def __init__(self, num_classes 1000, init_weights False):
super(AlexNet, self).__init__()
self.features nn.Sequential(
nn.Conv2d(3, 48, kernel_size 11, stride 4, padding 2), # input[3, 224, 224] output[48, 55, 55]
nn.ReLU(inplace True),
nn.MaxPool2d(kernel_size 3, stride 2), # output[48, 27, 27]
nn.Conv2d(48, 128, kernel_size 5, padding 2), # output[128, 27, 27]
nn.ReLU(inplace True),
nn.MaxPool2d(kernel_size 3, stride 2), # output[128, 13, 13]
nn.Conv2d(128, 192, kernel_size 3, padding 1), # output[192, 13, 13]
nn.ReLU(inplace True),
nn.Conv2d(192, 192, kernel_size 3, padding 1), # output[192, 13, 13]
nn.ReLU(inplace True),
nn.Conv2d(192, 128, kernel_size 3, padding 1), # output[128, 13, 13]
nn.ReLU(inplace True),
nn.MaxPool2d(kernel_size 3, stride 2), # output[128, 6, 6]
self.classifier nn.Sequential(
nn.Dropout(p 0.5),
nn.Linear(128 * 6 * 6, 2048),
nn.ReLU(inplace True),
nn.Dropout(p 0.5),
nn.Linear(2048, 2048),
nn.ReLU(inplace True),
nn.Linear(2048, num_classes),
if init_weights:
self._initialize_weights()
def forward(self, x):
x self.features(x)
x self.classifier(x)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode fan_out , nonlinearity relu )
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
if __name__ __main__ :
model AlexNet()
print( model children: )
for module in model.children():
print(module)
print( model modules: )
for module in model.modules():
print(module)
print( model named children: )
for name, module in model.named_children():
print( name: {}, module: {} .format(name, module))
print( model named modules: )
for name, module in model.named_modules():
print( name: {}, module: {} .format(name, module))
print( model named parameters: )
for name, parameter in model.named_parameters():
print( name: {}, parameter: {} .format(name, parameter))
print( parameters: )
for parameter in model.parameters():
print( parameter: {} .format(parameter))
