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384 lines
17 KiB
Python
384 lines
17 KiB
Python
# 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_vision_available
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from ...test_image_processing_common import ImageProcessingTestMixin
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if is_vision_available():
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from PIL import Image
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from transformers import MllamaImageProcessor
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if is_torch_available():
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import torch
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class MllamaImageProcessingTester:
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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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num_images=18,
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min_resolution=30,
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max_resolution=400,
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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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do_convert_rgb=True,
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do_pad=True,
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max_image_tiles=4,
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):
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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.max_image_tiles = max_image_tiles
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self.image_size = image_size
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self.num_images = num_images
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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_rescale = do_rescale
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self.rescale_factor = rescale_factor
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self.do_convert_rgb = do_convert_rgb
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self.do_pad = do_pad
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def prepare_image_processor_dict(self):
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return {
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"do_convert_rgb": self.do_convert_rgb,
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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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"do_pad": self.do_pad,
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"max_image_tiles": self.max_image_tiles,
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}
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def prepare_image_inputs(
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self,
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batch_size=None,
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min_resolution=None,
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max_resolution=None,
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num_channels=None,
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num_images=None,
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size_divisor=None,
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equal_resolution=False,
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numpify=False,
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torchify=False,
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):
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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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One can specify whether the images are of the same resolution or not.
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"""
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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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batch_size = batch_size if batch_size is not None else self.batch_size
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min_resolution = min_resolution if min_resolution is not None else self.min_resolution
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max_resolution = max_resolution if max_resolution is not None else self.max_resolution
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num_channels = num_channels if num_channels is not None else self.num_channels
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num_images = num_images if num_images is not None else self.num_images
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images_list = []
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for i in range(batch_size):
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images = []
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for j in range(num_images):
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if equal_resolution:
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width = height = max_resolution
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else:
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# To avoid getting image width/height 0
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if size_divisor is not None:
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# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
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min_resolution = max(size_divisor, min_resolution)
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width, height = np.random.choice(np.arange(min_resolution, max_resolution), 2)
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images.append(np.random.randint(255, size=(num_channels, width, height), dtype=np.uint8))
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images_list.append(images)
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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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images_list = [[Image.fromarray(np.moveaxis(image, 0, -1)) for image in images] for images in images_list]
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if torchify:
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images_list = [[torch.from_numpy(image) for image in images] for images in images_list]
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return images_list
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def expected_output_image_shape(self, images):
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expected_output_image_shape = (
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max(len(images) for images in images),
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self.max_image_tiles,
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self.num_channels,
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self.size["height"],
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self.size["width"],
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)
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return expected_output_image_shape
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@require_torch
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@require_vision
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class MllamaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = MllamaImageProcessor if is_vision_available() else None
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def setUp(self):
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super().setUp()
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self.image_processor_tester = MllamaImageProcessingTester(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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image_processing = self.image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
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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_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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self.assertTrue(hasattr(image_processing, "do_pad"))
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self.assertTrue(hasattr(image_processing, "max_image_tiles"))
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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_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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expected_output_image_shape = (
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max(len(images) for images in image_inputs),
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self.image_processor_tester.max_image_tiles,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.size["height"],
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self.image_processor_tester.size["width"],
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)
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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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# 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_image_inputs(equal_resolution=False)
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for images in image_inputs:
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for image in images:
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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_channels_last(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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# a white 1x1 pixel RGB image
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image_inputs = [[np.full(shape=(1, 1, 3), fill_value=1.0, dtype=float)]]
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encoded_images = image_processing(
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image_inputs, return_tensors="pt", input_data_format="channels_last"
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).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(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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def test_ambiguous_channel_pil_image(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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image_inputs = [[Image.new("RGB", (1, 1))], [Image.new("RGB", (100, 1))]]
