update DALIClassificationLoader to not use deprecated arguments (#4925)

* update DALIClassificationLoader to not use deprecated arguments

* fix line length

* dali version check added and changed args accordingly

* versions

* checking version using disutils.version.LooseVersion now

* .

* ver

* import

Co-authored-by: Jirka Borovec <[email protected]>
Co-authored-by: Jirka Borovec <[email protected]>

Author: gan3sh500

.drone.yml

@@ -36,8 +36,7 @@ steps:
     - pip install -r ./requirements/devel.txt --upgrade-strategy only-if-needed -v --no-cache-dir
     - pip install git+https://${AUTH_TOKEN}@github.com/PyTorchLightning/[email protected] -v --no-cache-dir
     # when Image has defined CUDa version we can switch to this package spec "nvidia-dali-cuda${CUDA_VERSION%%.*}0"
-    # todo: temprarl fix till https://github.com/PyTorchLightning/pytorch-lightning/pull/4922 is resolved
-    - pip install --extra-index-url https://developer.download.nvidia.com/compute/redist "nvidia-dali-cuda100<0.27" --upgrade-strategy only-if-needed
+    - pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali-cuda100 --upgrade-strategy only-if-needed
     - pip list
     - python -m coverage run --source pytorch_lightning -m pytest pytorch_lightning tests -v --durations=25 # --flake8
     # Running special tests

pl_examples/basic_examples/dali_image_classifier.py

@@ -15,6 +15,7 @@
 from argparse import ArgumentParser
 from random import shuffle
 from warnings import warn
+from distutils.version import LooseVersion
 
 import numpy as np
 import torch
@@ -31,12 +32,17 @@
     from tests.base.datasets import MNIST
 
 if DALI_AVAILABLE:
-    import nvidia.dali.ops as ops
+    from nvidia.dali import ops
     from nvidia.dali.pipeline import Pipeline
     from nvidia.dali.plugin.pytorch import DALIClassificationIterator
+    from nvidia.dali import __version__ as dali_version
+
+    NEW_DALI_API = LooseVersion(dali_version) >= LooseVersion('0.28.0')
+    if NEW_DALI_API:
+        from nvidia.dali.plugin.base_iterator import LastBatchPolicy
 else:
     warn('NVIDIA DALI is not available')
-    ops, Pipeline, DALIClassificationIterator = ..., ABC, ABC
+    ops, Pipeline, DALIClassificationIterator, LastBatchPolicy = ..., ABC, ABC, ABC
 
 
 class ExternalMNISTInputIterator(object):
@@ -98,11 +104,18 @@ def __init__(
             dynamic_shape=False,
             last_batch_padded=False,
     ):
-        super().__init__(pipelines, size, reader_name, auto_reset, fill_last_batch, dynamic_shape, last_batch_padded)
+        if NEW_DALI_API:
+            last_batch_policy = LastBatchPolicy.FILL if fill_last_batch else LastBatchPolicy.DROP
+            super().__init__(pipelines, size, reader_name, auto_reset, dynamic_shape,
+                             last_batch_policy=last_batch_policy, last_batch_padded=last_batch_padded)
+        else:
+            super().__init__(pipelines, size, reader_name, auto_reset, fill_last_batch,
+                             dynamic_shape, last_batch_padded)
+        self._fill_last_batch = fill_last_batch
 
     def __len__(self):
         batch_count = self._size // (self._num_gpus * self.batch_size)
-        last_batch = 1 if self._fill_last_batch else 0
+        last_batch = 1 if self._fill_last_batch else 1
         return batch_count + last_batch
 
 
@@ -179,7 +192,7 @@ def cli_main():
     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)
+    train_loader = DALIClassificationLoader(pipe_train, size=len(mnist_train), auto_reset=True, fill_last_batch=True)
 
     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)