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Add ViTImageProcessorFast to tests (#31424)

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* Add ViTImageProcessor to tests

* Correct data format

* Review comments
This commit is contained in:
amyeroberts
2024-06-25 13:36:58 +01:00
committed by GitHub
parent aab0829790
commit 0f67ba1d74
26 changed files with 230 additions and 87 deletions
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4
tests/models/bridgetower/test_image_processing_bridgetower.py
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@@ -17,6 +17,8 @@
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_vision_available
@@ -84,6 +86,8 @@ class BridgeTowerImageProcessingTester(unittest.TestCase):
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
elif isinstance(image, np.ndarray):
h, w = image.shape[0], image.shape[1]
else:
h, w = image.shape[1], image.shape[2]
scale = size / min(w, h)

4
tests/models/conditional_detr/test_image_processing_conditional_detr.py
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@@ -18,6 +18,8 @@ import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
@@ -87,6 +89,8 @@ class ConditionalDetrImageProcessingTester(unittest.TestCase):
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
elif isinstance(image, np.ndarray):
h, w = image.shape[0], image.shape[1]
else:
h, w = image.shape[1], image.shape[2]
if w < h:

4
tests/models/deformable_detr/test_image_processing_deformable_detr.py
View File

@@ -18,6 +18,8 @@ import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
@@ -87,6 +89,8 @@ class DeformableDetrImageProcessingTester(unittest.TestCase):
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
elif isinstance(image, np.ndarray):
h, w = image.shape[0], image.shape[1]
else:
h, w = image.shape[1], image.shape[2]
if w < h:

4
tests/models/detr/test_image_processing_detr.py
View File

@@ -17,6 +17,8 @@ import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
@@ -86,6 +88,8 @@ class DetrImageProcessingTester(unittest.TestCase):
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
elif isinstance(image, np.ndarray):
h, w = image.shape[0], image.shape[1]
else:
h, w = image.shape[1], image.shape[2]
if w < h:

2
tests/models/glpn/test_image_processing_glpn.py
View File

@@ -66,6 +66,8 @@ class GLPNImageProcessingTester(unittest.TestCase):
def expected_output_image_shape(self, images):
if isinstance(images[0], Image.Image):
width, height = images[0].size
elif isinstance(images[0], np.ndarray):
height, width = images[0].shape[0], images[0].shape[1]
else:
height, width = images[0].shape[1], images[0].shape[2]

4
tests/models/grounding_dino/test_image_processing_grounding_dino.py
View File

@@ -18,6 +18,8 @@ import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
@@ -93,6 +95,8 @@ class GroundingDinoImageProcessingTester(unittest.TestCase):
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
elif isinstance(image, np.ndarray):
h, w = image.shape[0], image.shape[1]
else:
h, w = image.shape[1], image.shape[2]
if w < h:

4
tests/models/idefics/test_image_processing_idefics.py
View File

@@ -16,6 +16,8 @@
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_torchvision, require_vision
from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
@@ -75,6 +77,8 @@ class IdeficsImageProcessingTester(unittest.TestCase):
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
elif isinstance(image, np.ndarray):
h, w = image.shape[0], image.shape[1]
else:
h, w = image.shape[1], image.shape[2]
scale = size / min(w, h)

