I used pytorch to build squeezenet before. I personally think pytorch is the best to use, but some projects require the caffe structure, so this article also uses caffe to build a squeezenet network.
Data processing
First, the data must be processed. Unlike pytorch, pytorch only needs to provide the directory where the data set is located to read data directly, while caffe requires an absolute path containing the absolute path of each image and its categorytxt file to read data from. Write a script to generate a txt file:
import os
import random
folder = 'cotta' # Relative path to the data set directory
names = os.listdir(folder)
f1 = open('/train_txt/train_cotta.txt', 'a') # Generated txt address
f2 = open('/train_txt/test_water_workcloth.txt', 'a')
for name in names:
imgnames = os.listdir(folder + '/' + name)
random.shuffle(imgnames)
numimg = len(imgnames)
for i in range(numimg):
f1.write('%s %s\
' % (folder + '/' + name + '/' + imgnames[i], name[0]))
# if i < int(0.9*numimg):
# f1.write('%s %s\
'%(folder + '/' + name + '/' + imgnames[i], name[0]))
#else:
# f2.write('%s %s\
'%(folder + '/' + name + '/' + imgnames[i], name[0]))
# f2.close()
f1.close()
The directory of the data set must also be consistent with pytorch. The data of a class is placed in a directory with the name of the class. And the script is at the same level as this directory.
The txt content generated after running the script is as follows:
/cotta/0_other/0_1_391_572_68_68.jpg 0 /cotta/1_longSleeves/9605_1_5_565_357_82_70.jpg 1 /cotta/2_cotta/713_0.99796_1_316_162_96_87.jpg 2 ... Relative path of the image Category of the image
Network structure configuration file
trainval.prototxt
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
transform_param {
mirror: true
crop_size: 96
}
image_data_param {
source: "/train_txt/train_cotta.txt" # Relative path of the generated txt
root_folder: "/data/" # Path to the directory where the data set is stored
batch_size: 64
shuffle: true
new_height: 96
new_width: 96
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 96
kernel_size: 3
stride: 1
pad: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "BatchNorm1"
type: "BatchNorm"
bottom: "conv1"
top: "BatchNorm1"
}
layer {
name: "relu_conv1"
type: "ReLU"
bottom: "BatchNorm1"
top: "BatchNorm1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "BatchNorm1"
top: "pool1"
pooling_param {
Pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "fire2/squeeze1x1"
type: "Convolution"
bottom: "pool1"
top: "fire2/squeeze1x1"
convolution_param {
num_output: 16
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire2/bn_squeeze1x1"
type: "BatchNorm"
bottom: "fire2/squeeze1x1"
top: "fire2/bn_squeeze1x1"
}
layer {
name: "fire2/relu_squeeze1x1"
type: "ReLU"
bottom: "fire2/bn_squeeze1x1"
top: "fire2/bn_squeeze1x1"
}
layer {
name: "fire2/expand1x1"
type: "Convolution"
bottom: "fire2/bn_squeeze1x1"
top: "fire2/expand1x1"
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire2/bn_expand1x1"
type: "BatchNorm"
bottom: "fire2/expand1x1"
top: "fire2/bn_expand1x1"
}
layer {
name: "fire2/relu_expand1x1"
type: "ReLU"
bottom: "fire2/bn_expand1x1"
top: "fire2/bn_expand1x1"
}
layer {
name: "fire2/expand3x3"
