QueryInst: Parallelly Supervised Mask Query for Instance Segmentation

Dear author: In Contrastive Tracking Head, the Track_Dynconv utilize the q*t-1 as shown in Equation (4). Why didn‘t it show in Figure 1: Overall pipeline of QueryTrack? There is no arrow connection between them. Thank you！

Dear authors,

Do you have training log files available? BTW, I got 3 warnings as shown below when training. I was wondering if you also got them? Is that fine?

1. queryinst/mmdet/models/backbones/resnet.py:400: UserWarning: DeprecationWarning: pretrained is a deprecated, please use "init_cfg" instead warnings.warn('DeprecationWarning: pretrained is a deprecated

2. python3.7/site-packages/mmcv/cnn/bricks/conv_module.py:107: UserWarning: ConvModule has norm and bias at the same time warnings.warn('ConvModule has norm and bias at the same time')

3. [W reducer.cpp:346] Warning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance. grad.sizes() = [80, 256, 1, 1], strides() = [256, 1, 256, 256]

Thank you for your nice work!

Since the training code of QueryTrack is not released, I hope you can share the following training details with me:

1. How is the instance embedding extracted from the reference frame? Is the process the same as that in the target frame? In the target frame, the corresponding embeddings of the ground-truth are extracted through the DynamicConv and HungarianAssigner.
2. Besides the instance segmentation loss of the target frame, do you also calculate it for the reference frame like CrossVIS?
3. During inference, the matching factor is changed. Therefore, I wonder if the association process is changed compared to MaskTrackRCNN?

Thanks again.

What variables correspond to queries q_{t-1} and q_t in the main module https://github.com/hustvl/QueryInst/blob/2e8db26965541eff5d290cd10772ed458c71abbc/mmdet/models/roi_heads/query_roi_head.py (if I understand it well)?

In what variable are q_0they stored? Is it happening in EmbeddingRPNHead?

Thank you!

While training a custom dataset having 2 coco classes with the config configs/queryinst/queryinst_swin_large_patch4_window7_fpn_300_proposals_crop_mstrain_400-1200_50e_coco.py

the model shows an error like this - Traceback (most recent call last): File "tools/train.py", line 188, in <module> main() File "tools/train.py", line 184, in main meta=meta) File "/content/QueryInst/mmdet/apis/train.py", line 193, in train_detector runner.run(data_loaders, cfg.workflow) File "/usr/local/lib/python3.7/dist-packages/mmcv/runner/epoch_based_runner.py", line 127, in run epoch_runner(data_loaders[i], **kwargs) File "/usr/local/lib/python3.7/dist-packages/mmcv/runner/epoch_based_runner.py", line 45, in train self.call_hook('before_train_epoch') File "/usr/local/lib/python3.7/dist-packages/mmcv/runner/base_runner.py", line 307, in call_hook getattr(hook, fn_name)(self) File "/content/QueryInst/mmdet/datasets/utils.py", line 155, in before_train_epoch self._check_head(runner) File "/content/QueryInst/mmdet/datasets/utils.py", line 142, in _check_head (f'Thenum_classes({module.num_classes}) in ' AssertionError: Thenum_classes(80) in DynamicMaskHead of MMDistributedDataParallel does not matches the length ofCLASSES2) in CocoDataset

The environment is google colab with Tesla-K80 GPU enabled at GPU:0

I've updated the classes in /content/QueryInst/mmdet/datasets/coco.py , /content/QueryInst/mmdet/core/evaluation/class_names.py and also in the base files of the corresponding config file. Please help !!

My command for training ./tools/dist_train.sh configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py 4

The training was stucked when model met an empty croped gt. My log is shown as below:

