[Docs] Add files for KD algo DFND

configs/distill/mmcls/dfnd/README.md

@@ -0,0 +1,31 @@
+# Learning Student Networks in the Wild (DFND)
+
+> [Learning Student Networks in the Wild](https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Learning_Student_Networks_in_the_Wild_CVPR_2021_paper.pdf)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Data-free learning for student networks is a new paradigm for solving users’ anxiety caused by the privacy problem of using original training data. Since the architectures of modern convolutional neural networks (CNNs) are compact and sophisticated, the alternative images or meta-data generated from the teacher network are often broken. Thus, the student network cannot achieve the comparable performance to that of the pre-trained teacher network especially on the large-scale image dataset. Different to previous works, we present to maximally utilize the massive available unlabeled data in the wild. Specifically, we first thoroughly analyze the output differences between teacher and student network on the original data and develop a data collection method. Then, a noisy knowledge distillation algorithm is proposed for achieving the performance of the student network. In practice, an adaptation matrix is learned with the student network for correcting the label noise produced by the teacher network on the collected unlabeled images. The effectiveness of our DFND (DataFree Noisy Distillation) method is then verified on several benchmarks to demonstrate its superiority over state-of-theart data-free distillation methods. Experiments on various datasets demonstrate that the student networks learned by the proposed method can achieve comparable performance with those using the original dataset.
+
+<img width="910" alt="pipeline" src="./dfnd.PNG">
+
+## Results and models
+
+### Classification
+
+|     Location      | Dataset |                                                     Teacher                                                     |                                                     Student                                                     |  Acc  | Acc(T) | Acc(S) |                           Config                            |                                                                                                                                                                                                                                                                                                                                                                                  |
+| :---------------: | :-----: | :-------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: | :---: | :----: | :----: | :---------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| backbone & logits | Cifar10 | [resnet34](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb16_cifar10.py) | [resnet18](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb16_cifar10.py) | 94.78 | 95.34 | 94.82 | [config](./dfnd_logits_resnet34_resnet18_8xb32_cifar10.py) | [student](https://drive.google.com/file/d/1_MekfTkCsEl68meWPqtdNZIxdJO2R2Eb/view?usp=drive_link) |
+
+## Citation
+
+```latex
+@inproceedings{chen2021learning,
+  title={Learning student networks in the wild},
+  author={Chen, Hanting and Guo, Tianyu and Xu, Chang and Li, Wenshuo and Xu, Chunjing and Xu, Chao and Wang, Yunhe},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={6428--6437},
+  year={2021}
+}
+```

configs/distill/mmcls/dfnd/dfnd_logits_resnet34_resnet18_8xb32_cifar10.py

@@ -0,0 +1,100 @@
+_base_ = ['mmcls::_base_/default_runtime.py']
+
+# optimizer
+optim_wrapper = dict(
+    optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001))
+# learning policy
+param_scheduler = dict(
+    type='MultiStepLR', by_epoch=True, milestones=[320, 640], gamma=0.1)
+
+# train, val, test setting
+train_cfg = dict(by_epoch=True, max_epochs=800, val_interval=1)
+test_cfg = dict()
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR
+# based on the actual training batch size.
+auto_scale_lr = dict(base_batch_size=128)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='RandomResizedCrop', scale=32),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='PackClsInputs'),
+]
+
+train_dataloader = dict(
+    batch_size=256,
+    num_workers=5,
+    dataset=dict(
+        type='ImageNet',
+        data_root='/cache/data/imagenet/',
+        data_prefix='train',
+        pipeline=train_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+)
+
+test_pipeline = [
+    dict(type='PackClsInputs'),
+]
+
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=2,
+    dataset=dict(
+        type='CIFAR10',
+        data_prefix='/cache/data/cifar',
+        test_mode=True,
+        pipeline=test_pipeline),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+)
+val_evaluator = dict(type='Accuracy', topk=(1, ))
+
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator
+
+teacher_ckpt = '/cache/models/resnet_model.pth'  # noqa: E501
+
+model = dict(
+    _scope_='mmrazor',
+    type='DFNDDistill',
+    calculate_student_loss=False,
+    data_preprocessor=dict(
+        type='ImgDataPreprocessor',
+        # RGB format normalization parameters
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        # convert image from BGR to RGB
+        bgr_to_rgb=True),
+    val_data_preprocessor=dict(
+        type='ImgDataPreprocessor',
+        # RGB format normalization parameters
+        mean=[125.307, 122.961, 113.8575],
+        std=[51.5865, 50.847, 51.255],
+        # convert image from BGR to RGB
+        bgr_to_rgb=False),
+    architecture=dict(
+        cfg_path='mmcls::resnet/resnet18_8xb16_cifar10.py', pretrained=False),
+    teacher=dict(
+        cfg_path='mmcls::resnet/resnet34_8xb16_cifar10.py', pretrained=False),
+    teacher_ckpt=teacher_ckpt,
+    distiller=dict(
+        type='ConfigurableDistiller',
+        student_recorders=dict(
+            fc=dict(type='ModuleOutputs', source='head.fc')),
+        teacher_recorders=dict(
+            fc=dict(type='ModuleOutputs', source='head.fc')),
+        distill_losses=dict(
+            loss_kl=dict(
+                type='DFNDLoss',
+                tau=4,
+                loss_weight=1,
+                num_classes=10,
+                batch_select=0.5)),
+        loss_forward_mappings=dict(
+            loss_kl=dict(
+                preds_S=dict(from_student=True, recorder='fc'),
+                preds_T=dict(from_student=False, recorder='fc')))))
+
+find_unused_parameters = True
+
+val_cfg = dict(type='mmrazor.DFNDValLoop')

