490 lines
22 KiB
Python
490 lines
22 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import pathlib
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import unittest
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from transformers.testing_utils import require_torch, require_vision, slow
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_image_processing_common import AnnotationFormatTestMixin, ImageProcessingTestMixin, prepare_image_inputs
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if is_torch_available():
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import torch
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if is_vision_available():
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from PIL import Image
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from transformers import DetaImageProcessor
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class DetaImageProcessingTester(unittest.TestCase):
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def __init__(
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self,
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parent,
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batch_size=7,
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num_channels=3,
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min_resolution=30,
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max_resolution=400,
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do_resize=True,
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size=None,
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do_normalize=True,
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image_mean=[0.5, 0.5, 0.5],
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image_std=[0.5, 0.5, 0.5],
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do_rescale=True,
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rescale_factor=1 / 255,
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do_pad=True,
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):
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# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
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size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.do_resize = do_resize
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self.size = size
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self.do_normalize = do_normalize
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self.image_mean = image_mean
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self.image_std = image_std
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self.do_rescale = do_rescale
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self.rescale_factor = rescale_factor
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self.do_pad = do_pad
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def prepare_image_processor_dict(self):
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return {
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"do_resize": self.do_resize,
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"size": self.size,
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"do_normalize": self.do_normalize,
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"do_rescale": self.do_rescale,
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"rescale_factor": self.rescale_factor,
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"do_pad": self.do_pad,
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}
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def get_expected_values(self, image_inputs, batched=False):
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"""
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This function computes the expected height and width when providing images to DetaImageProcessor,
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assuming do_resize is set to True with a scalar size.
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"""
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if not batched:
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image = image_inputs[0]
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if isinstance(image, Image.Image):
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w, h = image.size
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else:
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h, w = image.shape[1], image.shape[2]
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if w < h:
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expected_height = int(self.size["shortest_edge"] * h / w)
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expected_width = self.size["shortest_edge"]
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elif w > h:
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expected_height = self.size["shortest_edge"]
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expected_width = int(self.size["shortest_edge"] * w / h)
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else:
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expected_height = self.size["shortest_edge"]
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expected_width = self.size["shortest_edge"]
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else:
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expected_values = []
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for image in image_inputs:
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expected_height, expected_width = self.get_expected_values([image])
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expected_values.append((expected_height, expected_width))
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expected_height = max(expected_values, key=lambda item: item[0])[0]
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expected_width = max(expected_values, key=lambda item: item[1])[1]
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return expected_height, expected_width
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def expected_output_image_shape(self, images):
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height, width = self.get_expected_values(images, batched=True)
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return self.num_channels, height, width
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def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
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return prepare_image_inputs(
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batch_size=self.batch_size,
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num_channels=self.num_channels,
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min_resolution=self.min_resolution,
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max_resolution=self.max_resolution,
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equal_resolution=equal_resolution,
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numpify=numpify,
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torchify=torchify,
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)
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@require_torch
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@require_vision
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class DetaImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = DetaImageProcessor if is_vision_available() else None
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def setUp(self):
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self.image_processor_tester = DetaImageProcessingTester(self)
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@property
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def image_processor_dict(self):
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return self.image_processor_tester.prepare_image_processor_dict()
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def test_image_processor_properties(self):
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image_processing = self.image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processing, "image_mean"))
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self.assertTrue(hasattr(image_processing, "image_std"))
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self.assertTrue(hasattr(image_processing, "do_normalize"))
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self.assertTrue(hasattr(image_processing, "do_resize"))
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self.assertTrue(hasattr(image_processing, "do_rescale"))
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self.assertTrue(hasattr(image_processing, "do_pad"))
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self.assertTrue(hasattr(image_processing, "size"))
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def test_image_processor_from_dict_with_kwargs(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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self.assertEqual(image_processor.size, {"shortest_edge": 18, "longest_edge": 1333})
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self.assertEqual(image_processor.do_pad, True)
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@slow
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def test_call_pytorch_with_coco_detection_annotations(self):
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# prepare image and target
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f:
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target = json.loads(f.read())
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target = {"image_id": 39769, "annotations": target}
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# encode them
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image_processing = DetaImageProcessor()
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encoding = image_processing(images=image, annotations=target, return_tensors="pt")
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# verify pixel values
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expected_shape = torch.Size([1, 3, 800, 1066])
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self.assertEqual(encoding["pixel_values"].shape, expected_shape)
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expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
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self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
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# verify area
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expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
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self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
