From 00f3d48d02dc401fb905a6e8faf2c80390aac11e Mon Sep 17 00:00:00 2001 From: cuicheng01 <45199522+cuicheng01@users.noreply.github.com> Date: Sun, 11 Apr 2021 22:21:26 +0800 Subject: [PATCH] Add quick_start_professional.md (#663) * Update quick_start_professional.md --- ...ileNetV3_large_x1_0_CIFAR100_finetune.yaml | 78 +++++ ...50_vd_distill_MV3_large_x1_0_CIFAR100.yaml | 75 +++++ .../professional/ResNet50_vd_CIFAR100.yaml | 78 +++++ .../ResNet50_vd_CIFAR100_finetune.yaml | 78 +++++ .../ResNet50_vd_mixup_CIFAR100_finetune.yaml | 78 +++++ .../ResNet50_vd_ssld_CIFAR100_finetune.yaml | 78 +++++ .../tutorials/quick_start_professional.md | 293 ++++++++++++++++++ 7 files changed, 758 insertions(+) create mode 100644 configs/quick_start/professional/MobileNetV3_large_x1_0_CIFAR100_finetune.yaml create mode 100644 configs/quick_start/professional/R50_vd_distill_MV3_large_x1_0_CIFAR100.yaml create mode 100644 configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml create mode 100644 configs/quick_start/professional/ResNet50_vd_CIFAR100_finetune.yaml create mode 100644 configs/quick_start/professional/ResNet50_vd_mixup_CIFAR100_finetune.yaml create mode 100644 configs/quick_start/professional/ResNet50_vd_ssld_CIFAR100_finetune.yaml create mode 100644 docs/zh_CN/tutorials/quick_start_professional.md diff --git a/configs/quick_start/professional/MobileNetV3_large_x1_0_CIFAR100_finetune.yaml b/configs/quick_start/professional/MobileNetV3_large_x1_0_CIFAR100_finetune.yaml new file mode 100644 index 00000000..e9913365 --- /dev/null +++ b/configs/quick_start/professional/MobileNetV3_large_x1_0_CIFAR100_finetune.yaml @@ -0,0 +1,78 @@ +mode: 'train' +ARCHITECTURE: + name: 'MobileNetV3_large_x1_0' + +checkpoints: "" +pretrained_model: "./pretrained/MobileNetV3_large_x1_0_pretrained" +model_save_dir: "./output/" +classes_num: 100 +total_images: 50000 +save_interval: 1 +validate: True +valid_interval: 1 +epochs: 100 +topk: 5 +image_shape: [3, 32, 32] +use_mix: False + +LEARNING_RATE: + function: 'Cosine' + params: + lr: 0.04 + +OPTIMIZER: + function: 'Momentum' + params: + momentum: 0.9 + regularizer: + function: 'L2' + factor: 0.0001 + +TRAIN: + batch_size: 1024 + num_workers: 4 + file_list: "./dataset/CIFAR100/train_list.txt" + data_dir: "./dataset/CIFAR100/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - RandCropImage: + size: 32 + - RandFlipImage: + flip_code: 1 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + + mix: + - MixupOperator: + alpha: 0.2 + + +VALID: + batch_size: 256 + num_workers: 0 + file_list: "./dataset/CIFAR100/test_list.txt" + data_dir: "./dataset/CIFAR100/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - ResizeImage: + resize_short: 36 + - CropImage: + size: 32 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: diff --git a/configs/quick_start/professional/R50_vd_distill_MV3_large_x1_0_CIFAR100.yaml b/configs/quick_start/professional/R50_vd_distill_MV3_large_x1_0_CIFAR100.yaml new file mode 100644 index 00000000..c8bf6900 --- /dev/null +++ b/configs/quick_start/professional/R50_vd_distill_MV3_large_x1_0_CIFAR100.yaml @@ -0,0 +1,75 @@ +mode: 'train' +ARCHITECTURE: + name: 'ResNet50_vd_distill_MobileNetV3_large_x1_0' + +pretrained_model: + - "./pretrained/CIFAR100_R50_vd_final/ppcls" + - "./pretrained/MobileNetV3_large_x1_0_pretrained" +model_save_dir: "./output/" +classes_num: 100 +total_images: 50000 +save_interval: 1 +validate: True +valid_interval: 1 +epochs: 