From 657629a18cf8a6f35c5a8b161ee46be7d89ccac1 Mon Sep 17 00:00:00 2001
From: Fafa-DL <516451964@qq.com>
Date: Fri, 26 Mar 2021 08:39:57 +0800
Subject: [PATCH] Update_Transformer

---
 05 Transformer/代码/HW04.pdf         |  Bin 0 -> 770138 bytes
 05 Transformer/代码/ML2021_HW4.ipynb | 5783 ++++++++++++++++++++++++
 05 Transformer/范例/HW04.ipynb       | 5783 ++++++++++++++++++++++++
 05 Transformer/范例/HW04.pdf         |  Bin 0 -> 770138 bytes
 4 files changed, 11566 insertions(+)
 create mode 100644 05 Transformer/代码/HW04.pdf
 create mode 100644 05 Transformer/代码/ML2021_HW4.ipynb
 create mode 100644 05 Transformer/范例/HW04.ipynb
 create mode 100644 05 Transformer/范例/HW04.pdf

diff --git a/05 Transformer/代码/HW04.pdf b/05 Transformer/代码/HW04.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..93fce15986473a40580698f6a328379d96487e19
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diff --git a/05 Transformer/代码/ML2021_HW4.ipynb b/05 Transformer/代码/ML2021_HW4.ipynb
new file mode 100644
index 0000000..579781b
--- /dev/null
+++ b/05 Transformer/代码/ML2021_HW4.ipynb	
@@ -0,0 +1,5783 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "ML2021_HW4.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zC5KwRyl6Flp"
+      },
+      "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",
+        "  - Hard: Construct [conformer](https://arxiv.org/abs/2005.08100) which is a variety of transformer. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TPDoreyypeJE"
+      },
+      "source": [
+        "# Download dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QvpaILXnJIcw",
+        "outputId": "c2548949-1161-42c2-8318-076b987a1866"
+      },
+      "source": [
+        "# From Google drive\n",
+        "# !gdown --id '1T0RPnu-Sg5eIPwQPfYysipfcz81MnsYe' --output Dataset.zip\n",
+        "# !unzip Dataset.zip\n",
+        "\n",
+        "# From Dropbox\n",
+        "# If Dropbox is not work. Please use google drive.\n",
+        "!wget https://www.dropbox.com/s/vw324newiku0sz0/Dataset.tar.gz.aa?dl=0\n",
+        "!wget https://www.dropbox.com/s/z840g69e7lnkayo/Dataset.tar.gz.ab?dl=0\n",
+        "!wget https://www.dropbox.com/s/hl081e1ggonio81/Dataset.tar.gz.ac?dl=0\n",
+        "!wget https://www.dropbox.com/s/fh3zd8ow668c4th/Dataset.tar.gz.ad?dl=0\n",
+        "!wget https://www.dropbox.com/s/ydzygoy2pv6gw9d/Dataset.tar.gz.ae?dl=0\n",
+        "!cat Dataset.tar.gz.* | tar zxvf -"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\u001b[1;30;43m串流輸出內容已截斷至最後 5000 行。\u001b[0m\n",
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+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v1gYr_aoNDue"
+      },
+      "source": [
+        "# Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mz_NpuAipk3h"
+      },
+      "source": [
+        "## Dataset\n",
+        "- Original dataset is [Voxceleb1](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/).\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 Voxceleb1.\n",
+        "- We randomly select 600 speakers from Voxceleb1.\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",
+      "metadata": {
+        "id": "cd7hoGhYtbXQ"
+      },
+      "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",
+        "  def __init__(self, data_dir, segment_len=128):\n",
+        "    self.data_dir = data_dir\n",
+        "    self.segment_len = segment_len\n",
+        "\n",
+        "    # Load the mapping from speaker neme to their corresponding id. \n",
+        "    mapping_path = Path(data_dir) / \"mapping.json\"\n",
+        "    mapping = json.load(mapping_path.open())\n",
+        "    self.speaker2id = mapping[\"speaker2id\"]\n",
+        "\n",
+        "    # Load metadata of training data.\n",
+        "    metadata_path = Path(data_dir) / \"metadata.json\"\n",
+        "    metadata = json.load(open(metadata_path))[\"speakers\"]\n",
+        "\n",
+        "    # Get the total number of speaker.\n",
+        "    self.speaker_num = len(metadata.keys())\n",
+        "    self.data = []\n",
+        "    for speaker in metadata.keys():\n",
+        "      for utterances in metadata[speaker]:\n",
+        "        self.data.append([utterances[\"feature_path\"], self.speaker2id[speaker]])\n",
+        "\n",
+        "  def __len__(self):\n",
+        "    return len(self.data)\n",
+        "\n",
+        "  def __getitem__(self, index):\n",
+        "    feat_path, speaker = self.data[index]\n",
+        "    # Load preprocessed mel-spectrogram.\n",
+        "    mel = torch.load(os.path.join(self.data_dir, feat_path))\n",
+        "\n",
+        "    # Segmemt mel-spectrogram into \"segment_len\" frames.\n",
+        "    if len(mel) > self.segment_len:\n",
+        "      # Randomly get the starting point of the segment.\n",
+        "      start = random.randint(0, len(mel) - self.segment_len)\n",
+        "      # Get a segment with \"segment_len\" frames.\n",
+        "      mel = torch.FloatTensor(mel[start:start+self.segment_len])\n",
+        "    else:\n",
+        "      mel = torch.FloatTensor(mel)\n",
+        "    # Turn the speaker id into long for computing loss later.\n",
+        "    speaker = torch.FloatTensor([speaker]).long()\n",
+        "    return mel, speaker\n",
+        "\n",
+        "  def get_speaker_number(self):\n",
+        "    return self.speaker_num\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mqJxjoi_NGnB"
+      },
+      "source": [
+        "## Dataloader\n",
+        "- Split dataset into training dataset(90%) and validation dataset(10%).\n",
+        "- Create dataloader to iterate the data.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zuT1AuFENI8t"