2021-08-26 10:39:36,940 - mmdet - INFO - workflow: [('train', 1)], max: 36 epochs
2021-08-26 10:41:30,531 - mmdet - INFO - Epoch [1][50/14659]	lr: 1.249e-06, eta: 13 days, 20:51:37, time: 2.271, data_time: 0.978, memory: 9683, stage0_loss_cls: 2.2784, stage0_pos_acc: 2.0437, stage0_loss_bbox: 2.9789, stage0_loss_iou: 1.6788, stage0_loss_mask: 5.9337, stage1_loss_cls: 2.3385, stage1_pos_acc: 2.0824, stage1_loss_bbox: 4.2721, stage1_loss_iou: 1.8811, stage1_loss_mask: 6.2975, stage2_loss_cls: 2.3301, stage2_pos_acc: 0.9363, stage2_loss_bbox: 2.9707, stage2_loss_iou: 1.9285, stage2_loss_mask: 5.8670, stage3_loss_cls: 2.1712, stage3_pos_acc: 3.0306, stage3_loss_bbox: 3.1920, stage3_loss_iou: 2.2616, stage3_loss_mask: 6.0022, stage4_loss_cls: 2.2627, stage4_pos_acc: 1.7393, stage4_loss_bbox: 2.8403, stage4_loss_iou: 2.3233, stage4_loss_mask: 5.8660, stage5_loss_cls: 2.2882, stage5_pos_acc: 1.5126, stage5_loss_bbox: 2.8825, stage5_loss_iou: 2.3851, stage5_loss_mask: 5.8457, loss: 81.0761, grad_norm: 9063.7358
2021-08-26 10:42:34,279 - mmdet - INFO - Epoch [1][100/14659]	lr: 2.498e-06, eta: 10 days, 19:53:52, time: 1.276, data_time: 0.029, memory: 9683, stage0_loss_cls: 2.2928, stage0_pos_acc: 2.2991, stage0_loss_bbox: 2.4200, stage0_loss_iou: 1.5812, stage0_loss_mask: 5.5926, stage1_loss_cls: 2.3128, stage1_pos_acc: 1.5530, stage1_loss_bbox: 2.8068, stage1_loss_iou: 1.7299, stage1_loss_mask: 5.6873, stage2_loss_cls: 2.2738, stage2_pos_acc: 1.3359, stage2_loss_bbox: 2.0444, stage2_loss_iou: 1.7757, stage2_loss_mask: 5.1849, stage3_loss_cls: 2.0966, stage3_pos_acc: 3.8337, stage3_loss_bbox: 1.9937, stage3_loss_iou: 1.9307, stage3_loss_mask: 5.0764, stage4_loss_cls: 2.2168, stage4_pos_acc: 2.2209, stage4_loss_bbox: 1.8457, stage4_loss_iou: 1.9443, stage4_loss_mask: 4.9774, stage5_loss_cls: 2.1422, stage5_pos_acc: 2.2032, stage5_loss_bbox: 1.8217, stage5_loss_iou: 1.9223, stage5_loss_mask: 4.9423, loss: 68.6125, grad_norm: 5779.0945
2021-08-26 10:43:39,684 - mmdet - INFO - Epoch [1][150/14659]	lr: 3.746e-06, eta: 9 days, 21:06:05, time: 1.307, data_time: 0.030, memory: 9683, stage0_loss_cls: 2.1774, stage0_pos_acc: 2.4055, stage0_loss_bbox: 1.7417, stage0_loss_iou: 1.5327, stage0_loss_mask: 5.2476, stage1_loss_cls: 2.1912, stage1_pos_acc: 1.9714, stage1_loss_bbox: 1.5765, stage1_loss_iou: 1.6106, stage1_loss_mask: 4.8875, stage2_loss_cls: 2.1093, stage2_pos_acc: 3.1017, stage2_loss_bbox: 1.4834, stage2_loss_iou: 1.7319, stage2_loss_mask: 4.6163, stage3_loss_cls: 1.9555, stage3_pos_acc: 9.2308, stage3_loss_bbox: 1.4211, stage3_loss_iou: 1.7616, stage3_loss_mask: 4.4452, stage4_loss_cls: 2.0144, stage4_pos_acc: 13.1486, stage4_loss_bbox: 1.3939, stage4_loss_iou: 1.7237, stage4_loss_mask: 4.5496, stage5_loss_cls: 1.9050, stage5_pos_acc: 18.0571, stage5_loss_bbox: 1.4082, stage5_loss_iou: 1.6820, stage5_loss_mask: 4.6783, loss: 59.8446, grad_norm: 2041.8116
2021-08-26 10:44:43,974 - mmdet - INFO - Epoch [1][200/14659]	lr: 4.995e-06, eta: 9 days, 8:55:17, time: 1.286, data_time: 0.027, memory: 9683, stage0_loss_cls: 2.1020, stage0_pos_acc: 2.5095, stage0_loss_bbox: 1.4044, stage0_loss_iou: 1.4558, stage0_loss_mask: 4.7985, stage1_loss_cls: 2.0902, stage1_pos_acc: 3.7327, stage1_loss_bbox: 1.2853, stage1_loss_iou: 1.5602, stage1_loss_mask: 4.4117, stage2_loss_cls: 1.9464, stage2_pos_acc: 14.1383, stage2_loss_bbox: 1.2960, stage2_loss_iou: 1.5834, stage2_loss_mask: 4.4650, stage3_loss_cls: 1.9040, stage3_pos_acc: 18.7631, stage3_loss_bbox: 1.3171, stage3_loss_iou: 1.5608, stage3_loss_mask: 4.4906, stage4_loss_cls: 1.9529, stage4_pos_acc: 19.2329, stage4_loss_bbox: 1.4226, stage4_loss_iou: 1.6032, stage4_loss_mask: 4.6667, stage5_loss_cls: 1.8324, stage5_pos_acc: 21.7396, stage5_loss_bbox: 1.4392, stage5_loss_iou: 1.6493, stage5_loss_mask: 4.6460, loss: 56.8839, grad_norm: 1372.3742
2021-08-26 10:45:50,304 - mmdet - INFO - Epoch [1][250/14659]	lr: 6.244e-06, eta: 9 days, 2:47:51, time: 1.327, data_time: 0.028, memory: 9694, stage0_loss_cls: 2.0222, stage0_pos_acc: 5.2418, stage0_loss_bbox: 1.2542, stage0_loss_iou: 1.4832, stage0_loss_mask: 4.7497, stage1_loss_cls: 1.9436, stage1_pos_acc: 10.3808, stage1_loss_bbox: 1.1948, stage1_loss_iou: 1.5693, stage1_loss_mask: 4.5709, stage2_loss_cls: 1.9026, stage2_pos_acc: 18.4786, stage2_loss_bbox: 1.2399, stage2_loss_iou: 1.5680, stage2_loss_mask: 4.6608, stage3_loss_cls: 1.8653, stage3_pos_acc: 21.0621, stage3_loss_bbox: 1.2361, stage3_loss_iou: 1.5910, stage3_loss_mask: 4.5865, stage4_loss_cls: 1.8833, stage4_pos_acc: 22.2822, stage4_loss_bbox: 1.2228, stage4_loss_iou: 1.5849, stage4_loss_mask: 4.6193, stage5_loss_cls: 1.8216, stage5_pos_acc: 20.0130, stage5_loss_bbox: 1.2325, stage5_loss_iou: 1.5996, stage5_loss_mask: 4.6086, loss: 56.0107, grad_norm: 612.7333
2021-08-26 10:46:54,223 - mmdet - INFO - Epoch [1][300/14659]	lr: 7.493e-06, eta: 8 days, 21:30:13, time: 1.277, data_time: 0.028, memory: 9704, stage0_loss_cls: 1.9121, stage0_pos_acc: 13.8155, stage0_loss_bbox: 1.2645, stage0_loss_iou: 1.4868, stage0_loss_mask: 4.5622, stage1_loss_cls: 1.8526, stage1_pos_acc: 22.7398, stage1_loss_bbox: 1.2319, stage1_loss_iou: 1.5474, stage1_loss_mask: 4.4435, stage2_loss_cls: 1.8383, stage2_pos_acc: 23.9177, stage2_loss_bbox: 1.2318, stage2_loss_iou: 1.5391, stage2_loss_mask: 4.4818, stage3_loss_cls: 1.7926, stage3_pos_acc: 25.6336, stage3_loss_bbox: 1.2685, stage3_loss_iou: 1.5620, stage3_loss_mask: 4.5198, stage4_loss_cls: 1.8395, stage4_pos_acc: 25.4535, stage4_loss_bbox: 1.2195, stage4_loss_iou: 1.5786, stage4_loss_mask: 4.4744, stage5_loss_cls: 1.7601, stage5_pos_acc: 26.6780, stage5_loss_bbox: 1.2468, stage5_loss_iou: 1.5819, stage5_loss_mask: 4.4257, loss: 54.6615, grad_norm: 539.8647
2021-08-26 10:47:59,050 - mmdet - INFO - Epoch [1][350/14659]	lr: 8.741e-06, eta: 8 days, 18:07:15, time: 1.297, data_time: 0.027, memory: 10247, stage0_loss_cls: 1.8577, stage0_pos_acc: 21.1812, stage0_loss_bbox: 1.1807, stage0_loss_iou: 1.4646, stage0_loss_mask: 4.5113, stage1_loss_cls: 1.8366, stage1_pos_acc: 22.9034, stage1_loss_bbox: 1.1725, stage1_loss_iou: 1.5198, stage1_loss_mask: 4.4354, stage2_loss_cls: 1.8283, stage2_pos_acc: 24.0835, stage2_loss_bbox: 1.1699, stage2_loss_iou: 1.5183, stage2_loss_mask: 4.4518, stage3_loss_cls: 1.7548, stage3_pos_acc: 25.1505, stage3_loss_bbox: 1.1876, stage3_loss_iou: 1.5402, stage3_loss_mask: 4.3562, stage4_loss_cls: 1.7753, stage4_pos_acc: 25.4366, stage4_loss_bbox: 1.1960, stage4_loss_iou: 1.5559, stage4_loss_mask: 4.4155, stage5_loss_cls: 1.7162, stage5_pos_acc: 25.3584, stage5_loss_bbox: 1.1832, stage5_loss_iou: 1.5339, stage5_loss_mask: 4.4058, loss: 53.5674, grad_norm: 324.1903
2021-08-26 10:49:02,791 - mmdet - INFO - Epoch [1][400/14659]	lr: 9.990e-06, eta: 8 days, 15:11:47, time: 1.276, data_time: 0.029, memory: 10247, stage0_loss_cls: 1.8611, stage0_pos_acc: 25.3944, stage0_loss_bbox: 1.1805, stage0_loss_iou: 1.4688, stage0_loss_mask: 4.4686, stage1_loss_cls: 1.8132, stage1_pos_acc: 24.9371, stage1_loss_bbox: 1.1515, stage1_loss_iou: 1.4919, stage1_loss_mask: 4.3721, stage2_loss_cls: 1.7563, stage2_pos_acc: 27.1302, stage2_loss_bbox: 1.1182, stage2_loss_iou: 1.4570, stage2_loss_mask: 4.3585, stage3_loss_cls: 1.7043, stage3_pos_acc: 28.2956, stage3_loss_bbox: 1.1452, stage3_loss_iou: 1.4661, stage3_loss_mask: 4.3051, stage4_loss_cls: 1.7711, stage4_pos_acc: 28.3810, stage4_loss_bbox: 1.1635, stage4_loss_iou: 1.5052, stage4_loss_mask: 4.2992, stage5_loss_cls: 1.6963, stage5_pos_acc: 27.6587, stage5_loss_bbox: 1.2358, stage5_loss_iou: 1.5787, stage5_loss_mask: 4.3334, loss: 52.7017, grad_norm: 268.2526
