Pytorch转tflite方式

脚本专栏 2024/11/2 佚名

3 2 1

圆月山庄资源网 Design By www.vgjia.com

目标是想把在服务器上用pytorch训练好的模型转换为可以在移动端运行的tflite模型。

最直接的思路是想把pytorch模型转换为tensorflow的模型，然后转换为tflite。但是这个转换目前没有发现比较靠谱的方法。

经过调研发现最新的tflite已经支持直接从keras模型的转换，所以可以采用keras作为中间转换的桥梁，这样就能充分利用keras高层API的便利性。

转换的基本思想就是用pytorch中的各层网络的权重取出来后直接赋值给keras网络中的对应layer层的权重。

转换为Keras模型后，再通过tf.contrib.lite.TocoConverter把模型直接转为tflite.

下面是一个例子，假设转换的是一个两层的CNN网络。

import tensorflow as tf
from tensorflow import keras
import numpy as np

import torch
from torchvision import models
import torch.nn as nn
# import torch.nn.functional as F
from torch.autograd import Variable

class PytorchNet(nn.Module):
 def __init__(self):
 super(PytorchNet, self).__init__()
 conv1 = nn.Sequential(
  nn.Conv2d(3, 32, 3, 2),
  nn.BatchNorm2d(32),
  nn.ReLU(inplace=True),
  nn.MaxPool2d(2, 2))
 conv2 = nn.Sequential(
  nn.Conv2d(32, 64, 3, 1, groups=1),
  nn.BatchNorm2d(64),
  nn.ReLU(inplace=True),
  nn.MaxPool2d(2, 2))
 self.feature = nn.Sequential(conv1, conv2)
 self.init_weights()

 def forward(self, x):
 return self.feature(x)

 def init_weights(self):
 for m in self.modules():
  if isinstance(m, nn.Conv2d):
  nn.init.kaiming_normal_(
   m.weight.data, mode='fan_out', nonlinearity='relu')
  if m.bias is not None:
   m.bias.data.zero_()
  if isinstance(m, nn.BatchNorm2d):
  m.weight.data.fill_(1)
  m.bias.data.zero_()

def KerasNet(input_shape=(224, 224, 3)):
 image_input = keras.layers.Input(shape=input_shape)
 # conv1
 network = keras.layers.Conv2D(
 32, (3, 3), strides=(2, 2), padding="valid")(image_input)
 network = keras.layers.BatchNormalization(
 trainable=False, fused=False)(network)
 network = keras.layers.Activation("relu")(network)
 network = keras.layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2))(network)

 # conv2
 network = keras.layers.Conv2D(
 64, (3, 3), strides=(1, 1), padding="valid")(network)
 network = keras.layers.BatchNormalization(
 trainable=False, fused=True)(network)
 network = keras.layers.Activation("relu")(network)
 network = keras.layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2))(network)

 model = keras.Model(inputs=image_input, outputs=network)

 return model

class PytorchToKeras(object):
 def __init__(self, pModel, kModel):
 super(PytorchToKeras, self)
 self.__source_layers = []
 self.__target_layers = []
 self.pModel = pModel
 self.kModel = kModel
 tf.keras.backend.set_learning_phase(0)

 def __retrieve_k_layers(self):
 for i, layer in enumerate(self.kModel.layers):
  if len(layer.weights) > 0:
  self.__target_layers.append(i)

 def __retrieve_p_layers(self, input_size):

 input = torch.randn(input_size)
 input = Variable(input.unsqueeze(0))
 hooks = []

 def add_hooks(module):

  def hook(module, input, output):
  if hasattr(module, "weight"):
   # print(module)
   self.__source_layers.append(module)

  if not isinstance(module, nn.ModuleList) and not isinstance(module, nn.Sequential) and module != self.pModel:
  hooks.append(module.register_forward_hook(hook))

 self.pModel.apply(add_hooks)

 self.pModel(input)
 for hook in hooks:
  hook.remove()

 def convert(self, input_size):
 self.__retrieve_k_layers()
 self.__retrieve_p_layers(input_size)

 for i, (source_layer, target_layer) in enumerate(zip(self.__source_layers, self.__target_layers)):
  print(source_layer)
  weight_size = len(source_layer.weight.data.size())
  transpose_dims = []
  for i in range(weight_size):
  transpose_dims.append(weight_size - i - 1)
  if isinstance(source_layer, nn.Conv2d):
  transpose_dims = [2,3,1,0]
  self.kModel.layers[target_layer].set_weights([source_layer.weight.data.numpy(
  ).transpose(transpose_dims), source_layer.bias.data.numpy()])
  elif isinstance(source_layer, nn.BatchNorm2d):
  self.kModel.layers[target_layer].set_weights([source_layer.weight.data.numpy(), source_layer.bias.data.numpy(),
        source_layer.running_mean.data.numpy(), source_layer.running_var.data.numpy()])
 def save_model(self, output_file):
 self.kModel.save(output_file)

 def save_weights(self, output_file):
 self.kModel.save_weights(output_file, save_format='h5')

pytorch_model = PytorchNet()
keras_model = KerasNet(input_shape=(224, 224, 3))

torch.save(pytorch_model, 'test.pth')

