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test_collation.py
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import unittest
from collections import defaultdict
import numpy as np
import torch
from torch.utils.data import DataLoader
from trajdata.data_structures.agent import AgentType
from trajdata.dataset import UnifiedDataset
from trajdata.utils import arr_utils
class TestCollation(unittest.TestCase):
def test_convert_with_dir(self):
input = np.array([1, 2, 3])
converted = arr_utils.convert_with_dir(
input, torch.long, -1, arr_utils.PadDirection.AFTER
)
self.assertTrue(torch.allclose(torch.tensor(input), converted))
input = np.array([1, 2, 3])
converted = arr_utils.convert_with_dir(
input, torch.long, -1, arr_utils.PadDirection.BEFORE
)
self.assertTrue(torch.allclose(torch.tensor(np.array([3, 2, 1])), converted))
def test_pad_with_dir(self):
inputs = [
torch.tensor([[6], [5], [4], [3], [2], [1], [0]], dtype=torch.float),
torch.tensor([[3], [2], [1], [0]], dtype=torch.float),
torch.tensor([[9], [10], [11]], dtype=torch.float),
torch.tensor([[9], [10], [11], [12], [13], [14]], dtype=torch.float),
]
converted = arr_utils.pad_with_dir(
inputs,
-2,
arr_utils.PadDirection.AFTER,
batch_first=True,
padding_value=np.nan,
)
self.assertTrue(
torch.allclose(
torch.tensor(
[
[[6], [5], [4], [3], [2], [1], [0]],
[[3], [2], [1], [0], [np.nan], [np.nan], [np.nan]],
[[9], [10], [11], [np.nan], [np.nan], [np.nan], [np.nan]],
[[9], [10], [11], [12], [13], [14], [np.nan]],
],
),
converted,
equal_nan=True,
)
)
converted = arr_utils.pad_with_dir(
inputs,
-2,
arr_utils.PadDirection.BEFORE,
batch_first=True,
padding_value=np.nan,
)
self.assertTrue(
torch.allclose(
torch.tensor(
[
[[0], [1], [2], [3], [4], [5], [6]],
[[np.nan], [np.nan], [np.nan], [0], [1], [2], [3]],
[[np.nan], [np.nan], [np.nan], [np.nan], [11], [10], [9]],
[[np.nan], [14], [13], [12], [11], [10], [9]],
],
),
converted,
equal_nan=True,
)
)
def test_pad_sequences(self):
inputs = [
np.array([[6], [5], [4], [3], [2], [1], [0]], dtype=float),
np.array([[3], [2], [1], [0]], dtype=float),
np.array([[9], [10], [11]], dtype=float),
np.array([[9], [10], [11], [12], [13], [14]], dtype=float),
]
converted = arr_utils.pad_sequences(
inputs,
torch.float,
-2,
arr_utils.PadDirection.AFTER,
batch_first=True,
padding_value=np.nan,
)
self.assertTrue(
torch.allclose(
torch.tensor(
[
[[6], [5], [4], [3], [2], [1], [0]],
[[3], [2], [1], [0], [np.nan], [np.nan], [np.nan]],
[[9], [10], [11], [np.nan], [np.nan], [np.nan], [np.nan]],
[[9], [10], [11], [12], [13], [14], [np.nan]],
],
),
converted,
equal_nan=True,
)
)
converted = arr_utils.pad_sequences(
inputs,
torch.float,
-2,
arr_utils.PadDirection.BEFORE,
batch_first=True,
padding_value=np.nan,
)
self.assertTrue(
torch.allclose(
torch.tensor(
[
[[6], [5], [4], [3], [2], [1], [0]],
[[np.nan], [np.nan], [np.nan], [3], [2], [1], [0]],
[[np.nan], [np.nan], [np.nan], [np.nan], [9], [10], [11]],
[[np.nan], [9], [10], [11], [12], [13], [14]],
],
),
converted,
equal_nan=True,
)
)
def test_zero_neighbor_dict_collation(self):
dataset = UnifiedDataset(
desired_data=["lyft_sample-mini_val"],
centric="agent",
desired_dt=0.1,
history_sec=(3.2, 3.2),
future_sec=(4.8, 4.8),
only_predict=[AgentType.VEHICLE],
agent_interaction_distances=defaultdict(lambda: 0.0),
incl_robot_future=True,
incl_raster_map=True,
standardize_data=False,
raster_map_params={
"px_per_m": 2,
"map_size_px": 224,
"offset_frac_xy": (-0.5, 0.0),
},
num_workers=0,
verbose=True,
data_dirs={ # Remember to change this to match your filesystem!
"lyft_sample": "~/datasets/lyft_sample/scenes/sample.zarr",
},
)
dataloader = DataLoader(
dataset,
batch_size=4,
shuffle=True,
collate_fn=dataset.get_collate_fn(return_dict=True),
num_workers=0,
)
i = 0
for batch in dataloader:
i += 1
self.assertIsInstance(batch["curr_agent_state"], dataset.torch_state_type)
self.assertIsInstance(batch["agent_hist"], dataset.torch_obs_type)
self.assertIsInstance(batch["agent_fut"], dataset.torch_obs_type)
self.assertIsInstance(batch["robot_fut"], dataset.torch_obs_type)
if i == 5:
break
dataset = UnifiedDataset(
desired_data=["lyft_sample-mini_val"],
centric="scene",
desired_dt=0.1,
history_sec=(3.2, 3.2),
future_sec=(4.8, 4.8),
only_predict=[AgentType.VEHICLE],
agent_interaction_distances=defaultdict(lambda: 0.0),
incl_robot_future=True,
incl_raster_map=True,
standardize_data=False,
raster_map_params={
"px_per_m": 2,
"map_size_px": 224,
"offset_frac_xy": (-0.5, 0.0),
},
num_workers=0,
verbose=True,
data_dirs={ # Remember to change this to match your filesystem!
"lyft_sample": "~/datasets/lyft_sample/scenes/sample.zarr",
},
)
dataloader = DataLoader(
dataset,
batch_size=4,
shuffle=True,
collate_fn=dataset.get_collate_fn(return_dict=True),
num_workers=0,
)
i = 0
for batch in dataloader:
i += 1
self.assertIsInstance(
batch["centered_agent_state"], dataset.torch_state_type
)
self.assertIsInstance(batch["agent_hist"], dataset.torch_obs_type)
self.assertIsInstance(batch["agent_fut"], dataset.torch_obs_type)
self.assertIsInstance(batch["robot_fut"], dataset.torch_obs_type)
if i == 5:
break
if __name__ == "__main__":
unittest.main()