1.生成txt文件
分类网络可以不用制作lmdb数据，直接用txt文件作为输入源，一般习惯创建一个images文件夹，然后里面每一类单独一个文件夹，每个文件夹里面存放某一类的图片，然后用Python生成txt，脚本如下。
import os import random base_dir = "/data/chw/changjing_fenlei_20200622/yugeshuju/images" f_train = open("./train_origin.txt", 'w') #f_val = open("./val.txt", 'w') f_test = open("./test.txt", 'w') f_label = open("./label.txt", "w") for root, dirs, files in os.walk(base_dir): i = 0 for dirname in dirs: label_name = dirname f_label.write(label_name + " " + str(i) + " ") path = os.path.join(root, dirname) for jpeg_name in os.listdir(path): jpeg_path = os.path.join(path, jpeg_name) feed = random.randint(0, 10) if feed <= 9: f_train.write(jpeg_path + " " + str(i) + " ") if feed == 10: f_test.write(jpeg_path + " " + str(i) + " ") i=i + 1 f_train.close() #f_val.close() f_test.close() f_label.close()
生成txt文件里面，某一类的图片是紧挨着的，要把它打乱，用下面脚本
import os import random out = open("./train.txt",'w') lines=[] with open("./train_origin.txt", 'r') as infile: for line in infile: lines.append(line) random.shuffle(lines) random.shuffle(lines) random.shuffle(lines) random.shuffle(lines) random.shuffle(lines) for line in lines: out.write(line)
2.训练
首先修改train.prototxt
例如
# Enter your network definition here. # Use Shift+Enter to update the visualization. name: "shufflenet_v2" layer { name: "Data" type: "ImageData" top: "data" top: "label" include { phase: TRAIN } transform_param { mirror: true mean_value: 104 mean_value: 117 mean_value: 123 } image_data_param { mirror: true source: "./train.txt" #root_folder: "/images/" new_height: 224 new_ 224 batch_size: 8 shuffle: true #每个epoch都会进行shuffle #label_dim: 38 } } layer { name: "Data" type: "ImageData" top: "data" top: "label" include { phase: TEST } transform_param { mean_value: 104 mean_value: 117 mean_value: 123 } image_data_param { source: "./test.txt" #root_folder: "/images/" new_height: 224 new_ 224 batch_size: 8 #label_dim: 38 } } ########################################################## #layer { # name: "data" # type: "Data" # top: "data" # top: "label" # include { # phase: TRAIN # } # transform_param { # mirror: true # mean_file: "deepsort_mean.binaryproto" # } # data_param { # source: "deepsort_train_lmdb" # batch_size: 10 # backend: LMDB # } #} #layer { # name: "data" # type: "Data" # top: "data" # top: "label" # include { # phase: TEST # } # transform_param { # mirror: false # mean_file: "deepsort_mean.binaryproto" # } # data_param { # source: "deepsort_val_lmdb" # batch_size: 10 # backend: LMDB # } #} ########################################### layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" convolution_param { num_output: 24 pad: 1 kernel_size: 3 stride: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "conv1_bn" type: "BatchNorm" bottom: "conv1" top: "conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "conv1_scale" bottom: "conv1" top: "conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "conv1_relu" type: "ReLU" bottom: "conv1" top: "conv1" } layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name: "branch1_1_conv1" type: "ConvolutionDepthwise" bottom: "pool1" top: "branch1_1_conv1" convolution_param { num_output: 24 kernel_size: 3 stride: 2 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch1_1_conv1_bn" type: "BatchNorm" bottom: "branch1_1_conv1" top: "branch1_1_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch1_1_conv1_scale" bottom: "branch1_1_conv1" top: "branch1_1_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch1_1_conv2" type: "Convolution" bottom: "branch1_1_conv1" top: "branch1_1_conv2" convolution_param { num_output: 58 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch1_1_conv2_bn" type: "BatchNorm" bottom: "branch1_1_conv2" top: "branch1_1_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch1_1_conv2_scale" bottom: "branch1_1_conv2" top: "branch1_1_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch1_1_conv2_relu" type: "ReLU" bottom: "branch1_1_conv2" top: "branch1_1_conv2" } layer { name: "branch1_2_conv1" type: "Convolution" bottom: "pool1" top: "branch1_2_conv1" convolution_param { num_output: 58 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch1_2_conv1_bn" type: "BatchNorm" bottom: "branch1_2_conv1" top: "branch1_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch1_2_conv1_scale" bottom: "branch1_2_conv1" top: "branch1_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch1_2_conv1_relu" type: "ReLU" bottom: "branch1_2_conv1" top: "branch1_2_conv1" } layer { name: "branch1_2_conv2" type: "ConvolutionDepthwise" bottom: "branch1_2_conv1" top: "branch1_2_conv2" convolution_param { num_output: 58 kernel_size: 3 stride: 2 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch1_2_conv2_bn" type: "BatchNorm" bottom: "branch1_2_conv2" top: "branch1_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch1_2_conv2_scale" bottom: "branch1_2_conv2" top: "branch1_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch1_2_conv3" type: "Convolution" bottom: "branch1_2_conv2" top: "branch1_2_conv3" convolution_param { num_output: 58 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch1_2_conv3_bn" type: "BatchNorm" bottom: "branch1_2_conv3" top: "branch1_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch1_2_conv3_scale" bottom: "branch1_2_conv3" top: "branch1_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch1_2_conv3_relu" type: "ReLU" bottom: "branch1_2_conv3" top: "branch1_2_conv3" } layer { name: "concat1" type: "Concat" bottom: "branch1_1_conv2" bottom: "branch1_2_conv3" top: "concat1" } layer { name: "shuffle1" type: "ShuffleChannel" bottom: "concat1" top: "shuffle1" shuffle_channel_param { group: 2 } } layer { name: "slice2" type: "Slice" bottom: "shuffle1" top: "branch2_1" top: "branch2_2" slice_param { slice_point: 58 axis: 1 } } layer { name: "branch2_2_conv1" type: "Convolution" bottom: "branch2_2" top: "branch2_2_conv1" convolution_param { num_output: 58 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch2_2_conv1_bn" type: "BatchNorm" bottom: "branch2_2_conv1" top: "branch2_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch2_2_conv1_scale" bottom: "branch2_2_conv1" top: "branch2_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch2_2_conv1_relu" type: "ReLU" bottom: "branch2_2_conv1" top: "branch2_2_conv1" } layer { name: "branch2_2_conv2" type: "ConvolutionDepthwise" bottom: "branch2_2_conv1" top: "branch2_2_conv2" convolution_param { num_output: 58 kernel_size: 3 stride: 1 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch2_2_conv2_bn" type: "BatchNorm" bottom: "branch2_2_conv2" top: "branch2_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch2_2_conv2_scale" bottom: "branch2_2_conv2" top: "branch2_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch2_2_conv3" type: "Convolution" bottom: "branch2_2_conv2" top: "branch2_2_conv3" convolution_param { num_output: 58 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch2_2_conv3_bn" type: "BatchNorm" bottom: "branch2_2_conv3" top: "branch2_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch2_2_conv3_scale" bottom: "branch2_2_conv3" top: "branch2_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch2_2_conv3_relu" type: "ReLU" bottom: "branch2_2_conv3" top: "branch2_2_conv3" } layer { name: "concat2" type: "Concat" bottom: "branch2_1" bottom: "branch2_2_conv3" top: "concat2" } layer { name: "shuffle2" type: "ShuffleChannel" bottom: "concat2" top: "shuffle2" shuffle_channel_param { group: 2 } } layer { name: "slice3" type: "Slice" bottom: "shuffle2" top: "branch3_1" top: "branch3_2" slice_param { slice_point: 58 axis: 1 } } layer { name: "branch3_2_conv1" type: "Convolution" bottom: "branch3_2" top: "branch3_2_conv1" convolution_param { num_output: 58 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch3_2_conv1_bn" type: "BatchNorm" bottom: "branch3_2_conv1" top: "branch3_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch3_2_conv1_scale" bottom: "branch3_2_conv1" top: "branch3_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch3_2_conv1_relu" type: "ReLU" bottom: "branch3_2_conv1" top: "branch3_2_conv1" } layer { name: "branch3_2_conv2" type: "ConvolutionDepthwise" bottom: "branch3_2_conv1" top: "branch3_2_conv2" convolution_param { num_output: 58 kernel_size: 3 stride: 1 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch3_2_conv2_bn" type: "BatchNorm" bottom: "branch3_2_conv2" top: "branch3_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch3_2_conv2_scale" bottom: "branch3_2_conv2" top: "branch3_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch3_2_conv3" type: "Convolution" bottom: "branch3_2_conv2" top: "branch3_2_conv3" convolution_param { num_output: 58 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch3_2_conv3_bn" type: "BatchNorm" bottom: "branch3_2_conv3" top: "branch3_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch3_2_conv3_scale" bottom: "branch3_2_conv3" top: "branch3_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch3_2_conv3_relu" type: "ReLU" bottom: "branch3_2_conv3" top: "branch3_2_conv3" } layer { name: "concat3" type: "Concat" bottom: "branch3_1" bottom: "branch3_2_conv3" top: "concat3" } layer { name: "shuffle3" type: "ShuffleChannel" bottom: "concat3" top: "shuffle3" shuffle_channel_param { group: 2 } } layer { name: "slice4" type: "Slice" bottom: "shuffle3" top: "branch4_1" top: "branch4_2" slice_param { slice_point: 58 axis: 1 } } layer { name: "branch4_2_conv1" type: "Convolution" bottom: "branch4_2" top: "branch4_2_conv1" convolution_param { num_output: 58 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch4_2_conv1_bn" type: "BatchNorm" bottom: "branch4_2_conv1" top: "branch4_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch4_2_conv1_scale" bottom: "branch4_2_conv1" top: "branch4_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch4_2_conv1_relu" type: "ReLU" bottom: "branch4_2_conv1" top: "branch4_2_conv1" } layer { name: "branch4_2_conv2" type: "ConvolutionDepthwise" bottom: "branch4_2_conv1" top: "branch4_2_conv2" convolution_param { num_output: 58 kernel_size: 3 stride: 1 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch4_2_conv2_bn" type: "BatchNorm" bottom: "branch4_2_conv2" top: "branch4_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch4_2_conv2_scale" bottom: "branch4_2_conv2" top: "branch4_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch4_2_conv3" type: "Convolution" bottom: "branch4_2_conv2" top: "branch4_2_conv3" convolution_param { num_output: 58 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch4_2_conv3_bn" type: "BatchNorm" bottom: "branch4_2_conv3" top: "branch4_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch4_2_conv3_scale" bottom: "branch4_2_conv3" top: "branch4_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch4_2_conv3_relu" type: "ReLU" bottom: "branch4_2_conv3" top: "branch4_2_conv3" } layer { name: "concat4" type: "Concat" bottom: "branch4_1" bottom: "branch4_2_conv3" top: "concat4" } layer { name: "shuffle4" type: "ShuffleChannel" bottom: "concat4" top: "shuffle4" shuffle_channel_param { group: 2 } } layer { name: "branch5_1_conv1" type: "ConvolutionDepthwise" bottom: "shuffle4" top: "branch5_1_conv1" convolution_param { num_output: 116 kernel_size: 3 stride: 2 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch5_1_conv1_bn" type: "BatchNorm" bottom: "branch5_1_conv1" top: "branch5_1_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch5_1_conv1_scale" bottom: "branch5_1_conv1" top: "branch5_1_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch5_1_conv2" type: "Convolution" bottom: "branch5_1_conv1" top: "branch5_1_conv2" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch5_1_conv2_bn" type: "BatchNorm" bottom: "branch5_1_conv2" top: "branch5_1_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch5_1_conv2_scale" bottom: "branch5_1_conv2" top: "branch5_1_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch5_1_conv2_relu" type: "ReLU" bottom: "branch5_1_conv2" top: "branch5_1_conv2" } layer { name: "branch5_2_conv1" type: "Convolution" bottom: "shuffle4" top: "branch5_2_conv1" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch5_2_conv1_bn" type: "BatchNorm" bottom: "branch5_2_conv1" top: "branch5_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch5_2_conv1_scale" bottom: "branch5_2_conv1" top: "branch5_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch5_2_conv1_relu" type: "ReLU" bottom: "branch5_2_conv1" top: "branch5_2_conv1" } layer { name: "branch5_2_conv2" type: "ConvolutionDepthwise" bottom: "branch5_2_conv1" top: "branch5_2_conv2" convolution_param { num_output: 116 kernel_size: 3 stride: 2 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch5_2_conv2_bn" type: "BatchNorm" bottom: "branch5_2_conv2" top: "branch5_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch5_2_conv2_scale" bottom: "branch5_2_conv2" top: "branch5_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch5_2_conv3" type: "Convolution" bottom: "branch5_2_conv2" top: "branch5_2_conv3" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch5_2_conv3_bn" type: "BatchNorm" bottom: "branch5_2_conv3" top: "branch5_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch5_2_conv3_scale" bottom: "branch5_2_conv3" top: "branch5_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch5_2_conv3_relu" type: "ReLU" bottom: "branch5_2_conv3" top: "branch5_2_conv3" } layer { name: "concat5" type: "Concat" bottom: "branch5_1_conv2" bottom: "branch5_2_conv3" top: "concat5" } layer { name: "shuffle5" type: "ShuffleChannel" bottom: "concat5" top: "shuffle5" shuffle_channel_param { group: 2 } } layer { name: "slice6" type: "Slice" bottom: "shuffle5" top: "branch6_1" top: "branch6_2" slice_param { slice_point: 116 axis: 1 } } layer { name: "branch6_2_conv1" type: "Convolution" bottom: "branch6_2" top: "branch6_2_conv1" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch6_2_conv1_bn" type: "BatchNorm" bottom: "branch6_2_conv1" top: "branch6_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch6_2_conv1_scale" bottom: "branch6_2_conv1" top: "branch6_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch6_2_conv1_relu" type: "ReLU" bottom: "branch6_2_conv1" top: "branch6_2_conv1" } layer { name: "branch6_2_conv2" type: "ConvolutionDepthwise" bottom: "branch6_2_conv1" top: "branch6_2_conv2" convolution_param { num_output: 116 kernel_size: 3 stride: 1 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch6_2_conv2_bn" type: "BatchNorm" bottom: "branch6_2_conv2" top: "branch6_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch6_2_conv2_scale" bottom: "branch6_2_conv2" top: "branch6_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch6_2_conv3" type: "Convolution" bottom: "branch6_2_conv2" top: "branch6_2_conv3" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch6_2_conv3_bn" type: "BatchNorm" bottom: "branch6_2_conv3" top: "branch6_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch6_2_conv3_scale" bottom: "branch6_2_conv3" top: "branch6_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch6_2_conv3_relu" type: "ReLU" bottom: "branch6_2_conv3" top: "branch6_2_conv3" } layer { name: "concat6" type: "Concat" bottom: "branch6_1" bottom: "branch6_2_conv3" top: "concat6" } layer { name: "shuffle6" type: "ShuffleChannel" bottom: "concat6" top: "shuffle6" shuffle_channel_param { group: 2 } } layer { name: "slice7" type: "Slice" bottom: "shuffle6" top: "branch7_1" top: "branch7_2" slice_param { slice_point: 116 axis: 1 } } layer { name: "branch7_2_conv1" type: "Convolution" bottom: "branch7_2" top: "branch7_2_conv1" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch7_2_conv1_bn" type: "BatchNorm" bottom: "branch7_2_conv1" top: "branch7_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch7_2_conv1_scale" bottom: "branch7_2_conv1" top: "branch7_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch7_2_conv1_relu" type: "ReLU" bottom: "branch7_2_conv1" top: "branch7_2_conv1" } layer { name: "branch7_2_conv2" type: "ConvolutionDepthwise" bottom: "branch7_2_conv1" top: "branch7_2_conv2" convolution_param { num_output: 116 kernel_size: 3 stride: 1 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch7_2_conv2_bn" type: "BatchNorm" bottom: "branch7_2_conv2" top: "branch7_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch7_2_conv2_scale" bottom: "branch7_2_conv2" top: "branch7_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch7_2_conv3" type: "Convolution" bottom: "branch7_2_conv2" top: "branch7_2_conv3" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch7_2_conv3_bn" type: "BatchNorm" bottom: "branch7_2_conv3" top: "branch7_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch7_2_conv3_scale" bottom: "branch7_2_conv3" top: "branch7_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch7_2_conv3_relu" type: "ReLU" bottom: "branch7_2_conv3" top: "branch7_2_conv3" } layer { name: "concat7" type: "Concat" bottom: "branch7_1" bottom: "branch7_2_conv3" top: "concat7" } layer { name: "shuffle7" type: "ShuffleChannel" bottom: "concat7" top: "shuffle7" shuffle_channel_param { group: 2 } } layer { name: "slice8" type: "Slice" bottom: "shuffle7" top: "branch8_1" top: "branch8_2" slice_param { slice_point: 116 axis: 1 } } layer { name: "branch8_2_conv1" type: "Convolution" bottom: "branch8_2" top: "branch8_2_conv1" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch8_2_conv1_bn" type: "BatchNorm" bottom: "branch8_2_conv1" top: "branch8_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch8_2_conv1_scale" bottom: "branch8_2_conv1" top: "branch8_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch8_2_conv1_relu" type: "ReLU" bottom: "branch8_2_conv1" top: "branch8_2_conv1" } layer { name: "branch8_2_conv2" type: "ConvolutionDepthwise" bottom: "branch8_2_conv1" top: "branch8_2_conv2" convolution_param { num_output: 116 kernel_size: 3 stride: 1 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch8_2_conv2_bn" type: "BatchNorm" bottom: "branch8_2_conv2" top: "branch8_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch8_2_conv2_scale" bottom: "branch8_2_conv2" top: "branch8_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch8_2_conv3" type: "Convolution" bottom: "branch8_2_conv2" top: "branch8_2_conv3" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch8_2_conv3_bn" type: "BatchNorm" bottom: "branch8_2_conv3" top: "branch8_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch8_2_conv3_scale" bottom: "branch8_2_conv3" top: "branch8_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch8_2_conv3_relu" type: "ReLU" bottom: "branch8_2_conv3" top: "branch8_2_conv3" } layer { name: "concat8" type: "Concat" bottom: "branch8_1" bottom: "branch8_2_conv3" top: "concat8" } layer { name: "shuffle8" type: "ShuffleChannel" bottom: "concat8" top: "shuffle8" shuffle_channel_param { group: 2 } } layer { name: "slice9" type: "Slice" bottom: "shuffle8" top: "branch9_1" top: "branch9_2" slice_param { slice_point: 116 axis: 1 } } layer { name: "branch9_2_conv1" type: "Convolution" bottom: "branch9_2" top: "branch9_2_conv1" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch9_2_conv1_bn" type: "BatchNorm" bottom: "branch9_2_conv1" top: "branch9_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch9_2_conv1_scale" bottom: "branch9_2_conv1" top: "branch9_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch9_2_conv1_relu" type: "ReLU" bottom: "branch9_2_conv1" top: "branch9_2_conv1" } layer { name: "branch9_2_conv2" type: "ConvolutionDepthwise" bottom: "branch9_2_conv1" top: "branch9_2_conv2" convolution_param { num_output: 116 kernel_size: 3 stride: 1 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch9_2_conv2_bn" type: "BatchNorm" bottom: "branch9_2_conv2" top: "branch9_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch9_2_conv2_scale" bottom: "branch9_2_conv2" top: "branch9_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch9_2_conv3" type: "Convolution" bottom: "branch9_2_conv2" top: "branch9_2_conv3" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch9_2_conv3_bn" type: "BatchNorm" bottom: "branch9_2_conv3" top: "branch9_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch9_2_conv3_scale" bottom: "branch9_2_conv3" top: "branch9_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch9_2_conv3_relu" type: "ReLU" bottom: "branch9_2_conv3" top: "branch9_2_conv3" } layer { name: "concat9" type: "Concat" bottom: "branch9_1" bottom: "branch9_2_conv3" top: "concat9" } layer { name: "shuffle9" type: "ShuffleChannel" bottom: "concat9" top: "shuffle9" shuffle_channel_param { group: 2 } } layer { name: "slice10" type: "Slice" bottom: "shuffle9" top: "branch10_1" top: "branch10_2" slice_param { slice_point: 116 axis: 1 } } layer { name: "branch10_2_conv1" type: "Convolution" bottom: "branch10_2" top: "branch10_2_conv1" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch10_2_conv1_bn" type: "BatchNorm" bottom: "branch10_2_conv1" top: "branch10_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch10_2_conv1_scale" bottom: "branch10_2_conv1" top: "branch10_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch10_2_conv1_relu" type: "ReLU" bottom: "branch10_2_conv1" top: "branch10_2_conv1" } layer { name: "branch10_2_conv2" type: "ConvolutionDepthwise" bottom: "branch10_2_conv1" top: "branch10_2_conv2" convolution_param { num_output: 116 kernel_size: 3 stride: 1 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch10_2_conv2_bn" type: "BatchNorm" bottom: "branch10_2_conv2" top: "branch10_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch10_2_conv2_scale" bottom: "branch10_2_conv2" top: "branch10_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch10_2_conv3" type: "Convolution" bottom: "branch10_2_conv2" top: "branch10_2_conv3" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch10_2_conv3_bn" type: "BatchNorm" bottom: "branch10_2_conv3" top: "branch10_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch10_2_conv3_scale" bottom: "branch10_2_conv3" top: "branch10_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch10_2_conv3_relu" type: "ReLU" bottom: "branch10_2_conv3" top: "branch10_2_conv3" } layer { name: "concat10" type: "Concat" bottom: "branch10_1" bottom: "branch10_2_conv3" top: "concat10" } layer { name: "shuffle10" type: "ShuffleChannel" bottom: "concat10" top: "shuffle10" shuffle_channel_param { group: 2 } } layer { name: "slice11" type: "Slice" bottom: "shuffle10" top: "branch11_1" top: "branch11_2" slice_param { slice_point: 116 axis: 1 } } layer { name: "branch11_2_conv1" type: "Convolution" bottom: "branch11_2" top: "branch11_2_conv1" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch11_2_conv1_bn" type: "BatchNorm" bottom: "branch11_2_conv1" top: "branch11_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch11_2_conv1_scale" bottom: "branch11_2_conv1" top: "branch11_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch11_2_conv1_relu" type: "ReLU" bottom: "branch11_2_conv1" top: "branch11_2_conv1" } layer { name: "branch11_2_conv2" type: "ConvolutionDepthwise" bottom: "branch11_2_conv1" top: "branch11_2_conv2" convolution_param { num_output: 116 kernel_size: 3 stride: 1 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch11_2_conv2_bn" type: "BatchNorm" bottom: "branch11_2_conv2" top: "branch11_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch11_2_conv2_scale" bottom: "branch11_2_conv2" top: "branch11_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch11_2_conv3" type: "Convolution" bottom: "branch11_2_conv2" top: "branch11_2_conv3" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch11_2_conv3_bn" type: "BatchNorm" bottom: "branch11_2_conv3" top: "branch11_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch11_2_conv3_scale" bottom: "branch11_2_conv3" top: "branch11_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch11_2_conv3_relu" type: "ReLU" bottom: "branch11_2_conv3" top: "branch11_2_conv3" } layer { name: "concat11" type: "Concat" bottom: "branch11_1" bottom: "branch11_2_conv3" top: "concat11" } layer { name: "shuffle11" type: "ShuffleChannel" bottom: "concat11" top: "shuffle11" shuffle_channel_param { group: 2 } } layer { name: "slice12" type: "Slice" bottom: "shuffle11" top: "branch12_1" top: "branch12_2" slice_param { slice_point: 116 axis: 1 } } layer { name: "branch12_2_conv1" type: "Convolution" bottom: "branch12_2" top: "branch12_2_conv1" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch12_2_conv1_bn" type: "BatchNorm" bottom: "branch12_2_conv1" top: "branch12_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch12_2_conv1_scale" bottom: "branch12_2_conv1" top: "branch12_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch12_2_conv1_relu" type: "ReLU" bottom: "branch12_2_conv1" top: "branch12_2_conv1" } layer { name: "branch12_2_conv2" type: "ConvolutionDepthwise" bottom: "branch12_2_conv1" top: "branch12_2_conv2" convolution_param { num_output: 116 kernel_size: 3 stride: 1 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch12_2_conv2_bn" type: "BatchNorm" bottom: "branch12_2_conv2" top: "branch12_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch12_2_conv2_scale" bottom: "branch12_2_conv2" top: "branch12_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch12_2_conv3" type: "Convolution" bottom: "branch12_2_conv2" top: "branch12_2_conv3" convolution_param { num_output: 116 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch12_2_conv3_bn" type: "BatchNorm" bottom: "branch12_2_conv3" top: "branch12_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch12_2_conv3_scale" bottom: "branch12_2_conv3" top: "branch12_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch12_2_conv3_relu" type: "ReLU" bottom: "branch12_2_conv3" top: "branch12_2_conv3" } layer { name: "concat12" type: "Concat" bottom: "branch12_1" bottom: "branch12_2_conv3" top: "concat12" } layer { name: "shuffle12" type: "ShuffleChannel" bottom: "concat12" top: "shuffle12" shuffle_channel_param { group: 2 } } layer { name: "branch13_1_conv1" type: "ConvolutionDepthwise" bottom: "shuffle12" top: "branch13_1_conv1" convolution_param { num_output: 232 kernel_size: 3 stride: 2 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch13_1_conv1_bn" type: "BatchNorm" bottom: "branch13_1_conv1" top: "branch13_1_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch13_1_conv1_scale" bottom: "branch13_1_conv1" top: "branch13_1_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch13_1_conv2" type: "Convolution" bottom: "branch13_1_conv1" top: "branch13_1_conv2" convolution_param { num_output: 232 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch13_1_conv2_bn" type: "BatchNorm" bottom: "branch13_1_conv2" top: "branch13_1_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch13_1_conv2_scale" bottom: "branch13_1_conv2" top: "branch13_1_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch13_1_conv2_relu" type: "ReLU" bottom: "branch13_1_conv2" top: "branch13_1_conv2" } layer { name: "branch13_2_conv1" type: "Convolution" bottom: "shuffle12" top: "branch13_2_conv1" convolution_param { num_output: 232 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch13_2_conv1_bn" type: "BatchNorm" bottom: "branch13_2_conv1" top: "branch13_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch13_2_conv1_scale" bottom: "branch13_2_conv1" top: "branch13_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch13_2_conv1_relu" type: "ReLU" bottom: "branch13_2_conv1" top: "branch13_2_conv1" } layer { name: "branch13_2_conv2" type: "ConvolutionDepthwise" bottom: "branch13_2_conv1" top: "branch13_2_conv2" convolution_param { num_output: 232 kernel_size: 3 stride: 2 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch13_2_conv2_bn" type: "BatchNorm" bottom: "branch13_2_conv2" top: "branch13_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch13_2_conv2_scale" bottom: "branch13_2_conv2" top: "branch13_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch13_2_conv3" type: "Convolution" bottom: "branch13_2_conv2" top: "branch13_2_conv3" convolution_param { num_output: 232 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch13_2_conv3_bn" type: "BatchNorm" bottom: "branch13_2_conv3" top: "branch13_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch13_2_conv3_scale" bottom: "branch13_2_conv3" top: "branch13_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch13_2_conv3_relu" type: "ReLU" bottom: "branch13_2_conv3" top: "branch13_2_conv3" } layer { name: "concat13" type: "Concat" bottom: "branch13_1_conv2" bottom: "branch13_2_conv3" top: "concat13" } layer { name: "shuffle13" type: "ShuffleChannel" bottom: "concat13" top: "shuffle13" shuffle_channel_param { group: 2 } } layer { name: "slice14" type: "Slice" bottom: "shuffle13" top: "branch14_1" top: "branch14_2" slice_param { slice_point: 232 axis: 1 } } layer { name: "branch14_2_conv1" type: "Convolution" bottom: "branch14_2" top: "branch14_2_conv1" convolution_param { num_output: 232 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch14_2_conv1_bn" type: "BatchNorm" bottom: "branch14_2_conv1" top: "branch14_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch14_2_conv1_scale" bottom: "branch14_2_conv1" top: "branch14_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch14_2_conv1_relu" type: "ReLU" bottom: "branch14_2_conv1" top: "branch14_2_conv1" } layer { name: "branch14_2_conv2" type: "ConvolutionDepthwise" bottom: "branch14_2_conv1" top: "branch14_2_conv2" convolution_param { num_output: 232 kernel_size: 3 stride: 1 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch14_2_conv2_bn" type: "BatchNorm" bottom: "branch14_2_conv2" top: "branch14_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch14_2_conv2_scale" bottom: "branch14_2_conv2" top: "branch14_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch14_2_conv3" type: "Convolution" bottom: "branch14_2_conv2" top: "branch14_2_conv3" convolution_param { num_output: 232 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch14_2_conv3_bn" type: "BatchNorm" bottom: "branch14_2_conv3" top: "branch14_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch14_2_conv3_scale" bottom: "branch14_2_conv3" top: "branch14_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch14_2_conv3_relu" type: "ReLU" bottom: "branch14_2_conv3" top: "branch14_2_conv3" } layer { name: "concat14" type: "Concat" bottom: "branch14_1" bottom: "branch14_2_conv3" top: "concat14" } layer { name: "shuffle14" type: "ShuffleChannel" bottom: "concat14" top: "shuffle14" shuffle_channel_param { group: 2 } } layer { name: "slice15" type: "Slice" bottom: "shuffle14" top: "branch15_1" top: "branch15_2" slice_param { slice_point: 232 axis: 1 } } layer { name: "branch15_2_conv1" type: "Convolution" bottom: "branch15_2" top: "branch15_2_conv1" convolution_param { num_output: 232 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch15_2_conv1_bn" type: "BatchNorm" bottom: "branch15_2_conv1" top: "branch15_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch15_2_conv1_scale" bottom: "branch15_2_conv1" top: "branch15_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch15_2_conv1_relu" type: "ReLU" bottom: "branch15_2_conv1" top: "branch15_2_conv1" } layer { name: "branch15_2_conv2" type: "ConvolutionDepthwise" bottom: "branch15_2_conv1" top: "branch15_2_conv2" convolution_param { num_output: 232 kernel_size: 3 stride: 1 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch15_2_conv2_bn" type: "BatchNorm" bottom: "branch15_2_conv2" top: "branch15_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch15_2_conv2_scale" bottom: "branch15_2_conv2" top: "branch15_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch15_2_conv3" type: "Convolution" bottom: "branch15_2_conv2" top: "branch15_2_conv3" convolution_param { num_output: 232 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch15_2_conv3_bn" type: "BatchNorm" bottom: "branch15_2_conv3" top: "branch15_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch15_2_conv3_scale" bottom: "branch15_2_conv3" top: "branch15_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch15_2_conv3_relu" type: "ReLU" bottom: "branch15_2_conv3" top: "branch15_2_conv3" } layer { name: "concat15" type: "Concat" bottom: "branch15_1" bottom: "branch15_2_conv3" top: "concat15" } layer { name: "shuffle15" type: "ShuffleChannel" bottom: "concat15" top: "shuffle15" shuffle_channel_param { group: 2 } } layer { name: "slice16" type: "Slice" bottom: "shuffle15" top: "branch16_1" top: "branch16_2" slice_param { slice_point: 232 axis: 1 } } layer { name: "branch16_2_conv1" type: "Convolution" bottom: "branch16_2" top: "branch16_2_conv1" convolution_param { num_output: 232 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch16_2_conv1_bn" type: "BatchNorm" bottom: "branch16_2_conv1" top: "branch16_2_conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch16_2_conv1_scale" bottom: "branch16_2_conv1" top: "branch16_2_conv1" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch16_2_conv1_relu" type: "ReLU" bottom: "branch16_2_conv1" top: "branch16_2_conv1" } layer { name: "branch16_2_conv2" type: "ConvolutionDepthwise" bottom: "branch16_2_conv1" top: "branch16_2_conv2" convolution_param { num_output: 232 kernel_size: 3 stride: 1 pad: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch16_2_conv2_bn" type: "BatchNorm" bottom: "branch16_2_conv2" top: "branch16_2_conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch16_2_conv2_scale" bottom: "branch16_2_conv2" top: "branch16_2_conv2" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch16_2_conv3" type: "Convolution" bottom: "branch16_2_conv2" top: "branch16_2_conv3" convolution_param { num_output: 232 kernel_size: 1 stride: 1 pad: 0 bias_term: false weight_filler { type: "msra" } } } layer { name: "branch16_2_conv3_bn" type: "BatchNorm" bottom: "branch16_2_conv3" top: "branch16_2_conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "branch16_2_conv3_scale" bottom: "branch16_2_conv3" top: "branch16_2_conv3" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "branch16_2_conv3_relu" type: "ReLU" bottom: "branch16_2_conv3" top: "branch16_2_conv3" } layer { name: "concat16" type: "Concat" bottom: "branch16_1" bottom: "branch16_2_conv3" top: "concat16" } layer { name: "shuffle16" type: "ShuffleChannel" bottom: "concat16" top: "shuffle16" shuffle_channel_param { group: 2 } } layer { name: "conv5" type: "Convolution" bottom: "shuffle16" top: "conv5" convolution_param { num_output: 1024 pad: 0 kernel_size: 1 stride: 1 bias_term: false weight_filler { type: "msra" } } } layer { name: "conv5_bn" type: "BatchNorm" bottom: "conv5" top: "conv5" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } } layer { name: "conv5_scale" bottom: "conv5" top: "conv5" type: "Scale" scale_param { filler { value: 1 } bias_term: true bias_filler { value: 0 } } } layer { name: "conv5_relu" type: "ReLU" bottom: "conv5" top: "conv5" } layer { name: "pool_ave" type: "Pooling" bottom: "conv5" top: "pool_ave" pooling_param { global_pooling : true pool: AVE } } #####################################################################3 layer { name: "featuremap_" type: "InnerProduct" bottom: "pool_ave" top: "featuremap_" param{ lr_mult: 1 decay_mult: 1 } param{ lr_mult: 2 decay_mult: 0 } inner_product_param{ num_output:256 weight_filler{ type: "xavier" } bias_filler{ type: "constant" value: 0 } } } ############################################################################## layer { name: "fc6_" type: "InnerProduct" bottom: "featuremap_" top: "fc6_" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { #num_output: 4872 num_output: 15 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } } } layer { name: "softmax_loss_" type: "SoftmaxWithLoss" bottom: "fc6_" bottom: "label" top: "softmax_loss_" } layer { name:"accuracy_" type:"Accuracy" bottom:"fc6_" bottom:"label" top:"accuracy_" include:{ phase:TEST } }
然后是solver
net: "/data/chw/changjing_fenlei_20200622/yugeshuju/train_val.prototxt" test_iter: 100 test_interval: 1000 base_lr: 0.0001 lr_policy: "multistep" #stepsize: 10000 gamma: 0.1 stepvalue: 32000 stepvalue: 48000 stepvalue: 56000 display: 400 #average_loss: 100 max_iter: 100000 momentum: 0.9 weight_decay: 0.005 snapshot: 5000 snapshot_prefix: "./result/changjing" solver_mode: GPU #device_id: [2] #test_initialization: true
然后就可以用下面的命令进行训练了
nohup /data/chw/refinedet/build/tools/caffe train --solver=./solver.prototxt --weights=pretrain.caffemodel --gpu=0,1,2 >&log.txt &
3.测试
训练完成之后可以用Python接口进行测试，Python脚本如下
#coding=utf-8 import numpy as np import cv2 import os import shutil #caffe_root = '/data/chw/refinedet' import sys #os.chdir(caffe_root) #sys.path.insert(0, 'python') import caffe import pdb import time caffe.set_device(0) caffe.set_mode_gpu() #model_weights = '/data/chw/haixin_rentou_20200525/_iter_200000.caffemodel' model_weights = '/data/chw/car_class_small_big_20200616/result/ShuffleNet_srn_iter_165000.caffemodel' model_def = '/data/chw/car_class_small_big_20200616/deploy.prototxt' net = caffe.Net(model_def, model_weights, caffe.TEST) total_num=0 correct_num=0 f_result = open("./test_result.txt", "w") f = open("/data/chw/car_class_small_big_20200616/test.txt") lines = f.readlines() for line in lines: if ".jpg" in line: image_path = line.split(".jpg")[0] +".jpg" label = line.split(".jpg")[1].split(" ")[1] else: image_path = line.split(".jpeg")[0] +".jpeg" label = line.split(".jpeg")[1].split(" ")[1] print(image_path) print(label) #time.sleep(100) total_num = total_num + 1 #for image_name in os.listdir("/data/chw/car_class_small_big_20200616/test_folder"): #image_path= os.path.join("test_folder",image_name) image= cv2.imread(image_path) re_size = 224 image = cv2.resize(image,(re_size,re_size)) time_start=time.time() net.blobs['data'].reshape(1, 3, image.shape[0], image.shape[1]) transformer = caffe.io.Transformer({'data': net. blobs['data'].data.shape}) transformer.set_transpose('data', (2, 0, 1)) mean_file=np.array([104,117,123]) transformer.set_mean("data",mean_file) transformed_image = transformer.preprocess('data', image) net.blobs['data'].data[...] = transformed_image time_end=time.time() process_t = (time_end-time_start)*1000 time_start=time.time() output= net.forward() detections=output['conf_softmax'] print(detections) if detections[0][0] == max(detections[0]): label_result = "0" print("label_result:", label_result) elif detections[0][1] == max(detections[0]): label_result = "1" print("label_result:", label_result) if str(label.split(" ")[0]) == label_result: correct_num = correct_num + 1 elif str(label.split(" ")[0]) != label_result: copy_path = os.path.join("/data/chw/car_class_small_big_20200616/test_folder/", str(detections[0]) + "__" + str(label) + os.path.split(image_path)[1]) shutil.copy(image_path, copy_path); f_result.write(image_path + " ") f_result.write(str(detections)) f_result.write(" " + label + " ") print("-------------------------------------------------------------------------------------") print("correct_num:", correct_num) print("total_num:", total_num) f_result.close() f.close()