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Pytorch Lightning Siamese
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pytorch-lightning-siamese.ipynb
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{
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]
},
{
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"NIN = 10\n",
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],
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{
"cell_type": "code",
"metadata": {
"id": "gffkFURJtj0A",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 564
},
"outputId": "115241e5-3cf6-4eab-f66c-85a68d149680"
},
"source": [
"!pip install pytorch_lightning"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: pytorch_lightning in /usr/local/lib/python3.6/dist-packages (0.8.1)\n",
"Requirement already satisfied: tensorboard>=1.14 in /usr/local/lib/python3.6/dist-packages (from pytorch_lightning) (2.2.2)\n",
"Requirement already satisfied: torch>=1.3 in /usr/local/lib/python3.6/dist-packages (from pytorch_lightning) (1.5.1+cu101)\n",
"Requirement already satisfied: future>=0.17.1 in /usr/local/lib/python3.6/dist-packages (from pytorch_lightning) (0.18.2)\n",
"Requirement already satisfied: PyYAML>=5.1 in /usr/local/lib/python3.6/dist-packages (from pytorch_lightning) (5.3.1)\n",
"Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.6/dist-packages (from pytorch_lightning) (4.41.1)\n",
"Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from pytorch_lightning) (1.18.5)\n",
"Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning) (1.29.0)\n",
"Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning) (2.23.0)\n",
"Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning) (1.17.2)\n",
"Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning) (1.0.1)\n",
"Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning) (3.10.0)\n",
"Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning) (1.12.0)\n",
"Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning) (0.34.2)\n",
"Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning) (0.4.1)\n",
"Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning) (0.9.0)\n",
"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning) (3.2.2)\n",
"Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning) (47.3.1)\n",
"Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning) (1.6.0.post3)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard>=1.14->pytorch_lightning) (1.24.3)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard>=1.14->pytorch_lightning) (2.9)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard>=1.14->pytorch_lightning) (3.0.4)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard>=1.14->pytorch_lightning) (2020.4.5.2)\n",
"Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=1.14->pytorch_lightning) (0.2.8)\n",
"Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=1.14->pytorch_lightning) (4.6)\n",
"Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=1.14->pytorch_lightning) (4.1.0)\n",
"Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=1.14->pytorch_lightning) (1.3.0)\n",
"Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard>=1.14->pytorch_lightning) (1.6.1)\n",
"Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard>=1.14->pytorch_lightning) (0.4.8)\n",
"Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=1.14->pytorch_lightning) (3.1.0)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard>=1.14->pytorch_lightning) (3.1.0)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "49dYZjzTwamw",
"colab_type": "code",
"colab": {}
},
"source": [
"import logging\n",
"from collections import OrderedDict\n",
"\n",
"import pytorch_lightning as pl\n",
"from pytorch_lightning import LightningModule\n",
"from pytorch_lightning import Trainer\n",
"from torch import optim\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"from torch.utils.data import DataLoader\n",
"from torch.utils.data.distributed import DistributedSampler\n",
"# -\n",
"\n",
"import numpy as np\n",
"np.random.seed(0)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "83S_q6n9wfT1",
"colab_type": "text"
},
"source": [
"Create data set"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iL_-LDeIwcDL",
"colab_type": "code",
"colab": {}
},
"source": [
"X1 = np.random.random((EXAMPLES, NIN))\n",
"X2 = np.random.random((EXAMPLES, NIN))\n",
"DIST = np.random.random((EXAMPLES,))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "7ZJhOCMgwkdM",
"colab_type": "text"
},
"source": [
"Convert it into a torch Dataset"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Y5bXXCxpwjfO",
"colab_type": "code",
"colab": {}
},
"source": [
"class TableDistanceDataset(torch.utils.data.Dataset):\n",
" def __init__(self, x1, x2, dist):\n",
" self.dist = torch.Tensor(dist).float()\n",
" self.X1 = torch.Tensor(X1).float()\n",
" self.X2 = torch.Tensor(X2).float()\n",
"\n",
" def __len__(self):\n",
" return self.X1.shape[0]\n",
"\n",
" def __getitem__(self, index):\n",
" return self.X1[index,:], self.X2[index,:], self.dist[index]\n",
"\n",
"tableDistanceDataset = TableDistanceDataset(X1, X2, DIST)\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "huWdyY5lwo5d",
"colab_type": "text"
},
"source": [
"Module used to transform the input into a representation"
]
},
{
"cell_type": "code",
"metadata": {
"id": "D5mr9Bw_wsqc",
"colab_type": "code",
"colab": {}
},
"source": [
"class Table2Representation(pl.LightningModule):\n",
" def __init__(self):\n",
" super().__init__()\n",
"\n",
" self.nin = NIN\n",
" self.nhid = 32\n",
" self.nout = NOUT\n",
"\n",
" # build model\n",
" self.__build_model()\n",
" \n",
" def __build_model(self):\n",
" self.fc1 = nn.Linear(self.nin,\n",
" self.nhid)\n",
" self.do1 = nn.Dropout(0.2)\n",
" self.out = nn.Linear(self.nhid,\n",
" self.nout)\n",
"\n",
" def forward(self, x):\n",
" x = F.relu(self.fc1(x))\n",
" x = self.do1(x)\n",
" x = self.out(x)\n",
" return x"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zPuNuJIxwvI7",
"colab_type": "text"
},
"source": [
"Module to take the inputs, get two representations, measure their distance, and compare it against the target distance."
