mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-13 17:48:22 +06:00
a847d4aa6b
* added fast image processor for VitMatte including updated and new tests, fixed a bug in the slow image processor that processed images incorrectly for input format ChannelDimension.FIRST in which case the trimaps were not added in the correct dimension, this bug was also reflected in the tests through incorretly shaped trimaps being passed * final edits for fast vitmatte image processor and tests * final edits for fast vitmatte image processor and tests --------- Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com>
337 lines
16 KiB
Python
337 lines
16 KiB
Python
# Copyright 2023 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 time
|
|
import unittest
|
|
import warnings
|
|
|
|
import numpy as np
|
|
import requests
|
|
|
|
from transformers.testing_utils import is_flaky, require_torch, require_vision
|
|
from transformers.utils import is_torch_available, is_torchvision_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 VitMatteImageProcessor
|
|
|
|
if is_torchvision_available():
|
|
from transformers import VitMatteImageProcessorFast
|
|
|
|
|
|
class VitMatteImageProcessingTester:
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=7,
|
|
num_channels=3,
|
|
image_size=18,
|
|
min_resolution=30,
|
|
max_resolution=400,
|
|
do_rescale=True,
|
|
rescale_factor=0.5,
|
|
do_pad=True,
|
|
size_divisibility=10,
|
|
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.image_size = image_size
|
|
self.min_resolution = min_resolution
|
|
self.max_resolution = max_resolution
|
|
self.do_rescale = do_rescale
|
|
self.rescale_factor = rescale_factor
|
|
self.do_pad = do_pad
|
|
self.size_divisibility = size_divisibility
|
|
self.do_normalize = do_normalize
|
|
self.image_mean = image_mean
|
|
self.image_std = image_std
|
|
|
|
def prepare_image_processor_dict(self):
|
|
return {
|
|
"image_mean": self.image_mean,
|
|
"image_std": self.image_std,
|
|
"do_normalize": self.do_normalize,
|
|
"do_rescale": self.do_rescale,
|
|
"rescale_factor": self.rescale_factor,
|
|
"do_pad": self.do_pad,
|
|
"size_divisibility": self.size_divisibility,
|
|
}
|
|
|
|
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 VitMatteImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
|
|
image_processing_class = VitMatteImageProcessor if is_vision_available() else None
|
|
fast_image_processing_class = VitMatteImageProcessorFast if is_torchvision_available() else None
|
|
|
|
def setUp(self):
|
|
super().setUp()
|
|
self.image_processor_tester = VitMatteImageProcessingTester(self)
|
|
|
|
@property
|
|
def image_processor_dict(self):
|
|
return self.image_processor_tester.prepare_image_processor_dict()
|
|
|
|
def test_image_processor_properties(self):
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processing = 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_rescale"))
|
|
self.assertTrue(hasattr(image_processing, "rescale_factor"))
|
|
self.assertTrue(hasattr(image_processing, "do_pad"))
|
|
self.assertTrue(hasattr(image_processing, "size_divisibility"))
|
|
|
|
def test_call_numpy(self):
|
|
# create random numpy tensors
|
|
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
|
|
for image in image_inputs:
|
|
self.assertIsInstance(image, np.ndarray)
|
|
|
|
# Test not batched input (image processor does not support batched inputs)
|
|
image = image_inputs[0]
|
|
trimap = np.random.randint(0, 3, size=image.shape[:2])
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processing = image_processing_class(**self.image_processor_dict)
|
|
encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values
|
|
|
|
# Verify that width and height can be divided by size_divisibility and that correct dimensions got merged
|
|
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
|
|
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)
|
|
self.assertTrue(encoded_images.shape[-3] == 4)
|
|
|
|
def test_call_pytorch(self):
|
|
# 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 not batched input (image processor does not support batched inputs)
|
|
image = image_inputs[0]
|
|
trimap = np.random.randint(0, 3, size=image.shape[1:])
