Remove

README.md

@@ -11,7 +11,7 @@ PaddleClas is a toolset for image classification tasks prepared for the industry
 - 2021.06.16 PaddleClas release/2.2.
    - Add metric learning and vector search module.
    - Add product recognition, cartoon character recognition, car recognition and logo recognition.
-   - Added 30 pretrained models of LeViT, Twins, TNT, DLA, HarDNet, and RedNet, and the accuracy is roughly the same as that of the paper.
+   - Add 30 pretrained models of LeViT, Twins, TNT, DLA, HarDNet, and RedNet, and the accuracy is roughly the same as that of the paper.
 - [more](./docs/en/update_history_en.md)
 
 

ppcls/arch/backbone/__init__.py

@@ -45,7 +45,6 @@ from ppcls.arch.backbone.model_zoo.darknet import DarkNet53
 from ppcls.arch.backbone.model_zoo.regnet import RegNetX_200MF, RegNetX_4GF, RegNetX_32GF, RegNetY_200MF, RegNetY_4GF, RegNetY_32GF
 from ppcls.arch.backbone.model_zoo.vision_transformer import ViT_small_patch16_224, ViT_base_patch16_224, ViT_base_patch16_384, ViT_base_patch32_384, ViT_large_patch16_224, ViT_large_patch16_384, ViT_large_patch32_384, ViT_huge_patch16_224, ViT_huge_patch32_384
 from ppcls.arch.backbone.model_zoo.distilled_vision_transformer import DeiT_tiny_patch16_224, DeiT_small_patch16_224, DeiT_base_patch16_224, DeiT_tiny_distilled_patch16_224, DeiT_small_distilled_patch16_224, DeiT_base_distilled_patch16_224, DeiT_base_patch16_384, DeiT_base_distilled_patch16_384
-from ppcls.arch.backbone.model_zoo.distillation_models import ResNet50_vd_distill_MobileNetV3_large_x1_0, ResNeXt101_32x16d_wsl_distill_ResNet50_vd
 from ppcls.arch.backbone.model_zoo.swin_transformer import SwinTransformer_tiny_patch4_window7_224, SwinTransformer_small_patch4_window7_224, SwinTransformer_base_patch4_window7_224, SwinTransformer_base_patch4_window12_384, SwinTransformer_large_patch4_window7_224, SwinTransformer_large_patch4_window12_384
 from ppcls.arch.backbone.model_zoo.mixnet import MixNet_S, MixNet_M, MixNet_L
 from ppcls.arch.backbone.model_zoo.rexnet import ReXNet_1_0, ReXNet_1_3, ReXNet_1_5, ReXNet_2_0, ReXNet_3_0

ppcls/arch/backbone/model_zoo/distillation_models.py

-# copyright (c) 2020 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.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import math
-
-import paddle
-import paddle.nn as nn
-
-from .resnet_vd import ResNet50_vd
-from .mobilenet_v3 import MobileNetV3_large_x1_0
-from .resnext101_wsl import ResNeXt101_32x16d_wsl
-
-__all__ = [
-    'ResNet50_vd_distill_MobileNetV3_large_x1_0',
-    'ResNeXt101_32x16d_wsl_distill_ResNet50_vd'
-]
-
-
-class ResNet50_vd_distill_MobileNetV3_large_x1_0(nn.Layer):
-    def __init__(self, class_dim=1000, freeze_teacher=True, **args):
-        super(ResNet50_vd_distill_MobileNetV3_large_x1_0, self).__init__()
-
-        self.teacher = ResNet50_vd(class_dim=class_dim, **args)
-        self.student = MobileNetV3_large_x1_0(class_dim=class_dim, **args)
-
-        if freeze_teacher:
-            for param in self.teacher.parameters():
-                param.trainable = False
-
-    def forward(self, x):
-        teacher_label = self.teacher(x)
-        student_label = self.student(x)
-        return teacher_label, student_label
-
-
-class ResNeXt101_32x16d_wsl_distill_ResNet50_vd(nn.Layer):
-    def __init__(self, class_dim=1000, freeze_teacher=True, **args):
-        super(ResNeXt101_32x16d_wsl_distill_ResNet50_vd, self).__init__()
-
-        self.teacher = ResNeXt101_32x16d_wsl(class_dim=class_dim, **args)
-        self.student = ResNet50_vd(class_dim=class_dim, **args)
-
-        if freeze_teacher:
-            for param in self.teacher.parameters():
-                param.trainable = False
-
-    def forward(self, x):
-        teacher_label = self.teacher(x)
-        student_label = self.student(x)
-        return teacher_label, student_label