66 add encoders inputoutput shapes to docstrings

pangaea/decoders/upernet.py

@@ -173,8 +173,19 @@ def _forward_feature(self, inputs):
         feats = self.fpn_bottleneck(fpn_outs)
         return feats
 
-    def forward(self, img, output_shape=None):
-        """Forward function."""
+    def forward(self, img: dict[str, torch.Tensor], output_shape: torch.Size | None =None) -> torch.Tensor:
+        """Compute the segmentation output.
+
+        Args:
+            img (dict[str, torch.Tensor]): input data structured as a dictionary:
+            img = {modality1: tensor1, modality2: tensor2, ...}, e.g. img = {"optical": tensor1, "sar": tensor2}.
+            with tensor1 and tensor2 of shape (B C T=1 H W) with C the number of encoders'bands for the given modality.
+            output_shape (torch.Size | None, optional): output's spatial dims (H, W) (equals to the target spatial dims). 
+            Defaults to None.
+
+        Returns:
+            torch.Tensor: output tensor of shape (B, num_classes, H', W') with (H' W') coressponding to the output_shape.
+        """
 
         # img[modality] of shape [B C T=1 H W]
         if self.encoder.multi_temporal:
@@ -206,6 +217,8 @@ def forward(self, img, output_shape=None):
         # fixed bug just for optical single modality
         if output_shape is None:
             output_shape = img[list(img.keys())[0]].shape[-2:]
+
+        # interpolate to the target spatial dims
         output = F.interpolate(output, size=output_shape, mode="bilinear")
 
         return output
@@ -249,6 +262,19 @@ def __init__(
     def forward(
         self, img: dict[str, torch.Tensor], output_shape: torch.Size | None = None
     ) -> torch.Tensor:
+        """Compute the segmentation output for multi-temporal data.
+
+        Args:
+            img (dict[str, torch.Tensor]): input data structured as a dictionary:
+            img = {modality1: tensor1, modality2: tensor2, ...}, e.g. img = {"optical": tensor1, "sar": tensor2}.
+            with tensor1 and tensor2 of shape (B C T H W) with C the number of encoders'bands for the given modality, 
+            and T the number of time steps.
+            output_shape (torch.Size | None, optional): output's spatial dims (H, W) (equals to the target spatial dims). 
+            Defaults to None.
+
+        Returns:
+            torch.Tensor: output tensor of shape (B, num_classes, H', W') with (H' W') coressponding to the output_shape.
+        """
         # If the encoder handles multi_temporal we feed it with the input
         if self.encoder.multi_temporal:
             if not self.finetune:
@@ -293,6 +319,8 @@ def forward(
 
         if output_shape is None:
             output_shape = img[list(img.keys())[0]].shape[-2:]
+
+        # interpolate to the target spatial dims
         output = F.interpolate(output, size=output_shape, mode="bilinear")
 
         return output

pangaea/encoders/base.py

@@ -11,11 +11,26 @@
 
 
 class DownloadProgressBar:
-    def __init__(self, text="Downloading..."):
+    """Download progress bar.
+    """
+    def __init__(self, text: str="Downloading...") -> None:
+        """Initialize the DownloadProgressBar.
+
+        Args:
+            text (str, optional): pbar text. Defaults to "Downloading...".
+        """
         self.pbar = None
         self.text = text
 
-    def __call__(self, block_num, block_size, total_size):
+    def __call__(self, block_num: int, block_size: int, total_size: int) -> None:
+        """Update the progress bar.
+
+        Args:
+            block_num (int): number of blocks.
+            block_size (int): size of the blocks.
+            total_size (int): total size of the download.
+        """
+
         if self.pbar is None:
             self.pbar = tqdm.tqdm(
                 desc=self.text,
@@ -116,21 +131,30 @@ def freeze(self) -> None:
         for param in self.parameters():
             param.requires_grad = False
 
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """Compute the forward pass of the encoder.
+    def forward(self, x:dict[str, torch.Tensor]) -> list[torch.Tensor]:
+        """Foward pass of the encoder.
 
         Args:
-            x (torch.Tensor): input image.
-
+            x (dict[str, torch.Tensor]): encoder's input structured as a dictionary:
+            x = {modality1: tensor1, modality2: tensor2, ...}, e.g. x = {"optical": tensor1, "sar": tensor2}.
+            If the encoder is multi-temporal (self.multi_temporal==True), input tensor shape is (B C T H W) with C the
+            number of bands required by the encoder for the given modality and T the number of time steps. If the 
+            encoder is not multi-temporal, input tensor shape is (B C H W) with C the number of bands required by the 
+            encoder for the given modality.
         Raises:
             NotImplementedError: raise if the method is not implemented.
 
         Returns:
-            torch.Tensor: embedding generated by the encoder.
+            list[torch.Tensor]: list of the embeddings for each modality. For single-temporal encoders, the list's
+            elements are of shape (B, embed_dim, H', W'). For multi-temporal encoders, the list's elements are of shape
+            (B, C', T, H', W') with T the number of time steps if the encoder does not have any time-merging strategy,
+            else (B, C', H', W') if the encoder has a time-merging strategy (where C'==self.output_dim).
         """
         raise NotImplementedError
 
     def download_model(self) -> None:
+        """Download the model if the weights are not already downloaded.
+        """
         if self.download_url and not os.path.isfile(self.encoder_weights):
             # TODO: change this path
             os.makedirs("pretrained_models", exist_ok=True)