def test_py_transforms_with_c_vision():
"""
These examples will fail, as py_transforms.Random(Apply/Choice/Order) expect callable functions
"""
ds.config.set_seed(0)
def test_config(op_list):
data_dir = "../data/dataset/testImageNetData/train/"
data = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
data = data.map(operations=op_list)
res = []
for i in data.create_dict_iterator(output_numpy=True):
for col_name in output_cols:
res.append(i[col_name].tolist())
return res
with pytest.raises(ValueError) as error_info:
test_config(py_transforms.RandomApply([c_vision.RandomResizedCrop(200)]))
assert "transforms[0] is not callable." in str(error_info.value)
with pytest.raises(ValueError) as error_info:
test_config(py_transforms.RandomChoice([c_vision.RandomResizedCrop(200)]))
assert "transforms[0] is not callable." in str(error_info.value)
with pytest.raises(ValueError) as error_info:
test_config(py_transforms.RandomOrder([np.array, c_vision.RandomResizedCrop(200)]))
assert "transforms[1] is not callable." in str(error_info.value)
with pytest.raises(RuntimeError) as error_info:
test_config([py_transforms.OneHotOp(20, 0.1)])
assert "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" in str(
error_info.value)
def test_random_crop_and_resize_06():
"""
Test RandomCropAndResize with c_transforms: invalid values for scale,
expected to raise ValueError
"""
logger.info("test_random_crop_and_resize_05_c")
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
decode_op = c_vision.Decode()
try:
random_crop_and_resize_op = c_vision.RandomResizedCrop((256, 512),
scale="",
ratio=(1, 0.5))
data = data.map(operations=decode_op, input_columns=["image"])
data.map(operations=random_crop_and_resize_op, input_columns=["image"])
except TypeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Argument scale with value \"\" is not of type (<class 'tuple'>,)" in str(
e)
try:
random_crop_and_resize_op = c_vision.RandomResizedCrop((256, 512),
scale=(1, "2"),
ratio=(1, 0.5))
data = data.map(operations=decode_op, input_columns=["image"])
data.map(operations=random_crop_and_resize_op, input_columns=["image"])
except TypeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Argument scale[1] with value 2 is not of type (<class 'float'>, <class 'int'>)." in str(
e)
def test_random_crop_and_resize_op_c(plot=False):
"""
Test RandomCropAndResize op in c transforms
"""
logger.info("test_random_crop_and_resize_op_c")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
decode_op = c_vision.Decode()
# With these inputs we expect the code to crop the whole image
random_crop_and_resize_op = c_vision.RandomResizedCrop((256, 512), (2, 2), (1, 3))
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=random_crop_and_resize_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data2 = data2.map(operations=decode_op, 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"]
original = item2["image"]
# Note: resize the original image with the same size as the one applied RandomResizedCrop()
original = cv2.resize(original, (512, 256))
mse = diff_mse(crop_and_resize, original)
assert mse == 0
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 test_random_crop_and_resize_03():
"""
Test RandomCropAndResize with md5 check: max_attempts is 1, expected to pass
"""
logger.info("test_random_crop_and_resize_03")
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)
decode_op = c_vision.Decode()
random_crop_and_resize_op = c_vision.RandomResizedCrop((256, 512), max_attempts=1)
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=random_crop_and_resize_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.RandomResizedCrop((256, 512), max_attempts=1),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
data2 = data2.map(operations=transform, input_columns=["image"])
filename1 = "random_crop_and_resize_03_c_result.npz"
filename2 = "random_crop_and_resize_03_py_result.npz"
save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN)
save_and_check_md5(data2, filename2, 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_comp(plot=False):
"""
