Refactor image processor testers (#25450)
* 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
@@ -16,20 +16,13 @@
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import unittest
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import numpy as np
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, is_vision_available
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from transformers.utils import is_vision_available
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from ...test_image_processing_common import ImageProcessingSavingTestMixin
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from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
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if is_torch_available():
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import torch
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if is_vision_available():
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from PIL import Image
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from transformers import ChineseCLIPImageProcessor
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@@ -80,40 +73,24 @@ class ChineseCLIPImageProcessingTester(unittest.TestCase):
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"do_convert_rgb": self.do_convert_rgb,
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}
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def prepare_inputs(self, equal_resolution=False, numpify=False, torchify=False):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True.
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"""
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def expected_output_image_shape(self, images):
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return 3, self.crop_size["height"], self.crop_size["width"]
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assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
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if equal_resolution:
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image_inputs = []
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for i in range(self.batch_size):
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image_inputs.append(
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np.random.randint(
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255, size=(self.num_channels, self.max_resolution, self.max_resolution), dtype=np.uint8
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)
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)
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else:
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image_inputs = []
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for i in range(self.batch_size):
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width, height = np.random.choice(np.arange(self.min_resolution, self.max_resolution), 2)
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image_inputs.append(np.random.randint(255, size=(self.num_channels, width, height), dtype=np.uint8))
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if not numpify and not torchify:
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# PIL expects the channel dimension as last dimension
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image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
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if torchify:
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image_inputs = [torch.from_numpy(x) for x in image_inputs]
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return image_inputs
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def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
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return prepare_image_inputs(
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batch_size=self.batch_size,
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num_channels=self.num_channels,
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min_resolution=self.min_resolution,
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max_resolution=self.max_resolution,
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equal_resolution=equal_resolution,
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numpify=numpify,
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torchify=torchify,
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)
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@require_torch
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@require_vision
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class ChineseCLIPImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
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class ChineseCLIPImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = ChineseCLIPImageProcessor if is_vision_available() else None
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def setUp(self):
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@@ -143,109 +120,10 @@ class ChineseCLIPImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Te
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self.assertEqual(image_processor.size, {"shortest_edge": 42})
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self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
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def test_batch_feature(self):
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pass
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def test_call_pil(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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1,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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self.image_processor_tester.batch_size,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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def test_call_numpy(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False, numpify=True)
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for image in image_inputs:
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self.assertIsInstance(image, np.ndarray)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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1,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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self.image_processor_tester.batch_size,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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def test_call_pytorch(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False, torchify=True)
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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1,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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self.image_processor_tester.batch_size,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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@require_torch
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@require_vision
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class ChineseCLIPImageProcessingTestFourChannels(ImageProcessingSavingTestMixin, unittest.TestCase):
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class ChineseCLIPImageProcessingTestFourChannels(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = ChineseCLIPImageProcessor if is_vision_available() else None
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def setUp(self):
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@@ -267,37 +145,10 @@ class ChineseCLIPImageProcessingTestFourChannels(ImageProcessingSavingTestMixin,
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self.assertTrue(hasattr(image_processing, "image_std"))
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self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
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def test_batch_feature(self):
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pass
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@unittest.skip("ChineseCLIPImageProcessor does not support 4 channels yet") # FIXME Amy
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def test_call_numpy(self):
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return super().test_call_numpy()
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def test_call_pil_four_channels(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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1,
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self.expected_encoded_image_num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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encoded_images.shape,
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(
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self.image_processor_tester.batch_size,
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self.expected_encoded_image_num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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@unittest.skip("ChineseCLIPImageProcessor does not support 4 channels yet") # FIXME Amy
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def test_call_pytorch(self):
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return super().test_call_torch()
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