crnn-pytorch
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Holmeyoung
When I activate 4 gpus at the same time, I get the following error
Hello, I'm using a aws instance with 4 gpus and when activated (in the params.py file - True multigpu and 4 for the number) I get the following error: (P.S: For 4, 3, 2 and even 1 which is incomprehensible even for 1):
CRNN(
(cnn): Sequential(
(conv0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu0): ReLU(inplace=True)
(pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu1): ReLU(inplace=True)
(pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv2): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(batchnorm2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace=True)
(conv3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu3): ReLU(inplace=True)
(pooling2): MaxPool2d(kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False)
(conv4): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(batchnorm4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu4): ReLU(inplace=True)
(conv5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu5): ReLU(inplace=True)
(pooling3): MaxPool2d(kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False)
(conv6): Conv2d(512, 512, kernel_size=(2, 2), stride=(1, 1))
(batchnorm6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu6): ReLU(inplace=True)
)
(rnn): Sequential(
(0): BidirectionalLSTM(
(rnn): LSTM(512, 256, bidirectional=True)
(embedding): Linear(in_features=512, out_features=256, bias=True)
)
(1): BidirectionalLSTM(
(rnn): LSTM(256, 256, bidirectional=True)
(embedding): Linear(in_features=512, out_features=7116, bias=True)
)
)
)
/opt/conda/conda-bld/pytorch_1565272279342/work/aten/src/ATen/native/cudnn/RNN.cpp:1266: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters().
/opt/conda/conda-bld/pytorch_1565272279342/work/aten/src/ATen/native/cudnn/RNN.cpp:1266: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters().
/opt/conda/conda-bld/pytorch_1565272279342/work/aten/src/ATen/native/cudnn/RNN.cpp:1266: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters().
/opt/conda/conda-bld/pytorch_1565272279342/work/aten/src/ATen/native/cudnn/RNN.cpp:1266: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters().
/opt/conda/conda-bld/pytorch_1565272279342/work/aten/src/ATen/native/cudnn/RNN.cpp:1266: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters().
/opt/conda/conda-bld/pytorch_1565272279342/work/aten/src/ATen/native/cudnn/RNN.cpp:1266: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters().
Traceback (most recent call last):
File "train.py", line 253, in
Hi, can you try to make the ctcloss parallel
optimizer = nn.DataParallel(optimizer, device_ids=device_ids)
In the code there's already: gpu number (params.ngpu):
crnn = torch.nn.DataParallel(crnn, device_ids=range(params.ngpu))
Do I change it to something like cuda:0, cuda:1,...? like that?
device_ids = [0,1,2,3]
model = nn.DataParallel(model, device_ids=device_ids)
optimizer = nn.DataParallel(optimizer, device_ids=device_ids)
I forget to make the optimizer parallel. If the code above works, please tell me and i will fix the bug.
Thanks you~~~
Alright, I will check it, but can you check out this link about gpus parallalesim?
https://discuss.pytorch.org/t/multi-gpu-training-pipeline-in-0-4-1/32199
To recap what's written in this link: when we have an optimizer, the gpus will have one leader gpu to organize the data first, and then pass it to the other 3, then after all the work, one of the 4 wil get the whole data, and it must be the 1st one (the leader) who get these data (or else probably, we will get an error).
Do you think this assumption/what's written in the link, is right?
Thanks.
Alright, I got the following error:
AttributeError: 'RMSprop' object has no attribute 'cuda'
I only have one p40, so I can’t test the code. I can only give you some suggestions.
I haven't tested it yet too, but the error appears before the execution: the optimizer doesn't have cuda in it. I thought about deleting the optimizers (lol).
Now I erased the optimizer (just to see the result) and the error is about: RNN cannot be divided and that I should add the flatten_parameters function to it.
Problem here is that DataParallel parallelizes data on a specific dimension (which is by default 0). However, RNNs unless you use batch_first=True, chooses the 2nd dimension to be batch. Unfortunately convolutional part uses the 1st dimension for batch. DataParallel does good job with CNN part, but cannot work with RNN part. Only thing that can be done to solve this problem is to change RNN part to work with batch_first=True mode.
Thanks for the answer, I will try it. So the problem is about the dimensions and not about the optimizer?
In models/crnn.py file, do we change:
self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True, batch_first=True)?
Yes, that's the first step. But then you need to adjust internal calculations such that the first dimension is batch.
Okay, thanks. By internal calculations, you mean that the RNN input should be the 1st dimension of the batch? I kind of don't know where to put that?
@mariembenslama It's not very straightforward. I don't know the details of the code. Maybe @Holmeyoung may help you. Best.
Okay, thanks. By internal calculations, you mean that the RNN input should be the 1st dimension of the batch? I kind of don't know where to put that?
Dear mariembenslama : Have you solved your problem?
@JasonBoy1 Hello, Actually no, I have been waiting for @Holmeyoung since he said he will be editing the code.
Hi, i am so sorry i hav't fixed the bug yet. I have been doing recommendation these days. Can you try these code~~~
class BidirectionalLSTM(nn.Module):
def __init__(self, nIn, nHidden, nOut):
super(BidirectionalLSTM, self).__init__()
self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True, batch_first=True)
self.embedding = nn.Linear(nHidden * 2, nOut)
def forward(self, input):
recurrent, _ = self.rnn(input)
b, T, h = recurrent.size()
t_rec = recurrent.view(T * b, h)
output = self.embedding(t_rec) # [T * b, nOut]
output = output.view(T, b, -1)
return output
I have already solved this problem. This problem occurs due to the torch.nn.Dataparallel.
