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Add ViTMatte (#25843)

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* First draft

* Simplify image processor

* Fix rebase

* Address comments

* Address more comments

* Address more comments

* Address more comments

* Address more comments

* Improve pad_image

* Add tests

* Update integration test

* Fix image processor tests

* Fix model tests

* Convert checkpoints

* Fix doc tests

* Remove file

* Apply suggestions

* Address comments

* Fix typing hint

* Add batch_norm_eps

* Address comments

* Fix style
This commit is contained in:
NielsRogge
2023-09-19 15:56:10 +02:00
committed by GitHub
parent 04191ea1e6
commit 7d6354e047
25 changed files with 1524 additions and 0 deletions
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tests/models/vitmatte/test_image_processing_vitmatte.py Normal file
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# coding=utf-8
# 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 unittest
import numpy as np
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 torch
if is_vision_available():
from PIL import Image
from transformers import VitMatteImageProcessor
class VitMatteImageProcessingTester(unittest.TestCase):
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
def setUp(self):
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):
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_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):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# 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])
encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values
# Verify that width and height can be divided by size_divisibility
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)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# 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[:2])
encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values
# Verify that width and height can be divided by size_divisibility
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)
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# 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])
encoded_images = image_processing(images=image, trimaps=trimap, return_tensors="pt").pixel_values
# Verify that width and height can be divided by size_divisibility
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)
def test_call_numpy_4_channels(self):
# Test that can process images which have an arbitrary number of channels
# Initialize image_processing
image_processor = self.image_processing_class(**self.image_processor_dict)
# 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])
encoded_images = image_processor(
images=image,
trimaps=trimap,
input_data_format="channels_first",
image_mean=0,
image_std=1,
return_tensors="pt",
).pixel_values
# Verify that width and height can be divided by size_divisibility
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)
def test_padding(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)