def test_ten_crop_invalid_size_error_msg():
"""
Tests TenCrop error message when the size arg is not positive
"""
logger.info("test_ten_crop_invalid_size_error_msg")
with pytest.raises(ValueError) as info:
_ = [
vision.Decode(),
vision.TenCrop(0),
lambda images: np.stack(
[vision.ToTensor()(image)
for image in images]) # 4D stack of 10 images
]
error_msg = "Input is not within the required interval of (1 to 16777216)."
assert error_msg == str(info.value)
with pytest.raises(ValueError) as info:
_ = [
vision.Decode(),
vision.TenCrop(-10),
lambda images: np.stack(
[vision.ToTensor()(image)
for image in images]) # 4D stack of 10 images
]
assert error_msg == str(info.value)
def test_random_crop_op_py(plot=False):
"""
Test RandomCrop op in py transforms
"""
logger.info("test_random_crop_op_py")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms1 = [
py_vision.Decode(),
py_vision.RandomCrop([512, 512], [200, 200, 200, 200]),
py_vision.ToTensor()
]
transform1 = mindspore.dataset.transforms.py_transforms.Compose(transforms1)
data1 = data1.map(operations=transform1, input_columns=["image"])
# Second dataset
# Second dataset for comparison
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms2 = [
py_vision.Decode(),
py_vision.ToTensor()
]
transform2 = mindspore.dataset.transforms.py_transforms.Compose(transforms2)
data2 = data2.map(operations=transform2, input_columns=["image"])
crop_images = []
original_images = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
crop = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
crop_images.append(crop)
original_images.append(original)
if plot:
visualize_list(original_images, crop_images)
def test_auto_contrast_invalid_cutoff_param_py():
"""
Test AutoContrast python Op with invalid cutoff parameter
"""
logger.info("Test AutoContrast python Op with invalid cutoff parameter")
try:
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data_set = data_set.map(operations=[
mindspore.dataset.transforms.py_transforms.Compose([
F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(cutoff=-10.0),
F.ToTensor()
])
],
input_columns=["image"])
except ValueError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Input cutoff is not within the required interval of [0, 50)." in str(
error)
try:
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data_set = data_set.map(operations=[
mindspore.dataset.transforms.py_transforms.Compose([
F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(cutoff=120.0),
F.ToTensor()
])
],
input_columns=["image"])
except ValueError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Input cutoff is not within the required interval of [0, 50)." in str(
error)
def test_auto_contrast_invalid_ignore_param_py():
"""
Test AutoContrast python Op with invalid ignore parameter
"""
logger.info("Test AutoContrast python Op with invalid ignore parameter")
try:
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data_set = data_set.map(operations=[
mindspore.dataset.transforms.py_transforms.Compose([
F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(ignore=255.5),
F.ToTensor()
])
],
input_columns=["image"])
except TypeError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Argument ignore with value 255.5 is not of type" in str(error)
try:
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
data_set = data_set.map(operations=[
mindspore.dataset.transforms.py_transforms.Compose([
F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(ignore=(10, 100)),
F.ToTensor()
])
],
input_columns=["image"])
except TypeError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Argument ignore with value (10,100) is not of type" in str(
error)
def test_random_color_py(degrees=(0.1, 1.9), plot=False):
"""
Test Python RandomColor
"""
logger.info("Test RandomColor")
# Original Images
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = data.map(operations=transforms_original,
input_columns="image")
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image.asnumpy(),
(0, 2, 3, 1)),
axis=0)
# Random Color Adjusted Images
data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_random_color = mindspore.dataset.transforms.py_transforms.Compose(
[
F.Decode(),
F.Resize((224, 224)),
F.RandomColor(degrees=degrees),
F.ToTensor()
])
ds_random_color = data.map(operations=transforms_random_color,
input_columns="image")
ds_random_color = ds_random_color.batch(512)
for idx, (image, _) in enumerate(ds_random_color):
if idx == 0:
images_random_color = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_random_color = np.append(images_random_color,
np.transpose(
image.asnumpy(), (0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_random_color[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_random_color)
def test_equalize_py(plot=False):
"""
Test Equalize py op
"""
logger.info("Test Equalize")
# Original Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = data_set.map(operations=transforms_original,
input_columns="image")
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image.asnumpy(),
(0, 2, 3, 1)),
axis=0)
# Color Equalized Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_equalize = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(),
F.Resize((224, 224)),
