v1.1

v1.1 (25/01/2018)

• New experimental Multi30kDataset and ImageFolderDataset classes
• torchvision dependency added for CNN support
• nmtpy-coco-metrics now computes one METEOR without norm=True
• Mainloop mechanism is completely refactored with backward-incompatible
  configuration option changes for [train] section:
  • patience_delta option is removed
  • Added eval_batch_size to define batch size for GPU beam-search during training
  • eval_freq default is now 3000 which means per 3000 minibatches
  • eval_metrics now defaults to loss. As before, you can provide a list
    of metrics like bleu,meteor,loss to compute all of them and early-stop
    based on the first
  • Added eval_zero (default: False) which tells to evaluate the model
    once on dev set right before the training starts. Useful for sanity
    checking if you fine-tune a model initialized with pre-trained weights
  • Removed save_best_n: we no longer save the best N models on dev set
    w.r.t. early-stopping metric
  • Added save_best_metrics (default: True) which will save best models
    on dev set w.r.t each metric provided in eval_metrics. This kind of
    remedies the removal of save_best_n
  • checkpoint_freq now to defaults to 5000 which means per 5000
    minibatches.
  • Added n_checkpoints (default: 5) to define the number of last
    checkpoints that will be kept if checkpoint_freq > 0 i.e. checkpointing enabled
• Added ExtendedInterpolation support to configuration files:
  • You can now define intermediate variables in .conf files to avoid
    typing same paths again and again. A variable can be referenced
    from within its section using tensorboard_dir: ${save_path}/tb notation
    Cross-section references are also possible: ${data:root} will be replaced
    by the value of the root variable defined in the [data] section.
• Added -p/--pretrained to nmtpy train to initialize the weights of
  the model using another checkpoint .ckpt.
• Improved input/output handling for nmtpy translate:
  • -s accepts a comma-separated test sets defined in the configuration
    file of the experiment to translate them at once. Example: -s val,newstest2016,newstest2017
  • The mutually exclusive counterpart of -s is -S which receives a
    single input file of source sentences.
  • For both cases, an output prefix should now be provided with -o.
    In the case of multiple test sets, the output prefix will be appended
    the name of the test set and the beam size. If you just provide a single file with -S
    the final output name will only reflect the beam size information.
• Two new arguments for nmtpy-build-vocab:
  • -f: Stores frequency counts as well inside the final json vocabulary
  • -x: Does not add special markers <eos>,<bos>,<unk>,<pad> into the vocabulary

Layers/Architectures

• Added Fusion() layer to concat,sum,mul an arbitrary number of inputs
• Added experimental ImageEncoder() layer to seamlessly plug a VGG or ResNet
  CNN using torchvision pretrained models
• Attention layer arguments improved. You can now select the bottleneck
  dimensionality for MLP attention with att_bottleneck. The dot
  attention is still not tested and probably broken.

New stuff

• Added AttentiveMNMT which implements modality-specific multimodal attention
  from the paper Multimodal Attention for Neural Machine Translation
• Added ShowAttendAndTell model

Changes in NMT

• dec_init defaults to mean_ctx, i.e. the decoder will be initialized
  with the mean context computed from the source encoder
• enc_lnorm which was just a placeholder is now removed since we do not
  provided layer-normalization for now
• Beam Search is completely moved to GPU