2020-12-01 16:46:53 [INFO] ------------Environment Information------------- platform: Linux-3.10.0-1062.18.1.el7.x86_64-x86_64-with-centos-7.7.1908-Core Python: 3.7.7 (default, Mar 26 2020, 15:48:22) [GCC 7.3.0] Paddle compiled with cuda: True NVCC: Cuda compilation tools, release 10.2, V10.2.89 cudnn: 7.6 GPUs used: 4 CUDA_VISIBLE_DEVICES: 3,2,1,0 GPU: ['GPU 0: Tesla V100-SXM2-16GB', 'GPU 1: Tesla V100-SXM2-16GB', 'GPU 2: Tesla V100-SXM2-16GB', 'GPU 3: Tesla V100-SXM2-16GB', 'GPU 4: Tesla V100-SXM2-16GB', 'GPU 5: Tesla V100-SXM2-16GB', 'GPU 6: Tesla V100-SXM2-16GB', 'GPU 7: Tesla V100-SXM2-16GB'] GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-39) PaddlePaddle: 0.0.0 OpenCV: 4.2.0 ------------------------------------------------ 2020-12-01 16:46:53 [INFO] ---------------Config Information--------------- batch_size: 2 iters: 80000 learning_rate: decay: end_lr: 0.0 power: 0.9 type: poly value: 0.01 loss: coef: - 1 - 1 - 20 - 1 types: - ignore_index: 255 type: CrossEntropyLoss - ignore_index: 255 type: EdgeAttentionLoss - ignore_index: 255 type: BCELoss - ignore_index: 255 type: DualTaskLoss model: align_corners: false aspp_out_channels: 256 aspp_ratios: - 1 - 12 - 24 - 36 backbone: multi_grid: - 1 - 2 - 4 output_stride: 8 pretrained: https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz type: ResNet50_vd backbone_indices: - 0 - 1 - 2 - 3 num_classes: 19 pretrained: null type: GSCNN optimizer: momentum: 0.9 type: sgd weight_decay: 4.0e-05 train_dataset: dataset_root: data/cityscapes edge: true mode: train transforms: - max_scale_factor: 2.0 min_scale_factor: 0.5 scale_step_size: 0.25 type: ResizeStepScaling - crop_size: - 1024 - 512 type: RandomPaddingCrop - type: RandomHorizontalFlip - brightness_range: 0.4 contrast_range: 0.4 saturation_range: 0.4 type: RandomDistort - type: Normalize type: Cityscapes val_dataset: dataset_root: data/cityscapes mode: val transforms: - type: Normalize type: Cityscapes ------------------------------------------------ 2020-12-01 16:47:00 [INFO] Loading pretrained model from https://bj.bcebos.com/paddleseg/dygraph/resnet50_vd_ssld_v2.tar.gz 2020-12-01 16:47:01 [INFO] There are 275/275 variables loaded into ResNet_vd. 2020-12-01 16:49:57 [INFO] seg_loss:0.6920, att_loss: 0.9848, edge_loss: 2.5808, dual_loss: 0.0057 2020-12-01 16:49:58 [INFO] [TRAIN] epoch=1, iter=100/80000, loss=4.0993, lr=0.009989, batch_cost=1.6958, reader_cost=0.0717 | ETA 37:38:15 2020-12-01 16:52:30 [INFO] seg_loss:0.2689, att_loss: 0.7382, edge_loss: 1.0429, dual_loss: 0.0021 2020-12-01 16:52:31 [INFO] [TRAIN] epoch=1, iter=200/80000, loss=3.6187, lr=0.009978, batch_cost=1.5289, reader_cost=0.0003 | ETA 33:53:25 2020-12-01 16:55:07 [INFO] seg_loss:0.2431, att_loss: 0.7150, edge_loss: 2.1305, dual_loss: 0.0050 2020-12-01 16:55:08 [INFO] [TRAIN] epoch=1, iter=300/80000, loss=3.5264, lr=0.009966, batch_cost=1.5653, reader_cost=0.0005 | ETA 34:39:14 2020-12-01 16:57:48 [INFO] seg_loss:0.2779, att_loss: 0.3767, edge_loss: 0.9454, dual_loss: 0.0029 2020-12-01 16:57:49 [INFO] [TRAIN] epoch=2, iter=400/80000, loss=3.7500, lr=0.009955, batch_cost=1.6133, reader_cost=0.0535 | ETA 35:40:20 2020-12-01 17:00:25 [INFO] seg_loss:0.2073, att_loss: 0.3918, edge_loss: 1.4407, dual_loss: 0.0029 2020-12-01 17:00:27 [INFO] [TRAIN] epoch=2, iter=500/80000, loss=3.5024, lr=0.009944, batch_cost=1.5754, reader_cost=0.0004 | ETA 34:47:21 2020-12-01 17:03:02 [INFO] seg_loss:0.2929, att_loss: 0.9806, edge_loss: 2.6367, dual_loss: 0.0031 2020-12-01 17:03:03 [INFO] [TRAIN] epoch=2, iter=600/80000, loss=2.9734, lr=0.009933, batch_cost=1.5612, reader_cost=0.0003 | ETA 34:25:57 2020-12-01 17:05:37 [INFO] seg_loss:0.4342, att_loss: 0.6585, edge_loss: 1.0993, dual_loss: 0.0044 2020-12-01 17:05:38 [INFO] [TRAIN] epoch=2, iter=700/80000, loss=3.1019, lr=0.009921, batch_cost=1.5580, reader_cost=0.0003 | ETA 34:19:13 2020-12-01 17:08:23 [INFO] seg_loss:0.2735, att_loss: 0.3144, edge_loss: 0.9277, dual_loss: 0.0022 2020-12-01 17:08:24 [INFO] [TRAIN] epoch=3, iter=800/80000, loss=3.0559, lr=0.009910, batch_cost=1.6512, reader_cost=0.0730 | ETA 36:19:37 2020-12-01 17:11:04 [INFO] seg_loss:0.3076, att_loss: 0.5794, edge_loss: 2.8176, dual_loss: 0.0030 2020-12-01 17:11:05 [INFO] [TRAIN] epoch=3, iter=900/80000, loss=3.1238, lr=0.009899, batch_cost=1.6179, reader_cost=0.0004 | ETA 35:32:56 2020-12-01 17:13:48 [INFO] seg_loss:0.8015, att_loss: 0.8140, edge_loss: 1.0508, dual_loss: 0.0023 2020-12-01 17:13:49 [INFO] [TRAIN] epoch=3, iter=1000/80000, loss=3.0477, lr=0.009888, batch_cost=1.6396, reader_cost=0.0003 | ETA 35:58:45 2020-12-01 17:16:23 [INFO] seg_loss:0.2563, att_loss: 0.7822, edge_loss: 1.3642, dual_loss: 0.0022 2020-12-01 17:16:24 [INFO] [TRAIN] epoch=3, iter=1100/80000, loss=3.2664, lr=0.009876, batch_cost=1.5426, reader_cost=0.0006 | ETA 33:48:27 2020-12-01 17:19:07 [INFO] seg_loss:0.2124, att_loss: 0.7263, edge_loss: 2.9231, dual_loss: 0.0029 2020-12-01 17:19:08 [INFO] [TRAIN] epoch=4, iter=1200/80000, loss=3.2199, lr=0.009865, batch_cost=1.6414, reader_cost=0.0607 | ETA 35:55:45 2020-12-01 17:21:50 [INFO] seg_loss:1.2910, att_loss: 1.2760, edge_loss: 3.0938, dual_loss: 0.0059 2020-12-01 17:21:51 [INFO] [TRAIN] epoch=4, iter=1300/80000, loss=3.0464, lr=0.009854, batch_cost=1.6322, reader_cost=0.0003 | ETA 35:40:57 2020-12-01 17:24:30 [INFO] seg_loss:0.5079, att_loss: 0.8938, edge_loss: 1.4778, dual_loss: 0.0046 2020-12-01 17:24:31 [INFO] [TRAIN] epoch=4, iter=1400/80000, loss=2.9830, lr=0.009842, batch_cost=1.5998, reader_cost=0.0004 | ETA 34:55:46 2020-12-01 17:27:26 [INFO] seg_loss:0.1485, att_loss: 0.6029, edge_loss: 2.0103, dual_loss: 0.0024 2020-12-01 17:27:27 [INFO] [TRAIN] epoch=5, iter=1500/80000, loss=3.1691, lr=0.009831, batch_cost=1.7615, reader_cost=0.0853 | ETA 38:24:41 2020-12-01 17:30:14 [INFO] seg_loss:0.0761, att_loss: 0.6233, edge_loss: 1.4367, dual_loss: 0.0015 2020-12-01 17:30:15 [INFO] [TRAIN] epoch=5, iter=1600/80000, loss=3.0885, lr=0.009820, batch_cost=1.6795, reader_cost=0.0004 | ETA 36:34:31 2020-12-01 17:32:51 [INFO] seg_loss:0.1910, att_loss: 0.8417, edge_loss: 2.9149, dual_loss: 0.0028 2020-12-01 17:32:52 [INFO] [TRAIN] epoch=5, iter=1700/80000, loss=2.8575, lr=0.009809, batch_cost=1.5670, reader_cost=0.0003 | ETA 34:04:53 2020-12-01 17:35:31 [INFO] seg_loss:0.2611, att_loss: 0.8651, edge_loss: 2.9511, dual_loss: 0.0033 2020-12-01 17:35:32 [INFO] [TRAIN] epoch=5, iter=1800/80000, loss=3.0771, lr=0.009797, batch_cost=1.6061, reader_cost=0.0004 | ETA 34:53:15 2020-12-01 17:38:20 [INFO] seg_loss:0.0880, att_loss: 0.7091, edge_loss: 1.0623, dual_loss: 0.0011 2020-12-01 17:38:21 [INFO] [TRAIN] epoch=6, iter=1900/80000, loss=3.1072, lr=0.009786, batch_cost=1.6903, reader_cost=0.0645 | ETA 36:40:12 2020-12-01 17:40:59 [INFO] seg_loss:0.3642, att_loss: 0.7478, edge_loss: 2.1329, dual_loss: 0.0034 2020-12-01 17:41:00 [INFO] [TRAIN] epoch=6, iter=2000/80000, loss=2.9206, lr=0.009775, batch_cost=1.5867, reader_cost=0.0004 | ETA 34:22:45 2020-12-01 17:41:00 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-01 17:42:10 [INFO] [EVAL] #Images=500 mIoU=0.5668 Acc=0.9247 Kappa=0.9016 2020-12-01 17:42:10 [INFO] [EVAL] Class IoU: [0.9425 0.6227 0.8806 0.1997 0.3664 0.5498 0.6194 0.7105 0.895 0.4513 0.902 0.7464 0.4287 0.8965 0.2956 0.2749 0.0125 0.3045 0.6705] 2020-12-01 17:42:10 [INFO] [EVAL] Class Acc: [0.9534 0.8974 0.929 0.7572 0.7092 0.8604 0.754 0.8128 0.9319 0.7279 0.9222 0.8862 0.5048 0.9237 0.3585 0.5928 0.6455 0.3367 0.7738] 2020-12-01 17:42:19 [INFO] [EVAL] The model with the best validation mIoU (0.5668) was saved at iter 2000. 2020-12-01 17:45:01 [INFO] seg_loss:0.1379, att_loss: 0.7120, edge_loss: 2.0849, dual_loss: 0.0021 2020-12-01 17:45:02 [INFO] [TRAIN] epoch=6, iter=2100/80000, loss=2.8676, lr=0.009764, batch_cost=1.6260, reader_cost=0.0004 | ETA 35:11:02 2020-12-01 17:47:41 [INFO] seg_loss:0.3283, att_loss: 0.7180, edge_loss: 2.0151, dual_loss: 0.0031 2020-12-01 17:47:42 [INFO] [TRAIN] epoch=6, iter=2200/80000, loss=2.9619, lr=0.009752, batch_cost=1.5968, reader_cost=0.0004 | ETA 34:30:32 2020-12-01 17:50:24 [INFO] seg_loss:0.2876, att_loss: 0.6914, edge_loss: 1.8778, dual_loss: 0.0026 2020-12-01 17:50:25 [INFO] [TRAIN] epoch=7, iter=2300/80000, loss=3.0527, lr=0.009741, batch_cost=1.6362, reader_cost=0.0828 | ETA 35:18:51 2020-12-01 17:53:04 [INFO] seg_loss:0.1645, att_loss: 0.7150, edge_loss: 2.6085, dual_loss: 0.0025 2020-12-01 17:53:05 [INFO] [TRAIN] epoch=7, iter=2400/80000, loss=2.9946, lr=0.009730, batch_cost=1.5945, reader_cost=0.0005 | ETA 34:22:14 2020-12-01 17:55:44 [INFO] seg_loss:0.0772, att_loss: 0.4823, edge_loss: 1.7974, dual_loss: 0.0017 2020-12-01 17:55:46 [INFO] [TRAIN] epoch=7, iter=2500/80000, loss=2.8959, lr=0.009718, batch_cost=1.6080, reader_cost=0.0004 | ETA 34:36:56 2020-12-01 17:58:27 [INFO] seg_loss:0.7648, att_loss: 0.7566, edge_loss: 1.0374, dual_loss: 0.0021 2020-12-01 17:58:28 [INFO] [TRAIN] epoch=7, iter=2600/80000, loss=3.0571, lr=0.009707, batch_cost=1.6237, reader_cost=0.0006 | ETA 34:54:36 2020-12-01 18:01:21 [INFO] seg_loss:0.3388, att_loss: 0.7897, edge_loss: 2.6061, dual_loss: 0.0037 2020-12-01 18:01:22 [INFO] [TRAIN] epoch=8, iter=2700/80000, loss=3.1000, lr=0.009696, batch_cost=1.7450, reader_cost=0.1000 | ETA 37:28:07 2020-12-01 18:04:07 [INFO] seg_loss:0.2912, att_loss: 1.0197, edge_loss: 1.2142, dual_loss: 0.0018 2020-12-01 18:04:08 [INFO] [TRAIN] epoch=8, iter=2800/80000, loss=2.8836, lr=0.009685, batch_cost=1.6561, reader_cost=0.0004 | ETA 35:30:50 2020-12-01 18:06:52 [INFO] seg_loss:0.3567, att_loss: 0.8979, edge_loss: 2.4538, dual_loss: 0.0029 2020-12-01 18:06:53 [INFO] [TRAIN] epoch=8, iter=2900/80000, loss=2.8935, lr=0.009673, batch_cost=1.6540, reader_cost=0.0005 | ETA 35:25:19 2020-12-01 18:09:55 [INFO] seg_loss:0.1285, att_loss: 0.6900, edge_loss: 2.3978, dual_loss: 0.0019 2020-12-01 18:09:56 [INFO] [TRAIN] epoch=9, iter=3000/80000, loss=3.0357, lr=0.009662, batch_cost=1.8302, reader_cost=0.0637 | ETA 39:08:42 2020-12-01 18:12:40 [INFO] seg_loss:0.2682, att_loss: 0.9072, edge_loss: 2.3436, dual_loss: 0.0033 2020-12-01 18:12:41 [INFO] [TRAIN] epoch=9, iter=3100/80000, loss=2.9457, lr=0.009651, batch_cost=1.6475, reader_cost=0.0007 | ETA 35:11:33 2020-12-01 18:15:32 [INFO] seg_loss:0.1343, att_loss: 0.6822, edge_loss: 2.1033, dual_loss: 0.0020 2020-12-01 18:15:33 [INFO] [TRAIN] epoch=9, iter=3200/80000, loss=2.7637, lr=0.009639, batch_cost=1.7144, reader_cost=0.0008 | ETA 36:34:24 2020-12-01 18:18:27 [INFO] seg_loss:0.3291, att_loss: 0.9598, edge_loss: 2.9319, dual_loss: 0.0034 2020-12-01 18:18:28 [INFO] [TRAIN] epoch=9, iter=3300/80000, loss=2.8929, lr=0.009628, batch_cost=1.7558, reader_cost=0.0004 | ETA 37:24:32 2020-12-01 18:21:21 [INFO] seg_loss:0.2669, att_loss: 0.7526, edge_loss: 2.6832, dual_loss: 0.0031 2020-12-01 18:21:22 [INFO] [TRAIN] epoch=10, iter=3400/80000, loss=2.8416, lr=0.009617, batch_cost=1.7390, reader_cost=0.0894 | ETA 37:00:08 2020-12-01 18:24:11 [INFO] seg_loss:0.1617, att_loss: 0.6799, edge_loss: 1.3554, dual_loss: 0.0016 2020-12-01 18:24:12 [INFO] [TRAIN] epoch=10, iter=3500/80000, loss=2.7117, lr=0.009605, batch_cost=1.7006, reader_cost=0.0253 | ETA 36:08:14 2020-12-01 18:27:00 [INFO] seg_loss:0.2460, att_loss: 0.7910, edge_loss: 2.6868, dual_loss: 0.0031 2020-12-01 18:27:01 [INFO] [TRAIN] epoch=10, iter=3600/80000, loss=2.7893, lr=0.009594, batch_cost=1.6824, reader_cost=0.0005 | ETA 35:42:16 2020-12-01 18:29:48 [INFO] seg_loss:0.0826, att_loss: 0.4391, edge_loss: 1.2265, dual_loss: 0.0014 2020-12-01 18:29:49 [INFO] [TRAIN] epoch=10, iter=3700/80000, loss=2.9509, lr=0.009583, batch_cost=1.6884, reader_cost=0.0005 | ETA 35:47:03 2020-12-01 18:32:44 [INFO] seg_loss:0.4055, att_loss: 1.0454, edge_loss: 3.4441, dual_loss: 0.0032 2020-12-01 18:32:45 [INFO] [TRAIN] epoch=11, iter=3800/80000, loss=2.9407, lr=0.009572, batch_cost=1.7573, reader_cost=0.0798 | ETA 37:11:44 2020-12-01 18:35:31 [INFO] seg_loss:0.1154, att_loss: 0.4889, edge_loss: 1.3734, dual_loss: 0.0015 2020-12-01 18:35:32 [INFO] [TRAIN] epoch=11, iter=3900/80000, loss=2.8133, lr=0.009560, batch_cost=1.6654, reader_cost=0.0004 | ETA 35:12:13 2020-12-01 18:38:16 [INFO] seg_loss:0.1371, att_loss: 0.6370, edge_loss: 3.2617, dual_loss: 0.0024 2020-12-01 18:38:17 [INFO] [TRAIN] epoch=11, iter=4000/80000, loss=2.7274, lr=0.009549, batch_cost=1.6531, reader_cost=0.0004 | ETA 34:53:54 2020-12-01 18:38:17 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-01 18:39:30 [INFO] [EVAL] #Images=500 mIoU=0.6724 Acc=0.9406 Kappa=0.9227 2020-12-01 18:39:30 [INFO] [EVAL] Class IoU: [0.9577 0.7343 0.8979 0.3661 0.5086 0.62 0.6732 0.7302 0.9022 0.524 0.9289 0.7999 0.5708 0.9167 0.4635 0.6051 0.3873 0.4546 0.7346] 2020-12-01 18:39:30 [INFO] [EVAL] Class Acc: [0.9724 0.9086 0.9318 0.6336 0.6978 0.8146 0.8149 0.9179 0.9557 0.6471 0.9525 0.8943 0.736 0.9359 0.7598 0.8261 0.8654 0.8795 0.7971] 2020-12-01 18:39:41 [INFO] [EVAL] The model with the best validation mIoU (0.6724) was saved at iter 4000. 2020-12-01 18:42:32 [INFO] seg_loss:0.1138, att_loss: 0.6049, edge_loss: 2.3222, dual_loss: 0.0020 2020-12-01 18:42:33 [INFO] [TRAIN] epoch=12, iter=4100/80000, loss=2.8360, lr=0.009538, batch_cost=1.7188, reader_cost=0.1038 | ETA 36:14:14 2020-12-01 18:45:16 [INFO] seg_loss:0.2063, att_loss: 0.9669, edge_loss: 1.8232, dual_loss: 0.0025 2020-12-01 18:45:17 [INFO] [TRAIN] epoch=12, iter=4200/80000, loss=3.0867, lr=0.009526, batch_cost=1.6423, reader_cost=0.0006 | ETA 34:34:42 2020-12-01 18:48:04 [INFO] seg_loss:0.2327, att_loss: 0.8001, edge_loss: 2.5123, dual_loss: 0.0028 2020-12-01 18:48:05 [INFO] [TRAIN] epoch=12, iter=4300/80000, loss=2.7188, lr=0.009515, batch_cost=1.6759, reader_cost=0.0005 | ETA 35:14:23 2020-12-01 18:50:52 [INFO] seg_loss:0.1125, att_loss: 0.6155, edge_loss: 2.2646, dual_loss: 0.0022 2020-12-01 18:50:53 [INFO] [TRAIN] epoch=12, iter=4400/80000, loss=2.7635, lr=0.009504, batch_cost=1.6848, reader_cost=0.0005 | ETA 35:22:47 2020-12-01 18:53:47 [INFO] seg_loss:0.1243, att_loss: 0.7178, edge_loss: 1.3359, dual_loss: 0.0017 2020-12-01 18:53:48 [INFO] [TRAIN] epoch=13, iter=4500/80000, loss=2.9140, lr=0.009492, batch_cost=1.7438, reader_cost=0.1012 | ETA 36:34:17 2020-12-01 18:56:35 [INFO] seg_loss:0.0549, att_loss: 0.4445, edge_loss: 1.6385, dual_loss: 0.0011 2020-12-01 18:56:36 [INFO] [TRAIN] epoch=13, iter=4600/80000, loss=2.8432, lr=0.009481, batch_cost=1.6837, reader_cost=0.0004 | ETA 35:15:51 2020-12-01 18:59:22 [INFO] seg_loss:0.2789, att_loss: 0.8317, edge_loss: 3.3317, dual_loss: 0.0033 2020-12-01 18:59:23 [INFO] [TRAIN] epoch=13, iter=4700/80000, loss=2.6404, lr=0.009470, batch_cost=1.6727, reader_cost=0.0006 | ETA 34:59:11 2020-12-01 19:02:12 [INFO] seg_loss:0.1326, att_loss: 0.7024, edge_loss: 1.9094, dual_loss: 0.0015 2020-12-01 19:02:13 [INFO] [TRAIN] epoch=13, iter=4800/80000, loss=2.8547, lr=0.009458, batch_cost=1.6962, reader_cost=0.0005 | ETA 35:25:55 2020-12-01 19:05:00 [INFO] seg_loss:0.0773, att_loss: 0.4848, edge_loss: 1.8531, dual_loss: 0.0016 2020-12-01 19:05:01 [INFO] [TRAIN] epoch=14, iter=4900/80000, loss=2.7863, lr=0.009447, batch_cost=1.6788, reader_cost=0.1008 | ETA 35:01:20 2020-12-01 19:07:30 [INFO] seg_loss:0.0635, att_loss: 0.5539, edge_loss: 1.7185, dual_loss: 0.0012 2020-12-01 19:07:31 [INFO] [TRAIN] epoch=14, iter=5000/80000, loss=2.8879, lr=0.009436, batch_cost=1.4978, reader_cost=0.0003 | ETA 31:12:14 2020-12-01 19:09:54 [INFO] seg_loss:0.2095, att_loss: 0.7938, edge_loss: 2.2291, dual_loss: 0.0027 2020-12-01 19:09:55 [INFO] [TRAIN] epoch=14, iter=5100/80000, loss=2.7607, lr=0.009424, batch_cost=1.4481, reader_cost=0.0004 | ETA 30:07:44 2020-12-01 19:12:20 [INFO] seg_loss:0.1858, att_loss: 0.6618, edge_loss: 2.0258, dual_loss: 0.0024 2020-12-01 19:12:21 [INFO] [TRAIN] epoch=14, iter=5200/80000, loss=2.8543, lr=0.009413, batch_cost=1.4531, reader_cost=0.0002 | ETA 30:11:30 2020-12-01 19:14:53 [INFO] seg_loss:0.3534, att_loss: 0.8159, edge_loss: 1.6372, dual_loss: 0.0042 2020-12-01 19:14:54 [INFO] [TRAIN] epoch=15, iter=5300/80000, loss=2.9098, lr=0.009402, batch_cost=1.5287, reader_cost=0.0788 | ETA 31:43:13 2020-12-01 19:17:22 [INFO] seg_loss:0.0914, att_loss: 0.5747, edge_loss: 1.3854, dual_loss: 0.0013 2020-12-01 19:17:23 [INFO] [TRAIN] epoch=15, iter=5400/80000, loss=2.7452, lr=0.009391, batch_cost=1.4960, reader_cost=0.0005 | ETA 31:00:04 2020-12-01 19:19:51 [INFO] seg_loss:0.2734, att_loss: 0.8058, edge_loss: 3.2998, dual_loss: 0.0036 2020-12-01 19:19:52 [INFO] [TRAIN] epoch=15, iter=5500/80000, loss=2.7738, lr=0.009379, batch_cost=1.4846, reader_cost=0.0003 | ETA 30:43:24 2020-12-01 19:22:27 [INFO] seg_loss:0.3067, att_loss: 0.6990, edge_loss: 1.9509, dual_loss: 0.0025 2020-12-01 19:22:28 [INFO] [TRAIN] epoch=16, iter=5600/80000, loss=3.0505, lr=0.009368, batch_cost=1.5588, reader_cost=0.0585 | ETA 32:12:57 2020-12-01 19:24:54 [INFO] seg_loss:0.1033, att_loss: 0.3566, edge_loss: 0.8663, dual_loss: 0.0008 2020-12-01 19:24:55 [INFO] [TRAIN] epoch=16, iter=5700/80000, loss=2.8362, lr=0.009357, batch_cost=1.4702, reader_cost=0.0003 | ETA 30:20:33 2020-12-01 19:27:23 [INFO] seg_loss:0.2161, att_loss: 0.5741, edge_loss: 2.3088, dual_loss: 0.0031 2020-12-01 19:27:24 [INFO] [TRAIN] epoch=16, iter=5800/80000, loss=2.5955, lr=0.009345, batch_cost=1.4954, reader_cost=0.0004 | ETA 30:49:17 2020-12-01 19:29:53 [INFO] seg_loss:0.1536, att_loss: 0.7397, edge_loss: 2.2180, dual_loss: 0.0022 2020-12-01 19:29:54 [INFO] [TRAIN] epoch=16, iter=5900/80000, loss=2.8400, lr=0.009334, batch_cost=1.4942, reader_cost=0.0002 | ETA 30:45:20 2020-12-01 19:32:25 [INFO] seg_loss:0.1144, att_loss: 0.5411, edge_loss: 1.0494, dual_loss: 0.0021 2020-12-01 19:32:26 [INFO] [TRAIN] epoch=17, iter=6000/80000, loss=2.8788, lr=0.009323, batch_cost=1.5266, reader_cost=0.0501 | ETA 31:22:44 2020-12-01 19:32:26 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-01 19:33:33 [INFO] [EVAL] #Images=500 mIoU=0.6429 Acc=0.9395 Kappa=0.9216 2020-12-01 19:33:33 [INFO] [EVAL] Class IoU: [0.9672 0.7836 0.8953 0.1931 0.4772 0.6095 0.6836 0.7505 0.9041 0.5171 0.9292 0.7935 0.579 0.919 0.4822 0.2786 0.1825 0.5284 0.7414] 2020-12-01 19:33:33 [INFO] [EVAL] Class Acc: [0.9819 0.8765 0.952 0.8229 0.5353 0.7826 0.807 0.8789 0.9439 0.654 0.9694 0.9133 0.7286 0.9619 0.7268 0.4749 0.1875 0.6196 0.8423] 2020-12-01 19:33:38 [INFO] [EVAL] The model with the best validation mIoU (0.6724) was saved at iter 4000. 2020-12-01 19:36:05 [INFO] seg_loss:0.0290, att_loss: 0.4810, edge_loss: 0.7126, dual_loss: 0.0006 2020-12-01 19:36:06 [INFO] [TRAIN] epoch=17, iter=6100/80000, loss=2.8551, lr=0.009311, batch_cost=1.4807, reader_cost=0.0003 | ETA 30:23:46 2020-12-01 19:38:36 [INFO] seg_loss:0.1178, att_loss: 0.6183, edge_loss: 2.6571, dual_loss: 0.0020 2020-12-01 19:38:37 [INFO] [TRAIN] epoch=17, iter=6200/80000, loss=2.6890, lr=0.009300, batch_cost=1.5133, reader_cost=0.0007 | ETA 31:01:20 2020-12-01 19:41:05 [INFO] seg_loss:0.0736, att_loss: 0.5355, edge_loss: 1.5978, dual_loss: 0.0014 2020-12-01 19:41:06 [INFO] [TRAIN] epoch=17, iter=6300/80000, loss=2.8743, lr=0.009288, batch_cost=1.4884, reader_cost=0.0003 | ETA 30:28:16 2020-12-01 19:43:37 [INFO] seg_loss:0.1108, att_loss: 0.6025, edge_loss: 2.3259, dual_loss: 0.0021 2020-12-01 19:43:38 [INFO] [TRAIN] epoch=18, iter=6400/80000, loss=2.7811, lr=0.009277, batch_cost=1.5224, reader_cost=0.0554 | ETA 31:07:26 2020-12-01 19:46:05 [INFO] seg_loss:0.0209, att_loss: 0.3528, edge_loss: 1.0637, dual_loss: 0.0007 2020-12-01 19:46:06 [INFO] [TRAIN] epoch=18, iter=6500/80000, loss=2.6897, lr=0.009266, batch_cost=1.4756, reader_cost=0.0004 | ETA 30:07:38 2020-12-01 19:48:33 [INFO] seg_loss:0.1279, att_loss: 0.5636, edge_loss: 2.5882, dual_loss: 0.0023 2020-12-01 19:48:34 [INFO] [TRAIN] epoch=18, iter=6600/80000, loss=2.5682, lr=0.009254, batch_cost=1.4830, reader_cost=0.0002 | ETA 30:14:14 2020-12-01 19:51:08 [INFO] seg_loss:0.2768, att_loss: 0.5167, edge_loss: 2.4151, dual_loss: 0.0040 2020-12-01 19:51:09 [INFO] [TRAIN] epoch=19, iter=6700/80000, loss=2.8454, lr=0.009243, batch_cost=1.5467, reader_cost=0.0671 | ETA 31:29:32 2020-12-01 19:53:34 [INFO] seg_loss:0.2042, att_loss: 0.7149, edge_loss: 2.3542, dual_loss: 0.0025 2020-12-01 19:53:35 [INFO] [TRAIN] epoch=19, iter=6800/80000, loss=2.8214, lr=0.009232, batch_cost=1.4640, reader_cost=0.0003 | ETA 29:46:04 2020-12-01 19:56:03 [INFO] seg_loss:0.1160, att_loss: 0.6362, edge_loss: 2.3161, dual_loss: 0.0020 2020-12-01 19:56:04 [INFO] [TRAIN] epoch=19, iter=6900/80000, loss=2.6396, lr=0.009220, batch_cost=1.4879, reader_cost=0.0003 | ETA 30:12:42 2020-12-01 19:58:32 [INFO] seg_loss:0.2517, att_loss: 0.6532, edge_loss: 2.7311, dual_loss: 0.0025 2020-12-01 19:58:33 [INFO] [TRAIN] epoch=19, iter=7000/80000, loss=2.7032, lr=0.009209, batch_cost=1.4873, reader_cost=0.0003 | ETA 30:09:32 2020-12-01 20:01:04 [INFO] seg_loss:0.3509, att_loss: 1.0334, edge_loss: 1.8939, dual_loss: 0.0026 2020-12-01 20:01:05 [INFO] [TRAIN] epoch=20, iter=7100/80000, loss=2.8231, lr=0.009198, batch_cost=1.5256, reader_cost=0.0474 | ETA 30:53:37 2020-12-01 20:03:33 [INFO] seg_loss:0.1356, att_loss: 0.4808, edge_loss: 1.1940, dual_loss: 0.0020 2020-12-01 20:03:34 [INFO] [TRAIN] epoch=20, iter=7200/80000, loss=2.7278, lr=0.009186, batch_cost=1.4836, reader_cost=0.0003 | ETA 30:00:07 2020-12-01 20:06:04 [INFO] seg_loss:0.0978, att_loss: 0.5285, edge_loss: 1.6045, dual_loss: 0.0018 2020-12-01 20:06:05 [INFO] [TRAIN] epoch=20, iter=7300/80000, loss=2.6240, lr=0.009175, batch_cost=1.5112, reader_cost=0.0002 | ETA 30:31:01 2020-12-01 20:08:39 [INFO] seg_loss:0.2395, att_loss: 0.6909, edge_loss: 2.7505, dual_loss: 0.0028 2020-12-01 20:08:40 [INFO] [TRAIN] epoch=20, iter=7400/80000, loss=2.9082, lr=0.009164, batch_cost=1.5532, reader_cost=0.0003 | ETA 31:19:18 2020-12-01 20:11:19 [INFO] seg_loss:0.0979, att_loss: 0.5009, edge_loss: 1.6775, dual_loss: 0.0018 2020-12-01 20:11:20 [INFO] [TRAIN] epoch=21, iter=7500/80000, loss=2.7884, lr=0.009152, batch_cost=1.5997, reader_cost=0.0658 | ETA 32:12:56 2020-12-01 20:13:50 [INFO] seg_loss:0.2201, att_loss: 0.7980, edge_loss: 2.2873, dual_loss: 0.0021 2020-12-01 20:13:51 [INFO] [TRAIN] epoch=21, iter=7600/80000, loss=2.7279, lr=0.009141, batch_cost=1.5086, reader_cost=0.0004 | ETA 30:20:21 2020-12-01 20:16:19 [INFO] seg_loss:0.0267, att_loss: 0.1572, edge_loss: 0.6791, dual_loss: 0.0006 2020-12-01 20:16:20 [INFO] [TRAIN] epoch=21, iter=7700/80000, loss=2.6452, lr=0.009130, batch_cost=1.4927, reader_cost=0.0003 | ETA 29:58:41 2020-12-01 20:18:46 [INFO] seg_loss:0.0562, att_loss: 0.3583, edge_loss: 0.7322, dual_loss: 0.0011 2020-12-01 20:18:47 [INFO] [TRAIN] epoch=21, iter=7800/80000, loss=2.8269, lr=0.009118, batch_cost=1.4666, reader_cost=0.0003 | ETA 29:24:48 2020-12-01 20:21:38 [INFO] seg_loss:0.6341, att_loss: 0.8311, edge_loss: 2.2168, dual_loss: 0.0047 2020-12-01 20:21:39 [INFO] [TRAIN] epoch=22, iter=7900/80000, loss=2.9005, lr=0.009107, batch_cost=1.7209, reader_cost=0.1020 | ETA 34:27:55 2020-12-01 20:24:06 [INFO] seg_loss:0.0372, att_loss: 0.4790, edge_loss: 1.1694, dual_loss: 0.0007 2020-12-01 20:24:07 [INFO] [TRAIN] epoch=22, iter=8000/80000, loss=2.5880, lr=0.009095, batch_cost=1.4844, reader_cost=0.0005 | ETA 29:41:17 2020-12-01 20:24:07 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-01 20:25:14 [INFO] [EVAL] #Images=500 mIoU=0.7291 Acc=0.9489 Kappa=0.9335 2020-12-01 20:25:14 [INFO] [EVAL] Class IoU: [0.9633 0.7385 0.913 0.4173 0.549 0.6495 0.6915 0.7636 0.9195 0.6224 0.9385 0.8119 0.5823 0.9361 0.6054 0.7575 0.6583 0.5831 0.7518] 2020-12-01 20:25:14 [INFO] [EVAL] Class Acc: [0.9756 0.9016 0.958 0.798 0.6629 0.782 0.7702 0.9079 0.9482 0.8 0.956 0.9081 0.71 0.9587 0.9016 0.8664 0.7668 0.6673 0.8123] 2020-12-01 20:25:21 [INFO] [EVAL] The model with the best validation mIoU (0.7291) was saved at iter 8000. 2020-12-01 20:27:49 [INFO] seg_loss:0.0681, att_loss: 0.4978, edge_loss: 1.2986, dual_loss: 0.0009 2020-12-01 20:27:50 [INFO] [TRAIN] epoch=22, iter=8100/80000, loss=2.5657, lr=0.009084, batch_cost=1.4951, reader_cost=0.0005 | ETA 29:51:38 2020-12-01 20:30:28 [INFO] seg_loss:0.1825, att_loss: 0.5994, edge_loss: 2.4244, dual_loss: 0.0021 2020-12-01 20:30:29 [INFO] [TRAIN] epoch=23, iter=8200/80000, loss=2.8951, lr=0.009073, batch_cost=1.5888, reader_cost=0.0663 | ETA 31:41:13 2020-12-01 20:32:59 [INFO] seg_loss:0.1126, att_loss: 0.6564, edge_loss: 2.1170, dual_loss: 0.0018 2020-12-01 20:33:00 [INFO] [TRAIN] epoch=23, iter=8300/80000, loss=2.8894, lr=0.009061, batch_cost=1.5026, reader_cost=0.0003 | ETA 29:55:35 2020-12-01 20:35:29 [INFO] seg_loss:0.1023, att_loss: 0.6292, edge_loss: 2.1529, dual_loss: 0.0019 2020-12-01 20:35:30 [INFO] [TRAIN] epoch=23, iter=8400/80000, loss=2.5422, lr=0.009050, batch_cost=1.5026, reader_cost=0.0003 | ETA 29:53:05 2020-12-01 20:37:59 [INFO] seg_loss:0.2682, att_loss: 0.8107, edge_loss: 2.2457, dual_loss: 0.0022 2020-12-01 20:38:00 [INFO] [TRAIN] epoch=23, iter=8500/80000, loss=2.7723, lr=0.009039, batch_cost=1.4980, reader_cost=0.0003 | ETA 29:45:04 2020-12-01 20:40:36 [INFO] seg_loss:0.2493, att_loss: 0.7251, edge_loss: 2.3273, dual_loss: 0.0027 2020-12-01 20:40:38 [INFO] [TRAIN] epoch=24, iter=8600/80000, loss=2.8298, lr=0.009027, batch_cost=1.5776, reader_cost=0.0554 | ETA 31:17:23 2020-12-01 20:43:28 [INFO] seg_loss:0.1397, att_loss: 0.6932, edge_loss: 2.4309, dual_loss: 0.0021 2020-12-01 20:43:30 [INFO] [TRAIN] epoch=24, iter=8700/80000, loss=2.7025, lr=0.009016, batch_cost=1.7218, reader_cost=0.0005 | ETA 34:06:05 2020-12-01 20:46:03 [INFO] seg_loss:0.0999, att_loss: 0.6677, edge_loss: 1.4995, dual_loss: 0.0015 2020-12-01 20:46:04 [INFO] [TRAIN] epoch=24, iter=8800/80000, loss=2.5587, lr=0.009004, batch_cost=1.5403, reader_cost=0.0003 | ETA 30:27:46 2020-12-01 20:48:31 [INFO] seg_loss:0.1360, att_loss: 0.5707, edge_loss: 2.0770, dual_loss: 0.0021 2020-12-01 20:48:32 [INFO] [TRAIN] epoch=24, iter=8900/80000, loss=2.7633, lr=0.008993, batch_cost=1.4824, reader_cost=0.0002 | ETA 29:16:40 2020-12-01 20:51:07 [INFO] seg_loss:0.3134, att_loss: 0.8172, edge_loss: 2.9455, dual_loss: 0.0035 2020-12-01 20:51:08 [INFO] [TRAIN] epoch=25, iter=9000/80000, loss=2.6951, lr=0.008982, batch_cost=1.5591, reader_cost=0.0716 | ETA 30:44:58 2020-12-01 20:53:36 [INFO] seg_loss:0.3234, att_loss: 0.6157, edge_loss: 1.5240, dual_loss: 0.0026 2020-12-01 20:53:37 [INFO] [TRAIN] epoch=25, iter=9100/80000, loss=2.6402, lr=0.008970, batch_cost=1.4882, reader_cost=0.0004 | ETA 29:18:35 2020-12-01 20:56:03 [INFO] seg_loss:0.1632, att_loss: 0.7707, edge_loss: 1.4800, dual_loss: 0.0020 2020-12-01 20:56:04 [INFO] [TRAIN] epoch=25, iter=9200/80000, loss=2.6418, lr=0.008959, batch_cost=1.4703, reader_cost=0.0003 | ETA 28:54:59 2020-12-01 20:58:28 [INFO] seg_loss:0.0393, att_loss: 0.3994, edge_loss: 1.6394, dual_loss: 0.0010 2020-12-01 20:58:29 [INFO] [TRAIN] epoch=25, iter=9300/80000, loss=2.7346, lr=0.008948, batch_cost=1.4477, reader_cost=0.0004 | ETA 28:25:55 2020-12-01 21:01:06 [INFO] seg_loss:0.2918, att_loss: 0.8265, edge_loss: 3.0177, dual_loss: 0.0029 2020-12-01 21:01:08 [INFO] [TRAIN] epoch=26, iter=9400/80000, loss=2.7174, lr=0.008936, batch_cost=1.5891, reader_cost=0.0515 | ETA 31:09:50 2020-12-01 21:03:37 [INFO] seg_loss:0.1997, att_loss: 1.3110, edge_loss: 1.3053, dual_loss: 0.0013 2020-12-01 21:03:39 [INFO] [TRAIN] epoch=26, iter=9500/80000, loss=2.6568, lr=0.008925, batch_cost=1.5104, reader_cost=0.0003 | ETA 29:34:40 2020-12-01 21:06:09 [INFO] seg_loss:0.1058, att_loss: 0.8337, edge_loss: 2.3306, dual_loss: 0.0015 2020-12-01 21:06:10 [INFO] [TRAIN] epoch=26, iter=9600/80000, loss=2.7061, lr=0.008913, batch_cost=1.5172, reader_cost=0.0003 | ETA 29:40:13 2020-12-01 21:08:48 [INFO] seg_loss:0.1338, att_loss: 0.9483, edge_loss: 1.1113, dual_loss: 0.0015 2020-12-01 21:08:49 [INFO] [TRAIN] epoch=27, iter=9700/80000, loss=2.8243, lr=0.008902, batch_cost=1.5824, reader_cost=0.0477 | ETA 30:54:02 2020-12-01 21:11:17 [INFO] seg_loss:0.0370, att_loss: 0.4484, edge_loss: 1.4546, dual_loss: 0.0011 2020-12-01 21:11:18 [INFO] [TRAIN] epoch=27, iter=9800/80000, loss=2.6687, lr=0.008891, batch_cost=1.4982, reader_cost=0.0006 | ETA 29:12:50 2020-12-01 21:13:44 [INFO] seg_loss:0.0772, att_loss: 0.5765, edge_loss: 1.5354, dual_loss: 0.0013 2020-12-01 21:13:45 [INFO] [TRAIN] epoch=27, iter=9900/80000, loss=2.6004, lr=0.008879, batch_cost=1.4662, reader_cost=0.0006 | ETA 28:32:58 2020-12-01 21:16:11 [INFO] seg_loss:0.4273, att_loss: 1.0694, edge_loss: 1.8031, dual_loss: 0.0030 2020-12-01 21:16:12 [INFO] [TRAIN] epoch=27, iter=10000/80000, loss=2.7011, lr=0.008868, batch_cost=1.4705, reader_cost=0.0004 | ETA 28:35:31 2020-12-01 21:16:12 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-01 21:17:19 [INFO] [EVAL] #Images=500 mIoU=0.7446 Acc=0.9552 Kappa=0.9417 2020-12-01 21:17:19 [INFO] [EVAL] Class IoU: [0.9754 0.8146 0.917 0.422 0.5813 0.6551 0.6925 0.776 0.9174 0.5895 0.9409 0.8241 0.6145 0.9403 0.6753 0.8191 0.6325 0.5983 0.7608] 2020-12-01 21:17:19 [INFO] [EVAL] Class Acc: [0.9855 0.9269 0.9429 0.7823 0.7974 0.8471 0.8605 0.918 0.9531 0.8338 0.9617 0.9018 0.7385 0.9632 0.7465 0.8894 0.7397 0.7432 0.8718] 2020-12-01 21:17:27 [INFO] [EVAL] The model with the best validation mIoU (0.7446) was saved at iter 10000. 