100 +topk: 5 +image_shape: [3, 32, 32] + +use_distillation: True + +LEARNING_RATE: + function: 'Cosine' + params: + lr: 0.04 + +OPTIMIZER: + function: 'Momentum' + params: + momentum: 0.9 + regularizer: + function: 'L2' + factor: 0.0001 + +TRAIN: + batch_size: 1024 + num_workers: 0 + file_list: "./dataset/CIFAR100/train_list.txt" + data_dir: "./dataset/CIFAR100/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - RandCropImage: + size: 32 + - RandFlipImage: + flip_code: 1 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + +VALID: + batch_size: 256 + num_workers: 0 + file_list: "./dataset/CIFAR100/test_list.txt" + data_dir: "./dataset/CIFAR100/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - ResizeImage: + resize_short: 36 + - CropImage: + size: 32 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: diff --git a/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml b/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml new file mode 100644 index 00000000..6d3a82e1 --- /dev/null +++ b/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml @@ -0,0 +1,78 @@ +mode: 'train' +ARCHITECTURE: + name: 'ResNet50_vd' + +checkpoints: "" +pretrained_model: "" +model_save_dir: "./output/" +classes_num: 100 +total_images: 50000 +save_interval: 1 +validate: True +valid_interval: 1 +epochs: 100 +topk: 5 +image_shape: [3, 32, 32] +use_mix: False + +LEARNING_RATE: + function: 'Cosine' + params: + lr: 0.04 + +OPTIMIZER: + function: 'Momentum' + params: + momentum: 0.9 + regularizer: + function: 'L2' + factor: 0.0001 + +TRAIN: + batch_size: 1024 + num_workers: 4 + file_list: "./dataset/CIFAR100/train_list.txt" + data_dir: "./dataset/CIFAR100/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - RandCropImage: + size: 32 + - RandFlipImage: + flip_code: 1 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + + mix: + - MixupOperator: + alpha: 0.2 + + +VALID: + batch_size: 256 + num_workers: 0 + file_list: "./dataset/CIFAR100/test_list.txt" + data_dir: "./dataset/CIFAR100/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - ResizeImage: + resize_short: 36 + - CropImage: + size: 32 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: diff --git a/configs/quick_start/professional/ResNet50_vd_CIFAR100_finetune.yaml b/configs/quick_start/professional/ResNet50_vd_CIFAR100_finetune.yaml new file mode 100644 index 00000000..b9ce100c --- /dev/null +++ b/configs/quick_start/professional/ResNet50_vd_CIFAR100_finetune.yaml @@ -0,0 +1,78 @@ +mode: 'train' +ARCHITECTURE: + name: 'ResNet50_vd' + +checkpoints: "" +pretrained_model: "./pretrained/ResNet50_vd_pretrained" +model_save_dir: "./output/" +classes_num: 100 +total_images: 50000 +save_interval: 1 +validate: True +valid_interval: 1 +epochs: 100 +topk: 5 +image_shape: [3, 32, 32] +use_mix: False + +LEARNING_RATE: + function: 'Cosine' + params: + lr: 0.04 + +OPTIMIZER: + function: 'Momentum' + params: + momentum: 0.9 + regularizer: + function: 'L2' + factor: 0.0001 + +TRAIN: + batch_size: 1024 + num_workers: 4 + file_list: "./dataset/CIFAR100/train_list.txt" + data_dir: "./dataset/CIFAR100/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - RandCropImage: + size: 32 + - RandFlipImage: + flip_code: 1 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + + mix: + - MixupOperator: + alpha: 0.2 + + +VALID: + batch_size: 256 + num_workers: 0 + file_list: "./dataset/CIFAR100/test_list.txt" + data_dir: "./dataset/CIFAR100/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - ResizeImage: + resize_short: 36 + - CropImage: + size: 32 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: diff --git a/configs/quick_start/professional/ResNet50_vd_mixup_CIFAR100_finetune.yaml b/configs/quick_start/professional/ResNet50_vd_mixup_CIFAR100_finetune.yaml new file mode 100644 index 00000000..acbe0133 --- /dev/null +++ b/configs/quick_start/professional/ResNet50_vd_mixup_CIFAR100_finetune.yaml @@ -0,0 +1,78 @@ +mode: 'train' +ARCHITECTURE: + name: 'ResNet50_vd' + +checkpoints: "" +pretrained_model: "./pretrained/ResNet50_vd_pretrained" +model_save_dir: "./output/" +classes_num: 100 +total_images: 50000 +save_interval: 1 +validate: True +valid_interval: 1 +epochs: 100 +topk: 5 +image_shape: [3, 32, 32] +use_mix: True + +LEARNING_RATE: + function: 'Cosine' + params: + lr: 0.04 + +OPTIMIZER: + function: 'Momentum' + params: + momentum: 0.9 + regularizer: + function: 'L2' + factor: 0.0001 + +TRAIN: + batch_size: 1024 + num_workers: 4 + file_list: "./dataset/CIFAR100/train_list.txt" + data_dir: "./dataset/CIFAR100/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - RandCropImage: + size: 32 + - RandFlipImage: + flip_code: 1 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + + mix: + - MixupOperator: + alpha: 0.2 + + +VALID: + batch_size: 256 + num_workers: 0 + file_list: "./dataset/CIFAR100/test_list.txt" + data_dir: "./dataset/CIFAR100/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - ResizeImage: + resize_short: 36 + - CropImage: + size: 32 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: diff --git a/configs/quick_start/professional/ResNet50_vd_ssld_CIFAR100_finetune.yaml b/configs/quick_start/professional/ResNet50_vd_ssld_CIFAR100_finetune.yaml new file mode 100644 index 00000000..b90d08ec --- /dev/null +++ b/configs/quick_start/professional/ResNet50_vd_ssld_CIFAR100_finetune.yaml @@ -0,0 +1,78 @@ +mode: 'train' +ARCHITECTURE: + name: 'ResNet50_vd' + +checkpoints: "" +pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained" +model_save_dir: "./output/" +classes_num: 100 +total_images: 50000 +save_interval: 1 +validate: True +valid_interval: 1 +epochs: 100 +topk: 5 +image_shape: [3, 32, 32] +use_mix: False + +LEARNING_RATE: + function: 'Cosine' + params: + lr: 0.04 + +OPTIMIZER: + function: 'Momentum' + params: + momentum: 0.9 + regularizer: + function: 'L2' + factor: 0.0001 + +TRAIN: + batch_size: 1024 + num_workers: 4 + file_list: "./dataset/CIFAR100/train_list.txt" + data_dir: "./dataset/CIFAR100/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - RandCropImage: + size: 32 + - RandFlipImage: + flip_code: 1 + - NormalizeImage: + scale: 1./255. + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: + + mix: + - MixupOperator: + alpha: 0.2 + + +VALID: + batch_size: 256 + num_workers: 0 + file_list: "./dataset/CIFAR100/test_list.txt" + data_dir: "./dataset/CIFAR100/" + shuffle_seed: 0 + transforms: + - DecodeImage: + to_rgb: True + to_np: False + channel_first: False + - ResizeImage: + resize_short: 36 + - CropImage: + size: 32 + - NormalizeImage: + scale: 1.0/255.0 + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + order: '' + - ToCHWImage: diff --git a/docs/zh_CN/tutorials/quick_start_professional.md b/docs/zh_CN/tutorials/quick_start_professional.md new file mode 100644 index 00000000..eced9d8d --- /dev/null +++ b/docs/zh_CN/tutorials/quick_start_professional.md @@ -0,0 +1,293 @@ +# 30分钟玩转PaddleClas（专业版） + +此处提供了专业用户在linux操作系统上使用PaddleClas的快速上手教程，主要内容包括基于CIFAR-100数据集和NUS-WIDE-SCENE数据集，快速体验不同模型的单标签训练及多标签训练、加载不同预训练模型、SSLD知识蒸馏方案和数据增广的效果。