tensorflow模型转ncnn的操作方式

第一步把tensorflow保存的.ckpt模型转为pb模型, 并记下模型的输入输出名字.

第二步去ncnn的github上把仓库clone下来, 按照上面的要求装好依赖并make.

第三步是修改ncnn的CMakeList, 具体修改的位置有:

ncnn/CMakeList.txt 文件, 在文件开头处加入add_definitions(-std=c++11), 末尾处加上add_subdirectory(examples), 如果ncnn没有examples文件夹,就新建一个, 并加上CMakeList.txt文件.

ncnn/tools/CMakeList.txt 文件, 加入add_subdirectory(tensorflow)

原版的tools/tensorflow/tensorflow2ncnn.cpp里, 不支持tensorflow的elu, FusedBathNormalization, Conv2dBackpropback操作, 其实elu是支持的,只需要仿照relu的格式, 在.cpp文件里加上就行. FusedBatchNormalization就是ncnn/layer/里实现的batchnorm.cpp, 只是`tensorflow2ncnn里没有写上, 可以增加下面的内容：

else if (node.op() == "FusedBatchNorm")
{
 fprintf(pp, "%-16s", "BatchNorm");
}
...
else if (node.op() == "FusedBatchNorm")
{
 std::cout << "node name is FusedBatchNorm" << std::endl;
 tensorflow::TensorProto tensor;
 find_tensor_proto(weights, node, tensor);
 const tensorflow::TensorShapeProto& shape = tensor.tensor_shape();

 const tensorflow::TensorProto& gamma = weights[node.input(1)];
 const tensorflow::TensorProto& Beta = weights[node.input(2)];
 const tensorflow::TensorProto& mean = weights[node.input(3)];
 const tensorflow::TensorProto& var = weights[node.input(4)];

 int channels = gamma.tensor_shape().dim(0).size(); // data size
 int dtype = gamma.dtype();

 switch (dtype){
  case 1: 
  {

   const float * gamma_tensor = reinterpret_cast<const float *>(gamma.tensor_content().c_str());
   const float * mean_data = reinterpret_cast<const float *>(mean.tensor_content().c_str());
   const float * var_data = reinterpret_cast<const float *>(var.tensor_content().c_str());
   const float * b_data = reinterpret_cast<const float *>(Beta.tensor_content().c_str());
   for (int i=0; i< channels; ++i)
   {
    fwrite(gamma_tensor+i, sizeof(float), 1, bp);
   }
   for (int i=0; i< channels; ++i)
   {
    fwrite(mean_data+i, sizeof(float), 1, bp);
   }
   for (int i=0; i< channels; ++i)
   {
    fwrite(var_data+i, sizeof(float), 1, bp);
   }
   for (int i=0; i< channels; ++i)
   {
    fwrite(b_data+i, sizeof(float), 1, bp);
   }
  }
  default:
   std::cerr << "Type is not supported." << std::endl;

 }
 fprintf(pp, " 0=%d", channels);

 tensorflow::AttrValue value_epsilon;
 if (find_attr_value(node, "epsilon", value_epsilon)){
  float epsilon = value_epsilon.f();
  fprintf(pp, " 1=%f", epsilon);
 }
}

同理, Conv2dBackpropback其实就是ncnn里的反卷积操作, 只不过ncnn实现反卷积的操作和tensorflow内部实现反卷积的操作过程不一样, 但结果是一致的, 需要仿照普通卷积的写法加上去.

ncnn同样支持空洞卷积, 但无法识别tensorflow的空洞卷积, 具体原理可以看tensorflow空洞卷积的原理, tensorflow是改变featuremap做空洞卷积, 而ncnn是改变kernel做空洞卷积, 结果都一样. 需要对.proto文件修改即可完成空洞卷积.

总之ncnn对tensorflow的支持很不友好, 有的层还需要自己手动去实现, 还是很麻烦.

补充知识：pytorch模型转mxnet

介绍

gluon把mxnet再进行封装，封装的风格非常接近pytorch

使用gluon的好处是非常容易把pytorch模型向mxnet转化

唯一的问题是gluon封装还不成熟，封装好的layer不多，很多常用的layer 如concat,upsampling等layer都没有

这里关注如何把pytorch 模型快速转换成 mxnet基于symbol 和 exector设计的网络

pytorch转mxnet module

关键点:

mxnet 设计网络时symbol 名称要和pytorch初始化中各网络层名称对应

torch.load()读入pytorch模型checkpoint 字典，取当中的'state_dict'元素，也是一个字典

pytorch state_dict 字典中key是网络层参数的名称，val是参数ndarray

pytorch 的参数名称的组织形式和mxnet一样，但是连接符号不同，pytorch是'.'，而mxnet是'_'比如：

pytorch '0.conv1.0.weight'
mxnet '0_conv1_0_weight'

