def prune_bn2d(module: BatchNorm2d, keep_idxes):
module.num_features = len(keep_idxes)
module.weight.data = module.weight.data[keep_idxes]
module.weight.grad = None
module.bias.data = module.bias.data[keep_idxes]
module.bias.grad = None
module.running_mean = module.running_mean[keep_idxes]
module.running_var = module.running_var[keep_idxes]
def prune_conv_layer(
conv_layer: Union[nn.Conv2d, nn.Linear],
bn_layer: nn.BatchNorm2d,
sparse_layer: Union[nn.BatchNorm2d, SparseGate],
in_channel_mask: np.ndarray,
prune_output_mode: str,
pruner: Callable[[np.ndarray], float],
prune_mode: str,
sparse_layer_in: typing.Optional[SparseGate] = None,
prune_on="factor") -> typing.Tuple[np.ndarray, np.ndarray]:
"""
Note: if the sparse_layer is SparseGate, the gate will be replaced by BatchNorm
scaling factor. The value of the gate will be set to all ones.
:param prune_output_mode: how to handle the output channel (case insensitive)
"keep": keep the output channel intact
"same": keep the output channel as same as input channel
"prune": prune the output according to bn_layer
:param pruner: the method to determinate the pruning threshold.
:param in_channel_mask: a 0-1 vector indicates whether the corresponding channel should be pruned (0) or not (1)
:param bn_layer: the BatchNorm layer after the convolution layer
:param conv_layer: the convolution layer to be pruned
:param sparse_layer: the layer to determine the sparsity. Support BatchNorm2d and SparseGate.
:param prune_mode: pruning mode (`str`), case-insensitive:
- `"multiply"`: pruning threshold is determined by the multiplication of `sparse_layer` and `bn_layer`
only available when `sparse_layer` is `SparseGate`
- `None` or `"default"`: default behaviour. The pruning threshold is determined by `sparse_layer`
:param prune_on: 'factor' or 'weight'.
:param sparse_layer_in: the layer to determine the input sparsity. Support BatchNorm2d and SparseGate.
When the `sparse_layer_in` is None, there is no input sparse layers,
the input channel will be determined by the `in_channel_mask`
Note: `in_channel_mask` is CONFLICT with `sparse_layer_in`!
:return out_channel_mask
"""
assert isinstance(conv_layer, nn.Conv2d) or isinstance(
conv_layer, nn.Linear), f"conv_layer got {conv_layer}"
assert isinstance(sparse_layer, nn.BatchNorm2d) or \
isinstance(sparse_layer, nn.BatchNorm1d) or isinstance(sparse_layer, SparseGate), \
f"sparse_layer got {sparse_layer}"
if in_channel_mask is not None and sparse_layer_in is not None:
raise ValueError(
"Conflict option: in_channel_mask and sparse_layer_in")
prune_mode = prune_mode.lower() #lower() 方法转换字符串中所有大写字符为小写
prune_output_mode = str.lower(
prune_output_mode) #lower() 方法转换字符串中所有大写字符为小写
if prune_mode == 'multiply':
if bn_layer is None:
raise ValueError("Could not use multiply mode when bn is None")
if not isinstance(sparse_layer, SparseGate):
raise ValueError(
f"Do not support prune_mode {prune_mode} when the sparse_layer is {sparse_layer}"
)
with torch.no_grad():
conv_weight: torch.Tensor = conv_layer.weight.data.clone()
# prune the input channel of the conv layer
# if sparse_layer_in and in_channel_mask are both None, the input dim will NOT be pruned
if sparse_layer_in is not None:
if in_channel_mask is not None:
raise ValueError("")
sparse_weight_in: np.ndarray = sparse_layer_in.weight.view(
-1).data.cpu().numpy()
# the in_channel_mask will be overwrote
input_threshold = pruner(sparse_weight_in)
in_channel_mask: np.ndarray = sparse_weight_in > input_threshold #获得掩码
# convert mask to channel indexes
idx_in = np.squeeze(np.argwhere(np.asarray(
in_channel_mask))) #np.argwhere(a):返回非0的数组元组的索引，其中a是要索引数组的条件。
if len(idx_in.shape) == 0:
# expand the single scalar to array
idx_in = np.expand_dims(idx_in, 0)
# prune the input of the conv layer
if isinstance(conv_layer, nn.Conv2d):
if conv_layer.groups == 1:
a = conv_weight
conv_weight = conv_weight[:, idx_in.tolist(
), :, :] #tolist():将数组或者矩阵转换成列表
print(a - conv_weight)
else:
assert conv_weight.shape[
1] == 1, "only works for groups == num_channels"
elif isinstance(conv_layer, nn.Linear):
conv_weight = conv_weight[:, idx_in.tolist()]
else:
raise ValueError(f"unsupported conv layer type: {conv_layer}")
# prune the output channel of the conv layer
if prune_output_mode == "prune":
if prune_on == 'factor':
# the sparse_layer.weight need to be flatten, because the weight of SparseGate is not 1d
sparse_weight: np.ndarray = sparse_layer.weight.view(
-1).data.cpu().numpy()
