208 lines
6.7 KiB
Python
208 lines
6.7 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from abc import ABC
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from argparse import ArgumentParser
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from random import shuffle
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from warnings import warn
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import numpy as np
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import torch
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from torch.nn import functional as F
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from torch.utils.data import random_split
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import pytorch_lightning as pl
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from pl_examples import TORCHVISION_AVAILABLE, DALI_AVAILABLE
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if TORCHVISION_AVAILABLE:
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from torchvision.datasets.mnist import MNIST
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from torchvision import transforms
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else:
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from tests.base.datasets import MNIST
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if DALI_AVAILABLE:
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import nvidia.dali.ops as ops
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from nvidia.dali.pipeline import Pipeline
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from nvidia.dali.plugin.pytorch import DALIClassificationIterator
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else:
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warn('NVIDIA DALI is not available')
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ops, Pipeline, DALIClassificationIterator = ..., ABC, ABC
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class ExternalMNISTInputIterator(object):
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"""
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This iterator class wraps torchvision's MNIST dataset and returns the images and labels in batches
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"""
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def __init__(self, mnist_ds, batch_size):
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self.batch_size = batch_size
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self.mnist_ds = mnist_ds
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self.indices = list(range(len(self.mnist_ds)))
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shuffle(self.indices)
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def __iter__(self):
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self.i = 0
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self.n = len(self.mnist_ds)
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return self
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def __next__(self):
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batch = []
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labels = []
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for _ in range(self.batch_size):
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index = self.indices[self.i]
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img, label = self.mnist_ds[index]
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batch.append(img.numpy())
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labels.append(np.array([label], dtype=np.uint8))
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self.i = (self.i + 1) % self.n
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return (batch, labels)
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class ExternalSourcePipeline(Pipeline):
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"""
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This DALI pipeline class just contains the MNIST iterator
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"""
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def __init__(self, batch_size, eii, num_threads, device_id):
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super(ExternalSourcePipeline, self).__init__(batch_size, num_threads, device_id, seed=12)
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self.source = ops.ExternalSource(source=eii, num_outputs=2)
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self.build()
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def define_graph(self):
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images, labels = self.source()
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return images, labels
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class DALIClassificationLoader(DALIClassificationIterator):
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"""
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This class extends DALI's original DALIClassificationIterator with the __len__() function so that we can call len() on it
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"""
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def __init__(
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self,
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pipelines,
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size=-1,
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reader_name=None,
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auto_reset=False,
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fill_last_batch=True,
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dynamic_shape=False,
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last_batch_padded=False,
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):
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super().__init__(pipelines, size, reader_name, auto_reset, fill_last_batch, dynamic_shape, last_batch_padded)
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def __len__(self):
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batch_count = self._size // (self._num_gpus * self.batch_size)
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last_batch = 1 if self._fill_last_batch else 0
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return batch_count + last_batch
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class LitClassifier(pl.LightningModule):
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def __init__(self, hidden_dim=128, learning_rate=1e-3):
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super().__init__()
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self.save_hyperparameters()
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self.l1 = torch.nn.Linear(28 * 28, self.hparams.hidden_dim)
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self.l2 = torch.nn.Linear(self.hparams.hidden_dim, 10)
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def forward(self, x):
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x = x.view(x.size(0), -1)
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x = torch.relu(self.l1(x))
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x = torch.relu(self.l2(x))
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return x
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def split_batch(self, batch):
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return batch[0]["data"], batch[0]["label"].squeeze().long()
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def training_step(self, batch, batch_idx):
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x, y = self.split_batch(batch)
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y_hat = self(x)
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loss = F.cross_entropy(y_hat, y)
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return loss
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def validation_step(self, batch, batch_idx):
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x, y = self.split_batch(batch)
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y_hat = self(x)
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loss = F.cross_entropy(y_hat, y)
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self.log('valid_loss', loss)
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def test_step(self, batch, batch_idx):
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x, y = self.split_batch(batch)
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y_hat = self(x)
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loss = F.cross_entropy(y_hat, y)
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self.log('test_loss', loss)
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def configure_optimizers(self):
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return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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@staticmethod
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def add_model_specific_args(parent_parser):
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parser = ArgumentParser(parents=[parent_parser], add_help=False)
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parser.add_argument('--hidden_dim', type=int, default=128)
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parser.add_argument('--learning_rate', type=float, default=0.0001)
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return parser
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def cli_main():
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if not DALI_AVAILABLE:
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return
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pl.seed_everything(1234)
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# ------------
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# args
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# ------------
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parser = ArgumentParser()
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parser.add_argument('--batch_size', default=32, type=int)
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parser = pl.Trainer.add_argparse_args(parser)
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parser = LitClassifier.add_model_specific_args(parser)
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args = parser.parse_args()
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# ------------
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# data
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# ------------
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dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
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mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
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mnist_train, mnist_val = random_split(dataset, [55000, 5000])
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eii_train = ExternalMNISTInputIterator(mnist_train, args.batch_size)
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eii_val = ExternalMNISTInputIterator(mnist_val, args.batch_size)
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eii_test = ExternalMNISTInputIterator(mnist_test, args.batch_size)
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pipe_train = ExternalSourcePipeline(batch_size=args.batch_size, eii=eii_train, num_threads=2, device_id=0)
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train_loader = DALIClassificationLoader(pipe_train, size=len(mnist_train), auto_reset=True, fill_last_batch=False)
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pipe_val = ExternalSourcePipeline(batch_size=args.batch_size, eii=eii_val, num_threads=2, device_id=0)
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val_loader = DALIClassificationLoader(pipe_val, size=len(mnist_val), auto_reset=True, fill_last_batch=False)
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pipe_test = ExternalSourcePipeline(batch_size=args.batch_size, eii=eii_test, num_threads=2, device_id=0)
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test_loader = DALIClassificationLoader(pipe_test, size=len(mnist_test), auto_reset=True, fill_last_batch=False)
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# ------------
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# model
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# ------------
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model = LitClassifier(args.hidden_dim, args.learning_rate)
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# ------------
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# training
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# ------------
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trainer = pl.Trainer.from_argparse_args(args)
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trainer.fit(model, train_loader, val_loader)
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# ------------
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# testing
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# ------------
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trainer.test(test_dataloaders=test_loader)
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if __name__ == "__main__":
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cli_main()
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