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Datasets:
byeonghwikim
/
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

Raw RGB images with depth and object masks

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.

git clone https://huggingface.co/datasets/byeonghwikim/abp_images json_2.1.0

Pre-extracted features

To skip feature extraction from RGB images, you can use our pre-extracted features. 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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