2020-12-01 21:20:02 [INFO] seg_loss:0.1009, att_loss: 0.3680, edge_loss: 1.1135, dual_loss: 0.0015 2020-12-01 21:20:03 [INFO] [TRAIN] epoch=28, iter=10100/80000, loss=2.7805, lr=0.008856, batch_cost=1.5571, reader_cost=0.0752 | ETA 30:13:59 2020-12-01 21:22:32 [INFO] seg_loss:0.1098, att_loss: 0.5276, edge_loss: 2.8002, dual_loss: 0.0021 2020-12-01 21:22:33 [INFO] [TRAIN] epoch=28, iter=10200/80000, loss=2.5525, lr=0.008845, batch_cost=1.4959, reader_cost=0.0004 | ETA 29:00:13 2020-12-01 21:25:05 [INFO] seg_loss:0.0281, att_loss: 0.3490, edge_loss: 0.8521, dual_loss: 0.0007 2020-12-01 21:25:06 [INFO] [TRAIN] epoch=28, iter=10300/80000, loss=2.6391, lr=0.008834, batch_cost=1.5310, reader_cost=0.0003 | ETA 29:38:29 2020-12-01 21:27:34 [INFO] seg_loss:0.2149, att_loss: 0.3246, edge_loss: 0.9307, dual_loss: 0.0019 2020-12-01 21:27:35 [INFO] [TRAIN] epoch=28, iter=10400/80000, loss=2.7703, lr=0.008822, batch_cost=1.4958, reader_cost=0.0002 | ETA 28:55:10 2020-12-01 21:30:17 [INFO] seg_loss:0.1357, att_loss: 0.5800, edge_loss: 2.2707, dual_loss: 0.0018 2020-12-01 21:30:18 [INFO] [TRAIN] epoch=29, iter=10500/80000, loss=2.7262, lr=0.008811, batch_cost=1.6247, reader_cost=0.0802 | ETA 31:21:58 2020-12-01 21:32:47 [INFO] seg_loss:0.2209, att_loss: 0.7231, edge_loss: 2.1860, dual_loss: 0.0023 2020-12-01 21:32:48 [INFO] [TRAIN] epoch=29, iter=10600/80000, loss=2.5267, lr=0.008799, batch_cost=1.4973, reader_cost=0.0002 | ETA 28:51:51 2020-12-01 21:35:16 [INFO] seg_loss:0.1984, att_loss: 0.6276, edge_loss: 1.4601, dual_loss: 0.0019 2020-12-01 21:35:17 [INFO] [TRAIN] epoch=29, iter=10700/80000, loss=2.5758, lr=0.008788, batch_cost=1.4916, reader_cost=0.0005 | ETA 28:42:46 2020-12-01 21:37:49 [INFO] seg_loss:0.1871, att_loss: 0.6878, edge_loss: 2.0839, dual_loss: 0.0021 2020-12-01 21:37:50 [INFO] [TRAIN] epoch=30, iter=10800/80000, loss=2.8094, lr=0.008776, batch_cost=1.5271, reader_cost=0.0499 | ETA 29:21:18 2020-12-01 21:40:17 [INFO] seg_loss:0.2425, att_loss: 0.5707, edge_loss: 1.2218, dual_loss: 0.0028 2020-12-01 21:40:18 [INFO] [TRAIN] epoch=30, iter=10900/80000, loss=2.7452, lr=0.008765, batch_cost=1.4885, reader_cost=0.0003 | ETA 28:34:12 2020-12-01 21:42:44 [INFO] seg_loss:0.1364, att_loss: 0.6515, edge_loss: 3.0575, dual_loss: 0.0024 2020-12-01 21:42:45 [INFO] [TRAIN] epoch=30, iter=11000/80000, loss=2.5913, lr=0.008754, batch_cost=1.4663, reader_cost=0.0002 | ETA 28:06:17 2020-12-01 21:45:11 [INFO] seg_loss:0.1361, att_loss: 0.5397, edge_loss: 3.3173, dual_loss: 0.0025 2020-12-01 21:45:12 [INFO] [TRAIN] epoch=30, iter=11100/80000, loss=2.6958, lr=0.008742, batch_cost=1.4686, reader_cost=0.0003 | ETA 28:06:27 2020-12-01 21:47:48 [INFO] seg_loss:0.1025, att_loss: 0.5284, edge_loss: 1.3604, dual_loss: 0.0014 2020-12-01 21:47:49 [INFO] [TRAIN] epoch=31, iter=11200/80000, loss=2.7417, lr=0.008731, batch_cost=1.5665, reader_cost=0.0502 | ETA 29:56:11 2020-12-01 21:50:17 [INFO] seg_loss:0.0418, att_loss: 0.4279, edge_loss: 1.6741, dual_loss: 0.0011 2020-12-01 21:50:18 [INFO] [TRAIN] epoch=31, iter=11300/80000, loss=2.6776, lr=0.008719, batch_cost=1.4983, reader_cost=0.0003 | ETA 28:35:30 2020-12-01 21:52:47 [INFO] seg_loss:0.1726, att_loss: 0.7064, edge_loss: 2.7401, dual_loss: 0.0025 2020-12-01 21:52:49 [INFO] [TRAIN] epoch=31, iter=11400/80000, loss=2.6924, lr=0.008708, batch_cost=1.5007, reader_cost=0.0003 | ETA 28:35:47 2020-12-01 21:55:19 [INFO] seg_loss:0.2098, att_loss: 0.5756, edge_loss: 1.9747, dual_loss: 0.0020 2020-12-01 21:55:20 [INFO] [TRAIN] epoch=31, iter=11500/80000, loss=2.6337, lr=0.008697, batch_cost=1.5120, reader_cost=0.0005 | ETA 28:46:08 2020-12-01 21:57:55 [INFO] seg_loss:0.1665, att_loss: 0.7394, edge_loss: 1.5353, dual_loss: 0.0020 2020-12-01 21:57:56 [INFO] [TRAIN] epoch=32, iter=11600/80000, loss=2.7712, lr=0.008685, batch_cost=1.5676, reader_cost=0.0462 | ETA 29:47:02 2020-12-01 22:00:20 [INFO] seg_loss:0.1657, att_loss: 0.6658, edge_loss: 2.8760, dual_loss: 0.0025 2020-12-01 22:00:21 [INFO] [TRAIN] epoch=32, iter=11700/80000, loss=2.6622, lr=0.008674, batch_cost=1.4445, reader_cost=0.0005 | ETA 27:24:16 2020-12-01 22:02:49 [INFO] seg_loss:0.2279, att_loss: 0.5996, edge_loss: 1.1502, dual_loss: 0.0018 2020-12-01 22:02:50 [INFO] [TRAIN] epoch=32, iter=11800/80000, loss=2.5159, lr=0.008662, batch_cost=1.4873, reader_cost=0.0007 | ETA 28:10:36 2020-12-01 22:05:18 [INFO] seg_loss:0.0556, att_loss: 0.3355, edge_loss: 1.1724, dual_loss: 0.0010 2020-12-01 22:05:19 [INFO] [TRAIN] epoch=32, iter=11900/80000, loss=2.7423, lr=0.008651, batch_cost=1.4895, reader_cost=0.0003 | ETA 28:10:35 2020-12-01 22:07:56 [INFO] seg_loss:0.1268, att_loss: 0.5864, edge_loss: 2.2161, dual_loss: 0.0019 2020-12-01 22:07:57 [INFO] [TRAIN] epoch=33, iter=12000/80000, loss=2.7755, lr=0.008639, batch_cost=1.5833, reader_cost=0.0560 | ETA 29:54:26 2020-12-01 22:07:57 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-01 22:09:05 [INFO] [EVAL] #Images=500 mIoU=0.7272 Acc=0.9521 Kappa=0.9378 2020-12-01 22:09:05 [INFO] [EVAL] Class IoU: [0.9755 0.8153 0.9106 0.4202 0.6077 0.6182 0.7 0.782 0.9096 0.587 0.9318 0.8075 0.6118 0.9352 0.4274 0.7569 0.6511 0.5962 0.7722] 2020-12-01 22:09:05 [INFO] [EVAL] Class Acc: [0.9866 0.8963 0.9422 0.6764 0.799 0.8533 0.8156 0.8903 0.9527 0.7643 0.9444 0.8842 0.7746 0.9703 0.816 0.8642 0.7439 0.7818 0.8832] 2020-12-01 22:09:13 [INFO] [EVAL] The model with the best validation mIoU (0.7446) was saved at iter 10000. 2020-12-01 22:11:41 [INFO] seg_loss:0.0892, att_loss: 0.6346, edge_loss: 1.2276, dual_loss: 0.0011 2020-12-01 22:11:42 [INFO] [TRAIN] epoch=33, iter=12100/80000, loss=2.6009, lr=0.008628, batch_cost=1.4926, reader_cost=0.0003 | ETA 28:09:05 2020-12-01 22:14:10 [INFO] seg_loss:0.0645, att_loss: 0.4823, edge_loss: 1.6866, dual_loss: 0.0011 2020-12-01 22:14:11 [INFO] [TRAIN] epoch=33, iter=12200/80000, loss=2.6120, lr=0.008617, batch_cost=1.4878, reader_cost=0.0005 | ETA 28:01:10 2020-12-01 22:16:46 [INFO] seg_loss:0.1731, att_loss: 0.6990, edge_loss: 2.6224, dual_loss: 0.0025 2020-12-01 22:16:47 [INFO] [TRAIN] epoch=34, iter=12300/80000, loss=2.8891, lr=0.008605, batch_cost=1.5589, reader_cost=0.0544 | ETA 29:18:56 2020-12-01 22:19:14 [INFO] seg_loss:0.2631, att_loss: 0.8133, edge_loss: 3.1017, dual_loss: 0.0028 2020-12-01 22:19:15 [INFO] [TRAIN] epoch=34, iter=12400/80000, loss=2.6156, lr=0.008594, batch_cost=1.4866, reader_cost=0.0002 | ETA 27:54:51 2020-12-01 22:21:45 [INFO] seg_loss:0.0645, att_loss: 0.4986, edge_loss: 1.9948, dual_loss: 0.0014 2020-12-01 22:21:46 [INFO] [TRAIN] epoch=34, iter=12500/80000, loss=2.5679, lr=0.008582, batch_cost=1.5090, reader_cost=0.0004 | ETA 28:17:34 2020-12-01 22:24:16 [INFO] seg_loss:0.1122, att_loss: 0.5264, edge_loss: 2.0781, dual_loss: 0.0017 2020-12-01 22:24:17 [INFO] [TRAIN] epoch=34, iter=12600/80000, loss=2.6511, lr=0.008571, batch_cost=1.5051, reader_cost=0.0004 | ETA 28:10:43 2020-12-01 22:26:51 [INFO] seg_loss:0.1005, att_loss: 0.4765, edge_loss: 1.7760, dual_loss: 0.0015 2020-12-01 22:26:52 [INFO] [TRAIN] epoch=35, iter=12700/80000, loss=2.6101, lr=0.008559, batch_cost=1.5512, reader_cost=0.0620 | ETA 28:59:56 2020-12-01 22:29:20 [INFO] seg_loss:0.1190, att_loss: 0.5288, edge_loss: 1.7357, dual_loss: 0.0018 2020-12-01 22:29:21 [INFO] [TRAIN] epoch=35, iter=12800/80000, loss=2.5657, lr=0.008548, batch_cost=1.4924, reader_cost=0.0009 | ETA 27:51:29 2020-12-01 22:31:51 [INFO] seg_loss:0.1522, att_loss: 0.6891, edge_loss: 2.3151, dual_loss: 0.0021 2020-12-01 22:31:52 [INFO] [TRAIN] epoch=35, iter=12900/80000, loss=2.6342, lr=0.008536, batch_cost=1.5071, reader_cost=0.0005 | ETA 28:05:27 2020-12-01 22:34:18 [INFO] seg_loss:0.1614, att_loss: 0.6502, edge_loss: 1.4202, dual_loss: 0.0017 2020-12-01 22:34:20 [INFO] [TRAIN] epoch=35, iter=13000/80000, loss=2.7059, lr=0.008525, batch_cost=1.4773, reader_cost=0.0004 | ETA 27:29:35 2020-12-01 22:36:54 [INFO] seg_loss:0.0656, att_loss: 0.5046, edge_loss: 1.7711, dual_loss: 0.0013 2020-12-01 22:36:55 [INFO] [TRAIN] epoch=36, iter=13100/80000, loss=2.5780, lr=0.008514, batch_cost=1.5559, reader_cost=0.0657 | ETA 28:54:49 2020-12-01 22:39:24 [INFO] seg_loss:0.2531, att_loss: 0.6191, edge_loss: 2.3410, dual_loss: 0.0024 2020-12-01 22:39:25 [INFO] [TRAIN] epoch=36, iter=13200/80000, loss=2.5892, lr=0.008502, batch_cost=1.4954, reader_cost=0.0003 | ETA 27:44:49 2020-12-01 22:41:52 [INFO] seg_loss:0.1311, att_loss: 0.6728, edge_loss: 2.9949, dual_loss: 0.0022 2020-12-01 22:41:53 [INFO] [TRAIN] epoch=36, iter=13300/80000, loss=2.5013, lr=0.008491, batch_cost=1.4808, reader_cost=0.0004 | ETA 27:26:11 2020-12-01 22:44:30 [INFO] seg_loss:0.1296, att_loss: 0.5401, edge_loss: 2.5609, dual_loss: 0.0023 2020-12-01 22:44:31 [INFO] [TRAIN] epoch=37, iter=13400/80000, loss=2.7905, lr=0.008479, batch_cost=1.5781, reader_cost=0.0517 | ETA 29:11:44 2020-12-01 22:46:59 [INFO] seg_loss:0.1685, att_loss: 0.7222, edge_loss: 2.3153, dual_loss: 0.0022 2020-12-01 22:47:00 [INFO] [TRAIN] epoch=37, iter=13500/80000, loss=2.7597, lr=0.008468, batch_cost=1.4933, reader_cost=0.0005 | ETA 27:35:02 2020-12-01 22:49:28 [INFO] seg_loss:0.1306, att_loss: 0.6089, edge_loss: 1.5006, dual_loss: 0.0019 2020-12-01 22:49:29 [INFO] [TRAIN] epoch=37, iter=13600/80000, loss=2.4587, lr=0.008456, batch_cost=1.4905, reader_cost=0.0004 | ETA 27:29:28 2020-12-01 22:51:57 [INFO] seg_loss:0.0454, att_loss: 0.4764, edge_loss: 1.6296, dual_loss: 0.0012 2020-12-01 22:51:58 [INFO] [TRAIN] epoch=37, iter=13700/80000, loss=2.6818, lr=0.008445, batch_cost=1.4872, reader_cost=0.0003 | ETA 27:23:24 2020-12-01 22:54:34 [INFO] seg_loss:0.0524, att_loss: 0.4889, edge_loss: 1.0674, dual_loss: 0.0009 2020-12-01 22:54:35 [INFO] [TRAIN] epoch=38, iter=13800/80000, loss=2.8082, lr=0.008433, batch_cost=1.5778, reader_cost=0.0481 | ETA 29:00:50 2020-12-01 22:57:00 [INFO] seg_loss:0.0859, att_loss: 0.4637, edge_loss: 1.6408, dual_loss: 0.0017 2020-12-01 22:57:01 [INFO] [TRAIN] epoch=38, iter=13900/80000, loss=2.6042, lr=0.008422, batch_cost=1.4533, reader_cost=0.0004 | ETA 26:41:03 2020-12-01 22:59:29 [INFO] seg_loss:0.2083, att_loss: 0.7187, edge_loss: 2.7617, dual_loss: 0.0035 2020-12-01 22:59:30 [INFO] [TRAIN] epoch=38, iter=14000/80000, loss=2.5732, lr=0.008410, batch_cost=1.4897, reader_cost=0.0004 | ETA 27:18:38 2020-12-01 22:59:30 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-01 23:00:36 [INFO] [EVAL] #Images=500 mIoU=0.7398 Acc=0.9564 Kappa=0.9435 2020-12-01 23:00:36 [INFO] [EVAL] Class IoU: [0.9788 0.837 0.9177 0.4528 0.599 0.6671 0.7159 0.7906 0.9182 0.5953 0.9227 0.8031 0.5872 0.9436 0.7454 0.7226 0.5384 0.5506 0.7706] 2020-12-01 23:00:36 [INFO] [EVAL] Class Acc: [0.99 0.8952 0.9575 0.8218 0.8498 0.8183 0.803 0.8734 0.9523 0.8248 0.9315 0.8403 0.7447 0.971 0.8283 0.9017 0.794 0.7258 0.8611] 2020-12-01 23:00:41 [INFO] [EVAL] The model with the best validation mIoU (0.7446) was saved at iter 10000. 