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# verify boxes
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expected_boxes_shape = torch.Size([6, 4])
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self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
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expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
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self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
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# verify image_id
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expected_image_id = torch.tensor([39769])
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self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
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# verify is_crowd
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expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
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self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
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# verify class_labels
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expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
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self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
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# verify orig_size
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expected_orig_size = torch.tensor([480, 640])
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self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
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# verify size
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expected_size = torch.tensor([800, 1066])
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self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
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@slow
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def test_call_pytorch_with_coco_panoptic_annotations(self):
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# prepare image, target and masks_path
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f:
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target = json.loads(f.read())
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target = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
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masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")
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# encode them
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image_processing = DetaImageProcessor(format="coco_panoptic")
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encoding = image_processing(images=image, annotations=target, masks_path=masks_path, return_tensors="pt")
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# verify pixel values
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expected_shape = torch.Size([1, 3, 800, 1066])
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self.assertEqual(encoding["pixel_values"].shape, expected_shape)
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expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
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self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
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# verify area
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expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
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self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
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# verify boxes
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expected_boxes_shape = torch.Size([6, 4])
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self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
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expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
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self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
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# verify image_id
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expected_image_id = torch.tensor([39769])
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self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
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# verify is_crowd
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expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
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self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
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# verify class_labels
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expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
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self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
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# verify masks
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expected_masks_sum = 822873
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self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
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# verify orig_size
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expected_orig_size = torch.tensor([480, 640])
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self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
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# verify size
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expected_size = torch.tensor([800, 1066])
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self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
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@slow
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# Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_detection_annotations with Detr->Deta
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def test_batched_coco_detection_annotations(self):
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image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800))
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with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f:
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target = json.loads(f.read())
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annotations_0 = {"image_id": 39769, "annotations": target}
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annotations_1 = {"image_id": 39769, "annotations": target}
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# Adjust the bounding boxes for the resized image
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w_0, h_0 = image_0.size
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w_1, h_1 = image_1.size
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for i in range(len(annotations_1["annotations"])):
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coords = annotations_1["annotations"][i]["bbox"]
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new_bbox = [
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coords[0] * w_1 / w_0,
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coords[1] * h_1 / h_0,
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coords[2] * w_1 / w_0,
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coords[3] * h_1 / h_0,
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]
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annotations_1["annotations"][i]["bbox"] = new_bbox
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images = [image_0, image_1]
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annotations = [annotations_0, annotations_1]
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image_processing = DetaImageProcessor()
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encoding = image_processing(
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images=images,
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annotations=annotations,
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return_segmentation_masks=True,
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return_tensors="pt", # do_convert_annotations=True
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)
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# Check the pixel values have been padded
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postprocessed_height, postprocessed_width = 800, 1066
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expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width])
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self.assertEqual(encoding["pixel_values"].shape, expected_shape)
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# Check the bounding boxes have been adjusted for padded images
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self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
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self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
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expected_boxes_0 = torch.tensor(
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[
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[0.6879, 0.4609, 0.0755, 0.3691],
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[0.2118, 0.3359, 0.2601, 0.1566],
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[0.5011, 0.5000, 0.9979, 1.0000],
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[0.5010, 0.5020, 0.9979, 0.9959],
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[0.3284, 0.5944, 0.5884, 0.8112],
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[0.8394, 0.5445, 0.3213, 0.9110],
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]
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)
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expected_boxes_1 = torch.tensor(
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[
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[0.4130, 0.2765, 0.0453, 0.2215],
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[0.1272, 0.2016, 0.1561, 0.0940],
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[0.3757, 0.4933, 0.7488, 0.9865],
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[0.3759, 0.5002, 0.7492, 0.9955],
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[0.1971, 0.5456, 0.3532, 0.8646],
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[0.5790, 0.4115, 0.3430, 0.7161],
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]
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)
