init
This commit is contained in:
@@ -0,0 +1,218 @@
|
||||
# Copyright 2024 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.image_utils import PILImageResampling
|
||||
from transformers.testing_utils import 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 ChameleonImageProcessor
|
||||
|
||||
if is_torchvision_available():
|
||||
from transformers import ChameleonImageProcessorFast
|
||||
|
||||
|
||||
class ChameleonImageProcessingTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=7,
|
||||
num_channels=3,
|
||||
image_size=18,
|
||||
min_resolution=30,
|
||||
max_resolution=200,
|
||||
do_resize=True,
|
||||
size=None,
|
||||
do_center_crop=True,
|
||||
crop_size=None,
|
||||
do_normalize=True,
|
||||
image_mean=[1.0, 1.0, 1.0],
|
||||
image_std=[1.0, 1.0, 1.0],
|
||||
do_convert_rgb=True,
|
||||
resample=PILImageResampling.BILINEAR,
|
||||
):
|
||||
size = size if size is not None else {"shortest_edge": 18}
|
||||
crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
|
||||
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_center_crop = do_center_crop
|
||||
self.crop_size = crop_size
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
self.do_convert_rgb = do_convert_rgb
|
||||
self.resample = resample
|
||||
|
||||
def prepare_image_processor_dict(self):
|
||||
return {
|
||||
"do_resize": self.do_resize,
|
||||
"size": self.size,
|
||||
"do_center_crop": self.do_center_crop,
|
||||
"crop_size": self.crop_size,
|
||||
"do_normalize": self.do_normalize,
|
||||
"image_mean": self.image_mean,
|
||||
"image_std": self.image_std,
|
||||
"do_convert_rgb": self.do_convert_rgb,
|
||||
"resample": self.resample,
|
||||
}
|
||||
|
||||
# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.expected_output_image_shape
|
||||
def expected_output_image_shape(self, images):
|
||||
return self.num_channels, self.crop_size["height"], self.crop_size["width"]
|
||||
|
||||
# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.prepare_image_inputs
|
||||
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 ChameleonImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
|
||||
image_processing_class = ChameleonImageProcessor if is_vision_available() else None
|
||||
fast_image_processing_class = ChameleonImageProcessorFast if is_torchvision_available() else None
|
||||
|
||||
# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.setUp with CLIP->Chameleon
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
self.image_processor_tester = ChameleonImageProcessingTester(self)
|
||||
|
||||
@property
|
||||
# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.image_processor_dict
|
||||
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, "do_resize"))
|
||||
self.assertTrue(hasattr(image_processing, "size"))
|
||||
self.assertTrue(hasattr(image_processing, "do_center_crop"))
|
||||
self.assertTrue(hasattr(image_processing, "center_crop"))
|
||||
self.assertTrue(hasattr(image_processing, "do_normalize"))
|
||||
self.assertTrue(hasattr(image_processing, "image_mean"))
|
||||
self.assertTrue(hasattr(image_processing, "image_std"))
|
||||
self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
|
||||
|
||||
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, {"shortest_edge": 18})
|
||||
self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
|
||||
|
||||
image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
|
||||
self.assertEqual(image_processor.size, {"shortest_edge": 42})
|
||||
self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
|
||||
|
||||
def test_call_pil(self):
|
||||
for image_processing_class in self.image_processor_list:
|
||||
# Initialize image_processing
|
||||
image_processing = image_processing_class(**self.image_processor_dict)
|
||||
# create random PIL images
|
||||
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
|
||||
for image in image_inputs:
|
||||
self.assertIsInstance(image, Image.Image)
|
||||
|
||||
# Test not batched input
|
||||
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
|
||||
expected_output_image_shape = (1, 3, 18, 18)
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
|
||||
# Test batched
|
||||
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
|
||||
expected_output_image_shape = (7, 3, 18, 18)
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
|
||||
def test_call_numpy(self):
|
||||
for image_processing_class in self.image_processor_list:
|
||||
# Initialize image_processing
|
||||
image_processing = image_processing_class(**self.image_processor_dict)
|
||||
# create random numpy tensors
|
||||
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
|
||||
for image in image_inputs:
|
||||
self.assertIsInstance(image, np.ndarray)
|
||||
|
||||
# Test not batched input
|
||||
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
|
||||
expected_output_image_shape = (1, 3, 18, 18)
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
|
||||
# Test batched
|
||||
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
|
||||
expected_output_image_shape = (7, 3, 18, 18)
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
|
||||
def test_call_pytorch(self):
|
||||
for image_processing_class in self.image_processor_list:
|
||||
# Initialize image_processing
|
||||
image_processing = image_processing_class(**self.image_processor_dict)
|
||||
# create random PyTorch tensors
|
||||
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
|
||||
|
||||
for image in image_inputs:
|
||||
self.assertIsInstance(image, torch.Tensor)
|
||||
|
||||
# Test not batched input
|
||||
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
|
||||
expected_output_image_shape = (1, 3, 18, 18)
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
|
||||
# Test batched
|
||||
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
|
||||
expected_output_image_shape = (7, 3, 18, 18)
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
|
||||
def test_nested_input(self):
|
||||
for image_processing_class in self.image_processor_list:
|
||||
image_processing = image_processing_class(**self.image_processor_dict)
|
||||
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
|
||||
|
||||
# Test batched as a list of images
|
||||
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
|
||||
expected_output_image_shape = (7, 3, 18, 18)
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
|
||||
# Test batched as a nested list of images, where each sublist is one batch
|
||||
image_inputs_nested = [image_inputs[:3], image_inputs[3:]]
|
||||
encoded_images_nested = image_processing(image_inputs_nested, return_tensors="pt").pixel_values
|
||||
expected_output_image_shape = (7, 3, 18, 18)
|
||||
self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape)
|
||||
|
||||
# Image processor should return same pixel values, independently of input format
|
||||
self.assertTrue((encoded_images_nested == encoded_images).all())
|
||||
Reference in New Issue
Block a user