2020-12-01 23:03:08 [INFO] seg_loss:0.1045, att_loss: 0.4877, edge_loss: 2.2394, dual_loss: 0.0017 2020-12-01 23:03:09 [INFO] [TRAIN] epoch=38, iter=14100/80000, loss=2.6569, lr=0.008399, batch_cost=1.4755, reader_cost=0.0003 | ETA 27:00:32 2020-12-01 23:05:46 [INFO] seg_loss:0.0846, att_loss: 0.5797, edge_loss: 1.8364, dual_loss: 0.0014 2020-12-01 23:05:47 [INFO] [TRAIN] epoch=39, iter=14200/80000, loss=2.6665, lr=0.008387, batch_cost=1.5762, reader_cost=0.0501 | ETA 28:48:37 2020-12-01 23:08:14 [INFO] seg_loss:0.0501, att_loss: 0.5590, edge_loss: 1.7047, dual_loss: 0.0010 2020-12-01 23:08:16 [INFO] [TRAIN] epoch=39, iter=14300/80000, loss=2.6271, lr=0.008376, batch_cost=1.4883, reader_cost=0.0005 | ETA 27:09:43 2020-12-01 23:10:44 [INFO] seg_loss:0.1754, att_loss: 0.6570, edge_loss: 2.2164, dual_loss: 0.0027 2020-12-01 23:10:45 [INFO] [TRAIN] epoch=39, iter=14400/80000, loss=2.6646, lr=0.008364, batch_cost=1.4950, reader_cost=0.0003 | ETA 27:14:33 2020-12-01 23:13:13 [INFO] seg_loss:0.0912, att_loss: 0.5952, edge_loss: 1.8064, dual_loss: 0.0016 2020-12-01 23:13:14 [INFO] [TRAIN] epoch=39, iter=14500/80000, loss=2.7591, lr=0.008353, batch_cost=1.4929, reader_cost=0.0003 | ETA 27:09:47 2020-12-01 23:15:49 [INFO] seg_loss:0.1171, att_loss: 0.4496, edge_loss: 0.8401, dual_loss: 0.0010 2020-12-01 23:15:50 [INFO] [TRAIN] epoch=40, iter=14600/80000, loss=2.7839, lr=0.008342, batch_cost=1.5544, reader_cost=0.0558 | ETA 28:14:20 2020-12-01 23:18:16 [INFO] seg_loss:0.0969, att_loss: 0.6506, edge_loss: 1.2612, dual_loss: 0.0010 2020-12-01 23:18:17 [INFO] [TRAIN] epoch=40, iter=14700/80000, loss=2.5395, lr=0.008330, batch_cost=1.4743, reader_cost=0.0002 | ETA 26:44:32 2020-12-01 23:20:45 [INFO] seg_loss:0.2226, att_loss: 0.7352, edge_loss: 2.5499, dual_loss: 0.0026 2020-12-01 23:20:46 [INFO] [TRAIN] epoch=40, iter=14800/80000, loss=2.4683, lr=0.008319, batch_cost=1.4888, reader_cost=0.0004 | ETA 26:57:47 2020-12-01 23:23:20 [INFO] seg_loss:0.0992, att_loss: 0.5342, edge_loss: 2.5726, dual_loss: 0.0018 2020-12-01 23:23:21 [INFO] [TRAIN] epoch=41, iter=14900/80000, loss=2.8573, lr=0.008307, batch_cost=1.5448, reader_cost=0.0483 | ETA 27:56:05 2020-12-01 23:25:48 [INFO] seg_loss:0.0937, att_loss: 0.6154, edge_loss: 2.0467, dual_loss: 0.0014 2020-12-01 23:25:49 [INFO] [TRAIN] epoch=41, iter=15000/80000, loss=2.6593, lr=0.008296, batch_cost=1.4813, reader_cost=0.0005 | ETA 26:44:47 2020-12-01 23:28:17 [INFO] seg_loss:0.0598, att_loss: 0.4675, edge_loss: 1.7720, dual_loss: 0.0013 2020-12-01 23:28:18 [INFO] [TRAIN] epoch=41, iter=15100/80000, loss=2.5213, lr=0.008284, batch_cost=1.4879, reader_cost=0.0003 | ETA 26:49:26 2020-12-01 23:30:45 [INFO] seg_loss:0.0422, att_loss: 0.3643, edge_loss: 1.5525, dual_loss: 0.0011 2020-12-01 23:30:46 [INFO] [TRAIN] epoch=41, iter=15200/80000, loss=2.7967, lr=0.008273, batch_cost=1.4887, reader_cost=0.0005 | ETA 26:47:47 2020-12-01 23:33:20 [INFO] seg_loss:0.4877, att_loss: 0.7085, edge_loss: 1.3566, dual_loss: 0.0026 2020-12-01 23:33:21 [INFO] [TRAIN] epoch=42, iter=15300/80000, loss=2.6507, lr=0.008261, batch_cost=1.5460, reader_cost=0.0482 | ETA 27:47:07 2020-12-01 23:35:47 [INFO] seg_loss:0.0056, att_loss: 0.1513, edge_loss: 0.5924, dual_loss: 0.0003 2020-12-01 23:35:48 [INFO] [TRAIN] epoch=42, iter=15400/80000, loss=2.6934, lr=0.008250, batch_cost=1.4741, reader_cost=0.0004 | ETA 26:27:06 2020-12-01 23:38:17 [INFO] seg_loss:0.0732, att_loss: 0.4989, edge_loss: 1.8512, dual_loss: 0.0013 2020-12-01 23:38:18 [INFO] [TRAIN] epoch=42, iter=15500/80000, loss=2.6149, lr=0.008238, batch_cost=1.4970, reader_cost=0.0003 | ETA 26:49:13 2020-12-01 23:40:46 [INFO] seg_loss:0.1091, att_loss: 0.5001, edge_loss: 2.4261, dual_loss: 0.0020 2020-12-01 23:40:47 [INFO] [TRAIN] epoch=42, iter=15600/80000, loss=2.7982, lr=0.008227, batch_cost=1.4861, reader_cost=0.0005 | ETA 26:35:01 2020-12-01 23:43:22 [INFO] seg_loss:0.0620, att_loss: 0.4878, edge_loss: 2.1867, dual_loss: 0.0014 2020-12-01 23:43:23 [INFO] [TRAIN] epoch=43, iter=15700/80000, loss=2.6322, lr=0.008215, batch_cost=1.5654, reader_cost=0.0505 | ETA 27:57:34 2020-12-01 23:45:49 [INFO] seg_loss:0.0410, att_loss: 0.4815, edge_loss: 1.7012, dual_loss: 0.0009 2020-12-01 23:45:50 [INFO] [TRAIN] epoch=43, iter=15800/80000, loss=2.5870, lr=0.008204, batch_cost=1.4681, reader_cost=0.0003 | ETA 26:10:51 2020-12-01 23:48:20 [INFO] seg_loss:0.0910, att_loss: 0.5246, edge_loss: 2.6613, dual_loss: 0.0018 2020-12-01 23:48:21 [INFO] [TRAIN] epoch=43, iter=15900/80000, loss=2.5440, lr=0.008192, batch_cost=1.5064, reader_cost=0.0004 | ETA 26:49:18 2020-12-01 23:50:54 [INFO] seg_loss:0.0832, att_loss: 0.4427, edge_loss: 1.8144, dual_loss: 0.0016 2020-12-01 23:50:55 [INFO] [TRAIN] epoch=44, iter=16000/80000, loss=2.7677, lr=0.008181, batch_cost=1.5432, reader_cost=0.0510 | ETA 27:26:07 2020-12-01 23:50:55 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-01 23:52:03 [INFO] [EVAL] #Images=500 mIoU=0.7580 Acc=0.9575 Kappa=0.9447 2020-12-01 23:52:03 [INFO] [EVAL] Class IoU: [0.9765 0.8204 0.9218 0.5181 0.5245 0.6683 0.7226 0.8008 0.9232 0.6162 0.9489 0.8279 0.6219 0.9394 0.6281 0.842 0.7095 0.62 0.7726] 2020-12-01 23:52:03 [INFO] [EVAL] Class Acc: [0.9839 0.9252 0.9527 0.8745 0.8457 0.839 0.8348 0.8843 0.9499 0.8632 0.9728 0.8834 0.7732 0.9809 0.6518 0.8979 0.8597 0.7071 0.8641] 2020-12-01 23:52:12 [INFO] [EVAL] The model with the best validation mIoU (0.7580) was saved at iter 16000. 2020-12-01 23:54:36 [INFO] seg_loss:0.1877, att_loss: 0.6438, edge_loss: 2.7345, dual_loss: 0.0027 2020-12-01 23:54:37 [INFO] [TRAIN] epoch=44, iter=16100/80000, loss=2.6588, lr=0.008169, batch_cost=1.4500, reader_cost=0.0002 | ETA 25:44:12 2020-12-01 23:57:04 [INFO] seg_loss:0.1117, att_loss: 0.7381, edge_loss: 1.6285, dual_loss: 0.0015 2020-12-01 23:57:05 [INFO] [TRAIN] epoch=44, iter=16200/80000, loss=2.4786, lr=0.008158, batch_cost=1.4796, reader_cost=0.0003 | ETA 26:13:17 2020-12-01 23:59:32 [INFO] seg_loss:0.0646, att_loss: 0.4221, edge_loss: 2.2268, dual_loss: 0.0015 2020-12-01 23:59:33 [INFO] [TRAIN] epoch=44, iter=16300/80000, loss=2.5116, lr=0.008146, batch_cost=1.4824, reader_cost=0.0007 | ETA 26:13:51 2020-12-02 00:02:09 [INFO] seg_loss:0.2311, att_loss: 0.8870, edge_loss: 1.7769, dual_loss: 0.0021 2020-12-02 00:02:10 [INFO] [TRAIN] epoch=45, iter=16400/80000, loss=2.7739, lr=0.008135, batch_cost=1.5647, reader_cost=0.0699 | ETA 27:38:32 2020-12-02 00:04:38 [INFO] seg_loss:0.1629, att_loss: 0.5518, edge_loss: 1.3649, dual_loss: 0.0021 2020-12-02 00:04:39 [INFO] [TRAIN] epoch=45, iter=16500/80000, loss=2.6710, lr=0.008123, batch_cost=1.4951, reader_cost=0.0004 | ETA 26:22:18 2020-12-02 00:07:08 [INFO] seg_loss:0.0561, att_loss: 0.4454, edge_loss: 1.1986, dual_loss: 0.0010 2020-12-02 00:07:09 [INFO] [TRAIN] epoch=45, iter=16600/80000, loss=2.4612, lr=0.008112, batch_cost=1.4955, reader_cost=0.0005 | ETA 26:20:11 2020-12-02 00:09:36 [INFO] seg_loss:0.2577, att_loss: 0.6839, edge_loss: 2.5007, dual_loss: 0.0030 2020-12-02 00:09:38 [INFO] [TRAIN] epoch=45, iter=16700/80000, loss=2.7669, lr=0.008100, batch_cost=1.4880, reader_cost=0.0005 | ETA 26:09:49 2020-12-02 00:12:13 [INFO] seg_loss:0.0983, att_loss: 0.5417, edge_loss: 1.5115, dual_loss: 0.0013 2020-12-02 00:12:14 [INFO] [TRAIN] epoch=46, iter=16800/80000, loss=2.7380, lr=0.008089, batch_cost=1.5601, reader_cost=0.0559 | ETA 27:23:18 2020-12-02 00:14:41 [INFO] seg_loss:0.0897, att_loss: 0.4936, edge_loss: 2.7385, dual_loss: 0.0019 2020-12-02 00:14:42 [INFO] [TRAIN] epoch=46, iter=16900/80000, loss=2.6231, lr=0.008077, batch_cost=1.4881, reader_cost=0.0002 | ETA 26:04:59 2020-12-02 00:17:13 [INFO] seg_loss:0.0929, att_loss: 0.4819, edge_loss: 1.0882, dual_loss: 0.0014 2020-12-02 00:17:14 [INFO] [TRAIN] epoch=46, iter=17000/80000, loss=2.5479, lr=0.008066, batch_cost=1.5120, reader_cost=0.0002 | ETA 26:27:37 2020-12-02 00:19:43 [INFO] seg_loss:0.0480, att_loss: 0.3925, edge_loss: 0.9251, dual_loss: 0.0008 2020-12-02 00:19:44 [INFO] [TRAIN] epoch=46, iter=17100/80000, loss=2.6952, lr=0.008054, batch_cost=1.5036, reader_cost=0.0004 | ETA 26:16:14 2020-12-02 00:22:19 [INFO] seg_loss:0.1012, att_loss: 0.6603, edge_loss: 1.3568, dual_loss: 0.0014 2020-12-02 00:22:20 [INFO] [TRAIN] epoch=47, iter=17200/80000, loss=2.7869, lr=0.008042, batch_cost=1.5655, reader_cost=0.0662 | ETA 27:18:33 2020-12-02 00:24:50 [INFO] seg_loss:0.1842, att_loss: 0.6168, edge_loss: 2.1170, dual_loss: 0.0015 2020-12-02 00:24:51 [INFO] [TRAIN] epoch=47, iter=17300/80000, loss=2.4980, lr=0.008031, batch_cost=1.5019, reader_cost=0.0004 | ETA 26:09:28 2020-12-02 00:27:17 [INFO] seg_loss:0.1656, att_loss: 0.6608, edge_loss: 2.3887, dual_loss: 0.0024 2020-12-02 00:27:18 [INFO] [TRAIN] epoch=47, iter=17400/80000, loss=2.5606, lr=0.008019, batch_cost=1.4707, reader_cost=0.0003 | ETA 25:34:27 2020-12-02 00:29:55 [INFO] seg_loss:0.1345, att_loss: 0.6257, edge_loss: 2.6175, dual_loss: 0.0021 2020-12-02 00:29:56 [INFO] [TRAIN] epoch=48, iter=17500/80000, loss=2.8673, lr=0.008008, batch_cost=1.5851, reader_cost=0.0745 | ETA 27:31:05 2020-12-02 00:32:24 [INFO] seg_loss:0.0871, att_loss: 0.6090, edge_loss: 1.6212, dual_loss: 0.0014 2020-12-02 00:32:25 [INFO] [TRAIN] epoch=48, iter=17600/80000, loss=2.6136, lr=0.007996, batch_cost=1.4891, reader_cost=0.0005 | ETA 25:48:42 2020-12-02 00:34:55 [INFO] seg_loss:0.0758, att_loss: 0.3539, edge_loss: 1.6462, dual_loss: 0.0012 2020-12-02 00:34:56 [INFO] [TRAIN] epoch=48, iter=17700/80000, loss=2.5932, lr=0.007985, batch_cost=1.5095, reader_cost=0.0003 | ETA 26:07:21 2020-12-02 00:37:27 [INFO] seg_loss:0.0784, att_loss: 0.5588, edge_loss: 1.8676, dual_loss: 0.0015 2020-12-02 00:37:28 [INFO] [TRAIN] epoch=48, iter=17800/80000, loss=2.6455, lr=0.007973, batch_cost=1.5162, reader_cost=0.0004 | ETA 26:11:47 2020-12-02 00:40:01 [INFO] seg_loss:0.1062, att_loss: 0.5002, edge_loss: 1.9785, dual_loss: 0.0022 2020-12-02 00:40:02 [INFO] [TRAIN] epoch=49, iter=17900/80000, loss=2.6220, lr=0.007962, batch_cost=1.5424, reader_cost=0.0513 | ETA 26:36:23 2020-12-02 00:42:31 [INFO] seg_loss:0.1388, att_loss: 0.6080, edge_loss: 2.8694, dual_loss: 0.0020 2020-12-02 00:42:32 [INFO] [TRAIN] epoch=49, iter=18000/80000, loss=2.6548, lr=0.007950, batch_cost=1.4958, reader_cost=0.0003 | ETA 25:45:40 2020-12-02 00:42:32 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 00:43:38 [INFO] [EVAL] #Images=500 mIoU=0.6595 Acc=0.9430 Kappa=0.9261 2020-12-02 00:43:38 [INFO] [EVAL] Class IoU: [0.9692 0.7921 0.9016 0.3337 0.5204 0.6119 0.6636 0.7652 0.8942 0.5699 0.9422 0.6859 0.509 0.9043 0.4138 0.6595 0.402 0.2891 0.7024] 2020-12-02 00:43:38 [INFO] [EVAL] Class Acc: [0.9883 0.8694 0.9447 0.6813 0.8306 0.8148 0.8536 0.8804 0.9385 0.6872 0.9663 0.7162 0.725 0.929 0.6727 0.8074 0.81 0.8421 0.8873] 2020-12-02 00:43:43 [INFO] [EVAL] The model with the best validation mIoU (0.7580) was saved at iter 16000. 