2021-08-26 10:50:07,148 - mmdet - INFO - Epoch [1][450/14659]	lr: 1.124e-05, eta: 8 days, 13:06:45, time: 1.288, data_time: 0.028, memory: 10247, stage0_loss_cls: 1.8659, stage0_pos_acc: 21.0187, stage0_loss_bbox: 1.1456, stage0_loss_iou: 1.4673, stage0_loss_mask: 4.4616, stage1_loss_cls: 1.8124, stage1_pos_acc: 20.8431, stage1_loss_bbox: 1.1410, stage1_loss_iou: 1.5024, stage1_loss_mask: 4.3939, stage2_loss_cls: 1.7415, stage2_pos_acc: 22.2469, stage2_loss_bbox: 1.0791, stage2_loss_iou: 1.4552, stage2_loss_mask: 4.3218, stage3_loss_cls: 1.6844, stage3_pos_acc: 23.0229, stage3_loss_bbox: 1.0540, stage3_loss_iou: 1.4710, stage3_loss_mask: 4.2206, stage4_loss_cls: 1.7414, stage4_pos_acc: 23.0507, stage4_loss_bbox: 1.0820, stage4_loss_iou: 1.4799, stage4_loss_mask: 4.2415, stage5_loss_cls: 1.6787, stage5_pos_acc: 22.9782, stage5_loss_bbox: 1.1479, stage5_loss_iou: 1.5137, stage5_loss_mask: 4.2180, loss: 51.9207, grad_norm: 213.7757
2021-08-26 10:51:12,068 - mmdet - INFO - Epoch [1][500/14659]	lr: 1.249e-05, eta: 8 days, 11:35:48, time: 1.298, data_time: 0.028, memory: 10247, stage0_loss_cls: 1.8008, stage0_pos_acc: 26.0289, stage0_loss_bbox: 1.1152, stage0_loss_iou: 1.4964, stage0_loss_mask: 4.5369, stage1_loss_cls: 1.7177, stage1_pos_acc: 26.9426, stage1_loss_bbox: 1.0998, stage1_loss_iou: 1.4901, stage1_loss_mask: 4.4598, stage2_loss_cls: 1.6178, stage2_pos_acc: 28.2533, stage2_loss_bbox: 1.0045, stage2_loss_iou: 1.4458, stage2_loss_mask: 4.2719, stage3_loss_cls: 1.5983, stage3_pos_acc: 28.3737, stage3_loss_bbox: 0.9817, stage3_loss_iou: 1.4316, stage3_loss_mask: 4.1732, stage4_loss_cls: 1.6095, stage4_pos_acc: 28.0076, stage4_loss_bbox: 1.0100, stage4_loss_iou: 1.4404, stage4_loss_mask: 4.1736, stage5_loss_cls: 1.5825, stage5_pos_acc: 27.7242, stage5_loss_bbox: 1.0086, stage5_loss_iou: 1.4421, stage5_loss_mask: 4.1600, loss: 50.6682, grad_norm: 161.7756
2021-08-26 10:52:17,094 - mmdet - INFO - Epoch [1][550/14659]	lr: 1.374e-05, eta: 8 days, 10:22:02, time: 1.299, data_time: 0.031, memory: 10247, stage0_loss_cls: 1.7570, stage0_pos_acc: 26.8588, stage0_loss_bbox: 1.1083, stage0_loss_iou: 1.4725, stage0_loss_mask: 4.3113, stage1_loss_cls: 1.6231, stage1_pos_acc: 28.9210, stage1_loss_bbox: 1.0594, stage1_loss_iou: 1.4476, stage1_loss_mask: 4.2218, stage2_loss_cls: 1.5860, stage2_pos_acc: 28.7443, stage2_loss_bbox: 0.9882, stage2_loss_iou: 1.3822, stage2_loss_mask: 4.0420, stage3_loss_cls: 1.5382, stage3_pos_acc: 29.1749, stage3_loss_bbox: 0.9445, stage3_loss_iou: 1.3533, stage3_loss_mask: 3.9423, stage4_loss_cls: 1.5706, stage4_pos_acc: 29.3126, stage4_loss_bbox: 0.9957, stage4_loss_iou: 1.3977, stage4_loss_mask: 3.9466, stage5_loss_cls: 1.5491, stage5_pos_acc: 30.3116, stage5_loss_bbox: 1.0134, stage5_loss_iou: 1.4122, stage5_loss_mask: 3.9807, loss: 48.6438, grad_norm: 165.5006
2021-08-26 10:53:22,121 - mmdet - INFO - Epoch [1][600/14659]	lr: 1.499e-05, eta: 8 days, 9:22:05, time: 1.302, data_time: 0.029, memory: 10247, stage0_loss_cls: 1.8014, stage0_pos_acc: 24.0576, stage0_loss_bbox: 1.0937, stage0_loss_iou: 1.4583, stage0_loss_mask: 4.4055, stage1_loss_cls: 1.6502, stage1_pos_acc: 25.7283, stage1_loss_bbox: 1.0336, stage1_loss_iou: 1.4023, stage1_loss_mask: 4.2823, stage2_loss_cls: 1.6193, stage2_pos_acc: 25.9719, stage2_loss_bbox: 0.9329, stage2_loss_iou: 1.3439, stage2_loss_mask: 4.0294, stage3_loss_cls: 1.5818, stage3_pos_acc: 26.1097, stage3_loss_bbox: 0.9050, stage3_loss_iou: 1.3209, stage3_loss_mask: 3.9482, stage4_loss_cls: 1.6150, stage4_pos_acc: 25.8387, stage4_loss_bbox: 0.9309, stage4_loss_iou: 1.3462, stage4_loss_mask: 3.9348, stage5_loss_cls: 1.5781, stage5_pos_acc: 26.1663, stage5_loss_bbox: 0.9603, stage5_loss_iou: 1.3778, stage5_loss_mask: 3.9012, loss: 48.4532, grad_norm: 135.0550
2021-08-26 10:54:27,236 - mmdet - INFO - Epoch [1][650/14659]	lr: 1.623e-05, eta: 8 days, 8:30:35, time: 1.301, data_time: 0.029, memory: 10247, stage0_loss_cls: 1.7764, stage0_pos_acc: 24.2415, stage0_loss_bbox: 1.0727, stage0_loss_iou: 1.4545, stage0_loss_mask: 4.3936, stage1_loss_cls: 1.6191, stage1_pos_acc: 26.6580, stage1_loss_bbox: 0.9463, stage1_loss_iou: 1.4043, stage1_loss_mask: 4.0678, stage2_loss_cls: 1.5642, stage2_pos_acc: 26.5745, stage2_loss_bbox: 0.8532, stage2_loss_iou: 1.3563, stage2_loss_mask: 3.7875, stage3_loss_cls: 1.5627, stage3_pos_acc: 25.8296, stage3_loss_bbox: 0.8517, stage3_loss_iou: 1.3410, stage3_loss_mask: 3.7530, stage4_loss_cls: 1.5756, stage4_pos_acc: 27.2208, stage4_loss_bbox: 0.8791, stage4_loss_iou: 1.3547, stage4_loss_mask: 3.7740, stage5_loss_cls: 1.5621, stage5_pos_acc: 27.6374, stage5_loss_bbox: 0.8854, stage5_loss_iou: 1.3647, stage5_loss_mask: 3.7638, loss: 46.9637, grad_norm: 121.3612
2021-08-26 10:55:31,504 - mmdet - INFO - Epoch [1][700/14659]	lr: 1.748e-05, eta: 8 days, 7:37:41, time: 1.287, data_time: 0.026, memory: 10247, stage0_loss_cls: 1.7435, stage0_pos_acc: 26.5202, stage0_loss_bbox: 1.0671, stage0_loss_iou: 1.4283, stage0_loss_mask: 4.2096, stage1_loss_cls: 1.6122, stage1_pos_acc: 26.1169, stage1_loss_bbox: 0.9308, stage1_loss_iou: 1.3396, stage1_loss_mask: 3.8757, stage2_loss_cls: 1.5824, stage2_pos_acc: 28.1921, stage2_loss_bbox: 0.8364, stage2_loss_iou: 1.2766, stage2_loss_mask: 3.7162, stage3_loss_cls: 1.5652, stage3_pos_acc: 27.4108, stage3_loss_bbox: 0.8119, stage3_loss_iou: 1.2662, stage3_loss_mask: 3.6062, stage4_loss_cls: 1.5751, stage4_pos_acc: 28.9362, stage4_loss_bbox: 0.8734, stage4_loss_iou: 1.3164, stage4_loss_mask: 3.6785, stage5_loss_cls: 1.5723, stage5_pos_acc: 29.3925, stage5_loss_bbox: 0.9343, stage5_loss_iou: 1.3846, stage5_loss_mask: 3.6585, loss: 45.8611, grad_norm: 108.1211
2021-08-26 10:56:35,840 - mmdet - INFO - Epoch [1][750/14659]	lr: 1.873e-05, eta: 8 days, 6:51:16, time: 1.286, data_time: 0.025, memory: 10247, stage0_loss_cls: 1.7365, stage0_pos_acc: 25.9305, stage0_loss_bbox: 1.1240, stage0_loss_iou: 1.4425, stage0_loss_mask: 4.2803, stage1_loss_cls: 1.6124, stage1_pos_acc: 27.2223, stage1_loss_bbox: 0.9277, stage1_loss_iou: 1.3490, stage1_loss_mask: 3.8425, stage2_loss_cls: 1.5820, stage2_pos_acc: 27.2568, stage2_loss_bbox: 0.8401, stage2_loss_iou: 1.2861, stage2_loss_mask: 3.6427, stage3_loss_cls: 1.5647, stage3_pos_acc: 28.4669, stage3_loss_bbox: 0.8065, stage3_loss_iou: 1.2604, stage3_loss_mask: 3.5016, stage4_loss_cls: 1.5462, stage4_pos_acc: 28.9539, stage4_loss_bbox: 0.8224, stage4_loss_iou: 1.2827, stage4_loss_mask: 3.5201, stage5_loss_cls: 1.5790, stage5_pos_acc: 28.0625, stage5_loss_bbox: 0.8625, stage5_loss_iou: 1.2923, stage5_loss_mask: 3.5762, loss: 45.2801, grad_norm: 99.4212
2021-08-26 10:57:40,493 - mmdet - INFO - Epoch [1][800/14659]	lr: 1.998e-05, eta: 8 days, 6:14:34, time: 1.294, data_time: 0.029, memory: 10247, stage0_loss_cls: 1.7001, stage0_pos_acc: 27.6462, stage0_loss_bbox: 1.0656, stage0_loss_iou: 1.4303, stage0_loss_mask: 4.2057, stage1_loss_cls: 1.5513, stage1_pos_acc: 28.8365, stage1_loss_bbox: 0.8659, stage1_loss_iou: 1.3150, stage1_loss_mask: 3.6463, stage2_loss_cls: 1.5046, stage2_pos_acc: 29.7402, stage2_loss_bbox: 0.7838, stage2_loss_iou: 1.2468, stage2_loss_mask: 3.4932, stage3_loss_cls: 1.4943, stage3_pos_acc: 30.3968, stage3_loss_bbox: 0.7764, stage3_loss_iou: 1.2411, stage3_loss_mask: 3.4376, stage4_loss_cls: 1.4802, stage4_pos_acc: 31.3031, stage4_loss_bbox: 0.8314, stage4_loss_iou: 1.2847, stage4_loss_mask: 3.5074, stage5_loss_cls: 1.4886, stage5_pos_acc: 33.1703, stage5_loss_bbox: 0.8857, stage5_loss_iou: 1.3279, stage5_loss_mask: 3.5649, loss: 44.1290, grad_norm: 99.3313
