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# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# 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 unittest
from parameterized import parameterized
from transformers.testing_utils import require_torch, require_vision
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 numpy as np
import torch
from transformers.models.superglue.modeling_superglue import KeypointMatchingOutput
if is_vision_available():
from transformers import SuperGlueImageProcessor
def random_array(size):
return np.random.randint(255, size=size)
def random_tensor(size):
return torch.rand(size)
class SuperGlueImageProcessingTester:
def __init__(
self,
parent,
batch_size=6,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_grayscale=True,
):
size = size if size is not None else {"height": 480, "width": 640}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.do_grayscale = do_grayscale
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_grayscale": self.do_grayscale,
}
def expected_output_image_shape(self, images):
return 2, self.num_channels, self.size["height"], self.size["width"]
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False, pairs=True, batch_size=None):
batch_size = batch_size if batch_size is not None else self.batch_size
image_inputs = prepare_image_inputs(
batch_size=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,
)
if pairs:
image_inputs = [image_inputs[i : i + 2] for i in range(0, len(image_inputs), 2)]
return image_inputs
def prepare_keypoint_matching_output(self, pixel_values):
max_number_keypoints = 50
batch_size = len(pixel_values)
mask = torch.zeros((batch_size, 2, max_number_keypoints), dtype=torch.int)
keypoints = torch.zeros((batch_size, 2, max_number_keypoints, 2))
matches = torch.full((batch_size, 2, max_number_keypoints), -1, dtype=torch.int)
scores = torch.zeros((batch_size, 2, max_number_keypoints))
for i in range(batch_size):
random_number_keypoints0 = np.random.randint(10, max_number_keypoints)
random_number_keypoints1 = np.random.randint(10, max_number_keypoints)
random_number_matches = np.random.randint(5, min(random_number_keypoints0, random_number_keypoints1))
mask[i, 0, :random_number_keypoints0] = 1
mask[i, 1, :random_number_keypoints1] = 1
keypoints[i, 0, :random_number_keypoints0] = torch.rand((random_number_keypoints0, 2))
keypoints[i, 1, :random_number_keypoints1] = torch.rand((random_number_keypoints1, 2))
random_matches_indices0 = torch.randperm(random_number_keypoints1, dtype=torch.int)[:random_number_matches]
random_matches_indices1 = torch.randperm(random_number_keypoints0, dtype=torch.int)[:random_number_matches]
matches[i, 0, random_matches_indices1] = random_matches_indices0
matches[i, 1, random_matches_indices0] = random_matches_indices1
scores[i, 0, random_matches_indices1] = torch.rand((random_number_matches,))
scores[i, 1, random_matches_indices0] = torch.rand((random_number_matches,))
return KeypointMatchingOutput(mask=mask, keypoints=keypoints, matches=matches, matching_scores=scores)
@require_torch
@require_vision
class SuperGlueImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = SuperGlueImageProcessor if is_vision_available() else None
def setUp(self) -> None:
super().setUp()
self.image_processor_tester = SuperGlueImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processing(self):
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "do_rescale"))
self.assertTrue(hasattr(image_processing, "rescale_factor"))
self.assertTrue(hasattr(image_processing, "do_grayscale"))
def test_image_processor_from_dict_with_kwargs(self):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"height": 480, "width": 640})
image_processor = image_processing_class.from_dict(
self.image_processor_dict, size={"height": 42, "width": 42}
)
self.assertEqual(image_processor.size, {"height": 42, "width": 42})
@unittest.skip(reason="SuperPointImageProcessor is always supposed to return a grayscaled image")
def test_call_numpy_4_channels(self):
pass
def test_number_and_format_of_images_in_input(self):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class.from_dict(self.image_processor_dict)
# Cases where the number of images and the format of lists in the input is correct
image_input = self.image_processor_tester.prepare_image_inputs(pairs=False, batch_size=2)
