codingutf8 2022 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 orglicenseslicense2 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 for the postprocessfunctions this function computes the expected height and width when providing images to maskformerimageprocessor assuming doresize is set to true with a scalar size 1 for null class prepare image and target check if divisible check the batchsize this ensure padding has happened load 2 images and corresponding annotations from the hub get instance segmentations and instancetosegmentation mappings create mapping between instance ids and semantic category ids create a image processor prepare the images and annotations verify the pixel values and pixel mask verify the class labels verify the mask labels load 2 images and corresponding semantic annotations from the hub create a image processor prepare the images and annotations verify the pixel values and pixel mask verify the class labels verify the mask labels load 2 images and corresponding panoptic annotations from the hub convert rgb to segment ids per pixel 0 is the ignore label for which we don t need to make binary masks create mapping between segment ids and semantic classes create a image processor prepare the images and annotations verify the pixel values and pixel mask verify the class labels verify the mask labels get number of segments to be fused expected number of segments after fusing coding utf 8 2022 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 for the post_process_functions this function computes the expected height and width when providing images to maskformerimageprocessor assuming do_resize is set to true with a scalar size 1 for null class prepare image and target check if divisible check the batch_size this ensure padding has happened load 2 images and corresponding annotations from the hub get instance segmentations and instance to segmentation mappings create mapping between instance ids and semantic category ids create a image processor prepare the images and annotations verify the pixel values and pixel mask verify the class labels verify the mask labels load 2 images and corresponding semantic annotations from the hub create a image processor prepare the images and annotations verify the pixel values and pixel mask verify the class labels verify the mask labels load 2 images and corresponding panoptic annotations from the hub convert rgb to segment ids per pixel 0 is the ignore label for which we don t need to make binary masks create mapping between segment ids and semantic classes create a image processor prepare the images and annotations verify the pixel values and pixel mask verify the class labels fmt skip fmt skip verify the mask labels get number of segments to be fused expected number of segments after fusing
import unittest

import numpy as np
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

    if is_vision_available():
        from transformers import MaskFormerImageProcessor
        from transformers.models.maskformer.image_processing_maskformer import binary_mask_to_rle
        from transformers.models.maskformer.modeling_maskformer import MaskFormerForInstanceSegmentationOutput

if is_vision_available():
    from PIL import Image


class MaskFormerImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        min_resolution=30,
        max_resolution=400,
        size=None,
        do_resize=True,
        do_normalize=True,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
        num_labels=10,
        do_reduce_labels=True,
        ignore_index=255,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.do_resize = do_resize
        self.size = {"shortest_edge": 32, "longest_edge": 1333} if size is None else size
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.size_divisor = 0
        
        self.batch_size = 2
        self.num_queries = 3
        self.num_classes = 2
        self.height = 3
        self.width = 4
        self.num_labels = num_labels
        self.do_reduce_labels = do_reduce_labels
        self.ignore_index = ignore_index

    def prepare_image_processor_dict(self):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "size_divisor": self.size_divisor,
            "num_labels": self.num_labels,
            "do_reduce_labels": self.do_reduce_labels,
            "ignore_index": self.ignore_index,
        }

    def get_expected_values(self, image_inputs, batched=False):
        
        if not batched:
            image = image_inputs[0]
            if isinstance(image, Image.Image):
                w, h = image.size
            else:
                h, w = image.shape[1], image.shape[2]
            if w < h:
                expected_height = int(self.size["shortest_edge"] * h / w)
                expected_width = self.size["shortest_edge"]
            elif w > h:
                expected_height = self.size["shortest_edge"]
                expected_width = int(self.size["shortest_edge"] * w / h)
            else:
                expected_height = self.size["shortest_edge"]
                expected_width = self.size["shortest_edge"]

        else:
            expected_values = []
            for image in image_inputs:
                expected_height, expected_width = self.get_expected_values([image])
                expected_values.append((expected_height, expected_width))
            expected_height = max(expected_values, key=lambda item: item[0])[0]
            expected_width = max(expected_values, key=lambda item: item[1])[1]

        return expected_height, expected_width

    def get_fake_maskformer_outputs(self):
        return MaskFormerForInstanceSegmentationOutput(
            
            class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)),
            masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)),
        )

    def expected_output_image_shape(self, images):
        height, width = self.get_expected_values(images, batched=True)
        return self.num_channels, height, width

