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{
"metadata": {
"kernelspec": {
"language": "python",
"display_name": "Python 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"
},
"kaggle": {
"accelerator": "gpu",
"dataSources": [
{
"sourceId": 9679350,
"sourceType": "datasetVersion",
"datasetId": 5916065
}
],
"dockerImageVersionId": 30302,
"isInternetEnabled": true,
"language": "python",
"sourceType": "notebook",
"isGpuEnabled": true
},
"colab": {
"name": "Hack49-Training",
"provenance": []
}
},
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# IMPORTANT: SOME KAGGLE DATA SOURCES ARE PRIVATE\n",
"# RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES.\n",
"import kagglehub\n",
"kagglehub.login()\n"
],
"metadata": {
"id": "qWlTgQVSmgbJ"
},
"cell_type": "code",
"outputs": [],
"execution_count": null
},
{
"source": [
"# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,\n",
"# THEN FEEL FREE TO DELETE THIS CELL.\n",
"# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
"# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
"# NOTEBOOK.\n",
"\n",
"datajediai_hack49_alzheimer_dataset_path = kagglehub.dataset_download('datajediai/hack49-alzheimer-dataset')\n",
"\n",
"print('Data source import complete.')\n"
],
"metadata": {
"id": "1uar05ngmgbJ"
},
"cell_type": "code",
"outputs": [],
"execution_count": null
},
{
"cell_type": "code",
"source": [
"pip install boto3"
],
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"outputs": [
{
"name": "stdout",
"text": "Requirement already satisfied: boto3 in /opt/conda/lib/python3.7/site-packages (1.24.93)\nRequirement already satisfied: s3transfer<0.7.0,>=0.6.0 in /opt/conda/lib/python3.7/site-packages (from boto3) (0.6.0)\nRequirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.7/site-packages (from boto3) (1.0.1)\nRequirement already satisfied: botocore<1.28.0,>=1.27.93 in /opt/conda/lib/python3.7/site-packages (from boto3) (1.27.93)\nRequirement already satisfied: urllib3<1.27,>=1.25.4 in /opt/conda/lib/python3.7/site-packages (from botocore<1.28.0,>=1.27.93->boto3) (1.26.12)\nRequirement already satisfied: python-dateutil<3.0.0,>=2.1 in /opt/conda/lib/python3.7/site-packages (from botocore<1.28.0,>=1.27.93->boto3) (2.8.2)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.28.0,>=1.27.93->boto3) (1.15.0)\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[0mNote: you may need to restart the kernel to use updated packages.\n",
"output_type": "stream"
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"import pandas as pd\n",
"import torch\n",
"import torchaudio\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"# Custom collate function for DataLoader\n",
"import torch.nn.functional as F\n",
"from torch.utils.data import Dataset, DataLoader, random_split\n",
"dataset_dir = '/kaggle/input/hack49-alzheimer-dataset/Hack49-Alzheimer-Dataset'\n",
"# Custom Dataset class\n",
"class HealthAudioDataset(Dataset):\n",
" def __init__(self, root_dir, device='cpu'):\n",
" self.root_dir = root_dir\n",
" self.file_list = []\n",
" self.labels = []\n",
" self.dataframe = []\n",
" self.device = device\n",
" for label, subdir in enumerate(['Healthy', 'NotHealthy']):\n",
" subdir_path = os.path.join(root_dir, subdir)\n",
" for wav_file in os.listdir(subdir_path):\n",
" if wav_file.endswith('.wav'):\n",
" self.file_list.append(os.path.join(subdir_path, wav_file))\n",
