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Unverified Commit 41d56ea6 authored by amyeroberts's avatar amyeroberts Committed by GitHub
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Refactor image processor testers (#25450) 

* Refactor image processor test mixin

- Move test_call_numpy, test_call_pytorch, test_call_pil to mixin
- Rename mixin to reflect handling of logic more than saving
- Add prepare_image_inputs, expected_image_outputs for tests

* Fix for oneformer
parent 454957c9
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Showing with 434 additions and 1961 deletions
tests/models/beit/test_image_processing_beit.py
View file @ 41d56ea6
...@@ -16,13 +16,12 @@ ...@@ -16,13 +16,12 @@
import unittest import unittest
import numpy as np
from datasets import load_dataset from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available(): if is_torch_available():
...@@ -81,6 +80,20 @@ class BeitImageProcessingTester(unittest.TestCase): ...@@ -81,6 +80,20 @@ class BeitImageProcessingTester(unittest.TestCase):
"do_reduce_labels": self.do_reduce_labels, "do_reduce_labels": self.do_reduce_labels,
} }
def expected_output_image_shape(self, images):
return self.num_channels, self.crop_size["height"], self.crop_size["width"]
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,
)
def prepare_semantic_single_inputs(): def prepare_semantic_single_inputs():
dataset = load_dataset("hf-internal-testing/fixtures_ade20k", split="test") dataset = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
...@@ -104,7 +117,7 @@ def prepare_semantic_batch_inputs(): ...@@ -104,7 +117,7 @@ def prepare_semantic_batch_inputs():
@require_torch @require_torch
@require_vision @require_vision
class BeitImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class BeitImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = BeitImageProcessor if is_vision_available() else None image_processing_class = BeitImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -137,110 +150,11 @@ class BeitImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase) ...@@ -137,110 +150,11 @@ class BeitImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase)
self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84}) self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
self.assertEqual(image_processor.do_reduce_labels, True) self.assertEqual(image_processor.do_reduce_labels, True)
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
def test_call_segmentation_maps(self): def test_call_segmentation_maps(self):
# Initialize image_processing # Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict) image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors # create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
maps = [] maps = []
for image in image_inputs: for image in image_inputs:
self.assertIsInstance(image, torch.Tensor) self.assertIsInstance(image, torch.Tensor)
......
tests/models/blip/test_image_processing_blip.py
View file @ 41d56ea6
...@@ -16,20 +16,13 @@ ...@@ -16,20 +16,13 @@
import unittest import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available(): if is_vision_available():
from PIL import Image
from transformers import BlipImageProcessor from transformers import BlipImageProcessor
...@@ -76,40 +69,24 @@ class BlipImageProcessingTester(unittest.TestCase): ...@@ -76,40 +69,24 @@ class BlipImageProcessingTester(unittest.TestCase):
"do_pad": self.do_pad, "do_pad": self.do_pad,
} }
def prepare_inputs(self, equal_resolution=False, numpify=False, torchify=False): def expected_output_image_shape(self, images):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, return 3, self.size["height"], self.size["width"]
or a list of PyTorch tensors if one specifies torchify=True.
""" def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time" batch_size=self.batch_size,
num_channels=self.num_channels,
if equal_resolution: min_resolution=self.min_resolution,
image_inputs = [] max_resolution=self.max_resolution,
for i in range(self.batch_size): equal_resolution=equal_resolution,
image_inputs.append( numpify=numpify,
np.random.randint( torchify=torchify,
255, size=(self.num_channels, self.max_resolution, self.max_resolution), dtype=np.uint8
) )
)
else:
image_inputs = []
for i in range(self.batch_size):
width, height = np.random.choice(np.arange(self.min_resolution, self.max_resolution), 2)
image_inputs.append(np.random.randint(255, size=(self.num_channels, width, height), dtype=np.uint8))
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
if torchify:
image_inputs = [torch.from_numpy(x) for x in image_inputs]
return image_inputs
@require_torch @require_torch
@require_vision @require_vision
class BlipImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class BlipImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = BlipImageProcessor if is_vision_available() else None image_processing_class = BlipImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -128,109 +105,10 @@ class BlipImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase) ...@@ -128,109 +105,10 @@ class BlipImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase)
self.assertTrue(hasattr(image_processor, "image_std")) self.assertTrue(hasattr(image_processor, "image_std"))
self.assertTrue(hasattr(image_processor, "do_convert_rgb")) self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processor(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_numpy(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input
encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processor(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_pytorch(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processor(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
@require_torch @require_torch
@require_vision @require_vision
class BlipImageProcessingTestFourChannels(ImageProcessingSavingTestMixin, unittest.TestCase): class BlipImageProcessingTestFourChannels(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = BlipImageProcessor if is_vision_available() else None image_processing_class = BlipImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -250,37 +128,10 @@ class BlipImageProcessingTestFourChannels(ImageProcessingSavingTestMixin, unitte ...@@ -250,37 +128,10 @@ class BlipImageProcessingTestFourChannels(ImageProcessingSavingTestMixin, unitte
self.assertTrue(hasattr(image_processor, "image_std")) self.assertTrue(hasattr(image_processor, "image_std"))
self.assertTrue(hasattr(image_processor, "do_convert_rgb")) self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
def test_batch_feature(self): @unittest.skip("BlipImageProcessor does not support 4 channels yet") # FIXME Amy
pass def test_call_numpy(self):
return super().test_call_numpy()
def test_call_pil_four_channels(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = self.image_processor_tester.prepare_inputs(equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched @unittest.skip("BlipImageProcessor does not support 4 channels yet") # FIXME Amy
encoded_images = image_processor(image_inputs, return_tensors="pt").pixel_values def test_call_pytorch(self):
self.assertEqual( return super().test_call_torch()
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
