我有一个炬管t7模型，我想将其转换为pytorch模型。我使用了这种方法：
model = load_lua('xxx.t7', unknown_classes=True)
但是，出现以下错误：
AttributeError: type object 'torch.cuda.FloatStorage' has no attribute 'from_buffer'
有什么办法解决吗？
答案 0 :(得分：-2)
有一个非常有用的转换器。我用了很多时间。
使用方法；创建一个convert_torch.py文件并将其粘贴在下面的代码中。然后使用.t7参数运行代码。
python convert_torch.py -m xxx.t7
from __future__ import print_function
import os
import math
import torch
import argparse
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.legacy.nn as lnn
import torch.nn.functional as F
from functools import reduce
from torch.autograd import Variable
from torch.utils.serialization import load_lua
class LambdaBase(nn.Sequential):
def __init__(self, fn, *args):
super(LambdaBase, self).__init__(*args)
self.lambda_func = fn
def forward_prepare(self, input):
output = []
for module in self._modules.values():
output.append(module(input))
return output if output else input
class Lambda(LambdaBase):
def forward(self, input):
return self.lambda_func(self.forward_prepare(input))
class LambdaMap(LambdaBase):
def forward(self, input):
# result is Variables list [Variable1, Variable2, ...]
return list(map(self.lambda_func,self.forward_prepare(input)))
class LambdaReduce(LambdaBase):
def forward(self, input):
# result is a Variable
return reduce(self.lambda_func,self.forward_prepare(input))
def copy_param(m,n):
if m.weight is not None: n.weight.data.copy_(m.weight)
if m.bias is not None: n.bias.data.copy_(m.bias)
if hasattr(n,'running_mean'): n.running_mean.copy_(m.running_mean)
if hasattr(n,'running_var'): n.running_var.copy_(m.running_var)
def add_submodule(seq, *args):
for n in args:
seq.add_module(str(len(seq._modules)),n)
def lua_recursive_model(module,seq):
for m in module.modules:
name = type(m).__name__
real = m
if name == 'TorchObject':
name = m._typename.replace('cudnn.','')
m = m._obj
if name == 'SpatialConvolution' or name == 'nn.SpatialConvolutionMM':
if not hasattr(m,'groups') or m.groups is None: m.groups=1
n = nn.Conv2d(m.nInputPlane,m.nOutputPlane,(m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),1,m.groups,bias=(m.bias is not None))
copy_param(m,n)
add_submodule(seq,n)
elif name == 'SpatialBatchNormalization':
n = nn.BatchNorm2d(m.running_mean.size(0), m.eps, m.momentum, m.affine)
copy_param(m,n)
add_submodule(seq,n)
elif name == 'VolumetricBatchNormalization':
n = nn.BatchNorm3d(m.running_mean.size(0), m.eps, m.momentum, m.affine)
copy_param(m, n)
add_submodule(seq, n)
elif name == 'ReLU':
n = nn.ReLU()
add_submodule(seq,n)
elif name == 'Sigmoid':
n = nn.Sigmoid()
add_submodule(seq,n)
elif name == 'SpatialMaxPooling':
n = nn.MaxPool2d((m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),ceil_mode=m.ceil_mode)
add_submodule(seq,n)
elif name == 'SpatialAveragePooling':
n = nn.AvgPool2d((m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),ceil_mode=m.ceil_mode)
add_submodule(seq,n)
elif name == 'SpatialUpSamplingNearest':
n = nn.UpsamplingNearest2d(scale_factor=m.scale_factor)
add_submodule(seq,n)
elif name == 'View':
n = Lambda(lambda x: x.view(x.size(0),-1))
add_submodule(seq,n)
elif name == 'Reshape':
n = Lambda(lambda x: x.view(x.size(0),-1))
add_submodule(seq,n)
elif name == 'Linear':
# Linear in pytorch only accept 2D input
n1 = Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x )
n2 = nn.Linear(m.weight.size(1),m.weight.size(0),bias=(m.bias is not None))
copy_param(m,n2)
n = nn.Sequential(n1,n2)
add_submodule(seq,n)
elif name == 'Dropout':
m.inplace = False
n = nn.Dropout(m.p)
add_submodule(seq,n)
elif name == 'SoftMax':
n = nn.Softmax()
add_submodule(seq,n)
elif name == 'Identity':
n = Lambda(lambda x: x) # do nothing
add_submodule(seq,n)
elif name == 'SpatialFullConvolution':
n = nn.ConvTranspose2d(m.nInputPlane,m.nOutputPlane,(m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),(m.adjW,m.adjH))
