Resnet18 works!

rl_games/algos_torch/network_builder.py

@@ -1072,7 +1072,6 @@ def build(self, name, **kwargs):
 
 
 from torchvision import models
-from timm import create_model  # timm is required for ConvNeXt and ViT
 
 class VisionBackboneBuilder(NetworkBuilder):
     def __init__(self, **kwargs):
@@ -1172,6 +1171,8 @@ def forward(self, obs_dict):
                 proprio = obs_dict['proprio']
             else:
                 obs = obs_dict['obs']
+
+            # print('obs.shape: ', obs.shape)
             if self.permute_input:
                 obs = obs.permute((0, 3, 1, 2))
 
@@ -1181,7 +1182,10 @@ def forward(self, obs_dict):
 
             out = obs
             out = self.cnn(out)
+            #print(out.shape)
             out = out.flatten(1)
+            #print(out.shape)
+            #print('AAAAAAAAAAAAAAAAAaaa')
             out = self.flatten_act(out)
 
             if self.proprio_size > 0:
@@ -1281,19 +1285,20 @@ def _build_backbone(self, input_shape, backbone_params):
                 print('backbone_output_size: ', backbone_output_size)
                 backbone = nn.Sequential(*list(backbone.children())[:-1])
             elif backbone_type == 'convnext_tiny':
-                backbone = create_model('convnext_tiny', pretrained=pretrained)
-                # Modify the first convolution layer to match input shape if needed
-                #backbone.conv1 = nn.Conv2d(input_shape[0], 64, kernel_size=3, stride=1, padding=1, bias=False)
-                # Remove the fully connected layer
-                backbone_output_size = backbone.head.fc.in_features
+                backbone = models.convnext_tiny(pretrained=pretrained)
+                backbone_output_size = backbone.classifier[2].in_features
+                backbone.classifier = nn.Identity()
 
-                backbone = nn.Sequential(*list(backbone.children())[:-1])
+                # Do we need it?
+                # backbone = nn.Sequential(*list(backbone.children())[:-1])
             elif backbone_type == 'vit_tiny_patch16_224':
-                backbone = create_model('vit_tiny_patch16_224', pretrained=pretrained)
-                # # ViT outputs a single token, so no need to remove layers
-                # backbone = models.vit_small_patch16_224(pretrained=pretrained)
+                backbone = models.vit_small_patch16_224(pretrained=pretrained)
                 backbone_output_size = backbone.heads.head.in_features
                 backbone.heads.head = nn.Identity()
+
+                # ViT outputs a single token, so no need to remove layers
+                # Is it true?
+
             else:
                 raise ValueError(f'Unknown backbone type: {backbone_type}')
 

rl_games/configs/atari/ppo_pong_envpool_backbone.yaml

@@ -27,20 +27,20 @@ params:
     #     name: 'None'
 
     backbone:
-      type: resnet18 #vit_tiny_patch16_224 #convnext_tiny #resnet18
-      pretrained: False
-      permute_input: True
-      freeze: False
+      type: resnet18 #convnext_tiny #vit_tiny_patch16_224
+      pretrained: True
+      permute_input: False
+      freeze: True
 
       args:
-        zero_init_residual: False
+        zero_init_residual: True
         norm_layer: None
 
     mlp:
       units: [512]
       activation: relu
       regularizer:
-        name: 'None'
+        name: None
       initializer:
         name: default
 
@@ -49,35 +49,35 @@ params:
     #   units: 256
     #   layers: 1
   config:
-    name: pong_resnet18_nopretrained_novaluenorm_weightdecay_nomaxpool
+    name: pong_resnet18_pretrained_2_mini_epoch_1e-4_linear_lr_norm_value_frozen
     env_name: envpool
     reward_shaper:
       min_val: -1
       max_val: 1
 
-    mixed_precision: False
+    mixed_precision: True
     normalize_input: False
-    normalize_value: False
+    normalize_value: True
     normalize_advantage: True
-    gamma: 0.995
+    gamma: 0.99
     tau: 0.95
     learning_rate: 1e-4
 
     score_to_win: 100000
-    grad_norm: 1.5
+    grad_norm: 1.0
     entropy_coef: 0.01
     truncate_grads: True
 
     e_clip: 0.2
     clip_value: True
     save_best_after: 25
     save_frequency: 50
-    num_actors: 64
-    horizon_length: 128
-    minibatch_size: 2048
-    mini_epochs: 4
+    num_actors: 32
+    horizon_length: 64
+    minibatch_size: 512
+    mini_epochs: 2
     critic_coef: 1
-    lr_schedule: None
+    lr_schedule: linear
     kl_threshold: 0.01
     use_diagnostics: True
     seq_length: 8