tests/models/bridgetower/test_image_processing_bridgetower.py
View file @ 41d56ea6
...@@ -17,16 +17,11 @@ ...@@ -17,16 +17,11 @@
import unittest import unittest
from typing import Dict, List, Optional, Union from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available(): if is_vision_available():
from PIL import Image from PIL import Image
...@@ -119,10 +114,25 @@ class BridgeTowerImageProcessingTester(unittest.TestCase): ...@@ -119,10 +114,25 @@ class BridgeTowerImageProcessingTester(unittest.TestCase):
return expected_height, expected_width return expected_height, expected_width
def expected_output_image_shape(self, images):
height, width = self.get_expected_values(images, batched=True)
return self.num_channels, height, width
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_torch
@require_vision @require_vision
class BridgeTowerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class BridgeTowerImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = BridgeTowerImageProcessor if is_vision_available() else None image_processing_class = BridgeTowerImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -140,99 +150,3 @@ class BridgeTowerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Te ...@@ -140,99 +150,3 @@ class BridgeTowerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Te
self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "size_divisor")) self.assertTrue(hasattr(image_processing, "size_divisor"))
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image processor
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_numpy(self):
# Initialize image processor
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_pytorch(self):
# Initialize image processor
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
tests/models/chinese_clip/test_image_processing_chinese_clip.py
View file @ 41d56ea6
...@@ -16,20 +16,13 @@ ...@@ -16,20 +16,13 @@
import unittest import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available(): if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor from transformers import ChineseCLIPImageProcessor
...@@ -80,40 +73,24 @@ class ChineseCLIPImageProcessingTester(unittest.TestCase): ...@@ -80,40 +73,24 @@ class ChineseCLIPImageProcessingTester(unittest.TestCase):
"do_convert_rgb": self.do_convert_rgb, "do_convert_rgb": self.do_convert_rgb,
} }
def prepare_inputs(self, equal_resolution=False, numpify=False, torchify=False): def expected_output_image_shape(self, images):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, return 3, self.crop_size["height"], self.crop_size["width"]
or a list of PyTorch tensors if one specifies torchify=True.
""" def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time" batch_size=self.batch_size,
num_channels=self.num_channels,
if equal_resolution: min_resolution=self.min_resolution,
image_inputs = [] max_resolution=self.max_resolution,
for i in range(self.batch_size): equal_resolution=equal_resolution,
image_inputs.append( numpify=numpify,
np.random.randint( torchify=torchify,
255, size=(self.num_channels, self.max_resolution, self.max_resolution), dtype=np.uint8
) )
)
else:
image_inputs = []
for i in range(self.batch_size):
width, height = np.random.choice(np.arange(self.min_resolution, self.max_resolution), 2)
image_inputs.append(np.random.randint(255, size=(self.num_channels, width, height), dtype=np.uint8))
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
if torchify:
image_inputs = [torch.from_numpy(x) for x in image_inputs]
return image_inputs
@require_torch @require_torch
@require_vision @require_vision
class ChineseCLIPImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class ChineseCLIPImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = ChineseCLIPImageProcessor if is_vision_available() else None image_processing_class = ChineseCLIPImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -143,109 +120,10 @@ class ChineseCLIPImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Te ...@@ -143,109 +120,10 @@ class ChineseCLIPImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Te
self.assertEqual(image_processor.size, {"shortest_edge": 42}) self.assertEqual(image_processor.size, {"shortest_edge": 42})
self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84}) self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
def test_batch_feature(self):
pass
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_inputs(equal_resolution=False)
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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
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_inputs(equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
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_inputs(equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
@require_torch @require_torch
@require_vision @require_vision
class ChineseCLIPImageProcessingTestFourChannels(ImageProcessingSavingTestMixin, unittest.TestCase): class ChineseCLIPImageProcessingTestFourChannels(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = ChineseCLIPImageProcessor if is_vision_available() else None image_processing_class = ChineseCLIPImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -267,37 +145,10 @@ class ChineseCLIPImageProcessingTestFourChannels(ImageProcessingSavingTestMixin, ...@@ -267,37 +145,10 @@ class ChineseCLIPImageProcessingTestFourChannels(ImageProcessingSavingTestMixin,
self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "image_std"))
self.assertTrue(hasattr(image_processing, "do_convert_rgb")) self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
def test_batch_feature(self): @unittest.skip("ChineseCLIPImageProcessor does not support 4 channels yet") # FIXME Amy
pass def test_call_numpy(self):
return super().test_call_numpy()
def test_call_pil_four_channels(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_inputs(equal_resolution=False)
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
self.assertEqual(
encoded_images.shape,
(
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched @unittest.skip("ChineseCLIPImageProcessor does not support 4 channels yet") # FIXME Amy
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values def test_call_pytorch(self):
self.assertEqual( return super().test_call_torch()
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
tests/models/clip/test_image_processing_clip.py
View file @ 41d56ea6
...@@ -16,20 +16,13 @@ ...@@ -16,20 +16,13 @@
import unittest import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available(): if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor from transformers import CLIPImageProcessor
...@@ -80,40 +73,24 @@ class CLIPImageProcessingTester(unittest.TestCase): ...@@ -80,40 +73,24 @@ class CLIPImageProcessingTester(unittest.TestCase):
"do_convert_rgb": self.do_convert_rgb, "do_convert_rgb": self.do_convert_rgb,
} }
def prepare_inputs(self, equal_resolution=False, numpify=False, torchify=False): def expected_output_image_shape(self, images):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, return self.num_channels, self.crop_size["height"], self.crop_size["width"]
or a list of PyTorch tensors if one specifies torchify=True.