    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
        return prepare_image_inputs(
            batch_size=self.batch_size,
            num_channels=self.num_channels,
            min_resolution=self.min_resolution,
            max_resolution=self.max_resolution,
            equal_resolution=equal_resolution,
            numpify=numpify,
            torchify=torchify,
        )


@require_torch
@require_vision
class MaskFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = MaskFormerImageProcessor if (is_vision_available() and is_torch_available()) else None

    def setUp(self):
        self.image_processor_tester = MaskFormerImageProcessingTester(self)

    @property
    def image_processor_dict(self):
        return self.image_processor_tester.prepare_image_processor_dict()

    def test_image_processor_properties(self):
        image_processing = self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(image_processing, "image_mean"))
        self.assertTrue(hasattr(image_processing, "image_std"))
        self.assertTrue(hasattr(image_processing, "do_normalize"))
        self.assertTrue(hasattr(image_processing, "do_resize"))
        self.assertTrue(hasattr(image_processing, "size"))
        self.assertTrue(hasattr(image_processing, "ignore_index"))
        self.assertTrue(hasattr(image_processing, "num_labels"))

    def test_image_processor_from_dict_with_kwargs(self):
        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size, {"shortest_edge": 32, "longest_edge": 1333})
        self.assertEqual(image_processor.size_divisor, 0)

        image_processor = self.image_processing_class.from_dict(
            self.image_processor_dict, size=42, max_size=84, size_divisibility=8
        )
        self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84})
        self.assertEqual(image_processor.size_divisor, 8)

    def comm_get_image_processing_inputs(
        self, with_segmentation_maps=False, is_instance_map=False, segmentation_type="np"
    ):
        image_processing = self.image_processing_class(**self.image_processor_dict)
        
        num_labels = self.image_processor_tester.num_labels
        annotations = None
        instance_id_to_semantic_id = None
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
        if with_segmentation_maps:
            high = num_labels
            if is_instance_map:
                labels_expanded = list(range(num_labels)) * 2
                instance_id_to_semantic_id = dict(enumerate(labels_expanded))
            annotations = [
                np.random.randint(0, high * 2, (img.size[1], img.size[0])).astype(np.uint8) for img in image_inputs
            ]
            if segmentation_type == "pil":
                annotations = [Image.fromarray(annotation) for annotation in annotations]

        inputs = image_processing(
            image_inputs,
            annotations,
            return_tensors="pt",
            instance_id_to_semantic_id=instance_id_to_semantic_id,
            pad_and_return_pixel_mask=True,
        )

        return inputs

    def test_with_size_divisor(self):
        size_divisors = [8, 16, 32]
        weird_input_sizes = [(407, 802), (582, 1094)]
        for size_divisor in size_divisors:
            image_processor_dict = {**self.image_processor_dict, **{"size_divisor": size_divisor}}
            image_processing = self.image_processing_class(**image_processor_dict)
            for weird_input_size in weird_input_sizes:
                inputs = image_processing([np.ones((3, *weird_input_size))], return_tensors="pt")
                pixel_values = inputs["pixel_values"]
                
                self.assertTrue((pixel_values.shape[-1] % size_divisor) == 0)
                self.assertTrue((pixel_values.shape[-2] % size_divisor) == 0)

    def test_call_with_segmentation_maps(self):
        def common(is_instance_map=False, segmentation_type=None):
            inputs = self.comm_get_image_processing_inputs(
                with_segmentation_maps=True, is_instance_map=is_instance_map, segmentation_type=segmentation_type
            )

            mask_labels = inputs["mask_labels"]
            class_labels = inputs["class_labels"]
            pixel_values = inputs["pixel_values"]

