "For efficiency, we segment the mel-spectrograms into segments in the traing step." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d064d99a", + "metadata": { + "execution": { + "iopub.execute_input": "2022-11-15T11:07:54.433747Z", + "iopub.status.busy": "2022-11-15T11:07:54.433240Z", + "iopub.status.idle": "2022-11-15T11:07:54.445457Z", + "shell.execute_reply": "2022-11-15T11:07:54.444428Z" + }, + "id": "KpuGxl4CI2pr", + "papermill": { + "duration": 0.0222, + "end_time": "2022-11-15T11:07:54.448057", + "exception": false, + "start_time": "2022-11-15T11:07:54.425857", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import torch\n", + "import random\n", + "import numpy as np\n", + "from pathlib import Path\n", + "from torch.utils.data import Dataset\n", + "from torch.nn.utils.rnn import pad_sequence\n", + " \n", + " \n", + "class myDataset(Dataset):\n", + "\tdef __init__(self, data_dir, segment_len=448):\n", + "\t\tself.data_dir = data_dir\n", + "\t\tself.segment_len = segment_len\n", + "\t\n", + "\t\t# Load the mapping from speaker neme to their corresponding id. \n", + "\t\tmapping_path = Path(data_dir) / \"mapping.json\"\n", + "\t\tmapping = json.load(mapping_path.open())\n", + "\t\tself.speaker2id = mapping[\"speaker2id\"]\n", + "\t\n", + "\t\t# Load metadata of training data.\n", + "\t\tmetadata_path = Path(data_dir) / \"metadata.json\"\n", + "\t\tmetadata = json.load(open(metadata_path))[\"speakers\"]\n", + "\t\n", + "\t\t# Get the total number of speaker.\n", + "\t\tself.speaker_num = len(metadata.keys())\n", + "\t\tself.data = []\n", + "\t\tself.len_speach = []\n", + "\t\tfor speaker in metadata.keys():\n", + "\t\t\tfor utterances in metadata[speaker]:\n", + "\t\t\t\tself.data.append([utterances[\"feature_path\"], self.speaker2id[speaker]])\n", + "\t\t\t\tself.len_speach.append(int(utterances[\"mel_len\"])) \n", + "\t\tself.speak_len_array = np.array(self.len_speach)\n", + "\t\tprint(\"max len:\", self.speak_len_array.min())\n", + "\t\tprint(\"min_len:\", self.speak_len_array.max())\n", + "\t\tprint(\"average_len:\", self.speak_len_array.mean())\n", + " \n", + " \n", + " \n", + " \n", + "\tdef __len__(self):\n", + "\t\treturn len(self.data)\n", + " \n", + "\tdef __getitem__(self, index):\n", + "\t\tfeat_path, speaker = self.data[index]\n", + "\t\t# Load preprocessed mel-spectrogram.\n", + "\t\tmel = torch.load(os.path.join(self.data_dir, feat_path))\n", + "\n", + "\t\t# Segmemt mel-spectrogram into \"segment_len\" frames.\n", + "\t\tif len(mel) > self.segment_len:\n", + "\t\t\t# Randomly get the starting point of the segment.\n", + "\t\t\tstart = random.randint(0, len(mel) - self.segment_len)\n", + "\t\t\t# Get a segment with \"segment_len\" frames.\n", + "\t\t\tmel = torch.FloatTensor(mel[start:start+self.segment_len])\n", + "\t\telse:\n", + "\t\t\tmel = torch.FloatTensor(mel)\n", + "\t\t# Turn the speaker id into long for computing loss later.\n", + "\t\tspeaker = torch.FloatTensor([speaker]).long()\n", + "\t\treturn mel, speaker\n", + " \n", + "\tdef get_speaker_number(self):\n", + "\t\treturn self.speaker_num" + ] + }, + { + "cell_type": "markdown", + "id": "f00cc88a", + "metadata": { + "id": "668hverTMlGN", + "papermill": { + "duration": 0.005805, + "end_time": "2022-11-15T11:07:54.461739", + "exception": false, + "start_time": "2022-11-15T11:07:54.455934", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Dataloader\n", + "- Split dataset into training dataset(90%) and validation dataset(10%).