def test_agents_layer_out(hist_data: tuple, dmg: LocalDataManager, cfg: dict) -> None:
cfg["raster_params"]["map_type"] = "box_debug"
cfg["raster_params"]["filter_agents_threshold"] = 1.0
rasterizer = build_rasterizer(cfg, dmg)
out = rasterizer.rasterize(hist_data[0][:1], hist_data[1][:1], [])
assert out[..., 0].sum() == 0
cfg["raster_params"]["filter_agents_threshold"] = 0.0
rasterizer = build_rasterizer(cfg, dmg)
out = rasterizer.rasterize(hist_data[0][:1], hist_data[1][:1], [])
assert out[..., 0].sum() > 0
def main(model_name, plot_loss=True):
DIR_INPUT = cfg["data_path"]
os.environ["L5KIT_DATA_FOLDER"] = DIR_INPUT
dm = LocalDataManager(None)
rasterizer = build_rasterizer(cfg, dm)
train_dataset, train_dataset_ego, train_dataloader, train_dataloader_ego = get_train_dataloaders(
cfg, dm)
criterion = pytorch_neg_multi_log_likelihood_batch
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = CSP(cfg, device).to(device)
epochs = 10
optimizer = optim.Adam(model.parameters(), lr=1e-2)
train_losses = train(model, train_dataloader, train_dataset_ego,
train_dataset, criterion, device, epochs, optimizer)
torch.save(model, "models/{}.pt".format(model_name))
np.save("models/training_loss_{}.npy".format(model_name), train_losses)
eval_dataset, eval_dataloader, eval_dataset_ego, eval_gt_path = generate_eval_dataset(
cfg, dm, rasterizer)
eval(model, eval_dataloader, eval_dataset_ego, eval_dataset, criterion,
device, eval_gt_path, cfg)
if plot_loss:
plt.plot(np.arange(len(train_losses)),
train_losses,
label="train loss")
plt.legend()
plt.show()
def setup(self):
self.rasterizer = build_rasterizer(self.cfg, self.dm)
# called on every GPU
if self.is_test:
print("test mode setup")
self.test_path, test_zarr, self.test_dataset = self.load_zarr_dataset(
loader_name="test_data_loader")
else:
print("train mode setup")
self.train_path, train_zarr, self.train_dataset = self.load_zarr_dataset(
loader_name="train_data_loader")
self.val_path, val_zarr, self.val_dataset = self.load_zarr_dataset(
loader_name="val_data_loader")
self.plot_dataset(self.train_dataset)
if self.downsample_train:
print(
"downsampling agents, using only {} frames from each scene"
.format(len(lyft_utils.TRAIN_DSAMPLE_FRAMES)))
train_agents_list = lyft_utils.downsample_agents(
train_zarr,
self.train_dataset,
selected_frames=lyft_utils.TRAIN_DSAMPLE_FRAMES,
)
self.train_dataset = torch.utils.data.Subset(
self.train_dataset, train_agents_list)
# downsampling the validation dataset same as test dataset or
# l5kit.evaluation.create_chopped_dataset
val_agents_list = lyft_utils.downsample_agents(
val_zarr, self.val_dataset, selected_frames=VAL_SELECTED_FRAME)
self.val_dataset = torch.utils.data.Subset(self.val_dataset,
val_agents_list)
def test_simulation_agents_mock_insert(dmg: LocalDataManager, cfg: dict,
tmp_path: Path) -> None:
zarr_dataset = _mock_dataset()
rasterizer = build_rasterizer(cfg, dmg)
ego_dataset = EgoDataset(cfg, zarr_dataset, rasterizer)
sim_cfg = SimulationConfig(use_ego_gt=True,
use_agents_gt=True,
disable_new_agents=True,
distance_th_far=100,
distance_th_close=10)
dataset = SimulationDataset.from_dataset_indices(ego_dataset, [0], sim_cfg)
