VGG16提取图像特征 （torch7）

VGG16提取图像特征 （torch7）

VGG16

loadcaffe

torch7

1. 下载pretrained model,保存到当前目录下

1. th> caffemodel_url = 'http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel' 
   

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2. th> proto_url='https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-vgg_ilsvrc_16_layers_deploy-prototxt' 
   

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3. th> os.execute('wget VGG_ILSVRC_16_layers.caffemodel' .. caffemodel_url) 
   

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4. th> os.execute('wget VGG_ILSVRC_16_layers_deploy.prototxt' .. proto_url) 
   

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1. 使用loadcaffe提取图像特征

1. ---

2. require 'torch' -- 使用th命令，可忽略 
   

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3. require 'nn' -- 修改model用到nn包 
   

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4. require 'loadcaffe' -- 加在caffe训练的包 
   

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5. require 'image' -- 加载图像，处理图像，可以使用cv中函数替代 
   

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6. ---

7. local loadSize = {3,256,256} -- 加载图像scale尺寸 
   

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8. local sampleSize = {3,224,224} -- 样本尺寸，其实就是选取scale后图像的中间一块 
   

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9. ---

10. local function loadImage(input) 
    

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11. -- 将图像缩放到loadSize尺寸，为了保证aspect ratio不变，将短边缩放后，长边按比例缩放 
    

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12. if input:size(3) < input:size(2) then 
    

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13. input = image.scale(input,loadSize[2],loadSize[3]*input:size(2)/input:size(3)) 
    

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14. -- 注意image.scale(src,width,height),width对应的是input:size[3],height对应的是input:size[2] 
    

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15. else 
    

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16. input = image.scale(input,loadSize[2]*input:size(3)/input:size(2),loadSize[3]) 
    

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17. end 
    

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18. return input 
    

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19. end 
    

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20. ---

21. local bgr_means = {103.939,116.779,123.68} --VGG预训练中的均值 
    

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22. local function vggPreProcessing(img) 
    

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23. local img2=img:clone() 
    

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24. img2[{{1}}] =img2[{{3}}] -- image.load 加载图像是rgb格式，需转化维bgr 
    

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25. img2[{{3}}] = img[{{1}}] 
    

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26. img2:mul(255) -- image.load()加载的图像 pixel value \in [0,1] 
    

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27. for i=1,3 do 
    

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28. img2[i]:add(-bgr_means[i]) -- 中心化 
    

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29. end 
    

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30. return img2 
    

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31. end 
    

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32. ---

33. local function centerCrop(input) 
    

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34. local oH = sampleSize[2] 
    

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35. local oW = sampleSize[3] 
    

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36. local iW = input:size(3) 
    

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37. local iH = input:size(2) 
    

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38. local w1 = math.ceil((iW-oW)/2) 
    

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39. local h1 = math.ceil((iH-oH)/2) 
    

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40. local out = image.crop(input,w1,h1,w1+oW,h1+oH) 
    

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41. return out 
    

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42. end 
    

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43. ---

44. local function getPretrainedModel() 
    

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45. local proto = 'VGG_ILSVRC_16_layers_deploy.prototxt' 
    

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46. local caffemodel = '/home/zwzhou/modelZoo/VGG_ILSVRC_16_layers.caffemodel' 
    

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47. ---

48. local model = loadcaffe.load(proto,caffemodel,'nn') -- 加载pretrained model 
    

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49. for i=1,3 do -- 将最后3层舍掉 
    

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50. model.modules[#model.modules]=nil 
    

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51. end 
    

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52. -- 删除pretrained model的一些层官方方法 
    

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53. -- ========================== 
    

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54. -- for i= 40,38,-1 do 
    

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55. -- model:remove(i) 
    

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56. -- end 
    

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57. -- ========================== 
    

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58. model:add(nn.Normalize(2)) -- 添加一层正则化层，将输出向量归一化 
    

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59. ---

60. model:evaluate() -- self.training=false ，非训练，让网络参数不变 
    

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61. return model 
    

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62. end 
    

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63. ---

64. torch.setdefaulttensortype('torch.FloatTensor') 
    

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65. model = getPretrainedModel() 
    

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66. ---

67. filepath = '/home/zwzhou/MOT16/train/MOT16-02/img1/000001.jpg' 
    

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68. local img1=image.load(filepath) -- rgb图像 
    

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69. local input = image.crop(img1,910,480,910+97,480+110) -- 里面参数时选择原图像的一个区域，boundingbox 
    

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70. ---

71. input = loadImage(input) 
    

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72. local vggPreProcessed = vggPreProcessing(input) 
    

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73. local out = centerCrop(vggPreProcessed) 
    

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74. ---

75. local outputs = model:forward(out) 
    

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76. ---

77. print(outputs) 
    

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78. print(#outputs) 
    

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1. 参考项目
   
   VGG-19-feature-extractor
   
   Torch 7 利用已有VGG模型提取图片特征