最近使用pytorch时，需要用到一个预训练好的人脸识别模型提取人脸ID特征，想到很多人都在用用vgg-face，但是vgg-face没有pytorch的模型，于是写个vgg-face.mat转到pytorch模型的代码

#!/usr/bin/env python2# -*- coding: utf-8 -*-"""Created on Thu May 10 10:41:40 2018@author: hy"""import torchimport mathimport torch.nn as nnfrom torch.autograd import Variableimport numpy as npfrom scipy.io import loadmatimport scipy.misc as smimport matplotlib.pyplot as plt class vgg16_face(nn.Module): def __init__(self,num_classes=2622): super(vgg16_face,self).__init__() inplace = True self.conv1_1 = nn.Conv2d(3,64,kernel_size=(3,3),stride=(1,1),padding=(1,1)) self.relu1_1 = nn.ReLU(inplace) self.conv1_2 = nn.Conv2d(64,64,kernel_size=(3,3),stride=(1,1),padding=(1,1)) self.relu1_2 = nn.ReLU(inplace) self.pool1 = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False) self.conv2_1 = nn.Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.relu2_1 = nn.ReLU(inplace) self.conv2_2 = nn.Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.relu2_2 = nn.ReLU(inplace) self.pool2 = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False) self.conv3_1 = nn.Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.relu3_1 = nn.ReLU(inplace) self.conv3_2 = nn.Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.relu3_2 = nn.ReLU(inplace) self.conv3_3 = nn.Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.relu3_3 = nn.ReLU(inplace) self.pool3 = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False) self.conv4_1 = nn.Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.relu4_1 = nn.ReLU(inplace) self.conv4_2 = nn.Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.relu4_2 = nn.ReLU(inplace) self.conv4_3 = nn.Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.relu4_3 = nn.ReLU(inplace) self.pool4 = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False) self.conv5_1 = nn.Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.relu5_1 = nn.ReLU(inplace) self.conv5_2 = nn.Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.relu5_2 = nn.ReLU(inplace) self.conv5_3 = nn.Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.relu5_3 = nn.ReLU(inplace) self.pool5 = nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1), ceil_mode=False) self.fc6 = nn.Linear(in_features=25088, out_features=4096, bias=True) self.relu6 = nn.ReLU(inplace) self.drop6 = nn.Dropout(p=0.5) self.fc7 = nn.Linear(in_features=4096, out_features=4096, bias=True) self.relu7 = nn.ReLU(inplace) self.drop7 = nn.Dropout(p=0.5) self.fc8 = nn.Linear(in_features=4096, out_features=num_classes, bias=True) self._initialize_weights() def forward(self,x): out = self.conv1_1(x) x_conv1 = out out = self.relu1_1(out) out = self.conv1_2(out) out = self.relu1_2(out) out = self.pool1(out) x_pool1 = out out = self.conv2_1(out) out = self.relu2_1(out) out = self.conv2_2(out) out = self.relu2_2(out) out = self.pool2(out) x_pool2 = out out = self.conv3_1(out) out = self.relu3_1(out) out = self.conv3_2(out) out = self.relu3_2(out) out = self.conv3_3(out) out = self.relu3_3(out) out = self.pool3(out) x_pool3 = out out = self.conv4_1(out) out = self.relu4_1(out) out = self.conv4_2(out) out = self.relu4_2(out) out = self.conv4_3(out) out = self.relu4_3(out) out = self.pool4(out) x_pool4 = out out = self.conv5_1(out) out = self.relu5_1(out) out = self.conv5_2(out) out = self.relu5_2(out) out = self.conv5_3(out) out = self.relu5_3(out) out = self.pool5(out) x_pool5 = out out = out.view(out.size(0),-1) out = self.fc6(out) out = self.relu6(out) out = self.fc7(out) out = self.relu7(out) out = self.fc8(out) return out, x_pool1, x_pool2, x_pool3, x_pool4, x_pool5 def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): 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_() def copy(vgglayers, dstlayer,idx): layer = vgglayers[0][idx] kernel, bias = layer[0]['weights'][0][0] if idx in [33,35]: # fc7, fc8 kernel = kernel.squeeze() dstlayer.weight.data.copy_(torch.from_numpy(kernel.transpose([1,0]))) # matrix format: axb -> bxa elif idx == 31: # fc6 kernel = kernel.reshape(-1,4096) dstlayer.weight.data.copy_(torch.from_numpy(kernel.transpose([1,0]))) # matrix format: axb -> bxa else: dstlayer.weight.data.copy_(torch.from_numpy(kernel.transpose([3,2,1,0]))) # matrix format: axbxcxd -> dxcxbxa dstlayer.bias.data.copy_(torch.from_numpy(bias.reshape(-1))) def get_vggface(vgg_path): """1. define pytorch model""" model = vgg16_face() """2. get pre-trained weights and other params""" #vgg_path = "/home/hy/vgg-face.mat" # download from http://www.vlfeat.org/matconvnet/pretrained/ vgg_weights = loadmat(vgg_path) data = vgg_weights meta = data['meta'] classes = meta['classes'] class_names = classes[0][0]['description'][0][0] normalization = meta['normalization'] average_image = np.squeeze(normalization[0][0]['averageImage'][0][0][0][0]) image_size = np.squeeze(normalization[0][0]['imageSize'][0][0]) layers = data['layers'] # ============================================================================= # for idx,layer in enumerate(layers[0]): # name = layer[0]['name'][0][0] # print idx,name # """ # 0 conv1_1 # 1 relu1_1 # 2 conv1_2 # 3 relu1_2 # 4 pool1 # 5 conv2_1 # 6 relu2_1 # 7 conv2_2 # 8 relu2_2 # 9 pool2 # 10 conv3_1 # 11 relu3_1 # 12 conv3_2 # 13 relu3_2 # 14 conv3_3 # 15 relu3_3 # 16 pool3 # 17 conv4_1 # 18 relu4_1 # 19 conv4_2 # 20 relu4_2 # 21 conv4_3 # 22 relu4_3 # 23 pool4 # 24 conv5_1 # 25 relu5_1 # 26 conv5_2 # 27 relu5_2 # 28 conv5_3 # 29 relu5_3 # 30 pool5 # 31 fc6 # 32 relu6 # 33 fc7 # 34 relu7 # 35 fc8 # 36 prob # """ # ============================================================================= """3. load weights to pytorch model""" copy(layers,model.conv1_1,0) copy(layers,model.conv1_2,2) copy(layers,model.conv2_1,5) copy(layers,model.conv2_2,7) copy(layers,model.conv3_1,10) copy(layers,model.conv3_2,12) copy(layers,model.conv3_3,14) copy(layers,model.conv4_1,17) copy(layers,model.conv4_2,19) copy(layers,model.conv4_3,21) copy(layers,model.conv5_1,24) copy(layers,model.conv5_2,26) copy(layers,model.conv5_3,28) copy(layers,model.fc6,31) copy(layers,model.fc7,33) copy(layers,model.fc8,35) return model,class_names,average_image,image_size if __name__ == '__main__': """test""" vgg_path = "/home/hy/vgg-face.mat" # download from http://www.vlfeat.org/matconvnet/pretrained/ model,class_names,average_image,image_size = get_vggface(vgg_path) imgpath = "/home/hy/e/avg_face.jpg" img = sm.imread(imgpath) img = sm.imresize(img,[image_size[0],image_size[1]]) input_arr = np.float32(img)#-average_image # h,w,c x = torch.from_numpy(input_arr.transpose((2,0,1))) # c,h,w avg = torch.from_numpy(average_image) # avg = avg.view(3,1,1).expand(3,224,224) x = x - avg x = x.contiguous() x = x.view(1, x.size(0), x.size(1), x.size(2)) x = Variable(x) out, x_pool1, x_pool2, x_pool3, x_pool4, x_pool5 = model(x)# plt.imshow(x_pool1.data.numpy()[0,45]) # plot

以上这篇把vgg-face.mat权重迁移到pytorch模型示例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。