_ = dataset.rasterise_agents_frame_batch(0)
# insert (0, 1) in following frames
next_agent = np.zeros(1, dtype=AGENT_DTYPE)
next_agent["centroid"] = (-1, -1)
next_agent["yaw"] = -0.5
next_agent["track_id"] = 1
next_agent["extent"] = (1, 1, 1)
next_agent["label_probabilities"][:, 3] = 1
for frame_idx in [1, 2, 3]:
dataset.set_agents(frame_idx, {(0, 1): next_agent})
agents_dict = dataset.rasterise_agents_frame_batch(frame_idx)
assert len(agents_dict) == 1 and (0, 1) in agents_dict
assert np.allclose(agents_dict[(0, 1)]["centroid"], (-1, -1))
assert np.allclose(agents_dict[(0, 1)]["yaw"], -0.5)
def load_tune_data():
dm = get_dm()
eval_cfg = cfg["val_data_loader"]
eval_base_path = '/home/axot/lyft/data/scenes/validate_chopped_31'
eval_zarr_path = str(Path(eval_base_path) /
Path(dm.require(eval_cfg["key"])).name)
eval_mask_path = str(Path(eval_base_path) / "mask.npz")
eval_gt_path = str(Path(eval_base_path) / "gt.csv")
rasterizer = build_rasterizer(cfg, dm)
eval_zarr = ChunkedDataset(eval_zarr_path).open()
eval_mask = np.load(eval_mask_path)["arr_0"]
# ===== INIT DATASET AND LOAD MASK
eval_dataset = AgentDataset(
cfg, eval_zarr, rasterizer, agents_mask=eval_mask)
gt_dict = OrderedDict()
for el in read_gt_csv(eval_gt_path):
gt_dict[el["track_id"] + el["timestamp"]] = el
eval_dataloader = DataLoader(eval_dataset,
shuffle=eval_cfg["shuffle"],
batch_size=eval_cfg["batch_size"],
num_workers=eval_cfg["num_workers"])
return eval_dataloader, gt_dict
def test_simulation_agents_mock_disable(dmg: LocalDataManager, cfg: dict,
tmp_path: Path) -> None:
zarr_dataset = _mock_dataset()
rasterizer = build_rasterizer(cfg, dmg)
ego_dataset = EgoDataset(cfg, zarr_dataset, rasterizer)
sim_cfg = SimulationConfig(use_ego_gt=True,
use_agents_gt=True,
disable_new_agents=True,
distance_th_far=100,
distance_th_close=10)
dataset = SimulationDataset.from_dataset_indices(ego_dataset, [0], sim_cfg)
# nothing should be tracked
assert len(dataset._agents_tracked) == 0
agents_dict = dataset.rasterise_agents_frame_batch(0)
# only (0, 1) should be in
assert len(agents_dict) == 1 and (0, 1) in agents_dict
assert len(dataset._agents_tracked) == 1
agents_dict = dataset.rasterise_agents_frame_batch(1)
# again, only (0, 1) should be in
assert len(agents_dict) == 1
assert (0, 1) in agents_dict
assert len(dataset._agents_tracked) == 1
agents_dict = dataset.rasterise_agents_frame_batch(2)
assert len(agents_dict) == 0
assert len(dataset._agents_tracked) == 0
def test_simulation_dataset_build(zarr_cat_dataset: ChunkedDataset,
dmg: LocalDataManager, cfg: dict,
tmp_path: Path) -> None:
# modify one frame to ensure everything works also when scenes are different
zarr_cat_dataset.frames = np.asarray(zarr_cat_dataset.frames)
for scene_idx in range(len(zarr_cat_dataset.scenes)):
frame_slice = get_frames_slice_from_scenes(zarr_cat_dataset.scenes)
zarr_cat_dataset.frames[
frame_slice.start]["ego_translation"] += np.random.randn(3)
rasterizer = build_rasterizer(cfg, dmg)
ego_dataset = EgoDataset(cfg, zarr_cat_dataset, rasterizer)
sim_cfg = SimulationConfig(use_ego_gt=True,
use_agents_gt=True,
