def test_fprop_avgpool2d(self):
pool_layer = layers.AvgPool2D(pool_size=(2, 2),
strides=(1, 1),
padding=PaddingMode.valid,
data_format="channels_last")
self.create_small_net_with_pool_layer(pool_layer,
outputs_per_channel=9)
func = compile_func([self.input_layer.get_activation_vars()],
self.pool_layer.get_activation_vars())
np.testing.assert_almost_equal(
func([self.reference_inps[0], self.reference_inps[0] - 1]),
0.25 * np.array([[[[1, 3, 5], [6, 10, 4], [11, 16, 19]],
[[5, 7, 9], [10, 14, 8], [15, 20, 23]]],
[[[-3, -1, 1], [2, 6, 0], [7, 12, 15]],
[[1, 3, 5], [6, 10, 4], [11, 16, 19]]]
]).transpose(0, 2, 3, 1))
def test_backprop_avgpool2d(self):
pool_layer = layers.AvgPool2D(pool_size=(2, 2),
strides=(1, 1),
padding=PaddingMode.valid,
data_format="channels_last")
self.create_small_net_with_pool_layer(pool_layer,
outputs_per_channel=9)
self.dense_layer.update_task_index(task_index=0)
func = compile_func([
self.input_layer.get_activation_vars(),
self.input_layer.get_reference_vars()
], self.input_layer.get_mxts())
avg_pool_grads = np.array([[1, 2, 2, 1], [2, 4, 4, 2], [2, 4, 4, 2],
[1, 2, 2, 1]]).astype("float32")
np.testing.assert_almost_equal(
func([
self.backprop_test_inps,
np.ones_like(self.backprop_test_inps) * self.reference_inps
]),
np.array([[avg_pool_grads * 2 * 0.25, avg_pool_grads * 3 * 0.25],
[avg_pool_grads * 2 * 0.25,
avg_pool_grads * 3 * 0.25]]).transpose(0, 2, 3, 1))
def avgpool2d_conversion(config, name, verbose, **kwargs):
pool2d_kwargs = prep_pool2d_kwargs(config=config,
name=name,
verbose=verbose)
return [layers.AvgPool2D(**pool2d_kwargs)]