pytorch 的参数array 和mxnet 的参数array 完全一样，只要名称对上，直接赋值即可初始化mxnet模型

需要做的有以下几点：

设计和pytorch网络对应的mxnet网络

加载pytorch checkpoint

调整pytorch checkpoint state_dict 的key名称和mxnet命名格式一致

FlowNet2S PytorchToMxnet

pytorch flownet2S 的checkpoint 可以在github上搜到

import mxnet as mx
from symbol_util import *
import pickle
 
def get_loss(data, label, loss_scale, name, get_input=False, is_sparse = False, type='stereo'):
 
 if type == 'stereo':
  data = mx.sym.Activation(data=data, act_type='relu',name=name+'relu')
 # loss
 if is_sparse:
  loss =mx.symbol.Custom(data=data, label=label, name=name, loss_scale= loss_scale, is_l1=True,
   op_type='SparseRegressionLoss')
 else:
  loss = mx.sym.MAERegressionOutput(data=data, label=label, name=name, grad_scale=loss_scale)
 return (loss,data) if get_input else loss
 
def flownet_s(loss_scale, is_sparse=False, name=''):
 img1 = mx.symbol.Variable('img1')
 img2 = mx.symbol.Variable('img2')
 data = mx.symbol.concat(img1,img2,dim=1)
 labels = {'loss{}'.format(i): mx.sym.Variable('loss{}_label'.format(i)) for i in range(0, 7)}
 # print('labels: ',labels)
 prediction = {}# a dict for loss collection
 loss = []#a list
 
 #normalize
 data = (data-125)/255
 
 # extract featrue
 conv1 = mx.sym.Convolution(data, pad=(3, 3), kernel=(7, 7), stride=(2, 2), num_filter=64, name=name + 'conv1_0')
 conv1 = mx.sym.LeakyReLU(data=conv1, act_type='leaky', slope=0.1)
 
 conv2 = mx.sym.Convolution(conv1, pad=(2, 2), kernel=(5, 5), stride=(2, 2), num_filter=128, name=name + 'conv2_0')
 conv2 = mx.sym.LeakyReLU(data=conv2, act_type='leaky', slope=0.1)
 
 conv3a = mx.sym.Convolution(conv2, pad=(2, 2), kernel=(5, 5), stride=(2, 2), num_filter=256, name=name + 'conv3_0')
 conv3a = mx.sym.LeakyReLU(data=conv3a, act_type='leaky', slope=0.1)
 
 conv3b = mx.sym.Convolution(conv3a, pad=(1, 1), kernel=(3, 3), stride=(1, 1), num_filter=256, name=name + 'conv3_1_0')
 conv3b = mx.sym.LeakyReLU(data=conv3b, act_type='leaky', slope=0.1)
 
 conv4a = mx.sym.Convolution(conv3b, pad=(1, 1), kernel=(3, 3), stride=(2, 2), num_filter=512, name=name + 'conv4_0')
 conv4a = mx.sym.LeakyReLU(data=conv4a, act_type='leaky', slope=0.1)
 
 conv4b = mx.sym.Convolution(conv4a, pad=(1, 1), kernel=(3, 3), stride=(1, 1), num_filter=512, name=name + 'conv4_1_0')
 conv4b = mx.sym.LeakyReLU(data=conv4b, act_type='leaky', slope=0.1)
 
 conv5a = mx.sym.Convolution(conv4b, pad=(1, 1), kernel=(3, 3), stride=(2, 2), num_filter=512, name=name + 'conv5_0')
 conv5a = mx.sym.LeakyReLU(data=conv5a, act_type='leaky', slope=0.1)
 
 conv5b = mx.sym.Convolution(conv5a, pad=(1, 1), kernel=(3, 3), stride=(1, 1), num_filter=512, name=name + 'conv5_1_0')
 conv5b = mx.sym.LeakyReLU(data=conv5b, act_type='leaky', slope=0.1)
 
 conv6a = mx.sym.Convolution(conv5b, pad=(1, 1), kernel=(3, 3), stride=(2, 2), num_filter=1024, name=name + 'conv6_0')
 conv6a = mx.sym.LeakyReLU(data=conv6a, act_type='leaky', slope=0.1)
 
 conv6b = mx.sym.Convolution(conv6a, pad=(1, 1), kernel=(3, 3), stride=(1, 1), num_filter=1024,
        name=name + 'conv6_1_0')
 conv6b = mx.sym.LeakyReLU(data=conv6b, act_type='leaky', slope=0.1, )
 