""" def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
return prepare_image_inputs(
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time" batch_size=self.batch_size,
num_channels=self.num_channels,
if equal_resolution: min_resolution=self.min_resolution,
image_inputs = [] max_resolution=self.max_resolution,
for i in range(self.batch_size): equal_resolution=equal_resolution,
image_inputs.append( numpify=numpify,
np.random.randint( torchify=torchify,
255, size=(self.num_channels, self.max_resolution, self.max_resolution), dtype=np.uint8
)
) )
else:
image_inputs = []
for i in range(self.batch_size):
width, height = np.random.choice(np.arange(self.min_resolution, self.max_resolution), 2)
image_inputs.append(np.random.randint(255, size=(self.num_channels, width, height), dtype=np.uint8))
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
if torchify:
image_inputs = [torch.from_numpy(x) for x in image_inputs]
return image_inputs
@require_torch @require_torch
@require_vision @require_vision
class CLIPImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class CLIPImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = CLIPImageProcessor if is_vision_available() else None image_processing_class = CLIPImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -142,162 +119,3 @@ class CLIPImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase) ...@@ -142,162 +119,3 @@ class CLIPImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase)
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84) image_processor = self.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.size, {"shortest_edge": 42})
self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84}) self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
def test_batch_feature(self):
pass
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_inputs(equal_resolution=False)
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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
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_inputs(equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
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_inputs(equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
@require_torch
@require_vision
class CLIPImageProcessingTestFourChannels(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processing_class = CLIPImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = CLIPImageProcessingTester(self, num_channels=4)
self.expected_encoded_image_num_channels = 3
@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, "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_batch_feature(self):
pass
def test_call_pil_four_channels(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_inputs(equal_resolution=False)
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
self.assertEqual(
encoded_images.shape,
(
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
tests/models/conditional_detr/test_image_processing_conditional_detr.py
View file @ 41d56ea6
...@@ -18,12 +18,10 @@ import json ...@@ -18,12 +18,10 @@ import json
import pathlib import pathlib
import unittest import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available(): if is_torch_available():
...@@ -111,10 +109,25 @@ class ConditionalDetrImageProcessingTester(unittest.TestCase): ...@@ -111,10 +109,25 @@ class ConditionalDetrImageProcessingTester(unittest.TestCase):
return expected_height, expected_width return expected_height, expected_width
def expected_output_image_shape(self, images):
height, width = self.get_expected_values(images, batched=True)
return self.num_channels, height, width
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_torch
@require_vision @require_vision
class ConditionalDetrImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class ConditionalDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = ConditionalDetrImageProcessor if is_vision_available() else None image_processing_class = ConditionalDetrImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -143,107 +156,6 @@ class ConditionalDetrImageProcessingTest(ImageProcessingSavingTestMixin, unittes ...@@ -143,107 +156,6 @@ class ConditionalDetrImageProcessingTest(ImageProcessingSavingTestMixin, unittes
self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84}) self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84})
self.assertEqual(image_processor.do_pad, False) self.assertEqual(image_processor.do_pad, False)
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
@slow @slow
def test_call_pytorch_with_coco_detection_annotations(self): def test_call_pytorch_with_coco_detection_annotations(self):
# prepare image and target # prepare image and target
......
