def test_backprop_maxpool2d_gradients(self):
pool_layer = blobs.MaxPool2D(pool_size=(2,2),
strides=(1,1),
border_mode=B.BorderMode.valid,
ignore_border=True,
maxpool_deeplift_mode=MaxPoolDeepLiftMode.gradient,
channels_come_last=False)
self.create_small_net_with_pool_layer(pool_layer,
outputs_per_channel=9)
self.dense_layer.update_task_index(task_index=0)
func = B.function([
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]]))
def test_fprop_maxpool2d(self):
pool_layer = blobs.MaxPool2D(pool_size=(2,2),
strides=(1,1),
border_mode=B.BorderMode.valid,
ignore_border=True,
maxpool_deeplift_mode=MaxPoolDeepLiftMode.gradient,
channels_come_last=False)
self.create_small_net_with_pool_layer(pool_layer,
outputs_per_channel=9)
func = B.function([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]]]]))
def maxpool2d_conversion(layer, name, verbose, maxpool_deeplift_mode,
**kwargs):
pool2d_kwargs = prep_pool2d_kwargs(layer=layer, name=name, verbose=verbose)
return [
blobs.MaxPool2D(maxpool_deeplift_mode=maxpool_deeplift_mode,
**pool2d_kwargs)
]