+      },
+      "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",
+        "  # Process features within a batch.\n",
+        "  \"\"\"Collate a batch of data.\"\"\"\n",
+        "  mel, speaker = zip(*batch)\n",
+        "  # 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",
+        "  mel = pad_sequence(mel, batch_first=True, padding_value=-20)    # pad log 10^(-20) which is very small value.\n",
+        "  # mel: (batch size, length, 40)\n",
+        "  return mel, torch.FloatTensor(speaker).long()\n",
+        "\n",
+        "\n",
+        "def get_dataloader(data_dir, batch_size, n_workers):\n",
+        "  \"\"\"Generate dataloader\"\"\"\n",
+        "  dataset = myDataset(data_dir)\n",
+        "  speaker_num = dataset.get_speaker_number()\n",
+        "  # Split dataset into training dataset and validation dataset\n",
+        "  trainlen = int(0.9 * len(dataset))\n",
+        "  lengths = [trainlen, len(dataset) - trainlen]\n",
+        "  trainset, validset = random_split(dataset, lengths)\n",
+        "\n",
+        "  train_loader = DataLoader(\n",
+        "    trainset,\n",
+        "    batch_size=batch_size,\n",
+        "    shuffle=True,\n",
+        "    drop_last=True,\n",
+        "    num_workers=n_workers,\n",
+        "    pin_memory=True,\n",
+        "    collate_fn=collate_batch,\n",
+        "  )\n",
+        "  valid_loader = DataLoader(\n",
+        "    validset,\n",
+        "    batch_size=batch_size,\n",
+        "    num_workers=n_workers,\n",
+        "    drop_last=True,\n",
+        "    pin_memory=True,\n",
+        "    collate_fn=collate_batch,\n",
+        "  )\n",
+        "\n",
+        "  return train_loader, valid_loader, speaker_num\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X0x6eXiHpr4R"
+      },
+      "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",
+      "metadata": {
+        "id": "SHX4eVj4tjtd"
+      },
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "\n",
+        "\n",
+        "class Classifier(nn.Module):\n",
+        "  def __init__(self, d_model=80, n_spks=600, dropout=0.1):\n",
+        "    super().__init__()\n",
+        "    # Project the dimension of features from that of input into d_model.\n",
+        "    self.prenet = nn.Linear(40, d_model)\n",
+        "    # TODO:\n",
+        "    #   Change Transformer to Conformer.\n",
+        "    #   https://arxiv.org/abs/2005.08100\n",
+        "    self.encoder_layer = nn.TransformerEncoderLayer(\n",
+        "      d_model=d_model, dim_feedforward=256, nhead=2\n",
+        "    )\n",
+        "    # self.encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=2)\n",
+        "\n",
+        "    # Project the the dimension of features from d_model into speaker nums.\n",
+        "    self.pred_layer = nn.Sequential(\n",
+        "      nn.Linear(d_model, d_model),\n",
+        "      nn.ReLU(),\n",
+        "      nn.Linear(d_model, n_spks),\n",
+        "    )\n",
+        "\n",
+        "  def forward(self, mels):\n",
+        "    \"\"\"\n",
+        "    args:\n",
+        "      mels: (batch size, length, 40)\n",
+        "    return:\n",
+        "      out: (batch size, n_spks)\n",
+        "    \"\"\"\n",
+        "    # out: (batch size, length, d_model)\n",
+        "    out = self.prenet(mels)\n",
+        "    # out: (length, batch size, d_model)\n",
+        "    out = out.permute(1, 0, 2)\n",
+        "    # The encoder layer expect features in the shape of (length, batch size, d_model).\n",
+        "    out = self.encoder_layer(out)\n",
+        "    # out: (batch size, length, d_model)\n",
+        "    out = out.transpose(0, 1)\n",
+        "    # mean pooling\n",
+        "    stats = out.mean(dim=1)\n",
+        "\n",
+        "    # out: (batch, n_spks)\n",
+        "    out = self.pred_layer(stats)\n",
+        "    return out\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-__DolPGpvDZ"
+      },
+      "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",
+      "metadata": {
+        "id": "K-0816BntqT9"
+      },
+      "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",
+        "  optimizer: Optimizer,\n",
+        "  num_warmup_steps: int,\n",
+        "  num_training_steps: int,\n",
+        "  num_cycles: float = 0.5,\n",
+        "  last_epoch: int = -1,\n",
+        "):\n",
+        "  \"\"\"\n",
+        "  Create a schedule with a learning rate that decreases following the values of the cosine function between the\n",
+        "  initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the\n",
+        "  initial lr set in the optimizer.\n",
+        "\n",
+        "  Args:\n",
+        "    optimizer (:class:`~torch.optim.Optimizer`):\n",
+        "      The optimizer for which to schedule the learning rate.\n",
+        "    num_warmup_steps (:obj:`int`):\n",
+        "      The number of steps for the warmup phase.\n",
+        "    num_training_steps (:obj:`int`):\n",
+        "      The total number of training steps.\n",
+        "    num_cycles (:obj:`float`, `optional`, defaults to 0.5):\n",
+        "      The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0\n",
+        "      following a half-cosine).\n",
+        "    last_epoch (:obj:`int`, `optional`, defaults to -1):\n",
+        "      The index of the last epoch when resuming training.\n",
+        "\n",
+        "  Return:\n",
+        "    :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  def lr_lambda(current_step):\n",
+        "    # Warmup\n",
+        "    if current_step < num_warmup_steps:\n",
+        "      return float(current_step) / float(max(1, num_warmup_steps))\n",
