yejg2017 / metalearning-lstm

pytorch implementation of Optimization as a Model for Few-shot Learning

Optimization as a Model for Few-shot Learning

Pytorch implementation of Optimization as a Model for Few-shot Learning in ICLR 2017 (Oral)

Prerequisites

• python 3+
• pytorch 0.4+ (developed on 1.0.1 with cuda 9.0)
• pillow
• tqdm (a nice progress bar)

Data

• Mini-Imagenet as described here
  • You can download it from here (~2.7GB, google drive link)

Preparation

• Make sure Mini-Imagenet is split properly. For example:

  - data/
    - miniImagenet/
      - train/
        - n01532829/
          - n0153282900000005.jpg
          - ...
        - n01558993/
        - ...
      - val/
        - n01855672/
        - ...
      - test/
        - ...
  - main.py
  - ...
  

  • It'd be set if you download and extract Mini-Imagenet from the link above
• Check out scripts/train_5s_5c.sh, make sure --data-root is properly set

Run

For 5-shot, 5-class training, run

bash scripts/train_5s_5c.sh

Hyper-parameters are referred to the author's repo.

For 5-shot, 5-class evaluation, run (remember to change --resume and --seed arguments)

bash scripts/eval_5s_5c.sh

Notes

• Results (This repo is developed following the pytorch reproducibility guideline):

seed	train episodes	val episodes	val acc mean	val acc std	test episodes	test acc mean	test acc std
719	41000	100	59.08	9.9	100	56.59	8.4
-	-	-	-	-	250	57.85	8.6
-	-	-	-	-	600	57.76	8.6
53	44000	100	58.04	9.1	100	57.85	7.7
-	-	-	-	-	250	57.83	8.3
-	-	-	-	-	600	58.14	8.5

• The results I get from directly running the author's repo can be found here, I have slightly better performance (~5%) but neither results match the number in the paper (60%) (Discussion and help are welcome!).
• Training with the default settings takes ~2.5 hours on a single Titan Xp while occupying ~2GB GPU memory.
• The implementation replicates two learners similar to the author's repo:
  • learner_w_grad functions as a regular model, get gradients and loss as inputs to meta learner.
  • learner_wo_grad constructs the graph for meta learner:
    • All the parameters in learner_wo_grad are replaced by cI output by meta learner.
    • nn.Parameters in this model are casted to torch.Tensor to connect the graph to meta learner.
• Several ways to copy a parameters from meta learner to learner depends on the scenario:
  • copy_flat_params: we only need the parameter values and keep the original grad_fn.
  • transfer_params: we want the values as well as the grad_fn (from cI to learner_wo_grad).
    • .data.copy_ v.s. clone() -> the latter retains all the properties of a tensor including grad_fn.
    • To maintain the batch statistics, load_state_dict is used (from learner_w_grad to learner_wo_grad).

References

• CloserLookFewShot (Data loader)
• pytorch-meta-optimizer (Casting nn.Parameters to torch.Tensor inspired from here)
• meta-learning-lstm (Author's repo in Lua Torch)