]
},
{
"cell_type": "code",
"metadata": {
"id": "82DDXmMVfgcv",
"colab_type": "code",
"colab": {}
},
"source": [
"# Based upon https://github.com/PyTorchLightning/Siamese-Neural-Networks/blob/master/model.py\n",
"class TableDistanceModule(pl.LightningModule):\n",
" def __init__(self, tableDistanceDataset):\n",
" super().__init__()\n",
"\n",
" self.tableDistanceDataset = tableDistanceDataset\n",
" self.datatrain, self.dataval, self.datatest = \\\n",
" torch.utils.data.random_split(self.tableDistanceDataset,\n",
" [round(EXAMPLES*0.8),\n",
" round(EXAMPLES*0.1),\n",
" round(EXAMPLES*0.1)])\n",
"\n",
" self.table2Representation = Table2Representation()\n",
"\n",
" # build model\n",
" self.__build_model()\n",
" \n",
" def __build_model(self):\n",
" pass\n",
"\n",
" def forward(self, x1, x2):\n",
" z1 = self.table2Representation.forward(x1)\n",
" z2 = self.table2Representation.forward(x2)\n",
" dis = torch.mean(torch.abs(z1 - z2), axis=1)\n",
" return dis\n",
"\n",
" def loss(self, pred_dists, true_dists):\n",
" loss_val = F.mse_loss(pred_dists, true_dists)\n",
" return loss_val\n",
" \n",
" def _step(self, batch, batch_idx, name, training_step=False):\n",
" X1, X2, dist = batch\n",
" pred = self.forward(X1, X2)\n",
" loss_val = self.loss(pred, dist)\n",
" # in DP mode (default) make sure if result is scalar, there's another dim in the beginning\n",
" if self.trainer.use_dp or self.trainer.use_ddp2:\n",
" loss_val = loss_val.unsqueeze(0)\n",
" tqdm_dict = OrderedDict({name: loss_val})\n",
" if training_step:\n",
" return OrderedDict({\n",
" 'loss': loss_val,\n",
" 'progress_bar': tqdm_dict,\n",
" 'log': tqdm_dict\n",
" })\n",
" else:\n",
" return tqdm_dict\n",
" \n",
" def training_step(self, batch, batch_idx):\n",
" return self._step(batch, batch_idx, name=\"train_loss\", training_step=True)\n",
" def validation_step(self, batch, batch_idx):\n",
" return self._step(batch, batch_idx, name=\"val_loss\", training_step=False)\n",
" def test_step(self, batch, batch_idx):\n",
" return self._step(batch, batch_idx, name=\"test_loss\", training_step=False)\n",
"\n",
" def _epoch_end(self, outputs, name):\n",
" # With DP training I think you have to average the things individually? Not sure\n",
" # Look at the pytorch lightning siamese network code\n",
" #if self.trainer.use_dp or self.trainer.use_ddp2:\n",
" # val_acc = torch.mean(val_acc)\n",
" avg_loss = torch.stack([x[name] for x in outputs]).mean()\n",
" tqdm_dict = {name: avg_loss}\n",
" result = OrderedDict({name: avg_loss, 'progress_bar': tqdm_dict, 'log': tqdm_dict})\n",
" return result\n",
" \n",
" def validation_epoch_end(self, outputs):\n",
" return self._epoch_end(outputs, name=\"val_loss\")\n",
" def test_epoch_end(self, outputs):\n",
" return self._epoch_end(outputs, name=\"test_loss\")\n",
" \n",
" # ---------------------\n",
" # TRAINING SETUP\n",
" # ---------------------\n",
" def configure_optimizers(self):\n",
" \"\"\"\n",
" return whatever optimizers we want here\n",
" :return: list of optimizers\n",
" \"\"\"\n",
" optimizer = optim.SGD(self.parameters(),\n",
" lr=0.01, momentum=0.90)\n",
" scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer,\n",
" T_max=10)\n",
" return [optimizer], [scheduler]\n",
"\n",
" def __dataloader(self, train, dataset):\n",
" # when using multi-node (ddp) we need to add the datasampler\n",
" train_sampler = None\n",
" batch_size = BATCH_SIZE\n",
"\n",
" if self.use_ddp:\n",
" train_sampler = DistributedSampler(dataset)\n",
"\n",
" should_shuffle = train and train_sampler is None\n",
" loader = DataLoader(\n",
" dataset=dataset,\n",
" batch_size=batch_size,\n",
" shuffle=should_shuffle,\n",
" sampler=train_sampler,\n",
" num_workers=0,\n",
" drop_last=True\n",
" )\n",
"\n",
" return loader\n",
"\n",
" @pl.data_loader\n",
" def train_dataloader(self):\n",
" logging.info('training data loader called')\n",
