把vgg-face.mat权重迁移到pytorch模型示例-创新互联
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
企业建站必须是能够以充分展现企业形象为主要目的,是企业文化与产品对外扩展宣传的重要窗口,一个合格的网站不仅仅能为公司带来巨大的互联网上的收集和信息发布平台,成都创新互联面向各种领域:成都广告制作等网站设计、全网营销推广解决方案、网站设计等建站排名服务。#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Thu May 10 10:41:40 2018 @author: hy """ import torch import math import torch.nn as nn from torch.autograd import Variable import numpy as np from scipy.io import loadmat import scipy.misc as sm import 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
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