Fewshot-Deep-Conus

Recent few-shot learning methods adapted for atmospheric spatial data contained in the Deep Conus dataset. Repositories used:

Deep CONUS

Few-shot Meta-Baseline

Representations for Few-shot Learning (RFS)

Meta-learning with differentiable closed-form solvers (R2D2)

Prototypical Networks for Few-shot Learning

Generating data

1. Download data from Google Drive and move or extract to /deep-conus/data/.

2. Navigate to /deep-conus/ then run decompose.py. This will decompose the larger files into individual .pickle files, each of which contains data for one sample. (This may take several hours, but does not need to be repeated.)

3. Run splitmulti.py. Adjust parameters as desired. Use --help for a list of parameters. This will produce 3 .pickle files containing dictionaries for the training, validation, and testing splits. Note that in order to maintain compatibility with the few-shot methods, each split must have at least 5 classes each and each class must contain at least 20 samples.

4. Update the timestamp. Currently, this is hardcoded and should be changed in /few-shot-meta-baseline/datasets/mini_deep_conus.py, /rfs/dataset/mini_deep_conus.py, and r2d2/fewshots/data/mini_deep_conus.py before running experiments.

Training and Evaluation

Few-shot-meta-baseline

Navigate to /few-shot-meta-baseline/. Always run Classifier-Baseline before Meta-Baseline. Edit /configs/test_few_shot.yaml if you wish to evaluate with Classifier-Baseline instead of Meta-Baseline.

Train Classifier-Baseline: python train_classifier.py --config configs/train_classifier_mini_dc.yaml

Train Meta-Baseline: python train_meta.py --config configs/train_meta_mini_dc.yaml

Evaluate 1-shot: python test_few_shot.py --shot 1

Evaluate 5-shot: python test_few_shot.py --shot 5

Previously used methods

Representations for Few-Shot Learning (RFS)

Navigate to /rfs/. If you have enough memory or are using a cluster, set --num_workers 8.

Train: python train_supervised.py --trial pretrain --num_workers 1 --data_root ../deep-conus/data/ --dataset miniDeepConus

Self-distillation: python train_distillation.py -r 0.5 -a 0.5 --path_t ./models_pretrained/resnet12_miniDeepConus_lr_0.05_decay_0.0005_trans_A_trial_pretrain/resnet12_last.pth --trial born1 --num_workers 1 --data_root ../deep-conus/data/ --dataset miniDeepConus

Evaluate 1-shot: python eval_fewshot.py --model_path ./models_distilled/S-resnet12_T-resnet12_miniDeepConus_kd_r-0.5_a-0.5_b-0_trans_A_born1/resnet12_last.pth --num_workers 1 --data_root ../deep-conus/data/ --dataset miniDeepConus

Evaluate 5-shot: python eval_fewshot.py --model_path ./models_distilled/S-resnet12_T-resnet12_miniDeepConus_kd_r-0.5_a-0.5_b-0_trans_A_born1/resnet12_last.pth --num_workers 1 --data_root ../deep-conus/data/ --dataset miniDeepConus --n_shots 5

Meta-learning with differentiable closed-form solvers

Navigate to /r2d2/scripts/train/. I have included the set of parameters I have found to be most effective on Deep Conus, but feel free to experiment with other options. Note that this method requires that you have enough RAM available to store your entire training set and validation set.

Train: python run_train.py --log.exp_dir mini_r2d2 --data.dataset minideepconus --data.way 5 --data.root_dir ../../../deep-conus/data --model.drop 0.1 --base_learner.learn_lambda True --base_learner.lambda_base 2 --base_learner.init_lambda 8 --base_learner.adj_base 2

Evaluate 1-shot: python ../eval/run_eval.py --data.test_episodes 10000 --data.test_way 5 --data.test_shot 1 --model.model_path ../train/results/mini_r2d2/best_model.1shot.t7

Evaluate 5-shot: python ../eval/run_eval.py --data.test_episodes 10000 --data.test_way 5 --data.test_shot 5 --model.model_path ../train/results/mini_r2d2/best_model.5shot.t7