def test_depth_to_space(self, batch_size, scale, channels, rows, cols):
if K.image_data_format() == 'channels_first':
arr = np.arange(batch_size * channels * scale * scale * rows * cols)\
.reshape((batch_size, channels * scale * scale, rows, cols))
elif K.image_data_format() == 'channels_last':
arr = np.arange(batch_size * rows * cols * scale * scale * channels) \
.reshape((batch_size, rows, cols, channels * scale * scale))
arr_tf = KTF.variable(arr)
arr_th = KTH.variable(arr)
if K.image_data_format() == 'channels_first':
expected = arr.reshape((batch_size, scale, scale, channels, rows, cols))\
.transpose((0, 3, 4, 1, 5, 2))\
.reshape((batch_size, channels, rows * scale, cols * scale))
elif K.image_data_format() == 'channels_last':
expected = arr.reshape((batch_size, rows, cols, scale, scale, channels))\
.transpose((0, 1, 3, 2, 4, 5))\
.reshape((batch_size, rows * scale, cols * scale, channels))
tf_ans = KTF.eval(KCTF.depth_to_space(arr_tf, scale))
th_ans = KTH.eval(KCTH.depth_to_space(arr_th, scale))
assert tf_ans.shape == expected.shape
assert th_ans.shape == expected.shape
assert_allclose(expected, tf_ans, atol=1e-05)
assert_allclose(expected, th_ans, atol=1e-05)
def check_single_tensor_operation(function_name, input_shape, **kwargs):
val = np.random.random(input_shape) - 0.5
xth = KTH.variable(val)
xtf = KTF.variable(val)
zth = KTH.eval(getattr(KCTH, function_name)(xth, **kwargs))
ztf = KTF.eval(getattr(KCTF, function_name)(xtf, **kwargs))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
def check_two_tensor_operation(function_name, x_input_shape, y_input_shape,
**kwargs):
xval = np.random.random(x_input_shape) - 0.5
xth = KTH.variable(xval)
xtf = KTF.variable(xval)
yval = np.random.random(y_input_shape) - 0.5
yth = KTH.variable(yval)
ytf = KTF.variable(yval)
zth = KTH.eval(getattr(KCTH, function_name)(xth, yth, **kwargs))
ztf = KTF.eval(getattr(KCTF, function_name)(xtf, ytf, **kwargs))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)
def test_extract(self, input_shape, kernel_shape):
xval = np.random.random(input_shape)
kernel = [kernel_shape, kernel_shape]
strides = [kernel_shape, kernel_shape]
xth = KTH.variable(xval)
xtf = KTF.variable(xval)
ztf = KTF.eval(
KCTF.extract_image_patches(xtf,
kernel,
strides,
data_format='channels_first',
padding='valid'))
zth = KTH.eval(
KCTH.extract_image_patches(xth,
kernel,
strides,
data_format='channels_first',
padding='valid'))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-02)
def check_composed_tensor_operations(first_function_name, first_function_args,
second_function_name,
second_function_args, input_shape):
''' Creates a random tensor t0 with shape input_shape and compute
t1 = first_function_name(t0, **first_function_args)
t2 = second_function_name(t1, **second_function_args)
with both Theano and TensorFlow backends and ensures the answers match.
'''
val = np.random.random(input_shape) - 0.5
xth = KTH.variable(val)
xtf = KTF.variable(val)
yth = getattr(KCTH, first_function_name)(xth, **first_function_args)
ytf = getattr(KCTF, first_function_name)(xtf, **first_function_args)
zth = KTH.eval(
getattr(KCTH, second_function_name)(yth, **second_function_args))
ztf = KTF.eval(
getattr(KCTF, second_function_name)(ytf, **second_function_args))
assert zth.shape == ztf.shape
assert_allclose(zth, ztf, atol=1e-05)