\n", + "- Create dataloader to iterate the data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f02842d2", + "metadata": { + "execution": { + "iopub.execute_input": "2022-11-15T11:07:54.474751Z", + "iopub.status.busy": "2022-11-15T11:07:54.474462Z", + "iopub.status.idle": "2022-11-15T11:07:54.483375Z", + "shell.execute_reply": "2022-11-15T11:07:54.482552Z" + }, + "id": "B7c2gZYoJDRS", + "papermill": { + "duration": 0.017757, + "end_time": "2022-11-15T11:07:54.485383", + "exception": false, + "start_time": "2022-11-15T11:07:54.467626", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import DataLoader, random_split\n", + "from torch.nn.utils.rnn import pad_sequence\n", + "\n", + "\n", + "def collate_batch(batch):\n", + "\t# Process features within a batch.\n", + "\t\"\"\"Collate a batch of data.\"\"\"\n", + "\tmel, speaker = zip(*batch)\n", + "\t# Because we train the model batch by batch, we need to pad the features in the same batch to make their lengths the same.\n", + "\tmel = pad_sequence(mel, batch_first=True, padding_value=-20) # pad log 10^(-20) which is very small value.\n", + "\t# mel: (batch size, length, 40)\n", + "\treturn mel, torch.FloatTensor(speaker).long()\n", + "\n", + "\n", + "def get_dataloader(data_dir, batch_size, n_workers):\n", + "\t\"\"\"Generate dataloader\"\"\"\n", + "\tdataset = myDataset(data_dir)\n", + "\tspeaker_num = dataset.get_speaker_number()\n", + "\t# Split dataset into training dataset and validation dataset\n", + "\ttrainlen = int(0.9 * len(dataset))\n", + "\tlengths = [trainlen, len(dataset) - trainlen]\n", + "\ttrainset, validset = random_split(dataset, lengths)\n", + "\n", + "\ttrain_loader = DataLoader(\n", + "\t\ttrainset,\n", + "\t\tbatch_size=batch_size,\n", + "\t\tshuffle=True,\n", + "\t\tdrop_last=True,\n", + "\t\tnum_workers=n_workers,\n", + "\t\tpin_memory=True,\n", + "\t\tcollate_fn=collate_batch,\n", + "\t)\n", + "\tvalid_loader = DataLoader(\n", + "\t\tvalidset,\n", + "\t\tbatch_size=batch_size,\n", + "\t\tnum_workers=n_workers,\n", + "\t\tdrop_last=True,\n", + "\t\tpin_memory=True,\n", + "\t\tcollate_fn=collate_batch,\n", + "\t)\n", + "\n", + "\treturn train_loader, valid_loader, speaker_num" + ] + }, + { + "cell_type": "markdown", + "id": "8be88f72", + "metadata": { + "id": "5FOSZYxrMqhc", + "papermill": { + "duration": 0.005833, + "end_time": "2022-11-15T11:07:54.497089", + "exception": false, + "start_time": "2022-11-15T11:07:54.491256", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Model\n", + "- TransformerEncoderLayer:\n", + " - Base transformer encoder layer in [Attention Is All You Need](https://arxiv.org/abs/1706.03762)\n", + " - Parameters:\n", + " - d_model: the number of expected features of the input (required).\n", + "\n", + " - nhead: the number of heads of the multiheadattention models (required).\n", + "\n", + " - dim_feedforward: the dimension of the feedforward network model (default=2048).\n", + "\n", + " - dropout: the dropout value (default=0.1).\n", + "\n", + " - activation: the activation function of intermediate layer, relu or gelu (default=relu).\n", + "\n", + "- TransformerEncoder:\n", + " - TransformerEncoder is a stack of N transformer encoder layers\n", + " - Parameters:\n", + " - encoder_layer: an instance of the TransformerEncoderLayer() class (required).\n", + "\n", + " - num_layers: the number of sub-encoder-layers in the encoder (required).