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Refactor image processor testers (#25450)

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* Refactor image processor test mixin

- Move test_call_numpy, test_call_pytorch, test_call_pil to mixin
- Rename mixin to reflect handling of logic more than saving
- Add prepare_image_inputs, expected_image_outputs for tests

* Fix for oneformer
This commit is contained in:
amyeroberts
2023-08-11 11:30:18 +01:00
committed by GitHub
parent 454957c9bb
commit 41d56ea6dd
42 changed files with 993 additions and 3763 deletions
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94
tests/models/videomae/test_image_processing_videomae.py
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@@ -21,7 +21,7 @@ import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_video_inputs
if is_torch_available():
@@ -77,10 +77,25 @@ class VideoMAEImageProcessingTester(unittest.TestCase):
"crop_size": self.crop_size,
}
def expected_output_image_shape(self, images):
return self.num_frames, self.num_channels, self.crop_size["height"], self.crop_size["width"]
def prepare_video_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_video_inputs(
batch_size=self.batch_size,
num_frames=self.num_frames,
num_channels=self.num_channels,
min_resolution=self.min_resolution,
max_resolution=self.max_resolution,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch
@require_vision
class VideoMAEImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
class VideoMAEImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = VideoMAEImageProcessor if is_vision_available() else None
def setUp(self):
@@ -108,110 +123,65 @@ class VideoMAEImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestC
self.assertEqual(image_processor.size, {"shortest_edge": 42})
self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL videos
video_inputs = prepare_video_inputs(self.image_processor_tester, equal_resolution=False)
video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False)
for video in video_inputs:
self.assertIsInstance(video, list)
self.assertIsInstance(video[0], Image.Image)
# Test not batched input
encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_videos.shape,
(
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
expected_output_video_shape = self.image_processor_tester.expected_output_image_shape([encoded_videos[0]])
self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))
# Test batched
encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
expected_output_video_shape = self.image_processor_tester.expected_output_image_shape(encoded_videos)
self.assertEqual(
encoded_videos.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
tuple(encoded_videos.shape), (self.image_processor_tester.batch_size, *expected_output_video_shape)
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
video_inputs = prepare_video_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
for video in video_inputs:
self.assertIsInstance(video, list)
self.assertIsInstance(video[0], np.ndarray)
# Test not batched input
encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_videos.shape,
(
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
expected_output_video_shape = self.image_processor_tester.expected_output_image_shape([encoded_videos[0]])
self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))
# Test batched
encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
expected_output_video_shape = self.image_processor_tester.expected_output_image_shape(encoded_videos)
self.assertEqual(
encoded_videos.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
tuple(encoded_videos.shape), (self.image_processor_tester.batch_size, *expected_output_video_shape)
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
video_inputs = prepare_video_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
video_inputs = self.image_processor_tester.prepare_video_inputs(equal_resolution=False, torchify=True)
for video in video_inputs:
self.assertIsInstance(video, list)
self.assertIsInstance(video[0], torch.Tensor)
# Test not batched input
encoded_videos = image_processing(video_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_videos.shape,
(
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
expected_output_video_shape = self.image_processor_tester.expected_output_image_shape([encoded_videos[0]])
self.assertEqual(tuple(encoded_videos.shape), (1, *expected_output_video_shape))
# Test batched
encoded_videos = image_processing(video_inputs, return_tensors="pt").pixel_values
expected_output_video_shape = self.image_processor_tester.expected_output_image_shape(encoded_videos)
self.assertEqual(
encoded_videos.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
tuple(encoded_videos.shape), (self.image_processor_tester.batch_size, *expected_output_video_shape)
)