[feature] CONTRASTIVE REPRESENTATION DISTILLATION with dataset wrapper (

#281)

* init

* TD: CRDLoss

* complete UT

* fix docstrings

* fix ci

* update

* fix CI

* DONE

* maintain CRD dataset unique funcs as a mixin

* maintain CRD dataset unique funcs as a mixin

* maintain CRD dataset unique funcs as a mixin

* add UT: CRD_ClsDataset

* init

* TODO: UT test formatting.

* init

* crd dataset wrapper

* update docstring

Co-authored-by: huangpengsheng <huangpengsheng@sensetime.com>

configs/distill/mmcls/crd/README.md

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+# CONTRASTIVE REPRESENTATION DISTILLATION
+
+> [CONTRASTIVE REPRESENTATION DISTILLATION](https://arxiv.org/abs/1910.10699)
+
+## Abstract
+
+Often we wish to transfer representational knowledge from one neural network to another. Examples include distilling a large network into a smaller one, transferring knowledge from one sensory modality to a second, or ensembling a collection of models into a single estimator. Knowledge distillation, the standard approach to these problems, minimizes the KL divergence between the probabilistic outputs of a teacher and student network. We demonstrate that this objective ignores important structural knowledge of the teacher network. This motivates an alternative objective by which we train a student to capture significantly more information in the teacher’s representation of the data. We formulate this objective as contrastive learning. Experiments demonstrate that our resulting new objective outperforms knowledge distillation and other cutting-edge distillers on a variety of knowledge transfer tasks, including single model compression, ensemble distillation, and cross-modal transfer. Our method sets a new state-of-the-art in many transfer tasks, and sometimes even outperforms the teacher network when combined with knowledge distillation.[Original code](http://github.com/HobbitLong/RepDistiller)
+
+![pipeline](../../../../docs/en/imgs/model_zoo/crd/pipeline.jpg)
+
+## Citation
+
+```latex
+@article{tian2019contrastive,
+  title={Contrastive representation distillation},
+  author={Tian, Yonglong and Krishnan, Dilip and Isola, Phillip},
+  journal={arXiv preprint arXiv:1910.10699},
+  year={2019}
+}
+```
+
+## Results and models
+
+| Dataset | Model     | Teacher   | Top-1 (%) | Top-5 (%) | Configs                                     | Download                                                                                                                                     |
+| ------- | --------- | --------- | --------- | --------- | ------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
+| CIFAR10 | ResNet-18 | ResNet-50 | 94.79     | 99.86     | [config](crd_neck_r50_r18_8xb16_cifar10.py) | [teacher](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth) \|[model](<>) \| [log](<>) |
+
+## Acknowledgement
+
+Shout out to @chengshuang18 for his special contribution.

