DEX-6-caffe模型转成pytorch模型办法

在python2.7环境下

文件下载位置：https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/

1.可视化模型文件prototxt

1）在线可视化

网址为：https://ethereon.github.io/netscope/#/editor

将prototxt文件的内容复制到左边,然后按shift-enter键即可：

 

2）本地可视化

先安装：

(deeplearning2) userdeMacBook-Pro:~ user$ brew install graphviz

再安装：

(deeplearning2) userdeMacBook-Pro:~ user$ pip install pydot

前提是安装好了caffe，然后运行：

(deeplearning2) userdeMacBook-Pro:face_data user$ python /Users/user/caffe/python/draw_net.py /Users/user/pytorch/face_data/age_train.prototxt age_train.png --rankdir=BT
Drawing net to age_train.png

参数意思：

• 参数1：网络模型的prototxt文件
• 参数2：保存的图片路径及名字
• 参数3：--rankdir=x , x 有四种选项，分别是LR, RL, TB, BT 。用来表示网络的方向，分别是从左到右，从右到左，从上到小，从下到上。默认为LR

这是更改输入、损失和精确度层的图，为：

 

没更改过的为：

(deeplearning2) userdeMacBook-Pro:face_data user$ python /Users/user/caffe/python/draw_net.py /Users/user/pytorch/face_data/age_train.prototxt.txt age_train_2.png --rankdir=BT
Drawing net to age_train_2.png

图为：

 

2.网络结构说明

name: "VGG_ILSVRC_16_layers" # 声明该网络结构的名称
# 下面先是指明使用的数据集
layer {                                         #指明用于训练的数据，训练集
  top: "data"
  type: "ImageData"
  top: "label"
  name: "data"
  transform_param {
    mirror: true
    crop_size: 224
    mean_file: "imagenet_mean.binaryproto"
  }
  image_data_param {
    source: "train.txt"                       #指明存放位置
    batch_size: 10
    new_height: 256
    new_width: 256 
  }
  include: { phase: TRAIN }                       #指明用于训练
}
layer {                                         #指明用于验证的数据，验证集
  top: "data"
  top: "label"
  name: "data"
  type: "ImageData"
  image_data_param {
    new_height: 256
    new_width: 256
    source: "train.txt"
    batch_size: 10
  }
  transform_param {
    crop_size: 224
    mirror: false
    mean_file: "imagenet_mean.binaryproto"
  }
  include: { 
    phase: TEST
    stage: "test-on-train"
 }
}

layer {                                         #指明用于测试的数据，测试集
  top: "data"
  top: "label"
  name: "data"
  type: "ImageData"
  image_data_param {
    new_height: 256
    new_width: 256
    source: "test.txt"
    batch_size: 10
  }
  transform_param {
    crop_size: 224
    mirror: false
    mean_file: "imagenet_mean.binaryproto"
  }
  include: { 
    phase: TEST
    stage: "test-on-test"
 }
}

#下面就是网络结构了，这里以第一个卷积层为例

layer {
  bottom: "data"            #该层的上一层为data数据层，即其输入
  top: "conv1_1"            #该层的输出
  name: "conv1_1"           #该层的名称conv1_1
 param {                    #权重weight的参数
    lr_mult: 1              #权重weight的学习率
    decay_mult: 1           #权重weight的衰减系数
  }
  param {                   #偏置bias的参数
    lr_mult: 2              #偏置bias的学习率
    decay_mult: 0           #偏置bias的衰减系数
  }
  type: "Convolution"       #该层的类型，为卷积
  convolution_param {       #卷积层的参数
    num_output: 64          #卷积核个数，即输出的channels数
    pad: 1                  #padding大小
    kernel_size: 3          #卷积核的大小
  }
}
layer {
  bottom: "conv1_1"
  top: "conv1_1"
  name: "relu1_1"
  type: "ReLU"              #该层的类型，为激活函数，无参数
}
layer {
  bottom: "conv1_1"
  top: "conv1_2"
  name: "conv1_2"           #该层的名称conv1_1
 param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  type: "Convolution"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv1_2"
  top: "conv1_2"
  name: "relu1_2"
  type: "ReLU"              #该层的类型，为激活函数，无参数
}
layer {
  bottom: "conv1_2"
  top: "pool1"
  name: "pool1"
  type: "Pooling"           #该层的类型，为池化层
  pooling_param {
    pool: MAX               #为最大池化层
    kernel_size: 2          #卷积核的大小
    stride: 2               #步长大小
  }
}

# 中间其他卷积层省略
...

# 全连接层
layer {
  bottom: "pool5"
  top: "fc6"
  name: "fc6"
 param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  type: "InnerProduct"       #该层的类型，为全连接层
  inner_product_param {      #输出个数为4096
    num_output: 4096
  }
}
layer {
  bottom: "fc6"
  top: "fc6"
  name: "relu6"
  type: "ReLU"
}
layer {
  bottom: "fc6"
  top: "fc6"
  name: "drop6"
  type: "Dropout"             #该层的类型，为dropout层
  dropout_param {
    dropout_ratio: 0.5        #dropout比率为0.5
  }
}
layer {
  bottom: "fc6"
  top: "fc7"
  name: "fc7"
 param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  type: "InnerProduct"
  inner_product_param {
    num_output: 4096
  }
}
layer {
  bottom: "fc7"
  top: "fc7"
  name: "relu7"
  type: "ReLU"
}
layer {
  bottom: "fc7"
  top: "fc7"
  name: "drop7"
  type: "Dropout"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  bottom: "fc7"
  top: "fc8-101"
  name: "fc8-101"
 param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  type: "InnerProduct"
  inner_product_param {
    num_output: 101
  weight_filler {             #权重初始化方法参数
      type: "gaussian"        #使用的初始化方法为高斯分布
      std: 0.01               #标准差为0.01
    }
    bias_filler {             #偏置初始化方法参数
      type: "constant"        #初始化为定值0
      value: 0
    }
  }
}

layer {
  bottom: "fc8-101"
  bottom: "label"
  name: "loss"
  type: "SoftmaxWithLoss"      #最后对结果求损失，使用的方法为SoftmaxWithLoss
  include: { phase: TRAIN }
}

# 如果是测试则仅使用Softmax求概率

layer {
  name: "prob"
  type: "Softmax"
  bottom: "fc8-101"
  top: "prob"
    include {
    phase: TEST
  }
}