" return self.__dataloader(train=True, dataset=self.datatrain)\n",
"\n",
" @pl.data_loader\n",
" def val_dataloader(self):\n",
" logging.info('val data loader called')\n",
" return self.__dataloader(train=False, dataset=self.dataval)\n",
"\n",
" @pl.data_loader\n",
" def test_dataloader(self):\n",
" logging.info('val data loader called')\n",
" return self.__dataloader(train=False, dataset=self.datatest)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "EBEkzxkeukUl",
"colab_type": "text"
},
"source": [
"CPU training\n",
"\n",
"about 40it/s"
]
},
{
"cell_type": "code",
"metadata": {
"id": "KPG5887Jtc-y",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 375,
"referenced_widgets": [
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]
},
"outputId": "d59ff456-af03-45ad-eede-b0431d9ceddb"
},
"source": [
"model_cpu = TableDistanceModule(tableDistanceDataset)\n",
"trainer_cpu = Trainer(max_epochs=10)\n",
"trainer_cpu.fit(model_cpu)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"GPU available: True, used: False\n",
"INFO:lightning:GPU available: True, used: False\n",
"TPU available: False, using: 0 TPU cores\n",
"INFO:lightning:TPU available: False, using: 0 TPU cores\n",
"\n",
" | Name | Type | Params\n",
"--------------------------------------------------------------\n",
"0 | table2Representation | Table2Representation | 1 K \n",
"INFO:lightning:\n",
" | Name | Type | Params\n",
"--------------------------------------------------------------\n",
"0 | table2Representation | Table2Representation | 1 K \n",
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"\n",
"Even though GPU is active, it is also 40it/s and the data set and parameters are on the CPU"
]
},
{
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},
"outputId": "1daf18cc-830f-481b-8a5e-1e4125fc37e5"
},
"source": [
"model_gpu = TableDistanceModule(tableDistanceDataset)\n",
"trainer_gpu = Trainer(max_epochs=10, gpus=-1)\n",
"for i, (x, y, d) in enumerate(model_gpu.datatrain):\n",
" print(x.device)\n",
" print(y.device)\n",
" print(d.device)\n",
" break\n",
"for p in model_gpu.parameters():\n",
" print(p.device)\n",
"trainer_gpu.fit(model_gpu)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"GPU available: True, used: True\n",
"INFO:lightning:GPU available: True, used: True\n",
"TPU available: False, using: 0 TPU cores\n",
"INFO:lightning:TPU available: False, using: 0 TPU cores\n",
"CUDA_VISIBLE_DEVICES: [0]\n",
"INFO:lightning:CUDA_VISIBLE_DEVICES: [0]\n",
"\n",
" | Name | Type | Params\n",
"--------------------------------------------------------------\n",
"0 | table2Representation | Table2Representation | 1 K \n",
"INFO:lightning:\n",
" | Name | Type | Params\n",
"--------------------------------------------------------------\n",
"0 | table2Representation | Table2Representation | 1 K \n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"cpu\n",
"cpu\n",
"cpu\n",
"cpu\n",
"cpu\n",
"cpu\n",
"cpu\n"
],
"name": "stdout"
},
{
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"/usr/local/lib/python3.6/dist-packages/pytorch_lightning/utilities/distributed.py:25: UserWarning: The dataloader, val dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 2 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
" warnings.warn(*args, **kwargs)\n"
],
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},
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"/usr/local/lib/python3.6/dist-packages/pytorch_lightning/utilities/distributed.py:25: UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 2 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
" warnings.warn(*args, **kwargs)\n"
],
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"\n"
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]
},
{
"cell_type": "code",
"metadata": {
"id": "m5qRFnB_vr5o",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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