90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
import argparse
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import time
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import torch
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from mmcv import Config
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from mmcv.parallel import MMDataParallel
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from mmcv.runner import load_checkpoint, wrap_fp16_model
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from mmdet3d.datasets import build_dataloader, build_dataset
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from mmdet3d.models import build_fusion_model
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from torchpack.utils.config import configs
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from mmdet3d.utils import recursive_eval
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def parse_args():
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parser = argparse.ArgumentParser(description="MMDet benchmark a model")
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parser.add_argument("config", help="test config file path")
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parser.add_argument("checkpoint", help="checkpoint file")
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parser.add_argument("--samples", default=2000, help="samples to benchmark")
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parser.add_argument("--log-interval", default=50, help="interval of logging")
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parser.add_argument("--fp16", action="store_true")
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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configs.load(args.config, recursive=True)
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cfg = Config(recursive_eval(configs), filename=args.config)
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# set cudnn_benchmark
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if cfg.get("cudnn_benchmark", False):
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torch.backends.cudnn.benchmark = True
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cfg.model.pretrained = None
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cfg.data.test.test_mode = True
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# build the dataloader
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# TODO: support multiple images per gpu (only minor changes are needed)
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dataset = build_dataset(cfg.data.test)
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data_loader = build_dataloader(
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dataset,
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samples_per_gpu=1,
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workers_per_gpu=cfg.data.workers_per_gpu,
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dist=False,
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shuffle=False,
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)
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# build the model and load checkpoint
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cfg.model.train_cfg = None
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model = build_fusion_model(cfg.model, test_cfg=cfg.get("test_cfg"))
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if args.fp16:
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wrap_fp16_model(model)
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load_checkpoint(model, args.checkpoint, map_location="cpu")
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model = MMDataParallel(model, device_ids=[0])
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model.eval()
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# the first several iterations may be very slow so skip them
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num_warmup = 5
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pure_inf_time = 0
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# benchmark with several samples and take the average
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for i, data in enumerate(data_loader):
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torch.cuda.synchronize()
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start_time = time.perf_counter()
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with torch.no_grad():
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model(return_loss=False, rescale=True, **data)
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torch.cuda.synchronize()
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elapsed = time.perf_counter() - start_time
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if i >= num_warmup:
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pure_inf_time += elapsed
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if (i + 1) % args.log_interval == 0:
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fps = (i + 1 - num_warmup) / pure_inf_time
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print(
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f"Done image [{i + 1:<3}/ {args.samples}], "
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f"fps: {fps:.1f} img / s"
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)
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if (i + 1) == args.samples:
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pure_inf_time += elapsed
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fps = (i + 1 - num_warmup) / pure_inf_time
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print(f"Overall fps: {fps:.1f} img / s")
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break
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if __name__ == "__main__":
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main()
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