Fix Python formatting

dali/test/python/test_backend_impl_torch_dlpack.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2020-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,9 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from nvidia.dali.backend_impl import *
-from nvidia.dali.pipeline import Pipeline
-import nvidia.dali.ops as ops
+from nvidia.dali.tensors import TensorCPU, TensorGPU, TensorListCPU, TensorListGPU
 import nvidia.dali.tensors as tensors
 import numpy as np
 import torch
@@ -36,55 +34,59 @@ def test_dlpack_tensor_gpu_direct_creation():
     arr = torch.rand(size=[3, 5, 6], device="cuda")
     tensor = TensorGPU(to_dlpack(arr))
     dali_torch_tensor = convert_to_torch(tensor, device=arr.device, dtype=arr.dtype)
-    assert(torch.all(arr.eq(dali_torch_tensor)))
+    assert torch.all(arr.eq(dali_torch_tensor))
 
 
 def test_dlpack_tensor_gpu_to_cpu():
     arr = torch.rand(size=[3, 5, 6], device="cuda")
     tensor = TensorGPU(to_dlpack(arr))
     dali_torch_tensor = convert_to_torch(tensor, device=arr.device, dtype=arr.dtype)
-    assert(torch.all(arr.cpu().eq(dali_torch_tensor.cpu())))
+    assert torch.all(arr.cpu().eq(dali_torch_tensor.cpu()))
 
 
 def test_dlpack_tensor_list_gpu_direct_creation():
     arr = torch.rand(size=[3, 5, 6], device="cuda")
     tensor_list = TensorListGPU(to_dlpack(arr), "NHWC")
     dali_torch_tensor = convert_to_torch(tensor_list, device=arr.device, dtype=arr.dtype)
-    assert(torch.all(arr.eq(dali_torch_tensor)))
+    assert torch.all(arr.eq(dali_torch_tensor))
 
 
 def test_dlpack_tensor_list_gpu_to_cpu():
     arr = torch.rand(size=[3, 5, 6], device="cuda")
     tensor_list = TensorListGPU(to_dlpack(arr), "NHWC")
     dali_torch_tensor = convert_to_torch(tensor_list, device=arr.device, dtype=arr.dtype)
-    assert(torch.all(arr.cpu().eq(dali_torch_tensor.cpu())))
+    assert torch.all(arr.cpu().eq(dali_torch_tensor.cpu()))
 
 
 def check_dlpack_types_gpu(t):
     arr = torch.tensor([[-0.39, 1.5], [-1.5, 0.33]], device="cuda", dtype=t)
     tensor = TensorGPU(to_dlpack(arr), "NHWC")
-    dali_torch_tensor = convert_to_torch(tensor, device=arr.device, dtype=arr.dtype, size=tensor.shape())
-    assert(torch.all(arr.eq(dali_torch_tensor)))
+    dali_torch_tensor = convert_to_torch(tensor, device=arr.device, dtype=arr.dtype,
+                                         size=tensor.shape())
+    assert torch.all(arr.eq(dali_torch_tensor))
 
 
 def test_dlpack_interface_types():
-    for t in [torch.bool, torch.int8, torch.int16, torch.int32, torch.int64, torch.uint8,
-              torch.float64, torch.float32, torch.float16]:
+    for t in [
+            torch.bool, torch.int8, torch.int16, torch.int32, torch.int64, torch.uint8,
+            torch.float64, torch.float32, torch.float16
+    ]:
         yield check_dlpack_types_gpu, t
 
 
 def test_dlpack_tensor_cpu_direct_creation():
     arr = torch.rand(size=[3, 5, 6], device="cpu")
     tensor = TensorCPU(to_dlpack(arr))
     dali_torch_tensor = convert_to_torch(tensor, device=arr.device, dtype=arr.dtype)
-    assert(torch.all(arr.eq(dali_torch_tensor)))
+    assert torch.all(arr.eq(dali_torch_tensor))
 
 
 def test_dlpack_tensor_list_cpu_direct_creation():
     arr = torch.rand(size=[3, 5, 6], device="cpu")
     tensor_list = TensorListCPU(to_dlpack(arr), "NHWC")
     dali_torch_tensor = convert_to_torch(tensor_list, device=arr.device, dtype=arr.dtype)
-    assert(torch.all(arr.eq(dali_torch_tensor)))
+    assert torch.all(arr.eq(dali_torch_tensor))
+
 
