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. def write():
7. # basic setting
8. 10.     lmdb_file = 'lmdb_data'
9. 11.     batch_size = 256
10. 12.
11. 13.
12. 14.     lmdb_env = lmdb.open(lmdb_file, map_size =
    int(1e12))
13. 15.
14. 16.     lmdb_txn = lmdb_env.begin(write = True)
15. 17.
16. 18.     for x in range(batch_size):
17. 19.         data = np.ones((3, 64, 64), np.uint8)
18. 20.         label = x
19. 21.
20. 22.         datum =
    caffe.io.array_to_datum(data,label)
21. 23.         keystr = "{:0>8d}".format(x)
22. 24.
23. 25.         lmdb_txn.put(keystr,
    datum.SerializeToString())
24. 26.
25. 27.     lmdb_txn.commit()
26. 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. s =
   caffe_pb2.SolverParameter()
3. s.train_net = "train.prototxt"
4. s.test_net.append("test.prototxt")
5. s.test_interval = 100
6. s.test_iter.append(10)
7. 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. def create_net():
3. net = caffe.NetSpec()
4. net.data, net.label =
   caffe.layers.Data(source="data.lmdb",
5. backend=caffe.params.Data.LMDB,
6. batch_size=32,
7. ntop=2,  #数据层数据个数，分别为data,label
8. 10.                                             transform_param=dict(crop_size=40, mirror=True)
9. 11.                                             )
10. 12.
11. 13.     net.conv1 =
    caffe.layers.Convolution(net.data, num_output=20, kernel_size=5,
12. 14.                                          weight_filler={"type": "xavier"},
13. 15.                                          bias_filler={"type":"xavier"})  #卷积核参数
14. 16.
15. 17.     net.relu1 = caffe.layers.ReLU(net.conv1,
    in_place=True)
16. 18.
17. 19.     net.pool1 = caffe.layers.Pooling(net.relu1,
    pool=caffe.params.Pooling.MAX,
18. 20.                                      kernel_size=3, stride=2)
19. 21.
20. 22.     net.conv2 =
    caffe.layers.Convolution(net.pool1, num_output=32, kernel_size=3,
21. 23.                                          pad=1,
22. 24.                                          weight_filler={"type": "xavier"},
23. 25.                                          bias_filler={"type": "xavier"})
24. 26.
25. 27.     net.relu2 = caffe.layers.ReLU(net.conv2,
    in_place=True)
26. 28.
27. 29.     net.pool2 = caffe.layers.Pooling(net.relu2,
    pool=caffe.params.Pooling.MAX,
28. 30.                                      kernel_size=3, stride=2)
29. 31.     #下面为全连接层
30. 32.     net.fc3 =
    caffe.layers.InnerProduct(net.pool2, num_output=1024,
    weight_filler=dict(type='xavier'))
31. 33.
32. 34.     net.relu3 = caffe.layers.ReLU(net.fc3,
    in_place=True)
33. 35.
34. 36.     ##drop
35. 37.     net.drop = caffe.layers.Dropout(net.relu3,
    dropout_param=dict(dropout_ratio=0.5))
36. 38.
37. 39.     net.fc4 =
    caffe.layers.InnerProduct(net.drop, num_output=10,
    weight_filler=dict(type='xavier'))
38. 40.
39. 41.     net.loss =
    caffe.layers.SoftmaxWithLoss(net.fc4, net.label)
40. 42.
41. 43.     with open("net/tt.prototxt", 'w') as f:
42. 44.         f.write(str(net.to_proto()))
43. 45.
44. 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. solver =
   caffe.SGDSolver("/home/kuan/PycharmProjects/demo_cnn_net/cnn_net/alexnet/solver.prototxt")
5. 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. ##caffemodel
   deploy.prototxt
6. deploy = "/home/kuan/PycharmProjects/demo_cnn_net/cnn_net/alexnet/deploy.prototxt"

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.