copy_param(m,n)
add_submodule(seq,n)
elif name == 'VolumetricFullConvolution':
n = nn.ConvTranspose3d(m.nInputPlane,m.nOutputPlane,(m.kT,m.kW,m.kH),(m.dT,m.dW,m.dH),(m.padT,m.padW,m.padH),(m.adjT,m.adjW,m.adjH),m.groups)
copy_param(m,n)
add_submodule(seq, n)
elif name == 'SpatialReplicationPadding':
n = nn.ReplicationPad2d((m.pad_l,m.pad_r,m.pad_t,m.pad_b))
add_submodule(seq,n)
elif name == 'SpatialReflectionPadding':
n = nn.ReflectionPad2d((m.pad_l,m.pad_r,m.pad_t,m.pad_b))
add_submodule(seq,n)
elif name == 'Copy':
n = Lambda(lambda x: x) # do nothing
add_submodule(seq,n)
elif name == 'Narrow':
n = Lambda(lambda x,a=(m.dimension,m.index,m.length): x.narrow(*a))
add_submodule(seq,n)
elif name == 'SpatialCrossMapLRN':
lrn = lnn.SpatialCrossMapLRN(m.size,m.alpha,m.beta,m.k)
n = Lambda(lambda x,lrn=lrn: Variable(lrn.forward(x.data)))
add_submodule(seq,n)
elif name == 'Sequential':
n = nn.Sequential()
lua_recursive_model(m,n)
add_submodule(seq,n)
elif name == 'ConcatTable': # output is list
n = LambdaMap(lambda x: x)
lua_recursive_model(m,n)
add_submodule(seq,n)
elif name == 'CAddTable': # input is list
n = LambdaReduce(lambda x,y: x+y)
add_submodule(seq,n)
elif name == 'Concat':
dim = m.dimension
n = LambdaReduce(lambda x,y,dim=dim: torch.cat((x,y),dim))
lua_recursive_model(m,n)
add_submodule(seq,n)
elif name == 'TorchObject':
print('Not Implement',name,real._typename)
else:
print('Not Implement',name)
def lua_recursive_source(module):
s = []
for m in module.modules:
name = type(m).__name__
real = m
if name == 'TorchObject':
name = m._typename.replace('cudnn.','')
m = m._obj
if name == 'SpatialConvolution' or name == 'nn.SpatialConvolutionMM':
if not hasattr(m,'groups') or m.groups is None: m.groups=1
s += ['nn.Conv2d({},{},{},{},{},{},{},bias={}),#Conv2d'.format(m.nInputPlane,
m.nOutputPlane,(m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),1,m.groups,m.bias is not None)]
elif name == 'SpatialBatchNormalization':
s += ['nn.BatchNorm2d({},{},{},{}),#BatchNorm2d'.format(m.running_mean.size(0), m.eps, m.momentum, m.affine)]
elif name == 'VolumetricBatchNormalization':
s += ['nn.BatchNorm3d({},{},{},{}),#BatchNorm3d'.format(m.running_mean.size(0), m.eps, m.momentum, m.affine)]
elif name == 'ReLU':
s += ['nn.ReLU()']
elif name == 'Sigmoid':
s += ['nn.Sigmoid()']
elif name == 'SpatialMaxPooling':
s += ['nn.MaxPool2d({},{},{},ceil_mode={}),#MaxPool2d'.format((m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),m.ceil_mode)]
elif name == 'SpatialAveragePooling':
s += ['nn.AvgPool2d({},{},{},ceil_mode={}),#AvgPool2d'.format((m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),m.ceil_mode)]
elif name == 'SpatialUpSamplingNearest':
s += ['nn.UpsamplingNearest2d(scale_factor={})'.format(m.scale_factor)]
elif name == 'View':
s += ['Lambda(lambda x: x.view(x.size(0),-1)), # View']
elif name == 'Reshape':
s += ['Lambda(lambda x: x.view(x.size(0),-1)), # Reshape']
elif name == 'Linear':
s1 = 'Lambda(lambda x: x.view(1,-1) if 1==len(x.size()) else x )'
s2 = 'nn.Linear({},{},bias={})'.format(m.weight.size(1),m.weight.size(0),(m.bias is not None))
s += ['nn.Sequential({},{}),#Linear'.format(s1,s2)]
elif name == 'Dropout':
s += ['nn.Dropout({})'.format(m.p)]
elif name == 'SoftMax':
s += ['nn.Softmax()']
elif name == 'Identity':
s += ['Lambda(lambda x: x), # Identity']
elif name == 'SpatialFullConvolution':
s += ['nn.ConvTranspose2d({},{},{},{},{},{})'.format(m.nInputPlane,
m.nOutputPlane,(m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),(m.adjW,m.adjH))]
elif name == 'VolumetricFullConvolution':
s += ['nn.ConvTranspose3d({},{},{},{},{},{},{})'.format(m.nInputPlane,
m.nOutputPlane,(m.kT,m.kW,m.kH),(m.dT,m.dW,m.dH),(m.padT,m.padW,m.padH),(m.adjT,m.adjW,m.adjH),m.groups)]
elif name == 'SpatialReplicationPadding':