#Load the pretrained model
pytorch_model = torch.load('test.pth')

# #Time to transfer weights
converter = PytorchToKeras(pytorch_model, keras_model)
converter.convert((3, 224, 224))

# #Save the converted keras model for later use
# converter.save_weights("keras.h5")
converter.save_model("keras_model.h5")

# convert keras model to tflite model
converter = tf.contrib.lite.TocoConverter.from_keras_model_file(
 "keras_model.h5")
tflite_model = converter.convert()
open("convert_model.tflite", "wb").write(tflite_model)

补充知识：tensorflow模型转换成tensorflow lite模型

1.把graph和网络模型打包在一个文件中

 bazel build tensorflow/python/tools:freeze_graph &&  bazel-bin/tensorflow/python/tools/freeze_graph  --input_graph=eval_graph_def.pb  --input_checkpoint=checkpoint  --output_graph=frozen_eval_graph.pb  --output_node_names=outputs

For example:

 bazel-bin/tensorflow/python/tools/freeze_graph \ 
 --input_graph=./mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_eval.pbtxt  --input_checkpoint=./mobilenet_v1_1.0_224/mobilenet_v1_1.0_224.ckpt  --output_graph=./mobilenet_v1_1.0_224/frozen_eval_graph_test.pb  --output_node_names=MobilenetV1/Predictions/Reshape_1

2.把第一步中生成的tensorflow pb模型转换为tf lite模型

转换前需要先编译转换工具

bazel build tensorflow/contrib/lite/toco:toco

转换分两种，一种的转换为float的tf lite，另一种可以转换为对模型进行unit8的量化版本的模型。两种方式如下：

非量化的转换：

./bazel-bin/third_party/tensorflow/contrib/lite/toco/toco \ 官网给的这个路径不对       
./bazel-bin/tensorflow/contrib/lite/toco/toco \         
 —input_file=./mobilenet_v1_1.0_224/frozen_eval_graph_test.pb \  
 —output_file=./mobilenet_v1_1.0_224/tflite_model_test.tflite \  
 --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE \       
 --inference_type=FLOAT \           
 --input_shape="1,224, 224,3" \           
 --input_array=input \            
 --output_array=MobilenetV1/Predictions/Reshape_1

量化方式的转换(注意，只有量化训练的模型才能进行量化的tf_lite转换)：

./bazel-bin/third_party/tensorflow/contrib/lite/toco/toco ./bazel-bin/tensorflow/contrib/lite/toco/toco  --input_file=frozen_eval_graph.pb  --output_file=tflite_model.tflite  --input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE  --inference_type=QUANTIZED_UINT8  --input_shape="1,224, 224,3"  --input_array=input  --output_array=outputs  --std_value=127.5 --mean_value=127.5

以上这篇Pytorch转tflite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。

Pytorch,tflite

标签：

Pytorch,tflite

圆月山庄资源网 Design By www.vgjia.com

广告合作：本站广告合作请联系QQ：858582 申请时备注：广告合作（否则不回）
免责声明：本站文章均来自网站采集或用户投稿，网站不提供任何软件下载或自行开发的软件！如有用户或公司发现本站内容信息存在侵权行为，请邮件告知！ 858582#qq.com

圆月山庄资源网 Design By www.vgjia.com

评论“Pytorch转tflite方式”

暂无评论...

www.vgjia.com 圆月山庄资源网

139,976互联网资源

144,792高清电影

21,817无损音乐

631,128技术资源

Pytorch转tflite方式

Pytorch,tflite

基于pandas向csv添加新的行和列

Python如何把十进制数转换成ip地址

评论“Pytorch转tflite方式”

RTX 5090要首发性能要翻倍！三星展示GDDR7显存

更新日志

友情链接

Pytorch转tflite方式

Pytorch,tflite

基于pandas向csv添加新的行和列

Python如何把十进制数转换成ip地址

评论“Pytorch转tflite方式”

RTX 5090要首发 性能要翻倍！三星展示GDDR7显存

更新日志

友情链接

RTX 5090要首发性能要翻倍！三星展示GDDR7显存