add dist algo (#2133)

* add dist_kd

* add doc

* fix some typos

docs/zh_CN/advanced_tutorials/knowledge_distillation.md

@@ -14,6 +14,7 @@
         - [1.2.3 UDML](#1.2.3)
         - [1.2.4 AFD](#1.2.4)
         - [1.2.5 DKD](#1.2.5)
+        - [1.2.6 DIST](#1.2.6)
 - [2. 使用方法](#2)
     - [2.1 环境配置](#2.1)
     - [2.2 数据准备](#2.2)
@@ -444,6 +445,74 @@ Loss:
     - CELoss:
         weight: 1.0
 ```
+
+<a name='1.2.6'></a>
+
+#### 1.2.6 DIST
+
+##### 1.2.6.1 DIST 算法介绍
+
+论文信息：
+
+
+> [Knowledge Distillation from A Stronger Teacher](https://arxiv.org/pdf/2205.10536v1.pdf)
+>
+> Tao Huang, Shan You, Fei Wang, Chen Qian, Chang Xu
+>
+> 2022, under review
+
+使用KD方法进行模型蒸馏时，教师模型精度提升时，蒸馏的效果往往难以同步提升。本文提出DIST方法，使用皮尔逊相关系数（Pearson correlation coefficient）去表征学生模型与教师模型之间的差异，替代蒸馏过程中默认的KL散度，从而保证模型可以学到更加准确的相关性信息。
+
+在ImageNet1k公开数据集上，效果如下所示。
+
+| 策略 | 骨干网络 | 配置文件 | Top-1 acc | 下载链接 |
+| --- | --- | --- | --- | --- |
+| baseline | ResNet18 | [ResNet18.yaml](../../../ppcls/configs/ImageNet/ResNet/ResNet18.yaml) | 70.8% | - |
+| DIST | ResNet18 | [resnet34_distill_resnet18_dist.yaml](../../../ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dist.yaml) | 71.99%(**+1.19%**) | - |
+
+
+##### 1.2.6.2 DIST 配置
+
+DIST 配置如下所示。在模型构建Arch字段中，需要同时定义学生模型与教师模型，教师模型固定参数，且需要加载预训练模型。在损失函数Loss字段中，需要定义`DistillationDISTLoss`（学生与教师之间的DIST loss）以及`DistillationGTCELoss`（学生与教师关于真值标签的CE loss），作为训练的损失函数。
+
+
+```yaml
+Arch:
+  name: "DistillationModel"
+  # if not null, its lengths should be same as models
+  pretrained_list:
+  # if not null, its lengths should be same as models
+  freeze_params_list:
+  - True
+  - False
+  models:
+    - Teacher:
+        name: ResNet34
+        pretrained: True
+
+    - Student:
+        name: ResNet18
+        pretrained: False
+
+  infer_model_name: "Student"
+
+
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - DistillationGTCELoss:
+        weight: 1.0
+        model_names: ["Student"]
+    - DistillationDISTLoss:
+        weight: 2.0
+        model_name_pairs:
+        - ["Student", "Teacher"]
+  Eval:
+    - CELoss:
+        weight: 1.0
+```
+
+
 <a name="2"></a>
 
 ## 2. 模型训练、评估和预测
@@ -601,3 +670,5 @@ python3 tools/export_model.py \
 [11] Zhao B, Cui Q, Song R, et al. Decoupled Knowledge Distillation[J]. arXiv preprint arXiv:2203.08679, 2022.
 
 [12] Ji M, Heo B, Park S. Show, attend and distill: Knowledge distillation via attention-based feature matching[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2021, 35(9): 7945-7952.
+
+[13] Huang T, You S, Wang F, et al. Knowledge Distillation from A Stronger Teacher[J]. arXiv preprint arXiv:2205.10536, 2022.

ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dist.yaml

@@ -0,0 +1,152 @@
+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output/r34_r18_dist
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 100
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+  to_static: False
+
+# model architecture
+Arch:
+  name: "DistillationModel"
+  class_num: &class_num 1000
+  # if not null, its lengths should be same as models
+  pretrained_list:
+  # if not null, its lengths should be same as models
+  freeze_params_list:
+  - True
+  - False
+  infer_model_name: "Student"
+  models:
+    - Teacher:
+        name: ResNet34
+        class_num: *class_num
+        pretrained: True
+    - Student:
+        name: ResNet18
+        class_num: *class_num
+        pretrained: False
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - DistillationGTCELoss:
+        weight: 1.0
+        model_names: ["Student"]
+    - DistillationDISTLoss:
+        weight: 2.0
+        model_name_pairs:
+        - ["Student", "Teacher"]
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  weight_decay: 1e-4
+  lr:
+    name: Piecewise
+    learning_rate: 0.1
+    decay_epochs: [30, 60, 90]
+    values: [0.1, 0.01, 0.001, 0.0001]
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 64
