rename: v3 -> V3

docs/zh_CN/models/ImageNet1k/MobileViTV3.md

@@ -1,4 +1,4 @@
-# MobileviTv3
+# MobileviTV3
 -----
 
 ## 目录
@@ -24,8 +24,8 @@
 
 ### 1.1 模型简介
 
-MobileViTv3 是一个结合 CNN 和 ViT 的轻量级模型，用于移动视觉任务。通过 MobileViTv3-block 解决了 MobileViTv1 的扩展问题并简化了学习任务，从而得倒了 MobileViTv3-XXS、XS 和 S 模型，在 ImageNet-1k、ADE20K、COCO 和 PascalVOC2012 数据集上表现优于 MobileViTv1。
+MobileViTV3 是一个结合 CNN 和 ViT 的轻量级模型，用于移动视觉任务。通过 MobileViTV3-block 解决了 MobileViTV1 的扩展问题并简化了学习任务，从而得倒了 MobileViTV3-XXS、XS 和 S 模型，在 ImageNet-1k、ADE20K、COCO 和 PascalVOC2012 数据集上表现优于 MobileViTV1。
-通过将提出的融合块添加到 MobileViTv2 中，创建 MobileViTv3-0.5、0.75 和 1.0 模型，在ImageNet-1k、ADE20K、COCO和PascalVOC2012数据集上给出了比 MobileViTv2 更好的准确性数据。[论文地址](https://arxiv.org/abs/2209.15159)。
+通过将提出的融合块添加到 MobileViTV2 中，创建 MobileViTV3_x0_5、MobileViTV3_x0_75 和 MobileViTV3_x1_0 模型，在ImageNet-1k、ADE20K、COCO和PascalVOC2012数据集上给出了比 MobileViTV2 更好的准确性数据。[论文地址](https://arxiv.org/abs/2209.15159)。
 
 <a name='1.2'></a>
 
@@ -33,15 +33,15 @@ MobileViTv3 是一个结合 CNN 和 ViT 的轻量级模型，用于移动视觉
 
 | Models           | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPs<br>(G) | Params<br>(M) |
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
-| MobileViTv3_XXS    | 0.7087 | 0.8976 | 0.7098 | - |  289.02 | 1.25 |
+| MobileViTV3_XXS    | 0.7087 | 0.8976 | 0.7098 | - |  289.02 | 1.25 |
-| MobileViTv3_XS     | 0.7663 | 0.9332 | 0.7671 | - |  926.98 | 2.49 |
+| MobileViTV3_XS     | 0.7663 | 0.9332 | 0.7671 | - |  926.98 | 2.49 |
-| MobileViTv3_S      | 0.7928 | 0.9454 | 0.7930 | - | 1841.39 | 5.76 |
+| MobileViTV3_S      | 0.7928 | 0.9454 | 0.7930 | - | 1841.39 | 5.76 |
-| MobileViTv3_XXS_L2 | 0.7028 | 0.8942 | 0.7023 | - |  256.97 | 1.15 |
+| MobileViTV3_XXS_L2 | 0.7028 | 0.8942 | 0.7023 | - |  256.97 | 1.15 |
-| MobileViTv3_XS_L2  | 0.7607 | 0.9300 | 0.7610 | - |  852.82 | 2.26 |
+| MobileViTV3_XS_L2  | 0.7607 | 0.9300 | 0.7610 | - |  852.82 | 2.26 |
-| MobileViTv3_S_L2   | 0.7907 | 0.9440 | 0.7906 | - | 1651.96 | 5.17 |
+| MobileViTV3_S_L2   | 0.7907 | 0.9440 | 0.7906 | - | 1651.96 | 5.17 |
-| MobileViTv3_x0_5   | 0.7200 | 0.9083 | 0.7233 | - |  481.33 | 1.43 |
+| MobileViTV3_x0_5   | 0.7200 | 0.9083 | 0.7233 | - |  481.33 | 1.43 |
-| MobileViTv3_x0_75  | 0.7626 | 0.9308 | 0.7655 | - | 1064.48 | 3.00 |
+| MobileViTV3_x0_75  | 0.7626 | 0.9308 | 0.7655 | - | 1064.48 | 3.00 |
-| MobileViTv3_x1_0   | 0.7838 | 0.9421 | 0.7864 | - | 1875.96 | 5.14 |
+| MobileViTV3_x1_0   | 0.7838 | 0.9421 | 0.7864 | - | 1875.96 | 5.14 |
 
