if op_name in self.mask_calculated_ops:
assert op_name in self.mask_dict
return self.mask_dict.get(op_name)
mask = torch.ones(weight.size()).type_as(weight)
try:
filters = weight.size(0)
num_prune = int(filters * config.get("sparsity"))
if filters < 2 or num_prune < 1:
return mask
mask = self._get_mask(mask, weight, num_prune)
finally:
self.mask_dict.update({op_name: mask})
self.mask_calculated_ops.add(op_name)
return mask.detach()
class L1FilterPruner(RankFilterPruner):
After Change
if op_name in self.mask_calculated_ops:
assert op_name in self.mask_dict
return self.mask_dict.get(op_name)
mask_weight = torch.ones(weight.size()).type_as(weight).detach()
if hasattr(layer.module, "bias") and layer.module.bias is not None:
mask_bias = torch.ones(layer.module.bias.size()).type_as(layer.module.bias).detach()
else:
mask_bias = None
