yolov5 稀疏训练

Yolov5是一种目标检测算法，稀疏训练是一种优化模型大小和推理速度的方法。下面是yolov5稀疏训练的步骤和代码示例：

1. 安装依赖库sparseml和wandb：

pip install sparseml wandb

2. 导入依赖库和模型：

import torch
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

from sparseml.pytorch.models import ModelRegistry, yolo_v5s
from sparseml.pytorch.utils import DataLoaderIterator, ModuleExporterPyTorch, PyTorchModule

3. 加载数据集：

transform = transforms.Compose([
    transforms.Resize((640, 640)),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

train_dataset = datasets.CocoDetection(
    '/path/to/coco/train2017',
    '/path/to/coco/annotations/instances_train2017.json',
    transform=transform
)

train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)

4. 加载模型：

model = ModelRegistry.create('yolov5s')

5. 定义稀疏训练参数：

sparsity_params = {
    'start_epoch': 0,
    'end_epoch': 50,
    'target_sparsity': 0.5,
    'sparsity_function': 'exponential',
    'sparsity_function_params': {
        'initial_sparsity': 0.0,
        'final_sparsity': 0.5,
        'sparsity_half_life': 10,
    },
    'op_types': ['Conv2d'],
    'op_names': ['model.0.conv.conv', 'model.1.conv.conv', 'model.2.conv.conv'],
    'importance': 'layer',
    'magnitude_sparsity': False,
    'params': {
        'weight': {
            'schedule': 'polynomial',
            'power': 1,
            'initial_sparsity': 0.0,
            'final_sparsity': 0.5,
        },
        'bias': {
            'schedule': 'constant',
            'initial_sparsity': 0.0,
            'final_sparsity': 0.0,
        },
    },
}

6. 进行稀疏训练：

for epoch in range(50):
    model.train()
    train_loss = 0
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = compute_loss(output, target)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
        
        # apply sparsity
        if epoch >= sparsity_params['start_epoch'] and epoch <= sparsity_params['end_epoch']:
            sparsity_level = sparsity_params['sparsity_function_params']['initial_sparsity'] + \
                             (sparsity_params['sparsity_function_params']['final_sparsity'] - sparsity_params['sparsity_function_params']['initial_sparsity']) * \
                             (1 - (epoch - sparsity_params['start_epoch']) / (sparsity_params['end_epoch'] - sparsity_params['start_epoch']))
            model.apply_sparsity(sparsity_level, sparsity_params)

7. 导出稀疏模型：

exporter = ModuleExporterPyTorch(model, PyTorchModule)
exporter.export('sparse_yolov5s.pt')