\n", + "\n", + " - norm: the layer normalization component (optional)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a0bccae3", + "metadata": { + "execution": { + "iopub.execute_input": "2022-11-15T11:07:54.509944Z", + "iopub.status.busy": "2022-11-15T11:07:54.509695Z", + "iopub.status.idle": "2022-11-15T11:08:06.456562Z", + "shell.execute_reply": "2022-11-15T11:08:06.455261Z" + }, + "papermill": { + "duration": 11.956338, + "end_time": "2022-11-15T11:08:06.459317", + "exception": false, + "start_time": "2022-11-15T11:07:54.502979", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting conformer\r\n", + " Downloading conformer-0.2.5-py3-none-any.whl (4.1 kB)\r\n", + "Collecting einops\r\n", + " Downloading einops-0.6.0-py3-none-any.whl (41 kB)\r\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.6/41.6 kB\u001b[0m \u001b[31m320.1 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n", + "\u001b[?25hRequirement already satisfied: torch in /opt/conda/lib/python3.7/site-packages (from conformer) (1.11.0)\r\n", + "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch->conformer) (4.3.0)\r\n", + "Installing collected packages: einops, conformer\r\n", + "Successfully installed conformer-0.2.5 einops-0.6.0\r\n", + "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install conformer" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fa5a97a0", + "metadata": { + "execution": { + "iopub.execute_input": "2022-11-15T11:08:06.476414Z", + "iopub.status.busy": "2022-11-15T11:08:06.475643Z", + "iopub.status.idle": "2022-11-15T11:08:06.501437Z", + "shell.execute_reply": "2022-11-15T11:08:06.500560Z" + }, + "id": "iXZ5B0EKJGs8", + "papermill": { + "duration": 0.037717, + "end_time": "2022-11-15T11:08:06.503845", + "exception": false, + "start_time": "2022-11-15T11:08:06.466128", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from conformer import ConformerBlock\n", + "\n", + "class self_Attentive_pooling(nn.Module):\n", + " def __init__(self, dim):\n", + " super(self_Attentive_pooling, self).__init__()\n", + " self.sap_linear = nn.Linear(dim, dim)\n", + " self.attention = nn.Parameter(torch.FloatTensor(dim,1))\n", + " torch.nn.init.normal_(self.attention, std=.02)\n", + "\n", + " def forward(self, x):\n", + " h=torch.tanh(self.sap_linear(x))\n", + " w=torch.matmul(h, self.attention).squeeze(dim=2)\n", + " w=F.softmax(w, dim=1).view(x.size(0), x.size(1), 1)\n", + " x=torch.sum(x * w, dim=1)\n", + " return x\n", + "\n", + "class AMSoftmax(nn.Module):\n", + " def __init__(self, in_feats, n_classes, m=0.3, s=15, annealing=False):\n", + " super(AMSoftmax, self).__init__()\n", + " self.linear = nn.Linear(in_feats, n_classes, bias=False)\n", + " self.m = m\n", + " self.s = s\n", + "\n", + " def _am_logsumexp(self, logits):\n", + " max_x = torch.max(logits, dim=-1)[0].unsqueeze(-1)\n", + " term1 = (self.s*(logits - (max_x + self.m))).exp()\n", + " term2 = (self.s * (logits - max_x)).exp().sum(-1).unsqueeze(-1) - (self.s*(logits-max_x)).exp()\n", + " return self.s * max_x + (term2 + term1).log()\n", + "\n", + " def forward(self, *inputs):\n", + " x_vector = F.normalize(inputs[0], p=2, dim=-1)\n", + " self.linear.weight.data = F.normalize(self.linear.weight.data, p=2, dim=-1)\n", + " logits = self.linear(x_vector)\n", + " scaled_logits = (logits-self.m)*self.s\n", + " return scaled_logits - self._am_logsumexp(logits)\n", + "\n", + "class Classifier(nn.Module):\n", + "\tdef __init__(self, d_model=224, n_spks=600, dropout=0.3):\n", + "\t\tsuper().__init__()\n", + "\t\t# Project the dimension of features from that of input into d_model.\n", + "\t\tself.prenet = nn.Linear(40, d_model)\n", + "\t\t# TODO:\n", + "\t\t# Change Transformer to Conformer.