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9897e20 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | import torch
import numpy as np
# Parameters
MAX_NUM_OBJECTS = 32
MAX_MAP_POINTS = 3000
MAX_POLYLINES = 256
MAX_TRAFFIC_LIGHTS = 16
num_points_polyline = 30
def duplicate_batch(batch: dict, num_samples: int):
"""Duplicates the batch for the given number of samples."""
for key, value in batch.items():
if isinstance(value, torch.Tensor):
assert value.shape[0] == 1, "Only support batch size of 1"
batch[key] = torch.cat([value] * num_samples, dim=0)
return batch
def torch_dict_to_numpy(input: dict):
output = {}
for key, value in input.items():
if isinstance(value, torch.Tensor):
output[key] = value.detach().cpu().numpy()
else:
output[key] = value
return output
def stack_dict(input: list):
list_len = len(input)
if list_len == 0:
return {}
key_to_list = {}
for key in input[0].keys():
key_to_list[key] = [input[i][key] for i in range(list_len)]
output = {}
for key, value in key_to_list.items():
if isinstance(value[0], np.ndarray):
output[key] = np.stack(value, axis=0)
elif isinstance(value[0], dict):
output[key] = stack_dict(value)
else:
output[key] = value
return output
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