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 8
+      use_shared_memory: True
+
+  Eval:
+    dataset: 
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 256
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 8
+      use_shared_memory: True
+
+Infer:
+  infer_imgs: docs/images/inference_deployment/whl_demo.jpg
+  batch_size: 10
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        resize_short: 256
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        order: ''
+    - ToCHWImage:
+  PostProcess:
+    name: Topk
+    topk: 5
+    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+    Train:
+    - DistillationTopkAcc:
+        model_key: "Student"
+        topk: [1, 5]
+    Eval:
+    - DistillationTopkAcc:
+        model_key: "Student"
+        topk: [1, 5]
+

ppcls/loss/__init__.py

@@ -25,6 +25,8 @@ from .distillationloss import DistillationRKDLoss
 from .distillationloss import DistillationKLDivLoss
 from .distillationloss import DistillationDKDLoss
 from .distillationloss import DistillationMultiLabelLoss
+from .distillationloss import DistillationDISTLoss
+
 from .multilabelloss import MultiLabelLoss
 from .afdloss import AFDLoss
 

ppcls/loss/dist_loss.py

@@ -0,0 +1,52 @@
+# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+def cosine_similarity(a, b, eps=1e-8):
+    return (a * b).sum(1) / (a.norm(axis=1) * b.norm(axis=1) + eps)
+
+
+def pearson_correlation(a, b, eps=1e-8):
+    return cosine_similarity(a - a.mean(1).unsqueeze(1),
+                             b - b.mean(1).unsqueeze(1), eps)
+
+
+def inter_class_relation(y_s, y_t):
+    return 1 - pearson_correlation(y_s, y_t).mean()
+
+
+def intra_class_relation(y_s, y_t):
+    return inter_class_relation(y_s.transpose([1, 0]), y_t.transpose([1, 0]))
+
+
+class DISTLoss(nn.Layer):
+    # DISTLoss
+    # paper [Knowledge Distillation from A Stronger Teacher](https://arxiv.org/pdf/2205.10536v1.pdf)
+    # code reference: https://github.com/hunto/image_classification_sota/blob/d4f15a0494/lib/models/losses/dist_kd.py
+    def __init__(self, beta=1.0, gamma=1.0):
+        super().__init__()
+        self.beta = beta
+        self.gamma = gamma
+
+    def forward(self, z_s, z_t):
+        y_s = F.softmax(z_s, axis=-1)
+        y_t = F.softmax(z_t, axis=-1)
+        inter_loss = inter_class_relation(y_s, y_t)
+        intra_loss = intra_class_relation(y_s, y_t)
+        kd_loss = self.beta * inter_loss + self.gamma * intra_loss
+        return kd_loss

ppcls/loss/distillationloss.py

@@ -22,6 +22,7 @@ from .distanceloss import DistanceLoss
 from .rkdloss import RKdAngle, RkdDistance
 from .kldivloss import KLDivLoss
 from .dkdloss import DKDLoss
+from .dist_loss import DISTLoss
 from .multilabelloss import MultiLabelLoss
 
 
@@ -289,3 +290,32 @@ class DistillationMultiLabelLoss(MultiLabelLoss):
             for key in loss:
                 loss_dict["{}_{}".format(key, name)] = loss[key]
         return loss_dict
+
+
+class DistillationDISTLoss(DISTLoss):
+    """
+    DistillationDISTLoss
+    """
+
+    def __init__(self,
+                 model_name_pairs=[],
+                 key=None,
+                 beta=1.0,
+                 gamma=1.0,
+                 name="loss_dist"):
+        super().__init__(beta=beta, gamma=gamma)
+        self.key = key
+        self.model_name_pairs = model_name_pairs
+        self.name = name
+
+    def forward(self, predicts, batch):
+        loss_dict = dict()
+        for idx, pair in enumerate(self.model_name_pairs):
+            out1 = predicts[pair[0]]
+            out2 = predicts[pair[1]]
+            if self.key is not None:
+                out1 = out1[self.key]
+                out2 = out2[self.key]
+            loss = super().forward(out1, out2)
+            loss_dict[f"{self.name}_{pair[0]}_{pair[1]}"] = loss
+        return loss_dict