tests/models/convnext/test_image_processing_convnext.py
View file @ 41d56ea6
...@@ -16,20 +16,13 @@ ...@@ -16,20 +16,13 @@
import unittest import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available(): if is_vision_available():
from PIL import Image
from transformers import ConvNextImageProcessor from transformers import ConvNextImageProcessor
...@@ -73,10 +66,24 @@ class ConvNextImageProcessingTester(unittest.TestCase): ...@@ -73,10 +66,24 @@ class ConvNextImageProcessingTester(unittest.TestCase):
"crop_pct": self.crop_pct, "crop_pct": self.crop_pct,
} }
def expected_output_image_shape(self, images):
return self.num_channels, self.size["shortest_edge"], self.size["shortest_edge"]
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_torch
@require_vision @require_vision
class ConvNextImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class ConvNextImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = ConvNextImageProcessor if is_vision_available() else None image_processing_class = ConvNextImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -101,102 +108,3 @@ class ConvNextImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestC ...@@ -101,102 +108,3 @@ class ConvNextImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestC
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
self.assertEqual(image_processor.size, {"shortest_edge": 42}) self.assertEqual(image_processor.size, {"shortest_edge": 42})
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["shortest_edge"],
self.image_processor_tester.size["shortest_edge"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["shortest_edge"],
self.image_processor_tester.size["shortest_edge"],
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["shortest_edge"],
self.image_processor_tester.size["shortest_edge"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["shortest_edge"],
self.image_processor_tester.size["shortest_edge"],
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["shortest_edge"],
self.image_processor_tester.size["shortest_edge"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["shortest_edge"],
self.image_processor_tester.size["shortest_edge"],
),
)
tests/models/deformable_detr/test_image_processing_deformable_detr.py
View file @ 41d56ea6
...@@ -18,12 +18,10 @@ import json ...@@ -18,12 +18,10 @@ import json
import pathlib import pathlib
import unittest import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available(): if is_torch_available():
...@@ -111,10 +109,25 @@ class DeformableDetrImageProcessingTester(unittest.TestCase): ...@@ -111,10 +109,25 @@ class DeformableDetrImageProcessingTester(unittest.TestCase):
return expected_height, expected_width return expected_height, expected_width
def expected_output_image_shape(self, images):
height, width = self.get_expected_values(images, batched=True)
return self.num_channels, height, width
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_torch
@require_vision @require_vision
class DeformableDetrImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class DeformableDetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = DeformableDetrImageProcessor if is_vision_available() else None image_processing_class = DeformableDetrImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -145,107 +158,6 @@ class DeformableDetrImageProcessingTest(ImageProcessingSavingTestMixin, unittest ...@@ -145,107 +158,6 @@ class DeformableDetrImageProcessingTest(ImageProcessingSavingTestMixin, unittest
self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84}) self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84})
self.assertEqual(image_processor.do_pad, False) self.assertEqual(image_processor.do_pad, False)
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
@slow @slow
def test_call_pytorch_with_coco_detection_annotations(self): def test_call_pytorch_with_coco_detection_annotations(self):
# prepare image and target # prepare image and target
......
tests/models/deit/test_image_processing_deit.py
View file @ 41d56ea6
...@@ -16,20 +16,13 @@ ...@@ -16,20 +16,13 @@
import unittest import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available(): if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor from transformers import DeiTImageProcessor
...@@ -78,10 +71,24 @@ class DeiTImageProcessingTester(unittest.TestCase): ...@@ -78,10 +71,24 @@ class DeiTImageProcessingTester(unittest.TestCase):
"image_std": self.image_std, "image_std": self.image_std,
} }
def expected_output_image_shape(self, images):
return self.num_channels, self.crop_size["height"], self.crop_size["width"]
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_torch
@require_vision @require_vision
class DeiTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class DeiTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = DeiTImageProcessor if is_vision_available() else None image_processing_class = DeiTImageProcessor if is_vision_available() else None
test_cast_dtype = True test_cast_dtype = True
...@@ -110,102 +117,3 @@ class DeiTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase) ...@@ -110,102 +117,3 @@ class DeiTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase)
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
self.assertEqual(image_processor.size, {"height": 42, "width": 42}) self.assertEqual(image_processor.size, {"height": 42, "width": 42})
self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84}) self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
tests/models/deta/test_image_processing_deta.py
View file @ 41d56ea6
...@@ -18,12 +18,10 @@ import json ...@@ -18,12 +18,10 @@ import json
import pathlib import pathlib
import unittest import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available(): if is_torch_available():
...@@ -111,10 +109,25 @@ class DetaImageProcessingTester(unittest.TestCase): ...@@ -111,10 +109,25 @@ class DetaImageProcessingTester(unittest.TestCase):
return expected_height, expected_width return expected_height, expected_width
def expected_output_image_shape(self, images):
height, width = self.get_expected_values(images, batched=True)
return self.num_channels, height, width
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_torch
@require_vision @require_vision
class DetaImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class DetaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = DetaImageProcessor if is_vision_available() else None image_processing_class = DetaImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -139,107 +152,6 @@ class DetaImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase) ...@@ -139,107 +152,6 @@ class DetaImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase)
self.assertEqual(image_processor.size, {"shortest_edge": 18, "longest_edge": 1333}) self.assertEqual(image_processor.size, {"shortest_edge": 18, "longest_edge": 1333})
self.assertEqual(image_processor.do_pad, True) self.assertEqual(image_processor.do_pad, True)
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
@slow @slow
def test_call_pytorch_with_coco_detection_annotations(self): def test_call_pytorch_with_coco_detection_annotations(self):
# prepare image and target # prepare image and target
......