" self.labels.append(label)\n",
" self.dataframe = pd.DataFrame({'file_path': self.file_list, 'label': self.labels})\n",
"\n",
" def __len__(self):\n",
" return len(self.file_list)\n",
"\n",
" def __getitem__(self, idx):\n",
" wav_path = self.file_list[idx]\n",
" label = self.labels[idx]\n",
" waveform, sample_rate = torchaudio.load(wav_path)\n",
" waveform = waveform.to(self.device)\n",
" if sample_rate != bundle.sample_rate:\n",
" waveform = torchaudio.functional.resample(waveform, sample_rate, bundle.sample_rate)\n",
" return waveform, label\n",
"\n",
" def get_dataframe(self):\n",
" return self.dataframe\n",
"\n",
"\n",
"class SelfAttention(nn.Module):\n",
" def __init__(self, input_dim, num_heads):\n",
" super(SelfAttention, self).__init__()\n",
" self.multihead_attn = nn.MultiheadAttention(embed_dim=input_dim, num_heads=num_heads)\n",
" self.layer_norm = nn.LayerNorm(input_dim)\n",
"\n",
" def forward(self, x):\n",
" attn_output, _ = self.multihead_attn(x, x, x)\n",
" x = x + attn_output # Add & Normalize\n",
" x = self.layer_norm(x)\n",
" return x\n",
"\n",
"class TimeSeriesClassifier(nn.Module):\n",
" def __init__(self, input_dim, num_heads, hidden_dim, output_dim):\n",
" super(TimeSeriesClassifier, self).__init__()\n",
" self.self_attention = SelfAttention(input_dim, num_heads)\n",
" self.fc1 = nn.Linear(input_dim, hidden_dim)\n",
" self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
"\n",
" def forward(self, x):\n",
" # x: [batch_size, seq_len, input_dim]\n",
" x = x.permute(1, 0, 2) # Change to [seq_len, batch_size, input_dim]\n",
" x = self.self_attention(x)\n",
" x = x.permute(1, 0, 2) # Change back to [batch_size, seq_len, input_dim]\n",
" x = torch.mean(x, dim=1) # Global Average Pooling over the time dimension\n",
" x = torch.relu(self.fc1(x))\n",
" x = torch.sigmoid(self.fc2(x)) # Sigmoid for binary classification\n",
" return x\n",
"\n",
"\n",
"# Define the combined Encoder-Decoder model\n",
"class EncoderDecoder(nn.Module):\n",
" def __init__(self, encoder, decoder):\n",
" super(EncoderDecoder, self).__init__()\n",
" self.encoder = encoder\n",
" self.decoder = decoder\n",
" def forward(self, x):\n",
" emission, _ = self.encoder(x)\n",
" x = self.decoder(emission)\n",
" return x\n",
"\n",
"\n",
"\n",
"\n",
"def collate_fn(batch):\n",
" waveforms = [item[0] for item in batch]\n",
" labels = [item[1] for item in batch]\n",
"\n",
" # Find the length of the longest waveform in the batch\n",
" max_length = max(waveform.size(1) for waveform in waveforms)\n",
"\n",
" # Pad all waveforms to the length of the longest waveform\n",
" padded_waveforms = [F.pad(waveform, (0, max_length - waveform.size(1))) for waveform in waveforms]\n",
" padded_waveforms = torch.stack(padded_waveforms)\n",
"\n",
" labels = torch.tensor(labels)\n",
" return padded_waveforms, labels\n",
"\n",
"\n",
"\n",
"\n"
],
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"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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},
{
"cell_type": "code",
"source": [
"# Initialize everything\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"bundle = torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H\n",
"encoder = bundle.get_model().to(device)\n",
"\n",
"# Example usage\n",
"batch_size = 32\n",
"seq_len = 10 # Number of time steps\n",
"input_dim = 29\n",
"hidden_dim = 15\n",