# 当进行验证时进行的求top1、top5和top10的操作，求精确度
layer {
  name: "accuracy_train_top01"
  type: "Accuracy"
  bottom: "fc8-101"
  bottom: "label"
  top: "accuracy_train_top01"
  include {
    phase: TEST
    stage: "test-on-train"
  }
}

layer {
  name: "accuracy_train_top05"
  type: "Accuracy"
  bottom: "fc8-101"
  bottom: "label"
  top: "accuracy_train_top05"
  accuracy_param {
    top_k: 5
  }
  include {
    phase: TEST
    stage: "test-on-train"
  }
}

layer {
  name: "accuracy_train_top10"
  type: "Accuracy"
  bottom: "fc8-101"
  bottom: "label"
  top: "accuracy_train_top10"
  accuracy_param {
    top_k: 10
  }
  include {
    phase: TEST
    stage: "test-on-train"
  }
}

#测试时也一样，求top1、top5和top10的操作，求精确度
layer {
  name: "accuracy_test_top01"
  type: "Accuracy"
  bottom: "fc8-101"
  bottom: "label"
  top: "accuracy_test_top01"
  include {
    phase: TEST
    stage: "test-on-test"
  }
}

layer {
  name: "accuracy_test_top05"
  type: "Accuracy"
  bottom: "fc8-101"
  bottom: "label"
  top: "accuracy_test_top05"
  accuracy_param {
    top_k: 5
  }
  include {
    phase: TEST
    stage: "test-on-test"
  }
}

layer {
  name: "accuracy_test_top10"
  type: "Accuracy"
  bottom: "fc8-101"
  bottom: "label"
  top: "accuracy_test_top10"
  accuracy_param {
    top_k: 10
  }
  include {
    phase: TEST
    stage: "test-on-test"
  }
}

 

3.自己定义的VGG16模型-models.py

然后我自己定义的pytorch模型为：

# coding:utf-8
import torchvision.models as models
import torch.nn as nn
import torch
from torch.autograd import Variable
import torch.nn.functional as F

# 0-100岁
class AgeModel(nn.Module):
    def __init__(self, classes=101):
        super(AgeModel,self).__init__()
        model = models.vgg16()
        layers = list(model.children())
        # 一共只有3层，卷积层，池化层和全连接层
        # print(len(layers)) #3
        self.model = nn.Sequential(*layers[:-1])
        # -1层为整个连接层
        # print(layers[1]) #池化层AdaptiveAvgPool2d
        # print(layers[-1]) #全连接层
        self.fullCon = nn.Sequential(nn.Linear(7*7*512, 4096),
                                   nn.ReLU(True),
                                   nn.Dropout(0.5, inplace=True),
                                   nn.Linear(4096, 4096),
                                   nn.ReLU(True),
                                   nn.Dropout(0.5, inplace=True),
                                   nn.Linear(4096, classes) #将最后一层全连接层改成自己的输出101
                              )

    def forward(self, x):
        x = self.model(x)
        x = x.view(x.size(0), -1)
        x = self.fullCon(x)
        x = F.softmax(x, dim=1)
        return x

def test():
    net = AgeModel()
    for module in net.children():
        print(module)
    output = net(Variable(torch.randn(2,3,224,224)))
    print('output :', output.size())
    print(type(output))


if __name__ == '__main__':
    test()

 

4.下载对应的caffemodel模型并转换

(deeplearning2) userdeMacBook-Pro:pytorch-DEX-master user$ curl -o age_model_imdb.caffemodel https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/dex_imdb_wiki.caffemodel

然后转换代码为：

# coding:utf-8
import caffe
import torch

from models import AgeModel


def convert_age():
    age_torch_net = AgeModel()

    # 导入caffe模型，caffe.TRAIN表示使用训练模式，caffe.TEST表示使用测试模式
    # 不同模式使用的prototxt文件是不同的
    caffe_net = caffe.Net('age_train.prototxt', "age_model_imdb.caffemodel", caffe.TRAIN)
    # caffe_net = caffe.Net('age.prototxt', "age_model_imdb.caffemodel", caffe.TEST)

    # 对应得到的是权重weight和偏置bias的参数
    caffe_params = caffe_net.params
    print(caffe_params)

    # #查看索引表示
    # print(age_torch_net.model)
    # print(age_torch_net.model[0][1])
    #
    # print(age_torch_net.fullCon)
    # print(age_torch_net.fullCon[1])

    mappings = {
        'conv1_1': age_torch_net.model[0][0],
        'conv1_2': age_torch_net.model[0][2],
        'conv2_1': age_torch_net.model[0][5],
        'conv2_2': age_torch_net.model[0][7],
        'conv3_1': age_torch_net.model[0][10],
        'conv3_2': age_torch_net.model[0][12],
        'conv3_3': age_torch_net.model[0][14],
        'conv4_1': age_torch_net.model[0][17],
        'conv4_2': age_torch_net.model[0][19],
        'conv4_3': age_torch_net.model[0][21],
        'conv5_1': age_torch_net.model[0][24],
        'conv5_2': age_torch_net.model[0][26],
        'conv5_3': age_torch_net.model[0][28],
        'fc6': age_torch_net.fullCon[0],
        'fc7': age_torch_net.fullCon[3],
        'fc8-101': age_torch_net.fullCon[6],
    }

    for k, layer in mappings.items():
        # # 第一个迭代：k=conv1_1，layer=Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        # print(k, layer)

        # 将caffe模型中的参数信息导入定义好的pytorch模型中
        layer.weight.data.copy_(torch.from_numpy(caffe_params[k][0].data)) # 权重weight参数
        layer.bias.data.copy_(torch.from_numpy(caffe_params[k][1].data)) # 偏置bias的参数
    # 然后仅保存模型参数即可
    torch.save(age_torch_net.state_dict(), './age.pth')


if __name__ == '__main__':
    convert_age()

 

过程中出现了错误：

RuntimeError: unidentifiable C++ exception
Segmentation fault: 11

因为：

caffe_net = caffe.Net('age_train.prototxt.txt', "dex_imdb_wiki.caffemodel", caffe.TRAIN)

这里我下载时是age_train.prototxt.txt，但是运行是导入是age_train.prototxt，添加.txt即可，或者更改格式

 

然后还出现错误：

(deeplearning2) userdeMacBook-Pro:face_data user$ python convert.py
Traceback (most recent call last):
  File "convert.py", line 2, in <module>
    import caffe
  File "/Users/user/caffe/python/caffe/__init__.py", line 1, in <module>
    from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
  File "/Users/user/caffe/python/caffe/pycaffe.py", line 13, in <module>
    from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
ImportError: dlopen(/Users/user/caffe/python/caffe/_caffe.so, 2): Library not loaded: @rpath/libpython2.7.dylib
  Referenced from: /Users/user/caffe/python/caffe/_caffe.so
  Reason: image not found
(deeplearning2) userdeMacBook-Pro:face_data user$ python convert.py
Traceback (most recent call last):
  File "convert.py", line 2, in <module>
    import caffe
  File "/Users/user/caffe/python/caffe/__init__.py", line 1, in <module>
    from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver, NCCL, Timer
  File "/Users/user/caffe/python/caffe/pycaffe.py", line 13, in <module>
    from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
ImportError: dlopen(/Users/user/caffe/python/caffe/_caffe.so, 2): Library not loaded: @rpath/libpython2.7.dylib
  Referenced from: /Users/user/caffe/python/caffe/_caffe.so
  Reason: image not found

