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 = ov.parameter(data_shape, name="Data", dtype=np.float32)
filters_node = ov.parameter(filters_shape, name="Filters", dtype=np.float32)
output_shape_node = ov.constant(np.array([1, 14], dtype=np.int64))
model = ov.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 = ov.parameter(data_shape, name="Data", dtype=np.float32)
filters_node = ov.parameter(filters_shape,
name="Filters",
dtype=np.float32)
model = ov.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)