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:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[
F.ComposeOp([
F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(cutoff=-10.0),
F.ToTensor()
])
])
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 to 100)." in str(
error)
try:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[
F.ComposeOp([
F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(cutoff=120.0),
F.ToTensor()
])
])
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 to 100)." in str(
error)
def test_random_sharpness_py(degrees=(0.7, 0.7), plot=False):
"""
Test RandomSharpness python op
"""
logger.info("Test RandomSharpness python op")
# Original Images
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = data.map(input_columns="image",
operations=transforms_original())
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image, (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
# Random Sharpness Adjusted Images
data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
py_op = F.RandomSharpness()
if degrees is not None:
py_op = F.RandomSharpness(degrees)
transforms_random_sharpness = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)), py_op,
F.ToTensor()])
ds_random_sharpness = data.map(input_columns="image",
operations=transforms_random_sharpness())
ds_random_sharpness = ds_random_sharpness.batch(512)
for idx, (image, _) in enumerate(ds_random_sharpness):
if idx == 0:
images_random_sharpness = np.transpose(image, (0, 2, 3, 1))
else:
images_random_sharpness = np.append(images_random_sharpness,
np.transpose(
image, (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_sharpness[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize_list(images_original, images_random_sharpness)
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:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[
F.ComposeOp([
F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(ignore=255.5),
F.ToTensor()
])
])
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:
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
ds = ds.map(input_columns=["image"],
operations=[
F.ComposeOp([
F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(ignore=(10, 100)),
F.ToTensor()
])
])
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(degrees=(0.1, 1.9), plot=False):
"""
Test RandomColor
"""
logger.info("Test RandomColor")
# Original Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = ds.map(input_columns="image",
operations=transforms_original())
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image, (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
# Random Color Adjusted Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_random_color = F.ComposeOp([
F.Decode(),
F.Resize((224, 224)),
F.RandomColor(degrees=degrees),
F.ToTensor()
])
ds_random_color = ds.map(input_columns="image",
operations=transforms_random_color())
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, (0, 2, 3, 1))
else:
images_random_color = np.append(images_random_color,
np.transpose(image, (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_random_color[i] - images_original[i])**2)
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize(images_original, images_random_color)
def test_equalize_py(plot=False):
"""
Test Equalize py op
"""
logger.info("Test Equalize")
# Original Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = ds.map(input_columns="image",
operations=transforms_original())
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image, (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
# Color Equalized Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_equalize = F.ComposeOp(
[F.Decode(),
F.Resize((224, 224)),
F.Equalize(),
F.ToTensor()])
ds_equalize = ds.map(input_columns="image",
operations=transforms_equalize())
ds_equalize = ds_equalize.batch(512)
for idx, (image, _) in enumerate(ds_equalize):
if idx == 0:
images_equalize = np.transpose(image, (0, 2, 3, 1))
else:
images_equalize = np.append(images_equalize,
np.transpose(image, (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(plot=False):
"""
Test AutoContrast
"""
logger.info("Test AutoContrast")
# Original Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = ds.map(input_columns="image",
operations=transforms_original())
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image, (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
# AutoContrast Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_auto_contrast = F.ComposeOp(
[F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(),
F.ToTensor()])
ds_auto_contrast = ds.map(input_columns="image",
operations=transforms_auto_contrast())
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, (0, 2, 3, 1))
else:
images_auto_contrast = np.append(images_auto_contrast,
np.transpose(image, (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_auto_contrast[i] - images_original[i])**2)
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize(images_original, images_auto_contrast)
def test_random_apply_exception_random_crop_badinput():
"""
Test RandomApply: test invalid input for one of the transform functions,
expected to raise error
"""
logger.info("test_random_apply_exception_random_crop_badinput")
original_seed = config_get_set_seed(200)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# define map operations
transforms_list = [
py_vision.Resize([32, 32]),
py_vision.RandomCrop(100), # crop size > image size
py_vision.RandomRotation(30)
]
transforms = [
py_vision.Decode(),
py_vision.RandomApply(transforms_list, prob=0.6),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data = data.map(input_columns=["image"], operations=transform())
try:
_ = data.create_dict_iterator().get_next()
except RuntimeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Crop size" in str(e)
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def skip_test_random_perspective_md5():
"""
Test RandomPerspective with md5 comparison
"""
logger.info("test_random_perspective_md5")
original_seed = config_get_set_seed(5)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# define map operations
transforms = [
py_vision.Decode(),
py_vision.RandomPerspective(distortion_scale=0.3,
prob=0.7,
interpolation=Inter.BILINEAR),
py_vision.Resize(
1450), # resize to a smaller size to prevent round-off error
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data = data.map(input_columns=["image"], operations=transform())
# check results with md5 comparison
filename = "random_perspective_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_concat_14():
"""
Test concat: create dataset with different dataset folder, and do diffrent operation then concat
"""
logger.info("test_concat_14")
DATA_DIR = "../data/dataset/testPK/data"
DATA_DIR2 = "../data/dataset/testImageNetData/train/"
data1 = ds.ImageFolderDatasetV2(DATA_DIR, num_samples=3)
data2 = ds.ImageFolderDatasetV2(DATA_DIR2, num_samples=2)
transforms1 = F.ComposeOp([F.Decode(), F.Resize((224, 224)), F.ToTensor()])
data1 = data1.map(input_columns=["image"], operations=transforms1())
data2 = data2.map(input_columns=["image"], operations=transforms1())
data3 = data1 + data2
expected, output = [], []
for d in data1:
expected.append(d[0])
for d in data2:
expected.append(d[0])
for d in data3:
output.append(d[0])
assert len(expected) == len(output)
np.array_equal(np.array(output), np.array(expected))
assert sum([1 for _ in data3]) == 5
assert data3.get_dataset_size() == 5
def create_dataset_py(dataset_path, do_train, config, device_target, repeat_num=1, batch_size=32):
"""
create a train or eval dataset
Args:
dataset_path(string): the path of dataset.