 # Check if dlpack tensors behave correctly when created from temporary objects
 
@@ -94,6 +96,7 @@ def create_tmp(idx):
         a = np.full((4, 4), idx)
         dlt = to_dlpack(torch.from_numpy(a))
         return tensors.TensorCPU(dlt, "")
+
     out = [create_tmp(i) for i in range(4)]
     for i, t in enumerate(out):
         np.testing.assert_array_equal(np.array(t), np.full((4, 4), i))
@@ -104,6 +107,7 @@ def create_tmp(idx):
         a = np.full((4, 4), idx)
         dlt = to_dlpack(torch.from_numpy(a))
         return tensors.TensorListCPU(dlt, "")
+
     out = [create_tmp(i) for i in range(4)]
     for i, tl in enumerate(out):
         np.testing.assert_array_equal(tl.as_array(), np.full((4, 4), i))
@@ -114,6 +118,7 @@ def create_tmp(idx):
         a = np.full((4, 4), idx)
         dlt = to_dlpack(torch.from_numpy(a).cuda())
         return tensors.TensorGPU(dlt, "")
+
     out = [create_tmp(i) for i in range(4)]
     for i, t in enumerate(out):
         np.testing.assert_array_equal(np.array(t.as_cpu()), np.full((4, 4), i))
@@ -124,6 +129,7 @@ def create_tmp(idx):
         a = np.full((4, 4), idx)
         dlt = to_dlpack(torch.from_numpy(a).cuda())
         return tensors.TensorListGPU(dlt, "")
+
     out = [create_tmp(i) for i in range(4)]
     for i, tl in enumerate(out):
         np.testing.assert_array_equal(tl.as_cpu().as_array(), np.full((4, 4), i))
@@ -132,13 +138,16 @@ def create_tmp(idx):
 def check_dlpack_types_cpu(t):
     arr = torch.tensor([[-0.39, 1.5], [-1.5, 0.33]], device="cpu", dtype=t)
     tensor = TensorCPU(to_dlpack(arr), "NHWC")
-    dali_torch_tensor = convert_to_torch(tensor, device=arr.device, dtype=arr.dtype, size=tensor.shape())
-    assert(torch.all(arr.eq(dali_torch_tensor)))
+    dali_torch_tensor = convert_to_torch(tensor, device=arr.device, dtype=arr.dtype,
+                                         size=tensor.shape())
+    assert torch.all(arr.eq(dali_torch_tensor))
 
 
 def test_dlpack_interface_types_cpu():
-    for t in [torch.bool, torch.int8, torch.int16, torch.int32, torch.int64, torch.uint8,
-              torch.float64, torch.float32]:
+    for t in [
+            torch.bool, torch.int8, torch.int16, torch.int32, torch.int64, torch.uint8,
+            torch.float64, torch.float32
+    ]:
         yield check_dlpack_types_cpu, t
 
 

dali/test/python/test_coco_tfrecord.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2019-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,7 +14,6 @@
 
 import argparse
 import os
-from math import ceil, sqrt
 
 import nvidia.dali.ops as ops
 import nvidia.dali.types as types
@@ -37,7 +36,7 @@ def __init__(self, args):
             path=os.path.join(test_dummy_data_path, 'small_coco.tfrecord'),
             index_path=os.path.join(test_dummy_data_path, 'small_coco_index.idx'),
             features={
-                'image/encoded' : tfrec.FixedLenFeature((), tfrec.string, ""),
+                'image/encoded': tfrec.FixedLenFeature((), tfrec.string, ""),
                 'image/object/class/label':  tfrec.VarLenFeature([], tfrec.int64,  0),
                 'image/object/bbox':    tfrec.VarLenFeature([4], tfrec.float32, 0.0),
             },
@@ -52,7 +51,6 @@ def __init__(self, args):
             criteria=0.5,
             anchors=coco_anchors())
 
-
     def define_graph(self):
         inputs = self.input()
         input_images = inputs["image/encoded"]
@@ -89,7 +87,6 @@ def __init__(self, args, data_path=test_data_path):
             criteria=0.5,
             anchors=coco_anchors())
 
-
     def define_graph(self):
         inputs, boxes, labels = self.input(name="Reader")
         image_gpu = self.decode_gpu(inputs)

dali/test/python/test_dali_tf_dataset.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright (c) 2020-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -50,7 +50,7 @@ def test_python_operator_not_allowed_in_tf_dataset_error():
     dtypes = (tf.float32)
 
     with tf.device('/cpu:0'):
-        daliset = dali_tf.DALIDataset(
+        _ = dali_tf.DALIDataset(
             pipeline=pipeline,
             batch_size=1,
             output_shapes=shapes,

dali/test/python/test_dali_tf_dataset_mnist.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright (c) 2019-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,16 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import nvidia.dali as dali
 import nvidia.dali.plugin.tf as dali_tf
 import os
-import numpy as np
 from nvidia.dali.pipeline import Pipeline
 import nvidia.dali.fn as fn
 import nvidia.dali.types as types
-from test_utils_tensorflow import *
+from test_utils_tensorflow import num_available_gpus
 from shutil import rmtree as remove_directory
-from nose.tools import with_setup
 import tensorflow as tf
 import tensorflow.compat.v1 as tf_v1
 
