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_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_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_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_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_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_c_py_compose_vision_module(plot=False, run_golden=True):
"""
Test combining Python and C++ vision transforms
"""
original_seed = config_get_set_seed(10)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
def test_config(plot, file_name, op_list):
data_dir = "../data/dataset/testImageNetData/train/"
data1 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
data1 = data1.map(operations=op_list, input_columns=["image"])
data2 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
data2 = data2.map(operations=c_vision.Decode(), input_columns=["image"])
original_images = []
transformed_images = []
for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
transformed_images.append(item["image"])
for item in data2.create_dict_iterator(num_epochs=1, output_numpy=True):
original_images.append(item["image"])
if run_golden:
# Compare with expected md5 from images
save_and_check_md5(data1, file_name, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(original_images, transformed_images)
test_config(op_list=[c_vision.Decode(),
py_vision.ToPIL(),
py_vision.Resize((224, 224)),
np.array],
plot=plot, file_name="compose_c_py_1.npz")
test_config(op_list=[c_vision.Decode(),
c_vision.Resize((224, 244)),
py_vision.ToPIL(),
np.array,
c_vision.Resize((24, 24))],
plot=plot, file_name="compose_c_py_2.npz")
test_config(op_list=[py_vision.Decode(),
py_vision.Resize((224, 224)),
np.array,
c_vision.RandomColor()],
plot=plot, file_name="compose_c_py_3.npz")
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers((original_num_parallel_workers))
def test_concat_14():
"""
Test concat: Testing concat on two different source datasets with different dataset operations.
"""
logger.info("test_concat_14")
DATA_DIR = "../data/dataset/testPK/data"
DATA_DIR2 = "../data/dataset/testImageNetData/train/"
data1 = ds.ImageFolderDataset(DATA_DIR, num_samples=3)
data2 = ds.ImageFolderDataset(DATA_DIR2, num_samples=2)
transforms1 = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(), F.Resize((224, 224)),
F.ToTensor()])
data1 = data1.map(operations=transforms1, input_columns=["image"])
data2 = data2.map(operations=transforms1, input_columns=["image"])
data3 = data1 + data2
expected, output = [], []
for d in data1.create_tuple_iterator(output_numpy=True):
expected.append(d[0])
for d in data2.create_tuple_iterator(output_numpy=True):
expected.append(d[0])
for d in data3.create_tuple_iterator(output_numpy=True):
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 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 = 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"])
# 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 data_generator(args):
'''Build train dataloader.'''
mindrecord_path = args.mindrecord_path
dst_w = args.dst_w
dst_h = args.dst_h
batch_size = args.per_batch_size
attri_num = args.attri_num
max_epoch = args.max_epoch
transform_img = F2.Compose([
F.Decode(),
F.Resize((dst_w, dst_h)),
F.RandomHorizontalFlip(prob=0.5),
F.ToTensor(),
F.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
de_dataset = de.MindDataset(mindrecord_path + "0",
columns_list=["image", "label"],
num_shards=args.world_size,
shard_id=args.local_rank)
de_dataset = de_dataset.map(input_columns="image",
operations=transform_img,
num_parallel_workers=args.workers,
python_multiprocessing=True)
de_dataset = de_dataset.batch(batch_size, drop_remainder=True)
steps_per_epoch = de_dataset.get_dataset_size()
de_dataset = de_dataset.repeat(max_epoch)
de_dataloader = de_dataset.create_tuple_iterator(output_numpy=True)
num_classes = attri_num
return de_dataloader, steps_per_epoch, num_classes
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_transforms.RandomApply(transforms_list, prob=0.6),
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"])
