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bugfix #92

Merged
merged 6 commits into from
Nov 1, 2017
Merged

bugfix #92

merged 6 commits into from
Nov 1, 2017

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taras-sereda
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kylegao91 and others added 2 commits October 30, 2017 11:54
@kylegao91
0.1.5 (IBM#91)
e8250fb 
* Modified parameter order of DecoderRNN.forward (IBM#85)

* Updated TopKDecoder (IBM#86)

* Fixed topk decoder.

* Use torchtext from pipy (IBM#87)

* Use torchtext from pipe.

* Fixed torch text sorting order.

* attention is not required when only using teacher forcing in decoder (IBM#90)

* attention is not required when only using teacher forcing in decoder

* Updated docs and version.

* Fixed code style.
bugfix
2f35374
@kylegao91 kylegao91 changed the base branch from master to develop October 31, 2017 14:36
@kylegao91
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Thanks! Would you mind writing a small test case for it?

@taras-sereda
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I'm not sure this fix requires new test case. There was a wrong if statement. And next statement:
kwargs['include_lengths'] = True would be executed anyway. It would be safer to set values inside if statements, and by doing this, already present testCase would cover this issue.

@taras-sereda
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pushed new commit with safe assignment.

kylegao91
kylegao91 suggested changes Nov 1, 2017
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seq2seq/dataset/fields.py Outdated
logger.warning("Option include_lengths has to be set to use pytorch-seq2seq. Changed to True.")
kwargs['include_lengths'] = True
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kwargs['include_lengths'] has to be set and it has to be True. Your change doesn't make sure that it has a value, which leads to the test failure.

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oh, you are right

kylegao91
kylegao91 approved these changes Nov 1, 2017
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Thanks!

@kylegao91 kylegao91 merged commit feabc36 into IBM:develop Nov 1, 2017
kylegao91 added a commit that referenced this pull request Dec 4, 2017
@kylegao91
0.1.5 + minor fixes (#106)
96c6033 
* Modified parameter order of DecoderRNN.forward (#85)

* Updated TopKDecoder (#86)

* Fixed topk decoder.

* Use torchtext from pipy (#87)

* Use torchtext from pipe.

* Fixed torch text sorting order.

* attention is not required when only using teacher forcing in decoder (#90)

* attention is not required when only using teacher forcing in decoder

* Updated docs and version.

* Fixed code style.

* bugfix (#92)

Fixed field arguments validation.

* Removed `initial_lr` when resuming optimizer with scheduler. (#95)

* shuffle the training data (#97)

* 0.1.5 (#91)

* Modified parameter order of DecoderRNN.forward (#85)

* Updated TopKDecoder (#86)

* Fixed topk decoder.

* Use torchtext from pipy (#87)

* Use torchtext from pipe.

* Fixed torch text sorting order.

* attention is not required when only using teacher forcing in decoder (#90)

* attention is not required when only using teacher forcing in decoder

* Updated docs and version.

* Fixed code style.

* shuffle the training data

* fix example of inflate function in TopKDecoer.py (#98)

* fix example of inflate function in TopKDecoer.py

* Fix hidden_layer size for one-directional decoder (#99)

* Fix hidden_layer size for one-directional decoder

Hidden layer size of the decoder was given `hidden_size * 2 if bidirectional else 1`, resulting in a dimensionality error for non-bidirectional decoders.
Changed `1` to `hidden_size`.

* Adapt load to allow CPU loading of GPU models (#100)

* Adapt load to allow CPU loading of GPU models

Add storage parameter to torch.load to allow loading
models on a CPU that are trained on the GPU, depending
on availability of cuda.

* Fix wrong parameter use on DecoderRNN (#103)

* Fix wrong parameter use on DecoderRNN
kylegao91 added a commit that referenced this pull request May 4, 2018
@kylegao91
Release 0.1.6 (#137)
fede876 
* Modified parameter order of DecoderRNN.forward (#85)

* Updated TopKDecoder (#86)

* Fixed topk decoder.

* Use torchtext from pipy (#87)

* Use torchtext from pipe.

* Fixed torch text sorting order.

* attention is not required when only using teacher forcing in decoder (#90)

* attention is not required when only using teacher forcing in decoder

* Updated docs and version.

* Fixed code style.

* bugfix (#92)

Fixed field arguments validation.

* Removed `initial_lr` when resuming optimizer with scheduler. (#95)

* shuffle the training data (#97)

* 0.1.5 (#91)

* Modified parameter order of DecoderRNN.forward (#85)

* Updated TopKDecoder (#86)

* Fixed topk decoder.

* Use torchtext from pipy (#87)

* Use torchtext from pipe.

* Fixed torch text sorting order.

* attention is not required when only using teacher forcing in decoder (#90)

* attention is not required when only using teacher forcing in decoder

* Updated docs and version.

* Fixed code style.

* shuffle the training data

* fix example of inflate function in TopKDecoer.py (#98)

* fix example of inflate function in TopKDecoer.py

* Fix hidden_layer size for one-directional decoder (#99)

* Fix hidden_layer size for one-directional decoder

Hidden layer size of the decoder was given `hidden_size * 2 if bidirectional else 1`, resulting in a dimensionality error for non-bidirectional decoders.
Changed `1` to `hidden_size`.

* Adapt load to allow CPU loading of GPU models (#100)

* Adapt load to allow CPU loading of GPU models

Add storage parameter to torch.load to allow loading
models on a CPU that are trained on the GPU, depending
on availability of cuda.

* Fix wrong parameter use on DecoderRNN (#103)

* Fix wrong parameter use on DecoderRNN

* Upgrade to pytorch-0.3.0 (#111)

* Upgrade to pytorch-0.3.0

* Use pytorch 3.0 in travis env.

* Make sure tensor contiguous when attention's not used. (#112)

* Implementing the predict_n method. Using the beam search outputs it returns several seqs for a given seq (#116)

* Adding a predictor method to return n predicted seqs for a src_seq input
(intended to be used along to Beam Search using TopKDecoder)

* Checkpoint after batches not epochs (#119)

* Pytorch 0.4 (#134)

* add contiguous call to tensor (#127)

when attention is turned off, pytorch (well, 0.4 at least) gets angry about calling view on a non-contiguous tensor

* Fixed shape documentation (#131)

* Update to pytorch-0.4

* Remove pytorch manual install in travis.

* Allow using pre-trained embedding (#135)

* updated docs
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@taras-sereda @kylegao91