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processing = image_processing_class(**self.image_processor_dict)
|
|
encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values
|
|
|
|
# Verify that width and height can be divided by size_divisibility and that correct dimensions got merged
|
|
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
|
|
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)
|
|
self.assertTrue(encoded_images.shape[-3] == 4)
|
|
|
|
# create batched tensors
|
|
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
|
|
image_input = torch.stack(image_inputs, dim=0)
|
|
self.assertIsInstance(image_input, torch.Tensor)
|
|
self.assertTrue(image_input.shape[1] == 3)
|
|
|
|
trimap_shape = [image_input.shape[0]] + [1] + list(image_input.shape)[2:]
|
|
trimap_input = torch.randint(0, 3, trimap_shape, dtype=torch.uint8)
|
|
self.assertIsInstance(trimap_input, torch.Tensor)
|
|
self.assertTrue(trimap_input.shape[1] == 1)
|
|
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processing = image_processing_class(**self.image_processor_dict)
|
|
encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values
|
|
|
|
# Verify that width and height can be divided by size_divisibility and that correct dimensions got merged
|
|
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
|
|
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)
|
|
self.assertTrue(encoded_images.shape[-3] == 4)
|
|
|
|
def test_call_pil(self):
|
|
# create random PIL images
|
|
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
|
|
for image in image_inputs:
|
|
self.assertIsInstance(image, Image.Image)
|
|
|
|
# Test not batched input (image processor does not support batched inputs)
|
|
image = image_inputs[0]
|
|
trimap = np.random.randint(0, 3, size=image.size[::-1])
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processing = image_processing_class(**self.image_processor_dict)
|
|
encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values
|
|
|
|
# Verify that width and height can be divided by size_divisibility and that correct dimensions got merged
|
|
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
|
|
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)
|
|
self.assertTrue(encoded_images.shape[-3] == 4)
|
|
|
|
def test_call_numpy_4_channels(self):
|
|
# Test that can process images which have an arbitrary number of channels
|
|
|
|
# create random numpy tensors
|
|
self.image_processor_tester.num_channels = 4
|
|
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
|
|
|
|
# Test not batched input (image processor does not support batched inputs)
|
|
image = image_inputs[0]
|
|
trimap = np.random.randint(0, 3, size=image.shape[:2])
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processor = image_processing_class(**self.image_processor_dict)
|
|
encoded_images = image_processor(
|
|
images=image,
|
|
trimaps=trimap,
|
|
input_data_format="channels_last",
|
|
image_mean=0,
|
|
image_std=1,
|
|
return_tensors="pt",
|
|
).pixel_values
|
|
|
|
# Verify that width and height can be divided by size_divisibility and that correct dimensions got merged
|
|
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisibility == 0)
|
|
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisibility == 0)
|
|
self.assertTrue(encoded_images.shape[-3] == 5)
|
|
|
|
def test_padding_slow(self):
|
|
image_processing = self.image_processing_class(**self.image_processor_dict)
|
|
image = np.random.randn(3, 249, 491)
|
|
images = image_processing.pad_image(image)
|
|
assert images.shape == (3, 256, 512)
|
|
|
|
image = np.random.randn(3, 249, 512)
|
|
images = image_processing.pad_image(image)
|
|
assert images.shape == (3, 256, 512)
|
|
|
|
def test_padding_fast(self):
|
|
# extra test because name is different for fast image processor
|
|
image_processing = self.fast_image_processing_class(**self.image_processor_dict)
|
|
image = torch.rand(3, 249, 491)
|
|
images = image_processing._pad_image(image)
|
|
assert images.shape == (3, 256, 512)
|
|
|
|
image = torch.rand(3, 249, 512)
|
|
images = image_processing._pad_image(image)
|
|
assert images.shape == (3, 256, 512)
|
|
|
|
def test_image_processor_preprocess_arguments(self):
|
|
# vitmatte require additional trimap input for image_processor
|