            
            for mask_label, class_label in zip(mask_labels, class_labels):
                self.assertEqual(mask_label.shape[0], class_label.shape[0])
                
                self.assertEqual(mask_label.shape[1:], pixel_values.shape[2:])

        common()
        common(is_instance_map=True)
        common(is_instance_map=False, segmentation_type="pil")
        common(is_instance_map=True, segmentation_type="pil")

    def test_integration_instance_segmentation(self):
        
        repo_id = "nielsr/image-segmentation-toy-data"
        image1 = Image.open(
            hf_hub_download(repo_id=repo_id, filename="instance_segmentation_image_1.png", repo_type="dataset")
        )
        image2 = Image.open(
            hf_hub_download(repo_id=repo_id, filename="instance_segmentation_image_2.png", repo_type="dataset")
        )
        annotation1 = Image.open(
            hf_hub_download(repo_id=repo_id, filename="instance_segmentation_annotation_1.png", repo_type="dataset")
        )
        annotation2 = Image.open(
            hf_hub_download(repo_id=repo_id, filename="instance_segmentation_annotation_2.png", repo_type="dataset")
        )

        
        def get_instance_segmentation_and_mapping(annotation):
            instance_seg = np.array(annotation)[:, :, 1]
            class_id_map = np.array(annotation)[:, :, 0]
            class_labels = np.unique(class_id_map)

            
            inst2class = {}
            for label in class_labels:
                instance_ids = np.unique(instance_seg[class_id_map == label])
                inst2class.update({i: label for i in instance_ids})

            return instance_seg, inst2class

        instance_seg1, inst2class1 = get_instance_segmentation_and_mapping(annotation1)
        instance_seg2, inst2class2 = get_instance_segmentation_and_mapping(annotation2)

        
        image_processing = MaskFormerImageProcessor(reduce_labels=True, ignore_index=255, size=(512, 512))

        
        inputs = image_processing(
            [image1, image2],
            [instance_seg1, instance_seg2],
            instance_id_to_semantic_id=[inst2class1, inst2class2],
            return_tensors="pt",
        )

        
        self.assertEqual(inputs["pixel_values"].shape, (2, 3, 512, 512))
        self.assertEqual(inputs["pixel_mask"].shape, (2, 512, 512))

        
        self.assertEqual(len(inputs["class_labels"]), 2)
        self.assertTrue(torch.allclose(inputs["class_labels"][0], torch.tensor([30, 55])))
        self.assertTrue(torch.allclose(inputs["class_labels"][1], torch.tensor([4, 4, 23, 55])))

        
        self.assertEqual(len(inputs["mask_labels"]), 2)
        self.assertEqual(inputs["mask_labels"][0].shape, (2, 512, 512))
        self.assertEqual(inputs["mask_labels"][1].shape, (4, 512, 512))
        self.assertEquals(inputs["mask_labels"][0].sum().item(), 41527.0)
        self.assertEquals(inputs["mask_labels"][1].sum().item(), 26259.0)

    def test_integration_semantic_segmentation(self):
        
        repo_id = "nielsr/image-segmentation-toy-data"
        image1 = Image.open(
            hf_hub_download(repo_id=repo_id, filename="semantic_segmentation_image_1.png", repo_type="dataset")
        )
        image2 = Image.open(
            hf_hub_download(repo_id=repo_id, filename="semantic_segmentation_image_2.png", repo_type="dataset")
        )
        annotation1 = Image.open(
            hf_hub_download(repo_id=repo_id, filename="semantic_segmentation_annotation_1.png", repo_type="dataset")
        )
        annotation2 = Image.open(
            hf_hub_download(repo_id=repo_id, filename="semantic_segmentation_annotation_2.png", repo_type="dataset")
        )

        
        image_processing = MaskFormerImageProcessor(reduce_labels=True, ignore_index=255, size=(512, 512))

        
        inputs = image_processing(
            [image1, image2],
            [annotation1, annotation2],
            return_tensors="pt",
        )

        
        self.assertEqual(inputs["pixel_values"].shape, (2, 3, 512, 512))
        self.assertEqual(inputs["pixel_mask"].shape, (2, 512, 512))

        
        self.assertEqual(len(inputs["class_labels"]), 2)
        self.assertTrue(torch.allclose(inputs["class_labels"][0], torch.tensor([2, 4, 60])))
        self.assertTrue(torch.allclose(inputs["class_labels"][1], torch.tensor([0, 3, 7, 8, 15, 28, 30, 143])))