 #predict flow
 pr6 = mx.sym.Convolution(conv6b, pad=(1, 1), kernel=(3, 3), stride=(1, 1), num_filter=2,
        name=name + 'predict_flow6')
 prediction['loss6'] = pr6
 
 upsample_pr6to5 = mx.sym.Deconvolution(pr6, pad=(1, 1), kernel=(4, 4), stride=(2, 2), num_filter=2,
           name=name + 'upsampled_flow6_to_5', no_bias=True)
 upconv5 = mx.sym.Deconvolution(conv6b, pad=(1, 1), kernel=(4, 4), stride=(2, 2), num_filter=512,
         name=name + 'deconv5_0', no_bias=False)
 upconv5 = mx.sym.LeakyReLU(data=upconv5, act_type='leaky', slope=0.1)
 iconv5 = mx.sym.Concat(conv5b, upconv5, upsample_pr6to5, dim=1)
 
 
 pr5 = mx.sym.Convolution(iconv5, pad=(1, 1), kernel=(3, 3), stride=(1, 1), num_filter=2,
        name=name + 'predict_flow5')
 prediction['loss5'] = pr5
 
 upconv4 = mx.sym.Deconvolution(iconv5, pad=(1, 1), kernel=(4, 4), stride=(2, 2), num_filter=256,
         name=name + 'deconv4_0', no_bias=False)
 upconv4 = mx.sym.LeakyReLU(data=upconv4, act_type='leaky', slope=0.1)
 
 upsample_pr5to4 = mx.sym.Deconvolution(pr5, pad=(1, 1), kernel=(4, 4), stride=(2, 2), num_filter=2,
           name=name + 'upsampled_flow5_to_4', no_bias=True)
 
 iconv4 = mx.sym.Concat(conv4b, upconv4, upsample_pr5to4)
 
 pr4 = mx.sym.Convolution(iconv4, pad=(1, 1), kernel=(3, 3), stride=(1, 1), num_filter=2,
        name=name + 'predict_flow4')
 prediction['loss4'] = pr4
 
 upconv3 = mx.sym.Deconvolution(iconv4, pad=(1, 1), kernel=(4, 4), stride=(2, 2), num_filter=128,
         name=name + 'deconv3_0', no_bias=False)
 upconv3 = mx.sym.LeakyReLU(data=upconv3, act_type='leaky', slope=0.1)
 
 upsample_pr4to3 = mx.sym.Deconvolution(pr4, pad=(1, 1), kernel=(4, 4), stride=(2, 2), num_filter=2,
           name= name + 'upsampled_flow4_to_3', no_bias=True)
 iconv3 = mx.sym.Concat(conv3b, upconv3, upsample_pr4to3)
 
 pr3 = mx.sym.Convolution(iconv3, pad=(1, 1), kernel=(3, 3), stride=(1, 1), num_filter=2,
        name=name + 'predict_flow3')
 prediction['loss3'] = pr3
 
 upconv2 = mx.sym.Deconvolution(iconv3, pad=(1, 1), kernel=(4, 4), stride=(2, 2), num_filter=64,
         name=name + 'deconv2_0', no_bias=False)
 upconv2 = mx.sym.LeakyReLU(data=upconv2, act_type='leaky', slope=0.1)
 
 upsample_pr3to2 = mx.sym.Deconvolution(pr3, pad=(1, 1), kernel=(4, 4), stride=(2, 2), num_filter=2,
           name=name + 'upsampled_flow3_to_2', no_bias=True)
 iconv2 = mx.sym.Concat(conv2, upconv2, upsample_pr3to2)
 
 pr2 = mx.sym.Convolution(iconv2, pad=(1, 1), kernel=(3, 3), stride=(1, 1), num_filter=2,
        name=name + 'predict_flow2')
 prediction['loss2'] = pr2
 flow = mx.sym.UpSampling(arg0=pr2,scale=4,num_filter=2,num_args = 1,sample_type='nearest', name='upsample_flow2_to_1')
 # ignore the loss functions with loss scale of zero
 keys = loss_scale.keys()
 # keys.sort()
 #obtain the symbol of the losses
 for key in keys:
  # loss.append(get_loss(prediction[key] * 20, labels[key], loss_scale[key], name=key + name,get_input=False, is_sparse=is_sparse, type='flow'))
  loss.append(mx.sym.MAERegressionOutput(data=prediction[key] * 20, label=labels[key], name=key + name, grad_scale=loss_scale[key]))
 # print('loss: ',loss)
 #group 暂时不知道为嘛要group
 loss_group =mx.sym.Group(loss)
 # print('net: ',loss_group)
 return loss_group,flow
 
