Fix the soft_actor_critic model (#2326)

Summary:
Unfortunately, #2318 has a bug that breaks the `soft_actor_critic` model.

Pull Request resolved: #2326

Reviewed By: aaronenyeshi

Differential Revision: D58871386

Pulled By: xuzhao9

fbshipit-source-id: 5f8b5fbe00722ccb647b08a8089fd52a7719208c

torchbenchmark/models/soft_actor_critic/nets.py

@@ -4,7 +4,7 @@
 from torch import distributions as pyd
 from torch import nn
 
-from . import utils
+from . import sac_utils
 from torchbenchmark.util.distribution import SquashedNormal
 
 def weight_init(m):
@@ -30,11 +30,11 @@ def __init__(self, obs_shape, out_dim=50):
  self.conv3 = nn.Conv2d(32, 32, kernel_size=3, stride=1)
  self.conv4 = nn.Conv2d(32, 32, kernel_size=3, stride=1)
 
- output_height, output_width = utils.compute_conv_output(
+ output_height, output_width = sac_utils.compute_conv_output(
  obs_shape[1:], kernel_size=(3, 3), stride=(2, 2)
  )
  for _ in range(3):
- output_height, output_width = utils.compute_conv_output(
+ output_height, output_width = sac_utils.compute_conv_output(
  (output_height, output_width), kernel_size=(3, 3), stride=(1, 1)
  )
 
@@ -63,15 +63,15 @@ def __init__(self, obs_shape, out_dim=50):
  self.conv2 = nn.Conv2d(32, 64, kernel_size=4, stride=2)
  self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1)
 
- output_height, output_width = utils.compute_conv_output(
+ output_height, output_width = sac_utils.compute_conv_output(
  obs_shape[1:], kernel_size=(8, 8), stride=(4, 4)
  )
 
- output_height, output_width = utils.compute_conv_output(
+ output_height, output_width = sac_utils.compute_conv_output(
  (output_height, output_width), kernel_size=(4, 4), stride=(2, 2)
  )
 
- output_height, output_width = utils.compute_conv_output(
+ output_height, output_width = sac_utils.compute_conv_output(
  (output_height, output_width), kernel_size=(3, 3), stride=(1, 1)
  )
 

torchbenchmark/models/soft_actor_critic/sac.py

@@ -3,7 +3,7 @@
 import numpy as np
 import torch
 
-from . import envs, nets, replay, utils
+from . import envs, nets, replay, sac_utils
 
 
 class SACAgent: