rename v2 to V2

deb8e987 · Yang Nie · Tingquan Gao · be6a22be · deb8e987 · deb8e987
13 changed file
--- a/docs/zh_CN/models/ImageNet1k/MobileViTv2.md
+++ b/docs/zh_CN/models/ImageNet1k/MobileViTv2.md
-# MobileViTv2
+# MobileViTV2
 -----

 ## 目录
@@ -24,8 +24,7 @@

 ### 1.1 模型简介

-MobileViTv2 是一个结合 CNN 和 ViT 的轻量级模型，用于移动视觉任务。通过 MobileViTv2-block 解决了 MobileViTv1 的扩展问题并简化了学习任务，从而得倒了 MobileViTv2-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 MobileViTv2-0.5、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)。
+MobileViT 中的主要效率瓶颈是Transformer中的多头自注意力，影响资源受限设备上的延迟。MobileViTV2 提出了一种具有线性复杂度的可分离自我注意力方法，它使用一个简单而有效的逐元素操作来计算自我注意力，使其成为资源受限设备的良好选择，在移动设备上运行时比MobileViT快3.2倍。[论文地址](https://arxiv.org/abs/2206.02680)。

 <a name='1.2'></a>

@@ -33,10 +32,10 @@ MobileViTv2 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) |
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
-| MobileViTv2_x0_5   | 0.7017 | 0.89884 | 0.7018 | - |  480.46 |  1.37 |
-| MobileViTv2_x1_0   | 0.7813 | 0.94172 | 0.7809 | - | 1843.81 |  4.90 |
-| MobileViTv2_x1_5   | 0.8034 | 0.95094 | 0.8038 | - | 4090.07 | 10.60 |
-| MobileViTv2_x2_0   | 0.8116 | 0.95370 | 0.8117 | - | 7219.23 | 18.45 |
+| MobileViTV2_x0_5   | 0.7017 | 0.89884 | 0.7018 | - |  480.46 |  1.37 |
+| MobileViTV2_x1_0   | 0.7813 | 0.94172 | 0.7809 | - | 1843.81 |  4.90 |
+| MobileViTV2_x1_5   | 0.8034 | 0.95094 | 0.8038 | - | 4090.07 | 10.60 |
+| MobileViTV2_x2_0   | 0.8116 | 0.95370 | 0.8117 | - | 7219.23 | 18.45 |

 **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。

@@ -50,7 +49,7 @@ MobileViTv2 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/MobileViTv2/` 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/MobileViTV2/` 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。


--- a/docs/zh_CN/models/ImageNet1k/README.md
+++ b/docs/zh_CN/models/ImageNet1k/README.md
@@ -796,19 +796,19 @@ DeiT（Data-efficient Image Transformers）系列模型的精度、速度指标

 <a name="MobileViT"></a>

-## MobileViT 系列 <sup>[[42](#ref42)][[51](#ref51)]</sup>
+## MobileViT 系列 <sup>[[42](#ref42)][[51](#ref51)][[52](#ref52)]</sup>

-关于 MobileViT 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[MobileViT 系列模型文档](MobileViT.md)，[MobileViTv2 系列模型文档](MobileViTv2.md)，[MobileViTv3 系列模型文档](MobileViTv3.md)。
+关于 MobileViT 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[MobileViT 系列模型文档](MobileViT.md)，[MobileViTV2 系列模型文档](MobileViTV2.md)，[MobileViTv3 系列模型文档](MobileViTv3.md)。

