add docstring

mmedit/models/backbones/encoder_decoders/decoders/plain_decoder.py

@@ -11,10 +11,27 @@
 
 
 class MaxUnpool2dop(Function):
+    """We warp the `torch.nn.functional.max_unpool2d`
+    with an extra `symbolic` method, which is needed while exporting to ONNX.
+    Users should not call this function directly.
+    """
 
     @staticmethod
     def forward(ctx, input, indices, kernel_size, stride, padding,
                 output_size):
+        """Forward function of MaxUnpool2dop.
+
+        Args:
+            input (Tensor): Tensor needed to upsample.
+            indices (Tensor): Indices output of the previous MaxPool.
+            kernel_size (Tuple): Size of the max pooling window.
+            stride (Tuple): Stride of the max pooling window.
+            padding (Tuple): Padding that was added to the input.
+            output_size (List or Tuple): The shape of output tensor.
+
+        Returns:
+            Tensor: Output tensor.
+        """
         return F.max_unpool2d(input, indices, kernel_size, stride, padding,
                               output_size)
 
@@ -32,6 +49,15 @@ def symbolic(g, input, indices, kernel_size, stride, padding, output_size):
 
 
 class MaxUnpool2d(_MaxUnpoolNd):
+    """This module is modified from Pytorch `MaxUnpool2d` module.
+
+    Args:
+      kernel_size (int or tuple): Size of the max pooling window.
+      stride (int or tuple): Stride of the max pooling window.
+          Default: None (It is set to `kernel_size` by default).
+      padding (int or tuple): Padding that is added to the input.
+          Default: 0
+    """
 
     def __init__(self, kernel_size, stride=None, padding=0):
         super(MaxUnpool2d, self).__init__()
@@ -40,6 +66,17 @@ def __init__(self, kernel_size, stride=None, padding=0):
         self.padding = _pair(padding)
 
     def forward(self, input, indices, output_size=None):
+        """Forward function of MaxUnpool2d.
+
+        Args:
+            input (Tensor): Tensor needed to upsample.
+            indices (Tensor): Indices output of the previous MaxPool.
+            output_size (List or Tuple): The shape of output tensor.
+                Default: None.
+
+        Returns:
+            Tensor: Output tensor.
+        """
         return MaxUnpool2dop.apply(input, indices, self.kernel_size,
                                    self.stride, self.padding, output_size)
 

tools/pytorch2onnx.py

@@ -17,6 +17,19 @@ def pytorch2onnx(model,
                  show=False,
                  output_file='tmp.onnx',
                  verify=False):
+    """Export Pytorch model to ONNX model and verify the outputs are same
+    between Pytorch and ONNX.
+
+    Args:
+        model (nn.Module): Pytorch model we want to export.
+        input (dict): We need to use this input to execute the model.
+        opset_version (int): The onnx op version. Default: 11.
+        show (bool): Whether print the computation graph. Default: False.
+        output_file (string): The path to where we store the output ONNX model.
+            Default: `tmp.onnx`.
+        verify (bool): Whether compare the outputs between Pytorch and ONNX.
+            Default: False.
+    """
     model.cpu().eval()
     merged = input['merged'].unsqueeze(0)
     trimap = input['trimap'].unsqueeze(0)
@@ -68,8 +81,8 @@ def parse_args():
     parser.add_argument('img_path', help='path to input image file')
     parser.add_argument('trimap_path', help='path to input trimap file')
     parser.add_argument('--show', action='store_true', help='show onnx graph')
-    parser.add_argument('--output_file', type=str, default='tmp.onnx')
-    parser.add_argument('--opset_version', type=int, default=11)
+    parser.add_argument('--output-file', type=str, default='tmp.onnx')
+    parser.add_argument('--opset-version', type=int, default=11)
     parser.add_argument(
         '--verify',
         action='store_true',