Implementation of "The Senseiver: attention-based global field reconstruction from sparse observations" in Pytorch. This model provides an easy and efficient way to train a data driven to create a mapping between sensor measurements an a global field, the model operations are decoupled from the size of the domain which allows the model to be trained with very large n-D arrays. The application shown in our paper considered simulations, but the method is general, and should be applicable to any other application.

• data_name: str. Name of the dataset to be used for training.
• num_sensors: int. number of sensors to train with
• gpu_device: int. GPU to train on. MultiGPU support coming soon.
• training_frames: int. Number of frames (time steps) to train the model with.
• seed: int. If specified, it uses a seed to pick up sensors (if locations not specified) and frames.
• consecutive_train: bool. Whether to use consecutive frames to train or chosen at random.
• batch_frames: int. Number of frames per batch.
• batch_pixels: int. Number of pixels per batch.
• lr: float. Learning rate
• accum_grads: int. Number of batches to accumulate to perform an optimizer step.

• space_bands: int. Number of sine-cosine frequencies
• enc_preproc_ch: int. Size of the linear layer that processes the inputs (sensor value+positons)
• num_latents: int. Sequence size of the Q_in array.
• enc_num_latent_channels: int. Channel dimension of the Q_in array.
• num_layers: int. Number of model layers (depth).
• num_cross_attention_heads: int. Number of processsing attention heads.
• num_self_attention_layers_per_block: int. Number of self processing layers in each block.
• dec_preproc_ch: int. Size of the linear layer that processes the latent space sent to the decoder. This can act as a bolttleneck and reduce significantly the number of parameters.
• dec_num_latent_channels: int. Number of channels in the decoder.

• load_model_num: int. Load a model to test or to re-train on. Our library saves the models sequencially starting with 1.
• test: bool. To test the model in the entire dataset.

python train.py --gpu 0 --data cylinder --num_sensors 4 --training_frames 50 --cons False --seed 123 --enc_preproc 16 --dec_num_latent_channels 16 --enc_num_latent_channels 16 --num_latents 256 --dec_preproc_ch 16 --test False 

for num_frames in 100, 250, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000; do
    python train.py --gpu 1 --data pipe --num_sensors 6144 --cons False --seed 123 --enc_preproc 32 --dec_num_latent_channels 32 --enc_num_latent_channels 32 --num_latents 64 --dec_preproc_ch 32 --lr 1e-3 —training_frames $num_frames || break
done

The data is available at 10.5281/zenodo.8290039.