        
        self.assertEqual(len(inputs["mask_labels"]), 2)
        self.assertEqual(inputs["mask_labels"][0].shape, (3, 512, 512))
        self.assertEqual(inputs["mask_labels"][1].shape, (8, 512, 512))
        self.assertEquals(inputs["mask_labels"][0].sum().item(), 170200.0)
        self.assertEquals(inputs["mask_labels"][1].sum().item(), 257036.0)

    def test_integration_panoptic_segmentation(self):
        
        dataset = load_dataset("nielsr/ade20k-panoptic-demo")
        image1 = dataset["train"][0]["image"]
        image2 = dataset["train"][1]["image"]
        segments_info1 = dataset["train"][0]["segments_info"]
        segments_info2 = dataset["train"][1]["segments_info"]
        annotation1 = dataset["train"][0]["label"]
        annotation2 = dataset["train"][1]["label"]

        def rgb_to_id(color):
            if isinstance(color, np.ndarray) and len(color.shape) == 3:
                if color.dtype == np.uint8:
                    color = color.astype(np.int32)
                return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2]
            return int(color[0] + 256 * color[1] + 256 * 256 * color[2])

        def create_panoptic_map(annotation, segments_info):
            annotation = np.array(annotation)
            
            
            panoptic_map = rgb_to_id(annotation)

            
            inst2class = {segment["id"]: segment["category_id"] for segment in segments_info}

            return panoptic_map, inst2class

        panoptic_map1, inst2class1 = create_panoptic_map(annotation1, segments_info1)
        panoptic_map2, inst2class2 = create_panoptic_map(annotation2, segments_info2)

        
        image_processing = MaskFormerImageProcessor(ignore_index=0, do_resize=False)

        
        pixel_values_list = [np.moveaxis(np.array(image1), -1, 0), np.moveaxis(np.array(image2), -1, 0)]
        inputs = image_processing.encode_inputs(
            pixel_values_list,
            [panoptic_map1, panoptic_map2],
            instance_id_to_semantic_id=[inst2class1, inst2class2],
            return_tensors="pt",
        )

        
        self.assertEqual(inputs["pixel_values"].shape, (2, 3, 512, 711))
        self.assertEqual(inputs["pixel_mask"].shape, (2, 512, 711))

        
        self.assertEqual(len(inputs["class_labels"]), 2)
        expected_class_labels = torch.tensor([4, 17, 32, 42, 42, 42, 42, 42, 42, 42, 32, 12, 12, 12, 12, 12, 42, 42, 12, 12, 12, 42, 12, 12, 12, 12, 12, 3, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 5, 12, 12, 12, 12, 12, 12, 12, 0, 43, 43, 43, 96, 43, 104, 43, 31, 125, 31, 125, 138, 87, 125, 149, 138, 125, 87, 87])  
        self.assertTrue(torch.allclose(inputs["class_labels"][0], torch.tensor(expected_class_labels)))
        expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 3, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 5, 12, 12, 0, 115, 43, 43, 115, 43, 43, 43, 8, 8, 8, 138, 138, 125, 143])  
        self.assertTrue(torch.allclose(inputs["class_labels"][1], expected_class_labels))

        
        self.assertEqual(len(inputs["mask_labels"]), 2)
        self.assertEqual(inputs["mask_labels"][0].shape, (79, 512, 711))
        self.assertEqual(inputs["mask_labels"][1].shape, (61, 512, 711))
        self.assertEquals(inputs["mask_labels"][0].sum().item(), 315193.0)
        self.assertEquals(inputs["mask_labels"][1].sum().item(), 350747.0)

    def test_binary_mask_to_rle(self):
        fake_binary_mask = np.zeros((20, 50))
        fake_binary_mask[0, 20:] = 1
        fake_binary_mask[1, :15] = 1
        fake_binary_mask[5, :10] = 1

        rle = binary_mask_to_rle(fake_binary_mask)
        self.assertEqual(len(rle), 4)
        self.assertEqual(rle[0], 21)
        self.assertEqual(rle[1], 45)

    def test_post_process_segmentation(self):
        fature_extractor = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
        outputs = self.image_processor_tester.get_fake_maskformer_outputs()
        segmentation = fature_extractor.post_process_segmentation(outputs)