|
# that is why we override original common test
|
|
|
|
for image_processing_class in self.image_processor_list:
|
|
image_processor = image_processing_class(**self.image_processor_dict)
|
|
image = self.image_processor_tester.prepare_image_inputs()[0]
|
|
trimap = np.random.randint(0, 3, size=image.size[::-1])
|
|
|
|
with warnings.catch_warnings(record=True) as raised_warnings:
|
|
warnings.simplefilter("always")
|
|
image_processor(image, trimaps=trimap, extra_argument=True)
|
|
|
|
messages = " ".join([str(w.message) for w in raised_warnings])
|
|
self.assertGreaterEqual(len(raised_warnings), 1)
|
|
self.assertIn("extra_argument", messages)
|
|
|
|
@is_flaky()
|
|
def test_fast_is_faster_than_slow(self):
|
|
if not self.test_slow_image_processor or not self.test_fast_image_processor:
|
|
self.skipTest(reason="Skipping speed test")
|
|
|
|
if self.image_processing_class is None or self.fast_image_processing_class is None:
|
|
self.skipTest(reason="Skipping speed test as one of the image processors is not defined")
|
|
|
|
def measure_time(image_processor, images, trimaps):
|
|
# Warmup
|
|
for _ in range(5):
|
|
_ = image_processor(images, trimaps=trimaps, return_tensors="pt")
|
|
all_times = []
|
|
for _ in range(10):
|
|
start = time.time()
|
|
_ = image_processor(images, trimaps=trimaps, return_tensors="pt")
|
|
all_times.append(time.time() - start)
|
|
# Take the average of the fastest 3 runs
|
|
avg_time = sum(sorted(all_times[:3])) / 3.0
|
|
return avg_time
|
|
|
|
dummy_images = torch.randint(0, 255, (4, 3, 400, 800), dtype=torch.uint8)
|
|
dummy_trimaps = torch.randint(0, 3, (4, 400, 800), dtype=torch.uint8)
|
|
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
|
|
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
|
|
|
|
fast_time = measure_time(image_processor_fast, dummy_images, dummy_trimaps)
|
|
slow_time = measure_time(image_processor_slow, dummy_images, dummy_trimaps)
|
|
|
|
self.assertLessEqual(fast_time, slow_time)
|
|
|
|
def test_slow_fast_equivalence(self):
|
|
if not self.test_slow_image_processor or not self.test_fast_image_processor:
|
|
self.skipTest(reason="Skipping slow/fast equivalence test")
|
|
|
|
if self.image_processing_class is None or self.fast_image_processing_class is None:
|
|
self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
|
|
|
|
dummy_image = Image.open(
|
|
requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
|
|
)
|
|
dummy_trimap = np.random.randint(0, 3, size=dummy_image.size[::-1])
|
|
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
|
|
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
|
|
|
|
encoding_slow = image_processor_slow(dummy_image, trimaps=dummy_trimap, return_tensors="pt")
|
|
encoding_fast = image_processor_fast(dummy_image, trimaps=dummy_trimap, return_tensors="pt")
|
|
self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1))
|
|
self.assertLessEqual(
|
|
torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3
|
|
)
|
|
|
|
def test_slow_fast_equivalence_batched(self):
|
|
# this only checks on equal resolution, since the slow processor doesn't work otherwise
|
|
if not self.test_slow_image_processor or not self.test_fast_image_processor:
|
|
self.skipTest(reason="Skipping slow/fast equivalence test")
|
|
|
|
if self.image_processing_class is None or self.fast_image_processing_class is None:
|
|
self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
|
|
|
|
if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
|
|
self.skipTest(
|
|
reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
|
|
)
|
|
|
|
dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
|
|
dummy_trimaps = [np.random.randint(0, 3, size=image.shape[1:]) for image in dummy_images]
|
|
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
|
|
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
|
|
|
|
encoding_slow = image_processor_slow(dummy_images, trimaps=dummy_trimaps, return_tensors="pt")
|
|
encoding_fast = image_processor_fast(dummy_images, trimaps=dummy_trimaps, return_tensors="pt")
|
|
|
|
self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1))
|
|
self.assertLessEqual(
|
|
torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 1e-3
|
|
)
|