init

README.md

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+# Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
+
+Code for [Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks](https://arxiv.org/abs/2012.07176).
+
+<img src="block_diagrambw.png" width=750>
+
+## Setup
+Run the following in the project root to create the required directory structure.
+```
+mkdir ../data_root
+mkdir ../data_root/mini-imagenet
+mkdir ../data_root/tiered-imagenet
+mkdir ../data_root/cifarfs
+mkdir ../data_root/fc100
+```
+Download and extract the compressed file for each dataset to the corresponding directory.
+
+* [mini-ImageNet](https://drive.google.com/file/d/1ymtHXsfqdweRcQn6JE4pjt6vFXwcaHgk/view?usp=sharing) 
+* [tiered-ImageNet](https://drive.google.com/file/d/1_xXHOenGwT2scZ-d8MhnXCG-Uc5ht4sy/view?usp=sharing)
+* [cifarfs](https://drive.google.com/file/d/1-wPhCsKTvP1rVVqbqwL6yjkh6x4mvgWq/view?usp=sharing)
+* [fc100](https://drive.google.com/file/d/1ZacfPbfuebAuryX2t3GT9_mCH9SAxP17/view?usp=sharing)
+
+Download and extract the compressed meta-data file to `../data_root` directory.
+
+* [meta-data](https://drive.google.com/file/d/1FZwm_9vg2Z50SATcwFVKkeC4s5FUhCZG/view?usp=sharing)
+
+You can download our pre-trained variables for each model here:
+* Nearest Centroid Classifier (NCC) - [link](https://drive.google.com/drive/folders/16Fwz6fBmhvjShZdFMX02HDllwgP22_Wg?usp=sharing)
+* Relation Module (RM) - [link](https://drive.google.com/drive/folders/1BrG0Aiu7BL8TEpee2xXBYtRB_7981A3W?usp=sharing)
+* Embedding Adaptation (EA) - [link](https://drive.google.com/drive/folders/15QxLaXX1ltoSyNLylFh-v6j4bgrC34iH?usp=sharing)
+
+## Requirements
+
+Install the following packages in your environment.
+
+```text
+python=3.8.3
+torch=1.5.1
+cuda=10.1
+torchvision=0.6.1
+scipy=1.5.0
+scikit-learn=0.23.1
+pandas=1.1.1
+opencv-contrib-python=4.5.2.54
+flatten_dict=0.4.0
+filelock=3.0.12
+tqdm=4.61.1
+tensorboardx=2.4
+pyyaml=5.4.1
+h5py=2.10.0
+```
+
+
+## Evaluation
+
+To evaluate a model run the following command.
+Use the following command to evaluate the 1-shot NNC model with basic embeddings on mini-imagenet dataset with level0 auxiliary data.
+
+```commandline
+python test_efsl.py --encoder-conifg configs/resnet12.yaml 
+                    --model-checkpoint path/to/saved/parameters
+                    --data-dir path/to/dataset/files
+```
+
+Run `python test_efsl.py --help` for the list of possible command line options and their description.
+
+## Training
+
+**Feature Embedding.** Use the following to train the feature embedding parameters on mini-imagenet dataset with level0 auxiliary data. 
+
+```commandline
+python train_classifier.py --config configs/classifier.yaml 
+                           --data-dir path/to/dataset/files
+```
+
+Run `python train_classifier.py --help` for the list of possible command line options and their description.
+
+**Masking Module.** Use the following to train the masking module parameters for 1-shot task on mini-imagenet dataset with level0 auxiliary data. 
+
+
+```commandline
+python train_efsl.py --encoder-config configs/resnet12-mask.yaml
+                     --encoder-checkpoint path/to/resnet/parameters
+                     --data-dir path/to/dataset/files
+```
+Run `python train_efsl.py --help` for the list of possible command line options and their description.
+
+
+## Citation
+If you use this repo, please cite the corresponding paper.
+
+*Esfandiarpoor, R., Pu, A., Hajabdollahi, M. and Bach, S.H., 2020.*
+
+```
+@article{esfandiarpoor:arxiv20,
+  Author = {Esfandiarpoor, R. and Pu, A and Hajabdollahi, M. and Bach, S. H.},
+  Title = {Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks},
+  Volume = {arXiv:2012.07176 [cs.LG]},
+  Year = {2020}}
+```
+
+## Acknowledgement
+The code in this repo is based on the following repositories:
+* [A New Meta-Baseline for Few-Shot Learning](https://github.com/yinboc/few-shot-meta-baseline)
+* [Rethinking few-shot image classification: a good embedding is all you need?](https://github.com/WangYueFt/rfs/)
+* [Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions](https://github.com/Sha-Lab/FEAT)

configs/classifier.yaml

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+model: classifier
+model_args:
+    encoder: resnet12
+    encoder_args: {avg_pool: true, drop_rate: 0.1, dropblock_size: 5}
+    classifier: linear-classifier
+    classifier_args: {}

configs/resnet12-mask.yaml

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+encoder: masking-model
+encoder_args:
+  encoder: resnet12
+  encoder_args: {avg_pool: false, drop_rate: 0.1, dropblock_size: 5}
+  masking: multi-block-masking
+  masking_args: {channels: [640, 320, 1], final_relu: False, max_pool: False, activation: sigmoid, drop_rate: 0.1, dropblock_size: 5}

configs/resnet12.yaml

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+encoder: resnet12
+encoder_args: {avg_pool: false, drop_rate: 0.1, dropblock_size: 5}