codingutf8 2021 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 by setting sizelongestedge maxresolution we re effectively not testing this p this function computes the expected height and width when providing images to detrimageprocessor assuming doresize is set to true with a scalar size prepare image and target encode them verify pixel values verify area verify boxes verify imageid verify iscrowd verify classlabels verify origsize verify size prepare image target and maskspath encode them verify pixel values verify area verify boxes verify imageid verify iscrowd verify classlabels verify masks verify origsize verify size coding utf 8 2021 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 by setting size longest_edge max_resolution we re effectively not testing this p this function computes the expected height and width when providing images to detrimageprocessor assuming do_resize is set to true with a scalar size prepare image and target encode them verify pixel values verify area verify boxes verify image_id verify is_crowd verify class_labels verify orig_size verify size prepare image target and masks_path encode them verify pixel values verify area verify boxes verify image_id verify is_crowd verify class_labels verify masks verify orig_size verify size
import json
import pathlib
import unittest

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 ImageProcessingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DetrImageProcessor


class DetrImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        min_resolution=30,
        max_resolution=400,
        do_resize=True,
        size=None,
        do_rescale=True,
        rescale_factor=1 / 255,
        do_normalize=True,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
        do_pad=True,
    ):
        
        size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
        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 = size
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.do_pad = do_pad

    def prepare_image_processor_dict(self):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_pad": self.do_pad,
        }

    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 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 DetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = DetrImageProcessor if is_vision_available() else None

    def setUp(self):
        self.image_processor_tester = DetrImageProcessingTester(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_rescale"))
        self.assertTrue(hasattr(image_processing, "rescale_factor"))
        self.assertTrue(hasattr(image_processing, "do_resize"))
        self.assertTrue(hasattr(image_processing, "size"))
        self.assertTrue(hasattr(image_processing, "do_pad"))

    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": 18, "longest_edge": 1333})
        self.assertEqual(image_processor.do_pad, True)

        image_processor = self.image_processing_class.from_dict(
            self.image_processor_dict, size=42, max_size=84, pad_and_return_pixel_mask=False
        )
        self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84})
        self.assertEqual(image_processor.do_pad, False)

    @slow
    def test_call_pytorch_with_coco_detection_annotations(self):
        
        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f:
            target = json.loads(f.read())

        target = {"image_id": 39769, "annotations": target}

        
        image_processing = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
        encoding = image_processing(images=image, annotations=target, return_tensors="pt")

        
        expected_shape = torch.Size([1, 3, 800, 1066])
        self.assertEqual(encoding["pixel_values"].shape, expected_shape)

        expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))

        
        expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
        
        expected_boxes_shape = torch.Size([6, 4])
        self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
        expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
        
        expected_image_id = torch.tensor([39769])
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
        
        expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
        
        expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
        
        expected_orig_size = torch.tensor([480, 640])
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
        
        expected_size = torch.tensor([800, 1066])
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))

    @slow
    def test_call_pytorch_with_coco_panoptic_annotations(self):
        
        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
        with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f:
            target = json.loads(f.read())

        target = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

        masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

        
        image_processing = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic")
        encoding = image_processing(images=image, annotations=target, masks_path=masks_path, return_tensors="pt")

        
        expected_shape = torch.Size([1, 3, 800, 1066])
        self.assertEqual(encoding["pixel_values"].shape, expected_shape)

        expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))

        
        expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
        
        expected_boxes_shape = torch.Size([6, 4])
        self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
        expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
        
        expected_image_id = torch.tensor([39769])
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
        
        expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
        
        expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
        
        expected_masks_sum = 822873
        self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
        
        expected_orig_size = torch.tensor([480, 640])
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
        
        expected_size = torch.tensor([800, 1066])
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))