Caffe实现概述

Caffe实现概述

目录

一、caffe配置文件介绍

二、标准层的定义

 三、网络微调技巧

四、Linux脚本使用及LMDB文件生成

五、带你设计一个Caffe网络，用于分类任务

一、caffe配置文件介绍

 

 

 

 

 

   

 

 

 

 

 

 二、标准层的定义

 

 

 

 

 三、网络微调技巧

 

 

 

 

 其中，multistep最为常用

 

 

 

 

 四、Linux脚本使用及LMDB文件生成

 

 

 

 

 五、带你设计一个Caffe网络，用于分类任务

 

 

 

 

 下面：

使用pycaffe生成solver配置

使用pycaffe生成caffe测试网络和训练网络

 

数据集下载：

# demoCaffe

数据集下载，cifar mnist:
百度云盘:

链接: https://pan.baidu.com/s/1bHFQUz7Q6BMBZv25AhsXKQ 密码: dva9
链接: https://pan.baidu.com/s/1rPRjf2hanlYYjBQQDmIjNQ 密码: 5nhv

1. lmdb数据制作：

手动实现： https://blog.csdn.net/yx2017/article/details/72953537   

               https://www.jianshu.com/p/9d7ed35960cb

代码实现：https://www.cnblogs.com/leemo-o/p/4990021.html

                 https://www.jianshu.com/p/ef84715e0fdc

以下仅供对比阅读：

demo_lmdb.py：  生成lmdb格式数据

 

  1. import lmdb
  2. import numpy as np
  3. import cv2
  4. import caffe
  5. from caffe.proto import caffe_pb2
  6.  
  7. def write():
  8.     # basic setting
  9.  
  10. 10.     lmdb_file = 'lmdb_data'
  11. 11.     batch_size = 256
  12. 12.  
  13. 13.  
  14. 14.     lmdb_env = lmdb.open(lmdb_file, map_size = int(1e12))
  15. 15.  
  16. 16.     lmdb_txn = lmdb_env.begin(write = True)
  17. 17.  
  18. 18.     for x in range(batch_size):
  19. 19.         data = np.ones((3, 64, 64), np.uint8)
  20. 20.         label = x
  21. 21.  
  22. 22.         datum = caffe.io.array_to_datum(data,label)
  23. 23.         keystr = "{:0>8d}".format(x)
  24. 24.  
  25. 25.         lmdb_txn.put(keystr, datum.SerializeToString())
  26. 26.  
  27. 27.     lmdb_txn.commit()
  28. 28.  

29. def read():

  1. 30.     lmdb_env = lmdb.open('lmdb_data')
  2. 31.     lmdb_txt = lmdb_env.begin()
  3. 32.  
  4. 33.     datum = caffe_pb2.Datum()
  5. 34.  
  6. 35.     for key, value in lmdb_txt.cursor():
  7. 36.  
  8. 37.         datum.ParseFromString(value)
  9. 38.  
  10. 39.         label = datum.label
  11. 40.  
  12. 41.         data = caffe.io.datum_to_array(datum)
  13. 42.  
  14. 43.         print(label)
  15. 44.         print(data)
  16. 45.  
  17. 46.  

47. if __name__ == '__main__':

  1. 48.     write()
  2. 49.     read()

demo_create_solver.py:  生成solver配置文件

  1. from caffe.proto import caffe_pb2
  2.  
  3. s = caffe_pb2.SolverParameter()
  4.  
  5. s.train_net = "train.prototxt"
  6. s.test_net.append("test.prototxt")
  7.  
  8. s.test_interval = 100
  9. s.test_iter.append(10)
  10. 10.  

11. s.max_iter = 1000

  1. 12.  

13. s.base_lr = 0.1

  1. 14.  

15. s.weight_decay = 5e-4

  1. 16.  

17. s.lr_policy = "step"

  1. 18.  

19. s.display = 10

  1. 20.  

21. s.snapshot = 10

  1. 22.  

23. s.snapshot_prefix = "model"

  1. 24.  

25. s.type = "SGD"

  1. 26.  

27. s.solver_mode = caffe_pb2.SolverParameter.GPU

  1. 28.  

29. with open("net/s.prototxt", "w") as f:

  1. 30.     f.write(str(s))
  2. 31.  
  3. 32.  
  4. 33.  
  5. 34.  