if prune_mode == 'multiply':
bn_weight = bn_layer.weight.data.cpu().numpy()
sparse_weight = sparse_weight * bn_weight # element-wise multiplication
pass
elif prune_mode != 'default':
raise ValueError(f"Do not support prune_mode {prune_mode}")
# prune according the bn layer
output_threshold = pruner(sparse_weight)
out_channel_mask: np.ndarray = sparse_weight > output_threshold #获取掩码
else:
sparse_weight: np.ndarray = sparse_layer.weight.view(
-1).data.cpu().numpy()
# in this case, the sparse weight should be the conv or linear weight
out_channel_mask: np.ndarray = pruner(
conv_weight.data.cpu().numpy())
elif prune_output_mode == "keep":
# do not prune the output
out_channel_mask = np.ones(conv_layer.out_channels)
elif prune_output_mode == "same":
# prune the output channel with the input mask
# keep the conv layer in_channel == out_channel
out_channel_mask = in_channel_mask
else:
raise ValueError(f"invalid prune_output_mode: {prune_output_mode}")
if not np.any(out_channel_mask): #NumPy any() 判断矩阵中 是否 有一个元素 为True
# there is no channel left
return out_channel_mask, in_channel_mask
idx_out: np.ndarray = np.squeeze(
np.argwhere(np.asarray(out_channel_mask)))
if len(idx_out.shape) == 0:
# 0-d scalar
idx_out = np.expand_dims(idx_out, 0)
if isinstance(conv_layer, nn.Conv2d):
conv_weight = conv_weight[idx_out.tolist(), :, :, :] #获得输出权值
elif isinstance(conv_layer, nn.Linear):
conv_weight = conv_weight[idx_out.tolist(), :]
linear_bias = conv_layer.bias.clone()
linear_bias = linear_bias[idx_out.tolist()]
else:
raise ValueError(f"unsupported conv layer type: {conv_layer}")
# change the property of the conv layer
if isinstance(conv_layer, nn.Conv2d):
conv_layer.in_channels = len(idx_in)
conv_layer.out_channels = len(idx_out)
elif isinstance(conv_layer, nn.Linear):
conv_layer.in_features = len(idx_in)
conv_layer.out_features = len(idx_out)
conv_layer.weight.data = conv_weight
if isinstance(conv_layer, nn.Linear):
conv_layer.bias.data = linear_bias
if isinstance(conv_layer, nn.Conv2d) and conv_layer.groups != 1:
# set the new groups for dw layer (for MobileNet)
conv_layer.groups = conv_layer.in_channels
pass
# prune the bn layer
if bn_layer is not None:
bn_layer.weight.data = bn_layer.weight.data[
idx_out.tolist()].clone()
bn_layer.bias.data = bn_layer.bias.data[
idx_out.tolist()].clone() ##########
bn_layer.running_mean = bn_layer.running_mean[
idx_out.tolist()].clone()
bn_layer.running_var = bn_layer.running_var[
idx_out.tolist()].clone()
# set bn properties
bn_layer.num_features = len(idx_out)
# prune the gate
if isinstance(sparse_layer, SparseGate):
sparse_layer.prune(idx_out)
# multiply the bn weight and SparseGate weight
sparse_weight: torch.Tensor = sparse_layer.weight.view(-1)
if bn_layer is not None:
bn_layer.weight.data = (bn_layer.weight.data *
sparse_weight).clone()
bn_layer.bias.data = (bn_layer.bias.data *
sparse_weight).clone()
# the function of the SparseGate is now replaced by bn layers
# the SparseGate should be disabled
sparse_layer.set_ones()
return out_channel_mask, in_channel_mask
def prune_conv_layer(
conv_layer: nn.Conv2d,
bn_layer: nn.BatchNorm2d,
sparse_layer_out: typing.Optional[Union[nn.BatchNorm2d, SparseGate]],
in_channel_mask: typing.Optional[np.ndarray],
prune_output_mode: str,
pruner: Pruner,
prune_mode: str,
sparse_layer_in: typing.Optional[Union[nn.BatchNorm2d, SparseGate]],
) -> typing.Tuple[np.ndarray, np.ndarray]:
"""
Note: if the sparse_layer is SparseGate, the gate will be replaced by BatchNorm
scaling factor. The value of the gate will be set to all ones.
:param prune_output_mode: how to handle the output channel (case insensitive)
"keep": keep the output channel intact
"same": keep the output channel as same as input channel
"prune": prune the output according to bn_layer
:param pruner: the method to determinate the pruning mask.
:param in_channel_mask: a 0-1 vector indicates whether the corresponding channel should be pruned (0) or not (1)
:param bn_layer: the BatchNorm layer after the convolution layer
:param conv_layer: the convolution layer to be pruned
:param sparse_layer_out: the layer to determine the output sparsity. Support BatchNorm2d and SparseGate.
:param sparse_layer_in: the layer to determine the input sparsity. Support BatchNorm2d and SparseGate.
When the `sparse_layer_in` is None, there is no input sparse layers,
the input channel will be determined by the `in_channel_mask`
Note: `in_channel_mask` is CONFLICT with `sparse_layer_in`!
