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vgg16_c.py
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vgg16_c.py
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import numpy as np
import torch
import torchvision
import torch.nn as nn
import math
class VGG16_C(nn.Module):
""""""
def __init__(self, pretrain=None, logger=None):
super(VGG16_C, self).__init__()
self.conv1_1 = nn.Conv2d(3, 64, (3, 3), stride=1, padding=1)
self.relu1_1 = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv2d(64, 64, (3, 3), stride=1, padding=1)
self.relu1_2 = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.conv2_1 = nn.Conv2d(64, 128, (3, 3), stride=1, padding=1)
self.relu2_1 = nn.ReLU(inplace=True)
self.conv2_2 = nn.Conv2d(128, 128, (3, 3), stride=1, padding=1)
self.relu2_2 = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.conv3_1 = nn.Conv2d(128, 256, (3, 3), stride=1, padding=1)
self.relu3_1 = nn.ReLU(inplace=True)
self.conv3_2 = nn.Conv2d(256, 256, (3, 3), stride=1, padding=1)
self.relu3_2 = nn.ReLU(inplace=True)
self.conv3_3 = nn.Conv2d(256, 256, (3, 3), stride=1, padding=1)
self.relu3_3 = nn.ReLU(inplace=True)
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.conv4_1 = nn.Conv2d(256, 512, (3, 3), stride=1, padding=1)
self.relu4_1 = nn.ReLU(inplace=True)
self.conv4_2 = nn.Conv2d(512, 512, (3, 3), stride=1, padding=1)
self.relu4_2 = nn.ReLU(inplace=True)
self.conv4_3 = nn.Conv2d(512, 512, (3, 3), stride=1, padding=1)
self.relu4_3 = nn.ReLU(inplace=True)
self.pool4 = nn.MaxPool2d(2, stride=1, ceil_mode=True)
self.conv5_1 = nn.Conv2d(512, 512, (3, 3), stride=1, padding=2, dilation=2)
self.relu5_1 = nn.ReLU(inplace=True)
self.conv5_2 = nn.Conv2d(512, 512, (3, 3), stride=1, padding=2, dilation=2)
self.relu5_2 = nn.ReLU(inplace=True)
self.conv5_3 = nn.Conv2d(512, 512, (3, 3), stride=1, padding=2, dilation=2)
self.relu5_3 = nn.ReLU(inplace=True)
if pretrain:
if '.npy' in pretrain:
state_dict = np.load(pretrain).item()
for k in state_dict:
state_dict[k] = torch.from_numpy(state_dict[k])
else:
state_dict = torch.load(pretrain)
own_state_dict = self.state_dict()
for name, param in own_state_dict.items():
if name in state_dict:
if logger:
logger.info('copy the weights of %s from pretrained model' % name)
param.copy_(state_dict[name])
else:
if logger:
logger.info('init the weights of %s from mean 0, std 0.01 gaussian distribution'\
% name)
if 'bias' in name:
param.zero_()
else:
param.normal_(0, 0.01)
else:
self._initialize_weights(logger)
def forward(self, x):
conv1_1 = self.relu1_1(self.conv1_1(x))
conv1_2 = self.relu1_2(self.conv1_2(conv1_1))
pool1 = self.pool1(conv1_2)
conv2_1 = self.relu2_1(self.conv2_1(pool1))
conv2_2 = self.relu2_2(self.conv2_2(conv2_1))
pool2 = self.pool2(conv2_2)
conv3_1 = self.relu3_1(self.conv3_1(pool2))
conv3_2 = self.relu3_2(self.conv3_2(conv3_1))
conv3_3 = self.relu3_3(self.conv3_3(conv3_2))
pool3 = self.pool3(conv3_3)
conv4_1 = self.relu4_1(self.conv4_1(pool3))
conv4_2 = self.relu4_2(self.conv4_2(conv4_1))
conv4_3 = self.relu4_3(self.conv4_3(conv4_2))
pool4 = self.pool4(conv4_3)
# pool4 = conv4_3
conv5_1 = self.relu5_1(self.conv5_1(pool4))
conv5_2 = self.relu5_2(self.conv5_2(conv5_1))
conv5_3 = self.relu5_3(self.conv5_3(conv5_2))
side = [conv1_1, conv1_2, conv2_1, conv2_2,
conv3_1, conv3_2, conv3_3, conv4_1,
conv4_2, conv4_3, conv5_1, conv5_2, conv5_3]
return side
def _initialize_weights(self, logger=None):
for m in self.modules():
if isinstance(m, nn.Conv2d):
if logger:
logger.info('init the weights of %s from mean 0, std 0.01 gaussian distribution'\
% m)
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
if __name__ == '__main__':
model = VGG16_C()
# im = np.zeros((1,3,100,100))
# out = model(Variable(torch.from_numpy(im)))