do_train(bool): whether dataset is used for train or eval.
repeat_num(int): the repeat times of dataset. Default: 1.
batch_size(int): the batch size of dataset. Default: 32.
Returns:
dataset
"""
if device_target == "Ascend":
rank_size = int(os.getenv("RANK_SIZE"))
rank_id = int(os.getenv("RANK_ID"))
if do_train:
if rank_size == 1:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
else:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=rank_size, shard_id=rank_id)
else:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=False)
else:
raise ValueError("Unsupported device target.")
resize_height = config.image_height
if do_train:
buffer_size = 20480
# apply shuffle operations
ds = ds.shuffle(buffer_size=buffer_size)
# define map operations
decode_op = P.Decode()
resize_crop_op = P.RandomResizedCrop(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333))
horizontal_flip_op = P.RandomHorizontalFlip(prob=0.5)
resize_op = P.Resize(256)
center_crop = P.CenterCrop(resize_height)
to_tensor = P.ToTensor()
normalize_op = P.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
if do_train:
trans = [decode_op, resize_crop_op, horizontal_flip_op, to_tensor, normalize_op]
else:
trans = [decode_op, resize_op, center_crop, to_tensor, normalize_op]
compose = P.ComposeOp(trans)
ds = ds.map(input_columns="image", operations=compose(), num_parallel_workers=8, python_multiprocessing=True)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds
def test_rgb_hsv_pipeline():
# First dataset
transforms1 = [vision.Decode(), vision.Resize([64, 64]), vision.ToTensor()]
transforms1 = vision.ComposeOp(transforms1)
ds1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
ds1 = ds1.map(input_columns=["image"], operations=transforms1())
# Second dataset
transforms2 = [
vision.Decode(),
vision.Resize([64, 64]),
vision.ToTensor(),
vision.RgbToHsv(),
vision.HsvToRgb()
]
transform2 = vision.ComposeOp(transforms2)
ds2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
ds2 = ds2.map(input_columns=["image"], operations=transform2())
num_iter = 0
for data1, data2 in zip(ds1.create_dict_iterator(),
ds2.create_dict_iterator()):
num_iter += 1
ori_img = data1["image"]
cvt_img = data2["image"]
assert_allclose(ori_img.flatten(),
cvt_img.flatten(),
rtol=1e-5,
atol=0)
assert (ori_img.shape == cvt_img.shape)
def create_dataset(dataset_path,
do_train,
config,
platform,
repeat_num=1,
batch_size=100):
"""
create a train or eval dataset
Args:
dataset_path(string): the path of dataset.
do_train(bool): whether dataset is used for train or eval.
repeat_num(int): the repeat times of dataset. Default: 1
batch_size(int): the batch size of dataset. Default: 32
Returns:
dataset
"""
if platform == "Ascend":
rank_size = int(os.getenv("RANK_SIZE"))
rank_id = int(os.getenv("RANK_ID"))
if rank_size == 1:
ds = de.MindDataset(dataset_path,
num_parallel_workers=8,
shuffle=True)
else:
ds = de.MindDataset(dataset_path,
num_parallel_workers=8,
shuffle=True,
num_shards=rank_size,
shard_id=rank_id)
elif platform == "GPU":
if do_train:
from mindspore.communication.management import get_rank, get_group_size
ds = de.MindDataset(dataset_path,
num_parallel_workers=8,
shuffle=True,
num_shards=get_group_size(),
shard_id=get_rank())
else:
ds = de.MindDataset(dataset_path,
num_parallel_workers=8,
shuffle=False)
else:
raise ValueError("Unsupport platform.")