type: "Convolution"
bottom: "fire2/bn_expand1x1"
top: "fire2/expand3x3"
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire2/bn_expand3x3"
type: "BatchNorm"
bottom: "fire2/expand3x3"
top: "fire2/bn_expand3x3"
}
layer {
name: "fire2/relu_expand3x3"
type: "ReLU"
bottom: "fire2/bn_expand3x3"
top: "fire2/bn_expand3x3"
}
layer {
name: "fire2/concat"
type: "Concat"
bottom: "fire2/bn_expand1x1"
bottom: "fire2/bn_expand3x3"
top: "fire2/concat"
}
#fire2 ends: 128 channels
layer {
name: "fire3/squeeze1x1"
type: "Convolution"
bottom: "fire2/concat"
top: "fire3/squeeze1x1"
convolution_param {
num_output: 16
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire3/bn_squeeze1x1"
type: "BatchNorm"
bottom: "fire3/squeeze1x1"
top: "fire3/bn_squeeze1x1"
}
layer {
name: "fire3/relu_squeeze1x1"
type: "ReLU"
bottom: "fire3/bn_squeeze1x1"
top: "fire3/bn_squeeze1x1"
}
layer {
name: "fire3/expand1x1"
type: "Convolution"
bottom: "fire3/bn_squeeze1x1"
top: "fire3/expand1x1"
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire3/bn_expand1x1"
type: "BatchNorm"
bottom: "fire3/expand1x1"
top: "fire3/bn_expand1x1"
}
layer {
name: "fire3/relu_expand1x1"
type: "ReLU"
bottom: "fire3/bn_expand1x1"
top: "fire3/bn_expand1x1"
}
layer {
name: "fire3/expand3x3"
type: "Convolution"
bottom: "fire3/bn_expand1x1"
top: "fire3/expand3x3"
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire3/bn_expand3x3"
type: "BatchNorm"
bottom: "fire3/expand3x3"
top: "fire3/bn_expand3x3"
}
layer {
name: "fire3/relu_expand3x3"
type: "ReLU"
bottom: "fire3/bn_expand3x3"
top: "fire3/bn_expand3x3"
}
layer {
name: "fire3/concat"
type: "Concat"
bottom: "fire3/bn_expand1x1"
bottom: "fire3/bn_expand3x3"
top: "fire3/concat"
}
#fire3 ends: 128 channels
layer {
name: "bypass_23"
type: "Eltwise"
bottom: "fire2/concat"
bottom: "fire3/concat"
top: "fire3_EltAdd"
}
layer {
name: "fire4/squeeze1x1"
type: "Convolution"
bottom: "fire3_EltAdd"
top: "fire4/squeeze1x1"
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire4/bn_squeeze1x1"
type: "BatchNorm"
bottom: "fire4/squeeze1x1"
top: "fire4/bn_squeeze1x1"
}
layer {
name: "fire4/relu_squeeze1x1"
type: "ReLU"
bottom: "fire4/bn_squeeze1x1"
top: "fire4/bn_squeeze1x1"
}
layer {
name: "fire4/expand1x1"
type: "Convolution"
bottom: "fire4/bn_squeeze1x1"
top: "fire4/expand1x1"
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire4/bn_expand1x1"
type: "BatchNorm"
bottom: "fire4/expand1x1"
top: "fire4/bn_expand1x1"
}
layer {
name: "fire4/relu_expand1x1"
type: "ReLU"
bottom: "fire4/bn_expand1x1"
top: "fire4/bn_expand1x1"
}
layer {
name: "fire4/expand3x3"
type: "Convolution"
bottom: "fire4/bn_expand1x1"
top: "fire4/expand3x3"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire4/bn_expand3x3"
type: "BatchNorm"
bottom: "fire4/expand3x3"
top: "fire4/bn_expand3x3"
}
layer {
name: "fire4/relu_expand3x3"
type: "ReLU"
bottom: "fire4/bn_expand3x3"
top: "fire4/bn_expand3x3"
}
layer {
name: "fire4/concat"
type: "Concat"
bottom: "fire4/bn_expand1x1"
bottom: "fire4/bn_expand3x3"
top: "fire4/concat"
}
#fire4 ends: 256 channels
layer {
name: "pool4"
type: "Pooling"
bottom: "fire4/concat"
top: "pool4"
pooling_param {
Pool: MAX
kernel_size: 2
stride: 2
}
}