+        "    # decadence\n",
+        "    progress = float(current_step - num_warmup_steps) / float(\n",
+        "      max(1, num_training_steps - num_warmup_steps)\n",
+        "    )\n",
+        "    return max(\n",
+        "      0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))\n",
+        "    )\n",
+        "\n",
+        "  return LambdaLR(optimizer, lr_lambda, last_epoch)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IP03FFo9K8DS"
+      },
+      "source": [
+        "# Model Function\n",
+        "- Model forward function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fohaLEFJK9-t"
+      },
+      "source": [
+        "import torch\n",
+        "\n",
+        "\n",
+        "def model_fn(batch, model, criterion, device):\n",
+        "  \"\"\"Forward a batch through the model.\"\"\"\n",
+        "\n",
+        "  mels, labels = batch\n",
+        "  mels = mels.to(device)\n",
+        "  labels = labels.to(device)\n",
+        "\n",
+        "  outs = model(mels)\n",
+        "\n",
+        "  loss = criterion(outs, labels)\n",
+        "\n",
+        "  # Get the speaker id with highest probability.\n",
+        "  preds = outs.argmax(1)\n",
+        "  # Compute accuracy.\n",
+        "  accuracy = torch.mean((preds == labels).float())\n",
+        "\n",
+        "  return loss, accuracy\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F7cg-YrzLQcf"
+      },
+      "source": [
+        "# Validate\n",
+        "- Calculate accuracy of the validation set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mD-_p6nWLO2L"
+      },
+      "source": [
+        "from tqdm import tqdm\n",
+        "import torch\n",
+        "\n",
+        "\n",
+        "def valid(dataloader, model, criterion, device): \n",
+        "  \"\"\"Validate on validation set.\"\"\"\n",
+        "\n",
+        "  model.eval()\n",
+        "  running_loss = 0.0\n",
+        "  running_accuracy = 0.0\n",
+        "  pbar = tqdm(total=len(dataloader.dataset), ncols=0, desc=\"Valid\", unit=\" uttr\")\n",
+        "\n",
+        "  for i, batch in enumerate(dataloader):\n",
+        "    with torch.no_grad():\n",
+        "      loss, accuracy = model_fn(batch, model, criterion, device)\n",
+        "      running_loss += loss.item()\n",
+        "      running_accuracy += accuracy.item()\n",
+        "\n",
+        "    pbar.update(dataloader.batch_size)\n",
+        "    pbar.set_postfix(\n",
+        "      loss=f\"{running_loss / (i+1):.2f}\",\n",
+        "      accuracy=f\"{running_accuracy / (i+1):.2f}\",\n",
+        "    )\n",
+        "\n",
+        "  pbar.close()\n",
+        "  model.train()\n",
+        "\n",
+        "  return running_accuracy / len(dataloader)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "noHXyal5p1W5"
+      },
+      "source": [
+        "# Main function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "chRQE7oYtw62",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "45b0b120-3e86-4875-a8c0-2c4f0c980048"
+      },
+      "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",
+        "  \"\"\"arguments\"\"\"\n",
+        "  config = {\n",
+        "    \"data_dir\": \"./Dataset\",\n",
+        "    \"save_path\": \"model.ckpt\",\n",
+        "    \"batch_size\": 32,\n",
+        "    \"n_workers\": 8,\n",
+        "    \"valid_steps\": 2000,\n",
+        "    \"warmup_steps\": 1000,\n",
+        "    \"save_steps\": 10000,\n",
+        "    \"total_steps\": 70000,\n",
+        "  }\n",
+        "\n",
+        "  return config\n",
+        "\n",
+        "\n",
+        "def main(\n",
+        "  data_dir,\n",
+        "  save_path,\n",
+        "  batch_size,\n",
+        "  n_workers,\n",
+        "  valid_steps,\n",
+        "  warmup_steps,\n",
+        "  total_steps,\n",
+        "  save_steps,\n",
+        "):\n",
+        "  \"\"\"Main function.\"\"\"\n",
+        "  device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "  print(f\"[Info]: Use {device} now!\")\n",
+        "\n",
+        "  train_loader, valid_loader, speaker_num = get_dataloader(data_dir, batch_size, n_workers)\n",
+        "  train_iterator = iter(train_loader)\n",
+        "  print(f\"[Info]: Finish loading data!\",flush = True)\n",
+        "\n",
+        "  model = Classifier(n_spks=speaker_num).to(device)\n",
+        "  criterion = nn.CrossEntropyLoss()\n",
+        "  optimizer = AdamW(model.parameters(), lr=1e-3)\n",
+        "  scheduler = get_cosine_schedule_with_warmup(optimizer, warmup_steps, total_steps)\n",
+        "  print(f\"[Info]: Finish creating model!\",flush = True)\n",
+        "\n",
+        "  best_accuracy = -1.0\n",
+        "  best_state_dict = None\n",
+        "\n",
+        "  pbar = tqdm(total=valid_steps, ncols=0, desc=\"Train\", unit=\" step\")\n",
+        "\n",
+        "  for step in range(total_steps):\n",
+        "    # Get data\n",
+        "    try:\n",
+        "      batch = next(train_iterator)\n",
+        "    except StopIteration:\n",
+        "      train_iterator = iter(train_loader)\n",
+        "      batch = next(train_iterator)\n",
+        "\n",
+        "    loss, accuracy = model_fn(batch, model, criterion, device)\n",
+        "    batch_loss = loss.item()\n",
+        "    batch_accuracy = accuracy.item()\n",
+        "\n",
+        "    # Updata model\n",
+        "    loss.backward()\n",
+        "    optimizer.step()\n",
+        "    scheduler.step()\n",
+        "    optimizer.zero_grad()\n",
+        "    \n",
+        "    # Log\n",
+        "    pbar.update()\n",
+        "    pbar.set_postfix(\n",
