Add Dali MNIST example

.drone.yml

@@ -32,6 +32,8 @@ steps:
     - pip --version
     - nvidia-smi
     - pip install -r ./requirements/devel.txt --upgrade-strategy only-if-needed -v --no-cache-dir
+    # when Image has defined CUDa version we can switch to this package spec "nvidia-dali-cuda${CUDA_VERSION%%.*}0"
+    - pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali-cuda100 --upgrade-strategy only-if-needed
     - pip list
     - coverage run --source pytorch_lightning -m pytest pytorch_lightning tests -v --color=yes --durations=25 # --flake8
     - python -m pytest benchmarks pl_examples -v --color=yes --maxfail=2 --durations=0 # --flake8

pl_examples/basic_examples/README.md

@@ -14,7 +14,15 @@ python mnist.py
 python mnist.py --gpus 2 --distributed_backend 'dp'
 ```
 
----   
+---
+#### MNIST with DALI
+The MNIST example above using [NVIDIA DALI](https://developer.nvidia.com/DALI).
+Requires NVIDIA DALI to be installed based on your CUDA version, see [here](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/installation.html).
+```bash
+python mnist_dali.py
+```
+
+---
 #### Image classifier
 Generic image classifier with an arbitrary backbone (ie: a simple system)
 ```bash

pl_examples/basic_examples/mnist_dali.py

@@ -0,0 +1,206 @@
+# 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)
+
+
+class ExternalSourcePipeline(Pipeline):
+    """
+    This DALI pipeline class just contains the MNIST iterator
+    """
+
+    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)
+
+    def define_graph(self):
+        images, labels = self.source()
+        return images, labels
+
+
+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
+
+
+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)
+
+    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()
+
+    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
+
+
+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()
+
+    # ------------
+    # 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)
+    pipe_train.build()
+    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)
+    pipe_val.build()
+    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)
+    pipe_test.build()
+    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()

pl_examples/test_examples.py

@@ -1,6 +1,15 @@
+import platform
 from unittest import mock
-import torch
+
 import pytest
+import torch
+
+try:
+    from nvidia.dali import ops, types, pipeline, plugin
+except (ImportError, ModuleNotFoundError):
+    DALI_AVAILABLE = False
+else:
+    DALI_AVAILABLE = True
 
 dp_16_args = """
 --max_epochs 1 \
@@ -28,7 +37,7 @@
 --precision 16 \
 """
 
-
+# TODO
 # @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 # @pytest.mark.parametrize('cli_args', [dp_16_args])
 # def test_examples_dp_mnist(cli_args):
@@ -38,15 +47,17 @@
 #         cli_main()
 
 
+# TODO
 # @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 # @pytest.mark.parametrize('cli_args', [dp_16_args])
 # def test_examples_dp_image_classifier(cli_args):
 #     from pl_examples.basic_examples.image_classifier import cli_main
 #
 #     with mock.patch("argparse._sys.argv", ["any.py"] + cli_args.strip().split()):
 #         cli_main()
-#
-#
+
+
+# TODO
 # @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 # @pytest.mark.parametrize('cli_args', [dp_16_args])
 # def test_examples_dp_autoencoder(cli_args):
@@ -56,24 +67,27 @@
 #         cli_main()
 
 
+# TODO
 # @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 # @pytest.mark.parametrize('cli_args', [ddp_args])
 # def test_examples_ddp_mnist(cli_args):
 #     from pl_examples.basic_examples.mnist import cli_main
 #
 #     with mock.patch("argparse._sys.argv", ["any.py"] + cli_args.strip().split()):
 #         cli_main()
-#
-#
+
+
+# TODO
 # @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 # @pytest.mark.parametrize('cli_args', [ddp_args])
 # def test_examples_ddp_image_classifier(cli_args):
 #     from pl_examples.basic_examples.image_classifier import cli_main
 #
 #     with mock.patch("argparse._sys.argv", ["any.py"] + cli_args.strip().split()):
 #         cli_main()
-#
-#
+
+
+# TODO
 # @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
 # @pytest.mark.parametrize('cli_args', [ddp_args])
 # def test_examples_ddp_autoencoder(cli_args):
@@ -92,3 +106,14 @@ def test_examples_cpu(cli_args):
     for cli_cmd in [mnist_cli, ic_cli, ae_cli]:
         with mock.patch("argparse._sys.argv", ["any.py"] + cli_args.strip().split()):
             cli_cmd()
+
+
+@pytest.mark.skipif(not DALI_AVAILABLE, reason="Nvidia DALI required")
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
+@pytest.mark.skipif(platform.system() != 'Linux', reason='Only applies to Linux platform.')
+@pytest.mark.parametrize('cli_args', [cpu_args])
+def test_examples_mnist_dali(cli_args):
+    from pl_examples.basic_examples.mnist_dali import cli_main
+
+    with mock.patch("argparse._sys.argv", ["any.py"] + cli_args.strip().split()):
+        cli_main()

requirements/examples.txt

@@ -1,2 +1,2 @@
 torchvision>=0.4.1,<0.9.0
-gym>=0.17.0
+gym>=0.17.0