def backward_impl(self, inputs, outputs, prop_down, accum):
# inputs: [inputs_fwd_graph] + [inputs_bwd_graph] or
# [inputs_fwd_graph] + [outputs_fwd_graph] + [inputs_bwd_graph]
# Args
kernel = self.forward_func.info.args["kernel"]
stride = self.forward_func.info.args["stride"]
ignore_border = self.forward_func.info.args["ignore_border"]
pad = self.forward_func.info.args["pad"]
channel_last = self.forward_func.info.args["channel_last"]
# TODO: BHWC
assert channel_last == False, "`channel_last = False` is only supported now."
# Inputs
x0 = inputs[0].data
dy = inputs[1].data
# Outputs
dx0 = outputs[0].data
# Grads of inputs
g_x0 = inputs[0].grad
g_dy = inputs[1].grad
# Grads of outputs
g_dx0 = outputs[0].grad
# Computation
if prop_down[1]:
g_dy_ = F.sum_pooling(g_dx0, kernel, stride, ignore_border, pad,
channel_last)
if accum[1]:
g_dy += g_dy_
else:
g_dy.copy_from(g_dy_)
def sum_pooling_data_grad_backward(inputs,
kernel,
stride=None,
ignore_border=True,
pad=None,
channel_last=False):
"""
Args:
inputs (list of nn.Variable): Incomming grads/inputs to/of the forward function.
kwargs (dict of arguments): Dictionary of the corresponding function arguments.
Return:
list of Variable: Return the gradients wrt inputs of the corresponding function.
"""
gdx = inputs[0]
gdy = F.sum_pooling(gdx, kernel, stride, ignore_border, pad, channel_last)
return gdy