因为我是使用anaconda3的python2.7环境，其下面已经有libpython2.7.dylib，这里报错的原因是@rpath的导向中没有anaconda3的python2.7环境的路径，使用下面的命令添加即可：

(deeplearning2) userdeMacBook-Pro:caffe user$ install_name_tool -add_rpath /anaconda3/envs/deeplearning2/lib /Users/user/caffe/python/caffe/_caffe.so

 

还出现错误：

I0812 14:26:19.981719 245106112 layer_factory.hpp:77] Creating layer data
F0812 14:26:19.981747 245106112 layer_factory.hpp:81] Check failed: registry.count(type) == 1 (0 vs. 1) Unknown layer type: ImageData (known types: AbsVal, Accuracy, ArgMax, BNLL, BatchNorm, BatchReindex, Bias, Clip, Concat, ContrastiveLoss, Convolution, Crop, Data, Deconvolution, Dropout, DummyData, ELU, Eltwise, Embed, EuclideanLoss, Exp, Filter, Flatten, HDF5Data, HDF5Output, HingeLoss, Im2col, InfogainLoss, InnerProduct, Input, LRN, LSTM, LSTMUnit, Log, MVN, MemoryData, MultinomialLogisticLoss, PReLU, Parameter, Pooling, Power, RNN, ReLU, Reduction, Reshape, SPP, Scale, Sigmoid, SigmoidCrossEntropyLoss, Silence, Slice, Softmax, SoftmaxWithLoss, Split, Swish, TanH, Threshold, Tile)
*** Check failure stack trace: ***
Abort trap: 6

这是因为在配置Makefile.config时没有取消掉下面一行的注释，所以读取不了python写的layer：

# Uncomment to support layers written in Python (will link against Python libs)
WITH_PYTHON_LAYER := 1

将该行去掉，然后重新编译caffe，即make clean后，再从make all-make test-make runtest-make pycaffe,并且还要跟上面一样在~/.bash_profile文件的path中添加~/caffe/python这个caffe路径，并source /etc/profile

但是这样仍然没有解决问题,后面发现如果你的报错是：

Check failed: registry.count(type) == 1 (0 vs. 1) Unknown layer type:python

才使用上面的方法，但是注释掉这个总没有错。这个更改我已经合并到上面的完整配置代码中了，你如果跟着上面配置走应该就不会遇见该问题

 

后面查看该caffe模型的文件，其使用了ImageData来导入数据，但是我没有他指定的文件，所以将其的prototxt文件数据导入部分删除，改为：

input: "data"
input_dim: 10
input_dim: 3
input_dim: 224
input_dim: 224

把后面的一些损失和精确度内容也删除

然后再运行即可:

(deeplearning2) wanghuideMacBook-Pro:face_data wanghui$ python convert.py 
WARNING: Logging before InitGoogleLogging() is written to STDERR
W0812 16:24:50.179870 287970752 _caffe.cpp:139] DEPRECATION WARNING - deprecated use of Python interface
W0812 16:24:50.180747 287970752 _caffe.cpp:140] Use this instead (with the named "weights" parameter):
W0812 16:24:50.180763 287970752 _caffe.cpp:142] Net('age_train.prototxt', 0, weights='dex_imdb_wiki.caffemodel')
I0812 16:24:50.188591 287970752 upgrade_proto.cpp:69] Attempting to upgrade input file specified using deprecated input fields: age_train.prototxt
I0812 16:24:50.188863 287970752 upgrade_proto.cpp:72] Successfully upgraded file specified using deprecated input fields.
W0812 16:24:50.188877 287970752 upgrade_proto.cpp:74] Note that future Caffe releases will only support input layers and not input fields.
I0812 16:24:50.189504 287970752 net.cpp:53] Initializing net from parameters: 
name: "VGG_ILSVRC_16_layers"
state {
  phase: TRAIN
  level: 0
}
layer {
  name: "input"
  type: "Input"
  top: "data"
  input_param {
    shape {
      dim: 10
      dim: 3
      dim: 224
      dim: 224
    }
  }
}
layer {
  name: "conv1_1"
  type: "Convolution"
...
layer {
  name: "fc8-101"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8-101"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  inner_product_param {
    num_output: 101
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
I0812 16:24:50.190675 287970752 layer_factory.hpp:77] Creating layer input
I0812 16:24:50.190943 287970752 net.cpp:86] Creating Layer input
I0812 16:24:50.190958 287970752 net.cpp:382] input -> data
I0812 16:24:50.191754 287970752 net.cpp:124] Setting up input
I0812 16:24:50.191768 287970752 net.cpp:131] Top shape: 10 3 224 224 (1505280)
I0812 16:24:50.192245 287970752 net.cpp:139] Memory required for data: 6021120
I0812 16:24:50.192253 287970752 layer_factory.hpp:77] Creating layer conv1_1
I0812 16:24:50.192262 287970752 net.cpp:86] Creating Layer conv1_1
I0812 16:24:50.192267 287970752 net.cpp:408] conv1_1 <- data
I0812 16:24:50.192273 287970752 net.cpp:382] conv1_1 -> conv1_1
I0812 16:24:50.192760 287970752 net.cpp:124] Setting up conv1_1
I0812 16:24:50.192771 287970752 net.cpp:131] Top shape: 10 64 224 224 (32112640)
I0812 16:24:50.192780 287970752 net.cpp:139] Memory required for data: 134471680
I0812 16:24:50.192903 287970752 layer_factory.hpp:77] Creating layer relu1_1
I0812 16:24:50.192927 287970752 net.cpp:86] Creating Layer relu1_1
I0812 16:24:50.192940 287970752 net.cpp:408] relu1_1 <- conv1_1
I0812 16:24:50.192955 287970752 net.cpp:369] relu1_1 -> conv1_1 (in-place)
I0812 16:24:50.192987 287970752 net.cpp:124] Setting up relu1_1
I0812 16:24:50.193004 287970752 net.cpp:131] Top shape: 10 64 224 224 (32112640)
I0812 16:24:50.193015 287970752 net.cpp:139] Memory required for data: 262922240
I0812 16:24:50.193022 287970752 layer_factory.hpp:77] Creating layer conv1_2
I0812 16:24:50.193029 287970752 net.cpp:86] Creating Layer conv1_2
I0812 16:24:50.193034 287970752 net.cpp:408] conv1_2 <- conv1_1
I0812 16:24:50.193042 287970752 net.cpp:382] conv1_2 -> conv1_2
I0812 16:24:50.193079 287970752 net.cpp:124] Setting up conv1_2
I0812 16:24:50.193085 287970752 net.cpp:131] Top shape: 10 64 224 224 (32112640)
I0812 16:24:50.193092 287970752 net.cpp:139] Memory required for data: 391372800
I0812 16:24:50.193099 287970752 layer_factory.hpp:77] Creating layer relu1_2
I0812 16:24:50.193106 287970752 net.cpp:86] Creating Layer relu1_2
I0812 16:24:50.193111 287970752 net.cpp:408] relu1_2 <- conv1_2
I0812 16:24:50.193116 287970752 net.cpp:369] relu1_2 -> conv1_2 (in-place)
I0812 16:24:50.193122 287970752 net.cpp:124] Setting up relu1_2
I0812 16:24:50.193126 287970752 net.cpp:131] Top shape: 10 64 224 224 (32112640)
I0812 16:24:50.193132 287970752 net.cpp:139] Memory required for data: 519823360
I0812 16:24:50.193137 287970752 layer_factory.hpp:77] Creating layer pool1
I0812 16:24:50.193142 287970752 net.cpp:86] Creating Layer pool1
I0812 16:24:50.193147 287970752 net.cpp:408] pool1 <- conv1_2
I0812 16:24:50.193152 287970752 net.cpp:382] pool1 -> pool1
I0812 16:24:50.193373 287970752 net.cpp:124] Setting up pool1
I0812 16:24:50.193385 287970752 net.cpp:131] Top shape: 10 64 112 112 (8028160)
I0812 16:24:50.193394 287970752 net.cpp:139] Memory required for data: 551936000
I0812 16:24:50.193400 287970752 layer_factory.hpp:77] Creating layer conv2_1
I0812 16:24:50.193408 287970752 net.cpp:86] Creating Layer conv2_1
I0812 16:24:50.193414 287970752 net.cpp:408] conv2_1 <- pool1
I0812 16:24:50.193421 287970752 net.cpp:382] conv2_1 -> conv2_1
I0812 16:24:50.193460 287970752 net.cpp:124] Setting up conv2_1
I0812 16:24:50.193466 287970752 net.cpp:131] Top shape: 10 128 112 112 (16056320)
I0812 16:24:50.193473 287970752 net.cpp:139] Memory required for data: 616161280
I0812 16:24:50.193481 287970752 layer_factory.hpp:77] Creating layer relu2_1
I0812 16:24:50.193487 287970752 net.cpp:86] Creating Layer relu2_1
I0812 16:24:50.193492 287970752 net.cpp:408] relu2_1 <- conv2_1
I0812 16:24:50.193497 287970752 net.cpp:369] relu2_1 -> conv2_1 (in-place)
I0812 16:24:50.193504 287970752 net.cpp:124] Setting up relu2_1
I0812 16:24:50.193508 287970752 net.cpp:131] Top shape: 10 128 112 112 (16056320)
I0812 16:24:50.193514 287970752 net.cpp:139] Memory required for data: 680386560
I0812 16:24:50.193519 287970752 layer_factory.hpp:77] Creating layer conv2_2
I0812 16:24:50.193526 287970752 net.cpp:86] Creating Layer conv2_2
I0812 16:24:50.193531 287970752 net.cpp:408] conv2_2 <- conv2_1
I0812 16:24:50.193536 287970752 net.cpp:382] conv2_2 -> conv2_2
I0812 16:24:50.193588 287970752 net.cpp:124] Setting up conv2_2
I0812 16:24:50.193596 287970752 net.cpp:131] Top shape: 10 128 112 112 (16056320)
I0812 16:24:50.193604 287970752 net.cpp:139] Memory required for data: 744611840
I0812 16:24:50.193610 287970752 layer_factory.hpp:77] Creating layer relu2_2
I0812 16:24:50.193616 287970752 net.cpp:86] Creating Layer relu2_2
I0812 16:24:50.193620 287970752 net.cpp:408] relu2_2 <- conv2_2
I0812 16:24:50.193625 287970752 net.cpp:369] relu2_2 -> conv2_2 (in-place)
I0812 16:24:50.193631 287970752 net.cpp:124] Setting up relu2_2
I0812 16:24:50.193635 287970752 net.cpp:131] Top shape: 10 128 112 112 (16056320)
I0812 16:24:50.193641 287970752 net.cpp:139] Memory required for data: 808837120
I0812 16:24:50.193645 287970752 layer_factory.hpp:77] Creating layer pool2
