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* add MNIST DALI example, update README.md * Fix PEP8 warnings * reformatted using black * add mnist_dali to test_examples.py * Add documentation as docstrings * add nvidia-pyindex and nvidia-dali-cuda100 * replace nvidia-pyindex with --extra-index-url * mark mnist_dali test as Linux and GPU only * adjust CUDA docker and examples.txt, fix import error in test_examples.py * adjust the GPU check * Exit when DALI is not available * remove requirements-examples.txt and DALI pip install * Refactored example, moved to new logging api, added runtime check for test and dali script * Patch to reflect the mnist example module * add req. * Apply suggestions from code review * Removed requirement as it breaks CPU install, added note in README to install DALI * add DALI to Drone * test examples * Apply suggestions from code review * imports * ABC * cuda * cuda * pip DALI * Move build into init function Co-authored-by: SeanNaren <[email protected]> Co-authored-by: Jirka Borovec <[email protected]> Co-authored-by: Jirka Borovec <[email protected]> Co-authored-by: Sean Naren <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,204 @@ | ||
| # Copyright The PyTorch Lightning team. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| from abc import ABC | ||
| from argparse import ArgumentParser | ||
| from random import shuffle | ||
| from warnings import warn | ||
|
|
||
| import numpy as np | ||
| import torch | ||
| from torch.nn import functional as F | ||
| from torch.utils.data import random_split | ||
|
|
||
| import pytorch_lightning as pl | ||
|
|
||
| try: | ||
| from torchvision.datasets.mnist import MNIST | ||
| from torchvision import transforms | ||
| except Exception: | ||
| from tests.base.datasets import MNIST | ||
|
|
||
| try: | ||
| import nvidia.dali.ops as ops | ||
| import nvidia.dali.types as types | ||
| from nvidia.dali.pipeline import Pipeline | ||
| from nvidia.dali.plugin.pytorch import DALIClassificationIterator | ||
| except (ImportError, ModuleNotFoundError): | ||
| warn('NVIDIA DALI is not available') | ||
| ops, types, Pipeline, DALIClassificationIterator = ..., ..., ABC, ABC | ||
|
|
||
|
|
||
| class ExternalMNISTInputIterator(object): | ||
| """ | ||
| This iterator class wraps torchvision's MNIST dataset and returns the images and labels in batches | ||
| """ | ||
|
|
||
| def __init__(self, mnist_ds, batch_size): | ||
| self.batch_size = batch_size | ||
| self.mnist_ds = mnist_ds | ||
| self.indices = list(range(len(self.mnist_ds))) | ||
| shuffle(self.indices) | ||
|
|
||
| def __iter__(self): | ||
| self.i = 0 | ||
| self.n = len(self.mnist_ds) | ||
| return self | ||
|
|
||
| def __next__(self): | ||
| batch = [] | ||
| labels = [] | ||
| for _ in range(self.batch_size): | ||
| index = self.indices[self.i] | ||
| img, label = self.mnist_ds[index] | ||
| batch.append(img.numpy()) | ||
| labels.append(np.array([label], dtype=np.uint8)) | ||
| self.i = (self.i + 1) % self.n | ||
| return (batch, labels) | ||
|
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||
|
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||
| class ExternalSourcePipeline(Pipeline): | ||
| """ | ||
| This DALI pipeline class just contains the MNIST iterator | ||
| """ | ||
|
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||
| def __init__(self, batch_size, eii, num_threads, device_id): | ||
| super(ExternalSourcePipeline, self).__init__(batch_size, num_threads, device_id, seed=12) | ||
| self.source = ops.ExternalSource(source=eii, num_outputs=2) | ||
| self.build() | ||
|
|
||
| def define_graph(self): | ||
| images, labels = self.source() | ||
| return images, labels | ||
|
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||
|
|
||
| class DALIClassificationLoader(DALIClassificationIterator): | ||
| """ | ||
| This class extends DALI's original DALIClassificationIterator with the __len__() function so that we can call len() on it | ||
| """ | ||
|
|
||
| def __init__( | ||
| self, | ||
| pipelines, | ||
| size=-1, | ||
| reader_name=None, | ||
| auto_reset=False, | ||
| fill_last_batch=True, | ||
| dynamic_shape=False, | ||
| last_batch_padded=False, | ||
| ): | ||
| super().__init__(pipelines, size, reader_name, auto_reset, fill_last_batch, dynamic_shape, last_batch_padded) | ||