2021-08-26 10:58:45,254 - mmdet - INFO - Epoch [1][850/14659]	lr: 2.123e-05, eta: 8 days, 5:42:30, time: 1.295, data_time: 0.028, memory: 10247, stage0_loss_cls: 1.7266, stage0_pos_acc: 25.7188, stage0_loss_bbox: 1.0323, stage0_loss_iou: 1.4061, stage0_loss_mask: 4.1405, stage1_loss_cls: 1.5495, stage1_pos_acc: 26.3889, stage1_loss_bbox: 0.8425, stage1_loss_iou: 1.2886, stage1_loss_mask: 3.5787, stage2_loss_cls: 1.5065, stage2_pos_acc: 28.2313, stage2_loss_bbox: 0.7561, stage2_loss_iou: 1.2177, stage2_loss_mask: 3.4587, stage3_loss_cls: 1.4781, stage3_pos_acc: 29.3448, stage3_loss_bbox: 0.7380, stage3_loss_iou: 1.2051, stage3_loss_mask: 3.3795, stage4_loss_cls: 1.4777, stage4_pos_acc: 29.7030, stage4_loss_bbox: 0.7641, stage4_loss_iou: 1.2305, stage4_loss_mask: 3.4189, stage5_loss_cls: 1.4723, stage5_pos_acc: 30.1511, stage5_loss_bbox: 0.7887, stage5_loss_iou: 1.2514, stage5_loss_mask: 3.3970, loss: 43.1052, grad_norm: 91.7964
2021-08-26 10:59:50,234 - mmdet - INFO - Epoch [1][900/14659]	lr: 2.248e-05, eta: 8 days, 5:16:40, time: 1.300, data_time: 0.028, memory: 10247, stage0_loss_cls: 1.6663, stage0_pos_acc: 27.5676, stage0_loss_bbox: 1.0314, stage0_loss_iou: 1.3817, stage0_loss_mask: 4.0167, stage1_loss_cls: 1.5113, stage1_pos_acc: 29.0569, stage1_loss_bbox: 0.8031, stage1_loss_iou: 1.2455, stage1_loss_mask: 3.4317, stage2_loss_cls: 1.4575, stage2_pos_acc: 30.5629, stage2_loss_bbox: 0.7264, stage2_loss_iou: 1.1641, stage2_loss_mask: 3.2658, stage3_loss_cls: 1.4436, stage3_pos_acc: 32.9249, stage3_loss_bbox: 0.7004, stage3_loss_iou: 1.1511, stage3_loss_mask: 3.1839, stage4_loss_cls: 1.4497, stage4_pos_acc: 32.7798, stage4_loss_bbox: 0.7274, stage4_loss_iou: 1.1699, stage4_loss_mask: 3.2156, stage5_loss_cls: 1.4516, stage5_pos_acc: 32.8581, stage5_loss_bbox: 0.7502, stage5_loss_iou: 1.1905, stage5_loss_mask: 3.2463, loss: 41.3817, grad_norm: 82.7916
2021-08-26 11:00:55,297 - mmdet - INFO - Epoch [1][950/14659]	lr: 2.373e-05, eta: 8 days, 4:53:37, time: 1.301, data_time: 0.029, memory: 10247, stage0_loss_cls: 1.6926, stage0_pos_acc: 24.8302, stage0_loss_bbox: 1.0278, stage0_loss_iou: 1.4474, stage0_loss_mask: 4.1217, stage1_loss_cls: 1.5174, stage1_pos_acc: 26.3150, stage1_loss_bbox: 0.7981, stage1_loss_iou: 1.3047, stage1_loss_mask: 3.5163, stage2_loss_cls: 1.4542, stage2_pos_acc: 29.7413, stage2_loss_bbox: 0.6940, stage2_loss_iou: 1.2135, stage2_loss_mask: 3.3908, stage3_loss_cls: 1.4345, stage3_pos_acc: 30.6539, stage3_loss_bbox: 0.6771, stage3_loss_iou: 1.1896, stage3_loss_mask: 3.3288, stage4_loss_cls: 1.4294, stage4_pos_acc: 31.8011, stage4_loss_bbox: 0.6830, stage4_loss_iou: 1.1847, stage4_loss_mask: 3.3438, stage5_loss_cls: 1.4342, stage5_pos_acc: 30.7768, stage5_loss_bbox: 0.6908, stage5_loss_iou: 1.1846, stage5_loss_mask: 3.3271, loss: 42.0862, grad_norm: 80.4314
2021-08-26 11:01:59,821 - mmdet - INFO - Exp name: queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
2021-08-26 11:01:59,821 - mmdet - INFO - Epoch [1][1000/14659]	lr: 2.498e-05, eta: 8 days, 4:28:13, time: 1.290, data_time: 0.029, memory: 10247, stage0_loss_cls: 1.7185, stage0_pos_acc: 23.4859, stage0_loss_bbox: 1.0140, stage0_loss_iou: 1.3786, stage0_loss_mask: 3.8208, stage1_loss_cls: 1.5292, stage1_pos_acc: 27.5284, stage1_loss_bbox: 0.8249, stage1_loss_iou: 1.2268, stage1_loss_mask: 3.2860, stage2_loss_cls: 1.4810, stage2_pos_acc: 28.5340, stage2_loss_bbox: 0.7470, stage2_loss_iou: 1.1554, stage2_loss_mask: 3.1576, stage3_loss_cls: 1.4664, stage3_pos_acc: 31.6244, stage3_loss_bbox: 0.7178, stage3_loss_iou: 1.1216, stage3_loss_mask: 3.0968, stage4_loss_cls: 1.4632, stage4_pos_acc: 31.3865, stage4_loss_bbox: 0.7182, stage4_loss_iou: 1.1125, stage4_loss_mask: 3.1103, stage5_loss_cls: 1.4789, stage5_pos_acc: 31.1255, stage5_loss_bbox: 0.7303, stage5_loss_iou: 1.1205, stage5_loss_mask: 3.1161, loss: 40.5925, grad_norm: 91.0885
2021-08-26 11:03:05,687 - mmdet - INFO - Epoch [1][1050/14659]	lr: 2.500e-05, eta: 8 days, 4:16:47, time: 1.318, data_time: 0.028, memory: 10260, stage0_loss_cls: 1.6991, stage0_pos_acc: 26.2447, stage0_loss_bbox: 1.0155, stage0_loss_iou: 1.4066, stage0_loss_mask: 3.8848, stage1_loss_cls: 1.4980, stage1_pos_acc: 27.5659, stage1_loss_bbox: 0.7647, stage1_loss_iou: 1.2471, stage1_loss_mask: 3.2741, stage2_loss_cls: 1.4364, stage2_pos_acc: 29.7703, stage2_loss_bbox: 0.6777, stage2_loss_iou: 1.1569, stage2_loss_mask: 3.1341, stage3_loss_cls: 1.4320, stage3_pos_acc: 30.3810, stage3_loss_bbox: 0.6392, stage3_loss_iou: 1.1158, stage3_loss_mask: 3.0552, stage4_loss_cls: 1.4235, stage4_pos_acc: 30.6780, stage4_loss_bbox: 0.6377, stage4_loss_iou: 1.1067, stage4_loss_mask: 3.0394, stage5_loss_cls: 1.4280, stage5_pos_acc: 30.8946, stage5_loss_bbox: 0.6443, stage5_loss_iou: 1.1061, stage5_loss_mask: 3.0546, loss: 39.8778, grad_norm: 81.4806
2021-08-26 11:04:09,375 - mmdet - INFO - Epoch [1][1100/14659]	lr: 2.500e-05, eta: 8 days, 3:48:30, time: 1.274, data_time: 0.026, memory: 10260, stage0_loss_cls: 1.6987, stage0_pos_acc: 23.8894, stage0_loss_bbox: 1.0367, stage0_loss_iou: 1.3789, stage0_loss_mask: 3.7901, stage1_loss_cls: 1.4886, stage1_pos_acc: 28.3727, stage1_loss_bbox: 0.7877, stage1_loss_iou: 1.2206, stage1_loss_mask: 3.2159, stage2_loss_cls: 1.4322, stage2_pos_acc: 30.4321, stage2_loss_bbox: 0.6857, stage2_loss_iou: 1.1131, stage2_loss_mask: 3.1053, stage3_loss_cls: 1.4224, stage3_pos_acc: 32.5551, stage3_loss_bbox: 0.6554, stage3_loss_iou: 1.0726, stage3_loss_mask: 3.0383, stage4_loss_cls: 1.4080, stage4_pos_acc: 33.1763, stage4_loss_bbox: 0.6439, stage4_loss_iou: 1.0641, stage4_loss_mask: 3.0504, stage5_loss_cls: 1.4335, stage5_pos_acc: 32.6161, stage5_loss_bbox: 0.6368, stage5_loss_iou: 1.0557, stage5_loss_mask: 3.0440, loss: 39.4782, grad_norm: 78.5361
2021-08-26 11:05:14,090 - mmdet - INFO - Epoch [1][1150/14659]	lr: 2.500e-05, eta: 8 days, 3:30:28, time: 1.294, data_time: 0.029, memory: 10260, stage0_loss_cls: 1.6715, stage0_pos_acc: 27.3082, stage0_loss_bbox: 1.0007, stage0_loss_iou: 1.4006, stage0_loss_mask: 3.7686, stage1_loss_cls: 1.4621, stage1_pos_acc: 29.3495, stage1_loss_bbox: 0.7468, stage1_loss_iou: 1.2150, stage1_loss_mask: 3.1715, stage2_loss_cls: 1.4031, stage2_pos_acc: 32.7003, stage2_loss_bbox: 0.6451, stage2_loss_iou: 1.1141, stage2_loss_mask: 2.9907, stage3_loss_cls: 1.3903, stage3_pos_acc: 34.3246, stage3_loss_bbox: 0.6216, stage3_loss_iou: 1.0744, stage3_loss_mask: 2.9547, stage4_loss_cls: 1.3798, stage4_pos_acc: 34.3448, stage4_loss_bbox: 0.6325, stage4_loss_iou: 1.0721, stage4_loss_mask: 2.9729, stage5_loss_cls: 1.4005, stage5_pos_acc: 33.8655, stage5_loss_bbox: 0.6101, stage5_loss_iou: 1.0476, stage5_loss_mask: 2.9774, loss: 38.7239, grad_norm: 78.3448
2021-08-26 11:06:18,885 - mmdet - INFO - Epoch [1][1200/14659]	lr: 2.500e-05, eta: 8 days, 3:14:17, time: 1.295, data_time: 0.024, memory: 10260, stage0_loss_cls: 1.6741, stage0_pos_acc: 26.5082, stage0_loss_bbox: 1.0016, stage0_loss_iou: 1.3997, stage0_loss_mask: 3.8476, stage1_loss_cls: 1.4358, stage1_pos_acc: 29.2393, stage1_loss_bbox: 0.7487, stage1_loss_iou: 1.2229, stage1_loss_mask: 3.2792, stage2_loss_cls: 1.3819, stage2_pos_acc: 30.4406, stage2_loss_bbox: 0.6568, stage2_loss_iou: 1.1234, stage2_loss_mask: 3.1402, stage3_loss_cls: 1.3637, stage3_pos_acc: 33.2124, stage3_loss_bbox: 0.6305, stage3_loss_iou: 1.0950, stage3_loss_mask: 3.1075, stage4_loss_cls: 1.3658, stage4_pos_acc: 32.1867, stage4_loss_bbox: 0.6289, stage4_loss_iou: 1.0902, stage4_loss_mask: 3.0865, stage5_loss_cls: 1.3765, stage5_pos_acc: 33.1551, stage5_loss_bbox: 0.6318, stage5_loss_iou: 1.1011, stage5_loss_mask: 3.1301, loss: 39.5195, grad_norm: 76.9653
