mirror of
https://github.com/botastic/SoftGroup.git
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90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
import argparse
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import numpy as np
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import random
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import torch
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import yaml
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from munch import Munch
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from tqdm import tqdm
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import util.utils as utils
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from evaluation import ScanNetEval
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from model.softgroup import SoftGroup
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def get_args():
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parser = argparse.ArgumentParser('SoftGroup')
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parser.add_argument('config', type=str, help='path to config file')
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parser.add_argument('checkpoint', type=str, help='path to checkpoint')
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args = parser.parse_args()
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return args
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def evaluate_semantic_segmantation_accuracy(matches):
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seg_gt_list = []
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seg_pred_list = []
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for k, v in matches.items():
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seg_gt_list.append(v['seg_gt'])
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seg_pred_list.append(v['seg_pred'])
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seg_gt_all = torch.cat(seg_gt_list, dim=0).cuda()
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seg_pred_all = torch.cat(seg_pred_list, dim=0).cuda()
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assert seg_gt_all.shape == seg_pred_all.shape
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correct = (seg_gt_all[seg_gt_all != -100] == seg_pred_all[seg_gt_all != -100]).sum()
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whole = (seg_gt_all != -100).sum()
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seg_accuracy = correct.float() / whole.float()
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return seg_accuracy
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def evaluate_semantic_segmantation_miou(matches):
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seg_gt_list = []
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seg_pred_list = []
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for k, v in matches.items():
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seg_gt_list.append(v['seg_gt'])
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seg_pred_list.append(v['seg_pred'])
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seg_gt_all = torch.cat(seg_gt_list, dim=0).cuda()
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seg_pred_all = torch.cat(seg_pred_list, dim=0).cuda()
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pos_inds = seg_gt_all != -100
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seg_gt_all = seg_gt_all[pos_inds]
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seg_pred_all = seg_pred_all[pos_inds]
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assert seg_gt_all.shape == seg_pred_all.shape
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iou_list = []
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for _index in seg_gt_all.unique():
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if _index != -100:
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intersection = ((seg_gt_all == _index) & (seg_pred_all == _index)).sum()
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union = ((seg_gt_all == _index) | (seg_pred_all == _index)).sum()
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iou = intersection.float() / union
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iou_list.append(iou)
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iou_tensor = torch.tensor(iou_list)
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miou = iou_tensor.mean()
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return miou
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if __name__ == '__main__':
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test_seed = 567
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random.seed(test_seed)
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np.random.seed(test_seed)
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torch.manual_seed(test_seed)
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torch.cuda.manual_seed_all(test_seed)
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args = get_args()
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cfg = Munch.fromDict(yaml.safe_load(open(args.config, 'r')))
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torch.backends.cudnn.enabled = False
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model = SoftGroup(**cfg.model)
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print(f'Load state dict from {args.checkpoint}')
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model = utils.load_checkpoint(model, args.checkpoint)
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model.cuda()
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from data.scannetv2_inst import Dataset
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dataset = Dataset(**cfg.data.test)
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dataset.valLoader()
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dataloader = dataset.val_data_loader
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all_preds, all_gts = [], []
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with torch.no_grad():
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model = model.eval()
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for i, batch in tqdm(enumerate(dataloader), total=len(dataloader)):
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ret = model(batch)
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all_preds.append(ret['det_ins'])
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all_gts.append(ret['gt_ins'])
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scannet_eval = ScanNetEval(dataset.CLASSES)
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scannet_eval.evaluate(all_preds, all_gts)
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