| import argparse |
| import sys |
| import time |
|
|
| import torch |
| from omegaconf import OmegaConf |
|
|
|
|
| sys.path.append(".") |
| from utils.build_utils import build_from_cfg |
|
|
|
|
| parser = argparse.ArgumentParser( |
| prog="AMT", |
| description="Speed¶meter benchmark", |
| ) |
| parser.add_argument("-c", "--config", default="cfgs/AMT-S.yaml") |
| args = parser.parse_args() |
|
|
| cfg_path = args.config |
| network_cfg = OmegaConf.load(cfg_path).network |
| model = build_from_cfg(network_cfg) |
| model = model.cuda() |
| model.eval() |
|
|
| img0 = torch.randn(1, 3, 256, 448).cuda() |
| img1 = torch.randn(1, 3, 256, 448).cuda() |
| embt = torch.tensor(1 / 2).float().view(1, 1, 1, 1).cuda() |
|
|
| with torch.no_grad(): |
| for i in range(100): |
| out = model(img0, img1, embt, eval=True) |
| torch.cuda.synchronize() |
| time_stamp = time.time() |
| for i in range(1000): |
| out = model(img0, img1, embt, eval=True) |
| torch.cuda.synchronize() |
| print("Time: {:.5f}s".format((time.time() - time_stamp) / 1)) |
|
|
| total = sum([param.nelement() for param in model.parameters()]) |
| print("Parameters: {:.2f}M".format(total / 1e6)) |
|
|