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      2022 ML/04 Sequence as input/Machine Learning HW4.pdf
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      2022 ML/04 Sequence as input/hw04.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "C_jdZ5vHJ4A9"
},
"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://github.com/MachineLearningHW/ML_HW4_Dataset)\n",
"\n",
"# Download dataset\n",
"- Data is [here](https://github.com/MachineLearningHW/ML_HW4_Dataset)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LhLNWB-AK2Z5"
},
"outputs": [],
"source": [
"\"\"\"\n",
"If the links below become inaccessible, please connect TAs.\n",
"\"\"\"\n",
"\n",
"!wget https://github.com/MachineLearningHW/ML_HW4_Dataset/raw/0.0.1/Dataset.tar.gz.partaa\n",
"!wget https://github.com/MachineLearningHW/ML_HW4_Dataset/raw/0.0.1/Dataset.tar.gz.partab\n",
"!wget https://github.com/MachineLearningHW/ML_HW4_Dataset/raw/0.0.1/Dataset.tar.gz.partac\n",
"!wget https://github.com/MachineLearningHW/ML_HW4_Dataset/raw/0.0.1/Dataset.tar.gz.partad\n",
"\n",
"!cat Dataset.tar.gz.parta* > Dataset.tar.gz\n",
"\n",
"!tar zxvf Dataset.tar.gz"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ENWVAUDVJtVY"
},
"source": [
"## Fix Random Seed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "E6burzCXIyuA"
},
"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",
"metadata": {
"id": "k7dVbxW2LASN"
},
"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": null,
"metadata": {
"id": "KpuGxl4CI2pr"
},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"import torch\n",
"import random\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=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\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",
"metadata": {
"id": "668hverTMlGN"
},
"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": null,
"metadata": {
"id": "B7c2gZYoJDRS"
},
"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",
"metadata": {
"id": "5FOSZYxrMqhc"
},
"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": null,
"metadata": {
"id": "iXZ5B0EKJGs8"
},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"\n",
"class Classifier(nn.Module):\n",
"\tdef __init__(self, d_model=80, n_spks=600, dropout=0.1):\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=256, nhead=2\n",
"\t\t)\n",
"\t\t# self.encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=2)\n",
"\n",
"\t\t# Project the the dimension of features from d_model into speaker nums.\n",
"\t\tself.pred_layer = nn.Sequential(\n",
"\t\t\tnn.Linear(d_model, d_model),\n",
"\t\t\tnn.ReLU(),\n",
"\t\t\tnn.Linear(d_model, n_spks),\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\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_layer(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 = out.mean(dim=1)\n",
"\n",
"\t\t# out: (batch, n_spks)\n",
"\t\tout = self.pred_layer(stats)\n",
"\t\treturn out"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "W7yX8JinM5Ly"
},
"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": null,
"metadata": {
"id": "ykt0N1nVJJi2"
},
"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",
"metadata": {
"id": "-LN2XkteM_uH"
},
"source": [
"# Model Function\n",
"- Model forward function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "N-rr8529JMz0"
},
"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",
"metadata": {
"id": "cwM_xyOtNCI2"
},
"source": [
"# Validate\n",
"- Calculate accuracy of the validation set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YAiv6kpdJRTJ"
},
"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",
"metadata": {
"id": "g6ne9G-eNEdG"
},
"source": [
"# Main function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Usv9s-CuJSG7"
},
"outputs": [],
"source": [
"from tqdm import tqdm\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.optim import AdamW\n",
"from torch.utils.data import DataLoader, random_split\n",
"\n",
"\n",
"def parse_args():\n",
"\t\"\"\"arguments\"\"\"\n",
"\tconfig = {\n",
"\t\t\"data_dir\": \"./Dataset\",\n",
"\t\t\"save_path\": \"model.ckpt\",\n",
"\t\t\"batch_size\": 32,\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\": 70000,\n",
"\t}\n",
"\n",
"\treturn config\n",
"\n",
"\n",
"def 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",
"\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",
"\n",
"if __name__ == \"__main__\":\n",
"\tmain(**parse_args())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NLatBYAhNNMx"
},
"source": [
"# Inference\n",
"\n",
"## Dataset of inference"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "efS4pCmAJXJH"
},
"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",
"metadata": {
"id": "tl0WnYwxNK_S"
},
"source": [
"## Main funcrion of Inference"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "i8SAbuXEJb2A"
},
"outputs": [],
"source": [
"import json\n",
"import csv\n",
"from pathlib import Path\n",
"from tqdm.notebook import tqdm\n",
"\n",
"import torch\n",
"from torch.utils.data import DataLoader\n",
"\n",
"def parse_args():\n",
"\t\"\"\"arguments\"\"\"\n",
"\tconfig = {\n",
"\t\t\"data_dir\": \"./Dataset\",\n",
"\t\t\"model_path\": \"./model.ckpt\",\n",
"\t\t\"output_path\": \"./output.csv\",\n",
"\t}\n",
"\n",
"\treturn config\n",
"\n",
"\n",
"def 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",
"\n",
"if __name__ == \"__main__\":\n",
"\tmain(**parse_args())"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "hw04.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