disable_new_agents=False,
distance_th_far=30,
distance_th_close=10)
# we should be able to create the same object by using both constructor and factory
scene_indices = list(range(len(zarr_cat_dataset.scenes)))
scene_dataset_batch: Dict[int, EgoDataset] = {}
for scene_idx in scene_indices:
scene_dataset = ego_dataset.get_scene_dataset(scene_idx)
scene_dataset_batch[scene_idx] = scene_dataset
sim_1 = SimulationDataset(scene_dataset_batch, sim_cfg)
sim_2 = SimulationDataset.from_dataset_indices(ego_dataset, scene_indices,
sim_cfg)
for (k_1, v_1), (k_2, v_2) in zip(sim_1.scene_dataset_batch.items(),
sim_2.scene_dataset_batch.items()):
assert k_1 == k_2
assert np.allclose(v_1.dataset.frames["ego_translation"],
v_2.dataset.frames["ego_translation"])
def test_simulation_agents(zarr_cat_dataset: ChunkedDataset,
dmg: LocalDataManager, cfg: dict,
tmp_path: Path) -> None:
rasterizer = build_rasterizer(cfg, dmg)
scene_indices = list(range(len(zarr_cat_dataset.scenes)))
ego_dataset = EgoDataset(cfg, zarr_cat_dataset, rasterizer)
sim_cfg = SimulationConfig(use_ego_gt=True,
use_agents_gt=True,
disable_new_agents=False,
distance_th_far=100,
distance_th_close=30)
dataset = SimulationDataset.from_dataset_indices(ego_dataset,
scene_indices, sim_cfg)
# nothing should be tracked
assert len(dataset._agents_tracked) == 0
agents_dict = dataset.rasterise_agents_frame_batch(0)
# we should have the same agents in each scene
for k in agents_dict:
assert (0, k[1]) in agents_dict
# now everything should be tracked
assert len(dataset._agents_tracked) == len(agents_dict)
def __init__(self,
data_root: str,
config_path: str,
split: str,
show_progress=True,
turn_thresh=3.,
speed_thresh=0.5,
static_thresh=1.,
output_folder='preprocess',
autosave=True,
cache_size=1e9):
self.autosave = autosave
self.show_progress = show_progress
self.turn_thresh = turn_thresh
self.speed_thresh = speed_thresh
self.static_thresh = static_thresh
self.split = split
self.config = load_config_data(config_path)
self.output_folder = output_folder
self.data_manager = LocalDataManager(data_root)
self.rasterizer = build_rasterizer(self.config, self.data_manager)
self.data_zarr = ChunkedDataset(self.data_manager.require(split)).open(
cache_size_bytes=int(cache_size))
self.dataset = AgentDataset(self.config, self.data_zarr,
self.rasterizer)
self.data = defaultdict(list)
self.junk = defaultdict(list)
self.progress = None
def test_build_dataloader(scene_indices: tuple, dataset_cls: Callable) -> None:
cfg = load_config_data("./l5kit/tests/artefacts/config.yaml")
cfg["train_data_loader"]["datasets"][0]["scene_indices"] = scene_indices
dm = LocalDataManager("./l5kit/tests/artefacts/")
rasterizer = build_rasterizer(cfg, dm)
dl = build_dataloader(cfg, "train", dm, dataset_cls, rasterizer)
next(iter(dl))
def val_dataloader(self):
# created chopped dataset
rasterizer = build_rasterizer(cfg, dm)
eval_cfg = cfg["valid_data_loader"]
num_frames_to_chop = 100
eval_base_path = create_chopped_dataset(
dm.require(eval_cfg["key"]),
cfg["raster_params"]["filter_agents_threshold"],
num_frames_to_chop, cfg["model_params"]["future_num_frames"],
MIN_FUTURE_STEPS)
