# coding=utf-8 # Copyright 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. 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 ConditionalDetrImageProcessor class ConditionalDetrImageProcessingTester(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 ConditionalDetrImageProcessor, assuming do_resize is set to True with a scalar size. """ 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 ConditionalDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = ConditionalDetrImageProcessor if is_vision_available() else None def setUp(self): self.image_processor_tester = ConditionalDetrImageProcessingTester(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) @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 = ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50") encoding = image_processing(images=image, annotations=target, return_tensors="pt") # verify pixel values 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)) # verify area 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)) # 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, 1066]) 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 = ConditionalDetrImageProcessor(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, 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)) # verify area 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)) # 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 = 822873 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, 1066]) self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))