def active_rebuild(self, conv: L.Convolution2D):
mask = conv.W.array.sum(axis=(1, 2, 3)) != 0
self.logger.debug(log_shape(conv.W.array, mask))
conv.W.array = conv.W.array[mask].copy()
if conv.b is not None:
self.logger.debug(log_shape(conv.b.array, mask))
conv.b.array = conv.b.array[mask].copy()
return mask
def passive_rebuild(self, linear, mask):
# conv-linearだと影響を受ける
self.logger.debug(log_shape(linear.weight.data, mask))
input_shape = self.node.input_shape[0]
if len(input_shape) == 4 and input_shape[1] == len(mask):
# prev node is conv: conv-fc
n_out, n_in = linear.weight.shape
w = linear.weight.data.clone().reshape(n_out, *input_shape[1:])
w = w[:, mask, :, :]
w = w.reshape(n_out, -1)
else:
# conv-gap-fc, conv-view(flatten)-fc
n_out, n_in = linear.weight.shape
pixels_per_channel = input_shape[1] // len(mask)
assert mask.dim() == 1
# TODO(tkat0) refactor
# convert channel mask to pixel-level mask
flatten_mask = torch.zeros((n_in, ),
dtype=torch.uint8,
device=mask.device,
requires_grad=False)
for i, m in enumerate(mask):
m = int(m)
if m == 1:
begin, end = i * pixels_per_channel, (
i + 1) * pixels_per_channel
flatten_mask[begin:end] = m
w = linear.weight.data[:, flatten_mask].clone()
linear.weight.data = w
def passive_rebuild(self, conv, mask):
# 出力チャネルに影響は無いので、biasは変化なし
if conv.groups > 1:
# Depthwise Convolution
conv.weight.data = conv.weight.data[mask, :, :, :].clone()
self.logger.debug(log_shape(conv.weight.data, mask))
in_channels = int(conv.weight.shape[0])
conv.groups = in_channels
out_channels = conv.groups * int(conv.weight.shape[1])
conv.in_channels = in_channels
conv.out_channels = out_channels
else:
conv.weight.data = conv.weight.data[:, mask, :, :].clone(
) # oc, ic, kh, kwのうちicをrebuild
self.logger.debug(log_shape(conv.weight.data, mask))
in_channels = int(conv.weight.shape[1])
conv.in_channels = in_channels
def active_rebuild(self, conv: nn.Conv2d):
assert conv.groups == 1, 'Group Convolution is not supported.'
# mask_model is not None => conv-bnのbnを用いてconvをpruningする例外
# ocに関する全kernelの総和が0のチャネルは除外し、それ以外を残す
mask = conv.weight.data.sum(dim=(1, 2, 3)) != 0
self.logger.debug(log_shape(conv.weight.data, mask))
conv.weight.data = conv.weight.data[mask].clone()
if conv.bias is not None:
self.logger.debug(log_shape(conv.bias.data, mask))
conv.bias.data = conv.bias.data[mask].clone()
out_channels = int(conv.weight.shape[0])
conv.out_channels = out_channels
return mask
def _rebuild(self, bn, mask):
self.logger.debug(log_shape(bn.gamma.array, mask))
bn.gamma.array = bn.gamma.array[mask].copy()
w = bn.beta
if w is not None:
bn.beta.array = w.array[mask].copy()
bn.avg_var = bn.avg_var[mask].copy()
bn.avg_mean = bn.avg_mean[mask].copy()
return mask
def _rebuild(self, bn, mask):
self.logger.debug(log_shape(bn.weight.data, mask))
bn.weight.data = bn.weight.data[mask].clone()
w = bn.bias
if w is not None:
bn.bias.data = w.data[mask].clone()
bn.running_mean = bn.running_mean[mask].clone()
bn.running_var = bn.running_var[mask].clone()
bn.num_features = len(bn.weight)
return mask
def passive_rebuild(self, linear, mask):
self.logger.debug(log_shape(linear.W.array, mask))
input_shape = self.node.input_shape[0]
if len(input_shape) == 4 and input_shape[1] == len(mask):
# conv-fc
n_out, n_in = linear.W.shape
w = linear.W.array.copy().reshape(n_out, *input_shape[1:])
w = w[:, mask, :, :]
w = w.reshape(n_out, -1)
else:
# conv-gap-fc
w = linear.W.array[:, mask].copy()
linear.W.array = w
def passive_rebuild(self, conv: L.DepthwiseConvolution2D, mask):
self.logger.debug(log_shape(conv.W.array, mask))
conv.W.array = conv.W.array[:, mask].copy()
if conv.b is not None:
conv.b.array = conv.b.array[mask].copy()
def passive_rebuild(self, conv, mask):
# 出力チャネルに影響は無いので、biasは変化なし
conv.W.array = conv.W.array[:, mask, :, :].copy(
) # oc, ic, kh, kwのうちicをrebuild
self.logger.debug(log_shape(conv.W.array, mask))
