Open choodly opened 4 years ago
Thank you for your excellent work. On my own dataset, PQ calculation of stuff class is wrong. Can you tell me what else I should pay attention to?
2020-06-19 19:17:10,088 | upsnet_end2end_test.py | line 158: test config:{'dataset': {'dataset': 'coco', 'dataset_path': './data/coco/', 'image_set': 'train2017', 'num_classes': 4, 'num_seg_classes': 6, 'root_path': './data/', 'test_image_set': 'val2017'}, 'debug_mode': False, 'gpus': '1', 'model_prefix': 'upsnet_resnet_50coco', 'network': {'anchor_ratios': [0.5, 1, 2], 'anchor_scales': [8], 'backbone_fix_bn': True, 'backbone_freeze_at': 2, 'backbone_with_dconv': 100, 'backbone_with_dilation': False, 'backbone_with_dpyramid': False, 'bbox_reg_weights': [10.0, 10.0, 5.0, 5.0], 'binary_thresh': 0.5, 'cls_agnostic_bbox_reg': False, 'fcn_head': 'FCNHead', 'fcn_num_layers': 3, 'fcn_with_norm': 'none', 'fpn_feature_dim': 256, 'fpn_upsample_method': 'nearest', 'fpn_with_gap': True, 'fpn_with_norm': 'none', 'has_fcn_head': True, 'has_fpn': True, 'has_mask_head': True, 'has_mask_rcnn': True, 'has_panoptic_head': True, 'has_rcnn': True, 'has_rpn': True, 'mask_size': 28, 'num_anchors': 3, 'pixel_means': array([102.9801, 115.9465, 122.7717]), 'pretrained': './model/pretrained_model/resnet-50-caffe.pth', 'rcnn_feat_stride': 32, 'rcnn_with_norm': 'none', 'rpn_feat_stride': [4, 8, 16, 32, 64], 'rpn_with_norm': 'none', 'use_caffe_model': True, 'use_syncbn': False}, 'output_path': './output/upsnet/coco', 'symbol': 'resnet_50_upsnet', 'test': {'batch_size': 1, 'max_det': 100, 'max_size': 2000, 'nms_thresh': 0.5, 'panoptic_score_thresh': 0.6, 'panoptic_stuff_area_limit': 500000, 'rpn_min_size': 0, 'rpn_nms_thresh': 0.7, 'rpn_post_nms_top_n': 1000, 'rpn_pre_nms_top_n': 1000, 'scales': [1200], 'score_thresh': 0.05, 'test_iteration': 90000, 'vis_mask': False}, 'train': {'batch_rois': 512, 'batch_size': 1, 'bbox_loss_weight': 1.0, 'bbox_means': [0.0, 0.0, 0.0, 0.0], 'bbox_normalization_precomputed': True, 'bbox_regression_thresh': 0.5, 'bbox_stds': [0.1, 0.1, 0.2, 0.2], 'bbox_thresh': 0.5, 'bbox_weights': array([1., 1., 1., 1.]), 'begin_iteration': 0, 'bg_thresh_hi': 0.5, 'bg_thresh_lo': 0.0, 'crowd_filter_thresh': 0.7, 'decay_iteration': [60000, 80000], 'display_iter': 20, 'eval_data': True, 'fcn_focal_loss_gamma': 1.0, 'fcn_loss_weight': 0.2, 'fcn_use_focal_loss': False, 'fcn_with_negative_loss': False, 'fcn_with_roi_loss': True, 'fg_fraction': 0.25, 'fg_thresh': 0.5, 'flip': True, 'gt_min_area': -1, 'lr': 0.00125, 'lr_schedule': 'step', 'max_iteration': 90000, 'max_size': 2000, 'momentum': 0.9, 'panoptic_box_keep_fraction': 0.7, 'panoptic_focal_loss_gamma': 1.0, 'panoptic_loss_weight': 0.1, 'panoptic_use_focal_loss': False, 'panoptic_with_roi_loss': False, 'resume': False, 'rpn_batch_size': 256, 'rpn_bbox_weights': [1.0, 1.0, 1.0, 1.0], 'rpn_clobber_positive': False, 'rpn_fg_fraction': 0.5, 'rpn_individual_proposals': True, 'rpn_min_size': 0, 'rpn_negative_overlap': 0.3, 'rpn_nms_thresh': 0.7, 'rpn_positive_overlap': 0.7, 'rpn_positive_weight': -1.0, 'rpn_post_nms_top_n': 2000, 'rpn_pre_nms_top_n': 2000, 'rpn_straddle_thresh': 0, 'scales': [1200], 'shuffle': True, 'snapshot_step': 2000, 'use_horovod': False, 'warmup_iteration': 1500, 'wd': 0.0001}}
