convert torch 7 model and get an error :AttributeError: type object 'torch.cuda.FloatStorage' has no attribute 'from_buffer'

Question

convert torch 7 model and get an error :AttributeError: type object 'torch.cuda.FloatStorage' has no attribute 'from_buffer'

Mrxuefei opened this issue 7 years ago · 31 comments

wangjian@lhtserver-2:~/wj$ python convert_torch.py -m vgg.t7
Traceback (most recent call last):
File "convert_torch.py", line 286, in
torch_to_pytorch(args.model,args.output)
File "convert_torch.py", line 233, in torch_to_pytorch
model = load_lua(t7_filename,unknown_classes=True)
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 599, in load_lua
return reader.read()
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 584, in read
return self.read_object()
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 514, in wrapper
result = fn(self, *args, **kwargs)
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 537, in read_object
return reader_registry[cls_name](self, version)
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 242, in read_nn_class
attributes = reader.read()
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 586, in read
return self.read_table()
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 514, in wrapper
result = fn(self, *args, **kwargs)
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 563, in read_table
v = self.read()
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 586, in read
return self.read_table()
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 514, in wrapper
result = fn(self, *args, **kwargs)
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 563, in read_table
v = self.read()
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 584, in read
return self.read_object()
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 514, in wrapper
result = fn(self, *args, **kwargs)
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 539, in read_object
return TorchObject(cls_name, self.read())
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 586, in read
return self.read_table()
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 514, in wrapper
result = fn(self, *args, **kwargs)
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 563, in read_table
v = self.read()
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 584, in read
return self.read_object()
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 514, in wrapper
result = fn(self, *args, **kwargs)
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 537, in read_object
return reader_registry[cls_name](self, version)
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 146, in read_tensor
storage = reader.read()
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 584, in read
return self.read_object()
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 514, in wrapper
result = fn(self, *args, **kwargs)
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 537, in read_object
return reader_registry[cls_name](self, version)
File "/usr/local/anaconda2/lib/python2.7/site-packages/torch/utils/serialization/read_lua_file.py", line 165, in read_storage
return python_class.from_buffer(reader.f.read(size), 'native')
AttributeError: type object 'torch.cuda.FloatStorage' has no attribute 'from_buffer'

Answer 1 · 2017-05-23T10:24:47.000Z

Where did you download the vgg.t7? You can try check the size of the vgg.t7 and convert torch.cuda.FloatStorage to torch.FloatStorage before using it.

vgg model from https://github.com/jcjohnson/cnn-benchmarks has been tested, you can use this version.

Answer 2 · 2017-05-30T23:01:46.000Z

@clcarwin I got the same error. I try to use this library to convert a standalone training model (from my experiments; not vgg). So is that mean I cannot use this library to convert torch.cuda.FloatStorage?

Answer 3 · 2017-05-31T03:27:31.000Z

@johnny5550822 convert GPU to CPU model first.

m = torch.load('xxxx.t7')
m = m:float()
torch.save('xxxx.cpu.t7',m)

Answer 4 · 2017-05-31T08:10:21.000Z

Got it. Thanks!

Answer 5 · 2017-06-03T10:39:10.000Z

Thanks ;) worked for resnet152 from https://github.com/facebook/fb.resnet.torch

Answer 6 · 2017-07-07T09:44:53.000Z

@johnny5550822 do you get the problem " 'torch.FloatTensor' object has no attribute 'output' ",how should I do?

Answer 7 · 2017-07-08T00:08:13.000Z

@heyalqh did not get this problem. Can you share the screenshot of the code and error?

Answer 8 · 2017-07-08T02:13:18.000Z

@johnny5550822

Answer 9 · 2017-07-19T00:28:12.000Z

Does this mean that I cannot use the model on the GPU after conversion?