eval_zarr_path = str(
Path(eval_base_path) / Path(dm.require(eval_cfg["key"])).name)
eval_mask_path = str(Path(eval_base_path) / "mask.npz")
eval_gt_path = str(Path(eval_base_path) / "gt.csv")
self.eval_gt_path = eval_gt_path
eval_zarr = ChunkedDataset(eval_zarr_path).open(cache_size_bytes=10e9)
eval_mask = np.load(eval_mask_path)["arr_0"]
eval_dataset = AgentDataset(cfg,
eval_zarr,
rasterizer,
agents_mask=eval_mask)
eval_dataloader = DataLoader(eval_dataset,
shuffle=False,
batch_size=eval_cfg["batch_size"],
num_workers=8)
return eval_dataloader
def load_val_data():
dm = get_dm()
eval_cfg = cfg["val_data_loader"]
# MIN_FUTURE_STEPS = 10
# num_frames_to_chop = cfg['model_params']['history_num_frames']+1
# eval_base_path = create_chopped_dataset(dm.require(eval_cfg["key"]),
# cfg["raster_params"]["filter_agents_threshold"],
# num_frames_to_chop,
# cfg["model_params"]["future_num_frames"],
# MIN_FUTURE_STEPS)
eval_base_path = '/home/axot/lyft/data/scenes/validate_chopped_31'
eval_zarr_path = str(
Path(eval_base_path) / Path(dm.require(eval_cfg["key"])).name)
eval_mask_path = str(Path(eval_base_path) / "mask.npz")
eval_gt_path = str(Path(eval_base_path) / "gt.csv")
rasterizer = build_rasterizer(cfg, dm)
eval_zarr = ChunkedDataset(eval_zarr_path).open()
eval_mask = np.load(eval_mask_path)["arr_0"]
# ===== INIT DATASET AND LOAD MASK
eval_dataset = AgentDataset(cfg,
eval_zarr,
rasterizer,
agents_mask=eval_mask)
eval_dataloader = DataLoader(eval_dataset,
shuffle=eval_cfg["shuffle"],
batch_size=eval_cfg["batch_size"],
num_workers=eval_cfg["num_workers"])
return eval_dataloader
def test_agents_layer_out(hist_data: tuple) -> None:
cfg = load_config_data("./l5kit/tests/artefacts/config.yaml")
cfg["raster_params"]["map_type"] = "box_debug"
cfg["raster_params"]["filter_agents_threshold"] = 1.0
dm = LocalDataManager("./l5kit/tests/artefacts/")
rasterizer = build_rasterizer(cfg, dm)
out = rasterizer.rasterize(hist_data[0][:1], hist_data[1][:1])
assert out[..., 0].sum() == 0
cfg["raster_params"]["filter_agents_threshold"] = 0.0
rasterizer = build_rasterizer(cfg, dm)
out = rasterizer.rasterize(hist_data[0][:1], hist_data[1][:1])
assert out[..., 0].sum() > 0
def test_rasterizer_created_from_config(map_type: str,
dataset: ChunkedStateDataset) -> None:
cfg = load_config_data("./l5kit/tests/artefacts/config.yaml")
cfg["raster_params"]["map_type"] = map_type
dm = LocalDataManager("./l5kit/tests/artefacts/")
rasterizer = build_rasterizer(cfg, dm)
check_rasterizer(cfg, rasterizer, dataset)
def test_ego_layer_out_center_configs(ego_center: tuple, hist_data: tuple, dmg: LocalDataManager, cfg: dict) -> None:
cfg["raster_params"]["map_type"] = "box_debug"
cfg["raster_params"]["ego_center"] = np.asarray(ego_center)
rasterizer = build_rasterizer(cfg, dmg)
out = rasterizer.rasterize(hist_data[0][:1], hist_data[1][:1], [])
assert out[..., -1].sum() > 0
def get_loaders(train_batch_size=32, valid_batch_size=64):
"""Prepare loaders.
Args:
train_batch_size (int, optional): batch size for training dataset.
Default is `32`.
valid_batch_size (int, optional): batch size for validation dataset.
Default is `64`.
