Clean up resnet.py file (#780)

* clean up resnet.py

* make style and quality

* minor formatting

Author: Nathan Lambert

src/diffusers/models/resnet.py

@@ -9,9 +9,10 @@ class Upsample2D(nn.Module):
     """
     An upsampling layer with an optional convolution.
 
-    :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
-    applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 upsampling occurs in the inner-two dimensions.
+    Parameters:
+        channels: channels in the inputs and outputs.
+        use_conv: a bool determining if a convolution is applied.
+        dims: determines if the signal is 1D, 2D, or 3D. If 3D, then upsampling occurs in the inner-two dimensions.
     """
 
     def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
@@ -61,9 +62,10 @@ class Downsample2D(nn.Module):
     """
     A downsampling layer with an optional convolution.
 
-    :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is
-    applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
-                 downsampling occurs in the inner-two dimensions.
+    Parameters:
+        channels: channels in the inputs and outputs.
+        use_conv: a bool determining if a convolution is applied.
+        dims: determines if the signal is 1D, 2D, or 3D. If 3D, then downsampling occurs in the inner-two dimensions.
     """
 
     def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
@@ -115,21 +117,22 @@ def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=
     def _upsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1):
         """Fused `upsample_2d()` followed by `Conv2d()`.
 
-        Args:
         Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
-        efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary:
-        order.
-        x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
-            C]`.
-        weight: Weight tensor of the shape `[filterH, filterW, inChannels,
-            outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
-        kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
-            (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
-        factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
+        efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of
+        arbitrary order.
+
+        Args:
+            hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
+            weight: Weight tensor of the shape `[filterH, filterW, inChannels,
+                outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
+            kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
+                (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
+            factor: Integer upsampling factor (default: 2).
+            gain: Scaling factor for signal magnitude (default: 1.0).
 
         Returns:
-        Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same datatype as
-        `x`.
+            output: Tensor of the shape `[N, C, H * factor, W * factor]` or `[N, H * factor, W * factor, C]`, and same
+            datatype as `hidden_states`.
         """
 
         assert isinstance(factor, int) and factor >= 1
@@ -164,7 +167,6 @@ def _upsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1
                 output_shape[1] - (hidden_states.shape[3] - 1) * stride[1] - convW,
             )
             assert output_padding[0] >= 0 and output_padding[1] >= 0
-            inC = weight.shape[1]
             num_groups = hidden_states.shape[1] // inC
 
             # Transpose weights.
@@ -214,20 +216,23 @@ def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=
 
     def _downsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain=1):
         """Fused `Conv2d()` followed by `downsample_2d()`.
+        Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
+        efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of
+        arbitrary order.
 
         Args:
-        Padding is performed only once at the beginning, not between the operations. The fused op is considerably more
-        efficient than performing the same calculation using standard TensorFlow ops. It supports gradients of arbitrary:
-        order.
-            x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. w: Weight tensor of the shape `[filterH,
-            filterW, inChannels, outChannels]`. Grouped convolution can be performed by `inChannels = x.shape[0] //
-            numGroups`. k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] *
-            factor`, which corresponds to average pooling. factor: Integer downsampling factor (default: 2). gain:
-            Scaling factor for signal magnitude (default: 1.0).
+            hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
+            weight:
+                Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. Grouped convolution can be
+                performed by `inChannels = x.shape[0] // numGroups`.
+            kernel: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). The default is `[1] *
+            factor`, which corresponds to average pooling.
+            factor: Integer downsampling factor (default: 2).
+            gain: Scaling factor for signal magnitude (default: 1.0).
 
         Returns:
-            Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and same
-            datatype as `x`.
+            output: Tensor of the shape `[N, C, H // factor, W // factor]` or `[N, H // factor, W // factor, C]`, and
+            same datatype as `x`.
         """
 
         assert isinstance(factor, int) and factor >= 1
@@ -251,17 +256,17 @@ def _downsample_2d(self, hidden_states, weight=None, kernel=None, factor=2, gain
                 torch.tensor(kernel, device=hidden_states.device),
                 pad=((pad_value + 1) // 2, pad_value // 2),
             )
-            hidden_states = F.conv2d(upfirdn_input, weight, stride=stride_value, padding=0)
+            output = F.conv2d(upfirdn_input, weight, stride=stride_value, padding=0)
         else:
             pad_value = kernel.shape[0] - factor
-            hidden_states = upfirdn2d_native(
+            output = upfirdn2d_native(
                 hidden_states,
                 torch.tensor(kernel, device=hidden_states.device),
                 down=factor,
                 pad=((pad_value + 1) // 2, pad_value // 2),
             )
 