tests/models/detr/test_image_processing_detr.py
View file @ 41d56ea6
...@@ -18,12 +18,10 @@ import json ...@@ -18,12 +18,10 @@ import json
import pathlib import pathlib
import unittest import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available(): if is_torch_available():
...@@ -111,10 +109,25 @@ class DetrImageProcessingTester(unittest.TestCase): ...@@ -111,10 +109,25 @@ class DetrImageProcessingTester(unittest.TestCase):
return expected_height, expected_width return expected_height, expected_width
def expected_output_image_shape(self, images):
height, width = self.get_expected_values(images, batched=True)
return self.num_channels, height, width
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_torch
@require_vision @require_vision
class DetrImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class DetrImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = DetrImageProcessor if is_vision_available() else None image_processing_class = DetrImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -146,107 +159,6 @@ class DetrImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase) ...@@ -146,107 +159,6 @@ class DetrImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase)
self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84}) self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84})
self.assertEqual(image_processor.do_pad, False) self.assertEqual(image_processor.do_pad, False)
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
@slow @slow
def test_call_pytorch_with_coco_detection_annotations(self): def test_call_pytorch_with_coco_detection_annotations(self):
# prepare image and target # prepare image and target
......
tests/models/donut/test_image_processing_donut.py
View file @ 41d56ea6
...@@ -21,7 +21,7 @@ import numpy as np ...@@ -21,7 +21,7 @@ import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available(): if is_torch_available():
...@@ -78,10 +78,24 @@ class DonutImageProcessingTester(unittest.TestCase): ...@@ -78,10 +78,24 @@ class DonutImageProcessingTester(unittest.TestCase):
"image_std": self.image_std, "image_std": self.image_std,
} }
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
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_torch
@require_vision @require_vision
class DonutImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class DonutImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = DonutImageProcessor if is_vision_available() else None image_processing_class = DonutImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -113,15 +127,12 @@ class DonutImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase ...@@ -113,15 +127,12 @@ class DonutImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=(42, 84)) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=(42, 84))
self.assertEqual(image_processor.size, {"height": 84, "width": 42}) self.assertEqual(image_processor.size, {"height": 84, "width": 42})
def test_batch_feature(self):
pass
@is_flaky() @is_flaky()
def test_call_pil(self): def test_call_pil(self):
# Initialize image_processing # Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict) image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images # create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
for image in image_inputs: for image in image_inputs:
self.assertIsInstance(image, Image.Image) self.assertIsInstance(image, Image.Image)
...@@ -154,7 +165,7 @@ class DonutImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase ...@@ -154,7 +165,7 @@ class DonutImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase
# Initialize image_processing # Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict) image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors # create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
for image in image_inputs: for image in image_inputs:
self.assertIsInstance(image, np.ndarray) self.assertIsInstance(image, np.ndarray)
...@@ -187,7 +198,7 @@ class DonutImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase ...@@ -187,7 +198,7 @@ class DonutImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase
# Initialize image_processing # Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict) image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors # create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
for image in image_inputs: for image in image_inputs:
self.assertIsInstance(image, torch.Tensor) self.assertIsInstance(image, torch.Tensor)
......
tests/models/dpt/test_image_processing_dpt.py
View file @ 41d56ea6
...@@ -16,20 +16,13 @@ ...@@ -16,20 +16,13 @@
import unittest import unittest
import numpy as np from transformers.file_utils import is_vision_available
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available(): if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor from transformers import DPTImageProcessor
...@@ -70,10 +63,24 @@ class DPTImageProcessingTester(unittest.TestCase): ...@@ -70,10 +63,24 @@ class DPTImageProcessingTester(unittest.TestCase):
"size": self.size, "size": self.size,
} }
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
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_torch
@require_vision @require_vision
class DPTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class DPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = DPTImageProcessor if is_vision_available() else None image_processing_class = DPTImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -97,99 +104,3 @@ class DPTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): ...@@ -97,99 +104,3 @@ class DPTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
self.assertEqual(image_processor.size, {"height": 42, "width": 42}) self.assertEqual(image_processor.size, {"height": 42, "width": 42})
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
tests/models/efficientformer/test_image_processing_efficientformer.py
View file @ 41d56ea6
...@@ -16,20 +16,13 @@ ...@@ -16,20 +16,13 @@
import unittest import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available(): if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor from transformers import ViTImageProcessor
...@@ -70,18 +63,32 @@ class EfficientFormerImageProcessorTester(unittest.TestCase): ...@@ -70,18 +63,32 @@ class EfficientFormerImageProcessorTester(unittest.TestCase):
"size": self.size, "size": self.size,
} }
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
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_torch
@require_vision @require_vision
class EfficientFormerImageProcessorTest(ImageProcessingSavingTestMixin, unittest.TestCase): class EfficientFormerImageProcessorTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = ViTImageProcessor if is_vision_available() else None image_processing_class = ViTImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
self.image_proc_tester = EfficientFormerImageProcessorTester(self) self.image_processor_tester = EfficientFormerImageProcessorTester(self)
@property @property
def image_processor_dict(self): def image_processor_dict(self):
return self.image_proc_tester.prepare_image_processor_dict() return self.image_processor_tester.prepare_image_processor_dict()