#fire4 ends: 256 channels / pooled
layer {
name: "fire5/squeeze1x1"
type: "Convolution"
bottom: "pool4"
top: "fire5/squeeze1x1"
convolution_param {
num_output: 32
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire5/bn_squeeze1x1"
type: "BatchNorm"
bottom: "fire5/squeeze1x1"
top: "fire5/bn_squeeze1x1"
}
layer {
name: "fire5/relu_squeeze1x1"
type: "ReLU"
bottom: "fire5/bn_squeeze1x1"
top: "fire5/bn_squeeze1x1"
}
layer {
name: "fire5/expand1x1"
type: "Convolution"
bottom: "fire5/bn_squeeze1x1"
top: "fire5/expand1x1"
convolution_param {
num_output: 128
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire5/bn_expand1x1"
type: "BatchNorm"
bottom: "fire5/expand1x1"
top: "fire5/bn_expand1x1"
}
layer {
name: "fire5/relu_expand1x1"
type: "ReLU"
bottom: "fire5/bn_expand1x1"
top: "fire5/bn_expand1x1"
}
layer {
name: "fire5/expand3x3"
type: "Convolution"
bottom: "fire5/bn_expand1x1"
top: "fire5/expand3x3"
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire5/bn_expand3x3"
type: "BatchNorm"
bottom: "fire5/expand3x3"
top: "fire5/bn_expand3x3"
}
layer {
name: "fire5/relu_expand3x3"
type: "ReLU"
bottom: "fire5/bn_expand3x3"
top: "fire5/bn_expand3x3"
}
layer {
name: "fire5/concat"
type: "Concat"
bottom: "fire5/bn_expand1x1"
bottom: "fire5/bn_expand3x3"
top: "fire5/concat"
}
#fire5 ends: 256 channels
layer {
name: "bypass_45"
type: "Eltwise"
bottom: "pool4"
bottom: "fire5/concat"
top: "fire5_EltAdd"
}
layer {
name: "fire6/squeeze1x1"
type: "Convolution"
bottom: "fire5_EltAdd"
top: "fire6/squeeze1x1"
convolution_param {
num_output: 48
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire6/bn_squeeze1x1"
type: "BatchNorm"
bottom: "fire6/squeeze1x1"
top: "fire6/bn_squeeze1x1"
}
layer {
name: "fire6/relu_squeeze1x1"
type: "ReLU"
bottom: "fire6/bn_squeeze1x1"
top: "fire6/bn_squeeze1x1"
}
layer {
name: "fire6/expand1x1"
type: "Convolution"
bottom: "fire6/bn_squeeze1x1"
top: "fire6/expand1x1"
convolution_param {
num_output: 192
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire6/bn_expand1x1"
type: "BatchNorm"
bottom: "fire6/expand1x1"
top: "fire6/bn_expand1x1"
}
layer {
name: "fire6/relu_expand1x1"
type: "ReLU"
bottom: "fire6/bn_expand1x1"
top: "fire6/bn_expand1x1"
}
layer {
name: "fire6/expand3x3"
type: "Convolution"
bottom: "fire6/bn_expand1x1"
top: "fire6/expand3x3"
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire6/bn_expand3x3"
type: "BatchNorm"
bottom: "fire6/expand3x3"
top: "fire6/bn_expand3x3"
}
layer {
name: "fire6/relu_expand3x3"
type: "ReLU"
bottom: "fire6/bn_expand3x3"
top: "fire6/bn_expand3x3"
}
layer {
name: "fire6/concat"
type: "Concat"
bottom: "fire6/bn_expand1x1"
bottom: "fire6/bn_expand3x3"
top: "fire6/concat"
}
#fire6 ends: 384 channels
layer {
name: "fire7/squeeze1x1"
type: "Convolution"
bottom: "fire6/concat"
top: "fire7/squeeze1x1"
convolution_param {
num_output: 48
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire7/bn_squeeze1x1"
type: "BatchNorm"
bottom: "fire7/squeeze1x1"
top: "fire7/bn_squeeze1x1"
}
layer {
name: "fire7/relu_squeeze1x1"
type: "ReLU"
bottom: "fire7/bn_squeeze1x1"
top: "fire7/bn_squeeze1x1"
}
layer {
name: "fire7/expand1x1"
type: "Convolution"
bottom: "fire7/bn_squeeze1x1"
top: "fire7/expand1x1"
