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129 lines
4.7 KiB
Python
129 lines
4.7 KiB
Python
# Copyright 2022 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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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import 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() and is_torchvision_available():
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from transformers import Llama4ImageProcessorFast
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class Llama4ImageProcessingTester(unittest.TestCase):
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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=18,
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min_resolution=30,
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max_resolution=400,
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max_patches=1,
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do_resize=True,
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size=None,
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do_normalize=True,
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do_pad=False,
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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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do_convert_rgb=True,
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):
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super().__init__()
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size = size if size is not None else {"height": 20, "width": 20}
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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.max_patches = max_patches
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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_pad = do_pad
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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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"max_patches": self.max_patches,
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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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"do_pad": self.do_pad,
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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 Llama4ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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test_slow_image_processor = False
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fast_image_processing_class = Llama4ImageProcessorFast 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 = Llama4ImageProcessingTester(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_processor = image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processor, "do_resize"))
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self.assertTrue(hasattr(image_processor, "size"))
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self.assertTrue(hasattr(image_processor, "do_normalize"))
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self.assertTrue(hasattr(image_processor, "image_mean"))
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self.assertTrue(hasattr(image_processor, "image_std"))
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self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
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def test_split_tiles(self):
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class(**self.image_processor_dict)
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image = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)[0]
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processed_images = image_processor(
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image,
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max_patches=16,
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)
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self.assertEqual(len(processed_images.pixel_values), 1)
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self.assertEqual(processed_images.pixel_values[0].shape[0], 17)
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self.assertEqual(processed_images.pixel_values[0].shape[-2:], (20, 20))
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@unittest.skip("Broken on main right now. Should be fixable!")
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def test_image_processor_save_load_with_autoimageprocessor(self):
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pass
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