vision: Failed in fine-tuning inception_v3

I failed in using inception_v3 on my own dataset. (Ubuntu14.04, cuda8.0, python3.6.2)

It outputs warning when loaded:

/home/ljy/anaconda3/lib/python3.6/site-packages/torchvision-0.1.9-py3.6.egg/torchvision/models/inception.py:65: UserWarning: src is not broadcastable to dst, but they have the same number of elements.  Falling back to deprecated pointwise behavior.

It failed which training:

Traceback (most recent call last):
  File "/home/ljy/pytorch-examples-master/cub_pytorch/main.py", line 382, in <module>
    main()
  File "/home/ljy/pytorch-examples-master/cub_pytorch/main.py", line 213, in main
    train(train_loader, model, criterion, optimizer, epoch)
  File "/home/ljy/pytorch-examples-master/cub_pytorch/main.py", line 251, in train
    loss = criterion(output, target_var)
  File "/home/ljy/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 224, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/ljy/anaconda3/lib/python3.6/site-packages/torch/nn/modules/loss.py", line 482, in forward
    self.ignore_index)
  File "/home/ljy/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py", line 746, in cross_entropy
    return nll_loss(log_softmax(input), target, weight, size_average, ignore_index)
  File "/home/ljy/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py", line 537, in log_softmax
    return _functions.thnn.LogSoftmax.apply(input)
  File "/home/ljy/anaconda3/lib/python3.6/site-packages/torch/nn/_functions/thnn/auto.py", line 126, in forward
    ctx._backend = type2backend[type(input)]
  File "/home/ljy/anaconda3/lib/python3.6/site-packages/torch/_thnn/__init__.py", line 15, in __getitem__
    return self.backends[name].load()
KeyError: <class 'tuple'>

About this issue

Original URL
State: closed
Created 7 years ago
Comments: 35 (14 by maintainers)

Most upvoted comments

@jamiechoi1995 @MichaelLiang12, @TiRune is correct, inception_v3 has an aux branch, and if this is not disabled the forward function will return a tuple (see here), which when passed to the criterion will throw this error.

So you have two choices:

disable aux_logits when the model is created here by also passing aux_logits=False to the inception_v3 function.
edit your train function to accept and unpack the returned tuple here to be something like:

output, aux = model(input_var)

+43

alykhantejani on Nov 1, 2017

@tejasri19 for inference, don’t forget to set your model to eval() mode.

You don’t need to use the aux classifiers for inference, only for training

fmassa on Jul 10, 2019

Yeah, thanks.

rajasekharponakala on Mar 25, 2019

@rajasekharponakala one thing to note here is that GoogLeNet has two aux branches where as inception v3 only has one.

So for GoogLeNet you have to use: aux1, aux2, output = model(inputs)

TheCodez on Mar 25, 2019

@alykhantejani:Hi, why we have to disable the aux_logits?, what are these aux_logits? does they effect the training/validation?

I’m trying to reproduce the accuracy from a model trained using with the bvlc_googlenet (without pretrained weights). So when I do aux branch off with pytorch(googlenet) it works and reports val_acc with 50% which is very low when compared to the caffe. any other methods to reproduce the same accurcy using pytorch? Thanks.

@jamiechoi1995 @MichaelLiang12, @TiRune is correct, inception_v3 has an aux branch, and if this is not disabled the forward function will return a tuple (see here), which when passed to the criterion will throw this error.

So you have two choices:
1. disable `aux_logits` when the model is created [here](https://github.com/pytorch/examples/blob/master/imagenet/main.py#L75) by also passing `aux_logits=False` to the `inception_v3` function.

2. edit your `train` function to accept and unpack the returned tuple [here](https://github.com/pytorch/examples/blob/master/imagenet/main.py#L194) to be something like:
output, aux = model(input_var)

rajasekharponakala on Mar 23, 2019

Also the user warning you are getting when loading the model is fixed in master (via #231)

alykhantejani on Oct 18, 2017