+        "      loss=f\"{batch_loss:.2f}\",\n",
+        "      accuracy=f\"{batch_accuracy:.2f}\",\n",
+        "      step=step + 1,\n",
+        "    )\n",
+        "\n",
+        "    # Do validation\n",
+        "    if (step + 1) % valid_steps == 0:\n",
+        "      pbar.close()\n",
+        "\n",
+        "      valid_accuracy = valid(valid_loader, model, criterion, device)\n",
+        "\n",
+        "      # keep the best model\n",
+        "      if valid_accuracy > best_accuracy:\n",
+        "        best_accuracy = valid_accuracy\n",
+        "        best_state_dict = model.state_dict()\n",
+        "\n",
+        "      pbar = tqdm(total=valid_steps, ncols=0, desc=\"Train\", unit=\" step\")\n",
+        "\n",
+        "    # Save the best model so far.\n",
+        "    if (step + 1) % save_steps == 0 and best_state_dict is not None:\n",
+        "      torch.save(best_state_dict, save_path)\n",
+        "      pbar.write(f\"Step {step + 1}, best model saved. (accuracy={best_accuracy:.4f})\")\n",
+        "\n",
+        "  pbar.close()\n",
+        "\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "  main(**parse_args())\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[Info]: Use cuda now!\n",
+            "[Info]: Finish loading data!\n",
+            "[Info]: Finish creating model!\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Train: 100% 2000/2000 [03:34<00:00,  9.32 step/s, accuracy=0.31, loss=3.89, step=2000]\n",
+            "Valid: 100% 6944/6944 [00:23<00:00, 300.22 uttr/s, accuracy=0.18, loss=3.98]\n",
+            "Train: 100% 2000/2000 [03:04<00:00, 10.81 step/s, accuracy=0.19, loss=3.79, step=4000]\n",
+            "Valid: 100% 6944/6944 [00:24<00:00, 285.84 uttr/s, accuracy=0.29, loss=3.27]\n",
+            "Train: 100% 2000/2000 [02:50<00:00, 11.74 step/s, accuracy=0.41, loss=2.76, step=6000]\n",
+            "Valid: 100% 6944/6944 [00:25<00:00, 273.22 uttr/s, accuracy=0.35, loss=2.98]\n",
+            "Train: 100% 2000/2000 [02:47<00:00, 11.93 step/s, accuracy=0.53, loss=2.37, step=8000]\n",
+            "Valid: 100% 6944/6944 [00:25<00:00, 269.05 uttr/s, accuracy=0.41, loss=2.68]\n",
+            "Train:  71% 1416/2000 [02:01<01:06,  8.79 step/s, accuracy=0.50, loss=2.06, step=9416]"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0R2rx3AyHpQ-"
+      },
+      "source": [
+        "# Inference"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pSuI3WY9Fz78"
+      },
+      "source": [
+        "## Dataset of inference"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "4evns0055Dsx"
+      },
+      "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",
+        "  def __init__(self, data_dir):\n",
+        "    testdata_path = Path(data_dir) / \"testdata.json\"\n",
+        "    metadata = json.load(testdata_path.open())\n",
+        "    self.data_dir = data_dir\n",
+        "    self.data = metadata[\"utterances\"]\n",
+        "\n",
+        "  def __len__(self):\n",
+        "    return len(self.data)\n",
+        "\n",
+        "  def __getitem__(self, index):\n",
+        "    utterance = self.data[index]\n",
+        "    feat_path = utterance[\"feature_path\"]\n",
+        "    mel = torch.load(os.path.join(self.data_dir, feat_path))\n",
+        "\n",
+        "    return feat_path, mel\n",
+        "\n",
+        "\n",
+        "def inference_collate_batch(batch):\n",
+        "  \"\"\"Collate a batch of data.\"\"\"\n",
+        "  feat_paths, mels = zip(*batch)\n",
+        "\n",
+        "  return feat_paths, torch.stack(mels)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oAinHBG1GIWv"
+      },
+      "source": [
+        "## Main funcrion of Inference"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yQaTt7VDHoRI"
+      },
+      "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",
+        "  \"\"\"arguments\"\"\"\n",
+        "  config = {\n",
+        "    \"data_dir\": \"./Dataset\",\n",
+        "    \"model_path\": \"./model.ckpt\",\n",
+        "    \"output_path\": \"./output.csv\",\n",
+        "  }\n",
+        "\n",
+        "  return config\n",
+        "\n",
+        "\n",
+        "def main(\n",
+        "  data_dir,\n",
+        "  model_path,\n",
+        "  output_path,\n",
+        "):\n",
+        "  \"\"\"Main function.\"\"\"\n",
+        "  device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "  print(f\"[Info]: Use {device} now!\")\n",
+        "\n",
+        "  mapping_path = Path(data_dir) / \"mapping.json\"\n",
+        "  mapping = json.load(mapping_path.open())\n",
+        "\n",
+        "  dataset = InferenceDataset(data_dir)\n",
+        "  dataloader = DataLoader(\n",
+        "    dataset,\n",
+        "    batch_size=1,\n",
+        "    shuffle=False,\n",
+        "    drop_last=False,\n",
+        "    num_workers=8,\n",
+        "    collate_fn=inference_collate_batch,\n",
+        "  )\n",
+        "  print(f\"[Info]: Finish loading data!\",flush = True)\n",
+        "\n",
+        "  speaker_num = len(mapping[\"id2speaker\"])\n",
+        "  model = Classifier(n_spks=speaker_num).to(device)\n",
+        "  model.load_state_dict(torch.load(model_path))\n",
+        "  model.eval()\n",
+        "  print(f\"[Info]: Finish creating model!\",flush = True)\n",
+        "\n",
+        "  results = [[\"Id\", \"Category\"]]\n",
+        "  for feat_paths, mels in tqdm(dataloader):\n",
+        "    with torch.no_grad():\n",
+        "      mels = mels.to(device)\n",
+        "      outs = model(mels)\n",
+        "      preds = outs.argmax(1).cpu().numpy()\n",
+        "      for feat_path, pred in zip(feat_paths, preds):\n",