2021-08-26 11:07:25,167 - mmdet - INFO - Epoch [1][1250/14659]	lr: 2.500e-05, eta: 8 days, 3:10:12, time: 1.326, data_time: 0.035, memory: 10260, stage0_loss_cls: 1.6800, stage0_pos_acc: 25.1354, stage0_loss_bbox: 0.9629, stage0_loss_iou: 1.3814, stage0_loss_mask: 3.5957, stage1_loss_cls: 1.4227, stage1_pos_acc: 28.0095, stage1_loss_bbox: 0.7004, stage1_loss_iou: 1.1653, stage1_loss_mask: 3.0698, stage2_loss_cls: 1.3556, stage2_pos_acc: 31.2645, stage2_loss_bbox: 0.6103, stage2_loss_iou: 1.0676, stage2_loss_mask: 2.9250, stage3_loss_cls: 1.3398, stage3_pos_acc: 33.5218, stage3_loss_bbox: 0.5740, stage3_loss_iou: 1.0287, stage3_loss_mask: 2.8877, stage4_loss_cls: 1.3407, stage4_pos_acc: 34.1780, stage4_loss_bbox: 0.5645, stage4_loss_iou: 1.0191, stage4_loss_mask: 2.8721, stage5_loss_cls: 1.3556, stage5_pos_acc: 33.6427, stage5_loss_bbox: 0.5609, stage5_loss_iou: 1.0110, stage5_loss_mask: 2.8753, loss: 37.3662, grad_norm: 75.3087
2021-08-26 11:08:30,769 - mmdet - INFO - Epoch [1][1300/14659]	lr: 2.500e-05, eta: 8 days, 3:01:29, time: 1.312, data_time: 0.031, memory: 10260, stage0_loss_cls: 1.6479, stage0_pos_acc: 28.9296, stage0_loss_bbox: 0.9668, stage0_loss_iou: 1.4114, stage0_loss_mask: 3.8106, stage1_loss_cls: 1.3825, stage1_pos_acc: 32.6764, stage1_loss_bbox: 0.6959, stage1_loss_iou: 1.2048, stage1_loss_mask: 3.2734, stage2_loss_cls: 1.3125, stage2_pos_acc: 35.9537, stage2_loss_bbox: 0.6092, stage2_loss_iou: 1.1096, stage2_loss_mask: 3.1411, stage3_loss_cls: 1.3076, stage3_pos_acc: 37.4952, stage3_loss_bbox: 0.5831, stage3_loss_iou: 1.0698, stage3_loss_mask: 3.0958, stage4_loss_cls: 1.2939, stage4_pos_acc: 38.1969, stage4_loss_bbox: 0.5723, stage4_loss_iou: 1.0563, stage4_loss_mask: 3.0967, stage5_loss_cls: 1.3119, stage5_pos_acc: 37.5753, stage5_loss_bbox: 0.5783, stage5_loss_iou: 1.0544, stage5_loss_mask: 3.1193, loss: 38.7049, grad_norm: 69.7411
2021-08-26 11:09:37,150 - mmdet - INFO - Epoch [1][1350/14659]	lr: 2.500e-05, eta: 8 days, 2:58:17, time: 1.327, data_time: 0.031, memory: 10260, stage0_loss_cls: 1.6179, stage0_pos_acc: 29.8083, stage0_loss_bbox: 0.9617, stage0_loss_iou: 1.4000, stage0_loss_mask: 3.6221, stage1_loss_cls: 1.3624, stage1_pos_acc: 32.9784, stage1_loss_bbox: 0.7101, stage1_loss_iou: 1.1954, stage1_loss_mask: 3.0473, stage2_loss_cls: 1.3282, stage2_pos_acc: 34.8804, stage2_loss_bbox: 0.6097, stage2_loss_iou: 1.0835, stage2_loss_mask: 2.9072, stage3_loss_cls: 1.3201, stage3_pos_acc: 36.4125, stage3_loss_bbox: 0.5731, stage3_loss_iou: 1.0404, stage3_loss_mask: 2.8688, stage4_loss_cls: 1.3002, stage4_pos_acc: 37.3596, stage4_loss_bbox: 0.5613, stage4_loss_iou: 1.0244, stage4_loss_mask: 2.8589, stage5_loss_cls: 1.3196, stage5_pos_acc: 37.2872, stage5_loss_bbox: 0.5531, stage5_loss_iou: 1.0206, stage5_loss_mask: 2.8500, loss: 37.1359, grad_norm: 74.0133
2021-08-26 11:10:42,106 - mmdet - INFO - Epoch [1][1400/14659]	lr: 2.500e-05, eta: 8 days, 2:46:29, time: 1.299, data_time: 0.027, memory: 10260, stage0_loss_cls: 1.6639, stage0_pos_acc: 24.5354, stage0_loss_bbox: 0.9730, stage0_loss_iou: 1.3973, stage0_loss_mask: 3.6086, stage1_loss_cls: 1.4075, stage1_pos_acc: 27.8628, stage1_loss_bbox: 0.7055, stage1_loss_iou: 1.1755, stage1_loss_mask: 3.0696, stage2_loss_cls: 1.3575, stage2_pos_acc: 31.3315, stage2_loss_bbox: 0.6009, stage2_loss_iou: 1.0746, stage2_loss_mask: 2.9750, stage3_loss_cls: 1.3346, stage3_pos_acc: 35.5107, stage3_loss_bbox: 0.5721, stage3_loss_iou: 1.0375, stage3_loss_mask: 2.9445, stage4_loss_cls: 1.3261, stage4_pos_acc: 34.3865, stage4_loss_bbox: 0.5639, stage4_loss_iou: 1.0271, stage4_loss_mask: 2.9395, stage5_loss_cls: 1.3379, stage5_pos_acc: 34.6709, stage5_loss_bbox: 0.5691, stage5_loss_iou: 1.0290, stage5_loss_mask: 2.9497, loss: 37.6400, grad_norm: 71.6296
2021-08-26 11:11:47,091 - mmdet - INFO - Epoch [1][1450/14659]	lr: 2.500e-05, eta: 8 days, 2:35:03, time: 1.298, data_time: 0.026, memory: 10260, stage0_loss_cls: 1.6694, stage0_pos_acc: 25.8043, stage0_loss_bbox: 0.9478, stage0_loss_iou: 1.3652, stage0_loss_mask: 3.5024, stage1_loss_cls: 1.4135, stage1_pos_acc: 30.3336, stage1_loss_bbox: 0.6791, stage1_loss_iou: 1.1441, stage1_loss_mask: 2.9723, stage2_loss_cls: 1.3526, stage2_pos_acc: 33.2251, stage2_loss_bbox: 0.6031, stage2_loss_iou: 1.0438, stage2_loss_mask: 2.8801, stage3_loss_cls: 1.3430, stage3_pos_acc: 35.2084, stage3_loss_bbox: 0.5677, stage3_loss_iou: 1.0006, stage3_loss_mask: 2.8446, stage4_loss_cls: 1.3277, stage4_pos_acc: 35.9694, stage4_loss_bbox: 0.5671, stage4_loss_iou: 0.9964, stage4_loss_mask: 2.8584, stage5_loss_cls: 1.3488, stage5_pos_acc: 35.4904, stage5_loss_bbox: 0.5653, stage5_loss_iou: 0.9986, stage5_loss_mask: 2.8676, loss: 36.8592, grad_norm: 73.8468
2021-08-26 11:12:52,427 - mmdet - INFO - Epoch [1][1500/14659]	lr: 2.500e-05, eta: 8 days, 2:27:06, time: 1.308, data_time: 0.028, memory: 10260, stage0_loss_cls: 1.6548, stage0_pos_acc: 25.4055, stage0_loss_bbox: 0.9172, stage0_loss_iou: 1.3403, stage0_loss_mask: 3.5027, stage1_loss_cls: 1.3948, stage1_pos_acc: 30.2240, stage1_loss_bbox: 0.6886, stage1_loss_iou: 1.1475, stage1_loss_mask: 2.9771, stage2_loss_cls: 1.3370, stage2_pos_acc: 33.6155, stage2_loss_bbox: 0.5852, stage2_loss_iou: 1.0394, stage2_loss_mask: 2.8556, stage3_loss_cls: 1.3124, stage3_pos_acc: 37.1978, stage3_loss_bbox: 0.5461, stage3_loss_iou: 0.9921, stage3_loss_mask: 2.8452, stage4_loss_cls: 1.3030, stage4_pos_acc: 38.8616, stage4_loss_bbox: 0.5495, stage4_loss_iou: 0.9866, stage4_loss_mask: 2.8609, stage5_loss_cls: 1.3085, stage5_pos_acc: 38.3760, stage5_loss_bbox: 0.5476, stage5_loss_iou: 0.9827, stage5_loss_mask: 2.8690, loss: 36.5439, grad_norm: 70.3628
2021-08-26 11:13:57,001 - mmdet - INFO - Epoch [1][1550/14659]	lr: 2.500e-05, eta: 8 days, 2:15:13, time: 1.292, data_time: 0.027, memory: 10260, stage0_loss_cls: 1.6851, stage0_pos_acc: 24.5149, stage0_loss_bbox: 0.9397, stage0_loss_iou: 1.3625, stage0_loss_mask: 3.4770, stage1_loss_cls: 1.4110, stage1_pos_acc: 28.2324, stage1_loss_bbox: 0.6584, stage1_loss_iou: 1.1252, stage1_loss_mask: 2.8640, stage2_loss_cls: 1.3431, stage2_pos_acc: 32.4330, stage2_loss_bbox: 0.5522, stage2_loss_iou: 1.0091, stage2_loss_mask: 2.7325, stage3_loss_cls: 1.3222, stage3_pos_acc: 34.9220, stage3_loss_bbox: 0.5196, stage3_loss_iou: 0.9702, stage3_loss_mask: 2.7097, stage4_loss_cls: 1.3133, stage4_pos_acc: 35.7501, stage4_loss_bbox: 0.5125, stage4_loss_iou: 0.9522, stage4_loss_mask: 2.7075, stage5_loss_cls: 1.3295, stage5_pos_acc: 35.7375, stage5_loss_bbox: 0.5176, stage5_loss_iou: 0.9538, stage5_loss_mask: 2.7439, loss: 35.7117, grad_norm: 72.5540
