updated documentation

pyhealth/models/wordsat.py

@@ -234,13 +234,18 @@ def forward(self, **kwargs):
  Returns:
  A dictionary with the following keys:
  loss: a scalar tensor representing the loss.
- y_generated: a dictionary list of text representing the generated caption.
- The list contains only one element.
- y_true: a list of text representing the true caption.
- The list contains only one element.
+ y_generated: a dictionary with following key
+ - "patient_id": list of text representing the generated
+ text.
+ e.g.
+ {123: ["generated text"], 456: ["generated text"]}
+ y_true: a dictionary with following key
+ - "patient_id": list of text representing the true text.
+ e.g.
+ {123: ["true text"], 456: ["true text"]}
  """
  # Initialize the output
- output = {"loss": None,"y_generated": "","y_true": ""}
+ output = {"loss": None,"y_generated": [""],"y_true": [""]}
 
  # Get list of patient_ids
  patient_ids = kwargs["patient_id"]
@@ -286,21 +291,34 @@ def _prepare_batch_images(self,kwargs):
  return images
 
  def _prepare_batch_captions(self,captions):
- """Prepare caption for input.
+ """Prepare caption idx for input.
+
+ Args:
+ captions: list of captions. Each caption is a list of list, where
+ each list represents a sentence in the caption.
+ Following is an example of a caption
+ [
+ ["<start>","first","sentence","."],
+ [".", "second", "sentence",".", "<end>"]
+ ]
+ Returns:
+ captions_idx: an int tensor of size [batch_size,max_caption_length]
+ masks: a bool tensor of size [batch_size,max_caption_length]
  """
  samples = []
+ # Combine all sentences in each caption to create a single sentence
  for caption in captions:
  tokens = []
  tokens.extend(flatten_list(caption))
  text = ' '.join(tokens).replace('. .','.')
  samples.append([text.split()])
- #print(caption)
+
  x = self.caption_tokenizer.batch_encode_3d(samples)
- captions = torch.tensor(x, dtype=torch.long, device=self.device)
- masks = torch.sum(captions,dim=1) !=0
- captions = captions.squeeze(1)
+ captions_idx = torch.tensor(x, dtype=torch.long, device=self.device)
+ masks = torch.sum(captions_idx,dim=1) !=0
+ captions_idx = captions_idx.squeeze(1)
 
- return captions,masks
+ return captions_idx,masks
 
  def _forward_inference(self,patient_ids,cnn_features):
  """