rm temperature and edit docstrings

lerobot/common/policies/diffusion/modeling_diffusion.py

@@ -1,7 +1,6 @@
 """Diffusion Policy as per "Diffusion Policy: Visuomotor Policy Learning via Action Diffusion"
 
 TODO(alexander-soare):
-  - Remove reliance on Robomimic for SpatialSoftmax.
   - Remove reliance on diffusers for DDPMScheduler and LR scheduler.
 """
 
@@ -307,17 +306,15 @@ class SpatialSoftmax(nn.Module):
     We apply channel-wise softmax over the activations (512x120) and compute dot product with the coordinates (120x2)
     to get expected points of maximal activation (512x2).
 
-    Optionally, when num_kp != None, can learn a linear mapping from the feature maps to a lower/higher dimensional
+    Optionally, when num_kp != None, can learn a linear mapping (of shape (num_kp, H, W)) from the input feature maps
-    space using a conv1x1 before computing the softmax.
+    using a conv1x1 before computing softmax.
     """
 
-    def __init__(self, input_shape, num_kp=None, temperature=1.0, learnable_temperature=False):
+    def __init__(self, input_shape, num_kp=None):
         """
         Args:
             input_shape (list): (C, H, W) input feature map shape.
             num_kp (int): number of keypoints in output. If None, output will have the same number of channels as input.
-            temperature (float): temperature for softmax normalization.
-            learnable_temperature (bool): whether to learn the temperature parameter.
         """
         super().__init__()
 
@@ -331,7 +328,6 @@ class SpatialSoftmax(nn.Module):
             self.nets = None
             self._out_c = self._in_c
 
-        self.temperature = nn.Parameter(torch.tensor(temperature), requires_grad=learnable_temperature)
         # we could use torch.linspace directly but that seems to behave slightly differently than numpy
         # and causes a small degradation in pc_success of pre-trained models.
         pos_x, pos_y = np.meshgrid(np.linspace(-1.0, 1.0, self._in_w), np.linspace(-1.0, 1.0, self._in_h))
@@ -353,7 +349,7 @@ class SpatialSoftmax(nn.Module):
         # [B, K, H, W] -> [B * K, H * W] where K is number of keypoints
         features = features.reshape(-1, self._in_h * self._in_w)
         # 2d softmax normalization
-        attention = F.softmax(features / self.temperature, dim=-1)
+        attention = F.softmax(features, dim=-1)
         # [B * K, H * W] x [H * W, 2] -> [B * K, 2] for spatial coordinate mean in x and y dimensions
         expected_xy = attention @ self.pos_grid
         # reshape to [B, K, 2]