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_cut_out_md5():
"""
Test Cutout with md5 check
"""
logger.info("test_cut_out_md5")
original_seed = config_get_set_seed(2)
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.Decode()
cut_out_op = c.CutOut(100)
data1 = data1.map(input_columns=["image"], operations=decode_op)
data1 = data1.map(input_columns=["image"], operations=cut_out_op)
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms = [f.Decode(), f.ToTensor(), f.Cutout(100)]
transform = f.ComposeOp(transforms)
data2 = data2.map(input_columns=["image"], operations=transform())
# Compare with expected md5 from images
filename1 = "cut_out_01_c_result.npz"
save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN)
filename2 = "cut_out_01_py_result.npz"
save_and_check_md5(data2, filename2, generate_golden=GENERATE_GOLDEN)
# Restore config
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_cut_out_comp(plot=False):
"""
Test Cutout with c++ and python op comparison
"""
logger.info("test_cut_out_comp")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms_1 = [f.Decode(), f.ToTensor(), f.Cutout(200)]
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"],
shuffle=False)
transforms_2 = [c.Decode(), c.CutOut(200)]
data2 = data2.map(input_columns=["image"], operations=transforms_2)
num_iter = 0
image_list_1, image_list_2 = [], []
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"]
image_list_1.append(image_1)
image_list_2.append(image_2)
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(image_list_1, image_list_2, visualize_mode=2)