2020-12-02 00:46:10 [INFO] seg_loss:0.0676, att_loss: 0.6791, edge_loss: 1.1607, dual_loss: 0.0010 2020-12-02 00:46:11 [INFO] [TRAIN] epoch=49, iter=18100/80000, loss=2.4541, lr=0.007939, batch_cost=1.4774, reader_cost=0.0004 | ETA 25:24:10 2020-12-02 00:48:38 [INFO] seg_loss:0.3210, att_loss: 0.6646, edge_loss: 2.3714, dual_loss: 0.0030 2020-12-02 00:48:39 [INFO] [TRAIN] epoch=49, iter=18200/80000, loss=2.7977, lr=0.007927, batch_cost=1.4852, reader_cost=0.0005 | ETA 25:29:45 2020-12-02 00:51:14 [INFO] seg_loss:0.1231, att_loss: 0.5562, edge_loss: 2.1118, dual_loss: 0.0020 2020-12-02 00:51:15 [INFO] [TRAIN] epoch=50, iter=18300/80000, loss=2.5368, lr=0.007916, batch_cost=1.5612, reader_cost=0.0511 | ETA 26:45:28 2020-12-02 00:53:44 [INFO] seg_loss:0.1898, att_loss: 0.6292, edge_loss: 3.0413, dual_loss: 0.0026 2020-12-02 00:53:45 [INFO] [TRAIN] epoch=50, iter=18400/80000, loss=2.6409, lr=0.007904, batch_cost=1.4998, reader_cost=0.0003 | ETA 25:39:48 2020-12-02 00:56:14 [INFO] seg_loss:0.0612, att_loss: 0.6478, edge_loss: 1.1439, dual_loss: 0.0010 2020-12-02 00:56:16 [INFO] [TRAIN] epoch=50, iter=18500/80000, loss=2.5730, lr=0.007892, batch_cost=1.5001, reader_cost=0.0004 | ETA 25:37:39 2020-12-02 00:58:42 [INFO] seg_loss:0.1363, att_loss: 0.6817, edge_loss: 3.3123, dual_loss: 0.0023 2020-12-02 00:58:43 [INFO] [TRAIN] epoch=50, iter=18600/80000, loss=2.7441, lr=0.007881, batch_cost=1.4797, reader_cost=0.0002 | ETA 25:14:13 2020-12-02 01:01:18 [INFO] seg_loss:0.2043, att_loss: 0.7510, edge_loss: 2.4316, dual_loss: 0.0023 2020-12-02 01:01:19 [INFO] [TRAIN] epoch=51, iter=18700/80000, loss=2.7466, lr=0.007869, batch_cost=1.5596, reader_cost=0.0496 | ETA 26:33:25 2020-12-02 01:03:49 [INFO] seg_loss:0.1469, att_loss: 0.8223, edge_loss: 1.5603, dual_loss: 0.0015 2020-12-02 01:03:50 [INFO] [TRAIN] epoch=51, iter=18800/80000, loss=2.4823, lr=0.007858, batch_cost=1.5010, reader_cost=0.0006 | ETA 25:31:03 2020-12-02 01:06:18 [INFO] seg_loss:0.0559, att_loss: 0.4313, edge_loss: 1.3883, dual_loss: 0.0013 2020-12-02 01:06:19 [INFO] [TRAIN] epoch=51, iter=18900/80000, loss=2.5542, lr=0.007846, batch_cost=1.4898, reader_cost=0.0003 | ETA 25:17:09 2020-12-02 01:08:53 [INFO] seg_loss:0.1120, att_loss: 0.8208, edge_loss: 1.1688, dual_loss: 0.0014 2020-12-02 01:08:54 [INFO] [TRAIN] epoch=52, iter=19000/80000, loss=2.7783, lr=0.007835, batch_cost=1.5561, reader_cost=0.0623 | ETA 26:22:04 2020-12-02 01:11:23 [INFO] seg_loss:0.0793, att_loss: 0.5930, edge_loss: 1.9415, dual_loss: 0.0014 2020-12-02 01:11:24 [INFO] [TRAIN] epoch=52, iter=19100/80000, loss=2.5864, lr=0.007823, batch_cost=1.4986, reader_cost=0.0003 | ETA 25:21:05 2020-12-02 01:13:55 [INFO] seg_loss:0.0659, att_loss: 0.5067, edge_loss: 1.2602, dual_loss: 0.0011 2020-12-02 01:13:56 [INFO] [TRAIN] epoch=52, iter=19200/80000, loss=2.4648, lr=0.007812, batch_cost=1.5148, reader_cost=0.0004 | ETA 25:34:58 2020-12-02 01:16:21 [INFO] seg_loss:0.0700, att_loss: 0.5523, edge_loss: 1.1399, dual_loss: 0.0009 2020-12-02 01:16:22 [INFO] [TRAIN] epoch=52, iter=19300/80000, loss=2.6935, lr=0.007800, batch_cost=1.4607, reader_cost=0.0002 | ETA 24:37:45 2020-12-02 01:18:59 [INFO] seg_loss:0.0283, att_loss: 0.2714, edge_loss: 0.8764, dual_loss: 0.0006 2020-12-02 01:19:00 [INFO] [TRAIN] epoch=53, iter=19400/80000, loss=2.5856, lr=0.007788, batch_cost=1.5802, reader_cost=0.0673 | ETA 26:36:02 2020-12-02 01:21:29 [INFO] seg_loss:0.1067, att_loss: 0.5318, edge_loss: 2.3765, dual_loss: 0.0019 2020-12-02 01:21:30 [INFO] [TRAIN] epoch=53, iter=19500/80000, loss=2.5889, lr=0.007777, batch_cost=1.5031, reader_cost=0.0003 | ETA 25:15:35 2020-12-02 01:24:00 [INFO] seg_loss:0.1559, att_loss: 0.6080, edge_loss: 1.4315, dual_loss: 0.0012 2020-12-02 01:24:01 [INFO] [TRAIN] epoch=53, iter=19600/80000, loss=2.6009, lr=0.007765, batch_cost=1.5076, reader_cost=0.0005 | ETA 25:17:36 2020-12-02 01:26:32 [INFO] seg_loss:0.1476, att_loss: 0.2298, edge_loss: 0.7700, dual_loss: 0.0013 2020-12-02 01:26:33 [INFO] [TRAIN] epoch=53, iter=19700/80000, loss=2.7169, lr=0.007754, batch_cost=1.5263, reader_cost=0.0003 | ETA 25:33:56 2020-12-02 01:29:13 [INFO] seg_loss:0.0820, att_loss: 0.4545, edge_loss: 2.6614, dual_loss: 0.0017 2020-12-02 01:29:14 [INFO] [TRAIN] epoch=54, iter=19800/80000, loss=2.6285, lr=0.007742, batch_cost=1.6100, reader_cost=0.0598 | ETA 26:55:20 2020-12-02 01:31:44 [INFO] seg_loss:0.0805, att_loss: 0.4990, edge_loss: 1.1407, dual_loss: 0.0011 2020-12-02 01:31:45 [INFO] [TRAIN] epoch=54, iter=19900/80000, loss=2.5725, lr=0.007731, batch_cost=1.5080, reader_cost=0.0004 | ETA 25:10:32 2020-12-02 01:34:17 [INFO] seg_loss:0.0966, att_loss: 0.5946, edge_loss: 1.6303, dual_loss: 0.0014 2020-12-02 01:34:18 [INFO] [TRAIN] epoch=54, iter=20000/80000, loss=2.4329, lr=0.007719, batch_cost=1.5245, reader_cost=0.0003 | ETA 25:24:31 2020-12-02 01:34:18 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 01:35:25 [INFO] [EVAL] #Images=500 mIoU=0.7731 Acc=0.9603 Kappa=0.9485 2020-12-02 01:35:25 [INFO] [EVAL] Class IoU: [0.9796 0.8381 0.9278 0.4897 0.6239 0.674 0.7335 0.8085 0.9239 0.6095 0.9494 0.8416 0.646 0.9503 0.7036 0.8571 0.7393 0.6322 0.7618] 2020-12-02 01:35:25 [INFO] [EVAL] Class Acc: [0.9901 0.9062 0.9592 0.8492 0.8149 0.8497 0.8532 0.906 0.9563 0.7719 0.9672 0.9091 0.8237 0.9693 0.8485 0.9187 0.8326 0.8625 0.8002] 2020-12-02 01:35:33 [INFO] [EVAL] The model with the best validation mIoU (0.7731) was saved at iter 20000. 2020-12-02 01:38:10 [INFO] seg_loss:0.1384, att_loss: 0.5307, edge_loss: 2.2307, dual_loss: 0.0018 2020-12-02 01:38:11 [INFO] [TRAIN] epoch=55, iter=20100/80000, loss=2.7586, lr=0.007707, batch_cost=1.5776, reader_cost=0.0779 | ETA 26:14:55 2020-12-02 01:40:40 [INFO] seg_loss:0.0822, att_loss: 0.5041, edge_loss: 1.5549, dual_loss: 0.0014 2020-12-02 01:40:41 [INFO] [TRAIN] epoch=55, iter=20200/80000, loss=2.6179, lr=0.007696, batch_cost=1.5021, reader_cost=0.0004 | ETA 24:57:05 2020-12-02 01:43:12 [INFO] seg_loss:0.0985, att_loss: 0.5278, edge_loss: 2.9960, dual_loss: 0.0019 2020-12-02 01:43:13 [INFO] [TRAIN] epoch=55, iter=20300/80000, loss=2.5494, lr=0.007684, batch_cost=1.5135, reader_cost=0.0007 | ETA 25:05:56 2020-12-02 01:45:43 [INFO] seg_loss:0.0770, att_loss: 0.4439, edge_loss: 2.5710, dual_loss: 0.0017 2020-12-02 01:45:44 [INFO] [TRAIN] epoch=55, iter=20400/80000, loss=2.6358, lr=0.007673, batch_cost=1.5169, reader_cost=0.0003 | ETA 25:06:47 2020-12-02 01:48:21 [INFO] seg_loss:0.0695, att_loss: 0.6211, edge_loss: 1.4328, dual_loss: 0.0010 2020-12-02 01:48:22 [INFO] [TRAIN] epoch=56, iter=20500/80000, loss=2.7348, lr=0.007661, batch_cost=1.5713, reader_cost=0.0598 | ETA 25:58:14 2020-12-02 01:50:52 [INFO] seg_loss:0.1379, att_loss: 0.5406, edge_loss: 1.8338, dual_loss: 0.0014 2020-12-02 01:50:53 [INFO] [TRAIN] epoch=56, iter=20600/80000, loss=2.6152, lr=0.007650, batch_cost=1.5121, reader_cost=0.0003 | ETA 24:56:55 2020-12-02 01:53:23 [INFO] seg_loss:0.1477, att_loss: 0.6461, edge_loss: 2.5144, dual_loss: 0.0019 2020-12-02 01:53:24 [INFO] [TRAIN] epoch=56, iter=20700/80000, loss=2.5418, lr=0.007638, batch_cost=1.5111, reader_cost=0.0006 | ETA 24:53:29 2020-12-02 01:55:53 [INFO] seg_loss:0.1376, att_loss: 0.5717, edge_loss: 2.8484, dual_loss: 0.0023 2020-12-02 01:55:54 [INFO] [TRAIN] epoch=56, iter=20800/80000, loss=2.6917, lr=0.007626, batch_cost=1.5048, reader_cost=0.0005 | ETA 24:44:47 2020-12-02 01:58:31 [INFO] seg_loss:0.1273, att_loss: 0.5474, edge_loss: 1.6827, dual_loss: 0.0020 2020-12-02 01:58:32 [INFO] [TRAIN] epoch=57, iter=20900/80000, loss=2.5480, lr=0.007615, batch_cost=1.5741, reader_cost=0.0734 | ETA 25:50:29 2020-12-02 02:01:00 [INFO] seg_loss:0.0753, att_loss: 0.4698, edge_loss: 1.4504, dual_loss: 0.0012 2020-12-02 02:01:02 [INFO] [TRAIN] epoch=57, iter=21000/80000, loss=2.6625, lr=0.007603, batch_cost=1.4969, reader_cost=0.0004 | ETA 24:31:57 2020-12-02 02:03:32 [INFO] seg_loss:0.0701, att_loss: 0.5242, edge_loss: 1.8053, dual_loss: 0.0012 2020-12-02 02:03:33 [INFO] [TRAIN] epoch=57, iter=21100/80000, loss=2.4381, lr=0.007592, batch_cost=1.5154, reader_cost=0.0004 | ETA 24:47:39 2020-12-02 02:05:58 [INFO] seg_loss:0.0530, att_loss: 0.4506, edge_loss: 1.2740, dual_loss: 0.0009 2020-12-02 02:05:59 [INFO] [TRAIN] epoch=57, iter=21200/80000, loss=2.6956, lr=0.007580, batch_cost=1.4596, reader_cost=0.0002 | ETA 23:50:24 2020-12-02 02:08:38 [INFO] seg_loss:0.2114, att_loss: 0.6405, edge_loss: 2.4331, dual_loss: 0.0022 2020-12-02 02:08:39 [INFO] [TRAIN] epoch=58, iter=21300/80000, loss=2.7004, lr=0.007568, batch_cost=1.6005, reader_cost=0.0614 | ETA 26:05:50 2020-12-02 02:11:09 [INFO] seg_loss:0.0573, att_loss: 0.4012, edge_loss: 0.9905, dual_loss: 0.0010 2020-12-02 02:11:10 [INFO] [TRAIN] epoch=58, iter=21400/80000, loss=2.5220, lr=0.007557, batch_cost=1.5073, reader_cost=0.0004 | ETA 24:32:06 2020-12-02 02:13:39 [INFO] seg_loss:0.1002, att_loss: 0.4631, edge_loss: 1.8027, dual_loss: 0.0014 2020-12-02 02:13:40 [INFO] [TRAIN] epoch=58, iter=21500/80000, loss=2.5181, lr=0.007545, batch_cost=1.5005, reader_cost=0.0004 | ETA 24:22:59 2020-12-02 02:16:15 [INFO] seg_loss:0.1288, att_loss: 0.5128, edge_loss: 3.0653, dual_loss: 0.0021 2020-12-02 02:16:16 [INFO] [TRAIN] epoch=59, iter=21600/80000, loss=2.7633, lr=0.007534, batch_cost=1.5622, reader_cost=0.0646 | ETA 25:20:31 2020-12-02 02:18:44 [INFO] seg_loss:0.1905, att_loss: 0.5317, edge_loss: 2.0521, dual_loss: 0.0036 2020-12-02 02:18:45 [INFO] [TRAIN] epoch=59, iter=21700/80000, loss=2.5361, lr=0.007522, batch_cost=1.4907, reader_cost=0.0004 | ETA 24:08:24 2020-12-02 02:21:14 [INFO] seg_loss:0.1394, att_loss: 0.8103, edge_loss: 2.1011, dual_loss: 0.0019 2020-12-02 02:21:15 [INFO] [TRAIN] epoch=59, iter=21800/80000, loss=2.5182, lr=0.007510, batch_cost=1.4933, reader_cost=0.0003 | ETA 24:08:28 2020-12-02 02:23:45 [INFO] seg_loss:0.0827, att_loss: 0.5084, edge_loss: 2.1059, dual_loss: 0.0016 2020-12-02 02:23:46 [INFO] [TRAIN] epoch=59, iter=21900/80000, loss=2.7094, lr=0.007499, batch_cost=1.5109, reader_cost=0.0004 | ETA 24:23:02 2020-12-02 02:26:24 [INFO] seg_loss:0.3326, att_loss: 0.7455, edge_loss: 2.8322, dual_loss: 0.0034 2020-12-02 02:26:25 [INFO] [TRAIN] epoch=60, iter=22000/80000, loss=2.6786, lr=0.007487, batch_cost=1.5925, reader_cost=0.0662 | ETA 25:39:23 2020-12-02 02:26:25 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 02:27:32 [INFO] [EVAL] #Images=500 mIoU=0.7645 Acc=0.9600 Kappa=0.9480 2020-12-02 02:27:32 [INFO] [EVAL] Class IoU: [0.9788 0.836 0.9271 0.4958 0.6088 0.6774 0.7311 0.7832 0.9254 0.634 0.9489 0.833 0.6205 0.9522 0.6832 0.7716 0.6787 0.6569 0.782 ] 2020-12-02 02:27:32 [INFO] [EVAL] Class Acc: [0.9854 0.929 0.9641 0.8214 0.8077 0.7844 0.8159 0.8794 0.9548 0.8244 0.965 0.8805 0.7583 0.973 0.9331 0.8915 0.7831 0.8724 0.8799] 2020-12-02 02:27:37 [INFO] [EVAL] The model with the best validation mIoU (0.7731) was saved at iter 20000. 