+        "        results.append([feat_path, mapping[\"id2speaker\"][str(pred)]])\n",
+        "  \n",
+        "  with open(output_path, 'w', newline='') as csvfile:\n",
+        "    writer = csv.writer(csvfile)\n",
+        "    writer.writerows(results)\n",
+        "\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "  main(**parse_args())\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/05 Transformer/范例/HW04.ipynb b/05 Transformer/范例/HW04.ipynb
new file mode 100644
index 0000000..579781b
--- /dev/null
+++ b/05 Transformer/范例/HW04.ipynb	
@@ -0,0 +1,5783 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "ML2021_HW4.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zC5KwRyl6Flp"
+      },
+      "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",
+        "  - Hard: Construct [conformer](https://arxiv.org/abs/2005.08100) which is a variety of transformer. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TPDoreyypeJE"
+      },
+      "source": [
+        "# Download dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QvpaILXnJIcw",
+        "outputId": "c2548949-1161-42c2-8318-076b987a1866"
+      },
+      "source": [
+        "# From Google drive\n",
+        "# !gdown --id '1T0RPnu-Sg5eIPwQPfYysipfcz81MnsYe' --output Dataset.zip\n",
+        "# !unzip Dataset.zip\n",
+        "\n",
+        "# From Dropbox\n",
+        "# If Dropbox is not work. Please use google drive.\n",
+        "!wget https://www.dropbox.com/s/vw324newiku0sz0/Dataset.tar.gz.aa?dl=0\n",
+        "!wget https://www.dropbox.com/s/z840g69e7lnkayo/Dataset.tar.gz.ab?dl=0\n",
+        "!wget https://www.dropbox.com/s/hl081e1ggonio81/Dataset.tar.gz.ac?dl=0\n",
+        "!wget https://www.dropbox.com/s/fh3zd8ow668c4th/Dataset.tar.gz.ad?dl=0\n",
+        "!wget https://www.dropbox.com/s/ydzygoy2pv6gw9d/Dataset.tar.gz.ae?dl=0\n",
+        "!cat Dataset.tar.gz.* | tar zxvf -"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\u001b[1;30;43m串流輸出內容已截斷至最後 5000 行。\u001b[0m\n",
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+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v1gYr_aoNDue"
+      },
+      "source": [
+        "# Data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mz_NpuAipk3h"
+      },
+      "source": [
+        "## Dataset\n",
+        "- Original dataset is [Voxceleb1](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/).\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 Voxceleb1.\n",
+        "- We randomly select 600 speakers from Voxceleb1.\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",
+      "metadata": {
+        "id": "cd7hoGhYtbXQ"
+      },
+      "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",
+        "  def __init__(self, data_dir, segment_len=128):\n",
+        "    self.data_dir = data_dir\n",
+        "    self.segment_len = segment_len\n",
+        "\n",
+        "    # Load the mapping from speaker neme to their corresponding id. \n",
+        "    mapping_path = Path(data_dir) / \"mapping.json\"\n",
+        "    mapping = json.load(mapping_path.open())\n",
+        "    self.speaker2id = mapping[\"speaker2id\"]\n",
+        "\n",
+        "    # Load metadata of training data.\n",
+        "    metadata_path = Path(data_dir) / \"metadata.json\"\n",
+        "    metadata = json.load(open(metadata_path))[\"speakers\"]\n",
+        "\n",
+        "    # Get the total number of speaker.\n",
+        "    self.speaker_num = len(metadata.keys())\n",
+        "    self.data = []\n",
+        "    for speaker in metadata.keys():\n",
+        "      for utterances in metadata[speaker]:\n",
+        "        self.data.append([utterances[\"feature_path\"], self.speaker2id[speaker]])\n",
+        "\n",
+        "  def __len__(self):\n",
+        "    return len(self.data)\n",
+        "\n",
+        "  def __getitem__(self, index):\n",
+        "    feat_path, speaker = self.data[index]\n",
+        "    # Load preprocessed mel-spectrogram.\n",
+        "    mel = torch.load(os.path.join(self.data_dir, feat_path))\n",
+        "\n",
+        "    # Segmemt mel-spectrogram into \"segment_len\" frames.\n",
+        "    if len(mel) > self.segment_len:\n",
+        "      # Randomly get the starting point of the segment.\n",
+        "      start = random.randint(0, len(mel) - self.segment_len)\n",
+        "      # Get a segment with \"segment_len\" frames.\n",
+        "      mel = torch.FloatTensor(mel[start:start+self.segment_len])\n",
+        "    else:\n",
+        "      mel = torch.FloatTensor(mel)\n",
+        "    # Turn the speaker id into long for computing loss later.\n",
+        "    speaker = torch.FloatTensor([speaker]).long()\n",
+        "    return mel, speaker\n",
+        "\n",
+        "  def get_speaker_number(self):\n",
+        "    return self.speaker_num\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mqJxjoi_NGnB"
+      },
+      "source": [
+        "## Dataloader\n",
+        "- Split dataset into training dataset(90%) and validation dataset(10%).\n",
+        "- Create dataloader to iterate the data.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zuT1AuFENI8t"
+      },
+      "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",
+        "  # Process features within a batch.\n",
+        "  \"\"\"Collate a batch of data.\"\"\"\n",
+        "  mel, speaker = zip(*batch)\n",
+        "  # 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",
+        "  mel = pad_sequence(mel, batch_first=True, padding_value=-20)    # pad log 10^(-20) which is very small value.\n",
+        "  # mel: (batch size, length, 40)\n",
+        "  return mel, torch.FloatTensor(speaker).long()\n",
