feat: Add codes for meta pseudo labeling

README.md

@@ -60,10 +60,11 @@ Free online book on Artificial Intelligence to help people learn AI easily.
 
 - [Self-Supervised Learning](./SelfSupervisedLearning)
 
-- [Training](./Training)
-  * [Adversarial ML](./Training/AdversarialML/)
-  * [Knowledge Distillation](./Training/KnowledgeDistillation/)
-  * [Transfer Learning](./Training/TransferLearning/)
+- [TrainingTricks](./TrainingTricks)
+  * [Adversarial ML](./TrainingTricks/AdversarialML/)
+  * [Meta Pseudo Labeling](./TrainingTricks/meta_pseudo_label/)
+  * [Knowledge Distillation](./TrainingTricks/KnowledgeDistillation/)
+  * [Transfer Learning](./TrainingTricks/TransferLearning/)
 
 - [XAI](./XAI)
 

TrainingTricks/meta_pseudo_label/.gitignore

@@ -0,0 +1,148 @@
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TrainingTricks/meta_pseudo_label/README.md

@@ -0,0 +1,109 @@
+# Meta Pseudo Labels
+This is an unofficial PyTorch implementation of [Meta Pseudo Labels](https://arxiv.org/abs/2003.10580).
+The official Tensorflow implementation is [here](https://github.com/google-research/google-research/tree/master/meta_pseudo_labels).
+
+
+## Results
+
+|  | CIFAR-10-4K | SVHN-1K | ImageNet-10% |
+|:---:|:---:|:---:|:---:|
+| Paper (w/ finetune) | 96.11 ± 0.07 | 98.01 ± 0.07 | 73.89 |
+| This code (w/o finetune) | 96.01 | - | - |
+| This code (w/ finetune) | 96.08 | - | - |
+| Acc. curve | [w/o finetune](https://tensorboard.dev/experiment/ehMVEk39SrGiqM43ye2c7w/)<br>[w/ finetune](https://tensorboard.dev/experiment/vbqR7dt2Q9aw6rf8yVu56g/) | - | - |
+
+* February 2022, Retested.
+
+## Usage
+
+Train the model by 4000 labeled data of CIFAR-10 dataset:
+
+```
+python main.py \
+    --seed 2 \
+    --name cifar10-4K.2 \
+    --expand-labels \
+    --dataset cifar10 \
+    --num-classes 10 \
+    --num-labeled 4000 \
+    --total-steps 300000 \
+    --eval-step 1000 \
+    --randaug 2 16 \
+    --batch-size 128 \
+    --teacher_lr 0.05 \
+    --student_lr 0.05 \
+    --weight-decay 5e-4 \
+    --ema 0.995 \
+    --nesterov \
+    --mu 7 \
+    --label-smoothing 0.15 \
+    --temperature 0.7 \
+    --threshold 0.6 \
+    --lambda-u 8 \
+    --warmup-steps 5000 \
+    --uda-steps 5000 \
+    --student-wait-steps 3000 \
+    --teacher-dropout 0.2 \
+    --student-dropout 0.2 \
+    --finetune-epochs 625 \
+    --finetune-batch-size 512 \
+    --finetune-lr 3e-5 \
+    --finetune-weight-decay 0 \
+    --finetune-momentum 0.9 \
+    --amp
+```
+
+Train the model by 10000 labeled data of CIFAR-100 dataset by using DistributedDataParallel:
+```
+python -m torch.distributed.launch --nproc_per_node 4 main.py \
+    --seed 2 \
+    --name cifar100-10K.2 \
+    --dataset cifar100 \
+    --num-classes 100 \
+    --num-labeled 10000 \
+    --expand-labels \
+    --total-steps 300000 \
+    --eval-step 1000 \
+    --randaug 2 16 \
+    --batch-size 128 \
+    --teacher_lr 0.05 \
+    --student_lr 0.05 \
+    --weight-decay 5e-4 \
+    --ema 0.995 \
+    --nesterov \
+    --mu 7 \
+    --label-smoothing 0.15 \
+    --temperature 0.7 \
+    --threshold 0.6 \
+    --lambda-u 8 \
+    --warmup-steps 5000 \
+    --uda-steps 5000 \
+    --student-wait-steps 3000 \
+    --teacher-dropout 0.2 \
+    --student-dropout 0.2 \
+    --finetune-epochs 250 \
+    --finetune-batch-size 512 \
+    --finetune-lr 3e-5 \
+    --finetune-weight-decay 0 \
+    --finetune-momentum 0.9 \
+    --amp
+```
+
+Monitoring training progress
+
+tensorboard
+```
+tensorboard --logdir results
+```
+or
+
+Use wandb
+
+## Requirements
+- python 3.6+
+- torch 1.7+
+- torchvision 0.8+
+- tensorboard
+- wandb
+- numpy
+- tqdm