Returns:
train and validation data loaders
"""
rasterizer = build_rasterizer(cfg, dm)
train_zarr = ChunkedDataset(dm.require("scenes/train.zarr")).open()
train_dataset = CubicAgentDataset(cfg, train_zarr, rasterizer)
n_samples = len(train_dataset) // 5
# n_samples = 100
train_dataset = Subset(train_dataset, list(range(n_samples)))
train_loader = DataLoader(
train_dataset,
batch_size=train_batch_size,
num_workers=NUM_WORKERS,
shuffle=True,
worker_init_fn=seed_all,
drop_last=True,
)
print(f" * Number of elements in train dataset - {len(train_dataset)}")
print(f" * Number of elements in train loader - {len(train_loader)}")
return train_loader, None
def test_rasterizer_created_from_config(map_type: str,
zarr_dataset: ChunkedDataset,
dmg: LocalDataManager,
cfg: dict) -> None:
cfg["raster_params"]["map_type"] = map_type
rasterizer = build_rasterizer(cfg, dmg)
check_rasterizer(cfg, rasterizer, zarr_dataset)
def evaluate(self, data_path, file_name="submission.csv"):
# set env variable for data
os.environ["L5KIT_DATA_FOLDER"] = data_path
dm = LocalDataManager(None)
cfg = self.cfg
# ===== INIT DATASET
test_cfg = cfg["test_data_loader"]
# Rasterizer
rasterizer = build_rasterizer(cfg, dm)
# Test dataset/dataloader
test_zarr = ChunkedDataset(dm.require(test_cfg["key"])).open()
test_mask = np.load(f"{data_path}/scenes/mask.npz")["arr_0"]
test_dataset = AgentDataset(cfg,
test_zarr,
rasterizer,
agents_mask=test_mask)
test_dataloader = DataLoader(test_dataset,
shuffle=test_cfg["shuffle"],
batch_size=test_cfg["batch_size"],
num_workers=test_cfg["num_workers"])
test_dataloader = test_dataloader
print(test_dataloader)
# ==== EVAL LOOP
self.model.eval()
torch.set_grad_enabled(False)
criterion = nn.MSELoss(reduction="none")
# store information for evaluation
future_coords_offsets_pd = []
timestamps = []
pred_coords = []
confidences_list = []
agent_ids = []
progress_bar = tqdm(test_dataloader)
for data in progress_bar:
_, pred, confidences = self.forward(data, criterion)
# future_coords_offsets_pd.append(outputs.cpu().numpy().copy())
timestamps.append(data["timestamp"].numpy().copy())
agent_ids.append(data["track_id"].numpy().copy())
#
# pred, confidences = predictor(image)
pred_coords.append(pred.cpu().numpy().copy())
confidences_list.append(confidences.cpu().numpy().copy())
# ==== Save Results
pred_path = f"{os.getcwd()}/{file_name}"
write_pred_csv(pred_path,
timestamps=np.concatenate(timestamps),
track_ids=np.concatenate(agent_ids),
coords=np.concatenate(pred_coords),
confs=np.concatenate(confidences_list))
def prepare_train_data(self):
train_cfg = cfg["train_data_loader"]
rasterizer = build_rasterizer(cfg, dm)
train_zarr = ChunkedDataset(dm.require(train_cfg["key"])).open()
train_dataset = AgentDataset(cfg, train_zarr, rasterizer)
train_dataloader = DataLoader(train_dataset, shuffle=train_cfg["shuffle"], batch_size=train_cfg["batch_size"],
num_workers=train_cfg["num_workers"])
return train_dataloader
def test_agent_as_ego(hist_data: tuple, dmg: LocalDataManager, cfg: dict) -> None:
cfg["raster_params"]["map_type"] = "box_debug"
cfg["raster_params"]["filter_agents_threshold"] = -1 # take everything
rasterizer = build_rasterizer(cfg, dmg)
agents = hist_data[1][0]
for ag in agents:
out = rasterizer.rasterize(hist_data[0][:1], hist_data[1][:1], [], ag)
assert out[..., -1].sum() > 0
def test_rasterizer_created_from_config(map_type: str,
dataset: ChunkedStateDataset) -> None:
cfg = load_config_data("./l5kit/configs/default.yaml")
cfg["raster_params"]["map_type"] = map_type
with tempfile.TemporaryDirectory("", "rasterizer-test") as tmpdir:
setup_rasterizer_artifacts_and_config(tmpdir, cfg)
dm = LocalDataManager(tmpdir)
rasterizer = build_rasterizer(cfg, dm)
