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Text classification in PyTorch to refactor with PyTorch lightning.ipynb
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/davidefiocco/3b6c6b1e09c4f664b3a73e5bf24d1668/text-classification-in-pytorch-to-refactor-with-pytorch-lightning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SVhHUPSWPCHG"
},
"source": [
"The code below is a PyTorch text classifier obtained by getting code from https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html, attempting (clumsily) a PyTorch lightning refactor.\r\n",
"How can I make this work with PyTorch lightning?"
]
},
{
"cell_type": "code",
"metadata": {
"id": "8-un-rXKQ2ec"
},
"source": [
"%matplotlib inline"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "RWuv_rZYRdXz",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "04bf2b45-d31b-417f-8517-072d751e347e"
},
"source": [
"pip install torchtext pytorch-lightning --upgrade --quiet"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[K |████████████████████████████████| 7.0MB 27.1MB/s \n",
"\u001b[K |████████████████████████████████| 819kB 43.3MB/s \n",
"\u001b[K |████████████████████████████████| 829kB 52.9MB/s \n",
"\u001b[K |████████████████████████████████| 276kB 47.4MB/s \n",
"\u001b[K |████████████████████████████████| 112kB 56.5MB/s \n",
"\u001b[K |████████████████████████████████| 1.3MB 50.1MB/s \n",
"\u001b[K |████████████████████████████████| 143kB 60.6MB/s \n",
"\u001b[K |████████████████████████████████| 296kB 59.7MB/s \n",
"\u001b[?25h Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for PyYAML (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vOAdk0qdQ2ef",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4450f600-c758-49f6-9f28-59f5277015a9"
},
"source": [
"import torch\n",
"import torchtext\n",
"import torch.nn as nn\n",
"from torchtext.datasets import text_classification\n",
"NGRAMS = 2\n",
"import os\n",
"if not os.path.isdir('./.data'):\n",
"\tos.mkdir('./.data')\n",
"train_dataset, test_dataset = text_classification.DATASETS['AG_NEWS'](\n",
" root='./.data', ngrams=NGRAMS, vocab=None)\n",
"BATCH_SIZE = 16"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"ag_news_csv.tar.gz: 11.8MB [00:00, 67.7MB/s]\n",
"120000lines [00:06, 18254.78lines/s]\n",
"120000lines [00:14, 8170.78lines/s]\n",
"7600lines [00:00, 8609.89lines/s]\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "SUOxiQOJQ2el"
},
"source": [
"VOCAB_SIZE = len(train_dataset.get_vocab())\n",
"EMBED_DIM = 32\n",
"NUM_CLASS = len(train_dataset.get_labels())"
],
"execution_count": 13,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "YJXial03Q2em"
},
"source": [
"Functions used to generate batch\n",
"--------------------------------\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "36rTk-S5Q2en"
},
"source": [
"def generate_batch(batch):\n",
" label = torch.tensor([entry[0] for entry in batch])\n",
" text = [entry[1] for entry in batch]\n",
" offsets = [0] + [len(entry) for entry in text]\n",
" # torch.Tensor.cumsum returns the cumulative sum\n",
" # of elements in the dimension dim.\n",
" # torch.Tensor([1.0, 2.0, 3.0]).cumsum(dim=0)\n",
"\n",
" offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)\n",
" text = torch.cat(text)\n",
" return text, offsets, label"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "kiaOrOBhQ2ep"
},
"source": [
"Define functions to train the model and evaluate results.\n",
"---------------------------------------------------------\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "mMzZQ2e-Q2er"
},
"source": [
"import time\n",
"from torch.utils.data.dataset import random_split\n",
"\n",
"train_len = int(len(train_dataset) * 0.95)\n",
"sub_train_, sub_valid_ = \\\n",
