[Fix] Fix the no_grad bug for NavDP training & Support NavDP finetuning

internnav/dataset/navdp_dataset_lerobot.py

@@ -41,6 +41,7 @@ def __init__(
         image_size=224,
         scene_data_scale=1.0,
         trajectory_data_scale=1.0,
+        pixel_channel=7,
         debug=False,
         preload=False,
         random_digit=False,
@@ -61,6 +62,7 @@ def __init__(
         self.trajectory_afford_path = []
         self.random_digit = random_digit
         self.prior_sample = prior_sample
+        self.pixel_channel = pixel_channel
         self.item_cnt = 0
         self.batch_size = batch_size
         self.batch_time_sum = 0.0
@@ -509,7 +511,12 @@ def __getitem__(self, index):
             camera_intrinsic,
             trajectory_base_extrinsic,
         )
-        pixel_goal = np.concatenate((pixel_goal, memory_images[-1]), axis=-1)
+        # pixel channel == 7 represents the navdp works pixel navigation under asynchronous pace,
+        # pixel_mask (1), the history image with the assigned pixel goal (3), current image (3)
+        # if pixel_channel == 4, pixel goal is assigned at current frame, therefore,
+        # only pixel_mask (1) and current image (3) are needed
+        if self.pixel_channel == 7:
+            pixel_goal = np.concatenate((pixel_goal, memory_images[-1]), axis=-1)
 
         pred_actions = (pred_actions[1:] - pred_actions[:-1]) * 4.0
         augment_actions = (augment_actions[1:] - augment_actions[:-1]) * 4.0

internnav/model/basemodel/navdp/navdp_policy.py

@@ -7,7 +7,13 @@
 
 from internnav.configs.model.base_encoders import ModelCfg
 from internnav.configs.trainer.exp import ExpCfg
-from internnav.model.encoder.navdp_backbone import *
+from internnav.model.encoder.navdp_backbone import (
+    ImageGoalBackbone,
+    LearnablePositionalEncoding,
+    PixelGoalBackbone,
+    RGBDBackbone,
+    SinusoidalPosEmb,
+)
 
 
 class NavDPModelConfig(PretrainedConfig):
@@ -51,9 +57,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
         elif pretrained_model_name_or_path is None or len(pretrained_model_name_or_path) == 0:
             pass
         else:
-            incompatible_keys, _ = model.load_state_dict(
-                torch.load(pretrained_model_name_or_path)['state_dict'], strict=False
-            )
+            incompatible_keys, _ = model.load_state_dict(torch.load(pretrained_model_name_or_path), strict=False)
             if len(incompatible_keys) > 0:
                 print(f'Incompatible keys: {incompatible_keys}')
 
@@ -66,31 +70,32 @@ def __init__(self, config: NavDPModelConfig):
             self.model_config = ModelCfg(**config.model_cfg['model'])
         else:
             self.model_config = config
-
         self.config.model_cfg['il']
-
         self._device = torch.device(f"cuda:{config.model_cfg['local_rank']}")
         self.image_size = self.config.model_cfg['il']['image_size']
         self.memory_size = self.config.model_cfg['il']['memory_size']
         self.predict_size = self.config.model_cfg['il']['predict_size']
+        self.pixel_channel = self.config.model_cfg['il']['pixel_channel']
         self.temporal_depth = self.config.model_cfg['il']['temporal_depth']
         self.attention_heads = self.config.model_cfg['il']['heads']
         self.input_channels = self.config.model_cfg['il']['channels']
         self.dropout = self.config.model_cfg['il']['dropout']
         self.token_dim = self.config.model_cfg['il']['token_dim']
         self.scratch = self.config.model_cfg['il']['scratch']
         self.finetune = self.config.model_cfg['il']['finetune']
-        self.rgbd_encoder = NavDP_RGBD_Backbone(
+        self.rgbd_encoder = RGBDBackbone(
             self.image_size, self.token_dim, memory_size=self.memory_size, finetune=self.finetune, device=self._device
         )
-        self.pixel_encoder = NavDP_PixelGoal_Backbone(self.image_size, self.token_dim, device=self._device)
-        self.image_encoder = NavDP_ImageGoal_Backbone(self.image_size, self.token_dim, device=self._device)
+        self.pixel_encoder = PixelGoalBackbone(
+            self.image_size, self.token_dim, pixel_channel=self.pixel_channel, device=self._device
+        )
+        self.image_encoder = ImageGoalBackbone(self.image_size, self.token_dim, device=self._device)
         self.point_encoder = nn.Linear(3, self.token_dim)
 
         if not self.finetune:
-            for p in self.rgbd_encoder.parameters():
+            for p in self.rgbd_encoder.rgb_model.parameters():
                 p.requires_grad = False
-            self.rgbd_encoder.eval()
+            self.rgbd_encoder.rgb_model.eval()
 
         decoder_layer = nn.TransformerDecoderLayer(
             d_model=self.token_dim,
@@ -180,23 +185,6 @@ def predict_critic(self, predict_trajectory, rgbd_embed):
         return critic_output
 
