Chengfang Lu
7c43b44c57
Huaxu Sentinel Active Safety Platform with embedded algorithm code, Docker Compose setup, and vendored dataset scaffolds for clone-and-run. Co-authored-by: Cursor <cursoragent@cursor.com>
82 lines
3.4 KiB
Python
82 lines
3.4 KiB
Python
import argparse
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import torch
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from importmagician import import_from
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with import_from('./'):
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from utils.args import read_config, parse_arg_cfg, cmd_dict, add_shortcuts
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from utils.models import MODELS
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from utils.common import load_checkpoint
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from utils.profiling_utils import init_dataset, speed_evaluate_real, speed_evaluate_simple, model_profile
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if __name__ == '__main__':
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# Settings
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parser = argparse.ArgumentParser(description='PytorchAutoDrive Profiling', conflict_handler='resolve')
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add_shortcuts(parser)
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parser.add_argument('--config', type=str, help='Path to config file', required=True)
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# Optional args/to overwrite configs
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parser.add_argument('--height', type=int, default=288,
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help='Image input height (default: 288)')
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parser.add_argument('--width', type=int, default=800,
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help='Image input width (default: 800)')
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parser.add_argument('--mode', type=str, default='simple',
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help='Profiling mode (simple/real)')
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parser.add_argument('--times', type=int, default=1,
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help='Select test times')
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parser.add_argument('--cfg-options', type=cmd_dict,
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help='Override config options with \"x1=y1 x2=y2 xn=yn\"')
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group2 = parser.add_mutually_exclusive_group()
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group2.add_argument('--continue-from', type=str,
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help='[Deprecated] Continue training from a previous checkpoint')
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group2.add_argument('--checkpoint', type=str,
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help='Continue/Load from a previous checkpoint')
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args = parser.parse_args()
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# Parse configs and build model
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cfg = read_config(args.config)
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args, cfg = parse_arg_cfg(args, cfg)
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net = MODELS.from_dict(cfg['model'])
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device = torch.device('cpu')
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if torch.cuda.is_available():
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device = torch.device('cuda:0')
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print(device)
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net.to(device)
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if args.mode == 'simple':
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dummy = torch.ones((1, 3, args.height, args.width))
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print(dummy.dtype)
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fps = []
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for i in range(0, args.times):
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fps.append(speed_evaluate_simple(net=net, device=device, dummy=dummy, num=300))
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print('GPU FPS: {: .2f}'.format(max(fps)))
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elif args.mode == 'real':
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if cfg['test']['checkpoint'] is not None:
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load_checkpoint(net=net, optimizer=None, lr_scheduler=None, filename=cfg['test']['checkpoint'])
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val_loader = init_dataset(cfg['dataset'], cfg['test_augmentations'], (args.height, args.width))
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fps = []
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gpu_fps = []
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for i in range(0, args.times):
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fps_item, gpu_fps_item = speed_evaluate_real(net=net, device=device, loader=val_loader, num=300)
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fps.append(fps_item)
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gpu_fps.append(gpu_fps_item)
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print('Real FPS: {: .2f}'.format(max(fps)))
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print('GPU FPS: {: .2f}'.format(max(gpu_fps)))
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else:
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raise ValueError
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macs, _ = model_profile(net, args.height, args.width, device)
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flops = 2 * macs
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try:
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net.eval(profiling=True)
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except TypeError:
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net.eval()
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params = sum(p.numel() for p in net.parameters())
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print('FLOPs(G): {: .2f}'.format(flops / 1e9))
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print('Number of parameters: {: .2f}'.format(params / 1e6))
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print('Profiling, please clear your GPU memory before doing this.')
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