When I do module.cuda() and attempt to evaluate module.forward(data), I get the error:

RuntimeError: expected CPU tensor (got CUDA tensor)

When I put my data on the GPU data = Variable(torch.from_numpy(batch)).cuda()

I end up with this error instead:

Traceback (most recent call last):
  File "thing.py", line 51, in <module>
    module.forward(data.float())
  File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/container.py", line 64, in forward
    input = module(input)
  File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 206, in __call__
    result = self.forward(*input, **kwargs)
  File "/opt/home/d2curro/repos/thinslicing_pytorch/cpu_net.py", line 21, in forward
    return self.lambda_func(self.forward_prepare(input))
  File "/opt/home/d2curro/repos/thinslicing_pytorch/cpu_net.py", line 35, in <lambda>
    Lambda(lambda x,lrn=torch.legacy.nn.SpatialCrossMapLRN(*(5, 0.0005, 0.75, 2)): Variable(lrn.forward(x.data))),
  File "/usr/local/lib/python2.7/dist-packages/torch/legacy/nn/Module.py", line 33, in forward
    return self.updateOutput(input)
  File "/usr/local/lib/python2.7/dist-packages/torch/legacy/nn/SpatialCrossMapLRN.py", line 25, in updateOutput
    self._backend.SpatialCrossMapLRN_updateOutput(
  File "/usr/local/lib/python2.7/dist-packages/torch/_thnn/utils.py", line 22, in __getattr__
    raise NotImplementedError
NotImplementedError

Answer 10 · 2017-08-31T02:18:38.000Z

when I try to convert the following torch net to pytorch,

require 'torch'
require 'nn'
require 'nnx'
require 'optim'
require 'rnn'

function buildModel_MeanPool_RNN(nFltrs1,nFltrs2,nFltrs3,nPersonsTrain)

local nFilters = {nFltrs1,nFltrs2,nFltrs3}

local filtsize = {5,5,5}
local poolsize = {2,2,2}
local stepSize = {2,2,2}

-- remember this adds padding to ALL SIDES of the image
local padDim = 4

local cnn = nn.Sequential()

local ninputChannels = 5
cnn:add(nn.SpatialZeroPadding(padDim, padDim, padDim, padDim))
cnn:add(nn.SpatialConvolutionMM(ninputChannels, nFilters[1], filtsize[1], filtsize[1], 1, 1))
cnn:add(nn.Tanh())
cnn:add(nn.SpatialMaxPooling(poolsize[1],poolsize[1],stepSize[1],stepSize[1]))

ninputChannels = nFilters[1]
cnn:add(nn.SpatialZeroPadding(padDim, padDim, padDim, padDim))
cnn:add(nn.SpatialConvolutionMM(ninputChannels, nFilters[2], filtsize[2], filtsize[2], 1, 1))
cnn:add(nn.Tanh())
cnn:add(nn.SpatialMaxPooling(poolsize[2],poolsize[2],stepSize[2],stepSize[2]))

ninputChannels = nFilters[2]
cnn:add(nn.SpatialZeroPadding(padDim, padDim, padDim, padDim))
cnn:add(nn.SpatialConvolutionMM(ninputChannels, nFilters[3], filtsize[3], filtsize[3], 1, 1))
cnn:add(nn.Tanh())
cnn:add(nn.SpatialMaxPooling(poolsize[3],poolsize[3],stepSize[3],stepSize[3]))

local nFullyConnected = nFilters[3]108

cnn:add(nn.Reshape(1,nFullyConnected))
cnn:add(nn.Dropout(0.6))
cnn:add(nn.Linear(nFullyConnected,128))
-- cnn:cuda()

local h2h = nn.Sequential()
h2h:add(nn.Tanh())
h2h:add(nn.Dropout(0.6))
h2h:add(nn.Linear(128,128))
-- h2h:cuda()

local r1 = nn.Recurrent(
128,
cnn,
h2h,
nn.Identity(),
16)

local rnn1 = nn.Sequencer(
nn.Sequential()
:add(r1)
)

Combined_CNN_RNN_1 = nn.Sequential()
Combined_CNN_RNN_1:add(rnn1)
Combined_CNN_RNN_1:add(nn.JoinTable(1))
Combined_CNN_RNN_1:add(nn.Mean(1))

local r2 = nn.Recurrent(
128,
cnn:clone('weight','bias','gradWeight','gradBias'),
h2h:clone('weight','bias','gradWeight','gradBias'),
nn.Identity(),
16)

local rnn2 = nn.Sequencer(
nn.Sequential()
:add(r2)
)

