145 lines
5.6 KiB
Python
145 lines
5.6 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
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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 shutil
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import tempfile
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import unittest
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import numpy as np
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from transformers.testing_utils import (
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require_torch,
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require_torchvision,
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require_vision,
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)
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from transformers.utils import is_torch_available, is_vision_available
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if is_vision_available():
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from transformers import AutoProcessor, Sam2ImageProcessorFast, Sam2Processor
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if is_torch_available():
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import torch
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@require_vision
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@require_torchvision
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class Sam2ProcessorTest(unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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image_processor = Sam2ImageProcessorFast()
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processor = Sam2Processor(image_processor)
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processor.save_pretrained(self.tmpdirname)
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def get_image_processor(self, **kwargs):
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return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def prepare_image_inputs(self):
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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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"""
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image_inputs = torch.randint(0, 256, size=(1, 3, 30, 400), dtype=torch.uint8)
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# image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
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return image_inputs
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def prepare_mask_inputs(self):
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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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"""
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mask_inputs = torch.randint(0, 256, size=(1, 30, 400), dtype=torch.uint8)
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# mask_inputs = [Image.fromarray(x) for x in mask_inputs]
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return mask_inputs
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def test_save_load_pretrained_additional_features(self):
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image_processor = self.get_image_processor()
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processor = Sam2Processor(image_processor=image_processor)
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processor.save_pretrained(self.tmpdirname)
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image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
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processor = Sam2Processor.from_pretrained(self.tmpdirname, do_normalize=False, padding_value=1.0)
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self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.image_processor, Sam2ImageProcessorFast)
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def test_image_processor_no_masks(self):
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image_processor = self.get_image_processor()
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processor = Sam2Processor(image_processor=image_processor)
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image_input = self.prepare_image_inputs()
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input_feat_extract = image_processor(image_input)
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input_processor = processor(images=image_input)
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for key in input_feat_extract.keys():
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if key == "pixel_values":
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for input_feat_extract_item, input_processor_item in zip(
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input_feat_extract[key], input_processor[key]
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):
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np.testing.assert_array_equal(input_feat_extract_item, input_processor_item)
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else:
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self.assertEqual(input_feat_extract[key], input_processor[key])
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for image in input_feat_extract.pixel_values:
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self.assertEqual(image.shape, (3, 1024, 1024))
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for original_size in input_feat_extract.original_sizes:
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np.testing.assert_array_equal(original_size, np.array([30, 400]))
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def test_image_processor_with_masks(self):
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image_processor = self.get_image_processor()
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processor = Sam2Processor(image_processor=image_processor)
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image_input = self.prepare_image_inputs()
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mask_input = self.prepare_mask_inputs()
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input_feat_extract = image_processor(images=image_input, segmentation_maps=mask_input, return_tensors="pt")
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input_processor = processor(images=image_input, segmentation_maps=mask_input, return_tensors="pt")
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for key in input_feat_extract.keys():
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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for label in input_feat_extract.labels:
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self.assertEqual(label.shape, (256, 256))
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@require_torch
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def test_post_process_masks(self):
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image_processor = self.get_image_processor()
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processor = Sam2Processor(image_processor=image_processor)
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dummy_masks = [torch.ones((1, 3, 5, 5))]
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original_sizes = [[1764, 2646]]
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masks = processor.post_process_masks(dummy_masks, original_sizes)
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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masks = processor.post_process_masks(dummy_masks, torch.tensor(original_sizes))
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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# should also work with np
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dummy_masks = [np.ones((1, 3, 5, 5))]
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masks = processor.post_process_masks(dummy_masks, np.array(original_sizes))
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self.assertEqual(masks[0].shape, (1, 3, 1764, 2646))
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dummy_masks = [[1, 0], [0, 1]]
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with self.assertRaises(ValueError):
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masks = processor.post_process_masks(dummy_masks, np.array(original_sizes))
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