MXNet 2 pytorch 模型转换
1、先定义好pytorch的网络结构： 没怎么接触人脸识别 insightface提供r50 里是 IResBlock，第一个卷积还是3x3 而非7x7
# -*- coding: utf-8 -*-
"""
Created on 18-5-21 下午5:26
@author: ronghuaiyang
"""
import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch.nn.utils.weight_norm as weight_norm
import torch.nn.functional as F
# __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
# 'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class IRBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
super(IRBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(inplanes)
self.conv1 = conv3x3(inplanes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.relu1 = nn.LeakyReLU(0.25)#nn.PReLU()
self.conv2 = conv3x3(planes, planes, stride)
self.bn3 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.use_se = use_se
if self.use_se:
self.se = SEBlock(planes)
def forward(self, x):
residual = x
out = self.bn0(x)
out = self.conv1(out)
out = self.bn1(out)
out = self.prelu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.use_se:
out = self.se(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.prelu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class SEBlock(nn.Module):
def __init__(self, channel, reduction=16):
super(SEBlock, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction),
nn.PReLU(),
nn.Linear(channel // reduction, channel),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y
class ResNetFace(nn.Module):
def __init__(self, block, layers, use_se=False):
self.inplanes = 64
self.use_se = use_se
super(ResNetFace, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1,stride=2, bias=False)
self.bn0 = nn.BatchNorm2d(64)
self.prelu = nn.LeakyReLU(0.25)#nn.PReLU()
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.bn1 = nn.BatchNorm2d(512)
self.dropout = nn.Dropout()
self.pre_fc1 = nn.Linear(512 * 7 * 7, 512)
self.fc1 = nn.BatchNorm1d(512)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.xavier_normal_(m.weight)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
self.inplanes = planes
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, use_se=self.use_se))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.prelu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.bn4(x)
x = self.dropout(x)
x = x.view(x.size(0), -1)
x = self.fc5(x)
x = self.bn5(x)
return x
class ResNet(nn.Module):
def __init__(self, block, layers):
self.inplanes = 64
super(ResNet, self).__init__()
# self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
# bias=False)
self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
# self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
# self.avgpool = nn.AvgPool2d(8, stride=1)
# self.fc = nn.Linear(512 * block.expansion, num_classes)
self.fc5 = nn.Linear(512 * 8 * 8, 512)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
# x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
# x = nn.AvgPool2d(kernel_size=x.size()[2:])(x)
# x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc5(x)
return x
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
def resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model
def resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model
def resnet101(pretrained=False, **kwargs):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
return model
def resnet152(pretrained=False, **kwargs):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return model
def resnet_face18(use_se=True, **kwargs):
model = ResNetFace(IRBlock, [2, 2, 2, 2], use_se=use_se, **kwargs)
return model
2、转换代码
mxnet模型里json文件记录了模型相关的键名，
import torch.nn as nn import torch.utils.checkpoint as cp import mxnet as mx import torch from resnet import ResNetFace,IRBlock archs = ['iresnet50'] mx_model = './model-r50-am-lfw/model' mx_epoch = 14 _,arg_params, aux_params = mx.model.load_checkpoint(mx_model,mx_epoch) class convert_model(object): def init_model(self, model,param_dict,aux_params): # print(model) layer=[3,4,14,3] for n, m in model.named_modules(): print(n) n1 = n.split('.') if len(n1)>2: stage = n1[0][-1] unit = int(n1[1])+1 op = n1[2] print(stage,unit,op) if op=='relu': op = op+'1' if op == 'downsample': r='0000' if len(n1)>3: r =n1[3] if r=='0': op = 'conv1sc' else: op = 'sc' # # if op == '' n = 'stage'+stage+'_unit'+str(unit)+'_'+op print(n) if n=='conv1': self.conv1_init(n,m,param_dict) elif isinstance(m, nn.BatchNorm2d): if n=='bn0': self.bn_init('bn0', m, param_dict,aux_params) else: self.bn_init(n, m, param_dict,aux_params) elif isinstance(m, nn.Conv2d): self.conv_init(n, m, param_dict) elif isinstance(m, nn.Linear): self.fc_init(n, m, param_dict) elif isinstance(m, nn.PReLU): if n=='prelu': self.prelu_init('relu0', m, param_dict) else: self.prelu_init(n, m, param_dict) return model def bn_init(self, n, m, param_dict,aux_params): print(torch.FloatTensor(param_dict[n+'_gamma'].asnumpy()).size()) if not (m.weight is None): m.weight.data.copy_(torch.FloatTensor(param_dict[n+'_gamma'].asnumpy())) m.bias.data.copy_(torch.FloatTensor(param_dict[n+'_beta'].asnumpy())) m.running_mean.copy_(torch.FloatTensor(aux_params[n+'_moving_mean'].asnumpy())) m.running_var.copy_(torch.FloatTensor(aux_params[n+'_moving_var'].asnumpy())) def conv1_init(self, n, m, param_dict): # print('n = ', n) #$n = 'conv0' a = torch.FloatTensor(param_dict['conv0'+'_weight'].asnumpy()) print(a.size()) print(m.weight.size()) m.weight.data.copy_(torch.FloatTensor(param_dict['conv0'+'_weight'].asnumpy())) def conv_init(self, n, m, param_dict): # print('n = ', n) a = torch.FloatTensor(param_dict[n+'_weight'].asnumpy()) print(a.size(),n) print(m.weight.size()) m.weight.data.copy_(torch.FloatTensor(param_dict[n+'_weight'].asnumpy())) #if n in ['conv1_1', 'conv4_1', 'conv3_1', 'conv2_1']: #m.bias.data.copy_(torch.FloatTensor(param_dict[n + '_bias'].asnumpy())) def fc_init(self, n, m, param_dict): m.weight.data.copy_(torch.FloatTensor(param_dict[n+'_weight'].asnumpy())) m.bias.data.copy_(torch.FloatTensor(param_dict[n+'_bias'].asnumpy())) def prelu_init(self, n, m, param_dict): print(torch.FloatTensor(param_dict[n + '_gamma'].asnumpy()).size()) m.weight.data.copy_(torch.FloatTensor(param_dict[n + '_gamma'].asnumpy())) c = convert_model() r50 = ResNetFace(IRBlock, [3,4,14,3])#getattr(models, arch)()#ResNet(BasicBlock,[3,4,14,3]) model = c.init_model(r50,arg_params,aux_params) #r50.load_state_dict(torch.load('./r50.pkl')) #exit(0) torch.save(model.state_dict(),'./r50.pkl')
import torch.nn as nn import torch.utils.checkpoint as cp import mxnet as mx import torch from resnet import ResNetFace,IRBlock archs = ['iresnet50'] mx_model = './model-r50-am-lfw/model' mx_epoch = 14 _,arg_params, aux_params = mx.model.load_checkpoint(mx_model,mx_epoch) class convert_model(object): def init_model(self, model,param_dict,aux_params): # print(model) layer=[3,4,14,3] for n, m in model.named_modules(): print(n) n1 = n.split('.') if len(n1)>2: stage = n1[0][-1] unit = int(n1[1])+1 op = n1[2] print(stage,unit,op) if op=='relu': op = op+'1' if op == 'downsample': r='0000' if len(n1)>3: r =n1[3] if r=='0': op = 'conv1sc' else: op = 'sc' # # if op == '' n = 'stage'+stage+'_unit'+str(unit)+'_'+op print(n) if n=='conv1': self.conv1_init(n,m,param_dict) elif isinstance(m, nn.BatchNorm2d): if n=='bn0': self.bn_init('bn0', m, param_dict,aux_params) else: self.bn_init(n, m, param_dict,aux_params) elif isinstance(m, nn.Conv2d): self.conv_init(n, m, param_dict) elif isinstance(m, nn.Linear): self.fc_init(n, m, param_dict) elif isinstance(m, nn.PReLU): if n=='prelu': self.prelu_init('relu0', m, param_dict) else: self.prelu_init(n, m, param_dict) return model def bn_init(self, n, m, param_dict,aux_params): print(torch.FloatTensor(param_dict[n+'_gamma'].asnumpy()).size()) if not (m.weight is None): m.weight.data.copy_(torch.FloatTensor(param_dict[n+'_gamma'].asnumpy())) m.bias.data.copy_(torch.FloatTensor(param_dict[n+'_beta'].asnumpy())) m.running_mean.copy_(torch.FloatTensor(aux_params[n+'_moving_mean'].asnumpy())) m.running_var.copy_(torch.FloatTensor(aux_params[n+'_moving_var'].asnumpy())) def conv1_init(self, n, m, param_dict): # print('n = ', n) #$n = 'conv0' a = torch.FloatTensor(param_dict['conv0'+'_weight'].asnumpy()) print(a.size()) print(m.weight.size()) m.weight.data.copy_(torch.FloatTensor(param_dict['conv0'+'_weight'].asnumpy())) def conv_init(self, n, m, param_dict): # print('n = ', n) a = torch.FloatTensor(param_dict[n+'_weight'].asnumpy()) print(a.size(),n) print(m.weight.size()) m.weight.data.copy_(torch.FloatTensor(param_dict[n+'_weight'].asnumpy())) #if n in ['conv1_1', 'conv4_1', 'conv3_1', 'conv2_1']: #m.bias.data.copy_(torch.FloatTensor(param_dict[n + '_bias'].asnumpy())) def fc_init(self, n, m, param_dict): m.weight.data.copy_(torch.FloatTensor(param_dict[n+'_weight'].asnumpy())) m.bias.data.copy_(torch.FloatTensor(param_dict[n+'_bias'].asnumpy())) def prelu_init(self, n, m, param_dict): print(torch.FloatTensor(param_dict[n + '_gamma'].asnumpy()).size()) m.weight.data.copy_(torch.FloatTensor(param_dict[n + '_gamma'].asnumpy())) c = convert_model() r50 = ResNetFace(IRBlock, [3,4,14,3])#getattr(models, arch)()#ResNet(BasicBlock,[3,4,14,3]) model = c.init_model(r50,arg_params,aux_params) #r50.load_state_dict(torch.load('./r50.pkl')) #exit(0) torch.save(model.state_dict(),'./r50.pkl')
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