Combined_CNN_RNN_2 = nn.Sequential()
Combined_CNN_RNN_2:add(rnn2)
Combined_CNN_RNN_2:add(nn.JoinTable(1))
Combined_CNN_RNN_2:add(nn.Mean(1))

-- Combined_CNN_RNN_2 = Combined_CNN_RNN_1:clone('weight','bias','gradWeight','gradBias')

local mlp2 = nn.ParallelTable()
mlp2:add(Combined_CNN_RNN_1)
mlp2:add(Combined_CNN_RNN_2)
-- mlp2:cuda()

local mlp3 = nn.ConcatTable()
mlp3:add(nn.Identity())
mlp3:add(nn.Identity())
mlp3:add(nn.Identity())
-- mlp3:cuda()

local mlp4 = nn.ParallelTable()
mlp4:add(nn.Identity())
mlp4:add(nn.SelectTable(1))
mlp4:add(nn.SelectTable(2))
-- mlp4:cuda()

-- used to predict the identity of each person
local classifierLayer = nn.Linear(128,nPersonsTrain)

-- identification
local mlp6 = nn.Sequential()
mlp6:add(classifierLayer)
mlp6:add(nn.LogSoftMax())
-- mlp6:cuda()

local mlp7 = nn.Sequential()
mlp7:add(classifierLayer:clone('weight','bias','gradWeight','gradBias'))
mlp7:add(nn.LogSoftMax())
-- mlp7:cuda()

local mlp5 = nn.ParallelTable()
mlp5:add(nn.PairwiseDistance(2))
mlp5:add(mlp6)
mlp5:add(mlp7)
-- mlp5:cuda()

local fullModel = nn.Sequential()
fullModel:add(mlp2)
fullModel:add(mlp3)
fullModel:add(mlp4)
fullModel:add(mlp5)
-- fullModel:cuda()

local crit = nn.SuperCriterion()
crit:add(nn.HingeEmbeddingCriterion(2),1)
crit:add(nn.ClassNLLCriterion(),1)
crit:add(nn.ClassNLLCriterion(),1)

return fullModel, crit, Combined_CNN_RNN_1, cnn
end

fullModel, crit, Combined_CNN_RNN_1, cnn = buildModel_MeanPool_RNN(16,32,32,150)
torch.save('Combined_CNN_RNN.t7',Combined_CNN_RNN_1)

But it is not working:
Not Implement nn.Sequencer
Not Implement JoinTable
Not Implement Mean

Answer 11 · 2017-09-01T20:30:08.000Z

@clcarwin , when i try to convert the model to use float tensors (the original model was trained on GPUs and CUDA tensors is not usable in the code), I get this error:

 [string "m = m:float()"]:1: attempt to call method 'float' (a nil value)
stack traceback:
    [string "m = m:float()"]:1: in main chunk
[C]: in function 'xpcall'
/home/dravi/torch/install/share/lua/5.1/trepl/init.lua:679: in function 'repl'
...ravi/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:204: in main chunk
[C]: at 0x00405d50

I'm doing this via the command line 'th', is there any package that is missing and needs to be 'required' for the float tensor conversion?

Answer 12 · 2018-05-21T08:02:24.000Z

@stalagmite7, I have the same problem. Did you ever solve this? Thanks.

Answer 13 · 2018-08-06T22:12:23.000Z

@stalagmite7 @huntkao I am having the same issue! Did either of you end up solving this issue?

Thanks a lot!

Answer 14 · 2018-08-06T22:42:54.000Z

No, @huntkao and @Spandan-Madan . I finally ended up not going down this route because it was so buggy

Answer 15 · 2018-08-19T01:10:27.000Z

@clcarwin
Hi, I have SyntaxError on the line "m = m:float()". How to solve it? Thanks a lot!