I0812 16:24:50.193651 287970752 net.cpp:86] Creating Layer pool2
I0812 16:24:50.193655 287970752 net.cpp:408] pool2 <- conv2_2
I0812 16:24:50.193661 287970752 net.cpp:382] pool2 -> pool2
I0812 16:24:50.193668 287970752 net.cpp:124] Setting up pool2
I0812 16:24:50.193672 287970752 net.cpp:131] Top shape: 10 128 56 56 (4014080)
I0812 16:24:50.193678 287970752 net.cpp:139] Memory required for data: 824893440
I0812 16:24:50.193682 287970752 layer_factory.hpp:77] Creating layer conv3_1
I0812 16:24:50.193688 287970752 net.cpp:86] Creating Layer conv3_1
I0812 16:24:50.193694 287970752 net.cpp:408] conv3_1 <- pool2
I0812 16:24:50.193699 287970752 net.cpp:382] conv3_1 -> conv3_1
I0812 16:24:50.193792 287970752 net.cpp:124] Setting up conv3_1
I0812 16:24:50.193799 287970752 net.cpp:131] Top shape: 10 256 56 56 (8028160)
I0812 16:24:50.193805 287970752 net.cpp:139] Memory required for data: 857006080
I0812 16:24:50.193814 287970752 layer_factory.hpp:77] Creating layer relu3_1
I0812 16:24:50.193819 287970752 net.cpp:86] Creating Layer relu3_1
I0812 16:24:50.193825 287970752 net.cpp:408] relu3_1 <- conv3_1
I0812 16:24:50.193832 287970752 net.cpp:369] relu3_1 -> conv3_1 (in-place)
I0812 16:24:50.193838 287970752 net.cpp:124] Setting up relu3_1
I0812 16:24:50.193843 287970752 net.cpp:131] Top shape: 10 256 56 56 (8028160)
I0812 16:24:50.193850 287970752 net.cpp:139] Memory required for data: 889118720
I0812 16:24:50.193855 287970752 layer_factory.hpp:77] Creating layer conv3_2
I0812 16:24:50.193861 287970752 net.cpp:86] Creating Layer conv3_2
I0812 16:24:50.193866 287970752 net.cpp:408] conv3_2 <- conv3_1
I0812 16:24:50.193871 287970752 net.cpp:382] conv3_2 -> conv3_2
I0812 16:24:50.194085 287970752 net.cpp:124] Setting up conv3_2
I0812 16:24:50.194097 287970752 net.cpp:131] Top shape: 10 256 56 56 (8028160)
I0812 16:24:50.194105 287970752 net.cpp:139] Memory required for data: 921231360
I0812 16:24:50.194113 287970752 layer_factory.hpp:77] Creating layer relu3_2
I0812 16:24:50.194119 287970752 net.cpp:86] Creating Layer relu3_2
I0812 16:24:50.194124 287970752 net.cpp:408] relu3_2 <- conv3_2
I0812 16:24:50.194130 287970752 net.cpp:369] relu3_2 -> conv3_2 (in-place)
I0812 16:24:50.194137 287970752 net.cpp:124] Setting up relu3_2
I0812 16:24:50.194141 287970752 net.cpp:131] Top shape: 10 256 56 56 (8028160)
I0812 16:24:50.194147 287970752 net.cpp:139] Memory required for data: 953344000
I0812 16:24:50.194151 287970752 layer_factory.hpp:77] Creating layer conv3_3
I0812 16:24:50.194159 287970752 net.cpp:86] Creating Layer conv3_3
I0812 16:24:50.194162 287970752 net.cpp:408] conv3_3 <- conv3_2
I0812 16:24:50.194169 287970752 net.cpp:382] conv3_3 -> conv3_3
I0812 16:24:50.194345 287970752 net.cpp:124] Setting up conv3_3
I0812 16:24:50.194353 287970752 net.cpp:131] Top shape: 10 256 56 56 (8028160)
I0812 16:24:50.194360 287970752 net.cpp:139] Memory required for data: 985456640
I0812 16:24:50.194366 287970752 layer_factory.hpp:77] Creating layer relu3_3
I0812 16:24:50.194373 287970752 net.cpp:86] Creating Layer relu3_3
I0812 16:24:50.194377 287970752 net.cpp:408] relu3_3 <- conv3_3
I0812 16:24:50.194383 287970752 net.cpp:369] relu3_3 -> conv3_3 (in-place)
I0812 16:24:50.194388 287970752 net.cpp:124] Setting up relu3_3
I0812 16:24:50.194393 287970752 net.cpp:131] Top shape: 10 256 56 56 (8028160)
I0812 16:24:50.194398 287970752 net.cpp:139] Memory required for data: 1017569280
I0812 16:24:50.194403 287970752 layer_factory.hpp:77] Creating layer pool3
I0812 16:24:50.194411 287970752 net.cpp:86] Creating Layer pool3
I0812 16:24:50.194416 287970752 net.cpp:408] pool3 <- conv3_3
I0812 16:24:50.194422 287970752 net.cpp:382] pool3 -> pool3
I0812 16:24:50.194428 287970752 net.cpp:124] Setting up pool3
I0812 16:24:50.194433 287970752 net.cpp:131] Top shape: 10 256 28 28 (2007040)
I0812 16:24:50.194438 287970752 net.cpp:139] Memory required for data: 1025597440
I0812 16:24:50.194443 287970752 layer_factory.hpp:77] Creating layer conv4_1
I0812 16:24:50.194450 287970752 net.cpp:86] Creating Layer conv4_1
I0812 16:24:50.194454 287970752 net.cpp:408] conv4_1 <- pool3
I0812 16:24:50.194460 287970752 net.cpp:382] conv4_1 -> conv4_1
I0812 16:24:50.194834 287970752 net.cpp:124] Setting up conv4_1
I0812 16:24:50.194847 287970752 net.cpp:131] Top shape: 10 512 28 28 (4014080)
I0812 16:24:50.194854 287970752 net.cpp:139] Memory required for data: 1041653760
I0812 16:24:50.194861 287970752 layer_factory.hpp:77] Creating layer relu4_1
I0812 16:24:50.194867 287970752 net.cpp:86] Creating Layer relu4_1
I0812 16:24:50.194874 287970752 net.cpp:408] relu4_1 <- conv4_1
I0812 16:24:50.194880 287970752 net.cpp:369] relu4_1 -> conv4_1 (in-place)
I0812 16:24:50.194885 287970752 net.cpp:124] Setting up relu4_1
I0812 16:24:50.194890 287970752 net.cpp:131] Top shape: 10 512 28 28 (4014080)
I0812 16:24:50.194895 287970752 net.cpp:139] Memory required for data: 1057710080
I0812 16:24:50.194900 287970752 layer_factory.hpp:77] Creating layer conv4_2
I0812 16:24:50.194905 287970752 net.cpp:86] Creating Layer conv4_2
I0812 16:24:50.194911 287970752 net.cpp:408] conv4_2 <- conv4_1
I0812 16:24:50.194917 287970752 net.cpp:382] conv4_2 -> conv4_2
I0812 16:24:50.195816 287970752 net.cpp:124] Setting up conv4_2
I0812 16:24:50.195835 287970752 net.cpp:131] Top shape: 10 512 28 28 (4014080)
I0812 16:24:50.195843 287970752 net.cpp:139] Memory required for data: 1073766400
I0812 16:24:50.195854 287970752 layer_factory.hpp:77] Creating layer relu4_2
I0812 16:24:50.195863 287970752 net.cpp:86] Creating Layer relu4_2
I0812 16:24:50.195868 287970752 net.cpp:408] relu4_2 <- conv4_2
I0812 16:24:50.195874 287970752 net.cpp:369] relu4_2 -> conv4_2 (in-place)
I0812 16:24:50.195881 287970752 net.cpp:124] Setting up relu4_2
I0812 16:24:50.195886 287970752 net.cpp:131] Top shape: 10 512 28 28 (4014080)
I0812 16:24:50.195892 287970752 net.cpp:139] Memory required for data: 1089822720
I0812 16:24:50.195896 287970752 layer_factory.hpp:77] Creating layer conv4_3
I0812 16:24:50.195904 287970752 net.cpp:86] Creating Layer conv4_3
I0812 16:24:50.195907 287970752 net.cpp:408] conv4_3 <- conv4_2
I0812 16:24:50.195914 287970752 net.cpp:382] conv4_3 -> conv4_3
I0812 16:24:50.197036 287970752 net.cpp:124] Setting up conv4_3
I0812 16:24:50.197062 287970752 net.cpp:131] Top shape: 10 512 28 28 (4014080)
I0812 16:24:50.197072 287970752 net.cpp:139] Memory required for data: 1105879040