\n", + "\t\t# https://arxiv.org/abs/2005.08100\n", + "# \t\tself.encoder_layer = nn.TransformerEncoderLayer(\n", + "# \t\t\td_model=d_model, dim_feedforward=1024, nhead=2, dropout=dropout\n", + "# \t\t)\n", + "# \t\tself.encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=3)\n", + "\t\tself.blocks = nn.ModuleList([\n", + " ConformerBlock(\n", + " dim=d_model,\n", + " dim_head=64,\n", + " heads=8,\n", + " ff_mult=4,\n", + " conv_expansion_factor=2,\n", + " conv_kernel_size=15,\n", + " attn_dropout=dropout,\n", + " ff_dropout=dropout,\n", + " conv_dropout=dropout\n", + " )\n", + " for i in range(6)])\n", + "\t\t# Project the the dimension of features from d_model into speaker nums.\n", + "\t\tself.sap = self_Attentive_pooling(d_model)\n", + "\t\tself.pred_layer = AMSoftmax(d_model, n_spks)\n", + "\n", + "\tdef forward(self, mels):\n", + "\t\t\"\"\"\n", + "\t\targs:\n", + "\t\t\tmels: (batch size, length, 40)\n", + "\t\treturn:\n", + "\t\t\tout: (batch size, n_spks)\n", + "\t\t\"\"\"\n", + "\t\t# out: (batch size, length, d_model)\n", + "# \t\tprint(\"shape:\", mels.shape)\n", + "\t\tout = self.prenet(mels)\n", + "\t\t# out: (length, batch size, d_model)\n", + "# \t\tout = out.permute(1, 0, 2)\n", + "\t\t# The encoder layer expect features in the shape of (length, batch size, d_model).\n", + "# \t\tout = self.encoder(out)\n", + "\t\t# out: (batch size, length, d_model)\n", + "# \t\tout = out.transpose(0, 1)\n", + "\t\t# mean pooling\n", + "# \t\tstats = self.sap(out)\n", + "\t\tfor blk in self.blocks:\n", + "\t\t\tout = blk(out)\n", + "\t\tstats = self.sap(out)\n", + "\t\t# out: (batch, n_spks)\n", + "\t\tout = self.pred_layer(stats)\n", + "\t\treturn out" + ] + }, + { + "cell_type": "markdown", + "id": "f49dff06", + "metadata": { + "id": "W7yX8JinM5Ly", + "papermill": { + "duration": 0.006289, + "end_time": "2022-11-15T11:08:06.516767", + "exception": false, + "start_time": "2022-11-15T11:08:06.510478", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Learning rate schedule\n", + "- For transformer architecture, the design of learning rate schedule is different from that of CNN.\n", + "- Previous works show that the warmup of learning rate is useful for training models with transformer architectures.\n", + "- The warmup schedule\n", + " - Set learning rate to 0 in the beginning.\n", + " - The learning rate increases linearly from 0 to initial learning rate during warmup period." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1fbb7d8a", + "metadata": { + "execution": { + "iopub.execute_input": "2022-11-15T11:08:06.532603Z", + "iopub.status.busy": "2022-11-15T11:08:06.531195Z", + "iopub.status.idle": "2022-11-15T11:08:06.540359Z", + "shell.execute_reply": "2022-11-15T11:08:06.539464Z" + }, + "id": "ykt0N1nVJJi2", + "papermill": { + "duration": 0.019207, + "end_time": "2022-11-15T11:08:06.542571", + "exception": false, + "start_time": "2022-11-15T11:08:06.523364", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import math\n", + "\n", + "import torch\n", + "from torch.optim import Optimizer\n", + "from torch.optim.lr_scheduler import LambdaLR\n", + "\n", + "\n", + "def get_cosine_schedule_with_warmup(\n", + "\toptimizer: Optimizer,\n", + "\tnum_warmup_steps: int,\n", + "\tnum_training_steps: int,\n", + "\tnum_cycles: float = 0.5,\n", + "\tlast_epoch: int = -1,\n", + "):\n", + "\t\"\"\"\n", + "\tCreate a schedule with a learning rate that decreases following the values of the cosine function between the\n", + "\tinitial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the\n", + "\tinitial lr set in the optimizer.