2021-08-26 11:15:02,440 - mmdet - INFO - Epoch [1][1600/14659]	lr: 2.500e-05, eta: 8 days, 2:08:33, time: 1.309, data_time: 0.031, memory: 10260, stage0_loss_cls: 1.6480, stage0_pos_acc: 26.2355, stage0_loss_bbox: 0.9417, stage0_loss_iou: 1.3586, stage0_loss_mask: 3.4764, stage1_loss_cls: 1.3551, stage1_pos_acc: 31.0848, stage1_loss_bbox: 0.6607, stage1_loss_iou: 1.1333, stage1_loss_mask: 2.8353, stage2_loss_cls: 1.2772, stage2_pos_acc: 35.2232, stage2_loss_bbox: 0.5610, stage2_loss_iou: 1.0170, stage2_loss_mask: 2.7561, stage3_loss_cls: 1.2661, stage3_pos_acc: 36.9457, stage3_loss_bbox: 0.5299, stage3_loss_iou: 0.9758, stage3_loss_mask: 2.7287, stage4_loss_cls: 1.2547, stage4_pos_acc: 38.4415, stage4_loss_bbox: 0.5165, stage4_loss_iou: 0.9627, stage4_loss_mask: 2.7384, stage5_loss_cls: 1.2716, stage5_pos_acc: 39.2369, stage5_loss_bbox: 0.5176, stage5_loss_iou: 0.9582, stage5_loss_mask: 2.7461, loss: 35.4867, grad_norm: 70.7494
2021-08-26 11:16:07,147 - mmdet - INFO - Epoch [1][1650/14659]	lr: 2.500e-05, eta: 8 days, 1:58:19, time: 1.294, data_time: 0.027, memory: 10260, stage0_loss_cls: 1.6747, stage0_pos_acc: 25.6228, stage0_loss_bbox: 0.9274, stage0_loss_iou: 1.3638, stage0_loss_mask: 3.3953, stage1_loss_cls: 1.3673, stage1_pos_acc: 29.8191, stage1_loss_bbox: 0.6477, stage1_loss_iou: 1.1207, stage1_loss_mask: 2.8112, stage2_loss_cls: 1.3010, stage2_pos_acc: 33.2384, stage2_loss_bbox: 0.5509, stage2_loss_iou: 1.0006, stage2_loss_mask: 2.6553, stage3_loss_cls: 1.2719, stage3_pos_acc: 36.5612, stage3_loss_bbox: 0.5095, stage3_loss_iou: 0.9532, stage3_loss_mask: 2.6669, stage4_loss_cls: 1.2616, stage4_pos_acc: 38.9800, stage4_loss_bbox: 0.5050, stage4_loss_iou: 0.9380, stage4_loss_mask: 2.6746, stage5_loss_cls: 1.2729, stage5_pos_acc: 38.0502, stage5_loss_bbox: 0.4997, stage5_loss_iou: 0.9334, stage5_loss_mask: 2.6741, loss: 34.9768, grad_norm: 71.5673
2021-08-26 11:17:11,737 - mmdet - INFO - Epoch [1][1700/14659]	lr: 2.500e-05, eta: 8 days, 1:48:00, time: 1.292, data_time: 0.028, memory: 10260, stage0_loss_cls: 1.6531, stage0_pos_acc: 24.8506, stage0_loss_bbox: 0.9111, stage0_loss_iou: 1.3768, stage0_loss_mask: 3.3537, stage1_loss_cls: 1.3289, stage1_pos_acc: 29.6029, stage1_loss_bbox: 0.6354, stage1_loss_iou: 1.1256, stage1_loss_mask: 2.7265, stage2_loss_cls: 1.2492, stage2_pos_acc: 35.0890, stage2_loss_bbox: 0.5676, stage2_loss_iou: 1.0286, stage2_loss_mask: 2.6394, stage3_loss_cls: 1.2338, stage3_pos_acc: 40.2179, stage3_loss_bbox: 0.5471, stage3_loss_iou: 0.9936, stage3_loss_mask: 2.5983, stage4_loss_cls: 1.2161, stage4_pos_acc: 41.3352, stage4_loss_bbox: 0.5345, stage4_loss_iou: 0.9823, stage4_loss_mask: 2.5976, stage5_loss_cls: 1.2298, stage5_pos_acc: 40.0981, stage5_loss_bbox: 0.5292, stage5_loss_iou: 0.9787, stage5_loss_mask: 2.6037, loss: 34.6408, grad_norm: 74.2182
2021-08-26 11:18:17,203 - mmdet - INFO - Epoch [1][1750/14659]	lr: 2.500e-05, eta: 8 days, 1:42:19, time: 1.308, data_time: 0.029, memory: 10260, stage0_loss_cls: 1.6493, stage0_pos_acc: 27.4200, stage0_loss_bbox: 0.9352, stage0_loss_iou: 1.3797, stage0_loss_mask: 3.4129, stage1_loss_cls: 1.3249, stage1_pos_acc: 33.7146, stage1_loss_bbox: 0.6363, stage1_loss_iou: 1.1272, stage1_loss_mask: 2.7844, stage2_loss_cls: 1.2501, stage2_pos_acc: 38.5490, stage2_loss_bbox: 0.5377, stage2_loss_iou: 1.0149, stage2_loss_mask: 2.6762, stage3_loss_cls: 1.2411, stage3_pos_acc: 42.5941, stage3_loss_bbox: 0.5090, stage3_loss_iou: 0.9733, stage3_loss_mask: 2.6403, stage4_loss_cls: 1.2254, stage4_pos_acc: 42.4388, stage4_loss_bbox: 0.4923, stage4_loss_iou: 0.9571, stage4_loss_mask: 2.6582, stage5_loss_cls: 1.2398, stage5_pos_acc: 42.0586, stage5_loss_bbox: 0.4944, stage5_loss_iou: 0.9531, stage5_loss_mask: 2.6798, loss: 34.7925, grad_norm: 70.7024
2021-08-26 11:19:22,128 - mmdet - INFO - Epoch [1][1800/14659]	lr: 2.500e-05, eta: 8 days, 1:34:55, time: 1.300, data_time: 0.030, memory: 10379, stage0_loss_cls: 1.6151, stage0_pos_acc: 28.5230, stage0_loss_bbox: 0.8962, stage0_loss_iou: 1.3379, stage0_loss_mask: 3.3015, stage1_loss_cls: 1.3039, stage1_pos_acc: 33.2951, stage1_loss_bbox: 0.6194, stage1_loss_iou: 1.0892, stage1_loss_mask: 2.7000, stage2_loss_cls: 1.2418, stage2_pos_acc: 38.5387, stage2_loss_bbox: 0.5380, stage2_loss_iou: 0.9809, stage2_loss_mask: 2.5582, stage3_loss_cls: 1.2145, stage3_pos_acc: 42.3104, stage3_loss_bbox: 0.5076, stage3_loss_iou: 0.9360, stage3_loss_mask: 2.5354, stage4_loss_cls: 1.1984, stage4_pos_acc: 43.3533, stage4_loss_bbox: 0.5003, stage4_loss_iou: 0.9203, stage4_loss_mask: 2.5300, stage5_loss_cls: 1.2125, stage5_pos_acc: 42.7439, stage5_loss_bbox: 0.4876, stage5_loss_iou: 0.9110, stage5_loss_mask: 2.5421, loss: 33.6781, grad_norm: 74.8392
2021-08-26 11:20:26,881 - mmdet - INFO - Epoch [1][1850/14659]	lr: 2.500e-05, eta: 8 days, 1:26:36, time: 1.295, data_time: 0.026, memory: 10379, stage0_loss_cls: 1.6402, stage0_pos_acc: 26.3269, stage0_loss_bbox: 0.9378, stage0_loss_iou: 1.3596, stage0_loss_mask: 3.3698, stage1_loss_cls: 1.3408, stage1_pos_acc: 31.4008, stage1_loss_bbox: 0.6353, stage1_loss_iou: 1.0938, stage1_loss_mask: 2.7399, stage2_loss_cls: 1.2689, stage2_pos_acc: 35.9966, stage2_loss_bbox: 0.5364, stage2_loss_iou: 0.9779, stage2_loss_mask: 2.6163, stage3_loss_cls: 1.2561, stage3_pos_acc: 37.8304, stage3_loss_bbox: 0.5033, stage3_loss_iou: 0.9412, stage3_loss_mask: 2.5846, stage4_loss_cls: 1.2425, stage4_pos_acc: 38.1376, stage4_loss_bbox: 0.5033, stage4_loss_iou: 0.9371, stage4_loss_mask: 2.6020, stage5_loss_cls: 1.2553, stage5_pos_acc: 40.5602, stage5_loss_bbox: 0.4952, stage5_loss_iou: 0.9264, stage5_loss_mask: 2.6149, loss: 34.3788, grad_norm: 69.8435
2021-08-26 11:21:31,601 - mmdet - INFO - Epoch [1][1900/14659]	lr: 2.500e-05, eta: 8 days, 1:18:42, time: 1.295, data_time: 0.029, memory: 10379, stage0_loss_cls: 1.6763, stage0_pos_acc: 23.5548, stage0_loss_bbox: 0.9613, stage0_loss_iou: 1.3327, stage0_loss_mask: 3.2624, stage1_loss_cls: 1.3659, stage1_pos_acc: 28.5197, stage1_loss_bbox: 0.6521, stage1_loss_iou: 1.0761, stage1_loss_mask: 2.6724, stage2_loss_cls: 1.3015, stage2_pos_acc: 33.7910, stage2_loss_bbox: 0.5524, stage2_loss_iou: 0.9623, stage2_loss_mask: 2.5465, stage3_loss_cls: 1.2769, stage3_pos_acc: 37.5364, stage3_loss_bbox: 0.5105, stage3_loss_iou: 0.9124, stage3_loss_mask: 2.5400, stage4_loss_cls: 1.2598, stage4_pos_acc: 38.8722, stage4_loss_bbox: 0.5056, stage4_loss_iou: 0.8961, stage4_loss_mask: 2.5365, stage5_loss_cls: 1.2736, stage5_pos_acc: 40.1570, stage5_loss_bbox: 0.4917, stage5_loss_iou: 0.8863, stage5_loss_mask: 2.5463, loss: 33.9976, grad_norm: 70.5757
2021-08-26 11:22:36,294 - mmdet - INFO - Epoch [1][1950/14659]	lr: 2.500e-05, eta: 8 days, 1:10:51, time: 1.293, data_time: 0.027, memory: 10379, stage0_loss_cls: 1.6400, stage0_pos_acc: 26.8736, stage0_loss_bbox: 0.9084, stage0_loss_iou: 1.3418, stage0_loss_mask: 3.4105, stage1_loss_cls: 1.3184, stage1_pos_acc: 31.7751, stage1_loss_bbox: 0.6120, stage1_loss_iou: 1.0903, stage1_loss_mask: 2.7483, stage2_loss_cls: 1.2402, stage2_pos_acc: 37.1692, stage2_loss_bbox: 0.5336, stage2_loss_iou: 0.9890, stage2_loss_mask: 2.6649, stage3_loss_cls: 1.2257, stage3_pos_acc: 41.1638, stage3_loss_bbox: 0.5075, stage3_loss_iou: 0.9536, stage3_loss_mask: 2.6287, stage4_loss_cls: 1.2063, stage4_pos_acc: 42.5924, stage4_loss_bbox: 0.5062, stage4_loss_iou: 0.9484, stage4_loss_mask: 2.6098, stage5_loss_cls: 1.2333, stage5_pos_acc: 43.1168, stage5_loss_bbox: 0.5024, stage5_loss_iou: 0.9465, stage5_loss_mask: 2.6365, loss: 34.4023, grad_norm: 68.9732
2021-08-26 11:23:41,363 - mmdet - INFO - Exp name: queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
