IBM/pytorchpipe

# Training parameters:
training:
  task: 
    type: SimpleMolecules
    batch_size: &b 64
    split: training
    resize_image: [87, 87]
  # Resizing slows down the batch generation, so let's use many dataloaders.
  dataloader:
    num_workers: 4

  optimizer:
    type: Adam
    lr: 0.0001
  # settings parameters
  terminal_conditions:
    loss_stop_threshold: 0.15
    early_stop_validations: -1
    episode_limit: 10000
    epoch_limit: 10

# Validation parameters:
validation:
  task:
    type: SimpleMolecules
    batch_size: *b
    split: validation
    resize_image: [87, 87]
  dataloader:
    num_workers: 4

# Testing parameters:
test:
  task:
    type: SimpleMolecules
    batch_size: *b
    split: test
    resize_image: [87, 87]
  dataloader:
    num_workers: 4

test_mirror_blur:
  task:
    type: SimpleMolecules
    batch_size: *b
    split: test_mirror_blur
    resize_image: [87, 87]
  dataloader:
    num_workers: 4

test_on_grid:
  task:
    type: SimpleMolecules
    batch_size: *b
    split: test_on_grid
    resize_image: [87, 87]
  dataloader:
    num_workers: 4

test_handwritten:
  task:
    type: SimpleMolecules
    batch_size: *b
    split: test_handwritten
    resize_image: [87, 87]
  dataloader:
    num_workers: 4


# Default components shared between all molecule classification pipelines.
pipeline:
  #disable: image_viewer

  # Loss
  nllloss:
    type: NLLLoss
    priority: 4
    streams:
      targets: targets
      predictions: predictions

  accuracy:
    priority: 5
    type: AccuracyStatistics
    streams:
      targets: targets
      predictions: predictions

  answer_decoder:
    priority: 6
    type: WordDecoder
    import_word_mappings_from_globals: True
    globals:
      word_mappings: label_word_mappings
    streams:
      inputs: predictions
      outputs: predicted_answers

  stream_viewer:
    priority: 7
    type: StreamViewer
    input_streams: labels, targets, predictions, predicted_answers

  image_viewer:
    priority: 100.5
    type: ImageViewer
    streams:
      images: images
      labels: labels
      answers: predicted_answers