 | 模型       | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(M) | Params(M) | 预训练模型下载地址                                               | inference模型下载地址                                      |
 | ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
 |  MobileViT_XXS     | 0.6867 | 0.8878 | - | - | - |  337.24 | 1.28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XXS_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XXS_infer.tar) |
 |  MobileViT_XS      | 0.7454 | 0.9227 | - | - | - |  930.75 | 2.33 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XS_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XS_infer.tar) |
 |  MobileViT_S       | 0.7814 | 0.9413 | - | - | - | 1849.35 | 5.59 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_S_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_S_infer.tar) |
-| MobileViTv2_x0_5   | 0.7017 | 0.8988 | - | - | - |  480.46 |  1.37 | [下载链接]() | [下载链接]() |
-| MobileViTv2_x1_0   | 0.7813 | 0.9417 | - | - | - | 1843.81 |  4.90 | [下载链接]() | [下载链接]() |
-| MobileViTv2_x1_5   | 0.8034 | 0.9509 | - | - | - | 4090.07 | 10.60 | [下载链接]() | [下载链接]() |
-| MobileViTv2_x2_0   | 0.8116 | 0.9537 | - | - | - | 7219.23 | 18.45 | [下载链接]() | [下载链接]() |
+| MobileViTV2_x0_5   | 0.7017 | 0.8988 | - | - | - |  480.46 |  1.37 | [下载链接]() | [下载链接]() |
+| MobileViTV2_x1_0   | 0.7813 | 0.9417 | - | - | - | 1843.81 |  4.90 | [下载链接]() | [下载链接]() |
+| MobileViTV2_x1_5   | 0.8034 | 0.9509 | - | - | - | 4090.07 | 10.60 | [下载链接]() | [下载链接]() |
+| MobileViTV2_x2_0   | 0.8116 | 0.9537 | - | - | - | 7219.23 | 18.45 | [下载链接]() | [下载链接]() |
 | MobileViTv3_XXS    | 0.7087 | 0.8976 | - | - | - |  289.02 | 1.25 | [下载链接]() | [下载链接]() |
 | MobileViTv3_XS     | 0.7663 | 0.9332 | - | - | - |  926.98 | 2.49 | [下载链接]() | [下载链接]() |
 | MobileViTv3_S      | 0.7928 | 0.9454 | - | - | - | 1841.39 | 5.76 | [下载链接]() | [下载链接]() |
@@ -924,8 +924,6 @@ TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE.

 <a name="ref50">[50]</a>Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo. Swin Transformer V2: Scaling Up Capacity and Resolution

-<<<<<<< HEAD
-<a name="ref50">[51]</a>Wadekar, Shakti N. and Chaurasia, Abhishek. MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features
-=======
 <a name="ref51">[51]</a>Sachin Mehta and Mohammad Rastegari. Separable Self-attention for Mobile Vision Transformers
->>>>>>> add MobileViTv2
+
+<a name="ref52">[52]</a>Wadekar, Shakti N. and Chaurasia, Abhishek. MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features
--- a/ppcls/arch/backbone/__init__.py
+++ b/ppcls/arch/backbone/__init__.py
@@ -78,7 +78,7 @@ from .model_zoo.cae import cae_base_patch16_224, cae_large_patch16_224
 from .model_zoo.cvt import CvT_13_224, CvT_13_384, CvT_21_224, CvT_21_384, CvT_W24_384
 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_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 .variant_models.resnet_variant import ResNet50_last_stage_stride1

--- a/ppcls/arch/backbone/model_zoo/mobilevit_v2.py
+++ b/ppcls/arch/backbone/model_zoo/mobilevit_v2.py
@@ -25,13 +25,13 @@ import paddle.nn.functional as F
 from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url

 MODEL_URLS = {
-    "MobileViTv2_x0_5": "",
-    "MobileViTv2_x0_75": "",
-    "MobileViTv2_x1_0": "",
-    "MobileViTv2_x1_25": "",
-    "MobileViTv2_x1_5": "",
-    "MobileViTv2_x1_75": "",
-    "MobileViTv2_x2_0": "",
+    "MobileViTV2_x0_5": "",
+    "MobileViTV2_x0_75": "",
+    "MobileViTV2_x1_0": "",
+    "MobileViTV2_x1_25": "",
+    "MobileViTV2_x1_5": "",
+    "MobileViTV2_x1_75": "",
+    "MobileViTV2_x2_0": "",
 }

 layer_norm_2d = partial(nn.GroupNorm, num_groups=1)
@@ -179,9 +179,9 @@ class LinearAttnFFN(nn.Layer):
        return x


-class MobileViTv2Block(nn.Layer):
+class MobileViTV2Block(nn.Layer):
    """
-    This class defines the `MobileViTv2 block`
+    This class defines the `MobileViTV2 block`
    """

    def __init__(self,
@@ -302,9 +302,9 @@ class MobileViTv2Block(nn.Layer):
        return fm


-class MobileViTv2(nn.Layer):
+class MobileViTV2(nn.Layer):
    """
-        MobileViTv2
+        MobileViTV2
    """

    def __init__(self,
@@ -415,7 +415,7 @@ class MobileViTv2(nn.Layer):
            input_channel = cfg.get("out_channels")

        block.append(
-            MobileViTv2Block(
+            MobileViTV2Block(
                in_channels=input_channel,
                attn_unit_dim=cfg["attn_unit_dim"],
                ffn_multiplier=cfg.get("ffn_multiplier"),
@@ -540,64 +540,64 @@ def get_configuration(width_multiplier) -> Dict:
    return config