2020-06-19 19:17:17,126 | json_dataset.py | line 63 : Creating: coco_val2017 2020-06-19 19:17:17,789 | upsnet_end2end_test.py | line 274: Batch 1/85, data_time:0.182, net_time:0.000, post_time:0.021 2020-06-19 19:17:18,166 | upsnet_end2end_test.py | line 274: Batch 2/85, data_time:0.157, net_time:0.000, post_time:0.019 2020-06-19 19:17:18,539 | upsnet_end2end_test.py | line 274: Batch 3/85, data_time:0.151, net_time:0.000, post_time:0.018 2020-06-19 19:17:18,911 | upsnet_end2end_test.py | line 274: Batch 4/85, data_time:0.149, net_time:0.000, post_time:0.017 2020-06-19 19:17:19,321 | upsnet_end2end_test.py | line 274: Batch 5/85, data_time:0.147, net_time:0.000, post_time:0.025 2020-06-19 19:17:19,744 | upsnet_end2end_test.py | line 274: Batch 6/85, data_time:0.145, net_time:0.000, post_time:0.030 2020-06-19 19:17:20,141 | upsnet_end2end_test.py | line 274: Batch 7/85, data_time:0.143, net_time:0.000, post_time:0.032 2020-06-19 19:17:20,546 | upsnet_end2end_test.py | line 274: Batch 8/85, data_time:0.141, net_time:0.000, post_time:0.034 2020-06-19 19:17:20,958 | upsnet_end2end_test.py | line 274: Batch 9/85, data_time:0.142, net_time:0.000, post_time:0.034 2020-06-19 19:17:21,357 | upsnet_end2end_test.py | line 274: Batch 10/85, data_time:0.143, net_time:0.000, post_time:0.034 2020-06-19 19:17:21,749 | upsnet_end2end_test.py | line 274: Batch 11/85, data_time:0.141, net_time:0.214, post_time:0.035 2020-06-19 19:17:22,140 | upsnet_end2end_test.py | line 274: Batch 12/85, data_time:0.141, net_time:0.214, post_time:0.034 2020-06-19 19:17:22,561 | upsnet_end2end_test.py | line 274: Batch 13/85, data_time:0.141, net_time:0.213, post_time:0.036 2020-06-19 19:17:22,970 | upsnet_end2end_test.py | line 274: Batch 14/85, data_time:0.140, net_time:0.214, post_time:0.037 2020-06-19 19:17:23,388 | upsnet_end2end_test.py | line 274: Batch 15/85, data_time:0.140, net_time:0.214, post_time:0.038 2020-06-19 19:17:23,785 | upsnet_end2end_test.py | line 274: Batch 16/85, 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upsnet_end2end_test.py | line 274: Batch 81/85, data_time:0.132, net_time:0.216, post_time:0.042 2020-06-19 19:17:50,072 | upsnet_end2end_test.py | line 274: Batch 82/85, data_time:0.132, net_time:0.216, post_time:0.042 2020-06-19 19:17:50,446 | upsnet_end2end_test.py | line 274: Batch 83/85, data_time:0.132, net_time:0.216, post_time:0.042 2020-06-19 19:17:50,817 | upsnet_end2end_test.py | line 274: Batch 84/85, data_time:0.131, net_time:0.216, post_time:0.042 2020-06-19 19:17:51,193 | upsnet_end2end_test.py | line 274: Batch 85/85, data_time:0.131, net_time:0.216, post_time:0.042 2020-06-19 19:17:55,412 | base_dataset.py | line 194: Writing bbox results json to: /media/mubai/backupdata/UPSNet/output/upsnet/coco/upsnet_resnet50_coco_4gpu/val2017/results/bbox_coco_val2017_results.json 2020-06-19 19:17:55,687 | base_dataset.py | line 706: ~~ Mean and per-category AP @ IoU=[0.50,0.95] ~~ 2020-06-19 19:17:55,687 | base_dataset.py | line 714: 0.815 2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.693 2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.252 2020-06-19 