144
tests/models/idefics2/test_image_processing_idefics2.py
View File

@@ -99,6 +99,8 @@ class Idefics2ImageProcessingTester(unittest.TestCase):
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
elif isinstance(image, np.ndarray):
h, w = image.shape[0], image.shape[1]
else:
h, w = image.shape[1], image.shape[2]
@@ -176,6 +178,10 @@ class Idefics2ImageProcessingTester(unittest.TestCase):
if torchify:
images_list = [[torch.from_numpy(image) for image in images] for images in images_list]
if numpify:
# Numpy images are typically in channels last format
images_list = [[image.transpose(1, 2, 0) for image in images] for images in images_list]
return images_list
@@ -206,66 +212,100 @@ class Idefics2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
self.assertTrue(hasattr(image_processing, "do_image_splitting"))
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 sample_images in image_inputs:
for image in sample_images:
self.assertIsInstance(image, np.ndarray)
for image_processing_class in self.image_processor_list:
# 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 sample_images in image_inputs:
for image in sample_images:
self.assertIsInstance(image, np.ndarray)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertEqual(
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertEqual(
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
)
def test_call_numpy_4_channels(self):
for image_processing_class in self.image_processor_list:
# Initialize image_processing
image_processor_dict = self.image_processor_dict
image_processor_dict["image_mean"] = [0.5, 0.5, 0.5, 0.5]
image_processor_dict["image_std"] = [0.5, 0.5, 0.5, 0.5]
image_processing = self.image_processing_class(**image_processor_dict)
# create random numpy tensors
self.image_processor_tester.num_channels = 4
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
for sample_images in image_inputs:
for image in sample_images:
self.assertIsInstance(image, np.ndarray)
# Test not batched input
encoded_images = image_processing(
image_inputs[0], input_data_format="channels_last", return_tensors="pt"
).pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Test batched
encoded_images = image_processing(
image_inputs, input_data_format="channels_last", return_tensors="pt"
).pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertEqual(
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
)
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 images in image_inputs:
for image in images:
self.assertIsInstance(image, Image.Image)
for image_processing_class in self.image_processor_list:
# 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 images in image_inputs:
for image in images:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertEqual(
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
self.assertEqual(
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
for image_processing_class in self.image_processor_list:
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
for images in image_inputs:
for image in images:
self.assertIsInstance(image, torch.Tensor)
for images in image_inputs:
for image in images:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
# Test batched
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
tuple(encoded_images.shape),
(self.image_processor_tester.batch_size, *expected_output_image_shape),
)
# Test batched
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
tuple(encoded_images.shape),
(self.image_processor_tester.batch_size, *expected_output_image_shape),
)

2
tests/models/mask2former/test_image_processing_mask2former.py
View File

@@ -98,6 +98,8 @@ class Mask2FormerImageProcessingTester(unittest.TestCase):
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
elif isinstance(image, np.ndarray):
h, w = image.shape[0], image.shape[1]
else:
h, w = image.shape[1], image.shape[2]
if w < h:

2
tests/models/maskformer/test_image_processing_maskformer.py
View File

@@ -98,6 +98,8 @@ class MaskFormerImageProcessingTester(unittest.TestCase):
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
elif isinstance(image, np.ndarray):
h, w = image.shape[0], image.shape[1]
else:
h, w = image.shape[1], image.shape[2]
if w < h:

2
tests/models/oneformer/test_image_processing_oneformer.py
View File

@@ -106,6 +106,8 @@ class OneFormerImageProcessorTester(unittest.TestCase):
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
elif isinstance(image, np.ndarray):
h, w = image.shape[0], image.shape[1]
else:
h, w = image.shape[1], image.shape[2]
if w < h:

2
tests/models/oneformer/test_processor_oneformer.py
View File

@@ -143,6 +143,8 @@ class OneFormerProcessorTester(unittest.TestCase):
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
elif isinstance(image, np.ndarray):
h, w = image.shape[0], image.shape[1]
else:
h, w = image.shape[1], image.shape[2]
if w < h:

4
tests/models/pix2struct/test_image_processing_pix2struct.py
View File

@@ -232,7 +232,7 @@ class Pix2StructImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase)
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
encoded_images = image_processor(
image_inputs[0], return_tensors="pt", max_patches=max_patch, input_data_format="channels_first"
image_inputs[0], return_tensors="pt", max_patches=max_patch, input_data_format="channels_last"
).flattened_patches
self.assertEqual(
encoded_images.shape,
@@ -241,7 +241,7 @@ class Pix2StructImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase)
# Test batched
encoded_images = image_processor(
image_inputs, return_tensors="pt", max_patches=max_patch, input_data_format="channels_first"
image_inputs, return_tensors="pt", max_patches=max_patch, input_data_format="channels_last"
).flattened_patches
self.assertEqual(
encoded_images.shape,