 **备注：** PaddleClas 所提供的该系列模型的预训练模型权重，均是基于其官方提供的权重转得。
 
@@ -55,7 +55,7 @@ MobileViTv3 是一个结合 CNN 和 ViT 的轻量级模型，用于移动视觉
 
 ## 3. 模型训练、评估和预测
 
-此部分内容包括训练环境配置、ImageNet数据的准备、该模型在 ImageNet 上的训练、评估、预测等内容。在 `ppcls/configs/ImageNet/MobileViTv3/` 中提供了该模型的训练配置，启动训练方法可以参考：[ResNet50 模型训练、评估和预测](./ResNet.md#3-模型训练评估和预测)。
+此部分内容包括训练环境配置、ImageNet数据的准备、该模型在 ImageNet 上的训练、评估、预测等内容。在 `ppcls/configs/ImageNet/MobileViTV3/` 中提供了该模型的训练配置，启动训练方法可以参考：[ResNet50 模型训练、评估和预测](./ResNet.md#3-模型训练评估和预测)。
 
 **备注：** 由于 MobileViT 系列模型默认使用的 GPU 数量为 8 个，所以在训练时，需要指定8个GPU，如`python3 -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" tools/train.py -c xxx.yaml`, 如果使用 4 个 GPU 训练，默认学习率需要减小一半，精度可能有损。
 

ppcls/arch/backbone/__init__.py

@@ -79,8 +79,8 @@ from .model_zoo.cvt import CvT_13_224, CvT_13_384, CvT_21_224, CvT_21_384, CvT_W
 from .model_zoo.micronet import MicroNet_M0, MicroNet_M1, MicroNet_M2, MicroNet_M3
 from .model_zoo.mobilenext import MobileNeXt_x0_35, MobileNeXt_x0_5, MobileNeXt_x0_75, MobileNeXt_x1_0, MobileNeXt_x1_4
 from .model_zoo.mobilevit_v2 import MobileViTV2_x0_5, MobileViTV2_x0_75, MobileViTV2_x1_0, MobileViTV2_x1_25, MobileViTV2_x1_5, MobileViTV2_x1_75, MobileViTV2_x2_0
-from .model_zoo.mobilevit_v3 import MobileViTv3_XXS, MobileViTv3_XS, MobileViTv3_S, MobileViTv3_XXS_L2, MobileViTv3_XS_L2, MobileViTv3_S_L2, MobileViTv3_x0_5, MobileViTv3_x0_75, MobileViTv3_x1_0
 from .model_zoo.tinynet import TinyNet_A, TinyNet_B, TinyNet_C, TinyNet_D, TinyNet_E
+from .model_zoo.mobilevit_v3 import MobileViTV3_XXS, MobileViTV3_XS, MobileViTV3_S, MobileViTV3_XXS_L2, MobileViTV3_XS_L2, MobileViTV3_S_L2, MobileViTV3_x0_5, MobileViTV3_x0_75, MobileViTV3_x1_0
 
 from .variant_models.resnet_variant import ResNet50_last_stage_stride1
 from .variant_models.resnet_variant import ResNet50_adaptive_max_pool2d

ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_S.yaml

@@ -28,7 +28,7 @@ EMA:
 
 # model architecture
 Arch:
-  name: MobileViTv3_S
+  name: MobileViTV3_S
   class_num: 1000
   dropout: 0.1
 

ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_S_L2.yaml

@@ -28,7 +28,7 @@ EMA:
 
 # model architecture
 Arch:
-  name: MobileViTv3_S_L2
+  name: MobileViTV3_S_L2
   class_num: 1000
   dropout: 0.1
 

ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XS.yaml

@@ -28,7 +28,7 @@ EMA:
 
 # model architecture
 Arch:
-  name: MobileViTv3_XS
+  name: MobileViTV3_XS
   class_num: 1000
   dropout: 0.1
 

ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XS_L2.yaml

@@ -28,7 +28,7 @@ EMA:
 
