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jblitzar
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github-python

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tot_ep += 1
print(f"Task success: {task_success} (wandb_step: {wandb.run.step}). Current successes: {sum(successes)}/{len(successes)}")
scene_logs = calculcate_metric_means({scene_id: episode_infos})
wandb.log({f"{scene_id}_{k}": v for k, v in scene_logs[scene_id].items()})
high_level_env.close()
return episode_infos, tot_ep
def main():
np.set_printoptions(precision=3, suppress=True)
config_file = get_config("moma_llm.yaml")
# NOTE: igibson will reload the config file, so changes here won't be relfected! Just for wandb logging
cfg = parse_config(config_file)
if cfg["seed"] > 0:
np.random.seed(cfg["seed"])
if cfg["datasplit"] == "train":
scene_ids = TRAINING_SCENES
elif cfg["datasplit"] == "test":
scene_ids = TEST_SCENES
else:
raise ValueError(f"Unknown datasplit {cfg['datasplit']}")
cfg.update({"scene_ids": scene_ids, "agent": cfg["agent"]})
wandb.init(project="[scene-llm]",
entity="robot-learning-lab",
config=cfg,
mode="online" if cfg["wandb"] else "disabled",
#name=f"{agent}"
)
# copy config file to wandb run dir, so modifications to the main config file won't affect current runs
new_config_file = Path(wandb.run.dir) / Path(config_file).name
shutil.copy(config_file, new_config_file)
config_file = str(new_config_file)
episode_infos = defaultdict(list)
tot_ep = 0
if isinstance(scene_ids, str):
scene_ids = [scene_ids]
for scene_id in scene_ids:
infos, tot_ep = evaluate_scene(config_file=config_file, cfg=cfg, scene_id=scene_id, tot_ep=tot_ep)
episode_infos[scene_id] = infos
log_summary_table(episode_infos=episode_infos)
plot_efficiency_curves(episode_infos=episode_infos, max_hl_steps=cfg["max_high_level_steps"])
wandb.run.finish()
print("Done!")
if __name__ == "__main__":
main()
# <FILESEP>
from pydantic import BaseModel, Field
from typing import List
class VideoSegment(BaseModel):
path: str = Field(description="Path to the video segment")
start: float = Field(description="Start time of the video segment")
end: float = Field(description="End time of the video segment")
start_frame: int = Field(default=None, description="Start frame of the video segment")
end_frame: int = Field(default=None, description="End frame of the video segment")
def dimensions(self):
import cv2
cap = cv2.VideoCapture(self.path)
width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
return width, height
def duration(self):
return self.end - self.start
def fps(self):
import cv2
cap = cv2.VideoCapture(self.path)
fps = cap.get(cv2.CAP_PROP_FPS)
return fps
class Box(BaseModel):
class_id: int = Field(description="Class ID of the subject")
confidence: float = Field(description="Confidence of the subject")
x1: int = Field(description="X1 coordinate of the bounding box")
y1: int = Field(description="Y1 coordinate of the bounding box")
x2: int = Field(description="X2 coordinate of the bounding box")
y2: int = Field(description="Y2 coordinate of the bounding box")
id: int = Field(default=None, description="ID of the subject")
metadata: dict = Field(default=None, description="Metadata of the subject")
def area(self):
return (self.x2 - self.x1) * (self.y2 - self.y1)
def center(self):
return (self.x1 + self.x2) / 2, (self.y1 + self.y2) / 2
def width(self):