+ 3
- 0
2022 ML/HW DATA.txt View File

@@ -0,0 +1,3 @@
数据集太大了，UP放在了在线网盘中。

为方便所有网课资料与优质电子书籍的实时更新维护，创建了一个在线实时网盘文件夹，放在公众号【啥都会一点的研究生】，本节课对应序号【05】。

+ 10
- 1
README.md View File

@@ -7,10 +7,17 @@


## 重磅须知 ## 重磅须知
``` ```
（重磅须知，统一说明）为方便所有网课资料与优质电子书籍的实时更新维护，创建了一个在线实时网盘文件夹，放在公众号【啥都会一点的研究生】，本节课对应序号【05】。

UP将2021&2022所有作业的数据资料整理打包好了，由于文件太大，已同步放在上述所提在线网盘。

在线网盘能满足该课程所需资料的全部需求，链接挂掉也会及时更新，祝大家学习顺利。

2022仅在2021基础上进行小补充，2021内容变成了前置知识，UP会在视频标题打上2022的标签； 2022仅在2021基础上进行小补充，2021内容变成了前置知识，UP会在视频标题打上2022的标签；


ppt/pdf支持直链下载。 ppt/pdf支持直链下载。
``` ```
[![BILIBILI](https://raw.githubusercontent.com/Fafa-DL/readme-data/main/gzh.png)](https://space.bilibili.com/46880349)


## 更新日志 ## 更新日志


@@ -33,6 +40,7 @@ ppt/pdf支持直链下载。
|2022/02/21|更新Lecture 1:Introductionof Deep Learning补充内容，Github排版大更新| |2022/02/21|更新Lecture 1:Introductionof Deep Learning补充内容，Github排版大更新|
|2022/02/25|更新Lecture 2:What to do if my network fails to train补充内容与HW2| |2022/02/25|更新Lecture 2:What to do if my network fails to train补充内容与HW2|
|2022/03/05|更新Lecture 3:Images input，HW3| |2022/03/05|更新Lecture 3:Images input，HW3|
|2022/03/05|更新Lecture 4 Sequence as input，HW4<br>UP将2021&2022所有作业的数据资料整理打包好放在公众号【啥都会一点的研究生】|


