def test_backprop_maxpool2d_gradients(self):
pool_layer = layers.MaxPool2D(
pool_size=(2, 2),
strides=(1, 1),
padding=PaddingMode.valid,
maxpool_deeplift_mode=MaxPoolDeepLiftMode.gradient,
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())
np.testing.assert_almost_equal(
func([
self.backprop_test_inps,
np.ones_like(self.backprop_test_inps) * self.reference_inps
]),
np.array([[
np.array([[1, 0, 0, 0], [0, 0, 2, 0], [2, 1, 1, 0],
[0, 0, 1, 1]]) * 2,
np.array([[0, 0, 1, 1], [0, 1, 0, 0], [0, 2, 1, 0],
[1, 0, 1, 1]]) * 3
],
[
np.array([[0, 0, 1, 1], [0, 1, 0, 0], [0, 2, 1, 0],
[1, 0, 1, 1]]) * 2,
np.array([[1, 0, 0, 0], [0, 0, 2, 0], [2, 1, 1, 0],
[0, 0, 1, 1]]) * 3
]]).transpose(0, 2, 3, 1))
def maxpool2d_conversion(config, name, verbose, maxpool_deeplift_mode,
**kwargs):
pool2d_kwargs = prep_pool2d_kwargs(config=config,
name=name,
verbose=verbose)
return [
layers.MaxPool2D(maxpool_deeplift_mode=maxpool_deeplift_mode,
**pool2d_kwargs)
]
def test_fprop_maxpool2d(self):
pool_layer = layers.MaxPool2D(
pool_size=(2, 2),
strides=(1, 1),
padding=PaddingMode.valid,
maxpool_deeplift_mode=MaxPoolDeepLiftMode.gradient,
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]),
np.array([[[[1, 2, 3], [5, 5, 4], [6, 7, 8]],
[[2, 3, 4], [6, 6, 5], [7, 8, 9]]],
[[[0, 1, 2], [4, 4, 3], [5, 6, 7]],
[[1, 2, 3], [5, 5, 4], [6, 7,
8]]]]).transpose(0, 2, 3, 1))
