tests / test_feature_extraction_detr.py

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	# 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

	import numpy as np

	from transformers.file_utils import is_torch_available, is_vision_available
	from transformers.testing_utils import require_torch, require_vision, slow

	from .test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs


	if is_torch_available():
	import torch

	if is_vision_available():
	from PIL import Image

	from transformers import DetrFeatureExtractor


	class DetrFeatureExtractionTester(unittest.TestCase):
	def __init__(
	self,
	parent,
	batch_size=7,
	num_channels=3,
	min_resolution=30,
	max_resolution=400,
	do_resize=True,
	size=18,
	max_size=1333, # by setting max_size > max_resolution we're effectively not testing this :p
	do_normalize=True,
	image_mean=[0.5, 0.5, 0.5],
	image_std=[0.5, 0.5, 0.5],
	):
	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.max_size = max_size
	self.do_normalize = do_normalize
	self.image_mean = image_mean
	self.image_std = image_std

	def prepare_feat_extract_dict(self):
	return {
	"do_resize": self.do_resize,
	"size": self.size,
	"max_size": self.max_size,
	"do_normalize": self.do_normalize,
	"image_mean": self.image_mean,
	"image_std": self.image_std,
	}

	def get_expected_values(self, image_inputs, batched=False):
	"""
	This function computes the expected height and width when providing images to DetrFeatureExtractor,
	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 * h / w)
	expected_width = self.size
	elif w > h:
	expected_height = self.size
	expected_width = int(self.size * w / h)
	else:
	expected_height = self.size
	expected_width = self.size

	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


	@require_torch
	@require_vision
	class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):

	feature_extraction_class = DetrFeatureExtractor if is_vision_available() else None

	def setUp(self):
	self.feature_extract_tester = DetrFeatureExtractionTester(self)

	@property
	def feat_extract_dict(self):
	return self.feature_extract_tester.prepare_feat_extract_dict()

	def test_feat_extract_properties(self):
	feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
	self.assertTrue(hasattr(feature_extractor, "image_mean"))
	self.assertTrue(hasattr(feature_extractor, "image_std"))
	self.assertTrue(hasattr(feature_extractor, "do_normalize"))
	self.assertTrue(hasattr(feature_extractor, "do_resize"))
	self.assertTrue(hasattr(feature_extractor, "size"))
	self.assertTrue(hasattr(feature_extractor, "max_size"))

	def test_batch_feature(self):
	pass

	def test_call_pil(self):
	# Initialize feature_extractor
	feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
	# create random PIL images
	image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
	for image in image_inputs:
	self.assertIsInstance(image, Image.Image)

	# Test not batched input
	encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values

	expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)

	self.assertEqual(
	encoded_images.shape,
	(1, self.feature_extract_tester.num_channels, expected_height, expected_width),
	)

	# Test batched
	expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)

	encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
	self.assertEqual(
	encoded_images.shape,
	(
	self.feature_extract_tester.batch_size,
	self.feature_extract_tester.num_channels,
	expected_height,
	expected_width,
	),
	)

	def test_call_numpy(self):
	# Initialize feature_extractor
	feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
	# create random numpy tensors
	image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, numpify=True)
	for image in image_inputs:
	self.assertIsInstance(image, np.ndarray)

	# Test not batched input
	encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values

	expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)

	self.assertEqual(
	encoded_images.shape,
	(1, self.feature_extract_tester.num_channels, expected_height, expected_width),
	)

	# Test batched
	encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values

	expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)

	self.assertEqual(
	encoded_images.shape,
	(
	self.feature_extract_tester.batch_size,
	self.feature_extract_tester.num_channels,
	expected_height,
	expected_width,
	),
	)

	def test_call_pytorch(self):
	# Initialize feature_extractor
	feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
	# create random PyTorch tensors
	image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
	for image in image_inputs:
	self.assertIsInstance(image, torch.Tensor)

	# Test not batched input
	encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values

	expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs)

	self.assertEqual(
	encoded_images.shape,
	(1, self.feature_extract_tester.num_channels, expected_height, expected_width),
	)

	# Test batched
	encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values

	expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True)

	self.assertEqual(
	encoded_images.shape,
	(
	self.feature_extract_tester.batch_size,
	self.feature_extract_tester.num_channels,
	expected_height,
	expected_width,
	),
	)

	def test_equivalence_pad_and_create_pixel_mask(self):
	# Initialize feature_extractors
	feature_extractor_1 = self.feature_extraction_class(**self.feat_extract_dict)
	feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False)
	# create random PyTorch tensors
	image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
	for image in image_inputs:
	self.assertIsInstance(image, torch.Tensor)

	# Test whether the method "pad_and_return_pixel_mask" and calling the feature extractor return the same tensors
	encoded_images_with_method = feature_extractor_1.pad_and_create_pixel_mask(image_inputs, return_tensors="pt")
	encoded_images = feature_extractor_2(image_inputs, return_tensors="pt")

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

	@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
	# TODO replace by facebook/detr-resnet-50
	feature_extractor = DetrFeatureExtractor.from_pretrained("nielsr/detr-resnet-50")
	encoding = feature_extractor(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])
	assert 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])
	assert torch.allclose(encoding["target"][0]["area"], expected_area)
	# verify boxes
	expected_boxes_shape = torch.Size([6, 4])
	self.assertEqual(encoding["target"][0]["boxes"].shape, expected_boxes_shape)
	expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
	assert torch.allclose(encoding["target"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)
	# verify image_id
	expected_image_id = torch.tensor([39769])
	assert torch.allclose(encoding["target"][0]["image_id"], expected_image_id)
	# verify is_crowd
	expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
	assert torch.allclose(encoding["target"][0]["iscrowd"], expected_is_crowd)
	# verify class_labels
	expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
	assert torch.allclose(encoding["target"][0]["class_labels"], expected_class_labels)
	# verify orig_size
	expected_orig_size = torch.tensor([480, 640])
	assert torch.allclose(encoding["target"][0]["orig_size"], expected_orig_size)
	# verify size
	expected_size = torch.tensor([800, 1066])
	assert torch.allclose(encoding["target"][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
	# TODO replace by .from_pretrained facebook/detr-resnet-50-panoptic
	feature_extractor = DetrFeatureExtractor(format="coco_panoptic")
	encoding = feature_extractor(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])
	assert 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])
	assert torch.allclose(encoding["target"][0]["area"], expected_area)
	# verify boxes
	expected_boxes_shape = torch.Size([6, 4])
	self.assertEqual(encoding["target"][0]["boxes"].shape, expected_boxes_shape)
	expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
	assert torch.allclose(encoding["target"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)
	# verify image_id
	expected_image_id = torch.tensor([39769])
	assert torch.allclose(encoding["target"][0]["image_id"], expected_image_id)
	# verify is_crowd
	expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
	assert torch.allclose(encoding["target"][0]["iscrowd"], expected_is_crowd)
	# verify class_labels
	expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
	assert torch.allclose(encoding["target"][0]["class_labels"], expected_class_labels)
	# verify masks
	expected_masks_sum = 822338
	self.assertEqual(encoding["target"][0]["masks"].sum().item(), expected_masks_sum)
	# verify orig_size
	expected_orig_size = torch.tensor([480, 640])
	assert torch.allclose(encoding["target"][0]["orig_size"], expected_orig_size)
	# verify size
	expected_size = torch.tensor([800, 1066])
	assert torch.allclose(encoding["target"][0]["size"], expected_size)