|
|
||
| def __len__(self): | ||
| batch_count = self._size // (self._num_gpus * self.batch_size) | ||
| last_batch = 1 if self._fill_last_batch else 0 | ||
| return batch_count + last_batch | ||
|
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||
|
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| class LitClassifier(pl.LightningModule): | ||
| def __init__(self, hidden_dim=128, learning_rate=1e-3): | ||
| super().__init__() | ||
| self.save_hyperparameters() | ||
|
|
||
| self.l1 = torch.nn.Linear(28 * 28, self.hparams.hidden_dim) | ||
| self.l2 = torch.nn.Linear(self.hparams.hidden_dim, 10) | ||
|
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||
| def forward(self, x): | ||
| x = x.view(x.size(0), -1) | ||
| x = torch.relu(self.l1(x)) | ||
| x = torch.relu(self.l2(x)) | ||
| return x | ||
|
|
||
| def split_batch(self, batch): | ||
| return batch[0]["data"], batch[0]["label"].squeeze().long() | ||
|
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| def training_step(self, batch, batch_idx): | ||
| x, y = self.split_batch(batch) | ||
| y_hat = self(x) | ||
| loss = F.cross_entropy(y_hat, y) | ||
| return loss | ||
|
|
||
| def validation_step(self, batch, batch_idx): | ||
| x, y = self.split_batch(batch) | ||
| y_hat = self(x) | ||
| loss = F.cross_entropy(y_hat, y) | ||
| self.log('valid_loss', loss) | ||
|
|
||
| def test_step(self, batch, batch_idx): | ||
| x, y = self.split_batch(batch) | ||
| y_hat = self(x) | ||
| loss = F.cross_entropy(y_hat, y) | ||
| self.log('test_loss', loss) | ||
|
|
||
| def configure_optimizers(self): | ||
| return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate) | ||
|
|
||
| @staticmethod | ||
| def add_model_specific_args(parent_parser): | ||
| parser = ArgumentParser(parents=[parent_parser], add_help=False) | ||
| parser.add_argument('--hidden_dim', type=int, default=128) | ||
| parser.add_argument('--learning_rate', type=float, default=0.0001) | ||
| return parser | ||
|
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||
|
|
||
| def cli_main(): | ||
| pl.seed_everything(1234) | ||
|
|
||
| # ------------ | ||
| # args | ||
| # ------------ | ||
| parser = ArgumentParser() | ||
| parser.add_argument('--batch_size', default=32, type=int) | ||
| parser = pl.Trainer.add_argparse_args(parser) | ||
| parser = LitClassifier.add_model_specific_args(parser) | ||
| args = parser.parse_args() | ||
|
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||
| # ------------ | ||
| # data | ||
| # ------------ | ||
| dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor()) | ||
| mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor()) | ||
| mnist_train, mnist_val = random_split(dataset, [55000, 5000]) | ||
|
|
||
| eii_train = ExternalMNISTInputIterator(mnist_train, args.batch_size) | ||
| eii_val = ExternalMNISTInputIterator(mnist_val, args.batch_size) | ||
| eii_test = ExternalMNISTInputIterator(mnist_test, args.batch_size) | ||
|
|
||
| pipe_train = ExternalSourcePipeline(batch_size=args.batch_size, eii=eii_train, num_threads=2, device_id=0) | ||
| train_loader = DALIClassificationLoader(pipe_train, size=len(mnist_train), auto_reset=True, fill_last_batch=False) | ||
|
|
||
| pipe_val = ExternalSourcePipeline(batch_size=args.batch_size, eii=eii_val, num_threads=2, device_id=0) | ||
| val_loader = DALIClassificationLoader(pipe_val, size=len(mnist_val), auto_reset=True, fill_last_batch=False) | ||
|
|
||
| pipe_test = ExternalSourcePipeline(batch_size=args.batch_size, eii=eii_test, num_threads=2, device_id=0) | ||
| test_loader = DALIClassificationLoader(pipe_test, size=len(mnist_test), auto_reset=True, fill_last_batch=False) | ||
|
|
||
| # ------------ | ||
| # model | ||
| # ------------ | ||
| model = LitClassifier(args.hidden_dim, args.learning_rate) | ||
|
|
||
| # ------------ | ||
| # training | ||
| # ------------ | ||
| trainer = pl.Trainer.from_argparse_args(args) | ||
| trainer.fit(model, train_loader, val_loader) | ||
|
|
||
| # ------------ | ||
| # testing | ||
| # ------------ | ||
| trainer.test(test_dataloaders=test_loader) | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| cli_main() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,2 +1,2 @@ | ||
| torchvision>=0.4.1,<0.9.0 | ||
| gym>=0.17.0 | ||
| gym>=0.17.0 |