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https://github.com/huggingface/transformers.git
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369 lines
16 KiB
Python
369 lines
16 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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import numpy as np
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from datasets import load_dataset
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from transformers.file_utils import is_torch_available, is_vision_available
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torchvision_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 DPTImageProcessor
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if is_torchvision_available():
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from transformers import DPTImageProcessorFast
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class DPTImageProcessingTester:
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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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do_resize=True,
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size=None,
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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_reduce_labels=False,
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):
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size = size if size is not None else {"height": 18, "width": 18}
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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_reduce_labels = do_reduce_labels
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def prepare_image_processor_dict(self):
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return {
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"do_normalize": self.do_normalize,
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"do_resize": self.do_resize,
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"size": self.size,
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"do_reduce_labels": self.do_reduce_labels,
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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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# Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_single_inputs
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def prepare_semantic_single_inputs():
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dataset = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True)
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image = Image.open(dataset[0]["file"])
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map = Image.open(dataset[1]["file"])
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return image, map
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# Copied from transformers.tests.models.beit.test_image_processing_beit.prepare_semantic_batch_inputs
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def prepare_semantic_batch_inputs():
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ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test", trust_remote_code=True)
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image1 = Image.open(ds[0]["file"])
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map1 = Image.open(ds[1]["file"])
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image2 = Image.open(ds[2]["file"])
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map2 = Image.open(ds[3]["file"])
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return [image1, image2], [map1, map2]
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@require_torch
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@require_vision
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class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = DPTImageProcessor if is_vision_available() else None
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fast_image_processing_class = DPTImageProcessorFast 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 = DPTImageProcessingTester(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, "image_mean"))
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self.assertTrue(hasattr(image_processing, "image_std"))
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self.assertTrue(hasattr(image_processing, "do_normalize"))
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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_pad"))
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self.assertTrue(hasattr(image_processing, "size_divisor"))
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self.assertTrue(hasattr(image_processing, "do_reduce_labels"))
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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_processing_class = image_processing_class(**self.image_processor_dict)
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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": 18, "width": 18})
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image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42)
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self.assertEqual(image_processor.size, {"height": 42, "width": 42})
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def test_padding(self):
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for image_processing_class in self.image_processor_list:
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if image_processing_class == DPTImageProcessorFast:
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image = torch.arange(0, 366777, 1, dtype=torch.uint8).reshape(3, 249, 491)
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image_processor = image_processing_class(**self.image_processor_dict)
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padded_image = image_processor.pad_image(image, size_divisor=4)
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self.assertTrue(padded_image.shape[1] % 4 == 0)
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self.assertTrue(padded_image.shape[2] % 4 == 0)
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pixel_values = image_processor.preprocess(
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image, do_rescale=False, do_resize=False, do_pad=True, size_divisor=4, return_tensors="pt"
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).pixel_values
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self.assertTrue(pixel_values.shape[2] % 4 == 0)
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self.assertTrue(pixel_values.shape[3] % 4 == 0)
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else:
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image_processor = image_processing_class(**self.image_processor_dict)
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image = np.random.randn(3, 249, 491)
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image = image_processor.pad_image(image, size_divisor=4)
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self.assertTrue(image.shape[1] % 4 == 0)
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self.assertTrue(image.shape[2] % 4 == 0)
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pixel_values = image_processor.preprocess(
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image, do_rescale=False, do_resize=False, do_pad=True, size_divisor=4, return_tensors="pt"
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).pixel_values
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self.assertTrue(pixel_values.shape[2] % 4 == 0)
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self.assertTrue(pixel_values.shape[3] % 4 == 0)
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def test_keep_aspect_ratio(self):
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size = {"height": 512, "width": 512}
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class(size=size, keep_aspect_ratio=True, ensure_multiple_of=32)
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image = np.zeros((489, 640, 3))
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pixel_values = image_processor(image, return_tensors="pt").pixel_values
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self.assertEqual(list(pixel_values.shape), [1, 3, 512, 672])
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@unittest.skip("temporary to avoid failing on circleci")
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# Copied from transformers.tests.models.beit.test_image_processing_beit.BeitImageProcessingTest.test_call_segmentation_maps
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def test_call_segmentation_maps(self):
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for image_processing_class in self.image_processor_list:
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# Initialize image_processor
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image_processor = 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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maps = []