\n", + "\n", + "\tArgs:\n", + "\t\toptimizer (:class:`~torch.optim.Optimizer`):\n", + "\t\tThe optimizer for which to schedule the learning rate.\n", + "\t\tnum_warmup_steps (:obj:`int`):\n", + "\t\tThe number of steps for the warmup phase.\n", + "\t\tnum_training_steps (:obj:`int`):\n", + "\t\tThe total number of training steps.\n", + "\t\tnum_cycles (:obj:`float`, `optional`, defaults to 0.5):\n", + "\t\tThe number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0\n", + "\t\tfollowing a half-cosine).\n", + "\t\tlast_epoch (:obj:`int`, `optional`, defaults to -1):\n", + "\t\tThe index of the last epoch when resuming training.\n", + "\n", + "\tReturn:\n", + "\t\t:obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.\n", + "\t\"\"\"\n", + "\tdef lr_lambda(current_step):\n", + "\t\t# Warmup\n", + "\t\tif current_step < num_warmup_steps:\n", + "\t\t\treturn float(current_step) / float(max(1, num_warmup_steps))\n", + "\t\t# decadence\n", + "\t\tprogress = float(current_step - num_warmup_steps) / float(\n", + "\t\t\tmax(1, num_training_steps - num_warmup_steps)\n", + "\t\t)\n", + "\t\treturn max(\n", + "\t\t\t0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))\n", + "\t\t)\n", + "\n", + "\treturn LambdaLR(optimizer, lr_lambda, last_epoch)" + ] + }, + { + "cell_type": "markdown", + "id": "0dd9d7b0", + "metadata": { + "id": "-LN2XkteM_uH", + "papermill": { + "duration": 0.006377, + "end_time": "2022-11-15T11:08:06.555679", + "exception": false, + "start_time": "2022-11-15T11:08:06.549302", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Model Function\n", + "- Model forward function." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "13e18eee", + "metadata": { + "execution": { + "iopub.execute_input": "2022-11-15T11:08:06.570940Z", + "iopub.status.busy": "2022-11-15T11:08:06.569947Z", + "iopub.status.idle": "2022-11-15T11:08:06.576665Z", + "shell.execute_reply": "2022-11-15T11:08:06.575754Z" + }, + "id": "N-rr8529JMz0", + "papermill": { + "duration": 0.016667, + "end_time": "2022-11-15T11:08:06.578847", + "exception": false, + "start_time": "2022-11-15T11:08:06.562180", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "\n", + "def model_fn(batch, model, criterion, device):\n", + "\t\"\"\"Forward a batch through the model.\"\"\"\n", + "\n", + "\tmels, labels = batch\n", + "\tmels = mels.to(device)\n", + "\tlabels = labels.to(device)\n", + "\n", + "\touts = model(mels)\n", + "\n", + "\tloss = criterion(outs, labels)\n", + "\n", + "\t# Get the speaker id with highest probability.\n", + "\tpreds = outs.argmax(1)\n", + "\t# Compute accuracy.\n", + "\taccuracy = torch.mean((preds == labels).float())\n", + "\n", + "\treturn loss, accuracy" + ] + }, + { + "cell_type": "markdown", + "id": "026f8604", + "metadata": { + "id": "cwM_xyOtNCI2", + "papermill": { + "duration": 0.006378, + "end_time": "2022-11-15T11:08:06.591607", + "exception": false, + "start_time": "2022-11-15T11:08:06.585229", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Validate\n", + "- Calculate accuracy of the validation set." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dbce4a58", + "metadata": { + "execution": { + "iopub.execute_input": "2022-11-15T11:08:06.606709Z", + "iopub.status.busy": "2022-11-15T11:08:06.605691Z", + "iopub.status.idle": "2022-11-15T11:08:06.613733Z", + "shell.execute_reply": "2022-11-15T11:08:06.612810Z" + }, + "id": "YAiv6kpdJRTJ", + "papermill": { + "duration": 0.017695, + "end_time": "2022-11-15T11:08:06.615838", + "exception": false, + "start_time": "2022-11-15T11:08:06.598143", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "import torch\n", + "\n", + "\n", + "def valid(dataloader, model, criterion, device): \n", + "\t\"\"\"Validate on validation set.