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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch.nn as nn
from torch.nn import init
class BaseNetwork(nn.Module):
def __init__(self):
super(BaseNetwork, self).__init__()
@staticmethod
def modify_commandline_options(parser, is_train):
return parser
def print_network(self):
if isinstance(self, list):
self = self[0]
num_params = 0
for param in self.parameters():
num_params += param.numel()
print(
"Network [%s] was created. Total number of parameters: %.1f million. "
"To see the architecture, do print(network)." % (type(self).__name__, num_params / 1000000)
)
def init_weights(self, init_type="normal", gain=0.02):
def init_func(m):
classname = m.__class__.__name__
if classname.find("BatchNorm2d") != -1:
if hasattr(m, "weight") and m.weight is not None:
init.normal_(m.weight.data, 1.0, gain)
if hasattr(m, "bias") and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif hasattr(m, "weight") and (classname.find("Conv") != -1 or classname.find("Linear") != -1):
if init_type == "normal":
init.normal_(m.weight.data, 0.0, gain)
elif init_type == "xavier":
init.xavier_normal_(m.weight.data, gain=gain)
elif init_type == "xavier_uniform":
init.xavier_uniform_(m.weight.data, gain=1.0)
elif init_type == "kaiming":
init.kaiming_normal_(m.weight.data, a=0, mode="fan_in")
elif init_type == "orthogonal":
init.orthogonal_(m.weight.data, gain=gain)
elif init_type == "none": # uses pytorch's default init method
m.reset_parameters()
else:
raise NotImplementedError("initialization method [%s] is not implemented" % init_type)
if hasattr(m, "bias") and m.bias is not None:
init.constant_(m.bias.data, 0.0)
self.apply(init_func)
# propagate to children
for m in self.children():
if hasattr(m, "init_weights"):
m.init_weights(init_type, gain)
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