243 lines
11 KiB
Python
243 lines
11 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.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_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 JanusImageProcessor
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if is_torchvision_available():
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from transformers import JanusImageProcessorFast
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class JanusImageProcessingTester:
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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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image_size=384,
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min_resolution=30,
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max_resolution=200,
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do_resize=True,
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size=None,
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do_normalize=True,
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image_mean=[0.48145466, 0.4578275, 0.40821073],
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image_std=[0.26862954, 0.26130258, 0.27577711],
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do_convert_rgb=True,
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):
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size = size if size is not None else {"height": 384, "width": 384}
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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.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_resize = do_resize
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self.size = size
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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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self.do_convert_rgb = do_convert_rgb
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def prepare_image_processor_dict(self):
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return {
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"do_resize": self.do_resize,
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"size": self.size,
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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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"do_convert_rgb": self.do_convert_rgb,
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}
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# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.prepare_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 JanusImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = JanusImageProcessor if is_vision_available() else None
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fast_image_processing_class = JanusImageProcessorFast if is_torchvision_available() else None
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# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.setUp with CLIP->Janus
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def setUp(self):
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super().setUp()
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self.image_processor_tester = JanusImageProcessingTester(self)
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@property
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# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.image_processor_dict
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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_resize"))
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self.assertTrue(hasattr(image_processing, "size"))
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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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self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
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def test_image_processor_from_dict_with_kwargs(self):
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class.from_dict(self.image_processor_dict)
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self.assertEqual(image_processor.size, {"height": 384, "width": 384})
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self.assertEqual(image_processor.image_mean, [0.48145466, 0.4578275, 0.40821073])
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image_processor = image_processing_class.from_dict(
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self.image_processor_dict, size=42, image_mean=[1.0, 2.0, 1.0]
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)
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self.assertEqual(image_processor.size, {"height": 42, "width": 42})
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self.assertEqual(image_processor.image_mean, [1.0, 2.0, 1.0])
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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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image_processing = image_processing_class(**self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test Non 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 = (1, 3, 384, 384)
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self.assertEqual(tuple(encoded_images.shape), 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 = (7, 3, 384, 384)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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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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image_processing = image_processing_class(**self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
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for image in image_inputs:
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self.assertIsInstance(image, np.ndarray)
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = (1, 3, 384, 384)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 3, 384, 384)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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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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image_processing = image_processing_class(**self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = (1, 3, 384, 384)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 3, 384, 384)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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def test_nested_input(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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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
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# Test batched as a list of images.
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 3, 384, 384)
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self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
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# Test batched as a nested list of images, where each sublist is one batch.
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image_inputs_nested = [image_inputs[:3], image_inputs[3:]]
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encoded_images_nested = image_processing(image_inputs_nested, return_tensors="pt").pixel_values
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expected_output_image_shape = (7, 3, 384, 384)
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self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape)
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# Image processor should return same pixel values, independently of input format.
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self.assertTrue((encoded_images_nested == encoded_images).all())
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@require_vision
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@require_torch
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def test_slow_fast_equivalence_batched(self):
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if not self.test_slow_image_processor or not self.test_fast_image_processor:
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self.skipTest(reason="Skipping slow/fast equivalence test")
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if self.image_processing_class is None or self.fast_image_processing_class is None:
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self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
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if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
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self.skipTest(
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reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
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)
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dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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image_processor_slow = self.image_processing_class(**self.image_processor_dict)
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image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
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encoding_slow = image_processor_slow(dummy_images, return_tensors=None)
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encoding_fast = image_processor_fast(dummy_images, return_tensors=None)
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# Overwrite as the outputs are not always all of the same shape (kept for BC)
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for i in range(len(encoding_slow.pixel_values)):
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self._assert_slow_fast_tensors_equivalence(
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torch.from_numpy(encoding_slow.pixel_values[i]), encoding_fast.pixel_values[i]
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)
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@require_vision
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@require_torch
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def test_slow_fast_equivalence_postprocess(self):
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dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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dummy_images = [image / 255.0 for image in dummy_images]
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image_processor_slow = self.image_processing_class(**self.image_processor_dict)
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image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
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encoding_slow = image_processor_slow.postprocess(dummy_images, return_tensors=None)
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encoding_fast = image_processor_fast.postprocess(dummy_images, return_tensors=None)
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# Overwrite as the outputs are not always all of the same shape (kept for BC)
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for i in range(len(encoding_slow.pixel_values)):
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self._assert_slow_fast_tensors_equivalence(
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torch.from_numpy(encoding_slow.pixel_values[i]).float(), encoding_fast.pixel_values[i].float()
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)
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@unittest.skip(reason="Not supported")
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def test_call_numpy_4_channels(self):
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pass
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