| """ |
| python inference_speed_test.py \ |
| --model-variant mobilenetv3 \ |
| --resolution 1920 1080 \ |
| --downsample-ratio 0.25 \ |
| --precision float32 |
| """ |
|
|
| import argparse |
| import torch |
| from tqdm import tqdm |
|
|
| from model.model import MattingNetwork |
|
|
| torch.backends.cudnn.benchmark = True |
|
|
| class InferenceSpeedTest: |
| def __init__(self): |
| self.parse_args() |
| self.init_model() |
| self.loop() |
| |
| def parse_args(self): |
| parser = argparse.ArgumentParser() |
| parser.add_argument('--model-variant', type=str, required=True) |
| parser.add_argument('--resolution', type=int, required=True, nargs=2) |
| parser.add_argument('--downsample-ratio', type=float, required=True) |
| parser.add_argument('--precision', type=str, default='float32') |
| parser.add_argument('--disable-refiner', action='store_true') |
| self.args = parser.parse_args() |
| |
| def init_model(self): |
| self.device = 'cuda' |
| self.precision = {'float32': torch.float32, 'float16': torch.float16}[self.args.precision] |
| self.model = MattingNetwork(self.args.model_variant) |
| self.model = self.model.to(device=self.device, dtype=self.precision).eval() |
| self.model = torch.jit.script(self.model) |
| self.model = torch.jit.freeze(self.model) |
| |
| def loop(self): |
| w, h = self.args.resolution |
| src = torch.randn((1, 3, h, w), device=self.device, dtype=self.precision) |
| with torch.no_grad(): |
| rec = None, None, None, None |
| for _ in tqdm(range(1000)): |
| fgr, pha, *rec = self.model(src, *rec, self.args.downsample_ratio) |
| torch.cuda.synchronize() |
|
|
| if __name__ == '__main__': |
| InferenceSpeedTest() |
