| |
| |
| |
| import os |
| import sys |
| import pathlib |
|
|
| __dir__ = pathlib.Path(os.path.abspath(__file__)) |
| sys.path.append(str(__dir__)) |
| sys.path.append(str(__dir__.parent.parent)) |
|
|
| import argparse |
| import time |
| import paddle |
| from tqdm.auto import tqdm |
|
|
|
|
| class EVAL: |
| def __init__(self, model_path, gpu_id=0): |
| from models import build_model |
| from data_loader import get_dataloader |
| from post_processing import get_post_processing |
| from utils import get_metric |
|
|
| self.gpu_id = gpu_id |
| if ( |
| self.gpu_id is not None |
| and isinstance(self.gpu_id, int) |
| and paddle.device.is_compiled_with_cuda() |
| ): |
| paddle.device.set_device("gpu:{}".format(self.gpu_id)) |
| else: |
| paddle.device.set_device("cpu") |
| checkpoint = paddle.load(model_path) |
| config = checkpoint["config"] |
| config["arch"]["backbone"]["pretrained"] = False |
|
|
| self.validate_loader = get_dataloader( |
| config["dataset"]["validate"], config["distributed"] |
| ) |
|
|
| self.model = build_model(config["arch"]) |
| self.model.set_state_dict(checkpoint["state_dict"]) |
|
|
| self.post_process = get_post_processing(config["post_processing"]) |
| self.metric_cls = get_metric(config["metric"]) |
|
|
| def eval(self): |
| self.model.eval() |
| raw_metrics = [] |
| total_frame = 0.0 |
| total_time = 0.0 |
| for i, batch in tqdm( |
| enumerate(self.validate_loader), |
| total=len(self.validate_loader), |
| desc="test model", |
| ): |
| with paddle.no_grad(): |
| start = time.time() |
| preds = self.model(batch["img"]) |
| boxes, scores = self.post_process( |
| batch, preds, is_output_polygon=self.metric_cls.is_output_polygon |
| ) |
| total_frame += batch["img"].shape[0] |
| total_time += time.time() - start |
| raw_metric = self.metric_cls.validate_measure(batch, (boxes, scores)) |
| raw_metrics.append(raw_metric) |
| metrics = self.metric_cls.gather_measure(raw_metrics) |
| print("FPS:{}".format(total_frame / total_time)) |
| return { |
| "recall": metrics["recall"].avg, |
| "precision": metrics["precision"].avg, |
| "fmeasure": metrics["fmeasure"].avg, |
| } |
|
|
|
|
| def init_args(): |
| parser = argparse.ArgumentParser(description="DBNet.paddle") |
| parser.add_argument( |
| "--model_path", |
| required=False, |
| default="output/DBNet_resnet18_FPN_DBHead/checkpoint/1.pth", |
| type=str, |
| ) |
| args = parser.parse_args() |
| return args |
|
|
|
|
| if __name__ == "__main__": |
| args = init_args() |
| eval = EVAL(args.model_path) |
| result = eval.eval() |
| print(result) |
|
|