 # model architecture
 Arch:
-  name: MobileViTv3_XS_L2
+  name: MobileViTV3_XS_L2
   class_num: 1000
   dropout: 0.1
 

ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XXS.yaml

@@ -28,7 +28,7 @@ EMA:
 
 # model architecture
 Arch:
-  name: MobileViTv3_XXS
+  name: MobileViTV3_XXS
   class_num: 1000
   dropout: 0.05
 

ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XXS_L2.yaml

@@ -0,0 +1,150 @@
+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output/
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 300
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 256, 256]
+  save_inference_dir: ./inference
+  use_dali: False
+
+# mixed precision training
+AMP:
+  scale_loss: 65536
+  use_dynamic_loss_scaling: True
+  # O1: mixed fp16
+  level: O1
+
+# model ema
+EMA:
+  decay: 0.9995
+
+# model architecture
+Arch:
+  name: MobileViTV3_XXS_L2
+  class_num: 1000
+  dropout: 0.1
+
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+        epsilon: 0.1
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+Optimizer:
+  name: AdamW
+  beta1: 0.9
+  beta2: 0.999
+  epsilon: 1e-8
+  weight_decay: 0.01
+  lr:
+    name: Cosine
+    learning_rate: 0.002  # for total batch size 384
+    eta_min: 0.0002
+    warmup_epoch: 1  # 3000 iterations
+    warmup_start_lr: 0.0002
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: MultiScaleDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 256
+            interpolation: bilinear
+            use_log_aspect: True
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.0, 0.0, 0.0]
+            std: [1.0, 1.0, 1.0]
+            order: ''
+    # support to specify width and height respectively:
+    # scales: [(256,256) (160,160), (192,192), (224,224) (288,288) (320,320)]
+    sampler:
+      name: MultiScaleSampler
+      scales: [256, 160, 192, 224, 288, 320]
+      # first_bs: batch size for the first image resolution in the scales list
+      # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
+      first_bs: 48
+      divided_factor: 32
+      is_training: True
+    loader:
+      num_workers: 4
+      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: 288
+            interpolation: bilinear
+        - CropImage:
+            size: 256
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.0, 0.0, 0.0]
+            std: [1.0, 1.0, 1.0]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 48
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      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: 288
+        interpolation: bilinear
+    - CropImage:
+        size: 256
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.0, 0.0, 0.0]
+        std: [1.0, 1.0, 1.0]
+        order: ''
+    - ToCHWImage:
+  PostProcess:
+    name: Topk
+    topk: 5
+    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+  Train:
+    - TopkAcc:
+        topk: [1, 5]
+  Eval:
+    - TopkAcc:
+        topk: [1, 5]

ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_x0_5.yaml

@@ -28,7 +28,7 @@ EMA:
 
 # model architecture
 Arch:
-  name: MobileViTv3_x0_5
+  name: MobileViTV3_x0_5
   class_num: 1000
   classifier_dropout: 0.
 

ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_x0_75.yaml

@@ -28,7 +28,7 @@ EMA:
 
 # model architecture
 Arch:
-  name: MobileViTv3_x0_75
+  name: MobileViTV3_x0_75
   class_num: 1000
   classifier_dropout: 0.
 

ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_x1_0.yaml

@@ -28,7 +28,7 @@ EMA:
 
 # model architecture
 Arch:
-  name: MobileViTv3_x1_0
+  name: MobileViTV3_x1_0
   class_num: 1000
   classifier_dropout: 0.
 

ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XXS_L2.yaml

@@ -1,150 +0,0 @@
-# global configs
-Global:
-  checkpoints: null
-  pretrained_model: null
-  output_dir: ./output/
-  device: gpu
-  save_interval: 1
-  eval_during_train: True
-  eval_interval: 1
-  epochs: 300
-  print_batch_step: 10
-  use_visualdl: False
-  # used for static mode and model export
-  image_shape: [3, 256, 256]
-  save_inference_dir: ./inference
-  use_dali: False
-
-# mixed precision training
-AMP:
-  scale_loss: 65536
-  use_dynamic_loss_scaling: True
-  # O1: mixed fp16
-  level: O1
-
-# model ema
-EMA:
-  decay: 0.9995
-
-# model architecture
-Arch:
-  name: MobileViTv3_XXS_L2
-  class_num: 1000
-  dropout: 0.1
-
-# loss function config for traing/eval process
-Loss:
-  Train:
-    - CELoss:
-        weight: 1.0
-        epsilon: 0.1
-  Eval:
-    - CELoss:
-        weight: 1.0
-
-Optimizer:
-  name: AdamW
-  beta1: 0.9
-  beta2: 0.999
-  epsilon: 1e-8
-  weight_decay: 0.01
-  lr:
-    name: Cosine
-    learning_rate: 0.002  # for total batch size 384
-    eta_min: 0.0002
-    warmup_epoch: 1  # 3000 iterations
-    warmup_start_lr: 0.0002
-
-# data loader for train and eval
-DataLoader:
-  Train:
-    dataset:
-      name: MultiScaleDataset
-      image_root: ./dataset/ILSVRC2012/
-      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
-      transform_ops:
-        - DecodeImage:
-            to_rgb: True
-            channel_first: False
-        - RandCropImage:
-            size: 256
-            interpolation: bilinear
-            use_log_aspect: True
-        - RandFlipImage:
-            flip_code: 1
-        - NormalizeImage:
-            scale: 1.0/255.0
-            mean: [0.0, 0.0, 0.0]
-            std: [1.0, 1.0, 1.0]
-            order: ''
-    # support to specify width and height respectively:
-    # scales: [(256,256) (160,160), (192,192), (224,224) (288,288) (320,320)]
-    sampler:
-      name: MultiScaleSampler
-      scales: [256, 160, 192, 224, 288, 320]
-      # first_bs: batch size for the first image resolution in the scales list
-      # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
-      first_bs: 48
-      divided_factor: 32
-      is_training: True
-    loader:
-      num_workers: 4
-      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: 288
-            interpolation: bilinear
-        - CropImage:
-            size: 256
-        - NormalizeImage:
-            scale: 1.0/255.0
-            mean: [0.0, 0.0, 0.0]
-            std: [1.0, 1.0, 1.0]
-            order: ''
-    sampler:
-      name: DistributedBatchSampler
-      batch_size: 48
-      drop_last: False
-      shuffle: False
-    loader:
-      num_workers: 4
-      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: 288
-        interpolation: bilinear
-    - CropImage:
-        size: 256
-    - NormalizeImage:
-        scale: 1.0/255.0
-        mean: [0.0, 0.0, 0.0]
-        std: [1.0, 1.0, 1.0]
-        order: ''
-    - ToCHWImage:
-  PostProcess:
-    name: Topk
-    topk: 5
-    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
-
-Metric:
-  Train:
-    - TopkAcc:
-        topk: [1, 5]
-  Eval:
-    - TopkAcc:
-        topk: [1, 5]

test_tipc/configs/MobileViTv3/MobileViTV3_S_L2_train_infer_python.txt

@@ -1,5 +1,5 @@
 ===========================train_params===========================
-model_name:MobileViTv3_S_L2
+model_name:MobileViTV3_S_L2
 python:python3.7
 gpu_list:0|0,1
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_S_L2.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_S_L2.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params===========================
-eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_S_L2.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_S_L2.yaml
 null:null
 ##
 ===========================infer_params==========================
 -o Global.save_inference_dir:./inference
 -o Global.pretrained_model:
-norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_S_L2.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_S_L2.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

test_tipc/configs/MobileViTv3/MobileViTV3_S_train_infer_python.txt

@@ -1,5 +1,5 @@
 ===========================train_params===========================
-model_name:MobileViTv3_S
+model_name:MobileViTV3_S
 python:python3.7
 gpu_list:0|0,1
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_S.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_S.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params===========================
-eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_S.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_S.yaml
 null:null
 ##
 ===========================infer_params==========================
 -o Global.save_inference_dir:./inference
 -o Global.pretrained_model:
-norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_S.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_S.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

