add resnest fast config and fix flops

configs/ResNeSt/ResNeSt50_fast_1s1x64d.yaml

+mode: 'train'
+ARCHITECTURE:
+    name: 'ResNeSt50_fast_1s1x64d'
+
+pretrained_model: ""
+model_save_dir: "./output/"
+classes_num: 1000
+total_images: 1281167
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 300
+topk: 5
+image_shape: [3, 224, 224]
+
+use_mix: True
+ls_epsilon: 0.1
+
+LEARNING_RATE:
+    function: 'CosineWarmup'          
+    params:                   
+        lr: 0.1               
+
+OPTIMIZER:
+    function: 'Momentum'
+    params:
+        momentum: 0.9
+    regularizer:
+        function: 'L2'
+        factor: 0.000070
+
+TRAIN:
+    batch_size: 256
+    num_workers: 4
+    file_list: "./dataset/ILSVRC2012/train_list.txt"
+    data_dir: "./dataset/ILSVRC2012/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - AutoAugment:
+        - NormalizeImage:
+            scale: 1./255.
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - ToCHWImage:
+    mix:                       
+        - CutmixOperator:    
+            alpha: 0.2      
+
+VALID:
+    batch_size: 64
+    num_workers: 4
+    file_list: "./dataset/ILSVRC2012/val_list.txt"
+    data_dir: "./dataset/ILSVRC2012/"
+    shuffle_seed: 0
+    transforms:
+        - DecodeImage:
+            to_rgb: True
+            to_np: False
+            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:

docs/en/models/Mobile_en.md

@@ -58,9 +58,9 @@ Currently there are 32 pretrained models of the mobile series open source by Pad
 | ShuffleNetV2_x1_5                    | 0.716   | 0.902   | 0.726             |                   | 0.580        | 3.470             |
 | ShuffleNetV2_x2_0                    | 0.732   | 0.912   | 0.749             |                   | 1.120        | 7.320             |
 | ShuffleNetV2_swish                   | 0.700   | 0.892   |                   |                   | 0.290        | 2.260             |
-| GhostNet_x0_5                        | 0.668   | 0.869   | 0.662             | 0.866             | 0.041        | 2.600             |
-| GhostNet_x1_0                        | 0.740   | 0.916   | 0.739             | 0.914             | 0.147        | 5.200             |
-| GhostNet_x1_3                        | 0.757   | 0.925   | 0.757             | 0.927             | 0.220        | 7.300             |
+| GhostNet_x0_5                        | 0.668   | 0.869   | 0.662             | 0.866             | 0.082        | 2.600             |
+| GhostNet_x1_0                        | 0.740   | 0.916   | 0.739             | 0.914             | 0.294        | 5.200             |
+| GhostNet_x1_3                        | 0.757   | 0.925   | 0.757             | 0.927             | 0.440        | 7.300             |
 
 
 ## Inference speed and storage size based on SD855

docs/en/models/ResNeSt_RegNet_en.md

@@ -6,4 +6,5 @@ The ResNeSt series was proposed in 2020. The original resnet network structure h
 
 | Models           | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Parameters<br>(M) |
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
-| ResNeSt50        | 0.8102 | 0.9542|  0.8113 |            -|5.39     | 27.5   |
+| ResNeSt50_fast_1s1x64d        | 0.8035 | 0.9528|  0.8035 |            -| 8.68     | 26.3   |
+| ResNeSt50        | 0.8102 | 0.9542|  0.8113 |            -| 10.78     | 27.5   |

docs/en/models/models_intro_en.md

@@ -191,6 +191,13 @@ python tools/infer/predict.py \
     - [Fix_ResNeXt101_32x48d_wsl](https://paddle-imagenet-models-name.bj.bcebos.com/Fix_ResNeXt101_32x48d_wsl_pretrained.tar)
 
 
+
+- ResNeSt and RegNet series
+  - ResNeSt系列<sup>[[24](#ref24)]</sup>([paper link](https://arxiv.org/abs/2004.08955))
+    - [ResNeSt50_fast_1s1x64d)(https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_fast_1s1x64d_pretrained.tar)
+    - [ResNeSt50)(https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_pretrained.tar)
+
+
 - Other models
   - AlexNet series<sup>[[18](#ref18)]</sup>([paper link](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf))
     - [AlexNet](https://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.tar)
@@ -261,3 +268,5 @@ python tools/infer/predict.py \
 <a name="ref22">[22]</a> Ding X, Guo Y, Ding G, et al. Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1911-1920.
 