convolution_param {
num_output: 192
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire7/bn_expand1x1"
type: "BatchNorm"
bottom: "fire7/expand1x1"
top: "fire7/bn_expand1x1"
}
layer {
name: "fire7/relu_expand1x1"
type: "ReLU"
bottom: "fire7/bn_expand1x1"
top: "fire7/bn_expand1x1"
}
layer {
name: "fire7/expand3x3"
type: "Convolution"
bottom: "fire7/bn_expand1x1"
top: "fire7/expand3x3"
convolution_param {
num_output: 192
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire7/bn_expand3x3"
type: "BatchNorm"
bottom: "fire7/expand3x3"
top: "fire7/bn_expand3x3"
}
layer {
name: "fire7/relu_expand3x3"
type: "ReLU"
bottom: "fire7/bn_expand3x3"
top: "fire7/bn_expand3x3"
}
layer {
name: "fire7/concat"
type: "Concat"
bottom: "fire7/bn_expand1x1"
bottom: "fire7/bn_expand3x3"
top: "fire7/concat"
}
#fire7 ends: 384 channels
layer {
name: "bypass_67"
type: "Eltwise"
bottom: "fire6/concat"
bottom: "fire7/concat"
top: "fire7_EltAdd"
}
layer {
name: "fire8/squeeze1x1"
type: "Convolution"
bottom: "fire7_EltAdd"
top: "fire8/squeeze1x1"
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire8/bn_squeeze1x1"
type: "BatchNorm"
bottom: "fire8/squeeze1x1"
top: "fire8/bn_squeeze1x1"
}
layer {
name: "fire8/relu_squeeze1x1"
type: "ReLU"
bottom: "fire8/bn_squeeze1x1"
top: "fire8/bn_squeeze1x1"
}
layer {
name: "fire8/expand1x1"
type: "Convolution"
bottom: "fire8/bn_squeeze1x1"
top: "fire8/expand1x1"
convolution_param {
num_output: 256
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire8/bn_expand1x1"
type: "BatchNorm"
bottom: "fire8/expand1x1"
top: "fire8/bn_expand1x1"
}
layer {
name: "fire8/relu_expand1x1"
type: "ReLU"
bottom: "fire8/bn_expand1x1"
top: "fire8/bn_expand1x1"
}
layer {
name: "fire8/expand3x3"
type: "Convolution"
bottom: "fire8/bn_expand1x1"
top: "fire8/expand3x3"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire8/bn_expand3x3"
type: "BatchNorm"
bottom: "fire8/expand3x3"
top: "fire8/bn_expand3x3"
}
layer {
name: "fire8/relu_expand3x3"
type: "ReLU"
bottom: "fire8/bn_expand3x3"
top: "fire8/bn_expand3x3"
}
layer {
name: "fire8/concat"
type: "Concat"
bottom: "fire8/bn_expand1x1"
bottom: "fire8/bn_expand3x3"
top: "fire8/concat"
}
#fire8 ends: 512 channels
layer {
name: "pool8"
type: "Pooling"
bottom: "fire8/concat"
top: "pool8"
pooling_param {
Pool: MAX
kernel_size: 2
stride: 2
}
}
#fire8 ends: 512 channels
layer {
name: "fire9/squeeze1x1"
type: "Convolution"
bottom: "pool8"
top: "fire9/squeeze1x1"
convolution_param {
num_output: 64
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire9/bn_squeeze1x1"
type: "BatchNorm"
bottom: "fire9/squeeze1x1"
top: "fire9/bn_squeeze1x1"
}
layer {
name: "fire9/relu_squeeze1x1"
type: "ReLU"
bottom: "fire9/bn_squeeze1x1"
top: "fire9/bn_squeeze1x1"
}
layer {
name: "fire9/expand1x1"
type: "Convolution"
bottom: "fire9/bn_squeeze1x1"
top: "fire9/expand1x1"
convolution_param {
num_output: 256
kernel_size: 1
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire9/bn_expand1x1"
type: "BatchNorm"
bottom: "fire9/expand1x1"
top: "fire9/bn_expand1x1"
}
layer {
name: "fire9/relu_expand1x1"
type: "ReLU"
bottom: "fire9/bn_expand1x1"
top: "fire9/bn_expand1x1"
}
layer {
name: "fire9/expand3x3"
type: "Convolution"
