mindspore_gs.pruner.PrunerFtCompressAlgo
- class mindspore_gs.pruner.PrunerFtCompressAlgo(config=None)[source]
PrunerFtCompressAlgo is a subclass of CompAlgo that implements the ability to remove redundant convolution kernels and fully train the network.
- Parameters
config (dict) –
Configuration of PrunerFtCompressAlgo, keys are attribute names, values are attribute values. Supported attribute are listed below:
prune_rate (float): number in [0.0, 1.0).
- Raises
TypeError – If prune_rate is not float.
ValueError – If epoch_size is less than 0 or greater than or equal to 1.
- Supported Platforms:
Ascend
GPU
Examples
>>> from mindspore_gs.pruner import PrunerKfCompressAlgo, PrunerFtCompressAlgo >>> from mindspore import nn >>> class Net(nn.Cell): ... def __init__(self, num_channel=1): ... super(Net, 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 ... ... class NetToPrune(nn.Cell): ... def __init__(self): ... super(NetToPrune, self).__init__() ... self.layer = Net() ... ... def construct(self, x): ... x = self.layer(x) ... return x ... >>> net = NetToPrune() >>> kf_pruning = PrunerKfCompressAlgo({}) >>> net_pruning_kf = kf_pruning.apply(net) >>> ## 1) Define FineTune Algorithm >>> ft_pruning = PrunerFtCompressAlgo({'prune_rate': 0.5}) >>> ## 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< (layer): Net< (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<> > >