+        "\n",
+        "\n",
+        "def get_dataloader(data_dir, batch_size, n_workers):\n",
+        "  \"\"\"Generate dataloader\"\"\"\n",
+        "  dataset = myDataset(data_dir)\n",
+        "  speaker_num = dataset.get_speaker_number()\n",
+        "  # Split dataset into training dataset and validation dataset\n",
+        "  trainlen = int(0.9 * len(dataset))\n",
+        "  lengths = [trainlen, len(dataset) - trainlen]\n",
+        "  trainset, validset = random_split(dataset, lengths)\n",
+        "\n",
+        "  train_loader = DataLoader(\n",
+        "    trainset,\n",
+        "    batch_size=batch_size,\n",
+        "    shuffle=True,\n",
+        "    drop_last=True,\n",
+        "    num_workers=n_workers,\n",
+        "    pin_memory=True,\n",
+        "    collate_fn=collate_batch,\n",
+        "  )\n",
+        "  valid_loader = DataLoader(\n",
+        "    validset,\n",
+        "    batch_size=batch_size,\n",
+        "    num_workers=n_workers,\n",
+        "    drop_last=True,\n",
+        "    pin_memory=True,\n",
+        "    collate_fn=collate_batch,\n",
+        "  )\n",
+        "\n",
+        "  return train_loader, valid_loader, speaker_num\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X0x6eXiHpr4R"
+      },
+      "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",
+      "metadata": {
+        "id": "SHX4eVj4tjtd"
+      },
+      "source": [
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "\n",
+        "\n",
+        "class Classifier(nn.Module):\n",
+        "  def __init__(self, d_model=80, n_spks=600, dropout=0.1):\n",
+        "    super().__init__()\n",
+        "    # Project the dimension of features from that of input into d_model.\n",
+        "    self.prenet = nn.Linear(40, d_model)\n",
+        "    # TODO:\n",
+        "    #   Change Transformer to Conformer.\n",
+        "    #   https://arxiv.org/abs/2005.08100\n",
+        "    self.encoder_layer = nn.TransformerEncoderLayer(\n",
+        "      d_model=d_model, dim_feedforward=256, nhead=2\n",
+        "    )\n",
+        "    # self.encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=2)\n",
+        "\n",
+        "    # Project the the dimension of features from d_model into speaker nums.\n",
+        "    self.pred_layer = nn.Sequential(\n",
+        "      nn.Linear(d_model, d_model),\n",
+        "      nn.ReLU(),\n",
+        "      nn.Linear(d_model, n_spks),\n",
+        "    )\n",
+        "\n",
+        "  def forward(self, mels):\n",
+        "    \"\"\"\n",
+        "    args:\n",
+        "      mels: (batch size, length, 40)\n",
+        "    return:\n",
+        "      out: (batch size, n_spks)\n",
+        "    \"\"\"\n",
+        "    # out: (batch size, length, d_model)\n",
+        "    out = self.prenet(mels)\n",
+        "    # out: (length, batch size, d_model)\n",
+        "    out = out.permute(1, 0, 2)\n",
+        "    # The encoder layer expect features in the shape of (length, batch size, d_model).\n",
+        "    out = self.encoder_layer(out)\n",
+        "    # out: (batch size, length, d_model)\n",
+        "    out = out.transpose(0, 1)\n",
+        "    # mean pooling\n",
+        "    stats = out.mean(dim=1)\n",
+        "\n",
+        "    # out: (batch, n_spks)\n",
+        "    out = self.pred_layer(stats)\n",
+        "    return out\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-__DolPGpvDZ"
+      },
+      "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",
+      "metadata": {
+        "id": "K-0816BntqT9"
+      },
+      "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",
+        "  optimizer: Optimizer,\n",
+        "  num_warmup_steps: int,\n",
+        "  num_training_steps: int,\n",
+        "  num_cycles: float = 0.5,\n",
+        "  last_epoch: int = -1,\n",
+        "):\n",
+        "  \"\"\"\n",
+        "  Create a schedule with a learning rate that decreases following the values of the cosine function between the\n",
+        "  initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the\n",
+        "  initial lr set in the optimizer.\n",
+        "\n",
+        "  Args:\n",
+        "    optimizer (:class:`~torch.optim.Optimizer`):\n",
+        "      The optimizer for which to schedule the learning rate.\n",
+        "    num_warmup_steps (:obj:`int`):\n",
+        "      The number of steps for the warmup phase.\n",
+        "    num_training_steps (:obj:`int`):\n",
+        "      The total number of training steps.\n",
+        "    num_cycles (:obj:`float`, `optional`, defaults to 0.5):\n",
+        "      The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0\n",
+        "      following a half-cosine).\n",
+        "    last_epoch (:obj:`int`, `optional`, defaults to -1):\n",
+        "      The index of the last epoch when resuming training.\n",
+        "\n",
+        "  Return:\n",
+        "    :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  def lr_lambda(current_step):\n",
+        "    # Warmup\n",
+        "    if current_step < num_warmup_steps:\n",
+        "      return float(current_step) / float(max(1, num_warmup_steps))\n",
+        "    # decadence\n",
+        "    progress = float(current_step - num_warmup_steps) / float(\n",
+        "      max(1, num_training_steps - num_warmup_steps)\n",
+        "    )\n",
+        "    return max(\n",
+        "      0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))\n",
+        "    )\n",
+        "\n",
+        "  return LambdaLR(optimizer, lr_lambda, last_epoch)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IP03FFo9K8DS"
+      },