-        return hidden_states
+        return output
 
     def forward(self, hidden_states):
         if self.use_conv:
@@ -393,20 +398,20 @@ def forward(self, hidden_states):
 
 def upsample_2d(hidden_states, kernel=None, factor=2, gain=1):
     r"""Upsample2D a batch of 2D images with the given filter.
-
-    Args:
     Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
     filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the specified
-    `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is a:
-    multiple of the upsampling factor.
-        x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
-          C]`.
-        k: FIR filter of the shape `[firH, firW]` or `[firN]`
+    `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its shape is
+    a: multiple of the upsampling factor.
+
+    Args:
+        hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
+        kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
           (separable). The default is `[1] * factor`, which corresponds to nearest-neighbor upsampling.
-        factor: Integer upsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
+        factor: Integer upsampling factor (default: 2).
+        gain: Scaling factor for signal magnitude (default: 1.0).
 
     Returns:
-        Tensor of the shape `[N, C, H * factor, W * factor]`
+        output: Tensor of the shape `[N, C, H * factor, W * factor]`
     """
     assert isinstance(factor, int) and factor >= 1
     if kernel is None:
@@ -419,30 +424,31 @@ def upsample_2d(hidden_states, kernel=None, factor=2, gain=1):
 
     kernel = kernel * (gain * (factor**2))
     pad_value = kernel.shape[0] - factor
-    return upfirdn2d_native(
+    output = upfirdn2d_native(
         hidden_states,
         kernel.to(device=hidden_states.device),
         up=factor,
         pad=((pad_value + 1) // 2 + factor - 1, pad_value // 2),
     )
+    return output
 
 
 def downsample_2d(hidden_states, kernel=None, factor=2, gain=1):
     r"""Downsample2D a batch of 2D images with the given filter.
-
-    Args:
     Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the
     given filter. The filter is normalized so that if the input pixels are constant, they will be scaled by the
     specified `gain`. Pixels outside the image are assumed to be zero, and the filter is padded with zeros so that its
     shape is a multiple of the downsampling factor.
-        x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W,
-          C]`.
+
+    Args:
+        hidden_states: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
         kernel: FIR filter of the shape `[firH, firW]` or `[firN]`
           (separable). The default is `[1] * factor`, which corresponds to average pooling.
-        factor: Integer downsampling factor (default: 2). gain: Scaling factor for signal magnitude (default: 1.0).
+        factor: Integer downsampling factor (default: 2).
+        gain: Scaling factor for signal magnitude (default: 1.0).
 
     Returns:
-        Tensor of the shape `[N, C, H // factor, W // factor]`
+        output: Tensor of the shape `[N, C, H // factor, W // factor]`
     """
 
     assert isinstance(factor, int) and factor >= 1
@@ -456,34 +462,34 @@ def downsample_2d(hidden_states, kernel=None, factor=2, gain=1):
 
     kernel = kernel * gain
     pad_value = kernel.shape[0] - factor
-    return upfirdn2d_native(
+    output = upfirdn2d_native(
         hidden_states, kernel.to(device=hidden_states.device), down=factor, pad=((pad_value + 1) // 2, pad_value // 2)
     )
+    return output
 
 
-def upfirdn2d_native(input, kernel, up=1, down=1, pad=(0, 0)):
+def upfirdn2d_native(tensor, kernel, up=1, down=1, pad=(0, 0)):
     up_x = up_y = up
     down_x = down_y = down
     pad_x0 = pad_y0 = pad[0]
     pad_x1 = pad_y1 = pad[1]
 
-    _, channel, in_h, in_w = input.shape
-    input = input.reshape(-1, in_h, in_w, 1)
-    # Rename this variable (input); it shadows a builtin.sonarlint(python:S5806)
+    _, channel, in_h, in_w = tensor.shape
+    tensor = tensor.reshape(-1, in_h, in_w, 1)
 
-    _, in_h, in_w, minor = input.shape
+    _, in_h, in_w, minor = tensor.shape
     kernel_h, kernel_w = kernel.shape
 
-    out = input.view(-1, in_h, 1, in_w, 1, minor)
+    out = tensor.view(-1, in_h, 1, in_w, 1, minor)
 
     # Temporary workaround for mps specific issue: https://github.com/pytorch/pytorch/issues/84535
-    if input.device.type == "mps":
+    if tensor.device.type == "mps":
         out = out.to("cpu")
     out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
     out = out.view(-1, in_h * up_y, in_w * up_x, minor)
 
     out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
-    out = out.to(input.device)  # Move back to mps if necessary
+    out = out.to(tensor.device)  # Move back to mps if necessary
     out = out[
         :,
         max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),