Renamed x -> hidden_states in resnet.py (open-mmlab#676)

renamed x to hidden_states

src/diffusers/models/resnet.py

@@ -34,21 +34,21 @@ def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_chann
  else:
  self.Conv2d_0 = conv
 
- def forward(self, x):
- assert x.shape[1] == self.channels
+ def forward(self, hidden_states):
+ assert hidden_states.shape[1] == self.channels
  if self.use_conv_transpose:
- return self.conv(x)
+ return self.conv(hidden_states)
 
- x = F.interpolate(x, scale_factor=2.0, mode="nearest")
+ hidden_states = F.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
 
  # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
  if self.use_conv:
  if self.name == "conv":
- x = self.conv(x)
+ hidden_states = self.conv(hidden_states)
  else:
- x = self.Conv2d_0(x)
+ hidden_states = self.Conv2d_0(hidden_states)
 
- return x
+ return hidden_states
 
 
 class Downsample2D(nn.Module):
@@ -84,16 +84,16 @@ def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name=
  else:
  self.conv = conv
 
- def forward(self, x):
- assert x.shape[1] == self.channels
+ def forward(self, hidden_states):
+ assert hidden_states.shape[1] == self.channels
  if self.use_conv and self.padding == 0:
  pad = (0, 1, 0, 1)
- x = F.pad(x, pad, mode="constant", value=0)
+ hidden_states = F.pad(hidden_states, pad, mode="constant", value=0)
 
- assert x.shape[1] == self.channels
- x = self.conv(x)
+ assert hidden_states.shape[1] == self.channels
+ hidden_states = self.conv(hidden_states)
 
- return x
+ return hidden_states
 
 
 class FirUpsample2D(nn.Module):
@@ -174,12 +174,12 @@ def _upsample_2d(self, x, weight=None, kernel=None, factor=2, gain=1):
 
  return x
 
- def forward(self, x):
+ def forward(self, hidden_states):
  if self.use_conv:
- height = self._upsample_2d(x, self.Conv2d_0.weight, kernel=self.fir_kernel)
+ height = self._upsample_2d(hidden_states, self.Conv2d_0.weight, kernel=self.fir_kernel)
  height = height + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
  else:
- height = self._upsample_2d(x, kernel=self.fir_kernel, factor=2)
+ height = self._upsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
 
  return height
 
@@ -236,14 +236,14 @@ def _downsample_2d(self, x, weight=None, kernel=None, factor=2, gain=1):
 
  return x
 
- def forward(self, x):
+ def forward(self, hidden_states):
  if self.use_conv:
- x = self._downsample_2d(x, weight=self.Conv2d_0.weight, kernel=self.fir_kernel)
- x = x + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
+ hidden_states = self._downsample_2d(hidden_states, weight=self.Conv2d_0.weight, kernel=self.fir_kernel)
+ hidden_states = hidden_states + self.Conv2d_0.bias.reshape(1, -1, 1, 1)
  else:
- x = self._downsample_2d(x, kernel=self.fir_kernel, factor=2)
+ hidden_states = self._downsample_2d(hidden_states, kernel=self.fir_kernel, factor=2)
 
- return x
+ return hidden_states
 
 
 class ResnetBlock2D(nn.Module):