请事先参考[安装指南](install.md)配置运行环境和克隆PaddleClas代码。 + + +## 一、数据和模型准备 + +### 1.1 数据准备 + + +* 进入PaddleClas目录。 + +``` +cd path_to_PaddleClas +``` + +#### 1.1.1 准备CIFAR100 + +* 进入`dataset/`目录，下载并解压CIFAR100数据集。 + +```shell +cd dataset +wget https://paddle-imagenet-models-name.bj.bcebos.com/data/CIFAR100.tar +tar -xf CIFAR100.tar +cd ../ +``` + +#### 1.1.2 准备NUS-WIDE-SCENE + +* 创建并进入`dataset/NUS-WIDE-SCENE`目录，下载并解压NUS-WIDE-SCENE数据集。 + +```shell +mkdir dataset/NUS-WIDE-SCENE +cd dataset/NUS-WIDE-SCENE +wget https://paddle-imagenet-models-name.bj.bcebos.com/data/NUS-SCENE-dataset.tar +tar -xf NUS-SCENE-dataset.tar +``` + +* 返回`PaddleClas`根目录 + +``` +cd ../../ +``` + +### 1.2 模型准备 + +通过下面的命令下载所需要的预训练模型。 + +```bash +mkdir pretrained +cd pretrained +wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams +wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams +wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_pretrained.pdparams +cd ../ +``` + + +## 二、模型训练 + +### 2.1 单标签训练 + +#### 2.1.1 零基础训练：不加载预训练模型的训练 + +* 基于ResNet50_vd模型，训练脚本如下所示。 + +```shell +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + --gpus="0,1,2,3" \ + tools/train.py \ + -c ./configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml \ + -o model_save_dir="output_CIFAR" +``` + + +验证集的最高准确率为0.415左右。 + + +#### 2.1.2 迁移学习 + +* 基于ImageNet1k分类预训练模型ResNet50_vd_pretrained(准确率79.12\%)进行微调，训练脚本如下所示。 + +```shell +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + --gpus="0,1,2,3" \ + tools/train.py \ + -c ./configs/quick_start/professional/ResNet50_vd_CIFAR100_finetune.yaml \ + -o model_save_dir="output_CIFAR" +``` + +验证集最高准确率为0.718左右，加载预训练模型之后，CIFAR100数据集精度大幅提升，绝对精度涨幅30\%。 + +* 基于ImageNet1k分类预训练模型ResNet50_vd_ssld_pretrained(准确率82.39\%)进行微调，训练脚本如下所示。 + +```shell +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + --gpus="0,1,2,3" \ + tools/train.py \ + -c ./configs/quick_start/professional/ResNet50_vd_ssld_CIFAR100_finetune.yaml \ + -o model_save_dir="output_CIFAR" +``` + +最终CIFAR100验证集上精度指标为0.73，相对于79.12\%预训练模型的微调结构，新数据集指标可以再次提升1.2\%。 + +* 替换backbone为MobileNetV3_large_x1_0进行微调，训练脚本如下所示。 + +```shell +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + --gpus="0,1,2,3" \ + tools/train.py \ + -c ./configs/quick_start/professional/MobileNetV3_large_x1_0_CIFAR100_finetune.yaml \ + -o model_save_dir="output_CIFAR" +``` + +验证集最高准确率为0.601左右, 较ResNet50_vd低近12%。 + + +### 2.2 多标签训练 + +* 基于ImageNet1k分类预训练模型进行微调NUS-WIDE-SCENE数据集，该是数据集NUS-WIDE的一个子集，类别数目为33类，图片总数是17463张，训练脚本如下所示。 + +```shell +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + --gpus="0,1,2,3" \ + tools/train.py \ + -c ./configs/quick_start/ResNet50_vd_multilabel.yaml \ + -o model_save_dir="output_NUS-WIDE-SCENE" +``` + +训练10epoch之后，验证集最好的准确率应该在0.95左右。 + +* 零基础训练(不加载预训练模型)只需要将配置文件中的`pretrained_model`置为`""`即可。 + + +## 三、数据增广 + +PaddleClas包含了很多数据增广的方法，如Mixup、Cutout、RandomErasing等，具体的方法可以参考[数据增广的章节](../advanced_tutorials/image_augmentation/ImageAugment.md)。 + +### 数据增广的尝试-Mixup + +基于`3.3节`中的训练方法，结合Mixup的数据增广方式进行训练，具体的训练脚本如下所示。 + +```shell +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + --gpus="0,1,2,3" \ + tools/train.py \ + -c ./configs/quick_start/professional/ResNet50_vd_mixup_CIFAR100_finetune.yaml \ + -o model_save_dir="output_CIFAR" + +``` + +最终CIFAR100验证集上的精度为0.73，使用数据增广可以使得模型精度再次提升约1.2\%。 + + + +* **注意** + + * 其他数据增广的配置文件可以参考`configs/DataAugment`中的配置文件。 + + * 训练CIFAR100的迭代轮数较少，因此进行训练时，验证集的精度指标可能会有1\%左右的波动。 + + +## 四、知识蒸馏 + + +PaddleClas包含了自研的SSLD知识蒸馏方案，具体的内容可以参考[知识蒸馏章节](../advanced_tutorials/distillation/distillation.md)本小节将尝试使用知识蒸馏技术对MobileNetV3_large_x1_0模型进行训练，使用`2.1.2小节`训练得到的ResNet50_vd模型作为蒸馏所用的教师模型，首先将`2.1.2小节`训练得到的ResNet50_vd模型保存到指定目录，脚本如下。 + +```shell +cp -r output_CIFAR/ResNet50_vd/best_model/ ./pretrained/CIFAR100_R50_vd_final/ +``` + +配置文件中数据数量、模型结构、预训练地址以及训练的数据配置如下： + +```yaml +total_images: 50000 +ARCHITECTURE: + name: 'ResNet50_vd_distill_MobileNetV3_large_x1_0' +pretrained_model: + - "./pretrained/CIFAR100_R50_vd_final/ppcls" + - "./pretrained/MobileNetV3_large_x1_0_pretrained/” +``` + +最终的训练脚本如下所示。 + +```shell +export CUDA_VISIBLE_DEVICES=0,1,2,3 +python3 -m paddle.distributed.launch \ + --gpus="0,1,2,3" \ + tools/train.py \ + -c ./configs/quick_start/professional/R50_vd_distill_MV3_large_x1_0_CIFAR100.yaml \ + -o model_save_dir="output_CIFAR" + +``` + +最终CIFAR100验证集上的精度为64.4\%，使用教师模型进行知识蒸馏，MobileNetV3的精度涨幅4.3\%。 + +* **注意** + + * 蒸馏过程中，教师模型使用的预训练模型为CIFAR100数据集上的训练结果，学生模型使用的是ImageNet1k数据集上精度为75.32\%的MobileNetV3_large_x1_0预训练模型。 + + * 该蒸馏过程无须使用真实标签，所以可以使用更多的无标签数据，在使用过程中，可以将无标签数据生成假的train_list.txt，然后与真实的train_list.txt进行合并, 用户可以根据自己的数据自行体验。 + + +## 五、模型评估与推理 + +### 5.1 单标签分类模型评估与推理 + +#### 5.1.1 单标签分类模型评估。 + +训练好模型之后，可以通过以下命令实现对模型精度的评估。 + +```bash +python3 tools/eval.py \ + -c ./configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml \ + -o pretrained_model="./output_CIFAR/ResNet50_vd/best_model/ppcls" +``` + +#### 5.1.2 单标签分类模型预测 + +模型训练完成之后，可以加载训练得到的预训练模型，进行模型预测。在模型库的 `tools/infer/infer.py` 中提供了完整的示例，只需执行下述命令即可完成模型预测： + +```python +python3 tools/infer/infer.py \ + -i "./dataset/CIFAR100/test/0/0001.png" \ + --model ResNet50_vd \ + --pretrained_model "./output_CIFAR/ResNet50_vd/best_model/ppcls" \ + --use_gpu True +``` + + +#### 5.1.3 单标签分类使用inference模型进行模型推理 + +通过导出inference模型，PaddlePaddle支持使用预测引擎进行预测推理。接下来介绍如何用预测引擎进行推理： +首先，对训练好的模型进行转换： + +```bash +python3 tools/export_model.py \ + --model ResNet50_vd \ + --pretrained_model ./output_CIFAR/ResNet50_vd/best_model/ppcls \ + --output_path ./inference \ + --class_dim 100 \ + --img_size 32 +``` + +其中，参数`--model`用于指定模型名称，`--pretrained_model`用于指定模型文件路径，`--output_path`用于指定转换后模型的存储路径。 + +* **注意**： + * `--output_path`表示输出的inference模型文件夹路径，若`--output_path=./inference`，则会在`inference`文件夹下生成`inference.pdiparams`、`inference.pdmodel`和`inference.pdiparams.info`文件。 + + * 可以通过设置参数`--img_size`指定模型输入图像的`shape`，默认为`224`，表示图像尺寸为`224*224`，请根据实际情况修改。 + +上述命令将生成模型结构文件（`inference.pdmodel`）和模型权重文件（`inference.pdiparams`），然后可以使用预测引擎进行推理： + +```bash +python3 tools/infer/predict.py \ + --image_file "./dataset/CIFAR100/test/0/0001.png" \ + --model_file "./inference/inference.pdmodel" \ + --params_file "./inference/inference.pdiparams" \ + --use_gpu=True \ + --use_tensorrt=False +``` + +### 5.2 多标签分类模型评估与预测 + +#### 5.2.1 多标签分类模型评估 + +训练好模型之后，可以通过以下命令实现对模型精度的评估。 + +```bash +python3 tools/eval.py \ + -c ./configs/quick_start/ResNet50_vd_multilabel.yaml \ + -o pretrained_model="./output_NUS-WIDE-SCENE/ResNet50_vd/best_model/ppcls" +``` + +评估指标采用mAP，验证集的mAP应该在0.57左右。 + +#### 5.2.2 多标签分类模型预测 + +```bash +python3 tools/infer/infer.py \ + -i "./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images/0199_434752251.jpg" \ + --model ResNet50_vd \ + --pretrained_model "./output_NUS-WIDE-SCENE/ResNet50_vd/best_model/ppcls" \ + --use_gpu True \ + --multilabel True \ + --class_num 33 +``` -- GitLab