"# Creating a dummy input tensor with shape [batch_size, seq_len, input_dim]\n",
"dummy_input = torch.randn(batch_size, seq_len, input_dim)\n",
"\n",
"decoder = TimeSeriesClassifier(input_dim = input_dim,\n",
" num_heads = input_dim,\n",
" hidden_dim = hidden_dim,\n",
" output_dim = 1)\n",
"output = decoder(dummy_input)\n",
"print(output)\n",
"decoder = decoder.to(device)\n",
"model = EncoderDecoder(encoder, decoder).to(device)\n",
"\n",
"# Freeze the encoder parameters\n",
"for param in model.encoder.parameters():\n",
" param.requires_grad = False\n",
"\n",
"# Dataset and DataLoaders\n",
"\n",
"dataset = HealthAudioDataset(dataset_dir, device)\n",
"train_size = int(0.8 * len(dataset))\n",
"test_size = len(dataset) - train_size\n",
"train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
"\n",
"train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True, collate_fn=collate_fn)\n",
"test_loader = DataLoader(test_dataset, batch_size=2, shuffle=True, collate_fn=collate_fn)\n",
"\n",
"# Print the contents of the train_loader"
],
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"outputId": "d6b97111-e800-40db-8873-fff376726885"
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"outputs": [
{
"name": "stdout",
"text": "tensor([[0.5726],\n [0.5791],\n [0.5756],\n [0.5718],\n [0.6160],\n [0.5886],\n [0.5634],\n [0.5896],\n [0.5754],\n [0.6070],\n [0.5597],\n [0.5673],\n [0.5942],\n [0.5967],\n [0.5815],\n [0.5582],\n [0.5873],\n [0.6225],\n [0.5830],\n [0.5879],\n [0.5958],\n [0.5540],\n [0.5812],\n [0.6039],\n [0.5825],\n [0.5635],\n [0.6145],\n [0.5483],\n [0.5770],\n [0.5952],\n [0.6086],\n [0.5806]], grad_fn=<SigmoidBackward0>)\n",
"output_type": "stream"
}
]
},
{
"cell_type": "code",
"source": [
"# Loss and optimizer\n",
"criterion = nn.BCELoss()\n",
"optimizer = optim.Adam(model.parameters(), lr=1e-4)\n",
"\n",
"def verification(model):\n",
" # Testing script\n",
" test_loss = 0.0\n",
" with torch.no_grad():\n",
" for i, (inputs_batch, labels_batch) in enumerate(test_loader):\n",
" for batch_idx in range(inputs_batch.size(0)):\n",
" inputs, labels = inputs_batch[batch_idx], labels_batch[batch_idx]\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device).float().unsqueeze(0) # Convert to float and add batch dimension\n",
" outputs = model(inputs)\n",
" labels = labels.view(outputs.shape) # Ensure labels match the shape of outputs\n",
" loss = criterion(outputs, labels)\n",
" test_loss += loss.item()\n",
" ret = test_loss / len(test_loader)\n",
" print(f'Test Loss: {ret:.4f}')\n",
" return ret\n",
"verification(model)\n",
"# save the model\n"
],
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"execution": {
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"outputs": [
{
"name": "stdout",
"text": "Test Loss: 1.6243\n",
"output_type": "stream"
},
{
"execution_count": 99,
"output_type": "execute_result",
"data": {
"text/plain": "1.6242794593175252"
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},
{
"cell_type": "code",
"source": [
"print(f\"Training started... Using {device}\")\n",
"num_epochs = 0\n",
"while verification(model) > 0.10:\n",
" model.train()\n",
" running_loss = 0.0\n",
" for i, (inputs_batch, labels_batch) in enumerate(train_loader):\n",
"# print(f'Batch {i + 1}:')\n",
" for batch_idx in range(inputs_batch.size(0)):\n",
" inputs, labels = inputs_batch[batch_idx], labels_batch[batch_idx]\n",