-def MobileViTv2_x2_0(pretrained=False, use_ssld=False, **kwargs):
+def MobileViTV2_x2_0(pretrained=False, use_ssld=False, **kwargs):
    width_multiplier = 2.0
-    model = MobileViTv2(get_configuration(width_multiplier), **kwargs)
+    model = MobileViTV2(get_configuration(width_multiplier), **kwargs)

    _load_pretrained(
-        pretrained, model, MODEL_URLS["MobileViTv2_x2_0"], use_ssld=use_ssld)
+        pretrained, model, MODEL_URLS["MobileViTV2_x2_0"], use_ssld=use_ssld)
    return model


-def MobileViTv2_x1_75(pretrained=False, use_ssld=False, **kwargs):
+def MobileViTV2_x1_75(pretrained=False, use_ssld=False, **kwargs):
    width_multiplier = 1.75
-    model = MobileViTv2(get_configuration(width_multiplier), **kwargs)
+    model = MobileViTV2(get_configuration(width_multiplier), **kwargs)

    _load_pretrained(
-        pretrained, model, MODEL_URLS["MobileViTv2_x1_75"], use_ssld=use_ssld)
+        pretrained, model, MODEL_URLS["MobileViTV2_x1_75"], use_ssld=use_ssld)
    return model


-def MobileViTv2_x1_5(pretrained=False, use_ssld=False, **kwargs):
+def MobileViTV2_x1_5(pretrained=False, use_ssld=False, **kwargs):
    width_multiplier = 1.5
-    model = MobileViTv2(get_configuration(width_multiplier), **kwargs)
+    model = MobileViTV2(get_configuration(width_multiplier), **kwargs)

    _load_pretrained(
-        pretrained, model, MODEL_URLS["MobileViTv2_x1_5"], use_ssld=use_ssld)
+        pretrained, model, MODEL_URLS["MobileViTV2_x1_5"], use_ssld=use_ssld)
    return model


-def MobileViTv2_x1_25(pretrained=False, use_ssld=False, **kwargs):
+def MobileViTV2_x1_25(pretrained=False, use_ssld=False, **kwargs):
    width_multiplier = 1.25
-    model = MobileViTv2(get_configuration(width_multiplier), **kwargs)
+    model = MobileViTV2(get_configuration(width_multiplier), **kwargs)

    _load_pretrained(
-        pretrained, model, MODEL_URLS["MobileViTv2_x1_25"], use_ssld=use_ssld)
+        pretrained, model, MODEL_URLS["MobileViTV2_x1_25"], use_ssld=use_ssld)
    return model


-def MobileViTv2_x1_0(pretrained=False, use_ssld=False, **kwargs):
+def MobileViTV2_x1_0(pretrained=False, use_ssld=False, **kwargs):
    width_multiplier = 1.0
-    model = MobileViTv2(get_configuration(width_multiplier), **kwargs)
+    model = MobileViTV2(get_configuration(width_multiplier), **kwargs)

    _load_pretrained(
-        pretrained, model, MODEL_URLS["MobileViTv2_x1_0"], use_ssld=use_ssld)
+        pretrained, model, MODEL_URLS["MobileViTV2_x1_0"], use_ssld=use_ssld)
    return model


-def MobileViTv2_x0_75(pretrained=False, use_ssld=False, **kwargs):
+def MobileViTV2_x0_75(pretrained=False, use_ssld=False, **kwargs):
    width_multiplier = 0.75
-    model = MobileViTv2(get_configuration(width_multiplier), **kwargs)
+    model = MobileViTV2(get_configuration(width_multiplier), **kwargs)

    _load_pretrained(
-        pretrained, model, MODEL_URLS["MobileViTv2_x0_75"], use_ssld=use_ssld)
+        pretrained, model, MODEL_URLS["MobileViTV2_x0_75"], use_ssld=use_ssld)
    return model


-def MobileViTv2_x0_5(pretrained=False, use_ssld=False, **kwargs):
+def MobileViTV2_x0_5(pretrained=False, use_ssld=False, **kwargs):
    width_multiplier = 0.5
-    model = MobileViTv2(get_configuration(width_multiplier), **kwargs)
+    model = MobileViTV2(get_configuration(width_multiplier), **kwargs)

    _load_pretrained(
-        pretrained, model, MODEL_URLS["MobileViTv2_x0_5"], use_ssld=use_ssld)
+        pretrained, model, MODEL_URLS["MobileViTV2_x0_5"], use_ssld=use_ssld)
    return model
--- a/ppcls/configs/ImageNet/MobileViTv2/MobileViTv2_x0_5.yaml
+++ b/ppcls/configs/ImageNet/MobileViTv2/MobileViTv2_x0_5.yaml
@@ -28,7 +28,7 @@ EMA:

 # model architecture
 Arch:
-  name: MobileViTv2_x0_5
+  name: MobileViTV2_x0_5
  class_num: 1000

 # loss function config for traing/eval process

--- a/ppcls/configs/ImageNet/MobileViTv2/MobileViTv2_x1_0.yaml
+++ b/ppcls/configs/ImageNet/MobileViTv2/MobileViTv2_x1_0.yaml
@@ -28,7 +28,7 @@ EMA:

 # model architecture
 Arch:
-  name: MobileViTv2_x1_0
+  name: MobileViTV2_x1_0
  class_num: 1000

 # loss function config for traing/eval process

--- a/ppcls/configs/ImageNet/MobileViTv2/MobileViTv2_x1_5.yaml
+++ b/ppcls/configs/ImageNet/MobileViTv2/MobileViTv2_x1_5.yaml
@@ -29,7 +29,7 @@ EMA:

 # model architecture
 Arch:
-  name: MobileViTv2_x1_5
+  name: MobileViTV2_x1_5
  class_num: 1000

 # loss function config for traing/eval process

--- a/ppcls/configs/ImageNet/MobileViTv2/MobileViTv2_x2_0.yaml
+++ b/ppcls/configs/ImageNet/MobileViTv2/MobileViTv2_x2_0.yaml
@@ -28,7 +28,7 @@ EMA:

 # model architecture
 Arch:
-  name: MobileViTv2_x2_0
+  name: MobileViTV2_x2_0
  class_num: 1000

 # loss function config for traing/eval process

--- a/ppcls/data/preprocess/ops/randaugment.py
+++ b/ppcls/data/preprocess/ops/randaugment.py
@@ -264,7 +264,7 @@ class RandAugmentV2(RandAugment):


 class RandAugmentV3(RandAugment):
-    """Customed RandAugment for MobileViTv2"""
+    """Customed RandAugment for MobileViTV2"""

    def __init__(self,
                 num_layers=2,

--- a/test_tipc/configs/MobileViTv2/MobileViTv2_x1_0_train_infer_python.txt
+++ b/test_tipc/configs/MobileViTv2/MobileViTv2_x1_0_train_infer_python.txt
 ===========================train_params===========================
-model_name:MobileViTv2_x1_0
+model_name:MobileViTV2_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/MobileViTv2/MobileViTv2_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/MobileViTV2/MobileViTV2_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/MobileViTv2/MobileViTv2_x1_0.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_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/MobileViTv2/MobileViTv2_x1_0.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x0_5.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

--- a/test_tipc/configs/MobileViTv2/MobileViTv2_x1_5_train_infer_python.txt
+++ b/test_tipc/configs/MobileViTv2/MobileViTv2_x1_5_train_infer_python.txt
 ===========================train_params===========================
-model_name:MobileViTv2_x1_5
+model_name:MobileViTV2_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/MobileViTv2/MobileViTv2_x1_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/MobileViTV2/MobileViTV2_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/MobileViTv2/MobileViTv2_x1_5.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_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/MobileViTv2/MobileViTv2_x1_5.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x1_0.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

--- a/test_tipc/configs/MobileViTv2/MobileViTv2_x2_0_train_infer_python.txt
+++ b/test_tipc/configs/MobileViTv2/MobileViTv2_x2_0_train_infer_python.txt
 ===========================train_params===========================
-model_name:MobileViTv2_x2_0
+model_name:MobileViTV2_x1_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/MobileViTv2/MobileViTv2_x2_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/MobileViTV2/MobileViTV2_x1_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/MobileViTv2/MobileViTv2_x2_0.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x1_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/MobileViTv2/MobileViTv2_x2_0.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x1_5.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null

--- a/test_tipc/configs/MobileViTv2/MobileViTv2_x0_5_train_infer_python.txt
+++ b/test_tipc/configs/MobileViTv2/MobileViTv2_x0_5_train_infer_python.txt
 ===========================train_params===========================
-model_name:MobileViTv2_x0_5
+model_name:MobileViTV2_x2_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/MobileViTv2/MobileViTv2_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/MobileViTV2/MobileViTV2_x2_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/MobileViTv2/MobileViTv2_x0_5.yaml
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x2_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/MobileViTv2/MobileViTv2_x0_5.yaml
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x2_0.yaml
 quant_export:null
 fpgm_export:null
 distill_export:null