@@ -44,15 +41,12 @@ def mnist_pipeline(
     with pipeline:
         jpegs, labels = fn.readers.caffe2(
             path=path, random_shuffle=True, shard_id=shard_id, num_shards=num_shards)
-        images = fn.decoders.image(jpegs, device='mixed' if device == 'gpu' else 'cpu', output_type=types.GRAY)
+        images = fn.decoders.image(
+            jpegs, device='mixed' if device == 'gpu' else 'cpu', output_type=types.GRAY)
         if device == 'gpu':
             labels = labels.gpu()
         images = fn.crop_mirror_normalize(
-            images,
-            dtype=types.FLOAT,
-            mean=[0.],
-            std=[255.],
-            output_layout="CHW")
+            images, dtype=types.FLOAT, mean=[0.], std=[255.], output_layout="CHW")
 
         pipeline.set_outputs(images, labels)
 

dali/test/python/test_external_source_parallel_garbage_collection_order.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright (c) 2020-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -40,8 +40,8 @@ def simple_pipeline():
 
 def _test_no_segfault(method, workers_num):
     """
-    This may cause segmentation fault on Python teardown if shared memory wrappers managed by the py_pool
-    are garbage collected before pipeline's backend
+    This may cause segmentation fault on Python teardown if shared memory wrappers managed by the
+    py_pool are garbage collected before pipeline's backend
     """
     pipe = simple_pipeline(
         py_start_method=method, py_num_workers=workers_num,

dali/test/python/test_external_source_parallel_large_sample.py

@@ -33,7 +33,8 @@ def _test_large_sample(start_method):
     def create_pipeline():
         large = fn.external_source(
             large_sample_cb, batch=False, parallel=True, prefetch_queue_depth=1)
-        # iteration over array in Python is too slow, so reduce the number of elements to iterate over
+        # iteration over array in Python is too slow, so reduce the number of elements
+        # to iterate over
         reduced = fn.reductions.sum(large, axes=(1, 2))
         return reduced
 
@@ -49,9 +50,9 @@ def create_pipeline():
             a = np.array(out[idx_in_batch])
             assert a.shape == (512,), "Expected shape (512,) but got {}".format(a.shape)
             for val in a.flat:
-                assert val == expected_val, \
-                    "Unexpected value in batch: got {}, expected {}, for batch {}, sample {}".format(
-                        val, expected_val, batch_idx, idx_in_batch)
+                assert val == expected_val, (
+                    f"Unexpected value in batch: got {val}, expected {expected_val}, "
+                    f"for batch {batch_idx}, sample {idx_in_batch}")
 
 
 def test_large_sample():

dali/test/python/test_nvjpeg_cache.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2019-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -15,11 +15,7 @@
 from nvidia.dali.pipeline import Pipeline
 import nvidia.dali.ops as ops
 import nvidia.dali.types as types
-import nvidia.dali.tfrecord as tfrec
-from timeit import default_timer as timer
-import numpy as np
-import os
-from numpy.testing import assert_array_equal, assert_allclose
+from numpy.testing import assert_array_equal
 from test_utils import get_dali_extra_path
 
 seed = 1549361629
@@ -32,8 +28,7 @@
 def compare(tl1, tl2):
     tl1_cpu = tl1.as_cpu()
     tl2_cpu = tl2.as_cpu()
-    #tl2 = tl2.as_cpu()
-    assert(len(tl1_cpu) == len(tl2_cpu))
+    assert len(tl1_cpu) == len(tl2_cpu)
     for i in range(0, len(tl1_cpu)):
         assert_array_equal(tl1_cpu.at(i), tl2_cpu.at(i), "cached and non-cached images differ")
 
@@ -45,7 +40,9 @@ def __init__(self, batch_size, num_threads, device_id, cache_size):
         policy = None
         if cache_size > 0:
             policy = "threshold"
-        self.decode = ops.decoders.Image(device='mixed', output_type=types.RGB, cache_debug=False, cache_size=cache_size, cache_type=policy, cache_batch_copy=True)
+        self.decode = ops.decoders.Image(device='mixed', output_type=types.RGB, cache_debug=False,
+                                         cache_size=cache_size, cache_type=policy,
+                                         cache_batch_copy=True)
 
     def define_graph(self):
         jpegs, labels = self.input(name="Reader")