fixed pre commit checks from HW tests

.pre-commit-config.yaml

@@ -81,17 +81,3 @@ repos:
   rev: 3.0.0
   hooks:
   -  id: forbid-binary
-     # Note: to exclude files, uncomment the next line and provide the regex to the associated file or files
-     exclude: >
-       (?x)^(
-          docker/ros/web/.*|
-          docker/ros/rootfs/.*
-       )$
-- repo: local
-  hooks:
-    - id: check-bdai-deps
-      name: check bdai dependency rules
-      entry: ./scripts/check_dependency_rules.sh
-      language: script
-      files: \.py$
-      require_serial: true

config/experiments/reality.yaml

@@ -10,7 +10,7 @@ policy:
     name: "RealityITMPolicyV2"
     pointnav_policy_path: "data/pointnav_weights.pth"
     depth_image_shape: [212, 240]  # height, width
-    pointnav_stop_radius: 1.2
+    pointnav_stop_radius: 0.9
     use_max_confidence: False
     object_map_erosion_size: 5
     exploration_thresh: 0.0
@@ -21,7 +21,7 @@ policy:
     agent_radius: 0.2
 
 env:
-  max_body_cam_depth: 3.5
+  max_body_cam_depth: 2.5
   max_gripper_cam_depth: 5.0
   max_lin_dist: 0.2
   max_ang_dist: 0.523599

vlfm/mapping/obstacle_map.py

@@ -1,8 +1,9 @@
 # Copyright (c) 2023 Boston Dynamics AI Institute LLC. All rights reserved.
 
+from typing import Any, Union
+
 import cv2
 import numpy as np
-from typing import Union, Any
 from frontier_exploration.frontier_detection import detect_frontier_waypoints
 from frontier_exploration.utils.fog_of_war import reveal_fog_of_war
 

vlfm/mapping/traj_visualizer.py

@@ -36,7 +36,9 @@ def draw_trajectory(
         img = self._draw_agent(img, camera_positions[-1], camera_yaw)
         return img
 
-    def _draw_path(self, img: np.ndarray, camera_positions: Union[np.ndarray, List[np.ndarray]]) -> np.ndarray:
+    def _draw_path(
+        self, img: np.ndarray, camera_positions: Union[np.ndarray, List[np.ndarray]]
+    ) -> np.ndarray:
         """Draws the path on the image and returns it"""
         if len(camera_positions) < 2:
             return img

vlfm/policy/base_objectnav_policy.py

@@ -118,7 +118,7 @@ def act(
         prev_actions: Any,
         masks: Tensor,
         deterministic: bool = False,
-    ) -> Tuple[Tensor, Tensor]:
+    ) -> Any:
         """
         Starts the episode by 'initializing' and allowing robot to get its bearings
         (e.g., spinning in place to get a good view of the scene).

vlfm/policy/itm_policy.py

@@ -265,7 +265,7 @@ def act(
         prev_actions: Any,
         masks: Tensor,
         deterministic: bool = False,
-    ) -> Tuple[Tensor, Tensor]:
+    ) -> Any:
         self._pre_step(observations, masks)
         self._update_value_map()
         return super().act(

vlfm/policy/reality_policies.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2023 Boston Dynamics AI Institute LLC. All rights reserved.
 
 from dataclasses import dataclass
-from typing import Any, Dict, List, Tuple, Union, Optional
+from typing import Any, Dict, List, Union
 
 import numpy as np
 import torch
@@ -60,7 +60,7 @@ def act(
         prev_actions: Any,
         masks: Tensor,
         deterministic: bool = False,
-    ) -> Tuple[Tensor, Tensor]:
+    ) -> Dict[str, Any]:
         if observations["objectgoal"] not in self._non_coco_caption:
             self._non_coco_caption = (
                 observations["objectgoal"] + " . " + self._non_coco_caption
@@ -75,36 +75,31 @@ def act(
         # convert this numpy array to a dictionary with keys "angular" and "linear" so
         # that it can be passed to the Spot robot.
         if self._done_initializing:
-            angular = action[0][0].item()
-            linear = action[0][1].item()
-            arm_yaw = -1.0
+            action_dict = {
+                "angular": action[0][0].item(),
+                "linear": action[0][1].item(),
+                "arm_yaw": -1,
+                "info": self._policy_info,
+            }
         else:
-            angular = 0.0
-            linear = 0.0
-            arm_yaw = action[0][0].item()
+            action_dict = {
+                "angular": 0,
+                "linear": 0,
+                "arm_yaw": action[0][0].item(),
+                "info": self._policy_info,
+            }
 
