mirror of
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rename: v3 -> V3
This commit is contained in:
parent
2091a59ff5
commit
a1fa19cd29
@ -1,4 +1,4 @@
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# MobileviTv3
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# MobileviTV3
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-----
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## 目录
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@ -24,8 +24,8 @@
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### 1.1 模型简介
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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。
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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)。
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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。
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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)。
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<a name='1.2'></a>
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@ -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
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| Models | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPs<br>(G) | Params<br>(M) |
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|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
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| MobileViTv3_XXS | 0.7087 | 0.8976 | 0.7098 | - | 289.02 | 1.25 |
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| MobileViTv3_XS | 0.7663 | 0.9332 | 0.7671 | - | 926.98 | 2.49 |
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| MobileViTv3_S | 0.7928 | 0.9454 | 0.7930 | - | 1841.39 | 5.76 |
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| MobileViTv3_XXS_L2 | 0.7028 | 0.8942 | 0.7023 | - | 256.97 | 1.15 |
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| MobileViTv3_XS_L2 | 0.7607 | 0.9300 | 0.7610 | - | 852.82 | 2.26 |
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| MobileViTv3_S_L2 | 0.7907 | 0.9440 | 0.7906 | - | 1651.96 | 5.17 |
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| MobileViTv3_x0_5 | 0.7200 | 0.9083 | 0.7233 | - | 481.33 | 1.43 |
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| MobileViTv3_x0_75 | 0.7626 | 0.9308 | 0.7655 | - | 1064.48 | 3.00 |
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| MobileViTv3_x1_0 | 0.7838 | 0.9421 | 0.7864 | - | 1875.96 | 5.14 |
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| MobileViTV3_XXS | 0.7087 | 0.8976 | 0.7098 | - | 289.02 | 1.25 |
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| MobileViTV3_XS | 0.7663 | 0.9332 | 0.7671 | - | 926.98 | 2.49 |
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| MobileViTV3_S | 0.7928 | 0.9454 | 0.7930 | - | 1841.39 | 5.76 |
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| MobileViTV3_XXS_L2 | 0.7028 | 0.8942 | 0.7023 | - | 256.97 | 1.15 |
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| MobileViTV3_XS_L2 | 0.7607 | 0.9300 | 0.7610 | - | 852.82 | 2.26 |
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| MobileViTV3_S_L2 | 0.7907 | 0.9440 | 0.7906 | - | 1651.96 | 5.17 |
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| MobileViTV3_x0_5 | 0.7200 | 0.9083 | 0.7233 | - | 481.33 | 1.43 |
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| MobileViTV3_x0_75 | 0.7626 | 0.9308 | 0.7655 | - | 1064.48 | 3.00 |
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| MobileViTV3_x1_0 | 0.7838 | 0.9421 | 0.7864 | - | 1875.96 | 5.14 |
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**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。
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@ -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
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## 3. 模型训练、评估和预测
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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)。
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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)。
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**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。
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@ -79,8 +79,8 @@ from .model_zoo.cvt import CvT_13_224, CvT_13_384, CvT_21_224, CvT_21_384, CvT_W
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from .model_zoo.micronet import MicroNet_M0, MicroNet_M1, MicroNet_M2, MicroNet_M3
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from .model_zoo.mobilenext import MobileNeXt_x0_35, MobileNeXt_x0_5, MobileNeXt_x0_75, MobileNeXt_x1_0, MobileNeXt_x1_4
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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
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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
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from .model_zoo.tinynet import TinyNet_A, TinyNet_B, TinyNet_C, TinyNet_D, TinyNet_E
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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
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from .variant_models.resnet_variant import ResNet50_last_stage_stride1
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from .variant_models.resnet_variant import ResNet50_adaptive_max_pool2d
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@ -28,7 +28,7 @@ EMA:
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# model architecture
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Arch:
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name: MobileViTv3_S
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name: MobileViTV3_S
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class_num: 1000
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dropout: 0.1
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@ -28,7 +28,7 @@ EMA:
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# model architecture
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Arch:
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name: MobileViTv3_S_L2
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name: MobileViTV3_S_L2
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class_num: 1000
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dropout: 0.1
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@ -28,7 +28,7 @@ EMA:
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# model architecture
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Arch:
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name: MobileViTv3_XS
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name: MobileViTV3_XS
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class_num: 1000
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dropout: 0.1
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@ -28,7 +28,7 @@ EMA:
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# model architecture
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Arch:
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name: MobileViTv3_XS_L2
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name: MobileViTV3_XS_L2
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class_num: 1000
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dropout: 0.1
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@ -28,7 +28,7 @@ EMA:
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# model architecture
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Arch:
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name: MobileViTv3_XXS
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name: MobileViTV3_XXS
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class_num: 1000
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dropout: 0.05
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150
ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XXS_L2.yaml
Normal file
150
ppcls/configs/ImageNet/MobileViTV3/MobileViTV3_XXS_L2.yaml
Normal file
@ -0,0 +1,150 @@
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# global configs
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Global:
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checkpoints: null
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pretrained_model: null
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output_dir: ./output/
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device: gpu
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save_interval: 1
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eval_during_train: True
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eval_interval: 1
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epochs: 300
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print_batch_step: 10
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use_visualdl: False
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# used for static mode and model export
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image_shape: [3, 256, 256]
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save_inference_dir: ./inference