F.Equalize(),
F.ToTensor()])
ds_equalize = data_set.map(operations=transforms_equalize,
input_columns="image")
ds_equalize = ds_equalize.batch(512)
for idx, (image, _) in enumerate(ds_equalize):
if idx == 0:
images_equalize = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_equalize = np.append(images_equalize,
np.transpose(image.asnumpy(),
(0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_equalize[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_equalize)
def test_auto_contrast_py(plot=False):
"""
Test AutoContrast
"""
logger.info("Test AutoContrast Python Op")
# Original Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
F.Resize((224, 224)),
F.ToTensor()])
ds_original = data_set.map(operations=transforms_original, input_columns="image")
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image.asnumpy(), (0, 2, 3, 1)),
axis=0)
# AutoContrast Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_auto_contrast = \
mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(cutoff=10.0, ignore=[10, 20]),
F.ToTensor()])
ds_auto_contrast = data_set.map(operations=transforms_auto_contrast, input_columns="image")
ds_auto_contrast = ds_auto_contrast.batch(512)
for idx, (image, _) in enumerate(ds_auto_contrast):
if idx == 0:
images_auto_contrast = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_auto_contrast = np.append(images_auto_contrast,
np.transpose(image.asnumpy(), (0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_auto_contrast[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
# Compare with expected md5 from images
filename = "autocontrast_01_result_py.npz"
save_and_check_md5(ds_auto_contrast, filename, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(images_original, images_auto_contrast)
def test_cpp_uniform_augment(plot=False, num_ops=2):
"""
Test UniformAugment
"""
logger.info("Test CPP UniformAugment")
# Original Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = [C.Decode(), C.Resize(size=[224, 224]),
F.ToTensor()]
ds_original = data_set.map(operations=transforms_original, input_columns="image")
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image.asnumpy(), (0, 2, 3, 1)),
axis=0)
# UniformAugment Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]),
C.RandomHorizontalFlip(),
C.RandomVerticalFlip(),
C.RandomColorAdjust(),
C.RandomRotation(degrees=45)]
uni_aug = C.UniformAugment(transforms=transforms_ua, num_ops=num_ops)
transforms_all = [C.Decode(), C.Resize(size=[224, 224]),
uni_aug,
F.ToTensor()]
ds_ua = data_set.map(operations=transforms_all, input_columns="image", num_parallel_workers=1)
ds_ua = ds_ua.batch(512)
for idx, (image, _) in enumerate(ds_ua):
if idx == 0:
images_ua = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_ua = np.append(images_ua,
np.transpose(image.asnumpy(), (0, 2, 3, 1)),
axis=0)
if plot:
visualize_list(images_original, images_ua)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_ua[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
def test_invert_py(plot=False):
"""
Test Invert python op
"""
logger.info("Test Invert Python op")
# Original Images
ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = ds.map(operations=transforms_original, input_columns="image")
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image.asnumpy(),
(0, 2, 3, 1)),
axis=0)
# Color Inverted Images
ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transforms_invert = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(),
F.Resize((224, 224)),
F.Invert(),
F.ToTensor()])
ds_invert = ds.map(operations=transforms_invert, input_columns="image")
ds_invert = ds_invert.batch(512)
for idx, (image, _) in enumerate(ds_invert):
if idx == 0:
images_invert = np.transpose(image.asnumpy(), (0, 2, 3, 1))
else:
images_invert = np.append(images_invert,
np.transpose(image.asnumpy(),
(0, 2, 3, 1)),
axis=0)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = np.mean((images_invert[i] - images_original[i])**2)
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_invert)
def test_five_crop_op(plot=False):
"""
Test FiveCrop
"""
logger.info("test_five_crop")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms_1 = [
vision.Decode(),
vision.ToTensor(),
]
transform_1 = mindspore.dataset.transforms.py_transforms.Compose(
transforms_1)
data1 = data1.map(operations=transform_1, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms_2 = [
vision.Decode(),
vision.FiveCrop(200),
lambda images: np.stack([vision.ToTensor()(image)
for image in images]) # 4D stack of 5 images
]
transform_2 = mindspore.dataset.transforms.py_transforms.Compose(
transforms_2)
data2 = data2.map(operations=transform_2, input_columns=["image"])
num_iter = 0
for item1, item2 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
num_iter += 1
image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_2 = item2["image"]
logger.info("shape of image_1: {}".format(image_1.shape))
logger.info("shape of image_2: {}".format(image_2.shape))
logger.info("dtype of image_1: {}".format(image_1.dtype))
logger.info("dtype of image_2: {}".format(image_2.dtype))
if plot:
visualize_list(np.array([image_1] * 5),
(image_2 * 255).astype(np.uint8).transpose(
0, 2, 3, 1))
