PaddleX/examples/rebar_count/code/prune.py

import paddlex as pdx
from paddlex import transforms as T

# 定义训练和验证时的transforms
# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/transforms/operators.py
train_transforms = T.Compose([
    T.MixupImage(mixup_epoch=250), T.RandomDistort(),
    T.RandomExpand(im_padding_value=[123.675, 116.28, 103.53]), T.RandomCrop(),
    T.RandomHorizontalFlip(), T.BatchRandomResize(
        target_sizes=[320, 352, 384, 416, 448, 480, 512, 544, 576, 608],
        interp='RANDOM'), T.Normalize(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

eval_transforms = T.Compose([
    T.Resize(
        608, interp='CUBIC'), T.Normalize(
            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 定义训练和验证所用的数据集
# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/datasets/voc.py#L29
train_dataset = pdx.datasets.VOCDetection(
    data_dir='dataset',
    file_list='dataset/train_list.txt',
    label_list='dataset/labels.txt',
    transforms=train_transforms,
    shuffle=True)

eval_dataset = pdx.datasets.VOCDetection(
    data_dir='dataset',
    file_list='dataset/val_list.txt',
    label_list='dataset/labels.txt',
    transforms=eval_transforms,
    shuffle=False)

# 加载模型
model = pdx.load_model('output/yolov3_darknet53/best_model')

# Step 1/3: 分析模型各层参数在不同的剪裁比例下的敏感度
# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/base.py#L352
model.analyze_sensitivity(
    dataset=eval_dataset,
    batch_size=1,
    save_dir='output/yolov3_darknet53/prune')

# Step 2/3: 根据选择的FLOPs减小比例对模型进行剪裁
# API说明：https://github.com/PaddlePaddle/PaddleX/blob/95c53dec89ab0f3769330fa445c6d9213986ca5f/paddlex/cv/models/base.py#L394
model.prune(pruned_flops=.2)

# Step 3/3: 对剪裁后的模型重新训练
# API说明：https://github.com/PaddlePaddle/PaddleX/blob/release/2.0-rc/paddlex/cv/models/detector.py#L154
# 各参数介绍与调整说明：https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html
model.train(
    num_epochs=270,
    train_dataset=train_dataset,
    train_batch_size=8,
    eval_dataset=eval_dataset,
    learning_rate=0.001 / 8,
    warmup_steps=1000,
    warmup_start_lr=0.0,
    save_interval_epochs=5,
    lr_decay_epochs=[216, 243],
    pretrain_weights=None,
    save_dir='output/yolov3_darknet53/prune')