19:17:55,688 | base_dataset.py | line 716: 0.587 2020-06-19 19:17:55,688 | base_dataset.py | line 706: ~~ Mean and per-category AP @ IoU=[0.50,0.50] ~~ 2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.986 2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.945 2020-06-19 19:17:55,689 | base_dataset.py | line 714: 0.505 2020-06-19 19:17:55,689 | base_dataset.py | line 716: 0.812 2020-06-19 19:17:55,689 | base_dataset.py | line 718: ~~ Summary metrics ~~ 2020-06-19 19:17:55,692 | coco.py | line 214: Writing segmentation results json to: /media/mubai/backupdata/UPSNet/output/upsnet/coco/upsnet_resnet50_coco_4gpu/val2017/results/segmentations_coco_val2017_results.json 2020-06-19 19:17:56,087 | base_dataset.py | line 706: ~~ Mean and per-category AP @ IoU=[0.50,0.95] ~~ 2020-06-19 19:17:56,088 | base_dataset.py | line 714: 0.768 2020-06-19 19:17:56,088 | base_dataset.py | line 714: 0.626 2020-06-19 19:17:56,088 | base_dataset.py | line 714: 0.000 2020-06-19 19:17:56,088 | base_dataset.py | line 716: 0.465 2020-06-19 19:17:56,089 | base_dataset.py | line 706: ~~ Mean and per-category AP @ IoU=[0.50,0.50] ~~ 2020-06-19 19:17:56,089 | base_dataset.py | line 714: 0.986 2020-06-19 19:17:56,089 | base_dataset.py | line 714: 0.944 2020-06-19 19:17:56,089 | base_dataset.py | line 714: 0.000 2020-06-19 19:17:56,089 | base_dataset.py | line 716: 0.643 2020-06-19 19:17:56,089 | base_dataset.py | line 718: ~~ Summary metrics ~~ 2020-06-19 19:17:56,092 | upsnet_end2end_test.py | line 307: unified pano result: 2020-06-19 19:18:25,855 | base_dataset.py | line 301: | PQ SQ RQ N 2020-06-19 19:18:25,855 | base_dataset.py | line 302: -------------------------------------- 2020-06-19 19:18:25,855 | base_dataset.py | line 304: All | 2157.7 2206.5 63.1 6 2020-06-19 19:18:25,855 | base_dataset.py | line 304: Things | 52.1 56.1 61.8 3 2020-06-19 19:18:25,855 | base_dataset.py | line 304: Stuff | 4263.4 4356.8 64.4 3 2020-06-19 19:18:25,855 | base_dataset.py | line 306: IDX | PQ SQ RQ IoU TP FP FN 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 0 | 10119.1 10241.0 98.8 8500.1 83 1 1 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 1 | 0.0 0.0 0.0 0.0 0 0 30 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 2 | 2671.2 2829.4 94.4 2150.3 76 1 8 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 3 | 81.1 86.9 93.3 6.1 7 0 1 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 4 | 75.0 81.5 92.1 502.6 617 43 63 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 5 | 0.0 0.0 0.0 0.0 0 1 2 2020-06-19 19:18:30,706 | coco.py | line 260: evaluate segmentation: 2020-06-19 19:18:30,706 | coco.py | line 264: IU_array: 2020-06-19 19:18:30,706 | coco.py | line 266: 0.98239 2020-06-19 19:18:30,706 | coco.py | line 266: 0.87430 2020-06-19 19:18:30,707 | coco.py | line 266: 0.97881 2020-06-19 19:18:30,707 | coco.py | line 266: 0.75718 2020-06-19 19:18:30,707 | coco.py | line 266: 0.61269 2020-06-19 19:18:30,707 | coco.py | line 266: 0.08025 2020-06-19 19:18:30,707 | coco.py | line 267: meanIU:0.71427 2020-06-19 19:18:30,708 | coco.py | line 271: confusion_matrix: 2020-06-19 19:18:30,708 | coco.py | line 272: 0.996 0. 0.001 0. 0.002 0. 0.001 0.943 0.04 0. 0.015 0.
