def test_group_convolution_backprop_data_output_shape():
runtime = get_runtime()
data_shape = [1, 1, 1, 10]
filters_shape = [1, 1, 1, 1, 5]
strides = [1, 1]
data_node = ng.parameter(data_shape, name='Data', dtype=np.float32)
filters_node = ng.parameter(filters_shape,
name='Filters',
dtype=np.float32)
output_shape_node = ng.constant(np.array([1, 14], dtype=np.int64))
model = ng.group_convolution_backprop_data(data_node,
filters_node,
strides,
output_shape_node,
auto_pad='same_upper')
data_value = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0],
dtype=np.float32).reshape(data_shape)
filters_value = np.array([1.0, 2.0, 3.0, 2.0, 1.0],
dtype=np.float32).reshape(filters_shape)
computation = runtime.computation(model, data_node, filters_node)
result = computation(data_value, filters_value)
expected = np.array([
0.0, 1.0, 4.0, 10.0, 18.0, 27.0, 36.0, 45.0, 54.0, 63.0, 62.0, 50.0,
26.0, 9.0
],
dtype=np.float32).reshape(1, 1, 1, 14)
assert np.allclose(result, expected)
def test_group_convolution_backprop_data():
runtime = get_runtime()
data_shape = [1, 1, 3, 3]
filters_shape = [1, 1, 1, 3, 3]
strides = [2, 2]
output_padding = [1, 1]
pads_begin = [1, 1]
pads_end = [1, 1]
data_node = ng.parameter(data_shape, name='Data', dtype=np.float32)
filters_node = ng.parameter(filters_shape,
name='Filters',
dtype=np.float32)
model = ng.group_convolution_backprop_data(data_node,
filters_node,
strides,
None,
pads_begin,
pads_end,
output_padding=output_padding)
data_value = np.array([
0.16857791, -0.15161794, 0.08540368, 0.1820628, -0.21746576,
0.08245695, 0.1431433, -0.43156421, 0.30591947
],
dtype=np.float32).reshape(data_shape)
filters_value = np.array([
-0.06230065, 0.37932432, -0.25388849, 0.33878803, 0.43709868,
-0.22477469, 0.04118127, -0.44696793, 0.06373066
],
dtype=np.float32).reshape(filters_shape)
computation = runtime.computation(model, data_node, filters_node)
result = computation(data_value, filters_value)
expected = np.array([
0.07368518, -0.08925839, -0.06627201, 0.06301362, 0.03732984,
-0.01919658, -0.00628807, -0.02817563, -0.01472169, 0.04392925,
-0.00689478, -0.01549204, 0.07957941, -0.11459791, -0.09505399,
0.07681622, 0.03604182, -0.01853423, -0.0270785, -0.00680824,
-0.06650258, 0.08004665, 0.07918708, 0.0724144, 0.06256775,
-0.17838378, -0.18863615, 0.20064656, 0.133717, -0.06876295,
-0.06398046, -0.00864975, 0.19289537, -0.01490572, -0.13673618,
0.01949645
],
dtype=np.float32).reshape(1, 1, 6, 6)
assert np.allclose(result, expected)