s += ['nn.ReplicationPad2d({})'.format((m.pad_l,m.pad_r,m.pad_t,m.pad_b))]
elif name == 'SpatialReflectionPadding':
s += ['nn.ReflectionPad2d({})'.format((m.pad_l,m.pad_r,m.pad_t,m.pad_b))]
elif name == 'Copy':
s += ['Lambda(lambda x: x), # Copy']
elif name == 'Narrow':
s += ['Lambda(lambda x,a={}: x.narrow(*a))'.format((m.dimension,m.index,m.length))]
elif name == 'SpatialCrossMapLRN':
lrn = 'lnn.SpatialCrossMapLRN(*{})'.format((m.size,m.alpha,m.beta,m.k))
s += ['Lambda(lambda x,lrn={}: Variable(lrn.forward(x.data)))'.format(lrn)]
elif name == 'Sequential':
s += ['nn.Sequential( # Sequential']
s += lua_recursive_source(m)
s += [')']
elif name == 'ConcatTable':
s += ['LambdaMap(lambda x: x, # ConcatTable']
s += lua_recursive_source(m)
s += [')']
elif name == 'CAddTable':
s += ['LambdaReduce(lambda x,y: x+y), # CAddTable']
elif name == 'Concat':
dim = m.dimension
s += ['LambdaReduce(lambda x,y,dim={}: torch.cat((x,y),dim), # Concat'.format(m.dimension)]
s += lua_recursive_source(m)
s += [')']
else:
s += '# ' + name + ' Not Implement,\n'
s = map(lambda x: '\t{}'.format(x),s)
return s
def simplify_source(s):
s = map(lambda x: x.replace(',(1, 1),(0, 0),1,1,bias=True),#Conv2d',')'),s)
s = map(lambda x: x.replace(',(0, 0),1,1,bias=True),#Conv2d',')'),s)
s = map(lambda x: x.replace(',1,1,bias=True),#Conv2d',')'),s)
s = map(lambda x: x.replace(',bias=True),#Conv2d',')'),s)
s = map(lambda x: x.replace('),#Conv2d',')'),s)
s = map(lambda x: x.replace(',1e-05,0.1,True),#BatchNorm2d',')'),s)
s = map(lambda x: x.replace('),#BatchNorm2d',')'),s)
s = map(lambda x: x.replace(',(0, 0),ceil_mode=False),#MaxPool2d',')'),s)
s = map(lambda x: x.replace(',ceil_mode=False),#MaxPool2d',')'),s)
s = map(lambda x: x.replace('),#MaxPool2d',')'),s)
s = map(lambda x: x.replace(',(0, 0),ceil_mode=False),#AvgPool2d',')'),s)
s = map(lambda x: x.replace(',ceil_mode=False),#AvgPool2d',')'),s)
s = map(lambda x: x.replace(',bias=True)),#Linear',')), # Linear'),s)
s = map(lambda x: x.replace(')),#Linear',')), # Linear'),s)
s = map(lambda x: '{},\n'.format(x),s)
s = map(lambda x: x[1:],s)
s = reduce(lambda x,y: x+y, s)
return s
def torch_to_pytorch(t7_filename,outputname=None):
model = load_lua(t7_filename,unknown_classes=True)
if type(model).__name__=='hashable_uniq_dict': model=model.model
model.gradInput = None
slist = lua_recursive_source(lnn.Sequential().add(model))
s = simplify_source(slist)
header = '''
import torch
import torch.nn as nn
import torch.legacy.nn as lnn
from functools import reduce
from torch.autograd import Variable
class LambdaBase(nn.Sequential):
def __init__(self, fn, *args):
super(LambdaBase, self).__init__(*args)
self.lambda_func = fn
def forward_prepare(self, input):
output = []
for module in self._modules.values():
output.append(module(input))
return output if output else input
class Lambda(LambdaBase):
def forward(self, input):
return self.lambda_func(self.forward_prepare(input))
class LambdaMap(LambdaBase):
def forward(self, input):
return list(map(self.lambda_func,self.forward_prepare(input)))
class LambdaReduce(LambdaBase):
def forward(self, input):
return reduce(self.lambda_func,self.forward_prepare(input))
'''
varname = t7_filename.replace('.t7','').replace('.','_').replace('-','_')
s = '{}\n\n{} = {}'.format(header,varname,s[:-2])
if outputname is None: outputname=varname
with open(outputname+'.py', "w") as pyfile:
pyfile.write(s)
n = nn.Sequential()
lua_recursive_model(model,n)
torch.save(n.state_dict(),outputname+'.pth')
parser = argparse.ArgumentParser(description='Convert torch t7 model to pytorch')
parser.add_argument('--model','-m', type=str, required=True,
help='torch model file in t7 format')
parser.add_argument('--output', '-o', type=str, default=None,
help='output file name prefix, xxx.py xxx.pth')
args = parser.parse_args()
torch_to_pytorch(args.model,args.output)