     def forward(self, goal_point, goal_image, goal_pixel, input_images, input_depths, output_actions, augment_actions):
-        # """get device safely"""
-        # # get device safely
-        # try:
-        #     # try to get device through model parameters
-        #     device = next(self.parameters()).device
-        # except StopIteration:
-        #     # model has no parameters, use the default device
-        #     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        # # move all inputs to model device
-        # goal_point = goal_point.to(device)
-        # goal_image = goal_image.to(device)
-        # input_images = input_images.to(device)
-        # input_depths = input_depths.to(device)
-        # output_actions = output_actions.to(device)
-        # augment_actions = augment_actions.to(device)
-        # device = self._device
-        # print(f"self.parameters() is:{self.parameters()}")
         device = next(self.parameters()).device
 
         assert input_images.shape[1] == self.memory_size
@@ -325,7 +313,6 @@ def predict_pointgoal_batch_action_vel(self, goal_point, input_images, input_dep
                 naction = self.noise_scheduler.step(model_output=noise_pred, timestep=k, sample=naction).prev_sample
 
             critic_values = self.predict_critic(naction, rgbd_embed)
-            all_trajectory = torch.cumsum(naction / 4.0, dim=1)
 
             negative_trajectory = torch.cumsum(naction / 4.0, dim=1)[(critic_values).argsort()[0:8]]
             positive_trajectory = torch.cumsum(naction / 4.0, dim=1)[(-critic_values).argsort()[0:8]]
@@ -344,7 +331,6 @@ def predict_nogoal_batch_action_vel(self, input_images, input_depths, sample_num
                 naction = self.noise_scheduler.step(model_output=noise_pred, timestep=k, sample=naction).prev_sample
 
             critic_values = self.predict_critic(naction, rgbd_embed)
-            all_trajectory = torch.cumsum(naction / 4.0, dim=1)
 
             negative_trajectory = torch.cumsum(naction / 4.0, dim=1)[(critic_values).argsort()[0:8]]
             positive_trajectory = torch.cumsum(naction / 4.0, dim=1)[(-critic_values).argsort()[0:8]]

internnav/model/encoder/navdp_backbone.py

@@ -202,7 +202,7 @@ def forward(self, images, depths):
         return memory_token
 
 
-class NavDP_RGBD_Backbone(nn.Module):
+class RGBDBackbone(nn.Module):
     def __init__(
         self,
         image_size=224,
@@ -313,7 +313,7 @@ def _get_device(self):
         return torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
-class NavDP_ImageGoal_Backbone(nn.Module):
+class ImageGoalBackbone(nn.Module):
     def __init__(self, image_size=224, embed_size=512, device='cuda:0'):
         super().__init__()
         if device is None:
@@ -376,8 +376,8 @@ def _get_device(self):
         return torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
-class NavDP_PixelGoal_Backbone(nn.Module):
-    def __init__(self, image_size=224, embed_size=512, device='cuda:0'):
+class PixelGoalBackbone(nn.Module):
+    def __init__(self, image_size=224, embed_size=512, pixel_channel=7, device='cuda:0'):
         super().__init__()
         if device is None:
             device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -392,7 +392,7 @@ def __init__(self, image_size=224, embed_size=512, device='cuda:0'):
         self.pixelgoal_encoder = DepthAnythingV2(**model_configs['vits'])
         self.pixelgoal_encoder = self.pixelgoal_encoder.pretrained.float()
         self.pixelgoal_encoder.patch_embed.proj = nn.Conv2d(
-            in_channels=7,
+            in_channels=pixel_channel,
             out_channels=self.pixelgoal_encoder.patch_embed.proj.out_channels,
             kernel_size=self.pixelgoal_encoder.patch_embed.proj.kernel_size,
             stride=self.pixelgoal_encoder.patch_embed.proj.stride,

scripts/train/configs/navdp.py

@@ -29,7 +29,7 @@
     ),
     il=IlCfg(
         epochs=1000,
-        batch_size=16,
+        batch_size=32,
         lr=1e-4,
         num_workers=8,
         weight_decay=1e-4,  # TODO
@@ -57,6 +57,7 @@
         prior_sample=False,
         memory_size=8,
         predict_size=24,
+        pixel_channel=4,
         temporal_depth=16,
         heads=8,
         token_dim=384,

scripts/train/train.py

@@ -77,12 +77,12 @@ def main(config, model_class, model_config_class):
         """Main training function."""
         _make_dir(config)
 
-        print(f"=== Start training ===")
+        print("=== Start training ===")
         print(f"Current time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
         print(f"PyTorch version: {torch.__version__}")
         print(f"CUDA available: {torch.cuda.is_available()}")
         print(f"CUDA device count: {torch.cuda.device_count()}")
-        print(f"Environment variables:")
+        print("Environment variables:")
         print(f"  RANK: {os.getenv('RANK', 'Not set')}")
         print(f"  LOCAL_RANK: {os.getenv('LOCAL_RANK', 'Not set')}")
         print(f"  WORLD_SIZE: {os.getenv('WORLD_SIZE', 'Not set')}")
@@ -172,6 +172,7 @@ def main(config, model_class, model_config_class):
                 config.il.batch_size,
                 config.il.image_size,
                 config.il.scene_scale,
+                pixel_channel=config.il.pixel_channel,
                 preload=config.il.preload,
                 random_digit=config.il.random_digit,
                 prior_sample=config.il.prior_sample,