export_waymo_to_json.py

import json
from json import JSONEncoder
from mmcv import Config
import numpy 
import torch

from risk_biased.utils.waymo_dataloader import WaymoDataloaders


class NumpyArrayEncoder(JSONEncoder):
    def default(self, obj):
        if isinstance(obj, numpy.ndarray):
            return obj.tolist()
        return JSONEncoder.default(self, obj)

if __name__ == "__main__":
    output_path = "../risk_biased_dataset/data.json"
    config_path = "risk_biased/config/waymo_config.py"
    cfg = Config.fromfile(config_path)
    dataloaders = WaymoDataloaders(cfg)
    sample_dataloader = dataloaders.sample_dataloader()
    (
        x,
        mask_x,
        y,
        mask_y,
        mask_loss,
        map_data,
        mask_map,
        offset,
        x_ego,
        y_ego,
    ) = sample_dataloader.collate_fn(sample_dataloader.dataset)
    
    batch_size, n_agents, n_timesteps_past, n_features = x.shape
    n_timesteps_future = y.shape[2]
    n_features_map = map_data.shape[3]
    n_features_offset = offset.shape[2]
    
    print(x.shape)
    print(mask_x.shape)
    print(y.shape)
    print(mask_y.shape)
    print(mask_loss.shape)
    print(map_data.shape)
    print(mask_map.shape)
    print(offset.shape)
    print(x_ego.shape)
    print(y_ego.shape)
    
    
    data = {"x": x.numpy(),
            "mask_x": mask_x.numpy(),
            "y": y.numpy(),
            "mask_y": mask_y.numpy(),
            "mask_loss": mask_loss.numpy(),
            "map_data": map_data.numpy(),
            "mask_map": mask_map.numpy(),
            "offset": offset.numpy(),
            "x_ego": x_ego.numpy(),
            "y_ego": y_ego.numpy(),
    }
    
    json_data = json.dumps(data, cls=NumpyArrayEncoder)
    
    with open(output_path, "w+") as f:
        f.write(json_data)
            
    with open(output_path, "r") as f:
        decoded = json.load(f)
            
    x_c = torch.from_numpy(numpy.array(decoded["x"]).astype(numpy.float32))
    mask_x_c = torch.from_numpy(numpy.array(decoded["mask_x"]).astype(numpy.bool8))
    y_c = torch.from_numpy(numpy.array(decoded["y"]).astype(numpy.float32))
    mask_y_c = torch.from_numpy(numpy.array(decoded["mask_y"]).astype(numpy.bool8))
    mask_loss_c = torch.from_numpy( numpy.array(decoded["mask_loss"]).astype(numpy.bool8))
    map_data_c = torch.from_numpy(numpy.array(decoded["map_data"]).astype(numpy.float32))
    mask_map_c = torch.from_numpy(numpy.array(decoded["mask_map"]).astype(numpy.bool8))
    offset_c = torch.from_numpy(numpy.array(decoded["offset"]).astype(numpy.float32))
    x_ego_c = torch.from_numpy(numpy.array(decoded["x_ego"]).astype(numpy.float32))
    y_ego_c = torch.from_numpy(numpy.array(decoded["y_ego"]).astype(numpy.float32))
    
    assert torch.allclose(x, x_c)
    assert torch.allclose(mask_x, mask_x_c)
    assert torch.allclose(y, y_c)
    assert torch.allclose(mask_y, mask_y_c)
    assert torch.allclose(mask_loss, mask_loss_c)
    assert torch.allclose(map_data, map_data_c)
    assert torch.allclose(mask_map, mask_map_c)
    assert torch.allclose(offset, offset_c)
    assert torch.allclose(x_ego, x_ego_c)
    assert torch.allclose(y_ego, y_ego_c)
    
    print("All good!")