 <a name="ref23">[23]</a> Han K, Wang Y, Tian Q, et al. GhostNet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1580-1589.
+
+<a name="ref24">[24]</a> Zhang H, Wu C, Zhang Z, et al. Resnest: Split-attention networks[J]. arXiv preprint arXiv:2004.08955, 2020.

docs/zh_CN/models/Mobile.md

@@ -59,9 +59,9 @@ GhosttNet是华为于2020年提出的一种全新的轻量化网络结构，通
 | ShuffleNetV2_x1_5                    | 0.716   | 0.902   | 0.726             |                   | 0.580        | 3.470             |
 | ShuffleNetV2_x2_0                    | 0.732   | 0.912   | 0.749             |                   | 1.120        | 7.320             |
 | ShuffleNetV2_swish                   | 0.700   | 0.892   |                   |                   | 0.290        | 2.260             |
-| GhostNet_x0_5                        | 0.668   | 0.869   | 0.662             | 0.866             | 0.041        | 2.600             |
-| GhostNet_x1_0                        | 0.740   | 0.916   | 0.739             | 0.914             | 0.147        | 5.200             |
-| GhostNet_x1_3                        | 0.757   | 0.925   | 0.757             | 0.927             | 0.220        | 7.300             |
+| GhostNet_x0_5                        | 0.668   | 0.869   | 0.662             | 0.866             | 0.082        | 2.600             |
+| GhostNet_x1_0                        | 0.740   | 0.916   | 0.739             | 0.914             | 0.294        | 5.200             |
+| GhostNet_x1_3                        | 0.757   | 0.925   | 0.757             | 0.927             | 0.440        | 7.300             |
 
 
 ## 基于SD855的预测速度和存储大小

docs/zh_CN/models/ResNeSt_RegNet.md

@@ -9,6 +9,5 @@ ResNeSt系列模型是在2020年提出的，在原有的resnet网络结构上做
 
 | Models           | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Parameters<br>(M) |
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
-| ResNeSt50        | 0.8102 | 0.9542|  0.8113 |            -|5.39     | 27.5   |
-
-
+| ResNeSt50_fast_1s1x64d        | 0.8035 | 0.9528|  0.8035 |            -| 8.68     | 26.3   |
+| ResNeSt50        | 0.8102 | 0.9542|  0.8113 |            -| 10.78     | 27.5   |

docs/zh_CN/models/models_intro.md

@@ -190,6 +190,11 @@ python tools/infer/predict.py \
     - [ResNeXt101_32x48d_wsl](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x48d_wsl_pretrained.tar)
     - [Fix_ResNeXt101_32x48d_wsl](https://paddle-imagenet-models-name.bj.bcebos.com/Fix_ResNeXt101_32x48d_wsl_pretrained.tar)
 
+- ResNeSt与RegNet系列
+  - ResNeSt系列<sup>[[24](#ref24)]</sup>([论文地址](https://arxiv.org/abs/2004.08955))
+    - [ResNeSt50_fast_1s1x64d)(https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_fast_1s1x64d_pretrained.tar)
+    - [ResNeSt50)(https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_pretrained.tar)
+
 
 - 其他模型
   - AlexNet系列<sup>[[18](#ref18)]</sup>([论文地址](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf))
@@ -261,3 +266,5 @@ python tools/infer/predict.py \
 <a name="ref22">[22]</a> Ding X, Guo Y, Ding G, et al. Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1911-1920.
 
 <a name="ref23">[23]</a> Han K, Wang Y, Tian Q, et al. GhostNet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1580-1589.
+
+<a name="ref24">[24]</a> Zhang H, Wu C, Zhang Z, et al. Resnest: Split-attention networks[J]. arXiv preprint arXiv:2004.08955, 2020.