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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(tuple(encoded_images.shape), (2, *expected_output_image_shape))
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def test_resize_impractical_aspect_ratio(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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# Ensure that no error is raised even if the aspect ratio is impractical
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image_inputs = [[Image.new("RGB", (9999999, 1))]]
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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(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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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_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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def test_call_numpy_4_channels(self):
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self.skipTest("4 channels input is not supported yet")
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def test_image_correctly_tiled(self):
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def get_empty_tiles(pixel_values):
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# image has shape batch_size, max_num_images, max_image_tiles, num_channels, height, width
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# we want to get a binary mask of shape batch_size, max_num_images, max_image_tiles
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# of empty tiles, i.e. tiles that are completely zero
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return np.all(pixel_values == 0, axis=(3, 4, 5))
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image_processor_dict = {**self.image_processor_dict, "size": {"height": 50, "width": 50}, "max_image_tiles": 4}
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image_processor = self.image_processing_class(**image_processor_dict)
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# image fits 2x2 tiles grid (width x height)
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image = Image.new("RGB", (80, 95))
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inputs = image_processor(image, return_tensors="np")
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pixel_values = inputs.pixel_values
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empty_tiles = get_empty_tiles(pixel_values)[0, 0].tolist()
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self.assertEqual(empty_tiles, [False, False, False, False])
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aspect_ratio_ids = inputs.aspect_ratio_ids[0, 0]
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self.assertEqual(aspect_ratio_ids, 6)
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aspect_ratio_mask = inputs.aspect_ratio_mask[0, 0].tolist()
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self.assertEqual(aspect_ratio_mask, [1, 1, 1, 1])
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# image fits 3x1 grid (width x height)
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image = Image.new("RGB", (101, 50))
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inputs = image_processor(image, return_tensors="np")
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pixel_values = inputs.pixel_values
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empty_tiles = get_empty_tiles(pixel_values)[0, 0].tolist()
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self.assertEqual(empty_tiles, [False, False, False, True])
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aspect_ratio_ids = inputs.aspect_ratio_ids[0, 0]
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self.assertEqual(aspect_ratio_ids, 3)
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num_tiles = inputs.aspect_ratio_mask[0, 0].sum()
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self.assertEqual(num_tiles, 3)
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aspect_ratio_mask = inputs.aspect_ratio_mask[0, 0].tolist()
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self.assertEqual(aspect_ratio_mask, [1, 1, 1, 0])
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# image fits 1x1 grid (width x height)
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image = Image.new("RGB", (20, 39))
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inputs = image_processor(image, return_tensors="np")
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pixel_values = inputs.pixel_values
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empty_tiles = get_empty_tiles(pixel_values)[0, 0].tolist()
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self.assertEqual(empty_tiles, [False, True, True, True])
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aspect_ratio_ids = inputs.aspect_ratio_ids[0, 0]
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self.assertEqual(aspect_ratio_ids, 1)
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aspect_ratio_mask = inputs.aspect_ratio_mask[0, 0].tolist()
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self.assertEqual(aspect_ratio_mask, [1, 0, 0, 0])
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# image fits 2x1 grid (width x height)
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image = Image.new("RGB", (51, 20))
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inputs = image_processor(image, return_tensors="np")
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pixel_values = inputs.pixel_values
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empty_tiles = get_empty_tiles(pixel_values)[0, 0].tolist()
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self.assertEqual(empty_tiles, [False, False, True, True])
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aspect_ratio_ids = inputs.aspect_ratio_ids[0, 0]
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self.assertEqual(aspect_ratio_ids, 2)
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aspect_ratio_mask = inputs.aspect_ratio_mask[0, 0].tolist()
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self.assertEqual(aspect_ratio_mask, [1, 1, 0, 0])
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# image is greater than 2x2 tiles grid (width x height)
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image = Image.new("RGB", (150, 150))
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inputs = image_processor(image, return_tensors="np")
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pixel_values = inputs.pixel_values
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empty_tiles = get_empty_tiles(pixel_values)[0, 0].tolist()
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self.assertEqual(empty_tiles, [False, False, False, False])
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aspect_ratio_ids = inputs.aspect_ratio_ids[0, 0]
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self.assertEqual(aspect_ratio_ids, 6) # (2 - 1) * 4 + 2 = 6
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aspect_ratio_mask = inputs.aspect_ratio_mask[0, 0].tolist()
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self.assertEqual(aspect_ratio_mask, [1, 1, 1, 1])
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# batch of images
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image1 = Image.new("RGB", (80, 95))
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image2 = Image.new("RGB", (101, 50))
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image3 = Image.new("RGB", (23, 49))
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inputs = image_processor([[image1], [image2, image3]], return_tensors="np")
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pixel_values = inputs.pixel_values
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empty_tiles = get_empty_tiles(pixel_values).tolist()
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expected_empty_tiles = [
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# sample 1 with 1 image 2x2 grid
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[
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[False, False, False, False],
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[True, True, True, True], # padding
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],
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# sample 2
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[
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[False, False, False, True], # 3x1
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[False, True, True, True], # 1x1
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],
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]
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self.assertEqual(empty_tiles, expected_empty_tiles)
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aspect_ratio_ids = inputs.aspect_ratio_ids.tolist()
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expected_aspect_ratio_ids = [[6, 0], [3, 1]]
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self.assertEqual(aspect_ratio_ids, expected_aspect_ratio_ids)
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aspect_ratio_mask = inputs.aspect_ratio_mask.tolist()
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expected_aspect_ratio_mask = [
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[
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[1, 1, 1, 1],
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[1, 0, 0, 0],
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],
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[
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[1, 1, 1, 0],
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[1, 0, 0, 0],
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],
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]
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self.assertEqual(aspect_ratio_mask, expected_aspect_ratio_mask)
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