I also ran into this issue.
Thank you for your excellent work. On my own dataset, PQ calculation of stuff class is wrong. Can you tell me what else I should pay attention to?
2020-06-19 19:17:10,088 | upsnet_end2end_test.py | line 158: test config:{'dataset': {'dataset': 'coco', 'dataset_path': './data/coco/', 'image_set': 'train2017', 'num_classes': 4, 'num_seg_classes': 6, 'root_path': './data/', 'test_image_set': 'val2017'}, 'debug_mode': False, 'gpus': '1', 'model_prefix': 'upsnet_resnet_50coco', 'network': {'anchor_ratios': [0.5, 1, 2], 'anchor_scales': [8], 'backbone_fix_bn': True, 'backbone_freeze_at': 2, 'backbone_with_dconv': 100, 'backbone_with_dilation': False, 'backbone_with_dpyramid': False, 'bbox_reg_weights': [10.0, 10.0, 5.0, 5.0], 'binary_thresh': 0.5, 'cls_agnostic_bbox_reg': False, 'fcn_head': 'FCNHead', 'fcn_num_layers': 3, 'fcn_with_norm': 'none', 'fpn_feature_dim': 256, 'fpn_upsample_method': 'nearest', 'fpn_with_gap': True, 'fpn_with_norm': 'none', 'has_fcn_head': True, 'has_fpn': True, 'has_mask_head': True, 'has_mask_rcnn': True, 'has_panoptic_head': True, 'has_rcnn': True, 'has_rpn': True, 'mask_size': 28, 'num_anchors': 3, 'pixel_means': array([102.9801, 115.9465, 122.7717]), 'pretrained': './model/pretrained_model/resnet-50-caffe.pth', 'rcnn_feat_stride': 32, 'rcnn_with_norm': 'none', 'rpn_feat_stride': [4, 8, 16, 32, 64], 'rpn_with_norm': 'none', 'use_caffe_model': True, 'use_syncbn': False}, 'output_path': './output/upsnet/coco', 'symbol': 'resnet_50_upsnet', 'test': {'batch_size': 1, 'max_det': 100, 'max_size': 2000, 'nms_thresh': 0.5, 'panoptic_score_thresh': 0.6, 'panoptic_stuff_area_limit': 500000, 'rpn_min_size': 0, 'rpn_nms_thresh': 0.7, 'rpn_post_nms_top_n': 1000, 'rpn_pre_nms_top_n': 1000, 'scales': [1200], 'score_thresh': 0.05, 'test_iteration': 90000, 'vis_mask': False}, 'train': {'batch_rois': 512, 'batch_size': 1, 'bbox_loss_weight': 1.0, 'bbox_means': [0.0, 0.0, 0.0, 0.0], 'bbox_normalization_precomputed': True, 'bbox_regression_thresh': 0.5, 'bbox_stds': [0.1, 0.1, 0.2, 0.2], 'bbox_thresh': 0.5, 'bbox_weights': array([1., 1., 1., 1.]), 'begin_iteration': 0, 'bg_thresh_hi': 0.5, 'bg_thresh_lo': 0.0, 'crowd_filter_thresh': 0.7, 'decay_iteration': [60000, 80000], 'display_iter': 20, 'eval_data': True, 'fcn_focal_loss_gamma': 1.0, 'fcn_loss_weight': 0.2, 'fcn_use_focal_loss': False, 'fcn_with_negative_loss': False, 'fcn_with_roi_loss': True, 'fg_fraction': 0.25, 'fg_thresh': 0.5, 'flip': True, 'gt_min_area': -1, 'lr': 0.00125, 'lr_schedule': 'step', 'max_iteration': 90000, 'max_size': 2000, 'momentum': 0.9, 'panoptic_box_keep_fraction': 