2020-12-02 02:30:06 [INFO] seg_loss:0.1458, att_loss: 0.6056, edge_loss: 2.1969, dual_loss: 0.0020 2020-12-02 02:30:07 [INFO] [TRAIN] epoch=60, iter=22100/80000, loss=2.4560, lr=0.007475, batch_cost=1.5035, reader_cost=0.0005 | ETA 24:10:54 2020-12-02 02:32:37 [INFO] seg_loss:0.2418, att_loss: 0.6109, edge_loss: 2.9492, dual_loss: 0.0029 2020-12-02 02:32:38 [INFO] [TRAIN] epoch=60, iter=22200/80000, loss=2.6493, lr=0.007464, batch_cost=1.5114, reader_cost=0.0004 | ETA 24:15:59 2020-12-02 02:35:08 [INFO] seg_loss:0.0745, att_loss: 0.4884, edge_loss: 1.2185, dual_loss: 0.0014 2020-12-02 02:35:09 [INFO] [TRAIN] epoch=60, iter=22300/80000, loss=2.6868, lr=0.007452, batch_cost=1.5063, reader_cost=0.0004 | ETA 24:08:33 2020-12-02 02:37:44 [INFO] seg_loss:0.1518, att_loss: 0.6823, edge_loss: 2.8228, dual_loss: 0.0023 2020-12-02 02:37:45 [INFO] [TRAIN] epoch=61, iter=22400/80000, loss=2.6078, lr=0.007441, batch_cost=1.5571, reader_cost=0.0586 | ETA 24:54:46 2020-12-02 02:40:15 [INFO] seg_loss:0.0154, att_loss: 0.2327, edge_loss: 0.9713, dual_loss: 0.0005 2020-12-02 02:40:16 [INFO] [TRAIN] epoch=61, iter=22500/80000, loss=2.5091, lr=0.007429, batch_cost=1.5105, reader_cost=0.0007 | ETA 24:07:36 2020-12-02 02:42:45 [INFO] seg_loss:0.1020, att_loss: 0.5699, edge_loss: 2.5453, dual_loss: 0.0018 2020-12-02 02:42:46 [INFO] [TRAIN] epoch=61, iter=22600/80000, loss=2.5804, lr=0.007417, batch_cost=1.5005, reader_cost=0.0003 | ETA 23:55:30 2020-12-02 02:45:23 [INFO] seg_loss:0.0813, att_loss: 0.5446, edge_loss: 2.1875, dual_loss: 0.0017 2020-12-02 02:45:24 [INFO] [TRAIN] epoch=62, iter=22700/80000, loss=2.5943, lr=0.007406, batch_cost=1.5865, reader_cost=0.0644 | ETA 25:15:04 2020-12-02 02:47:57 [INFO] seg_loss:0.1439, att_loss: 0.7479, edge_loss: 2.3225, dual_loss: 0.0020 2020-12-02 02:47:58 [INFO] [TRAIN] epoch=62, iter=22800/80000, loss=2.6846, lr=0.007394, batch_cost=1.5342, reader_cost=0.0004 | ETA 24:22:36 2020-12-02 02:50:28 [INFO] seg_loss:0.1284, att_loss: 0.8099, edge_loss: 1.4640, dual_loss: 0.0016 2020-12-02 02:50:29 [INFO] [TRAIN] epoch=62, iter=22900/80000, loss=2.3312, lr=0.007382, batch_cost=1.5112, reader_cost=0.0005 | ETA 23:58:10 2020-12-02 02:53:01 [INFO] seg_loss:0.0577, att_loss: 0.4063, edge_loss: 1.8292, dual_loss: 0.0014 2020-12-02 02:53:02 [INFO] [TRAIN] epoch=62, iter=23000/80000, loss=2.5267, lr=0.007371, batch_cost=1.5341, reader_cost=0.0003 | ETA 24:17:22 2020-12-02 02:55:33 [INFO] seg_loss:0.0574, att_loss: 0.5745, edge_loss: 1.0981, dual_loss: 0.0009 2020-12-02 02:55:34 [INFO] [TRAIN] epoch=63, iter=23100/80000, loss=2.6765, lr=0.007359, batch_cost=1.5185, reader_cost=0.0425 | ETA 24:00:01 2020-12-02 02:58:03 [INFO] seg_loss:0.0630, att_loss: 0.4638, edge_loss: 1.7823, dual_loss: 0.0011 2020-12-02 02:58:04 [INFO] [TRAIN] epoch=63, iter=23200/80000, loss=2.5787, lr=0.007347, batch_cost=1.4965, reader_cost=0.0003 | ETA 23:36:39 2020-12-02 03:00:35 [INFO] seg_loss:0.0755, att_loss: 0.5154, edge_loss: 2.0996, dual_loss: 0.0018 2020-12-02 03:00:36 [INFO] [TRAIN] epoch=63, iter=23300/80000, loss=2.5059, lr=0.007336, batch_cost=1.5206, reader_cost=0.0003 | ETA 23:56:56 2020-12-02 03:03:04 [INFO] seg_loss:0.0871, att_loss: 0.5162, edge_loss: 2.2899, dual_loss: 0.0015 2020-12-02 03:03:05 [INFO] [TRAIN] epoch=63, iter=23400/80000, loss=2.6251, lr=0.007324, batch_cost=1.4897, reader_cost=0.0003 | ETA 23:25:19 2020-12-02 03:05:45 [INFO] seg_loss:0.1178, att_loss: 0.4390, edge_loss: 1.6204, dual_loss: 0.0019 2020-12-02 03:05:46 [INFO] [TRAIN] epoch=64, iter=23500/80000, loss=2.5326, lr=0.007313, batch_cost=1.6100, reader_cost=0.0664 | ETA 25:16:06 2020-12-02 03:08:16 [INFO] seg_loss:0.1392, att_loss: 0.4999, edge_loss: 2.0271, dual_loss: 0.0019 2020-12-02 03:08:17 [INFO] [TRAIN] epoch=64, iter=23600/80000, loss=2.5292, lr=0.007301, batch_cost=1.5067, reader_cost=0.0004 | ETA 23:36:18 2020-12-02 03:10:48 [INFO] seg_loss:0.1450, att_loss: 0.6117, edge_loss: 2.3366, dual_loss: 0.0020 2020-12-02 03:10:49 [INFO] [TRAIN] epoch=64, iter=23700/80000, loss=2.5702, lr=0.007289, batch_cost=1.5221, reader_cost=0.0003 | ETA 23:48:16 2020-12-02 03:13:18 [INFO] seg_loss:0.0920, att_loss: 0.5084, edge_loss: 1.9597, dual_loss: 0.0015 2020-12-02 03:13:19 [INFO] [TRAIN] epoch=64, iter=23800/80000, loss=2.6322, lr=0.007278, batch_cost=1.5048, reader_cost=0.0002 | ETA 23:29:30 2020-12-02 03:15:57 [INFO] seg_loss:0.0849, att_loss: 0.4918, edge_loss: 1.4362, dual_loss: 0.0015 2020-12-02 03:15:58 [INFO] [TRAIN] epoch=65, iter=23900/80000, loss=2.6611, lr=0.007266, batch_cost=1.5837, reader_cost=0.0475 | ETA 24:40:46 2020-12-02 03:18:26 [INFO] seg_loss:0.0468, att_loss: 0.3935, edge_loss: 1.5288, dual_loss: 0.0011 2020-12-02 03:18:27 [INFO] [TRAIN] epoch=65, iter=24000/80000, loss=2.4998, lr=0.007254, batch_cost=1.4965, reader_cost=0.0003 | ETA 23:16:45 2020-12-02 03:18:28 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 03:19:35 [INFO] [EVAL] #Images=500 mIoU=0.7844 Acc=0.9613 Kappa=0.9499 2020-12-02 03:19:35 [INFO] [EVAL] Class IoU: [0.9801 0.8455 0.9301 0.5202 0.6137 0.681 0.7324 0.8039 0.9268 0.6561 0.9495 0.7997 0.6304 0.9534 0.7926 0.8725 0.7676 0.6671 0.7804] 2020-12-02 03:19:35 [INFO] [EVAL] Class Acc: [0.9907 0.9243 0.9659 0.7812 0.7182 0.8085 0.8706 0.8742 0.9588 0.8343 0.9624 0.8595 0.7997 0.9748 0.8617 0.9485 0.829 0.8414 0.8408] 2020-12-02 03:19:46 [INFO] [EVAL] The model with the best validation mIoU (0.7844) was saved at iter 24000. 2020-12-02 03:22:18 [INFO] seg_loss:0.1281, att_loss: 0.5546, edge_loss: 1.5940, dual_loss: 0.0014 2020-12-02 03:22:20 [INFO] [TRAIN] epoch=65, iter=24100/80000, loss=2.5294, lr=0.007243, batch_cost=1.5335, reader_cost=0.0003 | ETA 23:48:42 2020-12-02 03:24:53 [INFO] seg_loss:0.1022, att_loss: 0.5632, edge_loss: 1.9882, dual_loss: 0.0017 2020-12-02 03:24:54 [INFO] [TRAIN] epoch=66, iter=24200/80000, loss=2.7894, lr=0.007231, batch_cost=1.5422, reader_cost=0.0561 | ETA 23:54:13 2020-12-02 03:27:26 [INFO] seg_loss:0.1374, att_loss: 0.5401, edge_loss: 1.2462, dual_loss: 0.0011 2020-12-02 03:27:27 [INFO] [TRAIN] epoch=66, iter=24300/80000, loss=2.4864, lr=0.007219, batch_cost=1.5349, reader_cost=0.0004 | ETA 23:44:55 2020-12-02 03:29:59 [INFO] seg_loss:0.0835, att_loss: 0.5241, edge_loss: 2.0585, dual_loss: 0.0016 2020-12-02 03:30:00 [INFO] [TRAIN] epoch=66, iter=24400/80000, loss=2.4941, lr=0.007208, batch_cost=1.5268, reader_cost=0.0004 | ETA 23:34:52 2020-12-02 03:32:29 [INFO] seg_loss:0.0990, att_loss: 0.5709, edge_loss: 2.0564, dual_loss: 0.0016 2020-12-02 03:32:30 [INFO] [TRAIN] epoch=66, iter=24500/80000, loss=2.6206, lr=0.007196, batch_cost=1.5002, reader_cost=0.0003 | ETA 23:07:41 2020-12-02 03:35:05 [INFO] seg_loss:0.2329, att_loss: 0.7821, edge_loss: 2.0155, dual_loss: 0.0026 2020-12-02 03:35:06 [INFO] [TRAIN] epoch=67, iter=24600/80000, loss=2.6846, lr=0.007184, batch_cost=1.5582, reader_cost=0.0631 | ETA 23:58:41 2020-12-02 03:37:35 [INFO] seg_loss:0.0162, att_loss: 0.2578, edge_loss: 0.7276, dual_loss: 0.0004 2020-12-02 03:37:36 [INFO] [TRAIN] epoch=67, iter=24700/80000, loss=2.5014, lr=0.007173, batch_cost=1.4984, reader_cost=0.0004 | ETA 23:01:03 2020-12-02 03:40:06 [INFO] seg_loss:0.1049, att_loss: 0.5480, edge_loss: 2.3056, dual_loss: 0.0016 2020-12-02 03:40:07 [INFO] [TRAIN] epoch=67, iter=24800/80000, loss=2.4980, lr=0.007161, batch_cost=1.5103, reader_cost=0.0003 | ETA 23:09:30 2020-12-02 03:42:35 [INFO] seg_loss:0.0717, att_loss: 0.4708, edge_loss: 1.8191, dual_loss: 0.0013 2020-12-02 03:42:36 [INFO] [TRAIN] epoch=67, iter=24900/80000, loss=2.7015, lr=0.007149, batch_cost=1.4963, reader_cost=0.0003 | ETA 22:54:03 2020-12-02 03:45:07 [INFO] seg_loss:0.1775, att_loss: 0.6935, edge_loss: 2.9704, dual_loss: 0.0026 2020-12-02 03:45:08 [INFO] [TRAIN] epoch=68, iter=25000/80000, loss=2.6467, lr=0.007138, batch_cost=1.5182, reader_cost=0.0537 | ETA 23:11:42 2020-12-02 03:47:28 [INFO] seg_loss:0.0356, att_loss: 0.4532, edge_loss: 1.5627, dual_loss: 0.0008 2020-12-02 03:47:29 [INFO] [TRAIN] epoch=68, iter=25100/80000, loss=2.6231, lr=0.007126, batch_cost=1.4052, reader_cost=0.0002 | ETA 21:25:47 2020-12-02 03:49:50 [INFO] seg_loss:0.0996, att_loss: 0.5659, edge_loss: 2.7350, dual_loss: 0.0018 2020-12-02 03:49:51 [INFO] [TRAIN] epoch=68, iter=25200/80000, loss=2.5024, lr=0.007114, batch_cost=1.4242, reader_cost=0.0003 | ETA 21:40:47 2020-12-02 03:52:18 [INFO] seg_loss:0.2081, att_loss: 0.5871, edge_loss: 2.9377, dual_loss: 0.0030 2020-12-02 03:52:19 [INFO] [TRAIN] epoch=69, iter=25300/80000, loss=2.7048, lr=0.007103, batch_cost=1.4765, reader_cost=0.0678 | ETA 22:26:03 2020-12-02 03:54:35 [INFO] seg_loss:0.1200, att_loss: 0.5960, edge_loss: 2.2006, dual_loss: 0.0016 2020-12-02 03:54:36 [INFO] [TRAIN] epoch=69, iter=25400/80000, loss=2.7546, lr=0.007091, batch_cost=1.3756, reader_cost=0.0002 | ETA 20:51:48 2020-12-02 03:56:56 [INFO] seg_loss:0.0905, att_loss: 0.5720, edge_loss: 1.4793, dual_loss: 