# The output data should be of a 4D tensor shape, a stack of 5 images.
assert len(image_2.shape) == 4
assert image_2.shape[0] == 5
def __init__(self,
data_dir,
training=True,
use_third_trsfm=False,
use_auto_augment=False,
num_parallel_workers=8,
device_num=1,
device_id=0):
if not training:
trsfm = Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
else:
if not use_third_trsfm:
trsfm = Compose([
transforms.ToPIL(),
transforms.RandomResizedCrop(size=32, scale=(0.2, 1.)),
transforms.RandomColorAdjust(0.4, 0.4, 0.4, 0.4),
transforms.RandomGrayscale(prob=0.2),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
else:
if use_auto_augment:
trsfm = Compose([
transforms.ToPIL(),
transforms.RandomResizedCrop(size=32, scale=(0.2, 1.)),
transforms.RandomHorizontalFlip(),
CIFAR10Policy(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
else:
rand_augment = RandAugment(n=2, m=10)
trsfm = Compose([
transforms.ToPIL(),
transforms.RandomResizedCrop(size=32, scale=(0.2, 1.)),
transforms.RandomHorizontalFlip(),
rand_augment,
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
self.trsfm = trsfm
self.data_dir = data_dir
self.num_parallel_workers = num_parallel_workers
self.device_num = device_num
self.device_id = device_id
def util_test_ten_crop(crop_size, vertical_flip=False, plot=False):
"""
Utility function for testing TenCrop. Input arguments are given by other tests
"""
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms_1 = [
vision.Decode(),
vision.ToTensor(),
]
transform_1 = mindspore.dataset.transforms.py_transforms.Compose(
transforms_1)
data1 = data1.map(operations=transform_1, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms_2 = [
vision.Decode(),
vision.TenCrop(crop_size, use_vertical_flip=vertical_flip),
lambda *images: np.stack(
[vision.ToTensor()(image)
for image in images]) # 4D stack of 10 images
]
transform_2 = mindspore.dataset.transforms.py_transforms.Compose(
transforms_2)
data2 = data2.map(operations=transform_2, input_columns=["image"])
num_iter = 0
for item1, item2 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
num_iter += 1
image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_2 = item2["image"]
logger.info("shape of image_1: {}".format(image_1.shape))
logger.info("shape of image_2: {}".format(image_2.shape))
logger.info("dtype of image_1: {}".format(image_1.dtype))
logger.info("dtype of image_2: {}".format(image_2.dtype))
if plot:
visualize_list(np.array([image_1] * 10),
(image_2 * 255).astype(np.uint8).transpose(
0, 2, 3, 1))
# The output data should be of a 4D tensor shape, a stack of 10 images.
assert len(image_2.shape) == 4
assert image_2.shape[0] == 10
def test_random_grayscale_input_grayscale_images():
"""
Test RandomGrayscale Op: valid parameter with grayscale images as input, expect to pass
"""
logger.info("test_random_grayscale_input_grayscale_images")
original_seed = config_get_set_seed(0)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms1 = [