2021-08-26 11:23:41,364 - mmdet - INFO - Epoch [1][2000/14659]	lr: 2.500e-05, eta: 8 days, 1:05:07, time: 1.302, data_time: 0.033, memory: 10379, stage0_loss_cls: 1.6157, stage0_pos_acc: 28.0797, stage0_loss_bbox: 0.8930, stage0_loss_iou: 1.3461, stage0_loss_mask: 3.2833, stage1_loss_cls: 1.2790, stage1_pos_acc: 33.3812, stage1_loss_bbox: 0.5989, stage1_loss_iou: 1.0778, stage1_loss_mask: 2.6179, stage2_loss_cls: 1.1971, stage2_pos_acc: 40.0282, stage2_loss_bbox: 0.5161, stage2_loss_iou: 0.9692, stage2_loss_mask: 2.4923, stage3_loss_cls: 1.1864, stage3_pos_acc: 41.1632, stage3_loss_bbox: 0.4747, stage3_loss_iou: 0.9215, stage3_loss_mask: 2.4593, stage4_loss_cls: 1.1722, stage4_pos_acc: 44.4289, stage4_loss_bbox: 0.4639, stage4_loss_iou: 0.9089, stage4_loss_mask: 2.4508, stage5_loss_cls: 1.1835, stage5_pos_acc: 44.9918, stage5_loss_bbox: 0.4557, stage5_loss_iou: 0.8996, stage5_loss_mask: 2.4687, loss: 32.9316, grad_norm: 67.7723
2021-08-26 11:24:46,291 - mmdet - INFO - Epoch [1][2050/14659]	lr: 2.500e-05, eta: 8 days, 0:58:58, time: 1.299, data_time: 0.025, memory: 10379, stage0_loss_cls: 1.6159, stage0_pos_acc: 28.6850, stage0_loss_bbox: 0.9238, stage0_loss_iou: 1.3584, stage0_loss_mask: 3.3194, stage1_loss_cls: 1.2727, stage1_pos_acc: 34.6746, stage1_loss_bbox: 0.6231, stage1_loss_iou: 1.1023, stage1_loss_mask: 2.7153, stage2_loss_cls: 1.1760, stage2_pos_acc: 40.8597, stage2_loss_bbox: 0.5384, stage2_loss_iou: 0.9938, stage2_loss_mask: 2.5986, stage3_loss_cls: 1.1673, stage3_pos_acc: 42.9596, stage3_loss_bbox: 0.5067, stage3_loss_iou: 0.9520, stage3_loss_mask: 2.5876, stage4_loss_cls: 1.1475, stage4_pos_acc: 46.5867, stage4_loss_bbox: 0.4939, stage4_loss_iou: 0.9400, stage4_loss_mask: 2.5573, stage5_loss_cls: 1.1630, stage5_pos_acc: 44.7366, stage5_loss_bbox: 0.4903, stage5_loss_iou: 0.9357, stage5_loss_mask: 2.5647, loss: 33.7436, grad_norm: 69.3526
2021-08-26 11:25:52,366 - mmdet - INFO - Epoch [1][2100/14659]	lr: 2.500e-05, eta: 8 days, 0:57:52, time: 1.322, data_time: 0.031, memory: 10379, stage0_loss_cls: 1.5835, stage0_pos_acc: 29.9282, stage0_loss_bbox: 0.8743, stage0_loss_iou: 1.3925, stage0_loss_mask: 3.4292, stage1_loss_cls: 1.2144, stage1_pos_acc: 36.4214, stage1_loss_bbox: 0.6154, stage1_loss_iou: 1.1387, stage1_loss_mask: 2.7780, stage2_loss_cls: 1.1328, stage2_pos_acc: 42.5968, stage2_loss_bbox: 0.5381, stage2_loss_iou: 1.0282, stage2_loss_mask: 2.6629, stage3_loss_cls: 1.1280, stage3_pos_acc: 46.1657, stage3_loss_bbox: 0.5008, stage3_loss_iou: 0.9818, stage3_loss_mask: 2.6260, stage4_loss_cls: 1.1079, stage4_pos_acc: 47.8904, stage4_loss_bbox: 0.4936, stage4_loss_iou: 0.9680, stage4_loss_mask: 2.6379, stage5_loss_cls: 1.1114, stage5_pos_acc: 49.2550, stage5_loss_bbox: 0.4914, stage5_loss_iou: 0.9609, stage5_loss_mask: 2.6372, loss: 34.0325, grad_norm: 67.0531
2021-08-26 11:26:55,927 - mmdet - INFO - Epoch [1][2150/14659]	lr: 2.500e-05, eta: 8 days, 0:46:29, time: 1.271, data_time: 0.022, memory: 10379, stage0_loss_cls: 1.6465, stage0_pos_acc: 27.2861, stage0_loss_bbox: 0.9112, stage0_loss_iou: 1.3809, stage0_loss_mask: 3.2773, stage1_loss_cls: 1.2806, stage1_pos_acc: 32.4545, stage1_loss_bbox: 0.5916, stage1_loss_iou: 1.0747, stage1_loss_mask: 2.6087, stage2_loss_cls: 1.2013, stage2_pos_acc: 37.1091, stage2_loss_bbox: 0.5088, stage2_loss_iou: 0.9662, stage2_loss_mask: 2.4953, stage3_loss_cls: 1.1934, stage3_pos_acc: 40.7695, stage3_loss_bbox: 0.4824, stage3_loss_iou: 0.9193, stage3_loss_mask: 2.4814, stage4_loss_cls: 1.1760, stage4_pos_acc: 41.7712, stage4_loss_bbox: 0.4698, stage4_loss_iou: 0.9015, stage4_loss_mask: 2.4674, stage5_loss_cls: 1.1843, stage5_pos_acc: 42.3751, stage5_loss_bbox: 0.4690, stage5_loss_iou: 0.8993, stage5_loss_mask: 2.4923, loss: 33.0790, grad_norm: 72.1277
2021-08-26 11:28:00,678 - mmdet - INFO - Epoch [1][2200/14659]	lr: 2.500e-05, eta: 8 days, 0:40:20, time: 1.295, data_time: 0.028, memory: 10379, stage0_loss_cls: 1.6484, stage0_pos_acc: 26.1184, stage0_loss_bbox: 0.9199, stage0_loss_iou: 1.3046, stage0_loss_mask: 3.1031, stage1_loss_cls: 1.2997, stage1_pos_acc: 33.3425, stage1_loss_bbox: 0.6182, stage1_loss_iou: 1.0203, stage1_loss_mask: 2.4607, stage2_loss_cls: 1.2138, stage2_pos_acc: 39.4199, stage2_loss_bbox: 0.5175, stage2_loss_iou: 0.9011, stage2_loss_mask: 2.3768, stage3_loss_cls: 1.1978, stage3_pos_acc: 42.3183, stage3_loss_bbox: 0.4882, stage3_loss_iou: 0.8593, stage3_loss_mask: 2.3531, stage4_loss_cls: 1.1829, stage4_pos_acc: 43.8389, stage4_loss_bbox: 0.4770, stage4_loss_iou: 0.8437, stage4_loss_mask: 2.3511, stage5_loss_cls: 1.1914, stage5_pos_acc: 43.3682, stage5_loss_bbox: 0.4723, stage5_loss_iou: 0.8356, stage5_loss_mask: 2.3603, loss: 31.9969, grad_norm: 71.5349
2021-08-26 11:29:05,539 - mmdet - INFO - Epoch [1][2250/14659]	lr: 2.500e-05, eta: 8 days, 0:34:48, time: 1.297, data_time: 0.026, memory: 10379, stage0_loss_cls: 1.6389, stage0_pos_acc: 26.4171, stage0_loss_bbox: 0.8738, stage0_loss_iou: 1.3478, stage0_loss_mask: 3.2712, stage1_loss_cls: 1.2597, stage1_pos_acc: 36.5073, stage1_loss_bbox: 0.5825, stage1_loss_iou: 1.0741, stage1_loss_mask: 2.5992, stage2_loss_cls: 1.1765, stage2_pos_acc: 42.4178, stage2_loss_bbox: 0.5101, stage2_loss_iou: 0.9813, stage2_loss_mask: 2.5105, stage3_loss_cls: 1.1682, stage3_pos_acc: 44.9137, stage3_loss_bbox: 0.4719, stage3_loss_iou: 0.9277, stage3_loss_mask: 2.4942, stage4_loss_cls: 1.1524, stage4_pos_acc: 47.3806, stage4_loss_bbox: 0.4631, stage4_loss_iou: 0.9198, stage4_loss_mask: 2.4841, stage5_loss_cls: 1.1615, stage5_pos_acc: 47.7888, stage5_loss_bbox: 0.4551, stage5_loss_iou: 0.9104, stage5_loss_mask: 2.5115, loss: 32.9455, grad_norm: 70.3203
2021-08-26 11:30:10,491 - mmdet - INFO - Epoch [1][2300/14659]	lr: 2.500e-05, eta: 8 days, 0:29:51, time: 1.299, data_time: 0.025, memory: 10379, stage0_loss_cls: 1.6181, stage0_pos_acc: 27.4700, stage0_loss_bbox: 0.8644, stage0_loss_iou: 1.3378, stage0_loss_mask: 3.2031, stage1_loss_cls: 1.2365, stage1_pos_acc: 34.6672, stage1_loss_bbox: 0.5978, stage1_loss_iou: 1.0615, stage1_loss_mask: 2.6021, stage2_loss_cls: 1.1551, stage2_pos_acc: 41.1225, stage2_loss_bbox: 0.5164, stage2_loss_iou: 0.9623, stage2_loss_mask: 2.5070, stage3_loss_cls: 1.1470, stage3_pos_acc: 43.9128, stage3_loss_bbox: 0.4866, stage3_loss_iou: 0.9210, stage3_loss_mask: 2.4700, stage4_loss_cls: 1.1324, stage4_pos_acc: 45.1144, stage4_loss_bbox: 0.4751, stage4_loss_iou: 0.9079, stage4_loss_mask: 2.4605, stage5_loss_cls: 1.1379, stage5_pos_acc: 45.8629, stage5_loss_bbox: 0.4707, stage5_loss_iou: 0.9008, stage5_loss_mask: 2.4844, loss: 32.6565, grad_norm: 66.8825
Ground Truth Not Found!
Ground Truth Not Found!
Ground Truth Not Found!
Ground Truth Not Found!
Ground Truth Not Found!
Ground Truth Not Found!
^CTraceback (most recent call last):
  File "/mnt/home1/programs/miniconda3/envs/usd/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/mnt/home1/programs/miniconda3/envs/usd/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/mnt/home1/programs/miniconda3/envs/usd/lib/python3.8/site-packages/torch/distributed/launch.py", line 260, in <module>
    main()
  File "/mnt/home1/programs/miniconda3/envs/usd/lib/python3.8/site-packages/torch/distributed/launch.py", line 253, in main
    process.wait()
  File "/mnt/home1/programs/miniconda3/envs/usd/lib/python3.8/subprocess.py", line 1083, in wait
    return self._wait(timeout=timeout)
  File "/mnt/home1/programs/miniconda3/envs/usd/lib/python3.8/subprocess.py", line 1806, in _wait
    (pid, sts) = self._try_wait(0)
  File "/mnt/home1/programs/miniconda3/envs/usd/lib/python3.8/subprocess.py", line 1764, in _try_wait
    (pid, sts) = os.waitpid(self.pid, wait_flags)
KeyboardInterrupt

