读取JPG文件作为caffe网络结构的输入

这种方式生成配置文件，必须有个前提，就是要先把原始图片转换成LMDB文件才行。如果我们已经把原始图片做成了一个列表清单（txt文件，一行一张图片），则可以不用LMDB格式作为输入数据，可以用ImageData作为数据源输入，即第一层由原来的Data类型，变成了ImageData类型，不需要LMDB文件和均值文件，但需要一个txt文件。
代码如下：

# -*- coding: utf-8 -*-

from caffe import layers as L,params as P,to_proto
path='/home/xxx/data/'
train_list=path+'train.txt'
val_list=path+'val.txt'           
train_proto=path+'train.prototxt'   
val_proto=path+'val.prototxt'       

def create_net(img_list,batch_size,include_acc=False):
    data,label=L.ImageData(source=img_list,batch_size=batch_size,new_width=48,new_height=48,ntop=2,
                           transform_param=dict(crop_size=40,mirror=True))

    conv1=L.Convolution(data, kernel_size=5, stride=1,num_output=16, pad=2,weight_filler=dict(type='xavier'))
    relu1=L.ReLU(conv1, in_place=True)
    pool1=L.Pooling(relu1, pool=P.Pooling.MAX, kernel_size=3, stride=2)
    conv2=L.Convolution(pool1, kernel_size=53, stride=1,num_output=32, pad=1,weight_filler=dict(type='xavier'))
    relu2=L.ReLU(conv2, in_place=True)
    pool2=L.Pooling(relu2, pool=P.Pooling.MAX, kernel_size=3, stride=2)
    conv3=L.Convolution(pool2, kernel_size=53, stride=1,num_output=32, pad=1,weight_filler=dict(type='xavier'))
    relu3=L.ReLU(conv3, in_place=True)
    pool3=L.Pooling(relu3, pool=P.Pooling.MAX, kernel_size=3, stride=2)
    fc4=L.InnerProduct(pool3, num_output=1024,weight_filler=dict(type='xavier'))
    relu4=L.ReLU(fc4, in_place=True)
    drop4 = L.Dropout(relu4, in_place=True)
    fc5 = L.InnerProduct(drop4, num_output=7,weight_filler=dict(type='xavier'))
    loss = L.SoftmaxWithLoss(fc5, label)

    if include_acc:             
        acc = L.Accuracy(fc5, label)
        return to_proto(loss, acc)
    else:
        return to_proto(loss)

def write_net():
    #
    with open(train_proto, 'w') as f:
        f.write(str(create_net(train_list,batch_size=64)))

    #    
    with open(val_proto, 'w') as f:
        f.write(str(create_net(val_list,batch_size=32, include_acc=True)))

if __name__ == '__main__':
    write_net()