232 lines
9.1 KiB
Python
232 lines
9.1 KiB
Python
# coding=utf-8
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# 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 datasets import load_dataset
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from transformers.testing_utils import require_torch, require_vision, slow
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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, prepare_image_inputs
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if is_torch_available():
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import torch
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from transformers.models.seggpt.modeling_seggpt import SegGptImageSegmentationOutput
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if is_vision_available():
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from transformers import SegGptImageProcessor
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class SegGptImageProcessingTester(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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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": 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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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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}
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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 expected_post_processed_shape(self):
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return self.size["height"] // 2, self.size["width"]
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def get_fake_image_segmentation_output(self):
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torch.manual_seed(42)
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return SegGptImageSegmentationOutput(
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pred_masks=torch.rand(self.batch_size, self.num_channels, self.size["height"], self.size["width"])
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)
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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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def prepare_mask():
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ds = load_dataset("EduardoPacheco/seggpt-example-data")["train"]
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return ds[0]["mask"].convert("L")
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def prepare_img():
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ds = load_dataset("EduardoPacheco/seggpt-example-data")["train"]
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images = [image.convert("RGB") for image in ds["image"]]
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masks = [image.convert("RGB") for image in ds["mask"]]
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return images, masks
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@require_torch
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@require_vision
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class SegGptImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = SegGptImageProcessor if is_vision_available() else None
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def setUp(self):
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self.image_processor_tester = SegGptImageProcessingTester(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, "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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def test_image_processor_from_dict_with_kwargs(self):
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image_processor = self.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 = self.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_image_processor_palette(self):
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num_labels = 3
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image_processing = self.image_processing_class(**self.image_processor_dict)
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palette = image_processing.get_palette(num_labels)
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self.assertEqual(len(palette), num_labels + 1)
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self.assertEqual(palette[0], (0, 0, 0))
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def test_mask_equivalence(self):
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image_processor = SegGptImageProcessor()
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mask_binary = prepare_mask()
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mask_rgb = mask_binary.convert("RGB")
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inputs_binary = image_processor(images=None, prompt_masks=mask_binary, return_tensors="pt")
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inputs_rgb = image_processor(images=None, prompt_masks=mask_rgb, return_tensors="pt")
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self.assertTrue((inputs_binary["prompt_masks"] == inputs_rgb["prompt_masks"]).all().item())
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def test_mask_to_rgb(self):
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image_processing = self.image_processing_class(**self.image_processor_dict)
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mask = prepare_mask()
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mask = np.array(mask)
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mask = (mask > 0).astype(np.uint8)
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def check_two_colors(image, color1=(0, 0, 0), color2=(255, 255, 255)):
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pixels = image.transpose(1, 2, 0).reshape(-1, 3)
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unique_colors = np.unique(pixels, axis=0)
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if len(unique_colors) == 2 and (color1 in unique_colors) and (color2 in unique_colors):
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return True
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else:
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return False
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num_labels = 1
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palette = image_processing.get_palette(num_labels)
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# Should only duplicate repeat class indices map, hence only (0,0,0) and (1,1,1)
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mask_duplicated = image_processing.mask_to_rgb(mask)
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# Mask using palette, since only 1 class is present we have colors (0,0,0) and (255,255,255)
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mask_painted = image_processing.mask_to_rgb(mask, palette=palette)
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self.assertTrue(check_two_colors(mask_duplicated, color2=(1, 1, 1)))
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self.assertTrue(check_two_colors(mask_painted, color2=(255, 255, 255)))
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def test_post_processing_semantic_segmentation(self):
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image_processor = self.image_processing_class(**self.image_processor_dict)
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outputs = self.image_processor_tester.get_fake_image_segmentation_output()
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post_processed = image_processor.post_process_semantic_segmentation(outputs)
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self.assertEqual(len(post_processed), self.image_processor_tester.batch_size)
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expected_semantic_map_shape = self.image_processor_tester.expected_post_processed_shape()
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self.assertEqual(post_processed[0].shape, expected_semantic_map_shape)
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@slow
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def test_pixel_values(self):
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images, masks = prepare_img()
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input_image = images[1]
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prompt_image = images[0]
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prompt_mask = masks[0]
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image_processor = SegGptImageProcessor.from_pretrained("BAAI/seggpt-vit-large")
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inputs = image_processor(
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images=input_image, prompt_images=prompt_image, prompt_masks=prompt_mask, return_tensors="pt"
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)
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# Verify pixel values
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expected_prompt_pixel_values = torch.tensor(
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[
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[[-0.6965, -0.6965, -0.6965], [-0.6965, -0.6965, -0.6965], [-0.6965, -0.6965, -0.6965]],
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[[1.6583, 1.6583, 1.6583], [1.6583, 1.6583, 1.6583], [1.6583, 1.6583, 1.6583]],
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[[2.3088, 2.3088, 2.3088], [2.3088, 2.3088, 2.3088], [2.3088, 2.3088, 2.3088]],
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]
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)
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expected_pixel_values = torch.tensor(
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[
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[[1.6324, 1.6153, 1.5810], [1.6153, 1.5982, 1.5810], [1.5810, 1.5639, 1.5639]],
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[[1.2731, 1.2556, 1.2206], [1.2556, 1.2381, 1.2031], [1.2206, 1.2031, 1.1681]],
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[[1.6465, 1.6465, 1.6465], [1.6465, 1.6465, 1.6465], [1.6291, 1.6291, 1.6291]],
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]
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)
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expected_prompt_masks = torch.tensor(
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[
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[[-2.1179, -2.1179, -2.1179], [-2.1179, -2.1179, -2.1179], [-2.1179, -2.1179, -2.1179]],
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[[-2.0357, -2.0357, -2.0357], [-2.0357, -2.0357, -2.0357], [-2.0357, -2.0357, -2.0357]],
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[[-1.8044, -1.8044, -1.8044], [-1.8044, -1.8044, -1.8044], [-1.8044, -1.8044, -1.8044]],
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]
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)
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self.assertTrue(torch.allclose(inputs.pixel_values[0, :, :3, :3], expected_pixel_values, atol=1e-4))
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self.assertTrue(
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torch.allclose(inputs.prompt_pixel_values[0, :, :3, :3], expected_prompt_pixel_values, atol=1e-4)
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)
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self.assertTrue(torch.allclose(inputs.prompt_masks[0, :, :3, :3], expected_prompt_masks, atol=1e-4))
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