Test RandomCropAndResize and compare between python and c image augmentation
"""
logger.info("test_random_crop_and_resize_comp")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
decode_op = c_vision.Decode()
random_crop_and_resize_op = c_vision.RandomResizedCrop(512, (1, 1), (0.5, 0.5))
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=random_crop_and_resize_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.RandomResizedCrop(512, (1, 1), (0.5, 0.5)),
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)
mse = diff_mse(c_image, py_image)
assert mse < 0.02 # rounding error
if plot:
visualize_list(image_c_cropped, image_py_cropped, visualize_mode=2)
def test_random_crop_decode_resize_op(plot=False):
"""
Test RandomCropDecodeResize op
"""
logger.info("test_random_decode_resize_op")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
decode_op = vision.Decode()
random_crop_decode_resize_op = vision.RandomCropDecodeResize((256, 512), (1, 1), (0.5, 0.5))
data1 = data1.map(operations=random_crop_decode_resize_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
random_crop_resize_op = vision.RandomResizedCrop((256, 512), (1, 1), (0.5, 0.5))
data2 = data2.map(operations=decode_op, input_columns=["image"])
data2 = data2.map(operations=random_crop_resize_op, 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)):
if num_iter > 0:
break
image1 = item1["image"]
image2 = item2["image"]
mse = diff_mse(image1, image2)
assert mse == 0
logger.info("random_crop_decode_resize_op_{}, mse: {}".format(num_iter + 1, mse))
if plot:
visualize_image(image1, image2, mse)
num_iter += 1
def create_dataset(args, shuffle=True, max_dataset_size=float("inf")):
"""Create dataset"""
dataroot = args.dataroot
phase = args.phase
batch_size = args.batch_size
device_num = args.device_num
rank = args.rank
cores = multiprocessing.cpu_count()
num_parallel_workers = min(8, int(cores / device_num))
image_size = args.image_size
mean = [0.5 * 255] * 3
std = [0.5 * 255] * 3
if phase == "train":
dataset = UnalignedDataset(dataroot,
phase,
max_dataset_size=max_dataset_size)
distributed_sampler = DistributedSampler(len(dataset),
device_num,
rank,
shuffle=shuffle)
ds = de.GeneratorDataset(dataset,
column_names=["image_A", "image_B"],
sampler=distributed_sampler,
num_parallel_workers=num_parallel_workers)
trans = [
C.RandomResizedCrop(image_size,
scale=(0.5, 1.0),
ratio=(0.75, 1.333)),
C.RandomHorizontalFlip(prob=0.5),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
ds = ds.map(operations=trans,
input_columns=["image_A"],
num_parallel_workers=num_parallel_workers)
ds = ds.map(operations=trans,
input_columns=["image_B"],
num_parallel_workers=num_parallel_workers)
ds = ds.batch(batch_size, drop_remainder=True)
ds = ds.repeat(1)
else:
datadir = os.path.join(dataroot, args.data_dir)
dataset = ImageFolderDataset(datadir,
max_dataset_size=max_dataset_size)
ds = de.GeneratorDataset(dataset,
column_names=["image", "image_name"],
num_parallel_workers=num_parallel_workers)
trans = [
C.Resize((image_size, image_size)),
C.Normalize(mean=mean, std=std),
C.HWC2CHW()
]
ds = ds.map(operations=trans,
input_columns=["image"],
num_parallel_workers=num_parallel_workers)
ds = ds.batch(1, drop_remainder=True)
ds = ds.repeat(1)
args.dataset_size = len(dataset)
return ds
def test_random_crop_and_resize_callable():
"""
Test RandomCropAndResize op is callable
"""
logger.info("test_random_crop_and_resize_callable")
img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8)
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
decode_op = c_vision.Decode()
img = decode_op(img)
random_crop_and_resize_op = c_vision.RandomResizedCrop((256, 512), (2, 2),
(1, 3))
img = random_crop_and_resize_op(img)
assert np.shape(img) == (256, 512, 3)
def test_random_crop_and_resize_05_c():
"""
Test RandomCropAndResize with c_transforms: invalid range of ratio (max<min),
expected to raise ValueError
"""
logger.info("test_random_crop_and_resize_05_c")
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
decode_op = c_vision.Decode()
try:
random_crop_and_resize_op = c_vision.RandomResizedCrop((256, 512), (1, 1), (1, 0.5))
