| |
| import argparse |
|
|
| import torch.nn.functional as F |
| import torchvision |
| import torchvision.transforms as transforms |
| from torch.optim import SGD |
|
|
| from mmengine.evaluator import BaseMetric |
| from mmengine.model import BaseModel |
| from mmengine.runner import Runner |
|
|
|
|
| class MMResNet50(BaseModel): |
|
|
| def __init__(self): |
| super().__init__() |
| self.resnet = torchvision.models.resnet50() |
|
|
| def forward(self, imgs, labels, mode): |
| x = self.resnet(imgs) |
| if mode == 'loss': |
| return {'loss': F.cross_entropy(x, labels)} |
| elif mode == 'predict': |
| return x, labels |
|
|
|
|
| class Accuracy(BaseMetric): |
|
|
| def process(self, data_batch, data_samples): |
| score, gt = data_samples |
| self.results.append({ |
| 'batch_size': len(gt), |
| 'correct': (score.argmax(dim=1) == gt).sum().cpu(), |
| }) |
|
|
| def compute_metrics(self, results): |
| total_correct = sum(item['correct'] for item in results) |
| total_size = sum(item['batch_size'] for item in results) |
| return dict(accuracy=100 * total_correct / total_size) |
|
|
|
|
| def parse_args(): |
| parser = argparse.ArgumentParser(description='Distributed Training') |
| parser.add_argument( |
| '--launcher', |
| choices=['none', 'pytorch', 'slurm', 'mpi'], |
| default='none', |
| help='job launcher') |
| parser.add_argument('--local_rank', type=int, default=0) |
|
|
| args = parser.parse_args() |
| return args |
|
|
|
|
| def main(): |
| args = parse_args() |
| norm_cfg = dict(mean=[0.491, 0.482, 0.447], std=[0.202, 0.199, 0.201]) |
| train_set = torchvision.datasets.CIFAR10( |
| 'data/cifar10', |
| train=True, |
| download=True, |
| transform=transforms.Compose([ |
| transforms.RandomCrop(32, padding=4), |
| transforms.RandomHorizontalFlip(), |
| transforms.ToTensor(), |
| transforms.Normalize(**norm_cfg) |
| ])) |
| valid_set = torchvision.datasets.CIFAR10( |
| 'data/cifar10', |
| train=False, |
| download=True, |
| transform=transforms.Compose( |
| [transforms.ToTensor(), |
| transforms.Normalize(**norm_cfg)])) |
| train_dataloader = dict( |
| batch_size=32, |
| dataset=train_set, |
| sampler=dict(type='DefaultSampler', shuffle=True), |
| collate_fn=dict(type='default_collate')) |
| val_dataloader = dict( |
| batch_size=32, |
| dataset=valid_set, |
| sampler=dict(type='DefaultSampler', shuffle=False), |
| collate_fn=dict(type='default_collate')) |
| runner = Runner( |
| model=MMResNet50(), |
| work_dir='./work_dir', |
| train_dataloader=train_dataloader, |
| optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)), |
| train_cfg=dict(by_epoch=True, max_epochs=2, val_interval=1), |
| val_dataloader=val_dataloader, |
| val_cfg=dict(), |
| val_evaluator=dict(type=Accuracy), |
| launcher=args.launcher, |
| ) |
| runner.train() |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