py_vision.Decode(),
py_vision.Grayscale(1),
# Note: If the input images is grayscale image with 1 channel.
py_vision.RandomGrayscale(0.5),
py_vision.ToTensor()
]
transform1 = mindspore.dataset.transforms.py_transforms.Compose(
transforms1)
data1 = data1.map(operations=transform1, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms2 = [py_vision.Decode(), py_vision.ToTensor()]
transform2 = mindspore.dataset.transforms.py_transforms.Compose(
transforms2)
data2 = data2.map(operations=transform2, input_columns=["image"])
image_gray = []
image = []
for item1, item2 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_gray.append(image1)
image.append(image2)
assert len(image1.shape) == 3
assert image1.shape[2] == 1
assert len(image2.shape) == 3
assert image2.shape[2] == 3
# Restore config
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_crop_and_resize_op_py(plot=False):
"""
Test RandomCropAndResize op in py transforms
"""
logger.info("test_random_crop_and_resize_op_py")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# With these inputs we expect the code to crop the whole image
transforms1 = [
py_vision.Decode(),
py_vision.RandomResizedCrop((256, 512), (2, 2), (1, 3)),
py_vision.ToTensor()
]
transform1 = mindspore.dataset.transforms.py_transforms.Compose(
transforms1)
data1 = data1.map(operations=transform1, input_columns=["image"])
# Second dataset
# Second dataset for comparison
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms2 = [py_vision.Decode(), py_vision.ToTensor()]
transform2 = mindspore.dataset.transforms.py_transforms.Compose(
transforms2)
data2 = data2.map(operations=transform2, input_columns=["image"])
num_iter = 0
crop_and_resize_images = []
original_images = []
for item1, item2 in zip(
data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
crop_and_resize = (item1["image"].transpose(1, 2, 0) * 255).astype(
np.uint8)
original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
original = cv2.resize(original, (512, 256))
mse = diff_mse(crop_and_resize, original)
# Due to rounding error the mse for Python is not exactly 0
assert mse <= 0.05
logger.info("random_crop_and_resize_op_{}, mse: {}".format(
num_iter + 1, mse))
num_iter += 1
crop_and_resize_images.append(crop_and_resize)
original_images.append(original)
if plot:
visualize_list(original_images, crop_and_resize_images)
def util_test_normalize(mean, std, op_type):
"""
Utility function for testing Normalize. Input arguments are given by other tests
"""
if op_type == "cpp":
# define map operations
decode_op = c_vision.Decode()
normalize_op = c_vision.Normalize(mean, std)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data = data.map(operations=decode_op, input_columns=["image"])
data = data.map(operations=normalize_op, input_columns=["image"])
elif op_type == "python":
# define map operations
transforms = [
py_vision.Decode(),
py_vision.ToTensor(),
py_vision.Normalize(mean, std)
]
transform = mindspore.dataset.transforms.py_transforms.Compose(
transforms)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data = data.map(operations=transform, input_columns=["image"])
else:
raise ValueError("Wrong parameter value")
return data
def test_serdes_pyvision(remove_json_files=True):
"""
Test serdes on py_transform pipeline.
"""
data_dir = [
"../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"
]
schema_file = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
data1 = ds.TFRecordDataset(data_dir,
schema_file,
columns_list=["image", "label"],
shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.CenterCrop([32, 32]),
py_vision.ToTensor()
]
data1 = data1.map(operations=py.Compose(transforms),
input_columns=["image"])
# Current python function derialization will be failed for pickle, so we disable this testcase
# as an exception testcase.
try:
util_check_serialize_deserialize_file(data1,
"pyvision_dataset_pipeline",
remove_json_files)
assert False
except NotImplementedError as e:
assert "python function is not yet supported" in str(e)
def __getitem__(self, index):
img_path = self.all_img_paths[index]
gt_path = self.all_gt_paths[index]
img = get_img(img_path)
bboxes, tags = get_bboxes(img, gt_path)
# multi-scale training
if self.is_transform:
img = random_scale(img, min_size=self.img_size[0])
# get gt_text and training_mask
img_h, img_w = img.shape[0: 2]
gt_text = np.zeros((img_h, img_w), dtype=np.float32)
training_mask = np.ones((img_h, img_w), dtype=np.float32)
if bboxes.shape[0] > 0:
bboxes = np.reshape(bboxes * ([img_w, img_h] * 4), (bboxes.shape[0], -1, 2)).astype('int32')
for i in range(bboxes.shape[0]):
cv2.drawContours(gt_text, [bboxes[i]], 0, i + 1, -1)
if not tags[i]:
cv2.drawContours(training_mask, [bboxes[i]], 0, 0, -1)
# get gt_kernels
gt_kernels = []
for i in range(1, self.kernel_num):
rate = 1.0 - (1.0 - self.min_scale) / (self.kernel_num - 1) * i
gt_kernel = np.zeros(img.shape[0:2], dtype=np.float32)
kernel_bboxes = shrink(bboxes, rate)
for j in range(kernel_bboxes.shape[0]):
cv2.drawContours(gt_kernel, [kernel_bboxes[j]], 0, 1, -1)
gt_kernels.append(gt_kernel)
# data augmentation
if self.is_transform:
imgs = [img, gt_text, training_mask]
imgs.extend(gt_kernels)
imgs = random_horizontal_flip(imgs)
imgs = random_rotate(imgs)
