def test_five_crop_op(plot=False):
"""
Test FiveCrop
"""
logger.info("test_five_crop")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms_1 = [
vision.Decode(),
vision.ToTensor(),
]
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.FiveCrop(200),
lambda images: np.stack([vision.ToTensor()(image)
for image in images]) # 4D stack of 5 images
]
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"]
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(np.array([image_1] * 5),
(image_2 * 255).astype(np.uint8).transpose(
0, 2, 3, 1))
# The output data should be of a 4D tensor shape, a stack of 5 images.
assert len(image_2.shape) == 4
assert image_2.shape[0] == 5
def test_five_crop_error_msg():
"""
Test FiveCrop error message.
"""
logger.info("test_five_crop_error_msg")
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms = [vision.Decode(), vision.FiveCrop(200), vision.ToTensor()]
transform = vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
with pytest.raises(RuntimeError):
data.create_tuple_iterator().__next__()
def test_five_crop_error_msg():
"""
Test FiveCrop error message.
"""
logger.info("test_five_crop_error_msg")
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms = [vision.Decode(), vision.FiveCrop(200), vision.ToTensor()]
transform = vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
with pytest.raises(RuntimeError) as info:
data.create_tuple_iterator().get_next()
error_msg = "TypeError: img should be PIL Image or Numpy array. Got <class 'tuple'>"
# error msg comes from ToTensor()
assert error_msg in str(info.value)
def test_five_crop_md5():
"""
Test FiveCrop with md5 check
"""
logger.info("test_five_crop_md5")
# First dataset
data = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms = [
vision.Decode(),
vision.FiveCrop(100),
lambda images: np.stack([vision.ToTensor()(image)
for image in images]) # 4D stack of 5 images
]
transform = vision.ComposeOp(transforms)
data = data.map(input_columns=["image"], operations=transform())
# Compare with expected md5 from images
filename = "five_crop_01_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
