def test_fprop_avgpool2d(self):
pool_layer = blobs.AvgPool2D(pool_size=(2,2),
strides=(1,1),
border_mode=B.BorderMode.valid,
ignore_border=True,
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]),
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]]]]))
def test_backprop_avgpool2d(self):
pool_layer = blobs.AvgPool2D(pool_size=(2,2),
strides=(1,1),
border_mode=B.BorderMode.valid,
ignore_border=True,
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())
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]]))
def avgpool2d_conversion(layer, name, verbose, **kwargs):
pool2d_kwargs = prep_pool2d_kwargs(layer=layer, name=name, verbose=verbose)
return [blobs.AvgPool2D(**pool2d_kwargs)]