def test_image_proc_properties(self): def test_image_proc_properties(self):
image_processor = self.image_processing_class(**self.image_processor_dict) image_processor = self.image_processing_class(**self.image_processor_dict)
...@@ -90,102 +97,3 @@ class EfficientFormerImageProcessorTest(ImageProcessingSavingTestMixin, unittest ...@@ -90,102 +97,3 @@ class EfficientFormerImageProcessorTest(ImageProcessingSavingTestMixin, unittest
self.assertTrue(hasattr(image_processor, "do_normalize")) self.assertTrue(hasattr(image_processor, "do_normalize"))
self.assertTrue(hasattr(image_processor, "do_resize")) self.assertTrue(hasattr(image_processor, "do_resize"))
self.assertTrue(hasattr(image_processor, "size")) self.assertTrue(hasattr(image_processor, "size"))
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_proc_tester, equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
),
)
# Test batched
encoded_images = image_processor(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
),
)
def test_call_numpy(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_proc_tester, equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input
encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
),
)
# Test batched
encoded_images = image_processor(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
),
)
def test_call_pytorch(self):
# Initialize image_processor
image_processor = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_proc_tester, equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
encoded_images = image_processor(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
),
)
# Test batched
encoded_images = image_processor(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size["height"],
self.image_proc_tester.size["width"],
),
)
tests/models/efficientnet/test_image_processing_efficientnet.py
View file @ 41d56ea6
...@@ -19,17 +19,12 @@ import unittest ...@@ -19,17 +19,12 @@ import unittest
import numpy as np import numpy as np
from transformers.testing_utils import require_torch, require_vision from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available(): if is_vision_available():
from PIL import Image
from transformers import EfficientNetImageProcessor from transformers import EfficientNetImageProcessor
...@@ -70,10 +65,24 @@ class EfficientNetImageProcessorTester(unittest.TestCase): ...@@ -70,10 +65,24 @@ class EfficientNetImageProcessorTester(unittest.TestCase):
"size": self.size, "size": self.size,
} }
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
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_torch
@require_vision @require_vision
class EfficientNetImageProcessorTest(ImageProcessingSavingTestMixin, unittest.TestCase): class EfficientNetImageProcessorTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = EfficientNetImageProcessor if is_vision_available() else None image_processing_class = EfficientNetImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -98,102 +107,6 @@ class EfficientNetImageProcessorTest(ImageProcessingSavingTestMixin, unittest.Te ...@@ -98,102 +107,6 @@ class EfficientNetImageProcessorTest(ImageProcessingSavingTestMixin, unittest.Te
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
self.assertEqual(image_processor.size, {"height": 42, "width": 42}) self.assertEqual(image_processor.size, {"height": 42, "width": 42})
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_rescale(self): def test_rescale(self):
# EfficientNet optionally rescales between -1 and 1 instead of the usual 0 and 1 # EfficientNet optionally rescales between -1 and 1 instead of the usual 0 and 1
image = np.arange(0, 256, 1, dtype=np.uint8).reshape(1, 8, 32) image = np.arange(0, 256, 1, dtype=np.uint8).reshape(1, 8, 32)
......
tests/models/flava/test_image_processing_flava.py
View file @ 41d56ea6
...@@ -21,7 +21,7 @@ import numpy as np ...@@ -21,7 +21,7 @@ import numpy as np
from transformers.testing_utils import require_torch, require_vision from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available(): if is_torch_available():
...@@ -156,10 +156,21 @@ class FlavaImageProcessingTester(unittest.TestCase): ...@@ -156,10 +156,21 @@ class FlavaImageProcessingTester(unittest.TestCase):
def get_expected_codebook_image_size(self): def get_expected_codebook_image_size(self):
return (self.codebook_size["height"], self.codebook_size["width"]) return (self.codebook_size["height"], self.codebook_size["width"])
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_torch
@require_vision @require_vision
class FlavaImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class FlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = FlavaImageProcessor if is_vision_available() else None image_processing_class = FlavaImageProcessor if is_vision_available() else None
maxDiff = None maxDiff = None
...@@ -207,14 +218,11 @@ class FlavaImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase ...@@ -207,14 +218,11 @@ class FlavaImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase
self.assertEqual(image_processor.codebook_size, {"height": 33, "width": 33}) self.assertEqual(image_processor.codebook_size, {"height": 33, "width": 33})
self.assertEqual(image_processor.codebook_crop_size, {"height": 66, "width": 66}) self.assertEqual(image_processor.codebook_crop_size, {"height": 66, "width": 66})
def test_batch_feature(self):
pass
def test_call_pil(self): def test_call_pil(self):
# Initialize image_processing # Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict) image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images # create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
for image in image_inputs: for image in image_inputs:
self.assertIsInstance(image, PIL.Image.Image) self.assertIsInstance(image, PIL.Image.Image)
...@@ -252,7 +260,7 @@ class FlavaImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase ...@@ -252,7 +260,7 @@ class FlavaImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase
# Initialize image_processing # Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict) image_processing = self.image_processing_class(**self.image_processor_dict)
# create random tensors # create random tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, **prepare_kwargs) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, **prepare_kwargs)
for image in image_inputs: for image in image_inputs:
self.assertIsInstance(image, instance_class) self.assertIsInstance(image, instance_class)
...@@ -336,7 +344,7 @@ class FlavaImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase ...@@ -336,7 +344,7 @@ class FlavaImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase
# Initialize image_processing # Initialize image_processing
random.seed(1234) random.seed(1234)
image_processing = self.image_processing_class(**self.image_processor_dict) image_processing = self.image_processing_class(**self.image_processor_dict)
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
# Test not batched input # Test not batched input
encoded_images = image_processing(image_inputs[0], return_image_mask=True, return_tensors="pt") encoded_images = image_processing(image_inputs[0], return_image_mask=True, return_tensors="pt")