结果如下：

  1. train_net: "/home/kuan/PycharmProjects/demo_cnn_net/net/train.prototxt"
  2. test_net: "/home/kuan/PycharmProjects/demo_cnn_net/net/test.prototxt"
  3. test_iter: 1000
  4. test_interval: 100
  5. base_lr: 0.10000000149
  6. display: 100
  7. max_iter: 100000
  8. lr_policy: "step"
  9. weight_decay: 0.000500000023749

10. snapshot: 100

11. snapshot_prefix: "/home/kuan/PycharmProjects/demo_cnn_net/cnn_model/mnist/lenet/"

12. solver_mode: GPU

13. type: "SGD"

demo_creat_net.py:    创建网络

  1. import caffe
  2.  
  3. def create_net():
  4.     net = caffe.NetSpec()
  5.  
  6.     net.data, net.label = caffe.layers.Data(source="data.lmdb",
  7.                                             backend=caffe.params.Data.LMDB,
  8.                                             batch_size=32,
  9.                                             ntop=2,  #数据层数据个数，分别为data,label
  10. 10.                                             transform_param=dict(crop_size=40, mirror=True)
  11. 11.                                             )
  12. 12.  
  13. 13.     net.conv1 = caffe.layers.Convolution(net.data, num_output=20, kernel_size=5,
  14. 14.                                          weight_filler={"type": "xavier"},
  15. 15.                                          bias_filler={"type":"xavier"})  #卷积核参数
  16. 16.  
  17. 17.     net.relu1 = caffe.layers.ReLU(net.conv1, in_place=True)
  18. 18.  
  19. 19.     net.pool1 = caffe.layers.Pooling(net.relu1, pool=caffe.params.Pooling.MAX,
  20. 20.                                      kernel_size=3, stride=2)
  21. 21.  
  22. 22.     net.conv2 = caffe.layers.Convolution(net.pool1, num_output=32, kernel_size=3,
  23. 23.                                          pad=1,
  24. 24.                                          weight_filler={"type": "xavier"},
  25. 25.                                          bias_filler={"type": "xavier"})
  26. 26.  
  27. 27.     net.relu2 = caffe.layers.ReLU(net.conv2, in_place=True)
  28. 28.  
  29. 29.     net.pool2 = caffe.layers.Pooling(net.relu2, pool=caffe.params.Pooling.MAX,
  30. 30.                                      kernel_size=3, stride=2)
  31. 31.     #下面为全连接层
  32. 32.     net.fc3 = caffe.layers.InnerProduct(net.pool2, num_output=1024, weight_filler=dict(type='xavier'))
  33. 33.  
  34. 34.     net.relu3 = caffe.layers.ReLU(net.fc3, in_place=True)
  35. 35.  
  36. 36.     ##drop
  37. 37.     net.drop = caffe.layers.Dropout(net.relu3, dropout_param=dict(dropout_ratio=0.5))
  38. 38.  
  39. 39.     net.fc4 = caffe.layers.InnerProduct(net.drop, num_output=10, weight_filler=dict(type='xavier'))
  40. 40.  
  41. 41.     net.loss = caffe.layers.SoftmaxWithLoss(net.fc4, net.label)
  42. 42.  
  43. 43.     with open("net/tt.prototxt", 'w') as f:
  44. 44.         f.write(str(net.to_proto()))
  45. 45.  
  46. 46.  

47. if __name__ == '__main__':

  1. 48.     create_net()

生成结果如下：

  1. layer {
  2.   name: "data"
  3.   type: "Data"
  4.   top: "data"
  5.   top: "label"
  6.   transform_param {
  7.     mirror: true
  8.     crop_size: 40
  9.   }
  10. 10.   data_param {
  11. 11.     source: "/home/kuan/PycharmProjects/demo_cnn_net/lmdb_data"
  12. 12.     batch_size: 32
  13. 13.     backend: LMDB
  14. 14.   }

15. }

16. layer {

  1. 17.   name: "conv1"
  2. 18.   type: "Convolution"
  3. 19.   bottom: "data"
  4. 20.   top: "conv1"
  5. 21.   convolution_param {
  6. 22.     num_output: 20
  7. 23.     kernel_size: 5
  8. 24.     weight_filler {
  9. 25.       type: "xavier"
  10. 26.     }
  11. 27.     bias_filler {
  12. 28.       type: "xavier"
  13. 29.     }
  14. 30.   }