:param prune_mode: pruning mode (`str`):
- `"multiply"`: pruning threshold is determined by the multiplication of `sparse_layer` and `bn_layer`
only available when `sparse_layer` is `SparseGate`
- `None` or `"default"`: default behaviour. The pruning threshold is determined by `sparse_layer`
:return out_channel_mask
"""
assert isinstance(conv_layer, nn.Conv2d), f"conv_layer got {conv_layer}"
assert isinstance(sparse_layer_out, nn.BatchNorm2d) or isinstance(
sparse_layer_out, SparseGate), f"sparse_layer got {sparse_layer_out}"
if in_channel_mask is not None and sparse_layer_in is not None:
raise ValueError(
"Conflict option: in_channel_mask and sparse_layer_in")
prune_mode = prune_mode.lower()
prune_output_mode = str.lower(prune_output_mode)
if prune_mode == 'multiply':
if not isinstance(sparse_layer_out, SparseGate):
raise ValueError(
f"Do not support prune_mode {prune_mode} when the sparse_layer is {sparse_layer_out}"
)
with torch.no_grad():
conv_weight: torch.Tensor = conv_layer.weight.data.clone()
# prune the input channel of the conv layer
# if sparse_layer_in and in_channel_mask are both None, the input dim will NOT be pruned
if sparse_layer_in is not None:
if in_channel_mask is not None:
raise ValueError("")
sparse_weight_in: np.ndarray = sparse_layer_in.weight.view(
-1).data.cpu().numpy()
# the in_channel_mask will be overwrote
in_channel_mask = pruner(sparse_weight_in)
if in_channel_mask is not None:
# prune the input channel according to the in_channel_mask
# convert mask to channel indexes
idx_in = np.squeeze(np.argwhere(np.asarray(in_channel_mask)))
if len(idx_in.shape) == 0:
# expand the single scalar to array
idx_in = np.expand_dims(idx_in, 0)
elif len(idx_in.shape) == 1 and idx_in.shape[0] == 0:
# nothing left, prune the whole block
out_channel_mask = np.full(conv_layer.out_channels, False)
return in_channel_mask, out_channel_mask
# prune the input of the conv layer
if conv_layer.groups == 1:
conv_weight = conv_weight[:, idx_in.tolist(), :, :]
else:
assert conv_weight.shape[
1] == 1, "only works for groups == num_channels"
# prune the output channel of the conv layer
if prune_output_mode == "prune":
# the sparse_layer.weight need to be flatten, because the weight of SparseGate is not 1d
sparse_weight_out: np.ndarray = sparse_layer_out.weight.view(
-1).data.cpu().numpy()
if prune_mode == 'multiply':
bn_weight = bn_layer.weight.data.cpu().numpy()
sparse_weight_out = sparse_weight_out * bn_weight # element-wise multiplication
elif prune_mode != 'default':
raise ValueError(f"Do not support prune_mode {prune_mode}")
# prune and get the pruned mask
out_channel_mask = pruner(sparse_weight_out)
elif prune_output_mode == "keep":
# do not prune the output
out_channel_mask = np.ones(conv_layer.out_channels)
elif prune_output_mode == "same":
# prune the output channel with the input mask
# keep the conv layer in_channel == out_channel
out_channel_mask = in_channel_mask
else:
raise ValueError(f"invalid prune_output_mode: {prune_output_mode}")
idx_out: np.ndarray = np.squeeze(
np.argwhere(np.asarray(out_channel_mask)))
if len(idx_out.shape) == 0:
# expand the single scalar to array
idx_out = np.expand_dims(idx_out, 0)
elif len(idx_out.shape) == 1 and idx_out.shape[0] == 0:
# no channel left
# return mask directly
# the block is supposed to be set as a identity mapping
return out_channel_mask, in_channel_mask
conv_weight = conv_weight[idx_out.tolist(), :, :, :]
# change the property of the conv layer
conv_layer.in_channels = len(idx_in)
conv_layer.out_channels = len(idx_out)
conv_layer.weight.data = conv_weight
if conv_layer.groups != 1:
# set the new groups for dw layer
conv_layer.groups = conv_layer.in_channels
pass
# prune the bn layer
bn_layer.weight.data = bn_layer.weight.data[idx_out.tolist()].clone()
bn_layer.bias.data = bn_layer.bias.data[idx_out.tolist()].clone()
bn_layer.running_mean = bn_layer.running_mean[idx_out.tolist()].clone()
bn_layer.running_var = bn_layer.running_var[idx_out.tolist()].clone()
# set bn properties
bn_layer.num_features = len(idx_out)
# prune the gate
if isinstance(sparse_layer_out, SparseGate):
sparse_layer_out.prune(idx_out)
# multiply the bn weight and SparseGate weight
sparse_weight_out: torch.Tensor = sparse_layer_out.weight.view(-1)
bn_layer.weight.data = (bn_layer.weight.data *
sparse_weight_out).clone()
bn_layer.bias.data = (bn_layer.bias.data *
sparse_weight_out).clone()
# the function of the SparseGate is now replaced by bn layers
# the SparseGate should be disabled
sparse_layer_out.set_ones()
return out_channel_mask, in_channel_mask
