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* add test and fast image processor * make style * Update src/transformers/models/perceiver/image_processing_perceiver_fast.py Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * make style --------- Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
228 lines
9.9 KiB
Python
228 lines
9.9 KiB
Python
# coding=utf-8
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# Copyright 2024 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from transformers.image_utils import PILImageResampling
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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_torchvision_available, is_vision_available
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from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
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if is_vision_available():
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from PIL import Image
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from transformers import PerceiverImageProcessor
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if is_torchvision_available():
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from transformers import PerceiverImageProcessorFast
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if is_torch_available():
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import torch
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class PerceiverImageProcessingTester:
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def __init__(
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self,
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parent,
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batch_size=7,
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num_channels=3,
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num_images=1,
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image_size=18,
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min_resolution=30,
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max_resolution=40,
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do_center_crop=True,
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crop_size=None,
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do_resize=True,
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size=None,
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do_rescale=True,
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rescale_factor=1 / 255,
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do_normalize=True,
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image_mean=[0.5, 0.5, 0.5],
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image_std=[0.5, 0.5, 0.5],
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resample=PILImageResampling.BICUBIC,
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):
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self.crop_size = crop_size if crop_size is not None else {"height": 256, "width": 256}
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self.size = size if size is not None else {"height": 224, "width": 224}
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.num_images = num_images
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self.image_size = image_size
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.do_center_crop = do_center_crop
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self.do_resize = do_resize
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self.resample = resample
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self.do_rescale = do_rescale
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self.rescale_factor = rescale_factor
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self.do_normalize = do_normalize
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self.image_mean = image_mean
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self.image_std = image_std
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def prepare_image_processor_dict(self):
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return {
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"do_center_crop": self.do_center_crop,
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"crop_size": self.crop_size,
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"do_resize": self.do_resize,
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"size": self.size,
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"do_rescale": self.do_rescale,
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"rescale_factor": self.rescale_factor,
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"do_normalize": self.do_normalize,
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"resample": self.resample,
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}
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def expected_output_image_shape(self, images):
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return self.num_channels, self.size["height"], self.size["width"]
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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 PerceiverImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = PerceiverImageProcessor if is_vision_available() else None
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fast_image_processing_class = PerceiverImageProcessorFast if is_torchvision_available() else None
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def setUp(self):
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super().setUp()
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self.image_processor_tester = PerceiverImageProcessingTester(self)
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@property
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def image_processor_dict(self):
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return self.image_processor_tester.prepare_image_processor_dict()
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def test_image_processor_properties(self):
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for image_processing_class in self.image_processor_list:
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image_processing = image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processing, "do_center_crop"))
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self.assertTrue(hasattr(image_processing, "crop_size"))
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self.assertTrue(hasattr(image_processing, "do_resize"))
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self.assertTrue(hasattr(image_processing, "size"))
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self.assertTrue(hasattr(image_processing, "resample"))
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self.assertTrue(hasattr(image_processing, "do_rescale"))
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self.assertTrue(hasattr(image_processing, "rescale_factor"))
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self.assertTrue(hasattr(image_processing, "do_normalize"))
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self.assertTrue(hasattr(image_processing, "image_mean"))
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self.assertTrue(hasattr(image_processing, "image_std"))
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def test_call_numpy(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = 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_image_inputs(equal_resolution=False, numpify=True)
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for sample_images in image_inputs:
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for image in sample_images:
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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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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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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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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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def test_call_numpy_4_channels(self):
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# Idefics3 always processes images as RGB, so it always returns images with 3 channels
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processor_dict = self.image_processor_dict
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image_processing = image_processing_class(**image_processor_dict)
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# create random numpy tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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for sample_images in image_inputs:
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for image in sample_images:
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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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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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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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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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def test_call_pil(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = 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_image_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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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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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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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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def test_call_pytorch(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = 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_image_inputs(equal_resolution=False, torchify=True)
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for images in image_inputs:
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for image in images:
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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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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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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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tuple(encoded_images.shape),
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(self.image_processor_tester.batch_size, *expected_output_image_shape),
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)
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