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self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3))
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self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3))
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# Check the masks have also been padded
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self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066]))
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self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066]))
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# Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height
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# format and not in the range [0, 1]
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encoding = image_processing(
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images=images,
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annotations=annotations,
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return_segmentation_masks=True,
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do_convert_annotations=False,
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return_tensors="pt",
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)
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self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
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self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
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# Convert to absolute coordinates
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unnormalized_boxes_0 = torch.vstack(
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[
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expected_boxes_0[:, 0] * postprocessed_width,
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expected_boxes_0[:, 1] * postprocessed_height,
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expected_boxes_0[:, 2] * postprocessed_width,
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expected_boxes_0[:, 3] * postprocessed_height,
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]
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).T
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unnormalized_boxes_1 = torch.vstack(
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[
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expected_boxes_1[:, 0] * postprocessed_width,
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expected_boxes_1[:, 1] * postprocessed_height,
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expected_boxes_1[:, 2] * postprocessed_width,
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expected_boxes_1[:, 3] * postprocessed_height,
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]
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).T
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# Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max
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expected_boxes_0 = torch.vstack(
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[
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unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2,
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unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2,
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unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2,
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unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2,
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]
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).T
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expected_boxes_1 = torch.vstack(
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[
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unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2,
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unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2,
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unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2,
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unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
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]
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).T
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self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1))
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self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1))
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@slow
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# Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_panoptic_annotations with Detr->Deta
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def test_batched_coco_panoptic_annotations(self):
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# prepare image, target and masks_path
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image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800))
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with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f:
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target = json.loads(f.read())
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annotation_0 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
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annotation_1 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
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w_0, h_0 = image_0.size
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w_1, h_1 = image_1.size
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for i in range(len(annotation_1["segments_info"])):
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coords = annotation_1["segments_info"][i]["bbox"]
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new_bbox = [
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coords[0] * w_1 / w_0,
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coords[1] * h_1 / h_0,
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coords[2] * w_1 / w_0,
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coords[3] * h_1 / h_0,
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]
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annotation_1["segments_info"][i]["bbox"] = new_bbox
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masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")
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images = [image_0, image_1]
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annotations = [annotation_0, annotation_1]
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# encode them
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image_processing = DetaImageProcessor(format="coco_panoptic")
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encoding = image_processing(
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images=images,
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annotations=annotations,
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masks_path=masks_path,
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return_tensors="pt",
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return_segmentation_masks=True,
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)
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# Check the pixel values have been padded
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postprocessed_height, postprocessed_width = 800, 1066
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expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width])
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self.assertEqual(encoding["pixel_values"].shape, expected_shape)
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# Check the bounding boxes have been adjusted for padded images
|
|
self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
|
|
self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
|
|
expected_boxes_0 = torch.tensor(
|
|
[
|
|
[0.2625, 0.5437, 0.4688, 0.8625],
|
|
[0.7719, 0.4104, 0.4531, 0.7125],
|
|
[0.5000, 0.4927, 0.9969, 0.9854],
|
|
[0.1688, 0.2000, 0.2063, 0.0917],
|
|
[0.5492, 0.2760, 0.0578, 0.2187],
|
|
[0.4992, 0.4990, 0.9984, 0.9979],
|
|
]
|
|
)
|
|
expected_boxes_1 = torch.tensor(
|
|
[
|
|
[0.1576, 0.3262, 0.2814, 0.5175],
|
|
[0.4634, 0.2463, 0.2720, 0.4275],
|
|
[0.3002, 0.2956, 0.5985, 0.5913],
|
|
[0.1013, 0.1200, 0.1238, 0.0550],
|
|
[0.3297, 0.1656, 0.0347, 0.1312],
|
|
[0.2997, 0.2994, 0.5994, 0.5987],
|
|
]
|
|
)
|
|
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3))
|
|
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3))
|
|
|
|
# Check the masks have also been padded
|
|
self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066]))
|
|
self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066]))
|
|
|
|
# Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height
|
|
# format and not in the range [0, 1]
|
|
encoding = image_processing(
|
|
images=images,
|
|
annotations=annotations,
|
|
masks_path=masks_path,
|
|
return_segmentation_masks=True,
|
|
do_convert_annotations=False,
|
|
return_tensors="pt",
|
|
)
|
|
self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4]))
|
|
self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4]))
|
|
# Convert to absolute coordinates
|
|
unnormalized_boxes_0 = torch.vstack(
|
|
[
|
|
expected_boxes_0[:, 0] * postprocessed_width,
|
|
expected_boxes_0[:, 1] * postprocessed_height,
|
|
expected_boxes_0[:, 2] * postprocessed_width,
|
|
expected_boxes_0[:, 3] * postprocessed_height,
|
|
]
|
|
).T
|
|
unnormalized_boxes_1 = torch.vstack(
|
|
[
|
|
expected_boxes_1[:, 0] * postprocessed_width,
|
|
expected_boxes_1[:, 1] * postprocessed_height,
|
|
expected_boxes_1[:, 2] * postprocessed_width,
|
|
expected_boxes_1[:, 3] * postprocessed_height,
|
|
]
|
|
).T
|
|
# Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max
|
|
expected_boxes_0 = torch.vstack(
|
|
[
|
|
unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2,
|
|
unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2,
|
|
unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2,
|
|
unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2,
|
|
]
|
|
).T
|
|
expected_boxes_1 = torch.vstack(
|
|
[
|
|
unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2,
|
|
unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2,
|
|
unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2,
|
|
unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2,
|
|
]
|
|
).T
|
|
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1))
|
|
self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1))
|