| | |
| | 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() |
| |
|
