244 lines
9.6 KiB
Python
244 lines
9.6 KiB
Python
# Copyright 2025 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 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() and is_torchvision_available():
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from transformers import Sam2ImageProcessorFast
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class Sam2ImageProcessingTester:
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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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mask_size=None,
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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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):
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size = size if size is not None else {"height": 20, "width": 20}
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mask_size = mask_size if mask_size is not None else {"height": 12, "width": 12}
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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.mask_size = mask_size
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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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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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"mask_size": self.mask_size,
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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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ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
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example = ds[0]
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return example["image"], example["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")
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return list(ds["image"][:2]), list(ds["map"][:2])
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@require_torch
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@require_vision
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class SamImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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fast_image_processing_class = Sam2ImageProcessorFast if is_torchvision_available() else None
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test_slow_image_processor = False
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def setUp(self):
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super().setUp()
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self.image_processor_tester = Sam2ImageProcessingTester(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, "mask_size"))
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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": 20, "width": 20})
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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_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.mask_size["height"],
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self.image_processor_tester.mask_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.mask_size["height"],
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self.image_processor_tester.mask_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.mask_size["height"],
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self.image_processor_tester.mask_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.mask_size["height"],
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self.image_processor_tester.mask_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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