mirror of
https://github.com/deepinsight/insightface.git
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117 lines
3.5 KiB
Python
Executable File
117 lines
3.5 KiB
Python
Executable File
import argparse
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import os
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import os.path as osp
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import pickle
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import numpy as np
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import datetime
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import warnings
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import mmcv
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import torch
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from mmcv import Config, DictAction
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from mmcv.cnn import fuse_conv_bn
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from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
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from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
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wrap_fp16_model)
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from mmdet.apis import multi_gpu_test, single_gpu_test
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from mmdet.datasets import (build_dataloader, build_dataset,
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replace_ImageToTensor)
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from mmdet.models import build_detector
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from mmdet.core.evaluation import wider_evaluation, get_widerface_gts
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#from torch.utils import mkldnn as mkldnn_utils
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def parse_args():
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parser = argparse.ArgumentParser(
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description='MMDet test (and eval) 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('--cpu', action="store_true", default=False, help='Use cpu inference')
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parser.add_argument(
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'--shape',
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type=int,
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nargs='+',
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default=[480, 640],
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help='input image size')
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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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cfg = Config.fromfile(args.config)
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cfg.model.pretrained = None
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# in case the test dataset is concatenated
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if isinstance(cfg.data.test, dict):
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cfg.data.test.test_mode = True
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elif isinstance(cfg.data.test, list):
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for ds_cfg in cfg.data.test:
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ds_cfg.test_mode = True
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pipelines = cfg.data.test.pipeline
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for pipeline in pipelines:
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if pipeline.type=='MultiScaleFlipAug':
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#pipeline.img_scale = (640, 640)
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pipeline.img_scale = None
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pipeline.scale_factor = 1.0
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transforms = pipeline.transforms
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for transform in transforms:
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if transform.type=='Pad':
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#transform.size = pipeline.img_scale
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transform.size = None
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transform.size_divisor = 1
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#print(cfg.data.test.pipeline)
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distributed = False
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# build the dataloader
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samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
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if samples_per_gpu > 1:
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# Replace 'ImageToTensor' to 'DefaultFormatBundle'
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cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
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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=samples_per_gpu,
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workers_per_gpu=cfg.data.workers_per_gpu,
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dist=distributed,
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shuffle=False)
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device = torch.device("cpu" if args.cpu else "cuda")
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# build the model and load checkpoint
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model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
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fp16_cfg = cfg.get('fp16', None)
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checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
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if 'CLASSES' in checkpoint['meta']:
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model.CLASSES = checkpoint['meta']['CLASSES']
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else:
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model.CLASSES = dataset.CLASSES
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model = model.to(device)
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model.eval()
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dataset = data_loader.dataset
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for i, data in enumerate(data_loader):
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img = data['img'][0]
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#print(img.shape)
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img = img[:,:,:args.shape[0],:args.shape[1]]
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img = img.to(device)
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with torch.no_grad():
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ta = datetime.datetime.now()
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result = model.feature_test(img)
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tb = datetime.datetime.now()
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print('cost:', (tb-ta).total_seconds())
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if __name__ == '__main__':
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main()
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