...@@ -346,7 +354,7 @@ class FlavaImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase ...@@ -346,7 +354,7 @@ class FlavaImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase
# Initialize image_processing # Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict) image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images # create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
for image in image_inputs: for image in image_inputs:
self.assertIsInstance(image, PIL.Image.Image) self.assertIsInstance(image, PIL.Image.Image)
......
tests/models/glpn/test_image_processing_glpn.py
View file @ 41d56ea6
...@@ -21,7 +21,7 @@ import numpy as np ...@@ -21,7 +21,7 @@ import numpy as np
from transformers.testing_utils import require_torch, require_vision from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available(): if is_torch_available():
...@@ -63,10 +63,33 @@ class GLPNImageProcessingTester(unittest.TestCase): ...@@ -63,10 +63,33 @@ class GLPNImageProcessingTester(unittest.TestCase):
"do_rescale": self.do_rescale, "do_rescale": self.do_rescale,
} }
def expected_output_image_shape(self, images):
if isinstance(images[0], Image.Image):
width, height = images[0].size
else:
height, width = images[0].shape[1], images[0].shape[2]
height = height // self.size_divisor * self.size_divisor
width = width // self.size_divisor * self.size_divisor
return self.num_channels, height, width
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,
size_divisor=self.size_divisor,
equal_resolution=equal_resolution,
numpify=numpify,
torchify=torchify,
)
@require_torch @require_torch
@require_vision @require_vision
class GLPNImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class GLPNImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = GLPNImageProcessor if is_vision_available() else None image_processing_class = GLPNImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -83,44 +106,41 @@ class GLPNImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase) ...@@ -83,44 +106,41 @@ class GLPNImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase)
self.assertTrue(hasattr(image_processing, "resample")) self.assertTrue(hasattr(image_processing, "resample"))
self.assertTrue(hasattr(image_processing, "do_rescale")) self.assertTrue(hasattr(image_processing, "do_rescale"))
def test_batch_feature(self):
pass
def test_call_pil(self): def test_call_pil(self):
# Initialize image_processing # Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict) image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images # create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
for image in image_inputs: for image in image_inputs:
self.assertIsInstance(image, Image.Image) self.assertIsInstance(image, Image.Image)
# Test not batched input (GLPNImageProcessor doesn't support batching) # Test not batched input (GLPNImageProcessor doesn't support batching)
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0) expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0) self.assertTrue(tuple(encoded_images.shape) == (1, *expected_output_image_shape))
def test_call_numpy(self): def test_call_numpy(self):
# Initialize image_processing # Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict) image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors # create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
for image in image_inputs: for image in image_inputs:
self.assertIsInstance(image, np.ndarray) self.assertIsInstance(image, np.ndarray)
# Test not batched input (GLPNImageProcessor doesn't support batching) # Test not batched input (GLPNImageProcessor doesn't support batching)
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0) expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0) self.assertTrue(tuple(encoded_images.shape) == (1, *expected_output_image_shape))
def test_call_pytorch(self): def test_call_pytorch(self):
# Initialize image_processing # Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict) image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors # create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True) image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
for image in image_inputs: for image in image_inputs:
self.assertIsInstance(image, torch.Tensor) self.assertIsInstance(image, torch.Tensor)
# Test not batched input (GLPNImageProcessor doesn't support batching) # Test not batched input (GLPNImageProcessor doesn't support batching)
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0) expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0) self.assertTrue(tuple(encoded_images.shape) == (1, *expected_output_image_shape))
tests/models/imagegpt/test_image_processing_imagegpt.py
View file @ 41d56ea6
...@@ -25,7 +25,7 @@ from datasets import load_dataset ...@@ -25,7 +25,7 @@ from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available(): if is_torch_available():
...@@ -75,10 +75,24 @@ class ImageGPTImageProcessingTester(unittest.TestCase): ...@@ -75,10 +75,24 @@ class ImageGPTImageProcessingTester(unittest.TestCase):
"do_normalize": self.do_normalize, "do_normalize": self.do_normalize,
} }
def expected_output_image_shape(self, images):
return (self.size["height"] * self.size["width"],)
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_torch
@require_vision @require_vision
class ImageGPTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class ImageGPTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = ImageGPTImageProcessor if is_vision_available() else None image_processing_class = ImageGPTImageProcessor if is_vision_available() else None
def setUp(self): def setUp(self):
...@@ -144,6 +158,68 @@ class ImageGPTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestC ...@@ -144,6 +158,68 @@ class ImageGPTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestC
def test_init_without_params(self): def test_init_without_params(self):
pass pass
# Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input
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
encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(encoded_images)
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids
self.assertEqual(
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
)
# Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input
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
encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(encoded_images)
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids
self.assertEqual(
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
)
# Override the test from ImageProcessingTestMixin as ImageGPT model takes input_ids as input
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)
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").input_ids
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").input_ids
self.assertEqual(
tuple(encoded_images.shape),
(self.image_processor_tester.batch_size, *expected_output_image_shape),
)
def prepare_images(): def prepare_images():
dataset = load_dataset("hf-internal-testing/fixtures_image_utils", split="test") dataset = load_dataset("hf-internal-testing/fixtures_image_utils", split="test")
......