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use_dali: False
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# mixed precision training
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AMP:
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scale_loss: 65536
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use_dynamic_loss_scaling: True
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# O1: mixed fp16
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level: O1
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# model ema
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EMA:
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decay: 0.9995
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# model architecture
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Arch:
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name: MobileViTV3_XXS_L2
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class_num: 1000
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dropout: 0.1
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# loss function config for traing/eval process
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Loss:
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Train:
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- CELoss:
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weight: 1.0
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epsilon: 0.1
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Eval:
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- CELoss:
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weight: 1.0
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Optimizer:
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name: AdamW
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beta1: 0.9
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beta2: 0.999
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epsilon: 1e-8
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weight_decay: 0.01
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lr:
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name: Cosine
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learning_rate: 0.002 # for total batch size 384
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eta_min: 0.0002
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warmup_epoch: 1 # 3000 iterations
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warmup_start_lr: 0.0002
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# data loader for train and eval
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DataLoader:
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Train:
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dataset:
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name: MultiScaleDataset
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image_root: ./dataset/ILSVRC2012/
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cls_label_path: ./dataset/ILSVRC2012/train_list.txt
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transform_ops:
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- DecodeImage:
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to_rgb: True
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channel_first: False
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- RandCropImage:
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size: 256
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interpolation: bilinear
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use_log_aspect: True
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- RandFlipImage:
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flip_code: 1
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.0, 0.0, 0.0]
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std: [1.0, 1.0, 1.0]
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order: ''
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# support to specify width and height respectively:
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# scales: [(256,256) (160,160), (192,192), (224,224) (288,288) (320,320)]
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sampler:
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name: MultiScaleSampler
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scales: [256, 160, 192, 224, 288, 320]
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# first_bs: batch size for the first image resolution in the scales list
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# divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
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first_bs: 48
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divided_factor: 32
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is_training: True
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loader:
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num_workers: 4
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use_shared_memory: True
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Eval:
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dataset:
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name: ImageNetDataset
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image_root: ./dataset/ILSVRC2012/
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cls_label_path: ./dataset/ILSVRC2012/val_list.txt
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transform_ops:
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- DecodeImage:
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to_rgb: True
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channel_first: False
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- ResizeImage:
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resize_short: 288
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interpolation: bilinear
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- CropImage:
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size: 256
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.0, 0.0, 0.0]
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std: [1.0, 1.0, 1.0]
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order: ''
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sampler:
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name: DistributedBatchSampler
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batch_size: 48
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drop_last: False
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shuffle: False
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loader:
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num_workers: 4
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use_shared_memory: True
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Infer:
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infer_imgs: docs/images/inference_deployment/whl_demo.jpg
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batch_size: 10
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transforms:
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- DecodeImage:
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to_rgb: True
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channel_first: False
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- ResizeImage:
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resize_short: 288
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interpolation: bilinear
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- CropImage:
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size: 256
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.0, 0.0, 0.0]
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std: [1.0, 1.0, 1.0]
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order: ''
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- ToCHWImage:
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PostProcess:
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name: Topk
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topk: 5
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class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
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Metric:
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Train:
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- TopkAcc:
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topk: [1, 5]
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Eval:
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- TopkAcc:
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topk: [1, 5]
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@ -28,7 +28,7 @@ EMA:
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# model architecture
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Arch:
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name: MobileViTv3_x0_5
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name: MobileViTV3_x0_5
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class_num: 1000
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classifier_dropout: 0.