I0812 16:24:50.197079 287970752 layer_factory.hpp:77] Creating layer relu4_3
I0812 16:24:50.197088 287970752 net.cpp:86] Creating Layer relu4_3
I0812 16:24:50.197094 287970752 net.cpp:408] relu4_3 <- conv4_3
I0812 16:24:50.197100 287970752 net.cpp:369] relu4_3 -> conv4_3 (in-place)
I0812 16:24:50.197108 287970752 net.cpp:124] Setting up relu4_3
I0812 16:24:50.197111 287970752 net.cpp:131] Top shape: 10 512 28 28 (4014080)
I0812 16:24:50.197116 287970752 net.cpp:139] Memory required for data: 1121935360
I0812 16:24:50.197120 287970752 layer_factory.hpp:77] Creating layer pool4
I0812 16:24:50.197126 287970752 net.cpp:86] Creating Layer pool4
I0812 16:24:50.197130 287970752 net.cpp:408] pool4 <- conv4_3
I0812 16:24:50.197136 287970752 net.cpp:382] pool4 -> pool4
I0812 16:24:50.197144 287970752 net.cpp:124] Setting up pool4
I0812 16:24:50.197149 287970752 net.cpp:131] Top shape: 10 512 14 14 (1003520)
I0812 16:24:50.197154 287970752 net.cpp:139] Memory required for data: 1125949440
I0812 16:24:50.197158 287970752 layer_factory.hpp:77] Creating layer conv5_1
I0812 16:24:50.197166 287970752 net.cpp:86] Creating Layer conv5_1
I0812 16:24:50.197171 287970752 net.cpp:408] conv5_1 <- pool4
I0812 16:24:50.197176 287970752 net.cpp:382] conv5_1 -> conv5_1
I0812 16:24:50.198091 287970752 net.cpp:124] Setting up conv5_1
I0812 16:24:50.198110 287970752 net.cpp:131] Top shape: 10 512 14 14 (1003520)
I0812 16:24:50.198118 287970752 net.cpp:139] Memory required for data: 1129963520
I0812 16:24:50.198124 287970752 layer_factory.hpp:77] Creating layer relu5_1
I0812 16:24:50.198132 287970752 net.cpp:86] Creating Layer relu5_1
I0812 16:24:50.198137 287970752 net.cpp:408] relu5_1 <- conv5_1
I0812 16:24:50.198143 287970752 net.cpp:369] relu5_1 -> conv5_1 (in-place)
I0812 16:24:50.198149 287970752 net.cpp:124] Setting up relu5_1
I0812 16:24:50.198153 287970752 net.cpp:131] Top shape: 10 512 14 14 (1003520)
I0812 16:24:50.198159 287970752 net.cpp:139] Memory required for data: 1133977600
I0812 16:24:50.198163 287970752 layer_factory.hpp:77] Creating layer conv5_2
I0812 16:24:50.198170 287970752 net.cpp:86] Creating Layer conv5_2
I0812 16:24:50.198174 287970752 net.cpp:408] conv5_2 <- conv5_1
I0812 16:24:50.198179 287970752 net.cpp:382] conv5_2 -> conv5_2
I0812 16:24:50.199494 287970752 net.cpp:124] Setting up conv5_2
I0812 16:24:50.199513 287970752 net.cpp:131] Top shape: 10 512 14 14 (1003520)
I0812 16:24:50.199522 287970752 net.cpp:139] Memory required for data: 1137991680
I0812 16:24:50.199528 287970752 layer_factory.hpp:77] Creating layer relu5_2
I0812 16:24:50.199539 287970752 net.cpp:86] Creating Layer relu5_2
I0812 16:24:50.199545 287970752 net.cpp:408] relu5_2 <- conv5_2
I0812 16:24:50.199551 287970752 net.cpp:369] relu5_2 -> conv5_2 (in-place)
I0812 16:24:50.199558 287970752 net.cpp:124] Setting up relu5_2
I0812 16:24:50.199563 287970752 net.cpp:131] Top shape: 10 512 14 14 (1003520)
I0812 16:24:50.199568 287970752 net.cpp:139] Memory required for data: 1142005760
I0812 16:24:50.199571 287970752 layer_factory.hpp:77] Creating layer conv5_3
I0812 16:24:50.199579 287970752 net.cpp:86] Creating Layer conv5_3
I0812 16:24:50.199582 287970752 net.cpp:408] conv5_3 <- conv5_2
I0812 16:24:50.199589 287970752 net.cpp:382] conv5_3 -> conv5_3
I0812 16:24:50.200711 287970752 net.cpp:124] Setting up conv5_3
I0812 16:24:50.200731 287970752 net.cpp:131] Top shape: 10 512 14 14 (1003520)
I0812 16:24:50.200739 287970752 net.cpp:139] Memory required for data: 1146019840
I0812 16:24:50.200747 287970752 layer_factory.hpp:77] Creating layer relu5_3
I0812 16:24:50.200753 287970752 net.cpp:86] Creating Layer relu5_3
I0812 16:24:50.200758 287970752 net.cpp:408] relu5_3 <- conv5_3
I0812 16:24:50.200764 287970752 net.cpp:369] relu5_3 -> conv5_3 (in-place)
I0812 16:24:50.200790 287970752 net.cpp:124] Setting up relu5_3
I0812 16:24:50.200794 287970752 net.cpp:131] Top shape: 10 512 14 14 (1003520)
I0812 16:24:50.200800 287970752 net.cpp:139] Memory required for data: 1150033920
I0812 16:24:50.200804 287970752 layer_factory.hpp:77] Creating layer pool5
I0812 16:24:50.200810 287970752 net.cpp:86] Creating Layer pool5
I0812 16:24:50.200814 287970752 net.cpp:408] pool5 <- conv5_3
I0812 16:24:50.200820 287970752 net.cpp:382] pool5 -> pool5
I0812 16:24:50.200830 287970752 net.cpp:124] Setting up pool5
I0812 16:24:50.200836 287970752 net.cpp:131] Top shape: 10 512 7 7 (250880)
I0812 16:24:50.200855 287970752 net.cpp:139] Memory required for data: 1151037440
I0812 16:24:50.200860 287970752 layer_factory.hpp:77] Creating layer fc6
I0812 16:24:50.200866 287970752 net.cpp:86] Creating Layer fc6
I0812 16:24:50.200870 287970752 net.cpp:408] fc6 <- pool5
I0812 16:24:50.200876 287970752 net.cpp:382] fc6 -> fc6
I0812 16:24:50.257817 287970752 net.cpp:124] Setting up fc6
I0812 16:24:50.257875 287970752 net.cpp:131] Top shape: 10 4096 (40960)
I0812 16:24:50.257887 287970752 net.cpp:139] Memory required for data: 1151201280
I0812 16:24:50.257897 287970752 layer_factory.hpp:77] Creating layer relu6
I0812 16:24:50.257906 287970752 net.cpp:86] Creating Layer relu6
I0812 16:24:50.257912 287970752 net.cpp:408] relu6 <- fc6
I0812 16:24:50.257920 287970752 net.cpp:369] relu6 -> fc6 (in-place)
I0812 16:24:50.257927 287970752 net.cpp:124] Setting up relu6
I0812 16:24:50.257931 287970752 net.cpp:131] Top shape: 10 4096 (40960)
I0812 16:24:50.257936 287970752 net.cpp:139] Memory required for data: 1151365120
I0812 16:24:50.257941 287970752 layer_factory.hpp:77] Creating layer drop6
I0812 16:24:50.257947 287970752 net.cpp:86] Creating Layer drop6
I0812 16:24:50.257952 287970752 net.cpp:408] drop6 <- fc6
I0812 16:24:50.257957 287970752 net.cpp:369] drop6 -> fc6 (in-place)
I0812 16:24:50.257974 287970752 net.cpp:124] Setting up drop6
I0812 16:24:50.257978 287970752 net.cpp:131] Top shape: 10 4096 (40960)
I0812 16:24:50.257983 287970752 net.cpp:139] Memory required for data: 1151528960
I0812 16:24:50.257987 287970752 layer_factory.hpp:77] Creating layer fc7
I0812 16:24:50.257994 287970752 net.cpp:86] Creating Layer fc7
I0812 16:24:50.257998 287970752 net.cpp:408] fc7 <- fc6
I0812 16:24:50.258003 287970752 net.cpp:382] fc7 -> fc7
I0812 16:24:50.303862 287970752 net.cpp:124] Setting up fc7
I0812 16:24:50.303889 287970752 net.cpp:131] Top shape: 10 4096 (40960)
I0812 16:24:50.303900 287970752 net.cpp:139] Memory required for data: 1151692800