-        self._done_initializing = len(self._initial_yaws) == 0
+        if "rho_theta" in self._policy_info:
+            action_dict["rho_theta"] = self._policy_info["rho_theta"]
 
-        action = torch.tensor([[angular, linear, arm_yaw]], dtype=torch.float32)
+        self._done_initializing = len(self._initial_yaws) == 0
 
-        return action, rnn_hidden_states
+        return action_dict
 
     def get_action(
         self, observations: Dict[str, Any], masks: Tensor, deterministic: bool = True
     ) -> Dict[str, Any]:
-        actions, _ = self.act(
-            observations, None, None, masks, deterministic=deterministic
-        )
-        action_dict = {
-            "angular": actions[0],
-            "linear": actions[1],
-            "arm_yaw": actions[2],
-            "info": self._policy_info,
-        }
-        if "rho_theta" in self._policy_info:
-            action_dict["rho_theta"] = self._policy_info["rho_theta"]
-
-        return action_dict
+        return self.act(observations, None, None, masks, deterministic=deterministic)
 
     def _reset(self: Union["RealityMixin", ITMPolicyV2]) -> None:
         parent_cls: ITMPolicyV2 = super()  # type: ignore

vlfm/policy/utils/non_habitat_policy/resnet.py

@@ -6,7 +6,7 @@
 # https://github.com/facebookresearch/habitat-lab/blob/main/habitat-baselines/habitat_baselines/rl/ddppo/policy/resnet.py
 # This is a filtered down version that only support ResNet-18
 
-from typing import List, Optional, Type, cast
+from typing import List, Optional, Type
 
 from torch import Tensor
 from torch import nn as nn

vlfm/policy/utils/non_habitat_policy/rnn_state_encoder.py

@@ -6,7 +6,7 @@
 # https://github.com/facebookresearch/habitat-lab/blob/main/habitat-baselines/habitat_baselines/rl/models/rnn_state_encoder.py
 # This is a filtered down version that only supports LSTM
 
-from typing import Dict, Optional, Tuple, Any
+from typing import Any, Dict, Optional, Tuple
 
 import torch
 import torch.nn as nn
@@ -20,6 +20,7 @@ class RNNStateEncoder(nn.Module):
     is that it takes an addition masks input that resets the hidden state between two adjacent
     timesteps to handle episodes ending in the middle of a rollout.
     """
+
     def __init__(
         self,
         input_size: int,
@@ -131,16 +132,16 @@ def __init__(
     ):
         super().__init__(input_size, hidden_size, num_layers)
 
-    # Note: Type handling mypy errors in pytorch libraries prevent 
+    # Note: Type handling mypy errors in pytorch libraries prevent
     # directly setting hidden_states type
     def pack_hidden(
-        self, hidden_states: Any # type is Tuple[torch.Tensor, torch.Tensor]
+        self, hidden_states: Any  # type is Tuple[torch.Tensor, torch.Tensor]
     ) -> torch.Tensor:
         return torch.cat(hidden_states, 0)
 
     def unpack_hidden(
         self, hidden_states: torch.Tensor
-    ) -> Any: # type is Tuple[torch.Tensor, torch.Tensor]
+    ) -> Any:  # type is Tuple[torch.Tensor, torch.Tensor]
         lstm_states = torch.chunk(hidden_states.contiguous(), 2, 0)
         return (lstm_states[0], lstm_states[1])
 
