mindspore_gs.pruner.PrunerFtCompressAlgo

class mindspore_gs.pruner.PrunerFtCompressAlgo(config)

Derived class of GoldenStick. Scop-algorithm. FineTune for recover net.

Parameters

config (Dict) – Configuration of PrunerFtCompressAlgo. There are no configurable options for PrunerFtCompressAlgo currently, but for compatibility, the config parameter in the constructor of class A is retained.

Supported Platforms:

Ascend GPU

Examples

>>> from mindspore_gs import PrunerKfCompressAlgo, PrunerFtCompressAlgo
>>> from models.resnet import resnet50
>>> class NetToPrune(nn.Cell):
...     def __init__(self, num_channel=1):
...         super(NetToPrune, self).__init__()
...         self.conv = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
...         self.bn = nn.BatchNorm2d(6)
...
...     def construct(self, x):
...         x = self.conv(x)
...         x = self.bn(x)
...         return x
...
>>> net = NetToPrune()
>>> kf_pruning = PrunerKfCompressAlgo()
>>> net_pruning_kf = kf_pruning.apply(net)
>>> ## 1) Define FineTune Algorithm
>>> ft_pruning = PrunerFtCompressAlgo()
>>> ## 2) Apply FineTune-algorithm to origin network
>>> net_pruning_ft = ft_pruning.apply(net_pruning_kf)
>>> ## 3) Print network and check the result. Conv2d and bn should be transformed to KfConv2d.
>>> print(net_pruning_ft)
NetToPrune<
  (conv): MaskedConv2dbn<
    (conv): Conv2d<input_channels=1, output_channels=6, kernel_size=(5, 5), stride=(1, 1), pad_mode=valid,
      padding=0, dilation=(1, 1), group=1, has_bias=False, weight_init=normal, bias_init=zeros, format=NCHW>
    (bn): BatchNorm2d<num_features=6, eps=1e-05, momentum=0.09999999999999998, gamma=Parameter
      (name=conv.bn.bn.gamma, shape=(6,), dtype=Float32, requires_grad=True), beta=Parameter
      (name=conv.bn.bn.beta, shape=(6,), dtype=Float32, requires_grad=True), moving_mean=Parameter
      (name=conv.bn.bn.moving_mean, shape=(6,), dtype=Float32, requires_grad=False), moving_variance=Parameter
      (name=conv.bn.bn.moving_variance, shape=(6,), dtype=Float32, requires_grad=False)>
    >
  (bn): SequentialCell<>
  >

apply(network)

Transform a knockoff network to a normal and pruned network.

Parameters

network (Cell) – Knockoff network.

Returns

Pruned network.