def test_ImageScaler(tmpdir):
input_height = 32
input_width = 32
channels = 3
image = np.ones([channels, input_height, input_width]).astype(np.float32)
scalar = 1.5
bias = [10, 20, 30]
model = C.image_scaler(image, scalar, bias)
verify_no_input(model, tmpdir, 'ImageScaler_0')
x = C.input_variable(np.shape(image))
model = C.image_scaler(x, scalar, bias)
verify_one_input(model, image, tmpdir, 'ImageScaler_1')
def test_ImageScaler(tmpdir, dtype):
with C.default_options(dtype = dtype):
input_height = 32
input_width = 32
channels = 3
image = np.ones([channels, input_height, input_width]).astype(dtype)
scalar = 1.5
bias = [10, 20, 30]
model = C.image_scaler(image, scalar, bias);
verify_no_input(model, tmpdir, 'ImageScaler_0')
x = C.input_variable(np.shape(image))
model = C.image_scaler(x, scalar, bias);
verify_one_input(model, image, tmpdir, 'ImageScaler_1')
def test_ImageScaler(tmpdir, dtype):
with C.default_options(dtype = dtype):
input_height = 32
input_width = 32
channels = 3
image = np.ones([channels, input_height, input_width]).astype(dtype)
scalar = 1.5
bias = [10, 20, 30]
model = C.image_scaler(image, scalar, bias);
verify_no_input(model, tmpdir, 'ImageScaler_0')
x = C.input_variable(np.shape(image))
model = C.image_scaler(x, scalar, bias);
verify_one_input(model, image, tmpdir, 'ImageScaler_1')