image_processed = image_processor.preprocess(image_input, return_tensors="pt")
self.assertEqual((1, 2, 3, 480, 640), tuple(image_processed["pixel_values"].shape))
image_input = self.image_processor_tester.prepare_image_inputs(pairs=True, batch_size=2)
image_processed = image_processor.preprocess(image_input, return_tensors="pt")
self.assertEqual((1, 2, 3, 480, 640), tuple(image_processed["pixel_values"].shape))
image_input = self.image_processor_tester.prepare_image_inputs(pairs=True, batch_size=4)
image_processed = image_processor.preprocess(image_input, return_tensors="pt")
self.assertEqual((2, 2, 3, 480, 640), tuple(image_processed["pixel_values"].shape))
image_input = self.image_processor_tester.prepare_image_inputs(pairs=True, batch_size=6)
image_processed = image_processor.preprocess(image_input, return_tensors="pt")
self.assertEqual((3, 2, 3, 480, 640), tuple(image_processed["pixel_values"].shape))
# Cases where the number of images or the format of lists in the input is incorrect
## List of 4 images
image_input = self.image_processor_tester.prepare_image_inputs(pairs=False, batch_size=4)
with self.assertRaises(ValueError) as cm:
image_processor.preprocess(image_input, return_tensors="pt")
self.assertEqual(ValueError, cm.exception.__class__)
## List of 3 images
image_input = self.image_processor_tester.prepare_image_inputs(pairs=False, batch_size=3)
with self.assertRaises(ValueError) as cm:
image_processor.preprocess(image_input, return_tensors="pt")
self.assertEqual(ValueError, cm.exception.__class__)
## List of 2 pairs and 1 image
image_input = self.image_processor_tester.prepare_image_inputs(pairs=True, batch_size=3)
with self.assertRaises(ValueError) as cm:
image_processor.preprocess(image_input, return_tensors="pt")
self.assertEqual(ValueError, cm.exception.__class__)
@parameterized.expand(
[
([random_array((3, 100, 200)), random_array((3, 100, 200))], (1, 2, 3, 480, 640)),
([[random_array((3, 100, 200)), random_array((3, 100, 200))]], (1, 2, 3, 480, 640)),
([random_tensor((3, 100, 200)), random_tensor((3, 100, 200))], (1, 2, 3, 480, 640)),
([random_tensor((3, 100, 200)), random_tensor((3, 100, 200))], (1, 2, 3, 480, 640)),
],
)
def test_valid_image_shape_in_input(self, image_input, output):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class.from_dict(self.image_processor_dict)
image_processed = image_processor.preprocess(image_input, return_tensors="pt")
self.assertEqual(output, tuple(image_processed["pixel_values"].shape))
@parameterized.expand(
[
(random_array((3, 100, 200)),),
([random_array((3, 100, 200))],),
(random_array((1, 3, 100, 200)),),
([[random_array((3, 100, 200))]],),
([[random_array((3, 100, 200))], [random_array((3, 100, 200))]],),
([random_array((1, 3, 100, 200)), random_array((1, 3, 100, 200))],),
(random_array((1, 1, 3, 100, 200)),),
],
)
def test_invalid_image_shape_in_input(self, image_input):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class.from_dict(self.image_processor_dict)
with self.assertRaises(ValueError) as cm:
image_processor(image_input, return_tensors="pt")
self.assertEqual(ValueError, cm.exception.__class__)
def test_input_images_properly_paired(self):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class.from_dict(self.image_processor_dict)
image_inputs = self.image_processor_tester.prepare_image_inputs()
pre_processed_images = image_processor(image_inputs, return_tensors="pt")
self.assertEqual(len(pre_processed_images["pixel_values"].shape), 5)
self.assertEqual(pre_processed_images["pixel_values"].shape[1], 2)
def test_input_not_paired_images_raises_error(self):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class.from_dict(self.image_processor_dict)
image_inputs = self.image_processor_tester.prepare_image_inputs(pairs=False)
with self.assertRaises(ValueError):
image_processor(image_inputs[0])
def test_input_image_properly_converted_to_grayscale(self):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class.from_dict(self.image_processor_dict)
image_inputs = self.image_processor_tester.prepare_image_inputs()
pre_processed_images = image_processor(image_inputs, return_tensors="pt")
for image_pair in pre_processed_images["pixel_values"]:
for image in image_pair:
self.assertTrue(
torch.all(image[0, ...] == image[1, ...]) and torch.all(image[1, ...] == image[2, ...])