\"\"\"\n", + "\n", + "\tmodel.eval()\n", + "\trunning_loss = 0.0\n", + "\trunning_accuracy = 0.0\n", + "\tpbar = tqdm(total=len(dataloader.dataset), ncols=0, desc=\"Valid\", unit=\" uttr\")\n", + "\n", + "\tfor i, batch in enumerate(dataloader):\n", + "\t\twith torch.no_grad():\n", + "\t\t\tloss, accuracy = model_fn(batch, model, criterion, device)\n", + "\t\t\trunning_loss += loss.item()\n", + "\t\t\trunning_accuracy += accuracy.item()\n", + "\n", + "\t\tpbar.update(dataloader.batch_size)\n", + "\t\tpbar.set_postfix(\n", + "\t\t\tloss=f\"{running_loss / (i+1):.2f}\",\n", + "\t\t\taccuracy=f\"{running_accuracy / (i+1):.2f}\",\n", + "\t\t)\n", + "\n", + "\tpbar.close()\n", + "\tmodel.train()\n", + "\n", + "\treturn running_accuracy / len(dataloader)" + ] + }, + { + "cell_type": "markdown", + "id": "5627abdf", + "metadata": { + "id": "g6ne9G-eNEdG", + "papermill": { + "duration": 0.006224, + "end_time": "2022-11-15T11:08:06.628864", + "exception": false, + "start_time": "2022-11-15T11:08:06.622640", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Main function" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5ec2668a", + "metadata": { + "execution": { + "iopub.execute_input": "2022-11-15T11:08:06.643526Z", + "iopub.status.busy": "2022-11-15T11:08:06.643199Z", + "iopub.status.idle": "2022-11-15T16:10:20.750920Z", + "shell.execute_reply": "2022-11-15T16:10:20.749671Z" + }, + "id": "Usv9s-CuJSG7", + "outputId": "0e4eb5c2-901f-419e-b917-61860e81c8ae", + "papermill": { + "duration": 18134.119889, + "end_time": "2022-11-15T16:10:20.755235", + "exception": false, + "start_time": "2022-11-15T11:08:06.635346", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Info]: Use cuda now!\n", + "max len: 91\n", + "min_len: 7193\n", + "average_len: 658.0912187202202\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/torch/utils/data/dataloader.py:490: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " cpuset_checked))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Info]: Finish loading data!\n", + "[Info]: Finish creating model!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Train: 100% 2000/2000 [23:53<00:00, 1.40 step/s, accuracy=0.64, loss=1.29, step=2000]\n", + "Valid: 99% 5632/5667 [00:21<00:00, 260.56 uttr/s, accuracy=0.43, loss=2.43]\n", + "Train: 100% 2000/2000 [23:47<00:00, 1.40 step/s, accuracy=0.89, loss=0.44, step=4000]\n", + "Valid: 99% 5632/5667 [00:21<00:00, 266.37 uttr/s, accuracy=0.70, loss=1.24]\n", + "Train: 100% 2000/2000 [23:47<00:00, 1.40 step/s, accuracy=0.86, loss=0.48, step=6000]\n", + "Valid: 99% 5632/5667 [00:21<00:00, 264.52 uttr/s, accuracy=0.84, loss=0.67]\n", + "Train: 100% 2000/2000 [24:10<00:00, 1.38 step/s, accuracy=0.98, loss=0.15, step=8000]\n", + "Valid: 99% 5632/5667 [00:21<00:00, 267.65 uttr/s, accuracy=0.88, loss=0.50]\n", + "Train: 100% 2000/2000 [23:48<00:00, 1.40 step/s, accuracy=0.98, loss=0.10, step=1e+4]\n", + "Valid: 99% 5632/5667 [00:20<00:00, 268.74 uttr/s, accuracy=0.89, loss=0.45]\n", + "Train: 0% 0/2000 [00:00 best_accuracy:\n", + "\t\t\t\tbest_accuracy = valid_accuracy\n", + "\t\t\t\tbest_state_dict = model.state_dict()\n", + "\n", + "\t\t\tpbar = tqdm(total=valid_steps, ncols=0, desc=\"Train\", unit=\" step\")\n", + "\n", + "\t\t# Save the best model so far.