def test_get_frame_indices_agent(frame_idx: int, zarr_dataset: ChunkedDataset,
dmg: LocalDataManager, cfg: dict) -> None:
cfg["raster_params"]["map_type"] = "box_debug"
rasterizer = build_rasterizer(cfg, dmg)
dataset = AgentDataset(cfg, zarr_dataset, rasterizer)
frame_indices = dataset.get_frame_indices(frame_idx)
# get valid agents from that frame only
agent_slice = get_agents_slice_from_frames(
dataset.dataset.frames[frame_idx])
agents = dataset.dataset.agents[agent_slice]
agents = agents[dataset.agents_mask[agent_slice]]
for agent, idx in zip(agents, frame_indices):
id_agent = dataset[idx]["track_id"]
assert id_agent == agent["track_id"]
confs=np.concatenate(confidences_list))
metrics = compute_metrics_csv(
eval_gt_path, pred_path, [neg_multi_log_likelihood, time_displace])
for metric_name, metric_mean in metrics.items():
print(metric_name, metric_mean)
gt_rows = {}
for row in read_gt_csv(eval_gt_path):
gt_rows[row["track_id"] + row["timestamp"]] = row["coord"]
eval_ego_dataset = EgoDataset(cfg, eval_dataset.dataset, rasterizer)
for frame_number in range(
99, len(eval_zarr.frames),
100): # start from last frame of scene_0 and increase by 100
agent_indices = eval_dataset.get_frame_indices(frame_number)
if not len(agent_indices):
continue
# get AV point-of-view frame
data_ego = eval_ego_dataset[frame_number]
im_ego = rasterizer.to_rgb(data_ego["image"].transpose(1, 2, 0))
center = np.asarray(cfg["raster_params"]["ego_center"]
) * cfg["raster_params"]["raster_size"]
predicted_positions = []
target_positions = []
for v_index in agent_indices:
data_agent = eval_dataset[v_index]
model.eval()
torch.set_grad_enabled(False)
# Uncomment to choose satelliter or semantic rasterizer
# validate_cfg["raster_params"]["map_type"] = "py_satellite"
validate_cfg["raster_params"]["map_type"] = "py_semantic"
rast = build_rasterizer(validate_cfg, dm)
eval_ego_dataset = EgoDataset(validate_cfg, valid_dataset.dataset, rast)
num_frames = 2 # randomly pick _ frames
random_frames = np.random.randint(0, len(eval_ego_dataset) - 1, (num_frames, ))
for frame_number in random_frames:
agent_indices = valid_dataset.get_frame_indices(frame_number)
if not len(agent_indices):
continue
# get AV point-of-view frame
data_ego = eval_ego_dataset[frame_number]
im_ego = rasterizer.to_rgb(data_ego["image"].transpose(1, 2, 0))
center = np.asarray(validate_cfg["raster_params"]["ego_center"]
) * validate_cfg["raster_params"]["raster_size"]
predicted_positions = []
target_positions = []
for v_index in agent_indices:
data_agent = valid_dataset[v_index]
