Random_clip_valid.py 文件源码

def parseNet(self, net, netstruct, istraining = True):
        for key in netstruct:
            if key[0] == "conv":
                net = self.conv3d(net, key[2], key[1],key[3], key[4])
            elif key[0] == "fc":
                net = self.fc(net, key[2], key[1], key[3], key[4],activation = key[-1])
            elif key[0] == "maxpool":
                net = tf.nn.max_pool3d(net, ksize = key[2], strides = key[2], padding = "SAME", name = key[1])
            elif key[0] == "dropout" and istraining:
                net = tf.nn.dropout(net, key[2], name = key[1])
            elif key[0] == "reshape":
                net = tf.reshape(net, key[-1])
            elif key[0] == "softmax":
                net = tf.nn.softmax(net)
            elif key[0] == "transpose":
                net = tf.transpose(net, perm=key[-1])
        return net