imgs = random_crop(imgs, self.img_size)
img, gt_text, training_mask, gt_kernels = imgs[0], imgs[1], imgs[2], imgs[3:]
gt_text[gt_text > 0] = 1
gt_kernels = np.array(gt_kernels)
if self.is_transform:
img = Image.fromarray(img)
img = img.convert('RGB')
img = py_transforms.RandomColorAdjust(brightness=32.0 / 255, saturation=0.5)(img)
else:
img = Image.fromarray(img)
img = img.convert('RGB')
img = py_transforms.ToTensor()(img)
img = py_transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(img)
gt_text = gt_text.astype(np.float32)
gt_kernels = gt_kernels.astype(np.float32)
training_mask = training_mask.astype(np.float32)
return img, gt_text, gt_kernels, training_mask
def test_random_apply_md5():
"""
Test RandomApply op with md5 check
"""
logger.info("test_random_apply_md5")
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# define map operations
transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)]
transforms = [
py_vision.Decode(),
# Note: using default value "prob=0.5"
py_transforms.RandomApply(transforms_list),
py_vision.ToTensor()
]
transform = py_transforms.Compose(transforms)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data = data.map(operations=transform, input_columns=["image"])
# check results with md5 comparison
filename = "random_apply_01_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers((original_num_parallel_workers))
def test_random_crop_and_resize_op_py_ANTIALIAS():
"""
Test RandomCropAndResize op in py transforms
"""
logger.info("test_random_crop_and_resize_op_py_ANTIALIAS")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# With these inputs we expect the code to crop the whole image
transforms1 = [
py_vision.Decode(),
py_vision.RandomResizedCrop((256, 512), (2, 2), (1, 3),
Inter.ANTIALIAS),
py_vision.ToTensor()
]
transform1 = mindspore.dataset.transforms.py_transforms.Compose(
transforms1)
data1 = data1.map(operations=transform1, input_columns=["image"])
num_iter = 0
for _ in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
num_iter += 1
logger.info(
"use RandomResizedCrop by Inter.ANTIALIAS process {} images.".format(
num_iter))
def test_random_crop_comp(plot=False):
"""
Test RandomCrop and compare between python and c image augmentation
"""
logger.info("Test RandomCrop with c_transform and py_transform comparison")
cropped_size = 512
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
random_crop_op = c_vision.RandomCrop(cropped_size)
decode_op = c_vision.Decode()
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=random_crop_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.RandomCrop(cropped_size),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data2 = data2.map(operations=transform, input_columns=["image"])
image_c_cropped = []
image_py_cropped = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
c_image = item1["image"]
py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_c_cropped.append(c_image)
image_py_cropped.append(py_image)
if plot:
visualize_list(image_c_cropped, image_py_cropped, visualize_mode=2)
def test_random_crop_06_py():
"""
Test RandomCrop op with py_transforms:
invalid size, expected to raise TypeError
"""
logger.info("test_random_crop_06_py")
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
try:
# Note: if size is neither an int nor a list of length 2, an exception will raise
transforms = [
py_vision.Decode(),
py_vision.RandomCrop([512, 512, 375]),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(
transforms)
data = data.map(operations=transform, input_columns=["image"])
except TypeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Size should be a single integer" in str(e)
def test_random_crop_05_py():
"""
Test RandomCrop op with py_transforms:
input image size < crop size but pad_if_needed is enabled,
expected to pass
"""
logger.info("test_random_crop_05_py")
original_seed = config_get_set_seed(0)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
# Note: The size of the image is 4032*2268
transforms = [
py_vision.Decode(),
py_vision.RandomCrop([2268, 4033], [200, 200, 200, 200], pad_if_needed=True),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data = data.map(operations=transform, input_columns=["image"])
filename = "random_crop_05_py_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
# Restore config setting
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_crop_02_py():
"""
Test RandomCrop op with py_transforms: size is a list/tuple with length 2, expected to pass
"""
logger.info("test_random_crop_02_py")
original_seed = config_get_set_seed(0)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
# Note: If size is a sequence of length 2, it should be (height, width).
transforms = [
py_vision.Decode(),
py_vision.RandomCrop([512, 375]),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data = data.map(operations=transform, input_columns=["image"])
filename = "random_crop_02_py_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
# Restore config setting
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_crop_01_py():
"""
Test RandomCrop op with py_transforms: size is a single integer, expected to pass
"""
logger.info("test_random_crop_01_py")
original_seed = config_get_set_seed(0)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