\n", + "\t\tif (step + 1) % save_steps == 0 and best_state_dict is not None:\n", + "\t\t\ttorch.save(best_state_dict, save_path)\n", + "\t\t\tpbar.write(f\"Step {step + 1}, best model saved. (accuracy={best_accuracy:.4f})\")\n", + "\n", + "\tpbar.close()\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + "\tmain(**parse_args())" + ] + }, + { + "cell_type": "markdown", + "id": "a0b24dca", + "metadata": { + "id": "NLatBYAhNNMx", + "papermill": { + "duration": 2.958346, + "end_time": "2022-11-15T16:10:26.609401", + "exception": false, + "start_time": "2022-11-15T16:10:23.651055", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Inference\n", + "\n", + "## Dataset of inference" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7baf48c5", + "metadata": { + "execution": { + "iopub.execute_input": "2022-11-15T16:10:32.654050Z", + "iopub.status.busy": "2022-11-15T16:10:32.653620Z", + "iopub.status.idle": "2022-11-15T16:10:32.663553Z", + "shell.execute_reply": "2022-11-15T16:10:32.662552Z" + }, + "id": "efS4pCmAJXJH", + "papermill": { + "duration": 2.943961, + "end_time": "2022-11-15T16:10:32.665669", + "exception": false, + "start_time": "2022-11-15T16:10:29.721708", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import torch\n", + "from pathlib import Path\n", + "from torch.utils.data import Dataset\n", + "\n", + "\n", + "class InferenceDataset(Dataset):\n", + "\tdef __init__(self, data_dir):\n", + "\t\ttestdata_path = Path(data_dir) / \"testdata.json\"\n", + "\t\tmetadata = json.load(testdata_path.open())\n", + "\t\tself.data_dir = data_dir\n", + "\t\tself.data = metadata[\"utterances\"]\n", + "\n", + "\tdef __len__(self):\n", + "\t\treturn len(self.data)\n", + "\n", + "\tdef __getitem__(self, index):\n", + "\t\tutterance = self.data[index]\n", + "\t\tfeat_path = utterance[\"feature_path\"]\n", + "\t\tmel = torch.load(os.path.join(self.data_dir, feat_path))\n", + "\n", + "\t\treturn feat_path, mel\n", + "\n", + "\n", + "def inference_collate_batch(batch):\n", + "\t\"\"\"Collate a batch of data.\"\"\"\n", + "\tfeat_paths, mels = zip(*batch)\n", + "\n", + "\treturn feat_paths, torch.stack(mels)" + ] + }, + { + "cell_type": "markdown", + "id": "a83ef398", + "metadata": { + "id": "tl0WnYwxNK_S", + "papermill": { + "duration": 3.126154, + "end_time": "2022-11-15T16:10:38.655472", + "exception": false, + "start_time": "2022-11-15T16:10:35.529318", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Main funcrion of Inference" + ] + }, + { + "cell_type": "raw", + "id": "6a857b1f", + "metadata": { + "papermill": { + "duration": 2.873059, + "end_time": "2022-11-15T16:10:44.875288", + "exception": false, + "start_time": "2022-11-15T16:10:42.002229", + "status": "completed" + }, + "tags": [] + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "cf2bff67", + "metadata": { + "execution": { + "iopub.execute_input": "2022-11-15T16:10:50.922588Z", + "iopub.status.busy": "2022-11-15T16:10:50.922197Z", + "iopub.status.idle": "2022-11-15T16:14:23.972994Z", + "shell.execute_reply": "2022-11-15T16:14:23.971874Z" + }, + "id": "i8SAbuXEJb2A", + "papermill": { + "duration": 216.162513, + "end_time": "2022-11-15T16:14:23.975237", + "exception": false, + "start_time": "2022-11-15T16:10:47.812724", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Info]: Use cuda now!\n", + "[Info]: Finish loading data!\n", + "[Info]: Finish creating model!\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "491dab589add4cc79d6c9b2121f26e38", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/8000 [00:00