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 oneformerimageprocessor assuming doresize is set to true with a scalar size 1 for null class only for testimageprocessingcommon testimageproctojsonstring prepare image and target check the batchsize this ensure padding has happened 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 oneformerimageprocessor assuming do_resize is set to true with a scalar size 1 for null class only for test_image_processing_common test_image_proc_to_json_string prepare image and target check the batch_size this ensure padding has happened
import json
import unittest

import numpy as np
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 OneFormerImageProcessor
        from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
        from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
    from PIL import Image


def prepare_metadata(class_info_file, repo_path="shi-labs/oneformer_demo"):
    with open(hf_hub_download(repo_path, class_info_file, repo_type="dataset"), "r") as f:
        class_info = json.load(f)
    metadata = {}
    class_names = []
    thing_ids = []
    for key, info in class_info.items():
        metadata[key] = info["name"]
        class_names.append(info["name"])
        if info["isthing"]:
            thing_ids.append(int(key))
    metadata["thing_ids"] = thing_ids
    metadata["class_names"] = class_names
    return metadata


class OneFormerImageProcessorTester(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=False,
        ignore_index=255,
        repo_path="shi-labs/oneformer_demo",
        class_info_file="ade20k_panoptic.json",
        num_text=10,
    ):
        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.class_info_file = class_info_file
        self.metadata = prepare_metadata(class_info_file, repo_path)
        self.num_text = num_text
        self.repo_path = repo_path

        
        self.batch_size = 2
        self.num_queries = 10
        self.num_classes = 10
        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,
            "num_labels": self.num_labels,
            "do_reduce_labels": self.do_reduce_labels,
            "ignore_index": self.ignore_index,
            "class_info_file": self.class_info_file,
            "metadata": self.metadata,
            "num_text": self.num_text,
        }

    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_oneformer_outputs(self):
        return OneFormerForUniversalSegmentationOutput(
            
            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 OneFormerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
    
    image_processing_class = image_processing_class

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

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

    def test_image_proc_properties(self):
        image_processor = self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(image_processor, "image_mean"))
        self.assertTrue(hasattr(image_processor, "image_std"))
        self.assertTrue(hasattr(image_processor, "do_normalize"))
        self.assertTrue(hasattr(image_processor, "do_resize"))
        self.assertTrue(hasattr(image_processor, "size"))
        self.assertTrue(hasattr(image_processor, "ignore_index"))
        self.assertTrue(hasattr(image_processor, "class_info_file"))
        self.assertTrue(hasattr(image_processor, "num_text"))
        self.assertTrue(hasattr(image_processor, "repo_path"))
        self.assertTrue(hasattr(image_processor, "metadata"))
        self.assertTrue(hasattr(image_processor, "do_reduce_labels"))

    def comm_get_image_processor_inputs(
        self, with_segmentation_maps=False, is_instance_map=False, segmentation_type="np"
    ):
        image_processor = 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_processor(
            image_inputs,
            ["semantic"] * len(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_init_without_params(self):
        pass

    def test_call_with_segmentation_maps(self):
        def common(is_instance_map=False, segmentation_type=None):
            inputs = self.comm_get_image_processor_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"]
            text_inputs = inputs["text_inputs"]

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

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

    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_semantic_segmentation(self):
        fature_extractor = self.image_processing_class(
            num_labels=self.image_processor_tester.num_classes,
            max_seq_length=77,
            task_seq_length=77,
            class_info_file="ade20k_panoptic.json",
            num_text=self.image_processor_tester.num_text,
            repo_path="shi-labs/oneformer_demo",
        )
        outputs = self.image_processor_tester.get_fake_oneformer_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,
            max_seq_length=77,
            task_seq_length=77,
            class_info_file="ade20k_panoptic.json",
            num_text=self.image_processor_tester.num_text,
            repo_path="shi-labs/oneformer_demo",
        )
        outputs = self.image_processor_tester.get_fake_oneformer_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)
            )

    def test_post_process_panoptic_segmentation(self):
        image_processor = self.image_processing_class(
            num_labels=self.image_processor_tester.num_classes,
            max_seq_length=77,
            task_seq_length=77,
            class_info_file="ade20k_panoptic.json",
            num_text=self.image_processor_tester.num_text,
            repo_path="shi-labs/oneformer_demo",
        )
        outputs = self.image_processor_tester.get_fake_oneformer_outputs()
        segmentation = image_processor.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)
            )