def test_cut_out_op_multicut():
"""
Test Cutout
"""
logger.info("test_cut_out")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
transforms_1 = [f.Decode(), f.ToTensor(), f.RandomErasing(value='random')]
transform_1 = f.ComposeOp(transforms_1)
data1 = data1.map(input_columns=["image"], operations=transform_1())
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"])
decode_op = c.Decode()
cut_out_op = c.CutOut(80, num_patches=10)
transforms_2 = [decode_op, cut_out_op]
data2 = data2.map(input_columns=["image"], operations=transforms_2)
num_iter = 0
for item1, item2 in zip(data1.create_dict_iterator(),
data2.create_dict_iterator()):
num_iter += 1
image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
# C image doesn't require transpose
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))
def test_random_erasing_md5():
"""
Test RandomErasing with md5 check
"""
logger.info("Test RandomErasing with md5 check")
original_seed = config_get_set_seed(5)
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)
transforms_1 = [
vision.Decode(),
vision.ToTensor(),
vision.RandomErasing(value='random')
]
transform_1 = vision.ComposeOp(transforms_1)
data = data.map(input_columns=["image"], operations=transform_1())
# Compare with expected md5 from images
filename = "random_erasing_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 create_imagenet_dataset(imagenet_dir):
ds = de.ImageFolderDatasetV2(imagenet_dir)
transform = F.ComposeOp([
F.Decode(),
F.RandomHorizontalFlip(0.5),
F.ToTensor(),
F.Normalize((0.491, 0.482, 0.447), (0.247, 0.243, 0.262)),
F.RandomErasing()
])
ds = ds.map(input_columns="image", operations=transform())
ds = ds.shuffle(buffer_size=5)
ds = ds.repeat(3)
return ds
def test_random_erasing_op(plot=False):
"""
Test RandomErasing and Cutout
"""
logger.info("test_random_erasing")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms_1 = [
vision.Decode(),
vision.ToTensor(),
vision.RandomErasing(value='random')
]
transform_1 = vision.ComposeOp(transforms_1)
data1 = data1.map(input_columns=["image"], operations=transform_1())
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms_2 = [vision.Decode(), vision.ToTensor(), vision.Cutout(80)]
transform_2 = vision.ComposeOp(transforms_2)
data2 = data2.map(input_columns=["image"], operations=transform_2())
num_iter = 0
for item1, item2 in zip(data1.create_dict_iterator(),
data2.create_dict_iterator()):
num_iter += 1
image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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))
mse = diff_mse(image_1, image_2)
if plot:
visualize_image(image_1, image_2, mse)