Implementing the predict_n method. Using the beam search outputs it returns several seqs for a given seq #116
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(intended to be used along to Beam Search using TopKDecoder)
Using the beam search outputs it returns several seqs for a given seq
…c_seq input" This reverts commit 4424310. # Conflicts: # seq2seq/evaluator/predictor.py
kylegao91
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Jan 18, 2018
seq2seq/evaluator/predictor.py
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| @@ -42,3 +43,31 @@ def predict(self, src_seq): | |||
| tgt_id_seq = [other['sequence'][di][0].data[0] for di in range(length)] | |||
| tgt_seq = [self.tgt_vocab.itos[tok] for tok in tgt_id_seq] | |||
| return tgt_seq | |||
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| def predict_n(self, src_seq, n=None): | |||
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I think n should be 1 by default because at line 67 you will do int(n).
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For sure. I checked that value on the application I'm building using this library, but it would be better to have in the method definition to ensure a safer running.
| if torch.cuda.is_available(): | ||
| src_id_seq = src_id_seq.cuda() | ||
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| softmax_list, _, other = self.model(src_id_seq, [len(src_seq)]) |
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Line 59-64 are exactly the same as line 35-40, it'd be better to extract a function for that.
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I placed this code in a new function called get_decoder_features
Removing unused variables returned by this function.
kylegao91
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Jan 18, 2018
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May 4, 2018
* 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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