v4.0.0

This release supports Pytorch >= 0.4.1 including the recent 1.0 release. The relevant
setup.py and environment.yml files will default to 1.0.0 installation.

v4.0.0 (18/12/2018)

• Critical: NumpyDataset now returns tensors of shape HxW, N, C for 3D/4D convolutional features, 1, N, C for 2D feature files. Models should be adjusted to adapt to this new shaping.
• An order_file per split (ord: path/to/txt file with integer per line) can be given from the configurations to change the feature order of numpy tensors to flexibly revert, shuffle, tile, etc. them.
• Better dimension checking to ensure that everything is OK.
• Added LabelDataset for single label input/outputs with associated Vocabulary for integer mapping.
• Added handle_oom=(True|False) argument for [train] section to recover from GPU out-of-memory (OOM) errors during training. This is disabled by default, you need to enable it from the experiment configuration file. Note that it is still possible to get an OOM during validation perplexity computation. If you hit that, reduce the eval_batch_size parameter.
• Added de-hyphen post-processing filter to stitch back the aggressive hyphen splitting of Moses during early-stopping evaluations.
• Added optional projection layer and layer normalization to TextEncoder.
• Added enc_lnorm, sched_sampling options to NMT to enable layer normalization for encoder and use scheduled sampling at a given probability.
• ConditionalDecoder can now be initialized with max-pooled encoder states or the last state as well.
• You can now experiment with different decoders for NMT by changing the dec_variant option.
• Collect all attention weights in self.history dictionary of the decoders.
• Added n-best output to nmtpy translate with the argument -N.
• Changed the way -S works for nmtpy translate. Now you need to give the split name with -s all the time but -S is used to override the input data sources defined for that split in the configuration file.
• Removed decoder-initialized multimodal NMT MNMTDecInit. Same functionality exists within the NMT model by using the model option dec_init=feats.
• New model MultimodalNMT: that supports encoder initialization, decoder initialization, both, concatenation of embeddings with visual features, prepending and appending. This model covers almost all the models from LIUM-CVC's WMT17 multimodal systems except the multiplicative interaction variants such as trgmul.
• New model MultimodalASR: encoder-decoder initialized ASR model. See the paper
• New Model AttentiveCaptioning: Similar but not an exact reproduction of show-attend-and-tell, it uses feature files instead of raw images.
• New model AttentiveMNMTFeaturesFA: LIUM-CVC's WMT18 multimodal system i.e. filtered attention
• New (experimental) model NLI: A simple LSTM-based NLI baseline for SNLI dataset:
  • direction should be defined as direction: pre:Text, hyp:Text -> lb:Label
  • pre, hyp and lb keys point to plain text files with one sentence per line. A vocabulary should be constructed even for the labels to fit the nmtpy architecture.
  • acc should be added to eval_metrics to compute accuracy.