# Note: If size is an int, a square crop of size (size, size) is returned.
transforms = [
py_vision.Decode(),
py_vision.RandomCrop(512),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data = data.map(operations=transform, input_columns=["image"])
filename = "random_crop_01_py_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
# Restore config setting
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_crop_and_resize_05_py():
"""
Test RandomCropAndResize with py_transforms: invalid range of ratio (max<min),
expected to raise ValueError
"""
logger.info("test_random_crop_and_resize_05_py")
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
try:
transforms = [
py_vision.Decode(),
# If input range of ratio is not in the order of (min, max), ValueError will be raised.
py_vision.RandomResizedCrop((256, 512), (1, 1), (1, 0.5)),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(
transforms)
data = data.map(operations=transform, input_columns=["image"])
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "ratio should be in (min,max) format. Got (max,min)." in str(e)
def __call__(self, imgs, ann, image_names, image_size, batch_info):
size = self.resize
decode = P.Decode()
resize_letter_box_op = ResizeLetterbox(input_dim=size)
to_tensor = P.ToTensor()
ret_imgs = []
ret_anno = []
for i, image in enumerate(imgs):
img_pil = decode(image)
input_data = img_pil, ann[i]
input_data = resize_letter_box_op(*input_data)
image_arr = to_tensor(input_data[0])
ret_imgs.append(image_arr)
ret_anno.append(input_data[1])
for i, anno in enumerate(ret_anno):
anno_count = anno.shape[0]
if anno_count < self.max_anno_count:
ret_anno[i] = np.concatenate(
(ret_anno[i],
np.zeros(
(self.max_anno_count - anno_count, 6), dtype=float)),
axis=0)
else:
ret_anno[i] = ret_anno[i][:self.max_anno_count]
return np.array(ret_imgs), np.array(ret_anno), image_names, image_size
def test_crop_grayscale(height=375, width=375):
"""
Test that centercrop works with pad and grayscale images
"""
# Note: image.transpose performs channel swap to allow py transforms to
# work with c transforms
transforms = [
py_vision.Decode(),
py_vision.Grayscale(1),
py_vision.ToTensor(),
(lambda image: (image.transpose(1, 2, 0) * 255).astype(np.uint8))
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data1 = data1.map(operations=transform, input_columns=["image"])
# If input is grayscale, the output dimensions should be single channel
crop_gray = vision.CenterCrop([height, width])
data1 = data1.map(operations=crop_gray, input_columns=["image"])
for item1 in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
c_image = item1["image"]
# Check that the image is grayscale
assert (c_image.ndim == 3 and c_image.shape[2] == 1)
def test_random_crop_08_py():
"""
Test RandomCrop op with py_transforms: padding_mode is Border.EDGE,
expected to pass
"""
logger.info("test_random_crop_08_py")
original_seed = config_get_set_seed(0)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
# Note: The padding_mode is Border.EDGE.
transforms = [
py_vision.Decode(),
py_vision.RandomCrop(512, [200, 200, 200, 200], padding_mode=mode.Border.EDGE),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data = data.map(operations=transform, input_columns=["image"])
filename = "random_crop_08_py_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
# Restore config setting
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_center_crop_comp(height=375, width=375, plot=False):
"""
Test CenterCrop between python and c image augmentation
"""
logger.info("Test CenterCrop")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
decode_op = vision.Decode()
center_crop_op = vision.CenterCrop([height, width])
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=center_crop_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.CenterCrop([height, width]),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data2 = data2.map(operations=transform, input_columns=["image"])
image_c_cropped = []
image_py_cropped = []
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
c_image = item1["image"]
py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
# Note: The images aren't exactly the same due to rounding error
assert diff_mse(py_image, c_image) < 0.001
image_c_cropped.append(c_image.copy())
image_py_cropped.append(py_image.copy())
if plot:
visualize_list(image_c_cropped, image_py_cropped, visualize_mode=2)
def test_random_crop_04_py():
"""
Test RandomCrop op with py_transforms:
input image size < crop size, expected to fail
"""
logger.info("test_random_crop_04_py")
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
# Note: The size of the image is 4032*2268
transforms = [
py_vision.Decode(),
py_vision.RandomCrop([2268, 4033]),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data = data.map(operations=transform, input_columns=["image"])
try:
data.create_dict_iterator(num_epochs=1).__next__()
except RuntimeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Crop size" in str(e)