@@ -150,7 +151,10 @@ def build_rnn_inputs(
     rnn_states: torch.Tensor,
     not_dones: torch.Tensor,
     rnn_build_seq_info: Dict[str, torch.Tensor],
-) -> Tuple[PackedSequence, torch.Tensor,]:
+) -> Tuple[
+    PackedSequence,
+    torch.Tensor,
+]:
     r"""Create a PackedSequence input for an RNN such that each
     set of steps that are part of the same episode are all part of
     a batch in the PackedSequence.

vlfm/policy/utils/pointnav_policy.py

@@ -191,6 +191,16 @@ def load_pointnav_policy(file_path: str) -> PointNavResNetTensorOutputPolicy:
         ckpt_dict = torch.load(file_path, map_location="cpu")
         pointnav_policy = PointNavResNetTensorOutputPolicy()
         current_state_dict = pointnav_policy.state_dict()
+        # Let old checkpoints work with new code
+        if "net.prev_action_embedding_cont.bias" not in ckpt_dict.keys():
+            ckpt_dict["net.prev_action_embedding_cont.bias"] = ckpt_dict[
+                "net.prev_action_embedding.bias"
+            ]
+        if "net.prev_action_embedding_cont.weights" not in ckpt_dict.keys():
+            ckpt_dict["net.prev_action_embedding_cont.weight"] = ckpt_dict[
+                "net.prev_action_embedding.weight"
+            ]
+
         pointnav_policy.load_state_dict(
             {k: v for k, v in ckpt_dict.items() if k in current_state_dict}
         )

vlfm/reality/pointnav_env.py

@@ -106,9 +106,7 @@ def _compute_velocities(self, action: Dict[str, Any]) -> Tuple[float, float]:
         lin_vel = lin_dist / self._time_step
         return ang_vel, lin_vel
 
-    def _compute_displacements(
-        self, action: Dict[str, Any]
-    ) -> Tuple[float, float]:
+    def _compute_displacements(self, action: Dict[str, Any]) -> Tuple[float, float]:
         displacements = []
         for action_key, max_dist in (
             ("angular", self._max_ang_dist),

vlfm/reality/robots/frame_ids.py

@@ -0,0 +1,8 @@
+class SpotFrameIds:
+    BODY: str = "body"
+    FLAT_BODY: str = "flat_body"
+    GPE: str = "gpe"
+    HAND: str = "hand"
+    LINK_WR1: str = "link_wr1"
+    ODOM: str = "odom"
+    VISION: str = "vision"

vlfm/utils/geometry_utils.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2023 Boston Dynamics AI Institute LLC. All rights reserved.
 
 import math
-from typing import Tuple, Union
+from typing import Tuple
 
 import numpy as np
 
@@ -83,8 +83,7 @@ def calculate_vfov(hfov: float, width: int, height: int) -> float:
     """
     # Calculate the diagonal field of view (DFOV)
     dfov = 2 * math.atan(
-        math.tan(hfov / 2)
-        * math.sqrt((width**2 + height**2) / (width**2 + height**2))
+        math.tan(hfov / 2) * math.sqrt((width**2 + height**2) / (width**2 + height**2))
     )
 
     # Calculate the vertical field of view (VFOV)

vlfm/vlm/detections.py

@@ -220,15 +220,15 @@ def draw_bounding_box(
 
         # reshape to a single dimensional array
         rand_color = single_pixel.reshape(3)
-        color = [int(c) for c in rand_color]  # type: ignore
+        bgr_color = [int(c) for c in rand_color]  # type: ignore
     else:
         # Convert RGB to BGR
-        bgr_color = color[::-1]
-        color = [int(c) for c in bgr_color]  # type: ignore
+        color = color[::-1]
+        bgr_color = [int(c) for c in color]
 
     # Draw bounding box on image
     box_thickness = 2
-    cv2.rectangle(img, point1, point2, color, thickness=box_thickness)
+    cv2.rectangle(img, point1, point2, bgr_color, thickness=box_thickness)
 
     # Draw class name and score on image
     text_label = f"{class_name}: {int(score * 100)}%"
@@ -242,7 +242,7 @@ def draw_bounding_box(
         img,
         (text_x, text_y - 2 * text_size[1]),
         (text_x + text_size[0], text_y - text_size[1]),
-        color,
+        bgr_color,
         -1,
     )
     cv2.putText(