bottom: "fire9/bn_expand1x1"
top: "fire9/expand3x3"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
}
}
layer {
name: "fire9/bn_expand3x3"
type: "BatchNorm"
bottom: "fire9/expand3x3"
top: "fire9/bn_expand3x3"
}
layer {
name: "fire9/relu_expand3x3"
type: "ReLU"
bottom: "fire9/bn_expand3x3"
top: "fire9/bn_expand3x3"
}
layer {
name: "fire9/concat"
type: "Concat"
bottom: "fire9/bn_expand1x1"
bottom: "fire9/bn_expand3x3"
top: "fire9/concat"
}
#fire9 ends: 512 channels
layer {
name: "conv10_new"
type: "Convolution"
bottom: "fire9/concat"
top: "conv10"
convolution_param {
num_output: 3
kernel_size: 1
weight_filler {
type: "gaussian"
mean: 0.0
std: 0.01
}
}
}
layer {
name: "pool10"
type: "Pooling"
bottom: "conv10"
top: "pool10"
pooling_param {
pool: AVE
global_pooling: true
}
}
# loss, top1, top5
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "pool10"
bottom: "label"
top: "loss"
include {
# phase: TRAIN
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "pool10"
bottom: "label"
top: "accuracy"
#include {
# phase: TEST
#}
}
The num_output in the last convolutional layer conv10 modifies the number of categories.
Model hyperparameter configuration file
solver.prototxt
test_iter: 2000 #not subject to iter_size test_interval: 1000000 # base_lr: 0.0001 base_lr: 0.005 # Learning rate display: 40 # max_iter: 600000 max_iter: 200000 #Number of iterations iter_size: 2 #global batch size = batch_size * iter_size lr_policy: "poly" power: 1.0 #linearly decrease LR momentum: 0.9 weight_decay: 0.0002 snapshot: 10000 # How many iterations to save a model snapshot_prefix: "/data/zxc/classfication/model/model_cotta/cotta_" # Model saving path solver_mode: GPU random_seed: 42 net: "./trainNets_drive/trainval.prototxt" # Path to the network structure configuration file test_initialization: false average_loss: 40
- max_iter: Caffe uses the number of iterations instead of the number of rounds of pytorch. In pytorch, training the entire training set is one round, while in caffe, training a batch_size of data is one iteration. If you want to be equivalent to the number of rounds, one round is equal to: len(train_data) / batch_size. If there is a remainder, it depends on whether the dataloader in pytorch is set to round off or to a batch. If it is rounded off, it will be rounded down. If it is not rounded off, it will be rounded up.
- snapshot_prefix: The last part is the prefix of each saved model, as shown in the figure:
Run command
Write the run command into the bash file:
train.sh
/home/seg/anaconda3/envs/zxc/bin/caffe train -gpu 1 -solver ./solvers/solver_3.prototxt -weights=/data/classfication/model/model_cotta/cotta__iter_200000.caffemodel 2> & amp; 1 | tee log_3_4_class.txt
- -gpu: Which card to choose, if only one card is 0;
- -solver: followed by the network hyperparameter configuration file path;
- -weights: followed by the pre-training model. You can use the official pre-training model of the caffe version of squeezenet. Here, the training is interrupted and the training is continued from the breakpoint.
After writing, source activate environment name enters the source environment, and then source train.sh runs the bash file to start training.