test_tipc/configs/MobileViTv3/MobileViTV3_XS_L2_train_infer_python.txt

@@ -1,5 +1,5 @@
 ===========================train_params===========================
-model_name:MobileViTv3_XS_L2
+model_name:MobileViTV3_XS_L2
 python:python3.7
 gpu_list:0|0,1
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XS_L2.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XS_L2.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params===========================
-eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XS_L2.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XS_L2.yaml
 null:null
 ##
 ===========================infer_params==========================
 -o Global.save_inference_dir:./inference
 -o Global.pretrained_model:
-norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XS_L2.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XS_L2.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

test_tipc/configs/MobileViTv3/MobileViTV3_XS_train_infer_python.txt

@@ -1,5 +1,5 @@
 ===========================train_params===========================
-model_name:MobileViTv3_XS
+model_name:MobileViTV3_XS
 python:python3.7
 gpu_list:0|0,1
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XS.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XS.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params===========================
-eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XS.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XS.yaml
 null:null
 ##
 ===========================infer_params==========================
 -o Global.save_inference_dir:./inference
 -o Global.pretrained_model:
-norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XS.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XS.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

test_tipc/configs/MobileViTv3/MobileViTV3_XXS_L2_train_infer_python.txt

@@ -1,5 +1,5 @@
 ===========================train_params===========================
-model_name:MobileViTv3_XXS_L2
+model_name:MobileViTV3_XXS_L2
 python:python3.7
 gpu_list:0|0,1
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XXS_L2.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XXS_L2.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params===========================
-eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XXS_L2.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XXS_L2.yaml
 null:null
 ##
 ===========================infer_params==========================
 -o Global.save_inference_dir:./inference
 -o Global.pretrained_model:
-norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XXS_L2.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XXS_L2.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

test_tipc/configs/MobileViTv3/MobileViTV3_XXS_train_infer_python.txt

@@ -1,5 +1,5 @@
 ===========================train_params===========================
-model_name:MobileViTv3_XXS
+model_name:MobileViTV3_XXS
 python:python3.7
 gpu_list:0|0,1
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XXS.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XXS.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params===========================
-eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XXS.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XXS.yaml
 null:null
 ##
 ===========================infer_params==========================
 -o Global.save_inference_dir:./inference
 -o Global.pretrained_model:
-norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_XXS.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XXS.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

test_tipc/configs/MobileViTv3/MobileViTV3_x0_5_train_infer_python.txt

@@ -1,5 +1,5 @@
 ===========================train_params===========================
-model_name:MobileViTv3_x0_5
+model_name:MobileViTV3_x0_5
 python:python3.7
 gpu_list:0|0,1
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params===========================
-eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_x0_5.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_x0_5.yaml
 null:null
 ##
 ===========================infer_params==========================
 -o Global.save_inference_dir:./inference
 -o Global.pretrained_model:
-norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_x0_5.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_x0_5.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

test_tipc/configs/MobileViTv3/MobileViTV3_x0_75_train_infer_python.txt

@@ -1,5 +1,5 @@
 ===========================train_params===========================
-model_name:MobileViTv3_x0_75
+model_name:MobileViTV3_x0_75
 python:python3.7
 gpu_list:0|0,1
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_x0_75.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_x0_75.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params===========================
-eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_x0_75.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_x0_75.yaml
 null:null
 ##
 ===========================infer_params==========================
 -o Global.save_inference_dir:./inference
 -o Global.pretrained_model:
-norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_x0_75.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_x0_75.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

test_tipc/configs/MobileViTv3/MobileViTV3_x1_0_train_infer_python.txt

@@ -1,5 +1,5 @@
 ===========================train_params===========================
-model_name:MobileViTv3_x1_0
+model_name:MobileViTV3_x1_0
 python:python3.7
 gpu_list:0|0,1
 -o Global.device:gpu
@@ -13,7 +13,7 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
 null:null
 ##
 trainer:norm_train
-norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Global.print_batch_step=1 -o Global.eval_during_train=False -o Global.save_interval=2
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -21,13 +21,13 @@ null:null
 null:null
 ##
 ===========================eval_params===========================
-eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_x1_0.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_x1_0.yaml
 null:null
 ##
 ===========================infer_params==========================
 -o Global.save_inference_dir:./inference
 -o Global.pretrained_model:
-norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTv3/MobileViTv3_x1_0.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_x1_0.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null