The torch.nn.Dataparallel would split your tensor in default of dim=0 when conducting function CRNN.forward(). The input tensor size in CRNN is (batch_size, channel, h, w ). While the output size is (pred_length, batch_size, n_class) because the CRNN permuted the tensor in the RNN code. Then the Dataparallel would split your input as:(batch_size/4, channel, h, w ) and cat your output as (pred_length*4, batch_size/4, n_class).
I recommend you to do:
class CRNN(nn.Module):
def __init__(self, imgH, nc, nclass, nh, n_rnn=2, leakyRelu=False):
....
def forward(self, input):
conv = self.cnn(input)
b, c, h, w = conv.size()
assert h == 1, "the height of conv must be 1"
conv = conv.squeeze(2)
conv = conv.permute(2, 0, 1) # [w, b, c]
output = self.rnn(conv).log_softmax(2)
output = output.permute(1, 0, 2)
return output
and
preds = crnn(image).permute(1, 0, 2)
So the batch_size would be at dim=0 and DataParallel would not cat your output wrong.
You can also change the dim inside self.rnn.
This is how I solve my problem.
Could you tell me what do we change in the self.rnn exactly?
That's the same as the first one and not so simple as that. You need to change 3 places
class CRNN(nn.Module):
def __init__(self, imgH, nc, nclass, nh, n_rnn=2, leakyRelu=False):
....
def forward(self, input):
conv = self.cnn(input)
b, c, h, w = conv.size()
assert h == 1, "the height of conv must be 1"
conv = conv.squeeze(2)
conv = conv.permute(0, 2, 1) # [b, w, c]
output = self.rnn(conv).log_softmax(2)
output = output.permute(1, 0, 2)
return output
class BidirectionalLSTM(nn.Module):
def __init__(self, nIn, nHidden, nOut):
super(BidirectionalLSTM, self).__init__()
self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True, batch_first=True)
self.embedding = nn.Linear(nHidden * 2, nOut)
def forward(self, input):
self.rnn.flatten_parameters()
recurrent, _ = self.rnn(input)
b, T, h = recurrent.size()
t_rec = recurrent.contiguous().view(T * b, h)
output = self.embedding(t_rec) # [T * b, nOut]
output = output.view(b, T, -1)
return output
preds = crnn(image).permute(1, 0, 2)
I don't think this will make any difference.
I cloned the project, directly changed as you told me and I got the following error:
Traceback (most recent call last):
File "train.py", line 254, in
I did that in www.github.com/meijieru/crnn.pytorch's code, and used warp_ctc loss. torch.nn.CTC_loss is different from warp_ctc. And I think you should use IPython.embed to debug and watch the size of the tensors.
@mineshmathew Hi, I got the same error when running with multiple GPUs: RuntimeError: input_lengths must be of size batch_size . Have you solved the problem? thanks
I have solved the problem. It seems that we cannot modify the tensor's shape easily. So, I just modify the input for CTC loss. The prediction matrix for my model is preds = model(inp).cpu(). Then, I use "preds.permute(1, 0, 2)" as the first input of CTC loss function. The problem is to keep the batch_size in the first dimension while your GPUs will combine them.
@JasonBoy1 s fix doesn't work for me (I use pytorch CTC, not warp CTC). It is even complicated when you have more than one rnn layer. See this thread here
Here is a solution if you use pytorch CTC, and my pytorch version is 1.10.2. Similar to @JasonBoy1, three places need to be changed. First, in the LSTM, you need specify batch_first=True.
class BidirectionalLSTM(nn.Module):
def __init__(self, nIn, nHidden, nOut):
super(BidirectionalLSTM, self).__init__()
self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True, batch_first=True)
self.embedding = nn.Linear(nHidden * 2, nOut)
def forward(self, input):
recurrent, _ = self.rnn(input)
b, T, h = recurrent.size()
#t_rec = recurrent.view(T * b, h)
t_rec = recurrent.reshape(T * b, h)
output = self.embedding(t_rec) # [T * b, nOut]
output = output.view(b, T, -1)
return output
nn.LSTM behaves differently when batch_first is specified, so the output should be modified too.
class CRNN(nn.Module):
def __init__(self, imgH, nc, nclass, nh, n_rnn=2, leakyRelu=False):
....
def forward(self, input):
# conv features
conv = self.cnn(input)
b, c, h, w = conv.size()
assert h == 1, "the height of conv must be 1"
conv = conv.squeeze(2)
#conv = conv.permute(2, 0, 1) # [w, b, c]
conv = conv.permute(0, 2, 1) # [b, w, c]
# rnn features
output = self.rnn(conv)
# add log_softmax to converge output
output = F.log_softmax(output, dim=2)
return output
And before the loss is calculated, according to the format of CTCLoss:
preds = crnn(image).permute(1, 0, 2)
Okay, I referred this issue and modified according with @JasonBoy1 and @Rabbit19731, it works fine.
In conclusion, it needs batch_first in __init__ of class CRNN, models/crnn.py to True (and it need to change view to reshape in line 19), and modify some order of permute.
Then preds = crnn(image).permute(1, 0, 2) is changed with preds=crnn(image) from train.py.
Be careful, this codes are placed two lines, in train and in val, so you have to chage all that lines (203 isval, 240 is train)
If you fix this in only one line, it will still show RuntimeError: input_lengths must be of size batch_size.
In my case, I have not changed it in train so still this error occured.
Plus, I added line for using multiple gpus under cudnn.benchmark=True in train.py like this : device_ids = list(range(params.ngpu)), so it can use in torch.nn.DataParallel.
Thanks all. I can stop wandering about using multi-gpus with this issue.
