test_image_processing_yolos.py

# coding=utf-8
# Copyright 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.


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 AnnotationFormatTestMixin, ImageProcessingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import YolosImageProcessor


class YolosImageProcessingTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        min_resolution=30,
        max_resolution=400,
        do_resize=True,
        size=None,
        do_normalize=True,
        image_mean=[0.5, 0.5, 0.5],
        image_std=[0.5, 0.5, 0.5],
        do_rescale=True,
        rescale_factor=1 / 255,
        do_pad=True,
    ):
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        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_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_pad = do_pad

    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,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
        }

    def get_expected_values(self, image_inputs, batched=False):
        """
        This function computes the expected height and width when providing images to YolosImageProcessor,
        assuming do_resize is set to True with a scalar size.
        """
        if not batched:
            image = image_inputs[0]
            if isinstance(image, Image.Image):
                width, height = image.size
            else:
                height, width = image.shape[1], image.shape[2]

            size = self.size["shortest_edge"]
            max_size = self.size.get("longest_edge", None)
            if max_size is not None:
                min_original_size = float(min((height, width)))
                max_original_size = float(max((height, width)))
                if max_original_size / min_original_size * size > max_size:
                    size = int(round(max_size * min_original_size / max_original_size))

            if width < height and width != size:
                height = int(size * height / width)
                width = size
            elif height < width and height != size:
                width = int(size * width / height)
                height = size
            width_mod = width % 16
            height_mod = height % 16
            expected_width = width - width_mod
            expected_height = height - height_mod

        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 YolosImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMixin, unittest.TestCase):
    image_processing_class = YolosImageProcessor if is_vision_available() else None

    def setUp(self):
        self.image_processor_tester = YolosImageProcessingTester(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"))

    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)

    def test_equivalence_padding(self):
        # Initialize image_processings
        image_processing_1 = self.image_processing_class(**self.image_processor_dict)
        image_processing_2 = self.image_processing_class(do_resize=False, do_normalize=False, do_rescale=False)
        # create random PyTorch tensors
        image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
        for image in image_inputs:
            self.assertIsInstance(image, torch.Tensor)

        # Test whether the method "pad" and calling the image processor return the same tensors
        encoded_images_with_method = image_processing_1.pad(image_inputs, return_tensors="pt")
        encoded_images = image_processing_2(image_inputs, return_tensors="pt")

        self.assertTrue(
            torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
        )

    def test_resize_max_size_respected(self):
        image_processor = self.image_processing_class(**self.image_processor_dict)

        # create torch tensors as image
        image = torch.randint(0, 256, (3, 100, 1500), dtype=torch.uint8)
        processed_image = image_processor(
            image, size={"longest_edge": 1333, "shortest_edge": 800}, do_pad=False, return_tensors="pt"
        )["pixel_values"]

        self.assertTrue(processed_image.shape[-1] <= 1333)
        self.assertTrue(processed_image.shape[-2] <= 800)

    @slow
    def test_call_pytorch_with_coco_detection_annotations(self):
        # prepare image and target
        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}

        # encode them
        image_processing = YolosImageProcessor.from_pretrained("hustvl/yolos-small")
        encoding = image_processing(images=image, annotations=target, return_tensors="pt")

        # verify pixel values
        expected_shape = torch.Size([1, 3, 800, 1056])
        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))

        # verify area
        expected_area = torch.tensor([5832.7256, 11144.6689, 484763.2500, 829269.8125, 146579.4531, 164177.6250])
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
        # verify boxes
        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))
        # verify image_id
        expected_image_id = torch.tensor([39769])
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
        # verify is_crowd
        expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
        # verify class_labels
        expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
        # verify orig_size
        expected_orig_size = torch.tensor([480, 640])
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
        # verify size
        expected_size = torch.tensor([800, 1056])
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))

    @slow
    def test_call_pytorch_with_coco_panoptic_annotations(self):
        # prepare image, target and masks_path
        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")

        # encode them
        image_processing = YolosImageProcessor(format="coco_panoptic")
        encoding = image_processing(images=image, annotations=target, masks_path=masks_path, return_tensors="pt")

        # verify pixel values
        expected_shape = torch.Size([1, 3, 800, 1056])
        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))

        # verify area
        expected_area = torch.tensor([146591.5000, 163974.2500, 480092.2500, 11187.0000, 5824.5000, 7562.5000])
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
        # verify boxes
        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))
        # verify image_id
        expected_image_id = torch.tensor([39769])
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
        # verify is_crowd
        expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
        # verify class_labels
        expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
        # verify masks
        expected_masks_sum = 815161
        self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
        # verify orig_size
        expected_orig_size = torch.tensor([480, 640])
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
        # verify size
        expected_size = torch.tensor([800, 1056])
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))