**** ****
@@ -58,6 +66,7 @@ ppt/pdf支持直链下载。
|---|---|---|---|---| |---|---|---|---|---|
|Lecture 1|[（上）机器学习基本概念简介](https://www.bilibili.com/video/BV1Wv411h7kN?p=3)<br/>[（下）机器学习基本概念简介](https://www.bilibili.com/video/BV1Wv411h7kN?p=4)|Video:<br/>[2022-机器学习相关规定](https://www.bilibili.com/video/BV1Wv411h7kN?p=1)<br/>[2022-Colab教学](https://www.bilibili.com/video/BV1Wv411h7kN?p=5)<br/>[2022-Pytorch Tutorial 1](https://www.bilibili.com/video/BV1Wv411h7kN?p=6)<br/>[2022-Pytorch Tutorial 2](https://www.bilibili.com/video/BV1Wv411h7kN?p=7)<br/><br/>PDF:<br/>[Rules](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/rule%20(v2).pdf)<br/>[Chinese class course intro](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/introduction%20(v2).pdf)<br/>[Pytorch Tutorial 1](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/Pytorch%20Tutorial%201.pdf)<br/>[Pytorch Tutorial 2](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/Pytorch%20Tutorial%202.pdf)<br/>[Colab Tutorial](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/Colab%20Tutorial%202022.pdf)<br/>[Environment Setup](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/EnvironmentSetup.pdf)|[深度学习简介](https://www.bilibili.com/video/BV1Wv411h7kN?p=13)<br/>[反向传播](https://www.bilibili.com/video/BV1Wv411h7kN?p=14)<br/>[预测-宝可梦](https://www.bilibili.com/video/BV1Wv411h7kN?p=15)<br/>[分类-宝可梦](https://www.bilibili.com/video/BV1Wv411h7kN?p=16)<br/>[逻辑回归](https://www.bilibili.com/video/BV1Wv411h7kN?p=17)|[Video](https://www.bilibili.com/video/BV1Wv411h7kN?p=11)<br/>[Slide](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/HW01.pdf)<br/>[Code](https://colab.research.google.com/drive/1FTcG6CE-HILnvFztEFKdauMlPKfQvm5Z#scrollTo=YdttVRkAfu2t)<br/>[Submission](https://www.kaggle.com/t/a3ebd5b5542f0f55e828d4f00de8e59a)| |Lecture 1|[（上）机器学习基本概念简介](https://www.bilibili.com/video/BV1Wv411h7kN?p=3)<br/>[（下）机器学习基本概念简介](https://www.bilibili.com/video/BV1Wv411h7kN?p=4)|Video:<br/>[2022-机器学习相关规定](https://www.bilibili.com/video/BV1Wv411h7kN?p=1)<br/>[2022-Colab教学](https://www.bilibili.com/video/BV1Wv411h7kN?p=5)<br/>[2022-Pytorch Tutorial 1](https://www.bilibili.com/video/BV1Wv411h7kN?p=6)<br/>[2022-Pytorch Tutorial 2](https://www.bilibili.com/video/BV1Wv411h7kN?p=7)<br/><br/>PDF:<br/>[Rules](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/rule%20(v2).pdf)<br/>[Chinese class course intro](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/introduction%20(v2).pdf)<br/>[Pytorch Tutorial 1](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/Pytorch%20Tutorial%201.pdf)<br/>[Pytorch Tutorial 2](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/Pytorch%20Tutorial%202.pdf)<br/>[Colab Tutorial](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/Colab%20Tutorial%202022.pdf)<br/>[Environment Setup](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/EnvironmentSetup.pdf)|[深度学习简介](https://www.bilibili.com/video/BV1Wv411h7kN?p=13)<br/>[反向传播](https://www.bilibili.com/video/BV1Wv411h7kN?p=14)<br/>[预测-宝可梦](https://www.bilibili.com/video/BV1Wv411h7kN?p=15)<br/>[分类-宝可梦](https://www.bilibili.com/video/BV1Wv411h7kN?p=16)<br/>[逻辑回归](https://www.bilibili.com/video/BV1Wv411h7kN?p=17)|[Video](https://www.bilibili.com/video/BV1Wv411h7kN?p=11)<br/>[Slide](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/HW01.pdf)<br/>[Code](https://colab.research.google.com/drive/1FTcG6CE-HILnvFztEFKdauMlPKfQvm5Z#scrollTo=YdttVRkAfu2t)<br/>[Submission](https://www.kaggle.com/t/a3ebd5b5542f0f55e828d4f00de8e59a)|
|Lecture 2|[（一）局部最小值 (local minima) 与鞍点 (saddle point)](https://www.bilibili.com/video/BV1Wv411h7kN?p=19)<br/>[（二）批次 (batch) 与动量 (momentum)](https://www.bilibili.com/video/BV1Wv411h7kN?p=20)<br/>[（三）自动调整学习率 (Learning Rate)](https://www.bilibili.com/video/BV1Wv411h7kN?p=21)<br/>[（四）损失函数 (Loss) 也可能有影响](https://www.bilibili.com/video/BV1Wv411h7kN?p=22)|Video:<br/>[2022-再探宝可梦、数码宝贝分类器 — 浅谈机器学习原理](https://www.bilibili.com/video/BV1Wv411h7kN?p=23)<br/><br/>PDF:<br/>[Theory](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/theory%20(v7).pdf)|[Gradient Descent (Demo by AOE)](https://www.bilibili.com/video/BV1Wv411h7kN?p=24)<br/>[ Beyond Adam (part 1)](https://www.bilibili.com/video/BV1Wv411h7kN?p=26)<br/>[ Beyond Adam (part 2)](https://www.bilibili.com/video/BV1Wv411h7kN?p=27)|[Video](https://www.bilibili.com/video/BV1Wv411h7kN?p=28)<br/>[Slide](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/hw2_slides%202022.pdf)<br/>[Code](https://colab.research.google.com/drive/1hmTFJ8hdcnqRz_0oJSXjTGhZLVU-bS1a?usp=sharing)<br/>[Submission](https://www.kaggle.com/c/ml2022spring-hw2)| |Lecture 2|[（一）局部最小值 (local minima) 与鞍点 (saddle point)](https://www.bilibili.com/video/BV1Wv411h7kN?p=19)<br/>[（二）批次 (batch) 与动量 (momentum)](https://www.bilibili.com/video/BV1Wv411h7kN?p=20)<br/>[（三）自动调整学习率 (Learning Rate)](https://www.bilibili.com/video/BV1Wv411h7kN?p=21)<br/>[（四）损失函数 (Loss) 也可能有影响](https://www.bilibili.com/video/BV1Wv411h7kN?p=22)|Video:<br/>[2022-再探宝可梦、数码宝贝分类器 — 浅谈机器学习原理](https://www.bilibili.com/video/BV1Wv411h7kN?p=23)<br/><br/>PDF:<br/>[Theory](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/theory%20(v7).pdf)|[Gradient Descent (Demo by AOE)](https://www.bilibili.com/video/BV1Wv411h7kN?p=24)<br/>[ Beyond Adam (part 1)](https://www.bilibili.com/video/BV1Wv411h7kN?p=26)<br/>[ Beyond Adam (part 2)](https://www.bilibili.com/video/BV1Wv411h7kN?p=27)|[Video](https://www.bilibili.com/video/BV1Wv411h7kN?p=28)<br/>[Slide](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/hw2_slides%202022.pdf)<br/>[Code](https://colab.research.google.com/drive/1hmTFJ8hdcnqRz_0oJSXjTGhZLVU-bS1a?usp=sharing)<br/>[Submission](https://www.kaggle.com/c/ml2022spring-hw2)|
|Lecture 3|[卷积神经网络CNN](https://www.bilibili.com/video/BV1Wv411h7kN?p=31)|Video:<br/>[为什么用了验证集还是过拟合](https://www.bilibili.com/video/BV1Wv411h7kN?p=32)<br/>[鱼与熊掌可以兼得的机器学习](https://www.bilibili.com/video/BV1Wv411h7kN?p=33)<br/><br/>PDF:<br/>[Validation](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/validation.pdf)<br/>[Why Deep](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/whydeep%20(v3).pdf)|[RNN(part 1)](https://www.bilibili.com/video/BV1Wv411h7kN?p=34)<br/>[RNN(part 2)](https://www.bilibili.com/video/BV1Wv411h7kN?p=35)<br/>[GNN(part 1)](https://www.bilibili.com/video/BV1Wv411h7kN?p=36)<br/>[GNN(part 2)](https://www.bilibili.com/video/BV1Wv411h7kN?p=37)|[Video](https://www.bilibili.com/video/BV1Wv411h7kN?p=38)<br/>[Slide](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/Machine%20Learning%20HW3%20-%20Image%20Classification.pdf)<br/>[Code](https://colab.research.google.com/drive/15hMu9YiYjE_6HY99UXon2vKGk2KwugWu)<br/>[Submission](https://www.kaggle.com/c/ml2022spring-hw3b)|
|Lecture 3|[卷积神经网络CNN](https://www.bilibili.com/video/BV1Wv411h7kN?p=31)|Video:<br/>[为什么用了验证集还是过拟合](https://www.bilibili.com/video/BV1Wv411h7kN?p=32)<br/>[鱼与熊掌可以兼得的机器学习](https://www.bilibili.com/video/BV1Wv411h7kN?p=33)<br/><br/>PDF:<br/>[Validation](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/validation.pdf)<br/>[Why Deep](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/whydeep%20(v3).pdf)|[Spatial Transformer Layer](https://www.bilibili.com/video/BV1Wv411h7kN?p=34)|[Video](https://www.bilibili.com/video/BV1Wv411h7kN?p=35)<br/>[Slide](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/Machine%20Learning%20HW3%20-%20Image%20Classification.pdf)<br/>[Code](https://colab.research.google.com/drive/15hMu9YiYjE_6HY99UXon2vKGk2KwugWu)<br/>[Submission](https://www.kaggle.com/c/ml2022spring-hw3b)|
|Lecture 4|[自注意力机制(Self-attention)（上）](https://www.bilibili.com/video/BV1Wv411h7kN?p=41)<br/>[自注意力机制(Self-attention)（下）](https://www.bilibili.com/video/BV1Wv411h7kN?p=42)|Video:<br/>[None]<br/><br/>PDF:<br/>[None]|[RNN(part 1)](https://www.bilibili.com/video/BV1Wv411h7kN?p=40)<br/>[RNN(part 2)](https://www.bilibili.com/video/BV1Wv411h7kN?p=41)<br/>[GNN(part 1)](https://www.bilibili.com/video/BV1Wv411h7kN?p=42)<br/>[GNN(part 2)](https://www.bilibili.com/video/BV1Wv411h7kN?p=43)|[Video](https://www.bilibili.com/video/BV1Wv411h7kN?p=45)<br/>[Slide](https://speech.ee.ntu.edu.tw/~hylee/ml/ml2022-course-data/Machine%20Learning%20HW4.pdf)<br/>[Code](https://colab.research.google.com/drive/1gC2Gojv9ov9MUQ1a1WDpVBD6FOcLZsog?usp=sharing)<br/>[Submission](https://www.kaggle.com/c/ml2022spring-hw4)|


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