" random_split(train_dataset, [train_len, len(train_dataset) - train_len])"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "drSdUCLuSuhK"
},
"source": [
"from torch.utils.data import DataLoader\r\n",
"\r\n",
"train_dl = DataLoader(sub_train_, batch_size=BATCH_SIZE, shuffle=True,\r\n",
" collate_fn=generate_batch)\r\n",
"valid_dl = DataLoader(sub_valid_, batch_size=BATCH_SIZE, shuffle=False,\r\n",
" collate_fn=generate_batch)"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "9-3usDU_TGY9"
},
"source": [
"import pytorch_lightning as pl\r\n",
"from torch import optim\r\n",
"from torch.nn import CrossEntropyLoss"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qFUFjqu_S9Gp"
},
"source": [
"class LitTextClassifier(pl.LightningModule):\r\n",
" def __init__(self, num_class, criterion = CrossEntropyLoss):\r\n",
" super().__init__()\r\n",
" self.embedding = nn.EmbeddingBag(VOCAB_SIZE, EMBED_DIM, sparse=False)\r\n",
" self.fc = nn.Linear(EMBED_DIM, num_class)\r\n",
" self.init_weights()\r\n",
" self.criterion = criterion()\r\n",
" self.accuracy = pl.metrics.Accuracy()\r\n",
"\r\n",
" def init_weights(self):\r\n",
" initrange = 0.5\r\n",
" self.embedding.weight.data.uniform_(-initrange, initrange)\r\n",
" self.fc.weight.data.uniform_(-initrange, initrange)\r\n",
" self.fc.bias.data.zero_()\r\n",
"\r\n",
" def forward(self, text, offsets):\r\n",
" embedded = self.embedding(text, offsets)\r\n",
" return self.fc(embedded)\r\n",
"\r\n",
" def configure_optimizers(self):\r\n",
" optimizer = optim.SGD(self.parameters(), lr=4.0)\r\n",
" return optimizer\r\n",
"\r\n",
" def training_step(self, batch, batch_idx):\r\n",
" text, offsets, cls = batch\r\n",
" output = self.forward(text, offsets)\r\n",
" loss = self.criterion(output, cls)\r\n",
" self.log(\"loss\", loss)\r\n",
" return loss\r\n",
"\r\n",
" def validation_step(self, batch, batch_idx):\r\n",
" text, offsets, cls = batch\r\n",
" output = self.forward(text, offsets)\r\n",
" loss = self.criterion(output, cls)\r\n",
" self.log('val_acc', self.accuracy(output.argmax(1), cls), prog_bar=True)\r\n",
" self.log(\"val_loss\", loss)\r\n",
" \r\n",
" return loss"
],
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "TgxhOV1YUCoD"
},
"source": [
"classifier = LitTextClassifier(NUM_CLASS)"
],
"execution_count": 15,
"outputs": []
},
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"source": [
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"trainer.fit(classifier, train_dl, valid_dl)"
],
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"text": [
"GPU available: True, used: True\n",
"INFO:lightning:GPU available: True, used: True\n",
"TPU available: None, using: 0 TPU cores\n",
"INFO:lightning:TPU available: None, using: 0 TPU cores\n",
"\n",
" | Name | Type | Params\n",
"-----------------------------------------------\n",
"0 | embedding | EmbeddingBag | 41.9 M\n",
"1 | fc | Linear | 132 \n",
"2 | criterion | CrossEntropyLoss | 0 \n",
"3 | accuracy | Accuracy | 0 \n",
"-----------------------------------------------\n",
"41.9 M Trainable params\n",
"0 Non-trainable params\n",
"41.9 M Total params\n",
"167.533 Total estimated model params size (MB)\n",
"INFO:lightning:\n",
" | Name | Type | Params\n",
"-----------------------------------------------\n",
"0 | embedding | EmbeddingBag | 41.9 M\n",
"1 | fc | Linear | 132 \n",
"2 | criterion | CrossEntropyLoss | 0 \n",
"3 | accuracy | Accuracy | 0 \n",
"-----------------------------------------------\n",
"41.9 M Trainable params\n",
"0 Non-trainable params\n",
"41.9 M Total params\n",
"167.533 Total estimated model params size (MB)\n"
],
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},
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