# Copyright 2024 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from datasets import load_dataset from transformers.testing_utils import require_torch, require_vision, slow from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ( ImageProcessingTester, ImageProcessingTestMixin, PostProcessSemanticSegmentationTestMixin, ) if is_torch_available(): import torch from transformers.models.seggpt.modeling_seggpt import SegGptImageSegmentationOutput if is_vision_available(): from PIL import Image class SegGptImageProcessingTester(ImageProcessingTester): def __init__(self, **kwargs): # Random test inputs kwargs kwargs.setdefault("num_segmentation_labels", 5) # Image processor init kwargs kwargs.setdefault("size", {"height": 18, "width": 18}) super().__init__(**kwargs) def expected_post_processed_shape(self): return self.size["height"] // 2, self.size["width"] def get_fake_image_segmentation_output(self): torch.manual_seed(42) return SegGptImageSegmentationOutput( pred_masks=torch.rand(self.batch_size, self.num_channels, self.size["height"], self.size["width"]) ) def prepare_post_process_semantic_segmentation_inputs(self): inputs = { "outputs": SegGptImageSegmentationOutput( pred_masks=torch.randn(self.batch_size, self.num_channels, self.size["height"], self.size["width"]) ), "num_labels": self.num_segmentation_labels, } expected_shape = { # extra background class "num_labels": self.num_segmentation_labels + 1, "height": self.size["height"] // 2, "width": self.size["width"], } return inputs, expected_shape def prepare_mask(): ds = load_dataset("EduardoPacheco/seggpt-example-data")["train"] return ds[0]["mask"].convert("L") def prepare_img(): ds = load_dataset("EduardoPacheco/seggpt-example-data")["train"] images = [image.convert("RGB") for image in ds["image"]] masks = [image.convert("RGB") for image in ds["mask"]] return images, masks @require_torch @require_vision class SegGptImageProcessingTest(ImageProcessingTestMixin, PostProcessSemanticSegmentationTestMixin, unittest.TestCase): image_processor_tester_class = SegGptImageProcessingTester def test_image_processor_palette(self): num_labels = 3 for image_processing_class in self.image_processing_classes.values(): image_processing = image_processing_class(**self.image_processor_dict) palette = image_processing.get_palette(num_labels) self.assertEqual(len(palette), num_labels + 1) self.assertEqual(palette[0], (0, 0, 0)) def test_mask_equivalence(self): for image_processing_class in self.image_processing_classes.values(): image_processor = image_processing_class() mask_binary = prepare_mask() mask_rgb = mask_binary.convert("RGB") inputs_binary = image_processor(images=None, prompt_masks=mask_binary, return_tensors="pt") inputs_rgb = image_processor(images=None, prompt_masks=mask_rgb, return_tensors="pt", do_convert_rgb=False) self.assertTrue((inputs_binary["prompt_masks"] == inputs_rgb["prompt_masks"]).all().item()) def test_mask_to_rgb(self): for image_processing_class in self.image_processing_classes.values(): image_processing = image_processing_class(**self.image_processor_dict) mask = prepare_mask() mask = np.array(mask) mask = (mask > 0).astype(np.uint8) def check_two_colors(image, color1=(0, 0, 0), color2=(255, 255, 255)): pixels = image.transpose(1, 2, 0).reshape(-1, 3) unique_colors = np.unique(pixels, axis=0) if len(unique_colors) == 2 and (color1 in unique_colors) and (color2 in unique_colors): return True else: return False num_labels = 1 palette = image_processing.get_palette(num_labels) # Should only duplicate class indices map, hence only (0,0,0) and (1,1,1) mask_duplicated = image_processing.mask_to_rgb(mask) # Mask using palette, since only 1 class we have colors (0,0,0) and (255,255,255) mask_painted = image_processing.mask_to_rgb(mask, palette=palette) self.assertTrue(check_two_colors(mask_duplicated, color2=(1, 1, 1))) self.assertTrue(check_two_colors(mask_painted, color2=(255, 255, 255))) def test_post_processing_semantic_segmentation(self): for image_processing_class in self.image_processing_classes.values(): image_processor = image_processing_class(**self.image_processor_dict) outputs = self.image_processor_tester.get_fake_image_segmentation_output() post_processed = image_processor.post_process_semantic_segmentation(outputs) self.assertEqual(len(post_processed), self.image_processor_tester.batch_size) expected_semantic_map_shape = self.image_processor_tester.expected_post_processed_shape() self.assertEqual(post_processed[0].shape, expected_semantic_map_shape) @slow def test_pixel_values(self): images, masks = prepare_img() input_image = images[1] prompt_image = images[0] prompt_mask = masks[0] for image_processing_class in self.image_processing_classes.values(): image_processor = image_processing_class.from_pretrained("BAAI/seggpt-vit-large") inputs = image_processor( images=input_image, prompt_images=prompt_image, prompt_masks=prompt_mask, return_tensors="pt", do_convert_rgb=False, ) # Verify pixel values expected_prompt_pixel_values = torch.tensor( [ [[-0.6965, -0.6965, -0.6965], [-0.6965, -0.6965, -0.6965], [-0.6965, -0.6965, -0.6965]], [[1.6583, 1.6583, 1.6583], [1.6583, 1.6583, 1.6583], [1.6583, 1.6583, 1.6583]], [[2.3088, 2.3088, 2.3088], [2.3088, 2.3088, 2.3088], [2.3088, 2.3088, 2.3088]], ] ) expected_pixel_values = torch.tensor( [ [[1.6324, 1.6153, 1.5810], [1.6153, 1.5982, 1.5810], [1.5810, 1.5639, 1.5639]], [[1.2731, 1.2556, 1.2206], [1.2556, 1.2381, 1.2031], [1.2206, 1.2031, 1.1681]], [[1.6465, 1.6465, 1.6465], [1.6465, 1.6465, 1.6465], [1.6291, 1.6291, 1.6291]], ] ) expected_prompt_masks = torch.tensor( [ [[-2.1179, -2.1179, -2.1179], [-2.1179, -2.1179, -2.1179], [-2.1179, -2.1179, -2.1179]], [[-2.0357, -2.0357, -2.0357], [-2.0357, -2.0357, -2.0357], [-2.0357, -2.0357, -2.0357]], [[-1.8044, -1.8044, -1.8044], [-1.8044, -1.8044, -1.8044], [-1.8044, -1.8044, -1.8044]], ] ) torch.testing.assert_close(inputs.pixel_values[0, :, :3, :3], expected_pixel_values, rtol=1e-4, atol=1e-4) torch.testing.assert_close( inputs.prompt_pixel_values[0, :, :3, :3], expected_prompt_pixel_values, rtol=1e-4, atol=1e-4 ) torch.testing.assert_close(inputs.prompt_masks[0, :, :3, :3], expected_prompt_masks, rtol=1e-4, atol=1e-4) def test_prompt_mask_equivalence(self): for image_processing_class in self.image_processing_classes.values(): image_processor = image_processing_class(**self.image_processor_dict) image_height, image_width = ( self.image_processor_tester.size["height"], self.image_processor_tester.size["width"], ) # Single Mask Examples expected_single_shape = [1, 3, image_height, image_width] # Single Semantic Map (2D) image_np_2d = np.ones((image_height, image_width), dtype=np.uint8) image_pt_2d = torch.ones((image_height, image_width), dtype=torch.uint8) image_pil_2d = Image.fromarray(image_np_2d) inputs_np_2d = image_processor(images=None, prompt_masks=image_np_2d, return_tensors="pt") inputs_pt_2d = image_processor(images=None, prompt_masks=image_pt_2d, return_tensors="pt") inputs_pil_2d = image_processor(images=None, prompt_masks=image_pil_2d, return_tensors="pt") self.assertTrue((inputs_np_2d["prompt_masks"] == inputs_pt_2d["prompt_masks"]).all().item()) self.assertTrue((inputs_np_2d["prompt_masks"] == inputs_pil_2d["prompt_masks"]).all().item()) self.assertEqual(list(inputs_np_2d["prompt_masks"].shape), expected_single_shape) # Single RGB Images (3D) image_np_3d = np.ones((3, image_height, image_width)) image_pt_3d = torch.ones((3, image_height, image_width)) image_pil_3d = Image.fromarray(image_np_3d.transpose(1, 2, 0).astype(np.uint8)) inputs_np_3d = image_processor( images=None, prompt_masks=image_np_3d, return_tensors="pt", do_convert_rgb=False ) inputs_pt_3d = image_processor( images=None, prompt_masks=image_pt_3d, return_tensors="pt", do_convert_rgb=False ) inputs_pil_3d = image_processor( images=None, prompt_masks=image_pil_3d, return_tensors="pt", do_convert_rgb=False ) self.assertTrue((inputs_np_3d["prompt_masks"] == inputs_pt_3d["prompt_masks"]).all().item()) self.assertTrue((inputs_np_3d["prompt_masks"] == inputs_pil_3d["prompt_masks"]).all().item()) self.assertEqual(list(inputs_np_3d["prompt_masks"].shape), expected_single_shape) # Batched Examples expected_batched_shape = [2, 3, image_height, image_width] # Batched Semantic Maps (3D) image_np_2d_batched = np.ones((2, image_height, image_width), dtype=np.uint8) image_pt_2d_batched = torch.ones((2, image_height, image_width), dtype=torch.uint8) inputs_np_2d_batched = image_processor(images=None, prompt_masks=image_np_2d_batched, return_tensors="pt") inputs_pt_2d_batched = image_processor(images=None, prompt_masks=image_pt_2d_batched, return_tensors="pt") self.assertTrue( (inputs_np_2d_batched["prompt_masks"] == inputs_pt_2d_batched["prompt_masks"]).all().item() ) self.assertEqual(list(inputs_np_2d_batched["prompt_masks"].shape), expected_batched_shape) # Batched RGB images image_np_4d = np.ones((2, 3, image_height, image_width)) image_pt_4d = torch.ones((2, 3, image_height, image_width)) inputs_np_4d = image_processor( images=None, prompt_masks=image_np_4d, return_tensors="pt", do_convert_rgb=False ) inputs_pt_4d = image_processor( images=None, prompt_masks=image_pt_4d, return_tensors="pt", do_convert_rgb=False ) self.assertTrue((inputs_np_4d["prompt_masks"] == inputs_pt_4d["prompt_masks"]).all().item()) self.assertEqual(list(inputs_np_4d["prompt_masks"].shape), expected_batched_shape) # Comparing Single and Batched Examples self.assertTrue((inputs_np_2d["prompt_masks"][0] == inputs_np_3d["prompt_masks"][0]).all().item()) self.assertTrue((inputs_np_2d_batched["prompt_masks"][0] == inputs_np_2d["prompt_masks"][0]).all().item()) self.assertTrue((inputs_np_2d_batched["prompt_masks"][0] == inputs_np_3d["prompt_masks"][0]).all().item()) self.assertTrue((inputs_np_2d_batched["prompt_masks"][0] == inputs_np_4d["prompt_masks"][0]).all().item()) self.assertTrue((inputs_np_2d_batched["prompt_masks"][0] == inputs_np_3d["prompt_masks"][0]).all().item()) def test_backends_equivalence(self): """Override to test equivalence across prompt_images and prompt_masks outputs as well.""" if len(self.image_processing_classes) < 2: self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends") image_height, image_width = ( self.image_processor_tester.size["height"], self.image_processor_tester.size["width"], ) image_np = np.random.randint(0, 256, (3, image_height, image_width), dtype=np.uint8) mask_np = np.zeros((image_height, image_width), dtype=np.uint8) encodings = {} for backend_name, image_processing_class in self.image_processing_classes.items(): image_processor = image_processing_class(**self.image_processor_dict) encodings[backend_name] = image_processor( images=image_np, prompt_images=image_np, prompt_masks=mask_np, return_tensors="pt", ) backend_names = list(encodings.keys()) reference_backend = backend_names[0] for backend_name in backend_names[1:]: self._assert_tensors_equivalence( encodings[reference_backend].pixel_values, encodings[backend_name].pixel_values ) self._assert_tensors_equivalence( encodings[reference_backend].prompt_pixel_values, encodings[backend_name].prompt_pixel_values ) self._assert_tensors_equivalence( encodings[reference_backend].prompt_masks, encodings[backend_name].prompt_masks )