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_protopath='/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()