Dear authors,

I have found on the repo a config of QueryInst with SwinTiny backbone. However, I see no results or checkpoints of this QueryInst version. Did you try to run this? If yes, do you have results and checkpoint saved?

Hi,

Thanks for your work!

Recently, I met two issues while training 'queryinst_r50_fpn_1x_coco'model with my own dataset.(samples_per_gpu=2,workers_per_gpu=2, optimizer = dict(type='AdamW', lr=2.5e-05, weight_decay=0.0001,lr_config = dict(policy='step', step=[27, 33])) just as default settings)

I noticed that learning rate represents an abnormal changing trend:

1. Starting with INCREASINGING trend in the first epoch ( all values are greater than the default lr setting value which is 2.5e-5)
2. Keeping decreasing to and then stay with the default lr setting value (2.5e-5) for next few epochs.
3. Decreasing again and stick to a value, for example, 2.5e-07.

This trending way is abnormal compared with some conventional lr trending patterns in which lr usually stays still or keeps deceasing during the training process.

The second issue I met is all the AP and AR values equal to zero all the time. I attached the training log here for review. 20210819_092034.log

Could you help me with this? Thanks a lot!

你好，我尝试在win10上用自己的数据集进行训练，但是返回了如下错误。

训练命令: python tools/train.py configs/queryinst/queryinst_r50_fpn_1x_coco_scratch.py 我把queryinst_r50_fpn_1x_coco_scratch.py中num_classes改为了我的数据集类别总数。

运行后出现下面问题: **Traceback (most recent call last): File "tools/train.py", line 188, in main() File "tools/train.py", line 161, in main test_cfg=cfg.get('test_cfg')) File "C:\Users\RTX3090.conda\envs\open-mmlab\lib\site-packages\mmdet\models\builder.py", line 58, in build_detector cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg)) File "d:\lbq\code\swin_tsf\mmsegmentation-master\mmcv\mmcv\utils\registry.py", line 210, in build return self.build_func(*args, **kwargs, registry=self) File "d:\lbq\code\swin_tsf\mmsegmentation-master\mmcv\mmcv\cnn\builder.py", line 26, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "d:\lbq\code\swin_tsf\mmsegmentation-master\mmcv\mmcv\utils\registry.py", line 44, in build_from_cfg f'{obj_type} is not in the {registry.name} registry') KeyError: 'QueryInst is not in the models registry' **

I took a look at the self.init_proposal_bboxes.weight from your trained model, but I found the boxes coordinates were not learned and kept around the initial values of 0.5 0.5 1 1. Is there any problem for this? Thanks

For the crop augmentation, since the following negative crop setting is alllowed, is anybody meet the "GroundTruth not found" error?
'allow_negative_crop': True

Hi, I just download your code and your pretrained paramters. I test the model in the sub-dataset of coco dataset. But I find that the results of bbox's mAP = 0. I have checked that there are no problems with the dataset.

here is the result:

Evaluating bbox... Loading and preparing results... DONE (t=1.75s) creating index... index created! Running per image evaluation... Evaluate annotation type bbox DONE (t=35.44s). Accumulating evaluation results... DONE (t=12.73s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.009 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.026 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.005 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.002 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.032 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.049 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.049 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.049 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.003 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.026 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.135

Evaluating segm... Loading and preparing results... UserWarning: The key "bbox" is deleted for more accurate mask AP of small/medium/large instances since v2.12.0. This does not change the overall mAP calculation. warnings.warn( DONE (t=4.94s) creating index... index created! Running per image evaluation... Evaluate annotation type segm DONE (t=40.55s). Accumulating evaluation results... DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) DONE (t=13.33s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.465 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.716 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.502 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.304 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.513 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.694 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.619 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.620 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.620 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.480 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.672 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.802 OrderedDict([('bbox_mAP', 0.009), ('bbox_mAP_50', 0.026), ('bbox_mAP_75', 0.005), ('bbox_mAP_s', 0.0), ('bbox_mAP_m', 0.002), ('bbox_mAP_l', 0.032), ('bbox_mAP_copypaste', '0.009 0.026 0.005 0.000 0.002 0.032'), ('segm_mAP', 0.465), ('segm_mAP_50', 0.716), ('segm_mAP_75', 0.502), ('segm_mAP_s', 0.304), ('segm_mAP_m', 0.513), ('segm_mAP_l', 0.694), ('segm_mAP_copypaste', '0.465 0.716 0.502 0.304 0.513 0.694')])

Hi all, I really impressed with your work and the performance of QueryInst so I am tried to train your work on my custom dataset. This dataset that I am successfully train on Swin Transformer. Unfortunately, I tried to train on your work so the loss values zero except loss_cls. Can you help me to solve that, I really appreciate it.

[stage0_loss_cls: 0.0025, stage0_pos_acc0, stage4_loss_iou: 0.0000, stage4_loss_mask: 0.0000, stage5_loss_cls: 0.0024, stage5_pos_acc: 0.0000, stage5_loss_bbox: 0.0000, stage5_loss_iou: 0.0000, stage5_loss_mask: 0.0000,0.0000, stage1_loss_mask: 0.0000, stage2_loss_cls: 0.0012, stage2_pos_acc: 0.0000, stage2_loss_bbox: 0.0000, stage2_loss_iou: 0.0000, stage2_loss_mask: 0.0000, stage3_loss_cls: 0.0012, stage3_pos_acc: 0.0000, stage3_loss_bbox: 0.0000, stage3_loss_iou: 0.0000, stage3_loss_mask: 0.0000, stage4_loss_cls: 0.0010, stage4_pos_acc: 0.0000, stage4_loss_bbox: 0.000](lr: 1.499e-05, eta: 17:44:38, time: 0.620, data_time: 0.004, memory: 10924, stage0_loss_cls: 125712.0807, stage0_pos_acc: 0.0000, stage0_loss_bbox: 0.0000, stage0_loss_iou: 0.0000, stage0_loss_mask: 0.0000, stage1_loss_cls: 90232.6852, stage1_pos_acc: 0.0000, stage1_loss_bbox: 0.0000, stage1_loss_iou: 0.0000, stage1_loss_mask: 0.0000, stage2_loss_cls: 46718.8418, stage2_pos_acc: 0.0000, stage2_loss_bbox: 0.0000, stage2_loss_iou: 0.0000, stage2_loss_mask: 0.0000, stage3_loss_cls: 32931.4105, stage3_pos_acc: 0.0000, stage3_loss_bbox: 0.0000, stage3_loss_iou: 0.0000, stage3_loss_mask: 0.0000, stage4_loss_cls: 41905.0223, stage4_pos_acc: 0.0000, stage4_loss_bbox: 0.0000, stage4_loss_iou: 0.0000, stage4_loss_mask: 0.0000, stage5_loss_cls: 47268.3891, stage5_pos_acc: 0.0000, stage5_loss_bbox: 0.0000, stage5_loss_iou: 0.0000, stage5_loss_mask: 0.0000, loss: 384768.4313, grad_norm: 1482184.3400)

looking for your reply!

i use mmcv 1.3.3 and 1.4，there will be an assertion: ca_forward miss in module _ext so, what's your version of mmcv test.py used? or maybe there are some other reasons?

The error that I have encountered when inferencing using the swin transformer on CPU was ERROR - upper bound and larger bound inconsistent with step sign

Such error disappears when inference was performed on GPU.

After some investigation it was found that when running on CPU, the bbox (batch size 1) that was provided to the function _do_paste_mask in the file mmdet/models/roi_heads/mask_heads/fcn_mask_head.py has negative coordinates, causing it to fail.