configs/distill/mmcls/crd/crd_neck_r50_r18_8xb16_cifar10.py

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+_base_ = [
+    'mmcls::_base_/datasets/cifar10_bs16.py',
+    'mmcls::_base_/schedules/cifar10_bs128.py',
+    'mmcls::_base_/default_runtime.py'
+]
+
+model = dict(
+    _scope_='mmrazor',
+    type='SingleTeacherDistill',
+    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),
+    architecture=dict(
+        cfg_path='mmcls::resnet/resnet18_8xb16_cifar10.py', pretrained=False),
+    teacher=dict(
+        cfg_path='mmcls::resnet/resnet50_8xb16_cifar10.py', pretrained=True),
+    teacher_ckpt='resnet50_b16x8_cifar10_20210528-f54bfad9.pth',
+    distiller=dict(
+        type='ConfigurableDistiller',
+        student_recorders=dict(
+            neck=dict(type='ModuleOutputs', source='neck.gap'),
+            data_samples=dict(type='ModuleInputs', source='')),
+        teacher_recorders=dict(
+            neck=dict(type='ModuleOutputs', source='neck.gap')),
+        distill_losses=dict(loss_crd=dict(type='CRDLoss', loss_weight=0.8)),
+        connectors=dict(
+            loss_crd_stu=dict(type='CRDConnector', dim_in=512, dim_out=128),
+            loss_crd_tea=dict(type='CRDConnector', dim_in=2048, dim_out=128)),
+        loss_forward_mappings=dict(
+            loss_crd=dict(
+                s_feats=dict(
+                    from_student=True,
+                    recorder='neck',
+                    connector='loss_crd_stu'),
+                t_feats=dict(
+                    from_student=False,
+                    recorder='neck',
+                    connector='loss_crd_tea'),
+                data_samples=dict(
+                    from_student=True, recorder='data_samples', data_idx=1)))))
+
+find_unused_parameters = True
+
+val_cfg = dict(_delete_=True, type='mmrazor.SingleTeacherDistillValLoop')
+
+# change `CIFAR10` dataset to `CRDDataset` dataset.
+dataset_type = 'CIFAR10'
+train_pipeline = [
+    dict(_scope_='mmcls', type='RandomCrop', crop_size=32, padding=4),
+    dict(_scope_='mmcls', type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(_scope_='mmrazor', type='PackCRDClsInputs'),
+]
+
+test_pipeline = [
+    dict(_scope_='mmrazor', type='PackCRDClsInputs'),
+]
+
+ori_train_dataset = dict(
+    _scope_='mmcls',
+    type=dataset_type,
+    data_prefix='data/cifar10',
+    test_mode=False,
+    pipeline=train_pipeline)
+
+crd_train_dataset = dict(
+    _scope_='mmrazor',
+    type='CRDDataset',
+    dataset=ori_train_dataset,
+    neg_num=16384,
+    sample_mode='exact',
+    percent=1.0)
+
+ori_test_dataset = dict(
+    _scope_='mmcls',
+    type=dataset_type,
+    data_prefix='data/cifar10/',
+    test_mode=True,
+    pipeline=test_pipeline)
+
+crd_test_dataset = dict(
+    _scope_='mmrazor',
+    type='CRDDataset',
+    dataset=ori_test_dataset,
+    neg_num=16384,
+    sample_mode='exact',
+    percent=1.0)
+
+train_dataloader = dict(
+    _delete_=True,
+    batch_size=16,
+    num_workers=2,
+    dataset=crd_train_dataset,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+)
+
+val_dataloader = dict(
+    _delete_=True,
+    batch_size=16,
+    num_workers=2,
+    dataset=crd_test_dataset,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)

configs/distill/mmcls/crd/datasets/crd_cifar10_bs16.py

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+# dataset settings
+dataset_type = 'CIFAR10'
+preprocess_cfg = dict(
+    # RGB format normalization parameters
+    mean=[125.307, 122.961, 113.8575],
+    std=[51.5865, 50.847, 51.255],
+    # loaded images are already RGB format
+    to_rgb=False)
+
+train_pipeline = [
+    dict(type='RandomCrop', crop_size=32, padding=4),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(type='PackClsInputs'),
+]
+
+test_pipeline = [
+    dict(type='PackClsInputs'),
+]
+
+neg_num = 16384
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_prefix='data/cifar10',
+        test_mode=False,
+        pipeline=train_pipeline,
+        neg_num=neg_num),
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    persistent_workers=True,
+)
+
+val_dataloader = dict(
+    batch_size=16,
+    num_workers=2,
+    dataset=dict(
+        type=dataset_type,
+        data_prefix='data/cifar10/',
+        test_mode=True,
+        pipeline=test_pipeline,
+        neg_num=neg_num),
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+val_evaluator = dict(type='Accuracy', topk=(1, ))
+
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

mmrazor/datasets/__init__.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from .crd_dataset_wrapper import CRDDataset
+from .transforms import PackCRDClsInputs
+
+__all__ = ['PackCRDClsInputs', 'CRDDataset']