def test_crop():
# Small network.
node_input = C.input_variable((1, 5, 5))
node_referent = C.input_variable((1, 5, 5))
node_output = C.layers.Sequential([
C.layers.Convolution2D(filter_shape=(3, 3),
num_filters=1,
init=1,
strides=(2, 2),
pad=True,
bias=False),
C.layers.MaxPooling(filter_shape=(3, 3), strides=(2, 2), pad=True),
C.layers.ConvolutionTranspose(filter_shape=(4, 4),
num_filters=1,
strides=(4, 4),
init=1,
bias=False)
])(node_input)
# Input data.
input_map = {
node_input: -np.arange(25).reshape(1, 1, 5, 5).astype(np.float32),
node_referent: np.zeros([1, 1, 5, 5]).astype(np.float32)
}
# Expected cropped output.
expected = [-12, -12, -12, -24, -24] * 3 + [-63, -63, -63, -81, -81] * 2
expected = np.asarray(expected, dtype=np.float32).reshape(1, 1, 5, 5)
# Test crop with explicitly specified offsets.
cropped = C.crop_manual(node_output, node_referent, 1, 1).eval(input_map)
assert np.array_equal(cropped, expected)
# Test crop with automatically computed offsets where inputs
# have common ancestor.
cropped = C.crop_automatic(node_output, node_input).eval(input_map)
assert np.array_equal(cropped, expected)
# Test crop with automatically computed offsets where inputs do not
# have common ancestor.
cropped = C.crop_automatic_with_ancestors(node_output, node_referent,
node_input,
node_referent).eval(input_map)
assert np.array_equal(cropped, expected)
def test_crop():
# Small network.
node_input = C.input_variable((1, 5, 5))
node_referent = C.input_variable((1, 5, 5))
node_output = C.layers.Sequential([
C.layers.Convolution2D(filter_shape = (3, 3),
num_filters = 1,
init = 1,
strides = (2, 2),
pad = True,
bias = False),
C.layers.MaxPooling(filter_shape = (3, 3),
strides = (2, 2),
pad = True),
C.layers.ConvolutionTranspose(filter_shape = (4, 4),
num_filters = 1,
strides = (4, 4),
init = 1,
bias = False)])(node_input)
# Input data.
input_map = {
node_input: -np.arange(25).reshape(1, 1, 5, 5).astype(np.float32),
node_referent: np.zeros([1, 1, 5, 5]).astype(np.float32)
}
# Expected cropped output.
expected = [-12, -12, -12, -24, -24] * 3 + [-63, -63, -63, -81, -81] * 2
expected = np.asarray(expected, dtype = np.float32).reshape(1, 1, 5, 5)
# Test crop with explicitly specified offsets.
cropped = C.crop_manual(node_output, node_referent, 1, 1).eval(input_map)
assert np.array_equal(cropped, expected)
# Test crop with automatically computed offsets where inputs
# have common ancestor.
cropped = C.crop_automatic(node_output, node_input).eval(input_map)
assert np.array_equal(cropped, expected)
# Test crop with automatically computed offsets where inputs do not
# have common ancestor.
cropped = C.crop_automatic_with_ancestors(
node_output, node_referent, node_input, node_referent).eval(input_map)
assert np.array_equal(cropped, expected)