The code is an unofficial pytorch implementation of SOLO: Segmenting Objects by Locations
The code is based on mmdetection. Please check Install.md for installation instructions.
Follows the same way as mmdetection. single GPU: (for pdb debug)
python tools/train.py configs/solo/r50_p2_p6_match_ml.py --work_dir r50_p2_p6_match_ml
multi GPU (for example 8):
./tools/dist_train.sh configs/solo/r50_p2_p6_match_mlaug.py 8 --work_dir r50_p2_p6_match_ml_3X
The code only implements the simplest version of SOLO:
- 1x:
r50_p2_p6_match_ml.py - 3x:
r50_p2_p6_match_ml_aug.py - using vanilla SOLO instead of Decoupled SOLO
- implemented the simplest mask-nms:
- still in progress
2020-02-14 02:34:20,390 - INFO - Epoch [1][50/7330] lr: 0.00399, eta: 1 day, 2:32:21, time: 1.087, data_time: 0.111, memory: 7960, loss_cls: 0.9476, loss_mask: 1.7644, loss: 2.7120
2020-02-14 02:35:06,882 - INFO - Epoch [1][100/7330] lr: 0.00465, eta: 1 day, 0:36:30, time: 0.930, data_time: 0.052, memory: 7960, loss_cls: 0.8290, loss_mask: 1.7436, loss: 2.5727
2020-02-14 02:35:54,244 - INFO - Epoch [1][150/7330] lr: 0.00532, eta: 1 day, 0:05:55, time: 0.947, data_time: 0.055, memory: 7960, loss_cls: 0.7803, loss_mask: 1.6476, loss: 2.4279
2020-02-14 02:36:45,649 - INFO - Epoch [1][200/7330] lr: 0.00599, eta: 1 day, 0:19:42, time: 1.028, data_time: 0.084, memory: 7960, loss_cls: 0.7539, loss_mask: 1.6324, loss: 2.3864
2020-02-14 02:37:32,214 - INFO - Epoch [1][250/7330] lr: 0.00665, eta: 23:59:22, time: 0.931, data_time: 0.054, memory: 7960, loss_cls: 0.7308, loss_mask: 1.6618, loss: 2.3925
2020-02-14 02:38:19,773 - INFO - Epoch [1][300/7330] lr: 0.00732, eta: 23:50:27, time: 0.951, data_time: 0.070, memory: 7960, loss_cls: 0.7185, loss_mask: 1.6393, loss: 2.3578
2020-02-14 02:39:08,042 - INFO - Epoch [1][350/7330] lr: 0.00799, eta: 23:46:46, time: 0.965, data_time: 0.070, memory: 7960, loss_cls: 0.7407, loss_mask: 1.6516, loss: 2.3923
2020-02-14 02:39:55,616 - INFO - Epoch [1][400/7330] lr: 0.00865, eta: 23:41:16, time: 0.951, data_time: 0.059, memory: 7960, loss_cls: 0.6650, loss_mask: 1.7049, loss: 2.3700
2020-02-14 02:40:43,378 - INFO - Epoch [1][450/7330] lr: 0.00932, eta: 23:37:24, time: 0.955, data_time: 0.063, memory: 7960, loss_cls: 0.6887, loss_mask: 1.5228, loss: 2.2115
2020-02-14 02:41:28,909 - INFO - Epoch [1][500/7330] lr: 0.00999, eta: 23:27:39, time: 0.911, data_time: 0.044, memory: 7960, loss_cls: 0.6639, loss_mask: 1.5959, loss: 2.2598
2020-02-14 02:42:17,590 - INFO - Epoch [1][550/7330] lr: 0.01000, eta: 23:27:55, time: 0.974, data_time: 0.048, memory: 7960, loss_cls: 0.6725, loss_mask: 1.5047, loss: 2.1771
2020-02-14 02:43:05,815 - INFO - Epoch [1][600/7330] lr: 0.01000, eta: 23:26:52, time: 0.965, data_time: 0.047, memory: 7960, loss_cls: 0.6454, loss_mask: 1.5493, loss: 2.1947
2020-02-14 02:43:54,693 - INFO - Epoch [1][650/7330] lr: 0.01000, eta: 23:27:19, time: 0.977, data_time: 0.064, memory: 7960, loss_cls: 0.6249, loss_mask: 1.5785, loss: 2.2034
2020-02-14 02:44:45,068 - INFO - Epoch [1][700/7330] lr: 0.01000, eta: 23:30:44, time: 1.008, data_time: 0.077, memory: 7960, loss_cls: 0.6156, loss_mask: 1.5290, loss: 2.1446
2020-02-14 02:45:34,278 - INFO - Epoch [1][750/7330] lr: 0.01000, eta: 23:31:18, time: 0.984, data_time: 0.050, memory: 7960, loss_cls: 0.6018, loss_mask: 1.3914, loss: 1.9932
2020-02-14 02:46:23,707 - INFO - Epoch [1][800/7330] lr: 0.01000, eta: 23:32:05, time: 0.989, data_time: 0.084, memory: 7960, loss_cls: 0.5828, loss_mask: 1.4481, loss: 2.0308
- Classification:
flatten_cls_scores: flattened w.r.t. all images and all levels
loss_cls = self.loss_cls(flatten_cls_scores, category_targets, avg_factor=num_pos + num_imgs)
- Mask: per-instance dict_loss, ave over all instances in a batch
loss_mask[j,i] = self.dict_loss_batch(pred_mask, ins_mask)
loss_mask = self.dict_weight * torch.mean(loss_mask)
- Dict loss register (02/15/2020)
- Fix class gt from bbox center to segm mess center
- 03/29: update Solo assignment in utils and detectors
./tools/dist_train.sh configs/solo/r50_p2_p6_match_ml.py 8 --work_dir fixgtv4_ml1X
score_thr=0.05
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.306
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.505
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.318
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.103
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.469
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.266
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.396
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.414
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.181
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.469
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.595
./tools/dist_train.sh configs/solo/soloassign_ml_aug.py 8 --work_dir soloassign_ml_3X
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.338
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.546
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.354
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.132
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.370
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.509
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.284
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.427
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.446
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.215
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.504
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.624