net_def.py 文件源码

def create_model(depth):
    model = caffe_pb2.NetParameter()
    model.name = 'ResNet_{}'.format(depth)
    configs = {
        50: [3, 4, 6, 3],
        101: [3, 4, 23, 3],
        152: [3, 8, 36, 3],
        200: [3, 24, 36, 3],
    }
    num = configs[depth]
    layers = []
    layers.append(Data('data', ['data', 'label'],
                       'examples/imagenet/ilsvrc12_train_lmdb', 32, 'train'))
    layers.append(Data('data', ['data', 'label'],
                       'examples/imagenet/ilsvrc12_val_lmdb', 25, 'test'))
    layers.append(Conv('conv1', 'data', 64, 7, 2, 3))
    layers.extend(Act('conv1', layers[-1].top[0]))
    layers.append(Pool('pool1', layers[-1].top[0], 'max', 3, 2, 0))
    layers.extend(ResLayer('conv2', layers[-1].top[0], num[0], 64, 1, 'first'))
    layers.extend(ResLayer('conv3', layers[-1].top[0], num[1], 128, 2))
    layers.extend(ResLayer('conv4', layers[-1].top[0], num[2], 256, 2))
    layers.extend(ResLayer('conv5', layers[-1].top[0], num[3], 512, 2))
    layers.extend(Act('conv5', layers[-1].top[0]))
    layers.append(Pool('pool5', layers[-1].top[0], 'ave', 7, 1, 0))
    layers.append(Linear('fc', layers[-1].top[0], 1000))
    layers.append(Loss('loss', ['fc', 'label']))
    layers.append(Accuracy('accuracy_top1', ['fc', 'label'], 1))
    layers.append(Accuracy('accuracy_top5', ['fc', 'label'], 5))
    model.layer.extend(layers)
    return model