" inputs = inputs.to(device)\n",
" labels = labels.to(device).float().unsqueeze(0) # Convert to float and add batch dimension\n",
"\n",
" # print(f' Input {batch_idx + 1}: {inputs.size()}, Label: {labels.size()}')\n",
" # print(f' Input data type: {inputs.dtype}, Label data type: {labels.dtype}')\n",
"\n",
" optimizer.zero_grad()\n",
" outputs = model(inputs)\n",
" # print(f' Output: {outputs.size()}')\n",
" labels = labels.view(outputs.shape) # Ensure labels match the shape of outputs\n",
" loss = criterion(outputs, labels)\n",
" loss.backward()\n",
" optimizer.step()\n",
" running_loss += loss.item()\n",
"\n",
"\n",
" print(f'Epoch [{epoch + 1}/{num_epochs}], Batch [{i + 1}], Loss: {running_loss / 100:.4f}')\n",
" epoch = epoch + 1\n",
"\n",
"print(\"Training completed.\")\n",
"# now generate the testing script\n",
"\n"
],
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"outputs": [
{
"name": "stdout",
"text": "Training started... Using cuda\nTest Loss: 0.1040\nEpoch [283/0], Batch [12], Loss: 0.0445\nTest Loss: 0.1708\nEpoch [284/0], Batch [12], Loss: 0.0302\nTest Loss: 0.1152\nEpoch [285/0], Batch [12], Loss: 0.0298\nTest Loss: 0.1269\nEpoch [286/0], Batch [12], Loss: 0.0319\nTest Loss: 0.1675\nEpoch [287/0], Batch [12], Loss: 0.0276\nTest Loss: 0.1064\nEpoch [288/0], Batch [12], Loss: 0.0250\nTest Loss: 0.1328\nEpoch [289/0], Batch [12], Loss: 0.0220\nTest Loss: 0.1303\nEpoch [290/0], Batch [12], Loss: 0.0219\nTest Loss: 0.1164\nEpoch [291/0], Batch [12], Loss: 0.0200\nTest Loss: 0.1139\nEpoch [292/0], Batch [12], Loss: 0.0342\nTest Loss: 0.1391\nEpoch [293/0], Batch [12], Loss: 0.0213\nTest Loss: 0.1166\nEpoch [294/0], Batch [12], Loss: 0.0271\nTest Loss: 0.0904\nTraining completed.\n",
"output_type": "stream"
}
]
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"cell_type": "code",
"source": [
"model"
],
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{
"cell_type": "code",
"source": [
"torch.save(model.state_dict(), 'hack49_encoder_decoder_model.pth')\n",
"import boto3\n",
"\n",
"def upload_to_s3(file_path, bucket_name, object_name, access_key, secret_key):\n",
" # Initialize a session using your AWS credentials\n",
" s3_client = boto3.client('s3',\n",
" region_name='us-east-2',\n",
" aws_access_key_id=access_key,\n",
" aws_secret_access_key=secret_key)\n",
"\n",
" try:\n",
" # Uploads the given file using a managed uploader\n",
" s3_client.upload_file(file_path, bucket_name, object_name)\n",
" print(f'Successfully uploaded {file_path} to {bucket_name}/{object_name}')\n",
" except Exception as e:\n",
" print(f'Error uploading file: {e}')\n",
"\n",
"# Example usage\n",
"file_path = 'hack49_encoder_decoder_model.pth'\n",
"bucket_name = 'my-ai-models-darcy'\n",
"# get time\n",
"import datetime\n",
"now = datetime.datetime.now()\n",
"object_name = f'hack49_encoder_decoder_model_{now.strftime(\"%Y-%m-%d_%H-%M-%S\")}.pth'\n",
"access_key = 'XXXX'\n",
"secret_key = 'XXXX'\n",
"\n",
"upload_to_s3(file_path, bucket_name, object_name, access_key, secret_key)\n"
],
"metadata": {
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"outputs": [
{
"name": "stdout",
"text": "Successfully uploaded hack49_encoder_decoder_model.pth to my-ai-models-darcy/hack49_encoder_decoder_model_2024-10-21_05-40-05.pth\n",
"output_type": "stream"
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}
]
} |