4
tests/models/swin2sr/test_image_processing_swin2sr.py
View File

@@ -72,6 +72,8 @@ class Swin2SRImageProcessingTester(unittest.TestCase):
if isinstance(img, Image.Image):
input_width, input_height = img.size
elif isinstance(img, np.ndarray):
input_height, input_width = img.shape[-3:-1]
else:
input_height, input_width = img.shape[-2:]
@@ -160,7 +162,7 @@ class Swin2SRImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
# Test not batched input
encoded_images = image_processing(
image_inputs[0], return_tensors="pt", input_data_format="channels_first"
image_inputs[0], return_tensors="pt", input_data_format="channels_last"
).pixel_values
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))

4
tests/models/video_llava/test_image_processing_video_llava.py
View File

@@ -285,7 +285,7 @@ class VideoLlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase)
encoded_images = image_processor(
image_inputs[0],
return_tensors="pt",
input_data_format="channels_first",
input_data_format="channels_last",
image_mean=0,
image_std=1,
).pixel_values_images
@@ -296,7 +296,7 @@ class VideoLlavaImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase)
encoded_images = image_processor(
image_inputs,
return_tensors="pt",
input_data_format="channels_first",
input_data_format="channels_last",
image_mean=0,
image_std=1,
).pixel_values_images

4
tests/models/vilt/test_image_processing_vilt.py
View File

@@ -16,6 +16,8 @@
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_vision_available
@@ -78,6 +80,8 @@ class ViltImageProcessingTester(unittest.TestCase):
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
elif isinstance(image, np.ndarray):
h, w = image.shape[0], image.shape[1]
else:
h, w = image.shape[1], image.shape[2]
scale = size / min(w, h)

6
tests/models/vit/test_image_processing_vit.py
View File

@@ -17,7 +17,7 @@
import unittest
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_vision_available
from transformers.utils import is_torchvision_available, is_vision_available
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
@@ -25,6 +25,9 @@ from ...test_image_processing_common import ImageProcessingTestMixin, prepare_im
if is_vision_available():
from transformers import ViTImageProcessor
if is_torchvision_available():
from transformers import ViTImageProcessorFast
class ViTImageProcessingTester(unittest.TestCase):
def __init__(
@@ -82,6 +85,7 @@ class ViTImageProcessingTester(unittest.TestCase):
@require_vision
class ViTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
image_processing_class = ViTImageProcessor if is_vision_available() else None
fast_image_processing_class = ViTImageProcessorFast if is_torchvision_available() else None
def setUp(self):
super().setUp()

3
tests/models/yolos/test_image_processing_yolos.py
View File

@@ -18,6 +18,7 @@ import json
import pathlib
import unittest
import numpy as np
from parameterized import parameterized
from transformers.testing_utils import require_torch, require_vision, slow
@@ -89,6 +90,8 @@ class YolosImageProcessingTester(unittest.TestCase):
image = image_inputs[0]
if isinstance(image, Image.Image):
width, height = image.size
elif isinstance(image, np.ndarray):
height, width = image.shape[0], image.shape[1]
else:
height, width = image.shape[1], image.shape[2]