try:
_ = data.create_dict_iterator(num_epochs=1).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 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()
rank_id = get_rank()
device_num = get_group_size()
if do_train:
if device_num == 1:
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
else:
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
num_shards=device_num, shard_id=rank_id)
else:
ds = de.ImageFolderDataset(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 = P2.Compose(trans)
ds = ds.map(operations=compose, input_columns="image", 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 = mindspore.dataset.transforms.py_transforms.Compose(
transforms1)
ds1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
ds1 = ds1.map(operations=transforms1, input_columns=["image"])
# Second dataset
transforms2 = [
vision.Decode(),
vision.Resize([64, 64]),
vision.ToTensor(),
vision.RgbToHsv(),
vision.HsvToRgb()
]
transform2 = mindspore.dataset.transforms.py_transforms.Compose(
transforms2)
ds2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
ds2 = ds2.map(operations=transform2, input_columns=["image"])
num_iter = 0
for data1, data2 in zip(ds1.create_dict_iterator(num_epochs=1),
ds2.create_dict_iterator(num_epochs=1)):
num_iter += 1
ori_img = data1["image"].asnumpy()
cvt_img = data2["image"].asnumpy()
assert_allclose(ori_img.flatten(),
cvt_img.flatten(),
rtol=1e-5,
atol=0)
assert ori_img.shape == cvt_img.shape
def create_dataset_val(batch_size=128,
val_data_url='',
workers=8,
distributed=False,
input_size=224):
"""Create ImageNet validation dataset"""
if not os.path.exists(val_data_url):
raise ValueError('Path not exists')
rank_id = get_rank() if distributed else 0
rank_size = get_group_size() if distributed else 1
dataset = ds.ImageFolderDataset(val_data_url,
num_parallel_workers=workers,
num_shards=rank_size,
shard_id=rank_id)
scale_size = None
if isinstance(input_size, tuple):
assert len(input_size) == 2
if input_size[-1] == input_size[-2]:
scale_size = int(math.floor(input_size[0] / DEFAULT_CROP_PCT))
else:
scale_size = tuple([int(x / DEFAULT_CROP_PCT) for x in input_size])
else:
scale_size = int(math.floor(input_size / DEFAULT_CROP_PCT))
type_cast_op = c_transforms.TypeCast(mstype.int32)
decode_op = py_vision.Decode()
resize_op = py_vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
center_crop = py_vision.CenterCrop(size=input_size)
to_tensor = py_vision.ToTensor()
normalize_op = py_vision.Normalize(IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD)
image_ops = py_transforms.Compose(
[decode_op, resize_op, center_crop, to_tensor, normalize_op])
dataset = dataset.map(input_columns=["label"],
operations=type_cast_op,
num_parallel_workers=workers)
dataset = dataset.map(input_columns=["image"],
operations=image_ops,
num_parallel_workers=workers)
dataset = dataset.batch(batch_size,
per_batch_map=split_imgs_and_labels,
input_columns=["image", "label"],
num_parallel_workers=2,
drop_remainder=True)
dataset = dataset.repeat(1)
return dataset
def test_resize_op_ANTIALIAS():
"""
Test resize_op
"""
logger.info("Test resize for ANTIALIAS")
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
# define map operations
decode_op = py_vision.Decode()
resize_op = py_vision.Resize(20, Inter.ANTIALIAS)
# apply map operations on images
data1 = data1.map(operations=[decode_op, resize_op, py_vision.ToTensor()], input_columns=["image"])
num_iter = 0
for _ in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
num_iter += 1
logger.info("use Resize by Inter.ANTIALIAS process {} images.".format(num_iter))
def load_images(paths, batch_size=128):
'''Load images.'''
ll = []
resize = V.Resize((96, 64))
transform = T.Compose(
[V.ToTensor(),
V.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
for i, _ in enumerate(paths):
im = Image.open(paths[i])
im = resize(im)
img = np.array(im)
ts = transform(img)
ll.append(ts[0])
if len(ll) == batch_size:
yield np.stack(ll, axis=0)
ll.clear()
if ll:
yield np.stack(ll, axis=0)
def data_generator_eval(args):
'''Build eval dataloader.'''