        self.assertEqual(
            segmentation.shape,
            (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_classes,
                self.image_processor_tester.height,
                self.image_processor_tester.width,
            ),
        )

        target_size = (1, 4)
        segmentation = fature_extractor.post_process_segmentation(outputs, target_size=target_size)

        self.assertEqual(
            segmentation.shape,
            (self.image_processor_tester.batch_size, self.image_processor_tester.num_classes, *target_size),
        )

    def test_post_process_semantic_segmentation(self):
        fature_extractor = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
        outputs = self.image_processor_tester.get_fake_maskformer_outputs()

        segmentation = fature_extractor.post_process_semantic_segmentation(outputs)

        self.assertEqual(len(segmentation), self.image_processor_tester.batch_size)
        self.assertEqual(
            segmentation[0].shape,
            (
                self.image_processor_tester.height,
                self.image_processor_tester.width,
            ),
        )

        target_sizes = [(1, 4) for i in range(self.image_processor_tester.batch_size)]
        segmentation = fature_extractor.post_process_semantic_segmentation(outputs, target_sizes=target_sizes)

        self.assertEqual(segmentation[0].shape, target_sizes[0])

    def test_post_process_instance_segmentation(self):
        image_processor = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
        outputs = self.image_processor_tester.get_fake_maskformer_outputs()
        segmentation = image_processor.post_process_instance_segmentation(outputs, threshold=0)

        self.assertTrue(len(segmentation) == self.image_processor_tester.batch_size)
        for el in segmentation:
            self.assertTrue("segmentation" in el)
            self.assertTrue("segments_info" in el)
            self.assertEqual(type(el["segments_info"]), list)
            self.assertEqual(
                el["segmentation"].shape, (self.image_processor_tester.height, self.image_processor_tester.width)
            )

        segmentation = image_processor.post_process_instance_segmentation(
            outputs, threshold=0, return_binary_maps=True
        )

        self.assertTrue(len(segmentation) == self.image_processor_tester.batch_size)
        for el in segmentation:
            self.assertTrue("segmentation" in el)
            self.assertTrue("segments_info" in el)
            self.assertEqual(type(el["segments_info"]), list)
            self.assertEqual(len(el["segmentation"].shape), 3)
            self.assertEqual(
                el["segmentation"].shape[1:], (self.image_processor_tester.height, self.image_processor_tester.width)
            )

    def test_post_process_panoptic_segmentation(self):
        image_processing = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
        outputs = self.image_processor_tester.get_fake_maskformer_outputs()
        segmentation = image_processing.post_process_panoptic_segmentation(outputs, threshold=0)

        self.assertTrue(len(segmentation) == self.image_processor_tester.batch_size)
        for el in segmentation:
            self.assertTrue("segmentation" in el)
            self.assertTrue("segments_info" in el)
            self.assertEqual(type(el["segments_info"]), list)
            self.assertEqual(
                el["segmentation"].shape, (self.image_processor_tester.height, self.image_processor_tester.width)
            )

    def test_post_process_label_fusing(self):
        image_processor = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
        outputs = self.image_processor_tester.get_fake_maskformer_outputs()

        segmentation = image_processor.post_process_panoptic_segmentation(
            outputs, threshold=0, mask_threshold=0, overlap_mask_area_threshold=0
        )
        unfused_segments = [el["segments_info"] for el in segmentation]

        fused_segmentation = image_processor.post_process_panoptic_segmentation(
            outputs, threshold=0, mask_threshold=0, overlap_mask_area_threshold=0, label_ids_to_fuse={1}
        )
        fused_segments = [el["segments_info"] for el in fused_segmentation]

        for el_unfused, el_fused in zip(unfused_segments, fused_segments):
            if len(el_unfused) == 0:
                self.assertEqual(len(el_unfused), len(el_fused))
                continue

            
            fuse_targets = [1 for el in el_unfused if el["label_id"] in {1}]
            num_to_fuse = 0 if len(fuse_targets) == 0 else sum(fuse_targets) - 1
            
            expected_num_segments = max([el["id"] for el in el_unfused]) - num_to_fuse
            num_segments_fused = max([el["id"] for el in el_fused])
            self.assertEqual(num_segments_fused, expected_num_segments)