84
tests/test_image_processing_common.py
View File

@@ -18,7 +18,9 @@ import json
import os
import pathlib
import tempfile
import time
import numpy as np
import requests
from transformers import AutoImageProcessor, BatchFeature
@@ -28,7 +30,6 @@ from transformers.utils import is_torch_available, is_vision_available
if is_torch_available():
import numpy as np
import torch
if is_vision_available():
@@ -72,6 +73,10 @@ def prepare_image_inputs(
if torchify:
image_inputs = [torch.from_numpy(image) for image in image_inputs]
if numpify:
# Numpy images are typically in channels last format
image_inputs = [image.transpose(1, 2, 0) for image in image_inputs]
return image_inputs
@@ -167,33 +172,28 @@ class ImageProcessingTestMixin:
encoding_slow = image_processor_slow(dummy_image, return_tensors="pt")
encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")
self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-3))
self.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-2))
@require_vision
@require_torch
def test_fast_is_faster_than_slow(self):
import time
def measure_time(self, image_processor, dummy_image):
start = time.time()
_ = image_processor(dummy_image, return_tensors="pt")
return time.time() - start
dummy_image = Image.open(
requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
)
if not self.test_slow_image_processor or not self.test_fast_image_processor:
self.skipTest("Skipping speed test")
if self.image_processing_class is None or self.fast_image_processing_class is None:
self.skipTest("Skipping speed test as one of the image processors is not defined")
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
def measure_time(image_processor, image):
start = time.time()
_ = image_processor(image, return_tensors="pt")
return time.time() - start
slow_time = self.measure_time(image_processor_slow, dummy_image)
fast_time = self.measure_time(image_processor_fast, dummy_image)
dummy_images = torch.randint(0, 255, (4, 3, 224, 224), dtype=torch.uint8)
image_processor_slow = self.image_processing_class(**self.image_processor_dict)
image_processor_fast = self.fast_image_processing_class()
fast_time = measure_time(image_processor_fast, dummy_images)
slow_time = measure_time(image_processor_slow, dummy_images)
self.assertLessEqual(fast_time, slow_time)
@@ -238,6 +238,52 @@ class ImageProcessingTestMixin:
self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
def test_save_load_fast_slow(self):
"Test that we can load a fast image processor from a slow one and vice-versa."
if self.image_processing_class is None or self.fast_image_processing_class is None:
self.skipTest("Skipping slow/fast save/load test as one of the image processors is not defined")
image_processor_dict = self.image_processor_tester.prepare_image_processor_dict()
image_processor_slow_0 = self.image_processing_class(**image_processor_dict)
# Load fast image processor from slow one
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_slow_0.save_pretrained(tmpdirname)
image_processor_fast_0 = self.fast_image_processing_class.from_pretrained(tmpdirname)
image_processor_fast_1 = self.fast_image_processing_class(**image_processor_dict)
# Load slow image processor from fast one
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_fast_1.save_pretrained(tmpdirname)
image_processor_slow_1 = self.image_processing_class.from_pretrained(tmpdirname)
self.assertEqual(image_processor_slow_0.to_dict(), image_processor_slow_1.to_dict())
self.assertEqual(image_processor_fast_0.to_dict(), image_processor_fast_1.to_dict())
def test_save_load_fast_slow_auto(self):
"Test that we can load a fast image processor from a slow one and vice-versa using AutoImageProcessor."
if self.image_processing_class is None or self.fast_image_processing_class is None:
self.skipTest("Skipping slow/fast save/load test as one of the image processors is not defined")
image_processor_dict = self.image_processor_tester.prepare_image_processor_dict()
image_processor_slow_0 = self.image_processing_class(**image_processor_dict)
# Load fast image processor from slow one
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_slow_0.save_pretrained(tmpdirname)
image_processor_fast_0 = AutoImageProcessor.from_pretrained(tmpdirname, use_fast=True)
image_processor_fast_1 = self.fast_image_processing_class(**image_processor_dict)
# Load slow image processor from fast one
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_fast_1.save_pretrained(tmpdirname)
image_processor_slow_1 = AutoImageProcessor.from_pretrained(tmpdirname, use_fast=False)
self.assertEqual(image_processor_slow_0.to_dict(), image_processor_slow_1.to_dict())
self.assertEqual(image_processor_fast_0.to_dict(), image_processor_fast_1.to_dict())
def test_init_without_params(self):
for image_processing_class in self.image_processor_list:
image_processor = image_processing_class()
@@ -358,7 +404,7 @@ class ImageProcessingTestMixin:
encoded_images = image_processor(
image_inputs[0],
return_tensors="pt",
input_data_format="channels_first",
input_data_format="channels_last",
image_mean=0,
image_std=1,
).pixel_values
@@ -369,7 +415,7 @@ class ImageProcessingTestMixin:
encoded_images = image_processor(
image_inputs,
return_tensors="pt",
input_data_format="channels_first",
input_data_format="channels_last",
image_mean=0,
image_std=1,
).pixel_values