def maxpool1d_conversion(config, name, verbose, maxpool_deeplift_mode,
**kwargs):
pool1d_kwargs = prep_pool1d_kwargs(config=config,
name=name,
verbose=verbose)
return [
layers.MaxPool1D(maxpool_deeplift_mode=maxpool_deeplift_mode,
**pool1d_kwargs)
]
def test_fprop_maxpool1d(self):
pool_layer = layers.MaxPool1D(pool_length=2,
stride=1,
padding=PaddingMode.valid,
maxpool_deeplift_mode=MaxPoolDeepLiftMode.gradient)
self.create_small_net_with_pool_layer(pool_layer,
outputs_per_channel=3)
func = compile_func([self.input_layer.get_activation_vars()],
self.pool_layer.get_activation_vars())
np.testing.assert_almost_equal(func(self.backprop_test_inps),
np.array(
[[
[1,4,4],
[3,2,1]],
[[ 0,-1,-2],
[-2,-1, 0]
]]).transpose(0,2,1))
def test_backprop_maxpool_gradients(self):
pool_layer = layers.MaxPool1D(pool_length=2,
stride=1,
padding=PaddingMode.valid,
maxpool_deeplift_mode=MaxPoolDeepLiftMode.gradient)
self.create_small_net_with_pool_layer(pool_layer,
outputs_per_channel=3)
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([0, 1, 2, 0])*2,
np.array([1, 1, 1, 0])*3],
[np.array([1, 1, 1, 0])*2,
np.array([0, 1, 1, 1])*3]]).transpose(0,2,1))