mindrecord_path = args.mindrecord_path
dst_w = args.dst_w
dst_h = args.dst_h
batch_size = 1
attri_num = args.attri_num
transform_img = F2.Compose([F.Decode(),
F.Resize((dst_w, dst_h)),
F.ToTensor(),
F.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
de_dataset = de.MindDataset(mindrecord_path + "0", columns_list=["image", "label"])
de_dataset = de_dataset.map(input_columns="image", operations=transform_img, num_parallel_workers=args.workers,
python_multiprocessing=True)
de_dataset = de_dataset.batch(batch_size)
de_dataloader = de_dataset.create_tuple_iterator(output_numpy=True)
steps_per_epoch = de_dataset.get_dataset_size()
print("image number:{0}".format(steps_per_epoch))
num_classes = attri_num
return de_dataloader, steps_per_epoch, num_classes
print("==> Loading data")
# Data Loading code
transform_train = Compose([
decode,
py_trans.Pad(10),
py_trans.RandomCrop((args.img_h, args.img_w)),
py_trans.RandomHorizontalFlip(),
py_trans.ToTensor(),
py_trans.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
transform_test = Compose([
decode,
py_trans.Resize((args.img_h, args.img_w)),
py_trans.ToTensor(),
py_trans.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
ifDebug_dic = {"yes": True, "no": False}
if dataset_type == "SYSU":
# train_set
ifDebug = {}
trainset_generator = SYSUDatasetGenerator(data_dir=data_path,
ifDebug=ifDebug_dic.get(
args.debug))
color_pos, thermal_pos = GenIdx(trainset_generator.train_color_label,
trainset_generator.train_thermal_label)
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:
data_set = ds.ImageFolderDataset(dataset_path,
num_parallel_workers=8,
shuffle=True)
else:
data_set = ds.ImageFolderDataset(dataset_path,
num_parallel_workers=8,
shuffle=True,
num_shards=rank_size,
shard_id=rank_id)
else:
data_set = ds.ImageFolderDataset(dataset_path,
num_parallel_workers=8,
shuffle=False)
else:
raise ValueError("Unsupported device target.")
resize_height = 224
if do_train:
buffer_size = 20480
# apply shuffle operations
data_set = data_set.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 = P2.Compose(trans)
data_set = data_set.map(operations=compose,
input_columns="image",
num_parallel_workers=8,
python_multiprocessing=True)
# apply batch operations
data_set = data_set.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
data_set = data_set.repeat(repeat_num)
return data_set
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:
data_set = ds.MindDataset(dataset_path,
num_parallel_workers=8,
shuffle=True)
else:
data_set = ds.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
data_set = ds.MindDataset(dataset_path,
num_parallel_workers=8,
shuffle=True,
num_shards=get_group_size(),
shard_id=get_rank())
else:
data_set = ds.MindDataset(dataset_path,
num_parallel_workers=8,
shuffle=False)
else:
raise ValueError("Unsupported 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 = P2.Compose(
[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)
data_set = data_set.map(input_columns="image",
operations=trans,
num_parallel_workers=8)
data_set = data_set.map(input_columns="label_list",
operations=type_cast_op,
num_parallel_workers=8)
# apply shuffle operations
data_set = data_set.shuffle(buffer_size=buffer_size)
# apply batch operations
data_set = data_set.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
data_set = data_set.repeat(repeat_num)
return data_set
def test_uniform_augment(plot=False, num_ops=2):
"""
Test UniformAugment
"""
logger.info("Test UniformAugment")
# 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)
# UniformAugment Images
data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
transform_list = [F.RandomRotation(45),
F.RandomColor(),
F.RandomSharpness(),
F.Invert(),
F.AutoContrast(),
F.Equalize()]
transforms_ua = \
mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
F.Resize((224, 224)),
F.UniformAugment(transforms=transform_list,
num_ops=num_ops),
F.ToTensor()])
ds_ua = data_set.map(operations=transforms_ua, input_columns="image")
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)
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))))
if plot:
visualize_list(images_original, images_ua)
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.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.create_tuple_iterator(output_numpy=True)):
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.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
py_op = F.RandomSharpness()
if degrees is not None:
py_op = F.RandomSharpness(degrees)
transforms_random_sharpness = mindspore.dataset.transforms.py_transforms.Compose(
[F.Decode(), F.Resize((224, 224)), py_op,
F.ToTensor()])
ds_random_sharpness = data.map(operations=transforms_random_sharpness,
input_columns="image")
ds_random_sharpness = ds_random_sharpness.batch(512)
for idx, (image, _) in enumerate(
ds_random_sharpness.create_tuple_iterator(output_numpy=True)):
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)