+      "source": [
+        "# Model Function\n",
+        "- Model forward function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fohaLEFJK9-t"
+      },
+      "source": [
+        "import torch\n",
+        "\n",
+        "\n",
+        "def model_fn(batch, model, criterion, device):\n",
+        "  \"\"\"Forward a batch through the model.\"\"\"\n",
+        "\n",
+        "  mels, labels = batch\n",
+        "  mels = mels.to(device)\n",
+        "  labels = labels.to(device)\n",
+        "\n",
+        "  outs = model(mels)\n",
+        "\n",
+        "  loss = criterion(outs, labels)\n",
+        "\n",
+        "  # Get the speaker id with highest probability.\n",
+        "  preds = outs.argmax(1)\n",
+        "  # Compute accuracy.\n",
+        "  accuracy = torch.mean((preds == labels).float())\n",
+        "\n",
+        "  return loss, accuracy\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F7cg-YrzLQcf"
+      },
+      "source": [
+        "# Validate\n",
+        "- Calculate accuracy of the validation set."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mD-_p6nWLO2L"
+      },
+      "source": [
+        "from tqdm import tqdm\n",
+        "import torch\n",
+        "\n",
+        "\n",
+        "def valid(dataloader, model, criterion, device): \n",
+        "  \"\"\"Validate on validation set.\"\"\"\n",
+        "\n",
+        "  model.eval()\n",
+        "  running_loss = 0.0\n",
+        "  running_accuracy = 0.0\n",
+        "  pbar = tqdm(total=len(dataloader.dataset), ncols=0, desc=\"Valid\", unit=\" uttr\")\n",
+        "\n",
+        "  for i, batch in enumerate(dataloader):\n",
+        "    with torch.no_grad():\n",
+        "      loss, accuracy = model_fn(batch, model, criterion, device)\n",
+        "      running_loss += loss.item()\n",
+        "      running_accuracy += accuracy.item()\n",
+        "\n",
+        "    pbar.update(dataloader.batch_size)\n",
+        "    pbar.set_postfix(\n",
+        "      loss=f\"{running_loss / (i+1):.2f}\",\n",
+        "      accuracy=f\"{running_accuracy / (i+1):.2f}\",\n",
+        "    )\n",
+        "\n",
+        "  pbar.close()\n",
+        "  model.train()\n",
+        "\n",
+        "  return running_accuracy / len(dataloader)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "noHXyal5p1W5"
+      },
+      "source": [
+        "# Main function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "chRQE7oYtw62",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "45b0b120-3e86-4875-a8c0-2c4f0c980048"
+      },
+      "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",
+        "  \"\"\"arguments\"\"\"\n",
+        "  config = {\n",
+        "    \"data_dir\": \"./Dataset\",\n",
+        "    \"save_path\": \"model.ckpt\",\n",
+        "    \"batch_size\": 32,\n",
+        "    \"n_workers\": 8,\n",
+        "    \"valid_steps\": 2000,\n",
+        "    \"warmup_steps\": 1000,\n",
+        "    \"save_steps\": 10000,\n",
+        "    \"total_steps\": 70000,\n",
+        "  }\n",
+        "\n",
+        "  return config\n",
+        "\n",
+        "\n",
+        "def main(\n",
+        "  data_dir,\n",
+        "  save_path,\n",
+        "  batch_size,\n",
+        "  n_workers,\n",
+        "  valid_steps,\n",
+        "  warmup_steps,\n",
+        "  total_steps,\n",
+        "  save_steps,\n",
+        "):\n",
+        "  \"\"\"Main function.\"\"\"\n",
+        "  device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "  print(f\"[Info]: Use {device} now!\")\n",
+        "\n",
+        "  train_loader, valid_loader, speaker_num = get_dataloader(data_dir, batch_size, n_workers)\n",
+        "  train_iterator = iter(train_loader)\n",
+        "  print(f\"[Info]: Finish loading data!\",flush = True)\n",
+        "\n",
+        "  model = Classifier(n_spks=speaker_num).to(device)\n",
+        "  criterion = nn.CrossEntropyLoss()\n",
+        "  optimizer = AdamW(model.parameters(), lr=1e-3)\n",
+        "  scheduler = get_cosine_schedule_with_warmup(optimizer, warmup_steps, total_steps)\n",
+        "  print(f\"[Info]: Finish creating model!\",flush = True)\n",
+        "\n",
+        "  best_accuracy = -1.0\n",
+        "  best_state_dict = None\n",
+        "\n",
+        "  pbar = tqdm(total=valid_steps, ncols=0, desc=\"Train\", unit=\" step\")\n",
+        "\n",
+        "  for step in range(total_steps):\n",
+        "    # Get data\n",
+        "    try:\n",
+        "      batch = next(train_iterator)\n",
+        "    except StopIteration:\n",
+        "      train_iterator = iter(train_loader)\n",
+        "      batch = next(train_iterator)\n",
+        "\n",
+        "    loss, accuracy = model_fn(batch, model, criterion, device)\n",
+        "    batch_loss = loss.item()\n",
+        "    batch_accuracy = accuracy.item()\n",
+        "\n",
+        "    # Updata model\n",
+        "    loss.backward()\n",
+        "    optimizer.step()\n",
+        "    scheduler.step()\n",
+        "    optimizer.zero_grad()\n",
+        "    \n",
+        "    # Log\n",
+        "    pbar.update()\n",
+        "    pbar.set_postfix(\n",
+        "      loss=f\"{batch_loss:.2f}\",\n",
+        "      accuracy=f\"{batch_accuracy:.2f}\",\n",
+        "      step=step + 1,\n",
+        "    )\n",
+        "\n",
+        "    # Do validation\n",
+        "    if (step + 1) % valid_steps == 0:\n",
+        "      pbar.close()\n",
+        "\n",
+        "      valid_accuracy = valid(valid_loader, model, criterion, device)\n",
+        "\n",
+        "      # keep the best model\n",
+        "      if valid_accuracy > best_accuracy:\n",
