def test_fprop_avgpool(self):
pool_layer = layers.AvgPool1D(pool_length=2,
stride=1,
padding=PaddingMode.valid)
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(
[[
[0.5,2.5,3.5],
[2.5,1.5,0.5]],
[[-0.5,-1.5,-2.5],
[-2.5,-1.5,-0.5]
]]).transpose(0,2,1))
def test_backprop_avgpool(self):
pool_layer = layers.AvgPool1D(pool_length=2, stride=1,
padding=PaddingMode.valid)
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())
avg_pool_grads = np.array([1, 2, 2, 1]).astype("float32")*0.5
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,
avg_pool_grads*3],
[avg_pool_grads*2,
avg_pool_grads*3]]).transpose(0,2,1))
def avgpool1d_conversion(config, name, verbose, **kwargs):
pool1d_kwargs = prep_pool1d_kwargs(config=config,
name=name,
verbose=verbose)
return [layers.AvgPool1D(**pool1d_kwargs)]