# If input range of ratio is not in the order of (min, max), ValueError will be raised.
data = data.map(operations=decode_op, input_columns=["image"])
data = data.map(operations=random_crop_and_resize_op, 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 create_dataset(batch_size,
train_data_url='',
workers=8,
distributed=False):
if not os.path.exists(train_data_url):
raise ValueError('Path not exists')
interpolation = str2MsInter(inter_str)
c_decode_op = C.Decode()
type_cast_op = C2.TypeCast(mstype.int32)
random_resize_crop_op = C.RandomResizedCrop(size=(resize_value,
resize_value),
scale=scale,
ratio=ratio,
interpolation=interpolation)
random_horizontal_flip_op = C.RandomHorizontalFlip(0.5)
efficient_rand_augment = RandAugment()
image_ops = [c_decode_op, random_resize_crop_op, random_horizontal_flip_op]
rank_id = get_rank() if distributed else 0
rank_size = get_group_size() if distributed else 1
dataset_train = ds.ImageFolderDataset(train_data_url,
num_parallel_workers=workers,
shuffle=True,
num_shards=rank_size,
shard_id=rank_id)
dataset_train = dataset_train.map(input_columns=["image"],
operations=image_ops,
num_parallel_workers=workers)
dataset_train = dataset_train.map(input_columns=["label"],
operations=type_cast_op,
num_parallel_workers=workers)
ds_train = dataset_train.batch(batch_size,
per_batch_map=efficient_rand_augment,
input_columns=["image", "label"],
num_parallel_workers=2,
drop_remainder=True)
ds_train = ds_train.repeat(1)
return ds_train
def test_random_crop_and_resize_04_c():
"""
Test RandomCropAndResize with c_tranforms: invalid range of scale (max<min),
expected to raise ValueError
"""
logger.info("test_random_crop_and_resize_04_c")
# Generate dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
decode_op = c_vision.Decode()
try:
# If input range of scale is not in the order of (min, max), ValueError will be raised.
random_crop_and_resize_op = c_vision.RandomResizedCrop(
(256, 512), (1, 0.5), (0.5, 0.5))
data = data.map(operations=decode_op, input_columns=["image"])
data = data.map(operations=random_crop_and_resize_op,
input_columns=["image"])
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Input is not within the required interval of (0 to 16777216)." in str(
e)
def create_dataset(args, dataset_mode, repeat_num=1):
"""
create a train or evaluate cifar10 dataset for SimCLR
"""
if args.dataset_name != "cifar10":
raise ValueError("Unsupported dataset.")
if dataset_mode in ("train_endcoder", "train_classifier"):
dataset_path = args.train_dataset_path
else:
dataset_path = args.eval_dataset_path
if args.run_distribute and args.device_target == "Ascend":
data_set = ds.Cifar10Dataset(dataset_path,
num_parallel_workers=8,
shuffle=True,
num_shards=args.device_num,
shard_id=args.device_id)
else:
data_set = ds.Cifar10Dataset(dataset_path,
num_parallel_workers=8,
shuffle=True)
# define map operations
trans = []
if dataset_mode == "train_endcoder":
if args.use_crop:
trans += [C.Resize(256, interpolation=Inter.BICUBIC)]
trans += [
C.RandomResizedCrop(size=(32, 32),
scale=(0.31, 1),
interpolation=Inter.BICUBIC,
max_attempts=100)
]
if args.use_flip:
trans += [C.RandomHorizontalFlip(prob=0.5)]
if args.use_color_jitter:
scale = 0.6
color_jitter = C.RandomColorAdjust(0.8 * scale, 0.8 * scale,
0.8 * scale, 0.2 * scale)
trans += [C2.RandomApply([color_jitter], prob=0.8)]
if args.use_color_gray:
trans += [
py_vision.ToPIL(),
py_vision.RandomGrayscale(prob=0.2), np.array
] # need to convert PIL image to a NumPy array to pass it to C++ operation
if args.use_blur:
trans += [C2.RandomApply([gaussian_blur], prob=0.8)]
if args.use_norm:
trans += [
C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
]
trans += [C2.TypeCast(mstype.float32), C.HWC2CHW()]
else:
trans += [C.Resize(32)]
trans += [C2.TypeCast(mstype.float32)]
if args.use_norm:
trans += [
C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010])
]
trans += [C.HWC2CHW()]
type_cast_op = C2.TypeCast(mstype.int32)
data_set = data_set.map(operations=type_cast_op,
input_columns="label",
num_parallel_workers=8)
data_set = data_set.map(operations=copy_column,
input_columns=["image", "label"],
output_columns=["image1", "image2", "label"],
column_order=["image1", "image2", "label"],
num_parallel_workers=8)
data_set = data_set.map(operations=trans,
input_columns=["image1"],
num_parallel_workers=8)
data_set = data_set.map(operations=trans,
input_columns=["image2"],
num_parallel_workers=8)
# apply batch operations
data_set = data_set.batch(args.batch_size, drop_remainder=True)
# apply dataset repeat operation
data_set = data_set.repeat(repeat_num)
return data_set