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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maps.append(torch.zeros(image.shape[-2:]).long())
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# Test not batched input
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encoding = image_processor(image_inputs[0], maps[0], return_tensors="pt")
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self.assertEqual(
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encoding["pixel_values"].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.size["height"],
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self.image_processor_tester.size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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1,
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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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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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self.assertTrue(encoding["labels"].min().item() >= 0)
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self.assertTrue(encoding["labels"].max().item() <= 255)
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# Test batched
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encoding = image_processor(image_inputs, maps, return_tensors="pt")
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self.assertEqual(
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encoding["pixel_values"].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.size["height"],
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self.image_processor_tester.size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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self.image_processor_tester.batch_size,
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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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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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self.assertTrue(encoding["labels"].min().item() >= 0)
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self.assertTrue(encoding["labels"].max().item() <= 255)
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# Test not batched input (PIL images)
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image, segmentation_map = prepare_semantic_single_inputs()
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encoding = image_processor(image, segmentation_map, return_tensors="pt")
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self.assertEqual(
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encoding["pixel_values"].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.size["height"],
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self.image_processor_tester.size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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1,
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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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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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self.assertTrue(encoding["labels"].min().item() >= 0)
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self.assertTrue(encoding["labels"].max().item() <= 255)
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# Test batched input (PIL images)
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images, segmentation_maps = prepare_semantic_batch_inputs()
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encoding = image_processor(images, segmentation_maps, return_tensors="pt")
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self.assertEqual(
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encoding["pixel_values"].shape,
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(
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2,
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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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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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2,
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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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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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self.assertTrue(encoding["labels"].min().item() >= 0)
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self.assertTrue(encoding["labels"].max().item() <= 255)
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@unittest.skip("temporary to avoid failing on circleci")
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def test_reduce_labels(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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# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
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image, map = prepare_semantic_single_inputs()
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encoding = image_processor(image, map, return_tensors="pt")
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labels_no_reduce = encoding["labels"].clone()
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self.assertTrue(labels_no_reduce.min().item() >= 0)
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self.assertTrue(labels_no_reduce.max().item() <= 150)
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# Get the first non-zero label coords and value, for comparison when do_reduce_labels is True
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non_zero_positions = (labels_no_reduce > 0).nonzero()
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first_non_zero_coords = tuple(non_zero_positions[0].tolist())
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first_non_zero_value = labels_no_reduce[first_non_zero_coords].item()
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image_processor.do_reduce_labels = True
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encoding = image_processor(image, map, return_tensors="pt")
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self.assertTrue(encoding["labels"].min().item() >= 0)
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self.assertTrue(encoding["labels"].max().item() <= 255)
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# Compare with non-reduced label to see if it's reduced by 1
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self.assertEqual(encoding["labels"][first_non_zero_coords].item(), first_non_zero_value - 1)
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@unittest.skip("temporary to avoid failing on circleci")
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def test_slow_fast_equivalence(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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dummy_image, dummy_map = prepare_semantic_single_inputs()
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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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image_encoding_slow = image_processor_slow(dummy_image, segmentation_maps=dummy_map, return_tensors="pt")
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image_encoding_fast = image_processor_fast(dummy_image, segmentation_maps=dummy_map, return_tensors="pt")
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self.assertTrue(torch.allclose(image_encoding_slow.pixel_values, image_encoding_fast.pixel_values, atol=1e-1))
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self.assertLessEqual(
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torch.mean(torch.abs(image_encoding_slow.pixel_values - image_encoding_fast.pixel_values)).item(), 1e-3
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)
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self.assertTrue(torch.allclose(image_encoding_slow.labels, image_encoding_fast.labels, atol=1e-1))
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@unittest.skip("temporary to avoid failing on circleci")
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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, dummy_maps = prepare_semantic_batch_inputs()
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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, segmentation_maps=dummy_maps, return_tensors="pt")
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encoding_fast = image_processor_fast(dummy_images, segmentation_maps=dummy_maps, return_tensors="pt")
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self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1))
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self.assertLessEqual(
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torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3
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)
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