mmrazor/engine/runner/__init__.py

@@ -1,7 +1,8 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .autoslim_greedy_search_loop import AutoSlimGreedySearchLoop
 from .darts_loop import DartsEpochBasedTrainLoop, DartsIterBasedTrainLoop
-from .distill_val_loop import SelfDistillValLoop, SingleTeacherDistillValLoop
+from .distill_val_loop import (DFNDValLoop, SelfDistillValLoop,
+                               SingleTeacherDistillValLoop)
 from .evolution_search_loop import EvolutionSearchLoop
 from .iteprune_val_loop import ItePruneValLoop
 from .quantization_loops import (LSQEpochBasedLoop, PTQLoop, QATEpochBasedLoop,
@@ -15,5 +16,5 @@
     'DartsIterBasedTrainLoop', 'SlimmableValLoop', 'EvolutionSearchLoop',
     'GreedySamplerTrainLoop', 'SubnetValLoop', 'SelfDistillValLoop',
     'ItePruneValLoop', 'AutoSlimGreedySearchLoop', 'QATEpochBasedLoop',
-    'PTQLoop', 'LSQEpochBasedLoop', 'QATValLoop'
+    'PTQLoop', 'LSQEpochBasedLoop', 'QATValLoop', 'DFNDValLoop'
 ]

mmrazor/engine/runner/distill_val_loop.py

@@ -125,3 +125,38 @@ def run(self):
 
         self.runner.call_hook('after_val_epoch', metrics=student_metrics)
         self.runner.call_hook('after_val')
+
+
+@LOOPS.register_module()
+class DFNDValLoop(SingleTeacherDistillValLoop):
+    """Validation loop for DFND. DFND requires different dataset for training
+    and validation.
+
+    Args:
+        runner (Runner): A reference of runner.
+        dataloader (Dataloader or dict): A dataloader object or a dict to
+            build a dataloader.
+        evaluator (Evaluator or dict or list): Used for computing metrics.
+        fp16 (bool): Whether to enable fp16 validation. Defaults to
+            False.
+    """
+
+    def __init__(self,
+                 runner,
+                 dataloader: Union[DataLoader, Dict],
+                 evaluator: Union[Evaluator, Dict, List],
+                 fp16: bool = False) -> None:
+        super().__init__(runner, dataloader, evaluator, fp16)
+        if self.runner.distributed:
+            assert hasattr(self.runner.model.module, 'teacher')
+            # TODO: remove hard code after mmcls add data_preprocessor
+            data_preprocessor = self.runner.model.module.val_data_preprocessor
+            self.teacher = self.runner.model.module.teacher
+            self.teacher.data_preprocessor = data_preprocessor
+
+        else:
+            assert hasattr(self.runner.model, 'teacher')
+            # TODO: remove hard code after mmcls add data_preprocessor
+            data_preprocessor = self.runner.model.val_data_preprocessor
+            self.teacher = self.runner.model.teacher
+            self.teacher.data_preprocessor = data_preprocessor

mmrazor/models/algorithms/distill/configurable/__init__.py

@@ -1,6 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .datafree_distillation import (DAFLDataFreeDistillation,
                                     DataFreeDistillation)
+from .dfnd_distill import DFNDDistill
 from .fpn_teacher_distill import FpnTeacherDistill
 from .overhaul_feature_distillation import OverhaulFeatureDistillation
 from .self_distill import SelfDistill
@@ -9,5 +10,5 @@
 __all__ = [
     'SelfDistill', 'SingleTeacherDistill', 'FpnTeacherDistill',
     'DataFreeDistillation', 'DAFLDataFreeDistillation',
-    'OverhaulFeatureDistillation'
+    'OverhaulFeatureDistillation', 'DFNDDistill'
 ]