I0812 16:24:50.303908 287970752 layer_factory.hpp:77] Creating layer relu7
I0812 16:24:50.303918 287970752 net.cpp:86] Creating Layer relu7
I0812 16:24:50.303923 287970752 net.cpp:408] relu7 <- fc7
I0812 16:24:50.303942 287970752 net.cpp:369] relu7 -> fc7 (in-place)
I0812 16:24:50.303951 287970752 net.cpp:124] Setting up relu7
I0812 16:24:50.303956 287970752 net.cpp:131] Top shape: 10 4096 (40960)
I0812 16:24:50.303961 287970752 net.cpp:139] Memory required for data: 1151856640
I0812 16:24:50.303966 287970752 layer_factory.hpp:77] Creating layer drop7
I0812 16:24:50.303972 287970752 net.cpp:86] Creating Layer drop7
I0812 16:24:50.303977 287970752 net.cpp:408] drop7 <- fc7
I0812 16:24:50.303982 287970752 net.cpp:369] drop7 -> fc7 (in-place)
I0812 16:24:50.303987 287970752 net.cpp:124] Setting up drop7
I0812 16:24:50.303992 287970752 net.cpp:131] Top shape: 10 4096 (40960)
I0812 16:24:50.303997 287970752 net.cpp:139] Memory required for data: 1152020480
I0812 16:24:50.304002 287970752 layer_factory.hpp:77] Creating layer fc8-101
I0812 16:24:50.304008 287970752 net.cpp:86] Creating Layer fc8-101
I0812 16:24:50.304013 287970752 net.cpp:408] fc8-101 <- fc7
I0812 16:24:50.304019 287970752 net.cpp:382] fc8-101 -> fc8-101
I0812 16:24:50.312885 287970752 net.cpp:124] Setting up fc8-101
I0812 16:24:50.312916 287970752 net.cpp:131] Top shape: 10 101 (1010)
I0812 16:24:50.312927 287970752 net.cpp:139] Memory required for data: 1152024520
I0812 16:24:50.312934 287970752 net.cpp:202] fc8-101 does not need backward computation.
I0812 16:24:50.312940 287970752 net.cpp:202] drop7 does not need backward computation.
I0812 16:24:50.312944 287970752 net.cpp:202] relu7 does not need backward computation.
I0812 16:24:50.312949 287970752 net.cpp:202] fc7 does not need backward computation.
I0812 16:24:50.312953 287970752 net.cpp:202] drop6 does not need backward computation.
I0812 16:24:50.312958 287970752 net.cpp:202] relu6 does not need backward computation.
I0812 16:24:50.312963 287970752 net.cpp:202] fc6 does not need backward computation.
I0812 16:24:50.312968 287970752 net.cpp:202] pool5 does not need backward computation.
I0812 16:24:50.312973 287970752 net.cpp:202] relu5_3 does not need backward computation.
I0812 16:24:50.312978 287970752 net.cpp:202] conv5_3 does not need backward computation.
I0812 16:24:50.312981 287970752 net.cpp:202] relu5_2 does not need backward computation.
I0812 16:24:50.312986 287970752 net.cpp:202] conv5_2 does not need backward computation.
I0812 16:24:50.312991 287970752 net.cpp:202] relu5_1 does not need backward computation.
I0812 16:24:50.312995 287970752 net.cpp:202] conv5_1 does not need backward computation.
I0812 16:24:50.313000 287970752 net.cpp:202] pool4 does not need backward computation.
I0812 16:24:50.313005 287970752 net.cpp:202] relu4_3 does not need backward computation.
I0812 16:24:50.313009 287970752 net.cpp:202] conv4_3 does not need backward computation.
I0812 16:24:50.313014 287970752 net.cpp:202] relu4_2 does not need backward computation.
I0812 16:24:50.313019 287970752 net.cpp:202] conv4_2 does not need backward computation.
I0812 16:24:50.313024 287970752 net.cpp:202] relu4_1 does not need backward computation.
I0812 16:24:50.313027 287970752 net.cpp:202] conv4_1 does not need backward computation.
I0812 16:24:50.313032 287970752 net.cpp:202] pool3 does not need backward computation.
I0812 16:24:50.313037 287970752 net.cpp:202] relu3_3 does not need backward computation.
I0812 16:24:50.313041 287970752 net.cpp:202] conv3_3 does not need backward computation.
I0812 16:24:50.313046 287970752 net.cpp:202] relu3_2 does not need backward computation.
I0812 16:24:50.313050 287970752 net.cpp:202] conv3_2 does not need backward computation.
I0812 16:24:50.313055 287970752 net.cpp:202] relu3_1 does not need backward computation.
I0812 16:24:50.313060 287970752 net.cpp:202] conv3_1 does not need backward computation.
I0812 16:24:50.313064 287970752 net.cpp:202] pool2 does not need backward computation.
I0812 16:24:50.313069 287970752 net.cpp:202] relu2_2 does not need backward computation.
I0812 16:24:50.313073 287970752 net.cpp:202] conv2_2 does not need backward computation.
I0812 16:24:50.313078 287970752 net.cpp:202] relu2_1 does not need backward computation.
I0812 16:24:50.313082 287970752 net.cpp:202] conv2_1 does not need backward computation.
I0812 16:24:50.313087 287970752 net.cpp:202] pool1 does not need backward computation.
I0812 16:24:50.313092 287970752 net.cpp:202] relu1_2 does not need backward computation.
I0812 16:24:50.313097 287970752 net.cpp:202] conv1_2 does not need backward computation.
I0812 16:24:50.313102 287970752 net.cpp:202] relu1_1 does not need backward computation.
I0812 16:24:50.313107 287970752 net.cpp:202] conv1_1 does not need backward computation.
I0812 16:24:50.313110 287970752 net.cpp:202] input does not need backward computation.
I0812 16:24:50.313114 287970752 net.cpp:244] This network produces output fc8-101
I0812 16:24:50.313129 287970752 net.cpp:257] Network initialization done.
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:537] Reading dangerously large protocol message.  If the message turns out to be larger than 2147483647 bytes, parsing will be halted for security reasons.  To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:78] The total number of bytes read was 538700275
I0812 16:24:51.490310 287970752 net.cpp:746] Ignoring source layer data
I0812 16:24:51.597520 287970752 net.cpp:746] Ignoring source layer loss
OrderedDict([('conv1_1', <caffe._caffe.BlobVec object at 0x13cafcf30>), ('conv1_2', <caffe._caffe.BlobVec object at 0x13cafcfa0>), ('conv2_1', <caffe._caffe.BlobVec object at 0x158bf0050>), ('conv2_2', <caffe._caffe.BlobVec object at 0x158bf00c0>), ('conv3_1', <caffe._caffe.BlobVec object at 0x158bf0130>), ('conv3_2', <caffe._caffe.BlobVec object at 0x158bf01a0>), ('conv3_3', <caffe._caffe.BlobVec object at 0x158bf0210>), ('conv4_1', <caffe._caffe.BlobVec object at 0x158bf0280>), ('conv4_2', <caffe._caffe.BlobVec object at 0x158bf02f0>), ('conv4_3', <caffe._caffe.BlobVec object at 0x158bf0360>), ('conv5_1', <caffe._caffe.BlobVec object at 0x158bf03d0>), ('conv5_2', <caffe._caffe.BlobVec object at 0x158bf0440>), ('conv5_3', <caffe._caffe.BlobVec object at 0x158bf04b0>), ('fc6', <caffe._caffe.BlobVec object at 0x158bf0520>), ('fc7', <caffe._caffe.BlobVec object at 0x158bf0590>), ('fc8-101', <caffe._caffe.BlobVec object at 0x158bf0600>)])