Answer 16 · 2018-08-19T02:03:32.000Z

In my specific case, this is what happened:- lua uses tables for all data arrays pretty much. So the only way to find out if the object is a model itself or a table containing model and other things is to look inside the table and see what it contains. In my case, the table object was a list of tables and one of the tables inside it was the model. So, I indexed inside to the right element. And then converted that model to CPU using model = model:float() and it worked perfectly. I know it sounds silly, but give it a shot. You never know you might have the same problem as me!

…

On Sat, Aug 18, 2018 at 6:10 PM, dmortem ***@***.***> wrote: @clcarwin <https://github.com/clcarwin> Hi, I have SyntaxError on the line "m = m:float()". How to solve it? Thanks a lot! — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#8 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AU8Xo45ExiBYx1z0Y8ItlYXr2WTROPgGks5uSLsDgaJpZM4NjX48> .

Answer 17 · 2018-09-06T13:22:11.000Z

@dmortem Hi, I have the same error with you, do you overcome it and how?

Answer 18 · 2018-09-06T14:59:15.000Z

@LaiPiXiong Hi, I solved it by put codes into a lua file, not python.

Answer 19 · 2018-09-07T02:43:43.000Z

@dmortem Thanks, do you change torch to pytorch successfully? I met error as @stalagmite7. And I try to download xnornet.py but it still show error "string m=m:float() attempt to call method 'float' (a nil value) ".

Answer 20 · 2018-09-07T02:51:36.000Z

@LaiPiXiong After I configuring torch environment, I put three sentences in a lua file, and execute it. The model has been converted successfully. And whether you can use this model in Pytorch depends on the original torch model since some torch structures cannot be used in Pytorch, so maybe we still cannot use the converted model.

Answer 21 · 2018-09-07T02:57:46.000Z

@dmortem I can't convert it right. Does "convert_torch.py" also based on torch?

Answer 22 · 2018-09-07T03:13:29.000Z

@LaiPiXiong I'm sorry, I didn't convert the model successfully, and my error is "torch.utils.serialization.read_lua_file.T7ReaderException: unknown type id 436207616. The file may be corrupted.". This error is because my model has some different layers which are absent in torch.legacy.
And what I do is to run lua file to convert the model of the GPU version into the CPU version, and then run python file to convert it into a pytorch model.

Answer 23 · 2018-09-07T06:14:44.000Z

@dmortem I can't convert model in GPU to CPU version. Do you install luarocks before torch?

Answer 24 · 2018-09-07T08:18:50.000Z

@dmortem Hi, I convert torch model to pytorch successfully. I use 'import 'cudnn'' before "m = torch.load(model-name)". I learn this from rwightman/pytorch-planet-amazon#1

Answer 25 · 2018-09-11T12:14:29.000Z

@LaiPiXiong It's rather

require 'cutorch'
require 'cudnn'

as it is in lua.

Answer 26 · 2018-09-11T12:21:46.000Z

Oh, I see, thanks.

Answer 27 · 2019-01-28T03:39:32.000Z

@johnny5550822
You should download the model from
https://github.com/jcjohnson/cnn-benchmarks
This does not contain the cudnn contents and can be successful.

Answer 28 · 2019-03-29T13:06:45.000Z

Somebody resolved the problem ?

Answer 29 · 2021-01-12T04:34:14.000Z

I have the same issue. Anyone help me please !

Answer 30 · 2023-08-17T11:57:47.000Z

I am trying to convert this model driveLink from this repo repoLink but it gives the error
return python_class.from_buffer(reader.f.read(size), 'native') AttributeError: type object 'torch.cuda.FloatStorage' has no attribute 'from_buffer'
i tried to convert it to cpu but the model wont load using torch with error magic_number = pickle_module.load(f) _pickle.UnpicklingError: invalid load key, '\x03'.
any help will be appreciated

Answer 31 · 2023-08-20T03:00:33.000Z

m = torch.load('xxxx.t7')

I am trying to use this way to convert the model to cpu but torch.load gives this types of error UnpicklingError: invalid load key, '\x03'.