import gluonbook as gb
import torch
from utils.frame_utils import *
import numpy as np
if __name__ == '__main__':
 checkpoint = torch.load("C:/Users/junjie.huang/PycharmProjects/flownet2_mxnet/flownet2_pytorch/FlowNet2-S_checkpoint.pth.tar")
 # # checkpoint是一个字典
 print(isinstance(checkpoint['state_dict'], dict))
 # # 打印checkpoint字典中的key名
 print('keys of checkpoint:')
 for i in checkpoint:
  print(i)
 print('')
 # # pytorch 模型参数保存在一个key名为'state_dict'的元素中
 state_dict = checkpoint['state_dict']
 # # state_dict也是一个字典
 print('keys of state_dict:')
 for i in state_dict:
  print(i)
  # print(state_dict[i].size())
 print('')
 # print(state_dict)
 #字典的value是torch.tensor
 print(torch.is_tensor(state_dict['conv1.0.weight']))
 #查看某个value的size
 print(state_dict['conv1.0.weight'].size())
 
 #flownet-mxnet init
 loss_scale={'loss2': 1.00,
    'loss3': 1.00,
    'loss4': 1.00,
    'loss5': 1.00,
    'loss6': 1.00}
 loss,flow = flownet_s(loss_scale=loss_scale,is_sparse=False)
 print('loss information: ')
 print('loss:',loss)
 print('type:',type(loss))
 print('list_arguments:',loss.list_arguments())
 print('list_outputs:',loss.list_outputs())
 print('list_inputs:',loss.list_inputs())
 print('')
 
 print('flow information: ')
 print('flow:',flow)
 print('type:',type(flow))
 print('list_arguments:',flow.list_arguments())
 print('list_outputs:',flow.list_outputs())
 print('list_inputs:',flow.list_inputs())
 print('')
 name_mxnet = symbol.list_arguments()
 print(type(name_mxnet))
 for key in name_mxnet:
  print(key)
 
 name_mxnet.sort()
 for key in name_mxnet:
  print(key)
 print(name_mxnet)
 
 shapes = (1, 3, 384, 512)
 ctx = gb.try_gpu()
 # exe = symbol.simple_bind(ctx=ctx, img1=shapes,img2=shapes)
 exe = flow.simple_bind(ctx=ctx, img1=shapes, img2=shapes)
 print('exe type: ',type(exe))
 print('exe: ',exe)
 #module
 # mod = mx.mod.Module(flow)
 # print('mod type: ', type(exe))
 # print('mod: ', exe)
 
 pim1 = read_gen("C:/Users/junjie.huang/PycharmProjects/flownet2_mxnet/data/0000007-img0.ppm")
 pim2 = read_gen("C:/Users/junjie.huang/PycharmProjects/flownet2_mxnet/data/0000007-img1.ppm")
 print(pim1.shape)
 
 '''使用pytorch 的state_dict 初始化 mxnet 模型参数'''
 for key in state_dict:
  # print(type(key))
  k_split = key.split('.')
  key_mx = '_'.join(k_split)
  # print(key,key_mx)
  try:
   exe.arg_dict[key_mx][:]=state_dict[key].data
  except:
   print(key,exe.arg_dict[key_mx].shape,state_dict[key].data.shape)
 
 exe.arg_dict['img1'][:] = pim1[np.newaxis, :, :, :].transpose(0, 3, 1, 2).data
 exe.arg_dict['img2'][:] = pim2[np.newaxis, :, :, :].transpose(0, 3, 1, 2).data
 
 result = exe.forward()
 print('result: ',type(result))
 # for tmp in result:
 #  print(type(tmp))
 #  print(tmp.shape)
 # color = flow2color(exe.outputs[0].asnumpy()[0].transpose(1, 2, 0))
 outputs = exe.outputs
 print('output type: ',type(outputs))
 # for tmp in outputs:
 #  print(type(tmp))
 #  print(tmp.shape)
 
 #来自pytroch flownet2
 from visualize import flow2color
 # color = flow2color(exe.outputs[0].asnumpy()[0].transpose(1,2,0))
 flow_color = flow2color(exe.outputs[0].asnumpy()[0].transpose(1, 2, 0))
 print('color type:',type(flow_color))
 import matplotlib.pyplot as plt
 #来自pytorch
 from torchvision.transforms import ToPILImage
 TF = ToPILImage()
 images = TF(flow_color)
 images.show()
 # plt.imshow(color)

以上这篇tensorflow模型转ncnn的操作方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持三水点靠木。