0.7, 'panoptic_focal_loss_gamma': 1.0, 'panoptic_loss_weight': 0.1, 'panoptic_use_focal_loss': False, 'panoptic_with_roi_loss': False, 'resume': False, 'rpn_batch_size': 256, 'rpn_bbox_weights': [1.0, 1.0, 1.0, 1.0], 'rpn_clobber_positive': False, 'rpn_fg_fraction': 0.5, 'rpn_individual_proposals': True, 'rpn_min_size': 0, 'rpn_negative_overlap': 0.3, 'rpn_nms_thresh': 0.7, 'rpn_positive_overlap': 0.7, 'rpn_positive_weight': -1.0, 'rpn_post_nms_top_n': 2000, 'rpn_pre_nms_top_n': 2000, 'rpn_straddle_thresh': 0, 'scales': [1200], 'shuffle': True, 'snapshot_step': 2000, 'use_horovod': False, 'warmup_iteration': 1500, 'wd': 0.0001}}
2020-06-19 19:17:17,126 | json_dataset.py | line 63 : Creating: coco_val2017 2020-06-19 19:17:17,789 | upsnet_end2end_test.py | line 274: Batch 1/85, data_time:0.182, net_time:0.000, post_time:0.021 2020-06-19 19:17:18,166 | upsnet_end2end_test.py | line 274: Batch 2/85, data_time:0.157, net_time:0.000, post_time:0.019 2020-06-19 19:17:18,539 | upsnet_end2end_test.py | line 274: Batch 3/85, data_time:0.151, net_time:0.000, post_time:0.018 2020-06-19 19:17:18,911 | upsnet_end2end_test.py | line 274: Batch 4/85, data_time:0.149, net_time:0.000, post_time:0.017 2020-06-19 19:17:19,321 | upsnet_end2end_test.py | line 274: Batch 5/85, data_time:0.147, net_time:0.000, post_time:0.025 2020-06-19 19:17:19,744 | upsnet_end2end_test.py | line 274: Batch 6/85, data_time:0.145, net_time:0.000, post_time:0.030 2020-06-19 19:17:20,141 | upsnet_end2end_test.py | line 274: Batch 7/85, data_time:0.143, net_time:0.000, post_time:0.032 2020-06-19 19:17:20,546 | upsnet_end2end_test.py | line 274: Batch 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~~ Mean and per-category AP @ IoU=[0.50,0.95] ~~ 2020-06-19 19:17:55,687 | base_dataset.py | line 714: 0.815 2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.693 2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.252 2020-06-19 19:17:55,688 | base_dataset.py | line 716: 0.587 2020-06-19 19:17:55,688 | base_dataset.py | line 706:~~ Mean and per-category AP @ IoU=[0.50,0.50] ~~ 2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.986 2020-06-19 19:17:55,688 | base_dataset.py | line 714: 0.945 2020-06-19 19:17:55,689 | base_dataset.py | line 714: 0.505 2020-06-19 19:17:55,689 | base_dataset.py | line 716: 0.812 2020-06-19 19:17:55,689 | base_dataset.py | line 718:~~ Summary metrics ~~ 2020-06-19 19:17:55,692 | coco.py | line 214: Writing segmentation results json to: /media/mubai/backupdata/UPSNet/output/upsnet/coco/upsnet_resnet50_coco_4gpu/val2017/results/segmentations_coco_val2017_results.json 