check_rasterizer(cfg, rasterizer, dataset)
def test_out_shape(hist_data: tuple, dmg: LocalDataManager, cfg: dict) -> None:
hist_length = 5
cfg["raster_params"]["map_type"] = "box_debug"
cfg["model_params"]["history_num_frames"] = hist_length
rasterizer = build_rasterizer(cfg, dmg)
out = rasterizer.rasterize(hist_data[0][: hist_length + 1], hist_data[1][: hist_length + 1], [])
assert out.shape == (224, 224, (hist_length + 1) * 2)
def test_get_frame_indices_ego(frame_idx: int, zarr_dataset: ChunkedDataset,
dmg: LocalDataManager, cfg: dict) -> None:
cfg["raster_params"]["map_type"] = "box_debug"
rasterizer = build_rasterizer(cfg, dmg)
dataset = EgoDataset(cfg, zarr_dataset, rasterizer)
frame_indices = dataset.get_frame_indices(frame_idx)
# this should be only one and match the index of the frame (i.e. it should be frame_idx)
assert frame_indices[0] == frame_idx
def test_get_scene_indices_ego(scene_idx: int, zarr_dataset: ChunkedDataset,
dmg: LocalDataManager, cfg: dict) -> None:
cfg["raster_params"]["map_type"] = "box_debug"
rasterizer = build_rasterizer(cfg, dmg)
dataset = EgoDataset(cfg, zarr_dataset, rasterizer)
scene_indices = dataset.get_scene_indices(scene_idx)
frame_slice = get_frames_slice_from_scenes(zarr_dataset.scenes[scene_idx])
assert scene_indices[0] == frame_slice.start
assert scene_indices[-1] == frame_slice.stop - 1
def test_ego_layer_out_center_configs(ego_center: tuple,
hist_data: tuple) -> None:
cfg = load_config_data("./l5kit/tests/artefacts/config.yaml")
cfg["raster_params"]["map_type"] = "box_debug"
cfg["raster_params"]["ego_center"] = np.asarray(ego_center)
dm = LocalDataManager("./l5kit/tests/artefacts/")
rasterizer = build_rasterizer(cfg, dm)
out = rasterizer.rasterize(hist_data[0][:1], hist_data[1][:1])
assert out[..., -1].sum() > 0
def __init__(self, args, data_root):
super().__init__()
self.args = args
if args.mode == "train":
self.cfg = utils.get_train_cfg(args)
else:
self.cfg = utils.get_test_cfg(args)
self.data_root = data_root
self.dm = LocalDataManager(data_root)
self.rast = build_rasterizer(self.cfg, self.dm)
def test_dataset_rasterizer(rast_name: str, dataset_cls: Callable,
zarr_dataset: ChunkedDataset,
dmg: LocalDataManager, cfg: dict) -> None:
rasterizer = build_rasterizer(cfg, dmg)
dataset = dataset_cls(cfg=cfg,
zarr_dataset=zarr_dataset,
rasterizer=rasterizer,
perturbation=None)
check_sample(cfg, dataset)
check_torch_loading(dataset)
def check_performance_default(num_samples=64 * 20):
"""
Defautl datset from l5kit w/o any optimizations
"""
scene_name = "train"
cfg_data = get_dataset_cfg(scene_name=scene_name, map_type="py_semantic")
dm = LocalDataManager(None)
rasterizer = build_rasterizer(cfg_data, dm)
zarr_dataset = ChunkedDataset(dm.require(f"scenes/{scene_name}.zarr")).open()
dataset = AgentDataset(cfg_data, zarr_dataset, rasterizer)
check_performance(dataset, "default")
def test_dataset_rasterizer(rast_name: str, dataset_cls: Callable,
zarr_dataset: ChunkedStateDataset) -> None:
cfg = load_config_data("./l5kit/tests/artefacts/config.yaml")
dm = LocalDataManager("./l5kit/tests/artefacts/")
rasterizer = build_rasterizer(cfg, dm)
dataset = dataset_cls(cfg=cfg,
zarr_dataset=zarr_dataset,
rasterizer=rasterizer,
perturbation=None)
check_sample(cfg, dataset)
check_torch_loading(dataset)
def test_agent_as_ego(hist_data: tuple) -> None:
cfg = load_config_data("./l5kit/tests/artefacts/config.yaml")
cfg["raster_params"]["map_type"] = "box_debug"
cfg["raster_params"]["filter_agents_threshold"] = -1 # take everything
dm = LocalDataManager("./l5kit/tests/artefacts/")
rasterizer = build_rasterizer(cfg, dm)
agents = hist_data[1][0]
for ag in agents:
out = rasterizer.rasterize(hist_data[0][:1], hist_data[1][:1], ag)
assert out[..., -1].sum() > 0