31. }

32. layer {

  1. 33.   name: "relu1"
  2. 34.   type: "ReLU"
  3. 35.   bottom: "conv1"
  4. 36.   top: "conv1"

37. }

38. layer {

  1. 39.   name: "pool1"
  2. 40.   type: "Pooling"
  3. 41.   bottom: "conv1"
  4. 42.   top: "pool1"
  5. 43.   pooling_param {
  6. 44.     pool: MAX
  7. 45.     kernel_size: 3
  8. 46.     stride: 2
  9. 47.   }

48. }

49. layer {

  1. 50.   name: "conv2"
  2. 51.   type: "Convolution"
  3. 52.   bottom: "pool1"
  4. 53.   top: "conv2"
  5. 54.   convolution_param {
  6. 55.     num_output: 32
  7. 56.     pad: 1
  8. 57.     kernel_size: 3
  9. 58.     weight_filler {
  10. 59.       type: "xavier"
  11. 60.     }
  12. 61.     bias_filler {
  13. 62.       type: "xavier"
  14. 63.     }
  15. 64.   }

65. }

66. layer {

  1. 67.   name: "relu2"
  2. 68.   type: "ReLU"
  3. 69.   bottom: "conv2"
  4. 70.   top: "conv2"

71. }

72. layer {

  1. 73.   name: "pool2"
  2. 74.   type: "Pooling"
  3. 75.   bottom: "conv2"
  4. 76.   top: "pool2"
  5. 77.   pooling_param {
  6. 78.     pool: MAX
  7. 79.     kernel_size: 3
  8. 80.     stride: 2
  9. 81.   }

82. }

83. layer {

  1. 84.   name: "fc3"
  2. 85.   type: "InnerProduct"
  3. 86.   bottom: "pool2"
  4. 87.   top: "fc3"
  5. 88.   inner_product_param {
  6. 89.     num_output: 1024
  7. 90.     weight_filler {
  8. 91.       type: "xavier"
  9. 92.     }
  10. 93.   }

94. }

95. layer {

  1. 96.   name: "relu3"
  2. 97.   type: "ReLU"
  3. 98.   bottom: "fc3"
  4. 99.   top: "fc3"
  5. }
  6. layer {
  7.   name: "drop"
  8.   type: "Dropout"
  9.   bottom: "fc3"
  10.   top: "drop"
  11.   dropout_param {
  12.     dropout_ratio: 0.5
  13.   }
  14. }
  15. layer {
  16.   name: "fc4"
  17.   type: "InnerProduct"
  18.   bottom: "drop"
  19.   top: "fc4"
  20.   inner_product_param {
  21.     num_output: 10
  22.     weight_filler {
  23.       type: "xavier"
  24.     }
  25.   }
  26. }
  27. layer {
  28.   name: "loss"
  29.   type: "SoftmaxWithLoss"
  30.   bottom: "fc4"
  31.   bottom: "label"
  32.   top: "loss"
  33. }

demo_train.py训练网络：

  1. import sys
  2. sys.path.append('/home/kuan/AM-softmax_caffe/python')
  3. import caffe
  4.  
  5. solver = caffe.SGDSolver("/home/kuan/PycharmProjects/demo_cnn_net/cnn_net/alexnet/solver.prototxt")
  6.  
  7. solver.solve()

demo_test.py:测试网络

  1. import sys
  2. sys.path.append('/home/kuan/AM-softmax_caffe/python')
  3. import caffe
  4. import numpy as np
  5.  
  6. ##caffemodel deploy.prototxt
  7.  
  8. deploy = "/home/kuan/PycharmProjects/demo_cnn_net/cnn_net/alexnet/deploy.prototxt"
  9.  

10. model = "/home/kuan/PycharmProjects/demo_cnn_net/cnn_model/cifar/alexnet/alexnet_iter_110.caffemodel"

  1. 11.  

12. net = caffe.Net(deploy, model, caffe.TEST)

  1. 13.  
  2. 14.  

15. net.blobs["data"].data[...] = np.ones((3,32,32),np.uint8)

  1. 16.  

17. net.forward()

  1. 18.  

19. prob = net.blobs["prob"].data[0]

  1. 20.  

21. print(prob)

  1. 22.  

 

 

 

 

 

 

posted @ 2021-02-09 06:44  吴建明wujianming  阅读(300)  评论(0)  编辑  收藏  举报