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Datasets:
byeonghwikim
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abp_dataset

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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    ValueError
Message:      Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('json', {}), NamedSplit('validation'): (None, {})}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1879, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1854, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1245, in get_module
                  module_name, default_builder_kwargs = infer_module_for_data_files(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 593, in infer_module_for_data_files
                  raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}")
              ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('json', {}), NamedSplit('validation'): (None, {})}

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ALFRED Dataset for ABP

We provide the ALFRED dataset used for ABP including ResNet-18 features of egocentric and surrounding views, annotations, etc. The surrdounding views are from four navigable actions defined in ALFRED: RotateLeft (90°), LookUp(15°), LookDown(15°), and RotateRight(90°). The file structure is almost identical to the ALFRED dataset, so refer to ALFRED for more details.

Download the dataset

Move to the root (denoted by ALFRED_ROOT below) of the ABP (or related work) repo and clone this repository by following the commands below.
Note: This dataset is quite large (~1.6T).

cd $ALFRED_ROOT/data
git clone https://huggingface.co/byeonghwikim/abp_dataset json_feat_2.1.0

After downloading the dataset, you may directly load a surrounding feature and the expected outcome is as below.

>> import torch
>> filename = 'train/look_at_obj_in_light-AlarmClock-None-DeskLamp-301/trial_T20190907_174127_043461/feat_conv_panoramic.pt'
>> im = torch.load(filename) # [5, T, 512, 7, 7], T the length of a trajectory
>> im.shape
torch.Size([5, T, 512, 7, 7])

The 0-dimension of the feature corresponds to the respective view directions as below.

  • 0: left view (RotateLeft)
  • 1: up view (LookUp)
  • 2: front (egocentric) view (no action)
  • 3: down view (LookDown)
  • 4: right view (RotateRight)

Inspired by MOCA, we apply image augmentation to the agent's visual observation. We apply two types of image augmentation: 1) swapping color channels of images and 2) AutoAugment.

  • No augmentation: (feat_conv_panoramic.pt)
  • Swapping color channels: (feat_conv_colorSwap1_panoramic.pt, feat_conv_colorSwap2_panoramic.pt)
  • AutoAugment: (feat_conv_onlyAutoAug1_panoramic.pt ~ feat_conv_onlyAutoAug4_panoramic.pt)

Related work that uses this dataset

Citation

If you find this repository useful, please cite this repository.

@inproceedings{kim2021agent,
  author    = {Kim, Byeonghwi and Bhambri, Suvaansh and Singh, Kunal Pratap and Mottaghi, Roozbeh and Choi, Jonghyun},
  title     = {Agent with the Big Picture: Perceiving Surroundings for Interactive Instruction Following},
  booktitle = {Embodied AI Workshop @ CVPR 2021},
  year      = {2021},
}
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