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Code accompanying our NeurIPS 2021 traffic4cast challenge

License: Apache License 2.0

Dockerfile 0.01% Python 0.67% Jupyter Notebook 99.33%

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aut-data-group rezaa89 roth-andreas shehel ninawie chouisgiser ejhill24 hujilin1229 346644054

neurips2021-traffic4cast's Issues

How to run on gpu

from official usage, I used

export PYTHONPATH="$PYTHONPATH:$PWD"

DEVICE=cuda
DATA_RAW_PATH="./data/raw"
GROUND_TRUTH=""

python baselines/baselines_cli.py --model_str=unet          --limit=2 --epochs=1 --batch_size=1 --num_workers=1 --data_raw_path=$DATA_RAW_PATH --device=$DEVICE $GROUND_TRUTH

, but it causes

AssertionError: Torch not compiled with CUDA enabled

1gpu has set into this environment,
is this right usage?

Thanks,

Low-res static map masking

Hi!

Thank you for the provided data and the competition in general.

I was working on my pipeline and considering adding masked MSE metric into my validation part, however, when performing some sanity checks I encountered the following inconsistency:

I was assuming that when multiplying the ground truth maps of the 6 frames we are predicting with the mask of the provided low-resolution static map, the number of non-zeros in the resulting tensor shouldn't decrease compared to beforehand. Yet the result was the contrary, meaning that in the ground truth labeling, some non-zero elements lie outside the provided low-resolution static map.

Please see the following code snippet for reproducing:

from data.dataset.dataset import T4CDataset
from baselines.unet import UNetTransfomer
from metrics.masking import get_static_mask

from functools import partial

import numpy as np
import torch

BASE_FOLDER = ...
CITY = "CHICAGO"

dataset = T4CDataset(
    BASE_FOLDER, f"{CITY}/training/2019*8ch.h5",
    transform=partial(
        UNetTransfomer.unet_pre_transform,
        stack_channels_on_time=True,
        zeropad2d=None, batch_dim=False)
)

city_static_map = get_static_mask(CITY, BASE_FOLDER)
city_static_map_torch = torch.from_numpy(city_static_map[:, :, 0])

for index in range(len(dataset)):
    inp_frames, out_frames = dataset[index]

    nonzero_frames = torch.count_nonzero(out_frames)
    nonzero_frames_masked = torch.count_nonzero(out_frames * city_static_map_torch)

    print(nonzero_frames)
    print(nonzero_frames_masked)

    assert torch.allclose(nonzero_frames, nonzero_frames_masked)

Is this the expected behavior? Should we trust or use the low-resolution static map then?

OS X: AttributeError: 'svmem' object has no attribute 'buffers'

(t4c) $ python baselines/baselines_cli.py  
[2021-06-15 20:29:52,217][INFO][38382][baselines_cli.py:main:287] Start build dataset
[2021-06-15 20:29:52,217][INFO][38382][baselines_cli.py:main:294] Check if files need to be untarred...
[2021-06-15 20:29:52,252][INFO][38382][baselines_cli.py:main:305] Dataset has size 519360
[2021-06-15 20:29:52,252][INFO][38382][baselines_cli.py:main:309] Create train_model.
[2021-06-15 20:29:52,407][INFO][38382][baselines_cli.py:main:323] Going to run train_model.
Traceback (most recent call last):
  File "baselines/baselines_cli.py", line 361, in <module>
    main(sys.argv[1:])
  File "baselines/baselines_cli.py", line 324, in main
    logging.info(system_status())
  File "/Users/che/workspaces/neurips2021-traffic4cast/util/monitoring.py", line 49, in system_status
    s += tabulate([[str(mem.__getattribute__(a)) for a in virtual_memory_fields]], headers=virtual_memory_fields) + "\n"
  File "/Users/che/workspaces/neurips2021-traffic4cast/util/monitoring.py", line 49, in <listcomp>
    s += tabulate([[str(mem.__getattribute__(a)) for a in virtual_memory_fields]], headers=virtual_memory_fields) + "\n"
AttributeError: 'svmem' object has no attribute 'buffers'

iarai / neurips2021-traffic4cast Goto Github PK

neurips2021-traffic4cast's People

Contributors

Stargazers

Watchers

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neurips2021-traffic4cast's Issues

How to run on gpu

Low-res static map masking

OS X: AttributeError: 'svmem' object has no attribute 'buffers'

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