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Add test_image_processing_common.py (#20785)

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* Add test_image_processing_common.py

* Fix typo

* Update imports and test fetcher

* Revert but keep test fetcher update

* Fix imports

* Fix all imports

* Formatting fix

* Update tests/test_image_processing_common.py
This commit is contained in:
amyeroberts
2023-01-23 13:48:30 +00:00
committed by GitHub
parent 96b2b2de12
commit 66459ce319
30 changed files with 367 additions and 168 deletions
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142
tests/test_feature_extraction_common.py
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@@ -25,16 +25,7 @@ from pathlib import Path
from huggingface_hub import HfFolder, delete_repo, set_access_token
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
from transformers.testing_utils import (
TOKEN,
USER,
check_json_file_has_correct_format,
get_tests_dir,
is_staging_test,
require_torch,
require_vision,
)
from transformers.utils import is_torch_available, is_vision_available
from transformers.testing_utils import TOKEN, USER, check_json_file_has_correct_format, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
@@ -42,105 +33,9 @@ sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
if is_torch_available():
import numpy as np
import torch
if is_vision_available():
from PIL import Image
SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = get_tests_dir("fixtures")
def prepare_image_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
or a list of PyTorch tensors if one specifies torchify=True.
One can specify whether the images are of the same resolution or not.
"""
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
image_inputs = []
for i in range(feature_extract_tester.batch_size):
if equal_resolution:
width = height = feature_extract_tester.max_resolution
else:
# To avoid getting image width/height 0
min_resolution = feature_extract_tester.min_resolution
if getattr(feature_extract_tester, "size_divisor", None):
# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
min_resolution = max(feature_extract_tester.size_divisor, min_resolution)
width, height = np.random.choice(np.arange(min_resolution, feature_extract_tester.max_resolution), 2)
image_inputs.append(
np.random.randint(
255,
size=(
feature_extract_tester.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(image, 0, -1)) for image in image_inputs]
if torchify:
image_inputs = [torch.from_numpy(image) for image in image_inputs]
return image_inputs
def prepare_video(feature_extract_tester, width=10, height=10, numpify=False, torchify=False):
"""This function prepares a video as a list of PIL images/NumPy arrays/PyTorch tensors."""
video = []
for i in range(feature_extract_tester.num_frames):
video.append(np.random.randint(255, size=(feature_extract_tester.num_channels, width, height), dtype=np.uint8))
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
video = [Image.fromarray(np.moveaxis(frame, 0, -1)) for frame in video]
if torchify:
video = [torch.from_numpy(frame) for frame in video]
return video
def prepare_video_inputs(feature_extract_tester, equal_resolution=False, numpify=False, torchify=False):
"""This function prepares a batch of videos: a list of list of PIL images, or a list of list of numpy arrays if
one specifies numpify=True, or a list of list of PyTorch tensors if one specifies torchify=True.
One can specify whether the videos are of the same resolution or not.
"""
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
video_inputs = []
for i in range(feature_extract_tester.batch_size):
if equal_resolution:
width = height = feature_extract_tester.max_resolution
else:
width, height = np.random.choice(
np.arange(feature_extract_tester.min_resolution, feature_extract_tester.max_resolution), 2
)
video = prepare_video(
feature_extract_tester=feature_extract_tester,
width=width,
height=height,
numpify=numpify,
torchify=torchify,
)
video_inputs.append(video)
return video_inputs
class FeatureExtractionSavingTestMixin:
test_cast_dtype = None
@@ -174,41 +69,6 @@ class FeatureExtractionSavingTestMixin:
feat_extract = self.feature_extraction_class()
self.assertIsNotNone(feat_extract)
@require_torch
@require_vision
def test_cast_dtype_device(self):
if self.test_cast_dtype is not None:
# Initialize feature_extractor
feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
encoding = feature_extractor(image_inputs, return_tensors="pt")
# for layoutLM compatiblity
self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
self.assertEqual(encoding.pixel_values.dtype, torch.float32)
encoding = feature_extractor(image_inputs, return_tensors="pt").to(torch.float16)
self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
self.assertEqual(encoding.pixel_values.dtype, torch.float16)
encoding = feature_extractor(image_inputs, return_tensors="pt").to("cpu", torch.bfloat16)
self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
self.assertEqual(encoding.pixel_values.dtype, torch.bfloat16)
with self.assertRaises(TypeError):
_ = feature_extractor(image_inputs, return_tensors="pt").to(torch.bfloat16, "cpu")
# Try with text + image feature
encoding = feature_extractor(image_inputs, return_tensors="pt")
encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
encoding = encoding.to(torch.float16)
self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
self.assertEqual(encoding.pixel_values.dtype, torch.float16)
self.assertEqual(encoding.input_ids.dtype, torch.long)
class FeatureExtractorUtilTester(unittest.TestCase):
def test_cached_files_are_used_when_internet_is_down(self):