+        "        best_accuracy = valid_accuracy\n",
+        "        best_state_dict = model.state_dict()\n",
+        "\n",
+        "      pbar = tqdm(total=valid_steps, ncols=0, desc=\"Train\", unit=\" step\")\n",
+        "\n",
+        "    # Save the best model so far.\n",
+        "    if (step + 1) % save_steps == 0 and best_state_dict is not None:\n",
+        "      torch.save(best_state_dict, save_path)\n",
+        "      pbar.write(f\"Step {step + 1}, best model saved. (accuracy={best_accuracy:.4f})\")\n",
+        "\n",
+        "  pbar.close()\n",
+        "\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "  main(**parse_args())\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[Info]: Use cuda now!\n",
+            "[Info]: Finish loading data!\n",
+            "[Info]: Finish creating model!\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Train: 100% 2000/2000 [03:34<00:00,  9.32 step/s, accuracy=0.31, loss=3.89, step=2000]\n",
+            "Valid: 100% 6944/6944 [00:23<00:00, 300.22 uttr/s, accuracy=0.18, loss=3.98]\n",
+            "Train: 100% 2000/2000 [03:04<00:00, 10.81 step/s, accuracy=0.19, loss=3.79, step=4000]\n",
+            "Valid: 100% 6944/6944 [00:24<00:00, 285.84 uttr/s, accuracy=0.29, loss=3.27]\n",
+            "Train: 100% 2000/2000 [02:50<00:00, 11.74 step/s, accuracy=0.41, loss=2.76, step=6000]\n",
+            "Valid: 100% 6944/6944 [00:25<00:00, 273.22 uttr/s, accuracy=0.35, loss=2.98]\n",
+            "Train: 100% 2000/2000 [02:47<00:00, 11.93 step/s, accuracy=0.53, loss=2.37, step=8000]\n",
+            "Valid: 100% 6944/6944 [00:25<00:00, 269.05 uttr/s, accuracy=0.41, loss=2.68]\n",
+            "Train:  71% 1416/2000 [02:01<01:06,  8.79 step/s, accuracy=0.50, loss=2.06, step=9416]"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0R2rx3AyHpQ-"
+      },
+      "source": [
+        "# Inference"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pSuI3WY9Fz78"
+      },
+      "source": [
+        "## Dataset of inference"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "4evns0055Dsx"
+      },
+      "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",
+        "  def __init__(self, data_dir):\n",
+        "    testdata_path = Path(data_dir) / \"testdata.json\"\n",
+        "    metadata = json.load(testdata_path.open())\n",
+        "    self.data_dir = data_dir\n",
+        "    self.data = metadata[\"utterances\"]\n",
+        "\n",
+        "  def __len__(self):\n",
+        "    return len(self.data)\n",
+        "\n",
+        "  def __getitem__(self, index):\n",
+        "    utterance = self.data[index]\n",
+        "    feat_path = utterance[\"feature_path\"]\n",
+        "    mel = torch.load(os.path.join(self.data_dir, feat_path))\n",
+        "\n",
+        "    return feat_path, mel\n",
+        "\n",
+        "\n",
+        "def inference_collate_batch(batch):\n",
+        "  \"\"\"Collate a batch of data.\"\"\"\n",
+        "  feat_paths, mels = zip(*batch)\n",
+        "\n",
+        "  return feat_paths, torch.stack(mels)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oAinHBG1GIWv"
+      },
+      "source": [
+        "## Main funcrion of Inference"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yQaTt7VDHoRI"
+      },
+      "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",
+        "  \"\"\"arguments\"\"\"\n",
+        "  config = {\n",
+        "    \"data_dir\": \"./Dataset\",\n",
+        "    \"model_path\": \"./model.ckpt\",\n",
+        "    \"output_path\": \"./output.csv\",\n",
+        "  }\n",
+        "\n",
+        "  return config\n",
+        "\n",
+        "\n",
+        "def main(\n",
+        "  data_dir,\n",
+        "  model_path,\n",
+        "  output_path,\n",
+        "):\n",
+        "  \"\"\"Main function.\"\"\"\n",
+        "  device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "  print(f\"[Info]: Use {device} now!\")\n",
+        "\n",
+        "  mapping_path = Path(data_dir) / \"mapping.json\"\n",
+        "  mapping = json.load(mapping_path.open())\n",
+        "\n",
+        "  dataset = InferenceDataset(data_dir)\n",
+        "  dataloader = DataLoader(\n",
+        "    dataset,\n",
+        "    batch_size=1,\n",
+        "    shuffle=False,\n",
+        "    drop_last=False,\n",
+        "    num_workers=8,\n",
+        "    collate_fn=inference_collate_batch,\n",
+        "  )\n",
+        "  print(f\"[Info]: Finish loading data!\",flush = True)\n",
+        "\n",
+        "  speaker_num = len(mapping[\"id2speaker\"])\n",
+        "  model = Classifier(n_spks=speaker_num).to(device)\n",
+        "  model.load_state_dict(torch.load(model_path))\n",
+        "  model.eval()\n",
+        "  print(f\"[Info]: Finish creating model!\",flush = True)\n",
+        "\n",
+        "  results = [[\"Id\", \"Category\"]]\n",
+        "  for feat_paths, mels in tqdm(dataloader):\n",
+        "    with torch.no_grad():\n",
+        "      mels = mels.to(device)\n",
+        "      outs = model(mels)\n",
+        "      preds = outs.argmax(1).cpu().numpy()\n",
+        "      for feat_path, pred in zip(feat_paths, preds):\n",
+        "        results.append([feat_path, mapping[\"id2speaker\"][str(pred)]])\n",
+        "  \n",
+        "  with open(output_path, 'w', newline='') as csvfile:\n",
+        "    writer = csv.writer(csvfile)\n",
+        "    writer.writerows(results)\n",
+        "\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "  main(**parse_args())\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/05 Transformer/范例/HW04.pdf b/05 Transformer/范例/HW04.pdf
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