resize_height = config.image_height
buffer_size = 1000
# define map operations
resize_crop_op = C.RandomCropDecodeResize(resize_height,
scale=(0.08, 1.0),
ratio=(0.75, 1.333))
horizontal_flip_op = C.RandomHorizontalFlip(prob=0.5)
color_op = C.RandomColorAdjust(brightness=0.4,
contrast=0.4,
saturation=0.4)
rescale_op = C.Rescale(1 / 255.0, 0)
normalize_op = C.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
change_swap_op = C.HWC2CHW()
# define python operations
decode_p = P.Decode()
resize_p = P.Resize(256, interpolation=Inter.BILINEAR)
center_crop_p = P.CenterCrop(224)
totensor = P.ToTensor()
normalize_p = P.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
composeop = P.ComposeOp(
[decode_p, resize_p, center_crop_p, totensor, normalize_p])
if do_train:
trans = [
resize_crop_op, horizontal_flip_op, color_op, rescale_op,
normalize_op, change_swap_op
]
else:
trans = composeop()
type_cast_op = C2.TypeCast(mstype.int32)
ds = ds.map(input_columns="image",
operations=trans,
num_parallel_workers=8)
ds = ds.map(input_columns="label_list",
operations=type_cast_op,
num_parallel_workers=8)
# apply shuffle operations
ds = ds.shuffle(buffer_size=buffer_size)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds
def test_uniform_augment(plot=False, num_ops=2):
"""
Test UniformAugment
"""
logger.info("Test UniformAugment")
# Original Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = ds.map(input_columns="image",
operations=transforms_original())
ds_original = ds_original.batch(512)
for idx, (image, label) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image, (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
# UniformAugment Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transform_list = [
F.RandomRotation(45),
F.RandomColor(),
F.RandomSharpness(),
F.Invert(),
F.AutoContrast(),
F.Equalize()
]
transforms_ua = F.ComposeOp([
F.Decode(),
F.Resize((224, 224)),
F.UniformAugment(transforms=transform_list, num_ops=num_ops),
F.ToTensor()
])
ds_ua = ds.map(input_columns="image", operations=transforms_ua())
ds_ua = ds_ua.batch(512)
for idx, (image, label) in enumerate(ds_ua):
if idx == 0:
images_ua = np.transpose(image, (0, 2, 3, 1))
else:
images_ua = np.append(images_ua,
np.transpose(image, (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_ua[i] - images_original[i])**2)
logger.info("MSE= {}".format(str(np.mean(mse))))
if plot:
visualize(images_original, images_ua)
def test_auto_contrast_py(plot=False):
"""
Test AutoContrast
"""
logger.info("Test AutoContrast Python Op")
# Original Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_original = F.ComposeOp(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
ds_original = ds.map(input_columns="image",
operations=transforms_original())
ds_original = ds_original.batch(512)
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = np.transpose(image, (0, 2, 3, 1))
else:
images_original = np.append(images_original,
np.transpose(image, (0, 2, 3, 1)),
axis=0)
# AutoContrast Images
ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
transforms_auto_contrast = F.ComposeOp(
[F.Decode(),
F.Resize((224, 224)),
F.AutoContrast(),
F.ToTensor()])
ds_auto_contrast = ds.map(input_columns="image",
operations=transforms_auto_contrast())
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, (0, 2, 3, 1))
else:
images_auto_contrast = np.append(images_auto_contrast,
np.transpose(image, (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 = "autcontrast_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 create_dataset_py(dataset_path,
do_train,
repeat_num=1,
batch_size=32,
target="Ascend"):
"""
create a train or eval dataset
Args:
dataset_path(string): the path of dataset.
do_train(bool): whether dataset is used for train or eval.
repeat_num(int): the repeat times of dataset. Default: 1
batch_size(int): the batch size of dataset. Default: 32
target(str): the device target. Default: Ascend
Returns:
dataset
"""
if target == "Ascend":
device_num = int(os.getenv("RANK_SIZE"))
rank_id = int(os.getenv("RANK_ID"))
else:
init("nccl")
rank_id = get_rank()
device_num = get_group_size()
if do_train:
if device_num == 1:
ds = de.ImageFolderDatasetV2(dataset_path,
num_parallel_workers=8,
shuffle=True)
else:
ds = de.ImageFolderDatasetV2(dataset_path,
num_parallel_workers=8,
shuffle=True,
num_shards=device_num,
shard_id=rank_id)
else:
ds = de.ImageFolderDatasetV2(dataset_path,
num_parallel_workers=8,
shuffle=False)
image_size = 224
# define map operations
decode_op = P.Decode()
resize_crop_op = P.RandomResizedCrop(image_size,
scale=(0.08, 1.0),
ratio=(0.75, 1.333))
horizontal_flip_op = P.RandomHorizontalFlip(prob=0.5)
resize_op = P.Resize(256)
center_crop = P.CenterCrop(image_size)
to_tensor = P.ToTensor()
normalize_op = P.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# define map operations
if do_train:
trans = [
decode_op, resize_crop_op, horizontal_flip_op, to_tensor,
normalize_op
]
else:
trans = [decode_op, resize_op, center_crop, to_tensor, normalize_op]
compose = P.ComposeOp(trans)
ds = ds.map(input_columns="image",
operations=compose(),
num_parallel_workers=8,
python_multiprocessing=True)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
ds = ds.repeat(repeat_num)
return ds