2020-06-19 19:17:56,087 | base_dataset.py | line 706:~~ Mean and per-category AP @ IoU=[0.50,0.95] ~~ 2020-06-19 19:17:56,088 | base_dataset.py | line 714: 0.768 2020-06-19 19:17:56,088 | base_dataset.py | line 714: 0.626 2020-06-19 19:17:56,088 | base_dataset.py | line 714: 0.000 2020-06-19 19:17:56,088 | base_dataset.py | line 716: 0.465 2020-06-19 19:17:56,089 | base_dataset.py | line 706:~~ Mean and per-category AP @ IoU=[0.50,0.50] ~~ 2020-06-19 19:17:56,089 | base_dataset.py | line 714: 0.986 2020-06-19 19:17:56,089 | base_dataset.py | line 714: 0.944 2020-06-19 19:17:56,089 | base_dataset.py | line 714: 0.000 2020-06-19 19:17:56,089 | base_dataset.py | line 716: 0.643 2020-06-19 19:17:56,089 | base_dataset.py | line 718:~~ Summary metrics ~~ 2020-06-19 19:17:56,092 | upsnet_end2end_test.py | line 307: unified pano result: 2020-06-19 19:18:25,855 | base_dataset.py | line 301: | PQ SQ RQ N 2020-06-19 19:18:25,855 | base_dataset.py | line 302: -------------------------------------- 2020-06-19 19:18:25,855 | base_dataset.py | line 304: All | 2157.7 2206.5 63.1 6 2020-06-19 19:18:25,855 | base_dataset.py | line 304: Things | 52.1 56.1 61.8 3 2020-06-19 19:18:25,855 | base_dataset.py | line 304: Stuff | 4263.4 4356.8 64.4 3 2020-06-19 19:18:25,855 | base_dataset.py | line 306: IDX | PQ SQ RQ IoU TP FP FN 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 0 | 10119.1 10241.0 98.8 8500.1 83 1 1 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 1 | 0.0 0.0 0.0 0.0 0 0 30 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 2 | 2671.2 2829.4 94.4 2150.3 76 1 8 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 3 | 81.1 86.9 93.3 6.1 7 0 1 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 4 | 75.0 81.5 92.1 502.6 617 43 63 2020-06-19 19:18:25,856 | base_dataset.py | line 309: 5 | 0.0 0.0 0.0 0.0 0 1 2 2020-06-19 19:18:30,706 | coco.py | line 260: evaluate segmentation: 2020-06-19 19:18:30,706 | coco.py | line 264: IU_array: 2020-06-19 19:18:30,706 | coco.py | line 266: 0.98239 2020-06-19 19:18:30,706 | coco.py | line 266: 0.87430 2020-06-19 19:18:30,707 | coco.py | line 266: 0.97881 2020-06-19 19:18:30,707 | coco.py | line 266: 0.75718 2020-06-19 19:18:30,707 | coco.py | line 266: 0.61269 2020-06-19 19:18:30,707 | coco.py | line 266: 0.08025 2020-06-19 19:18:30,707 | coco.py | line 267: meanIU:0.71427 2020-06-19 19:18:30,708 | coco.py | line 271: confusion_matrix: 2020-06-19 19:18:30,708 | coco.py | line 272: 0.996 0. 0.001 0. 0.002 0.0.001 0.943 0.04 0. 0.015 0.
0.14 0. 0. 0.817 0.043 0.
0.164 0.011 0.184 0. 0.642 0.
0.276 0. 0. 0. 0.639 0.085