然后就成功生成了age_train.pth模型

 

5.测试

使用保存好的pytorch模型来测试图片的年龄：

from PIL import Image
from torchvision import transforms as T
import torch
from models import AgeModel

def expected_age(outputs):
    age_output = []

    for batch, output in enumerate(outputs):
        age = 0
        for i, j in enumerate(output):
            age += i*j
        age_output.append(age.item())

    return torch.FloatTensor(age_output)

def test(img_path, model_path):
    transfrom = T.Compose([
        T.Resize(256),
        T.CenterCrop(224),
        T.ToTensor(),
        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
    img = Image.open(img_path)
    img = transfrom(img).unsqueeze(0)

    model = AgeModel()
    model.load_state_dict(torch.load(model_path))
    age = model(img)
    print(age)
    print(age.size())
    age = expected_age(age)
    print(age)
    print(age.size())

if __name__ == '__main__':
    img_path = './Tom_Hanks_54745.png'
    model_path = './age_train.pth'
    test(img_path, model_path)

返回：

(deeplearning2) userdeMacBook-Pro:face_data user$ python check_model.py 
tensor([[0.0032, 0.0039, 0.0027, 0.0020, 0.0026, 0.0031, 0.0029, 0.0035, 0.0051,
         0.0046, 0.0056, 0.0069, 0.0073, 0.0065, 0.0071, 0.0071, 0.0090, 0.0143,
         0.0225, 0.0269, 0.0288, 0.0373, 0.0335, 0.0343, 0.0282, 0.0303, 0.0256,
         0.0263, 0.0227, 0.0210, 0.0210, 0.0176, 0.0140, 0.0144, 0.0135, 0.0121,
         0.0107, 0.0112, 0.0110, 0.0088, 0.0109, 0.0092, 0.0086, 0.0140, 0.0096,
         0.0106, 0.0123, 0.0128, 0.0139, 0.0132, 0.0110, 0.0111, 0.0144, 0.0127,
         0.0110, 0.0153, 0.0103, 0.0113, 0.0117, 0.0136, 0.0147, 0.0124, 0.0096,
         0.0091, 0.0091, 0.0097, 0.0070, 0.0072, 0.0068, 0.0077, 0.0067, 0.0069,
         0.0062, 0.0065, 0.0056, 0.0047, 0.0045, 0.0045, 0.0048, 0.0049, 0.0040,
         0.0031, 0.0031, 0.0026, 0.0033, 0.0024, 0.0023, 0.0020, 0.0020, 0.0021,
         0.0020, 0.0016, 0.0021, 0.0016, 0.0016, 0.0014, 0.0014, 0.0013, 0.0015,
         0.0013, 0.0020]], grad_fn=<SoftmaxBackward>)
(1, 101)
tensor([39.2897])
(1,)

该图片为：

 

中间有出现一个错误：

RuntimeError: The size of tensor a (4) must match the size of tensor b (3) at non-singleton dimension 0

然后查看输入的图像，发现其为RGBA格式的：

<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=524x473 at 0x10138FFD0>

所以要将其更改为RGB格式：

img = img.convert("RGB")