tests/models/layoutlmv2/test_image_processing_layoutlmv2.py
View file @ 41d56ea6
...@@ -16,16 +16,11 @@ ...@@ -16,16 +16,11 @@
import unittest import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available from transformers.utils import is_pytesseract_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available(): if is_pytesseract_available():
from PIL import Image from PIL import Image
...@@ -60,10 +55,24 @@ class LayoutLMv2ImageProcessingTester(unittest.TestCase): ...@@ -60,10 +55,24 @@ class LayoutLMv2ImageProcessingTester(unittest.TestCase):
def prepare_image_processor_dict(self): def prepare_image_processor_dict(self):
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr} return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
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_torch
@require_pytesseract @require_pytesseract
class LayoutLMv2ImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class LayoutLMv2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = LayoutLMv2ImageProcessor if is_pytesseract_available() else None image_processing_class = LayoutLMv2ImageProcessor if is_pytesseract_available() else None
def setUp(self): def setUp(self):
...@@ -86,108 +95,6 @@ class LayoutLMv2ImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Tes ...@@ -86,108 +95,6 @@ class LayoutLMv2ImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Tes
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
self.assertEqual(image_processor.size, {"height": 42, "width": 42}) self.assertEqual(image_processor.size, {"height": 42, "width": 42})
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoding = image_processing(image_inputs[0], return_tensors="pt")
self.assertEqual(
encoding.pixel_values.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
self.assertIsInstance(encoding.words, list)
self.assertIsInstance(encoding.boxes, list)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_layoutlmv2_integration_test(self): def test_layoutlmv2_integration_test(self):
# with apply_OCR = True # with apply_OCR = True
image_processing = LayoutLMv2ImageProcessor() image_processing = LayoutLMv2ImageProcessor()
......
tests/models/layoutlmv3/test_image_processing_layoutlmv3.py
View file @ 41d56ea6
...@@ -16,16 +16,11 @@ ...@@ -16,16 +16,11 @@
import unittest import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available from transformers.utils import is_pytesseract_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available(): if is_pytesseract_available():
from PIL import Image from PIL import Image
...@@ -60,10 +55,24 @@ class LayoutLMv3ImageProcessingTester(unittest.TestCase): ...@@ -60,10 +55,24 @@ class LayoutLMv3ImageProcessingTester(unittest.TestCase):
def prepare_image_processor_dict(self): def prepare_image_processor_dict(self):
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr} return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
def expected_output_image_shape(self, images):
return self.num_channels, self.size["height"], self.size["width"]
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_torch
@require_pytesseract @require_pytesseract
class LayoutLMv3ImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): class LayoutLMv3ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = LayoutLMv3ImageProcessor if is_pytesseract_available() else None image_processing_class = LayoutLMv3ImageProcessor if is_pytesseract_available() else None
def setUp(self): def setUp(self):
...@@ -86,108 +95,6 @@ class LayoutLMv3ImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Tes ...@@ -86,108 +95,6 @@ class LayoutLMv3ImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Tes
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
self.assertEqual(image_processor.size, {"height": 42, "width": 42}) self.assertEqual(image_processor.size, {"height": 42, "width": 42})
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoding = image_processing(image_inputs[0], return_tensors="pt")
self.assertEqual(
encoding.pixel_values.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
self.assertIsInstance(encoding.words, list)
self.assertIsInstance(encoding.boxes, list)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, 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
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_LayoutLMv3_integration_test(self): def test_LayoutLMv3_integration_test(self):
# with apply_OCR = True # with apply_OCR = True
image_processing = LayoutLMv3ImageProcessor() image_processing = LayoutLMv3ImageProcessor()
......
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