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# model architecture
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Arch:
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name: MobileViTv3_x0_75
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name: MobileViTV3_x0_75
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class_num: 1000
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classifier_dropout: 0.
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# model architecture
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Arch:
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name: MobileViTv3_x1_0
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name: MobileViTV3_x1_0
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class_num: 1000
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classifier_dropout: 0.
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@ -1,150 +0,0 @@
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# global configs
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Global:
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checkpoints: null
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pretrained_model: null
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output_dir: ./output/
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device: gpu
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save_interval: 1
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eval_during_train: True
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eval_interval: 1
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epochs: 300
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print_batch_step: 10
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use_visualdl: False
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# used for static mode and model export
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image_shape: [3, 256, 256]
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save_inference_dir: ./inference
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use_dali: False
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# mixed precision training
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AMP:
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scale_loss: 65536
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use_dynamic_loss_scaling: True
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# O1: mixed fp16
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level: O1
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# model ema
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EMA:
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decay: 0.9995
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# model architecture
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Arch:
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name: MobileViTv3_XXS_L2
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class_num: 1000
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dropout: 0.1
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|
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# loss function config for traing/eval process
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Loss:
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Train:
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- CELoss:
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weight: 1.0
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epsilon: 0.1
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Eval:
|
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- CELoss:
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weight: 1.0
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Optimizer:
|
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name: AdamW
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beta1: 0.9
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beta2: 0.999
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epsilon: 1e-8
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weight_decay: 0.01
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lr:
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name: Cosine
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learning_rate: 0.002 # for total batch size 384
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eta_min: 0.0002
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warmup_epoch: 1 # 3000 iterations
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warmup_start_lr: 0.0002
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|
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# data loader for train and eval
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DataLoader:
|
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Train:
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dataset:
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name: MultiScaleDataset
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image_root: ./dataset/ILSVRC2012/
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cls_label_path: ./dataset/ILSVRC2012/train_list.txt
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transform_ops:
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- DecodeImage:
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to_rgb: True
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channel_first: False
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- RandCropImage:
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size: 256
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interpolation: bilinear
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use_log_aspect: True
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- RandFlipImage:
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flip_code: 1
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.0, 0.0, 0.0]
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std: [1.0, 1.0, 1.0]
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order: ''
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# support to specify width and height respectively:
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# scales: [(256,256) (160,160), (192,192), (224,224) (288,288) (320,320)]
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sampler:
|
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name: MultiScaleSampler
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scales: [256, 160, 192, 224, 288, 320]
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# first_bs: batch size for the first image resolution in the scales list
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# divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
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first_bs: 48
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divided_factor: 32
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is_training: True
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loader:
|
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num_workers: 4
|
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use_shared_memory: True
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Eval:
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dataset:
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name: ImageNetDataset
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image_root: ./dataset/ILSVRC2012/
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cls_label_path: ./dataset/ILSVRC2012/val_list.txt
|
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transform_ops:
|
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- DecodeImage:
|
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to_rgb: True
|
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channel_first: False
|
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- ResizeImage:
|
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resize_short: 288
|
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interpolation: bilinear
|
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- CropImage:
|
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size: 256
|
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- NormalizeImage:
|
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scale: 1.0/255.0
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mean: [0.0, 0.0, 0.0]
|
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std: [1.0, 1.0, 1.0]
|
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order: ''
|
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sampler:
|
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name: DistributedBatchSampler
|
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batch_size: 48
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
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num_workers: 4
|
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use_shared_memory: True
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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]
|
@ -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
|
@ -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
|
@ -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
|
@ -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
|
@ -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
|
@ -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
|
@ -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
|
@ -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
|
@ -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
|
Loading…
x
Reference in New Issue
Block a user