)
def test_call_numpy(self):
# Test overwritten because SuperGlueImageProcessor combines images by pair to feed it into SuperGlue
# Initialize image_processing
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_pairs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
for image_pair in image_pairs:
self.assertEqual(len(image_pair), 2)
expected_batch_size = int(self.image_processor_tester.batch_size / 2)
# Test with 2 images
encoded_images = image_processing(image_pairs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs[0])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Test with list of pairs
encoded_images = image_processing(image_pairs, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs)
self.assertEqual(tuple(encoded_images.shape), (expected_batch_size, *expected_output_image_shape))
# Test without paired images
image_pairs = self.image_processor_tester.prepare_image_inputs(
equal_resolution=False, numpify=True, pairs=False
)
with self.assertRaises(ValueError):
image_processing(image_pairs, return_tensors="pt").pixel_values
def test_call_pil(self):
# Test overwritten because SuperGlueImageProcessor combines images by pair to feed it into SuperGlue
# Initialize image_processing
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class(**self.image_processor_dict)
# create random PIL images
image_pairs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
for image_pair in image_pairs:
self.assertEqual(len(image_pair), 2)
expected_batch_size = int(self.image_processor_tester.batch_size / 2)
# Test with 2 images
encoded_images = image_processing(image_pairs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs[0])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Test with list of pairs
encoded_images = image_processing(image_pairs, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs)
self.assertEqual(tuple(encoded_images.shape), (expected_batch_size, *expected_output_image_shape))
# Test without paired images
image_pairs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, pairs=False)
with self.assertRaises(ValueError):
image_processing(image_pairs, return_tensors="pt").pixel_values
def test_call_pytorch(self):
# Test overwritten because SuperGlueImageProcessor combines images by pair to feed it into SuperGlue
# Initialize image_processing
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_pairs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
for image_pair in image_pairs:
self.assertEqual(len(image_pair), 2)
expected_batch_size = int(self.image_processor_tester.batch_size / 2)
# Test with 2 images
encoded_images = image_processing(image_pairs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs[0])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Test with list of pairs
encoded_images = image_processing(image_pairs, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs)
self.assertEqual(tuple(encoded_images.shape), (expected_batch_size, *expected_output_image_shape))
# Test without paired images
image_pairs = self.image_processor_tester.prepare_image_inputs(
equal_resolution=False, torchify=True, pairs=False
)
with self.assertRaises(ValueError):
image_processing(image_pairs, return_tensors="pt").pixel_values
def test_image_processor_with_list_of_two_images(self):
for image_processing_class in self.image_processor_list:
image_processing = image_processing_class(**self.image_processor_dict)
image_pairs = self.image_processor_tester.prepare_image_inputs(
equal_resolution=False, numpify=True, batch_size=2, pairs=False
)
self.assertEqual(len(image_pairs), 2)
self.assertTrue(isinstance(image_pairs[0], np.ndarray))
self.assertTrue(isinstance(image_pairs[1], np.ndarray))
expected_batch_size = 1
encoded_images = image_processing(image_pairs, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_pairs[0])
self.assertEqual(tuple(encoded_images.shape), (expected_batch_size, *expected_output_image_shape))
@require_torch
def test_post_processing_keypoint_matching(self):
def check_post_processed_output(post_processed_output, image_pair_size):
for post_processed_output, (image_size0, image_size1) in zip(post_processed_output, image_pair_size):
self.assertTrue("keypoints0" in post_processed_output)
self.assertTrue("keypoints1" in post_processed_output)
self.assertTrue("matching_scores" in post_processed_output)
keypoints0 = post_processed_output["keypoints0"]
keypoints1 = post_processed_output["keypoints1"]
all_below_image_size0 = torch.all(keypoints0[:, 0] <= image_size0[1]) and torch.all(
keypoints0[:, 1] <= image_size0[0]
)
all_below_image_size1 = torch.all(keypoints1[:, 0] <= image_size1[1]) and torch.all(
keypoints1[:, 1] <= image_size1[0]
)
all_above_zero0 = torch.all(keypoints0[:, 0] >= 0) and torch.all(keypoints0[:, 1] >= 0)
all_above_zero1 = torch.all(keypoints0[:, 0] >= 0) and torch.all(keypoints0[:, 1] >= 0)
self.assertTrue(all_below_image_size0)
self.assertTrue(all_below_image_size1)
self.assertTrue(all_above_zero0)
self.assertTrue(all_above_zero1)
all_scores_different_from_minus_one = torch.all(post_processed_output["matching_scores"] != -1)
self.assertTrue(all_scores_different_from_minus_one)
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class.from_dict(self.image_processor_dict)
image_inputs = self.image_processor_tester.prepare_image_inputs()
pre_processed_images = image_processor.preprocess(image_inputs, return_tensors="pt")
outputs = self.image_processor_tester.prepare_keypoint_matching_output(**pre_processed_images)
tuple_image_sizes = [
((image_pair[0].size[0], image_pair[0].size[1]), (image_pair[1].size[0], image_pair[1].size[1]))
for image_pair in image_inputs
]
tuple_post_processed_outputs = image_processor.post_process_keypoint_matching(outputs, tuple_image_sizes)
check_post_processed_output(tuple_post_processed_outputs, tuple_image_sizes)
tensor_image_sizes = torch.tensor(
[(image_pair[0].size, image_pair[1].size) for image_pair in image_inputs]
).flip(2)
tensor_post_processed_outputs = image_processor.post_process_keypoint_matching(outputs, tensor_image_sizes)
check_post_processed_output(tensor_post_processed_outputs, tensor_image_sizes)