0.0014 2020-12-02 03:56:57 [INFO] [TRAIN] epoch=69, iter=25500/80000, loss=2.4515, lr=0.007079, batch_cost=1.4093, reader_cost=0.0002 | ETA 21:20:08 2020-12-02 03:59:17 [INFO] seg_loss:0.1459, att_loss: 0.5761, edge_loss: 2.8537, dual_loss: 0.0022 2020-12-02 03:59:18 [INFO] [TRAIN] epoch=69, iter=25600/80000, loss=2.5627, lr=0.007067, batch_cost=1.4050, reader_cost=0.0002 | ETA 21:13:52 2020-12-02 04:01:44 [INFO] seg_loss:0.2389, att_loss: 0.7414, edge_loss: 2.9017, dual_loss: 0.0030 2020-12-02 04:01:45 [INFO] [TRAIN] epoch=70, iter=25700/80000, loss=2.7175, lr=0.007056, batch_cost=1.4711, reader_cost=0.0522 | ETA 22:11:20 2020-12-02 04:04:05 [INFO] seg_loss:0.0145, att_loss: 0.2381, edge_loss: 0.5786, dual_loss: 0.0004 2020-12-02 04:04:06 [INFO] [TRAIN] epoch=70, iter=25800/80000, loss=2.4959, lr=0.007044, batch_cost=1.4073, reader_cost=0.0002 | ETA 21:11:18 2020-12-02 04:06:26 [INFO] seg_loss:0.1226, att_loss: 0.6392, edge_loss: 2.6329, dual_loss: 0.0019 2020-12-02 04:06:27 [INFO] [TRAIN] epoch=70, iter=25900/80000, loss=2.5559, lr=0.007032, batch_cost=1.4132, reader_cost=0.0003 | ETA 21:14:13 2020-12-02 04:08:46 [INFO] seg_loss:0.0749, att_loss: 0.4792, edge_loss: 1.5101, dual_loss: 0.0014 2020-12-02 04:08:47 [INFO] [TRAIN] epoch=70, iter=26000/80000, loss=2.6016, lr=0.007021, batch_cost=1.4034, reader_cost=0.0004 | ETA 21:03:05 2020-12-02 04:08:47 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 04:09:51 [INFO] [EVAL] #Images=500 mIoU=0.7494 Acc=0.9558 Kappa=0.9426 2020-12-02 04:09:51 [INFO] [EVAL] Class IoU: [0.9743 0.819 0.9188 0.3622 0.6148 0.6867 0.7332 0.8106 0.9151 0.5918 0.9498 0.8206 0.6318 0.943 0.7801 0.7196 0.6209 0.5749 0.7715] 2020-12-02 04:09:51 [INFO] [EVAL] Class Acc: [0.9865 0.9232 0.9587 0.8031 0.865 0.8155 0.8331 0.9151 0.9348 0.8818 0.9663 0.8641 0.7699 0.9649 0.9223 0.7857 0.8524 0.6315 0.8782] 2020-12-02 04:09:55 [INFO] [EVAL] The model with the best validation mIoU (0.7844) was saved at iter 24000. 2020-12-02 04:12:19 [INFO] seg_loss:0.1156, att_loss: 0.5695, edge_loss: 1.6352, dual_loss: 0.0012 2020-12-02 04:12:20 [INFO] [TRAIN] epoch=71, iter=26100/80000, loss=2.5234, lr=0.007009, batch_cost=1.4501, reader_cost=0.0411 | ETA 21:42:42 2020-12-02 04:14:40 [INFO] seg_loss:0.1308, att_loss: 0.4932, edge_loss: 2.8009, dual_loss: 0.0019 2020-12-02 04:14:41 [INFO] [TRAIN] epoch=71, iter=26200/80000, loss=2.4524, lr=0.006997, batch_cost=1.4063, reader_cost=0.0004 | ETA 21:01:00 2020-12-02 04:16:59 [INFO] seg_loss:0.0514, att_loss: 0.4336, edge_loss: 1.2116, dual_loss: 0.0009 2020-12-02 04:17:00 [INFO] [TRAIN] epoch=71, iter=26300/80000, loss=2.4639, lr=0.006986, batch_cost=1.3934, reader_cost=0.0002 | ETA 20:47:06 2020-12-02 04:19:21 [INFO] seg_loss:0.0260, att_loss: 0.3036, edge_loss: 0.9845, dual_loss: 0.0006 2020-12-02 04:19:22 [INFO] [TRAIN] epoch=71, iter=26400/80000, loss=2.7059, lr=0.006974, batch_cost=1.4201, reader_cost=0.0005 | ETA 21:08:36 2020-12-02 04:21:51 [INFO] seg_loss:0.4001, att_loss: 0.8247, edge_loss: 2.0681, dual_loss: 0.0031 2020-12-02 04:21:52 [INFO] [TRAIN] epoch=72, iter=26500/80000, loss=2.7705, lr=0.006962, batch_cost=1.4963, reader_cost=0.0437 | ETA 22:14:13 2020-12-02 04:24:12 [INFO] seg_loss:0.0515, att_loss: 0.2828, edge_loss: 1.1192, dual_loss: 0.0008 2020-12-02 04:24:14 [INFO] [TRAIN] epoch=72, iter=26600/80000, loss=2.4818, lr=0.006950, batch_cost=1.4187, reader_cost=0.0002 | ETA 21:02:41 2020-12-02 04:26:30 [INFO] seg_loss:0.0892, att_loss: 0.5999, edge_loss: 1.4628, dual_loss: 0.0012 2020-12-02 04:26:31 [INFO] [TRAIN] epoch=72, iter=26700/80000, loss=2.3794, lr=0.006939, batch_cost=1.3791, reader_cost=0.0002 | ETA 20:25:04 2020-12-02 04:28:56 [INFO] seg_loss:0.0523, att_loss: 0.4414, edge_loss: 2.0601, dual_loss: 0.0012 2020-12-02 04:28:57 [INFO] [TRAIN] epoch=73, iter=26800/80000, loss=2.6593, lr=0.006927, batch_cost=1.4549, reader_cost=0.0406 | ETA 21:30:02 2020-12-02 04:31:16 [INFO] seg_loss:0.0631, att_loss: 0.4949, edge_loss: 1.3987, dual_loss: 0.0009 2020-12-02 04:31:17 [INFO] [TRAIN] epoch=73, iter=26900/80000, loss=2.5243, lr=0.006915, batch_cost=1.3997, reader_cost=0.0005 | ETA 20:38:45 2020-12-02 04:33:36 [INFO] seg_loss:0.0531, att_loss: 0.4141, edge_loss: 1.6617, dual_loss: 0.0012 2020-12-02 04:33:37 [INFO] [TRAIN] epoch=73, iter=27000/80000, loss=2.4543, lr=0.006904, batch_cost=1.3985, reader_cost=0.0003 | ETA 20:35:21 2020-12-02 04:35:57 [INFO] seg_loss:0.1604, att_loss: 0.6529, edge_loss: 1.6515, dual_loss: 0.0014 2020-12-02 04:35:58 [INFO] [TRAIN] epoch=73, iter=27100/80000, loss=2.5582, lr=0.006892, batch_cost=1.4088, reader_cost=0.0002 | ETA 20:42:07 2020-12-02 04:38:22 [INFO] seg_loss:0.0383, att_loss: 0.3903, edge_loss: 1.5877, dual_loss: 0.0009 2020-12-02 04:38:23 [INFO] [TRAIN] epoch=74, iter=27200/80000, loss=2.6071, lr=0.006880, batch_cost=1.4514, reader_cost=0.0434 | ETA 21:17:15 2020-12-02 04:40:42 [INFO] seg_loss:0.1062, att_loss: 0.6380, edge_loss: 2.8540, dual_loss: 0.0018 2020-12-02 04:40:44 [INFO] [TRAIN] epoch=74, iter=27300/80000, loss=2.5742, lr=0.006868, batch_cost=1.4069, reader_cost=0.0002 | ETA 20:35:42 2020-12-02 04:43:01 [INFO] seg_loss:0.0608, att_loss: 0.5727, edge_loss: 1.3695, dual_loss: 0.0010 2020-12-02 04:43:02 [INFO] [TRAIN] epoch=74, iter=27400/80000, loss=2.4636, lr=0.006857, batch_cost=1.3831, reader_cost=0.0002 | ETA 20:12:32 2020-12-02 04:45:21 [INFO] seg_loss:0.1890, att_loss: 0.4904, edge_loss: 2.1818, dual_loss: 0.0019 2020-12-02 04:45:22 [INFO] [TRAIN] epoch=74, iter=27500/80000, loss=2.7227, lr=0.006845, batch_cost=1.3982, reader_cost=0.0002 | ETA 20:23:27 2020-12-02 04:47:49 [INFO] seg_loss:0.1502, att_loss: 0.5348, edge_loss: 2.7129, dual_loss: 0.0023 2020-12-02 04:47:50 [INFO] [TRAIN] epoch=75, iter=27600/80000, loss=2.4998, lr=0.006833, batch_cost=1.4817, reader_cost=0.0443 | ETA 21:33:59 2020-12-02 04:50:08 [INFO] seg_loss:0.2394, att_loss: 0.7182, edge_loss: 2.5007, dual_loss: 0.0022 2020-12-02 04:50:09 [INFO] [TRAIN] epoch=75, iter=27700/80000, loss=2.5530, lr=0.006821, batch_cost=1.3966, reader_cost=0.0002 | ETA 20:17:23 2020-12-02 04:52:29 [INFO] seg_loss:0.0258, att_loss: 0.3298, edge_loss: 1.0831, dual_loss: 0.0007 2020-12-02 04:52:30 [INFO] [TRAIN] epoch=75, iter=27800/80000, loss=2.4233, lr=0.006810, batch_cost=1.4059, reader_cost=0.0002 | ETA 20:23:10 2020-12-02 04:54:49 [INFO] seg_loss:0.1207, att_loss: 0.5699, edge_loss: 2.4333, dual_loss: 0.0017 2020-12-02 04:54:50 [INFO] [TRAIN] epoch=75, iter=27900/80000, loss=2.6999, lr=0.006798, batch_cost=1.4008, reader_cost=0.0003 | ETA 20:16:20 2020-12-02 04:57:15 [INFO] seg_loss:0.1031, att_loss: 0.5245, edge_loss: 2.2467, dual_loss: 0.0019 2020-12-02 04:57:16 [INFO] [TRAIN] epoch=76, iter=28000/80000, loss=2.6189, lr=0.006786, batch_cost=1.4563, reader_cost=0.0456 | ETA 21:02:08 2020-12-02 04:57:16 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 04:58:20 [INFO] [EVAL] #Images=500 mIoU=0.7755 Acc=0.9600 Kappa=0.9482 2020-12-02 04:58:20 [INFO] [EVAL] Class IoU: [0.9791 0.8408 0.9267 0.51 0.5858 0.6842 0.7394 0.8098 0.9222 0.6006 0.9487 0.8315 0.6011 0.951 0.794 0.8662 0.7072 0.6517 0.7835] 2020-12-02 04:58:20 [INFO] [EVAL] Class Acc: [0.9916 0.9047 0.9628 0.8017 0.8846 0.8048 0.8358 0.9027 0.9467 0.825 0.9671 0.9078 0.6533 0.9763 0.8403 0.9525 0.7564 0.7623 0.8655] 2020-12-02 04:58:24 [INFO] [EVAL] The model with the best validation mIoU (0.7844) was saved at iter 24000. 2020-12-02 05:00:44 [INFO] seg_loss:0.0916, att_loss: 0.4771, edge_loss: 1.1204, dual_loss: 0.0011 2020-12-02 05:00:45 [INFO] [TRAIN] epoch=76, iter=28100/80000, loss=2.4543, lr=0.006774, batch_cost=1.4025, reader_cost=0.0002 | ETA 20:13:10 2020-12-02 05:03:05 [INFO] seg_loss:0.1520, att_loss: 0.5009, edge_loss: 2.0810, dual_loss: 0.0016 2020-12-02 05:03:06 [INFO] [TRAIN] epoch=76, iter=28200/80000, loss=2.5491, lr=0.006763, batch_cost=1.4122, reader_cost=0.0003 | ETA 20:19:12 2020-12-02 05:05:33 [INFO] seg_loss:0.1053, att_loss: 0.6113, edge_loss: 1.1351, dual_loss: 0.0010 2020-12-02 05:05:34 [INFO] [TRAIN] epoch=77, iter=28300/80000, loss=2.7415, lr=0.006751, batch_cost=1.4812, reader_cost=0.0425 | ETA 21:16:19 2020-12-02 05:07:54 [INFO] seg_loss:0.0328, att_loss: 0.3420, edge_loss: 1.7024, dual_loss: 0.0009 2020-12-02 05:07:56 [INFO] [TRAIN] epoch=77, iter=28400/80000, loss=2.5589, lr=0.006739, batch_cost=1.4148, reader_cost=0.0005 | ETA 20:16:45 2020-12-02 05:10:17 [INFO] seg_loss:0.0689, att_loss: 0.4650, edge_loss: 2.0831, dual_loss: 0.0013 2020-12-02 05:10:18 [INFO] [TRAIN] epoch=77, iter=28500/80000, loss=2.3606, lr=0.006727, batch_cost=1.4286, reader_cost=0.0002 | ETA 20:26:11 2020-12-02 05:12:37 [INFO] seg_loss:0.0807, att_loss: 0.3564, edge_loss: 0.9352, dual_loss: 0.0009 2020-12-02 05:12:38 [INFO] [TRAIN] epoch=77, iter=28600/80000, loss=2.5707, lr=0.006716, batch_cost=1.3980, reader_cost=0.0006 | ETA 19:57:36 2020-12-02 05:15:06 [INFO] seg_loss:0.0262, att_loss: 0.2739, edge_loss: 1.3295, dual_loss: 0.0007 2020-12-02 05:15:07 [INFO] [TRAIN] epoch=78, iter=28700/80000, loss=2.5914, lr=0.006704, batch_cost=1.4921, reader_cost=0.0520 | ETA 21:15:43 2020-12-02 05:17:28 [INFO] seg_loss:0.0678, att_loss: 0.4345, edge_loss: 2.4058, dual_loss: 0.0016 2020-12-02 05:17:29 [INFO] [TRAIN] epoch=78, iter=28800/80000, loss=2.4887, lr=0.006692, batch_cost=1.4139, reader_cost=0.0013 | ETA 20:06:32 2020-12-02 05:19:45 [INFO] seg_loss:0.0053, att_loss: 0.1387, edge_loss: 0.6867, dual_loss: 0.0003 2020-12-02 05:19:46 [INFO] [TRAIN] epoch=78, iter=28900/80000, loss=2.5380, lr=0.006680, batch_cost=1.3695, reader_cost=0.0009 | ETA 19:26:21 2020-12-02 05:22:02 [INFO] seg_loss:0.0244, att_loss: 0.1821, edge_loss: 0.6710, dual_loss: 0.0005 2020-12-02 05:22:03 [INFO] [TRAIN] epoch=78, iter=29000/80000, loss=2.6882, lr=0.006669, batch_cost=1.3705, reader_cost=0.0011 | ETA 19:24:56 2020-12-02 05:24:29 [INFO] seg_loss:0.0683, att_loss: 0.4451, edge_loss: 2.2062, dual_loss: 0.0015 2020-12-02 05:24:30 [INFO] [TRAIN] epoch=79, iter=29100/80000, loss=2.6170, lr=0.006657, batch_cost=1.4759, reader_cost=0.0422 | ETA 20:52:02 2020-12-02 05:26:53 [INFO] seg_loss:0.2307, att_loss: 0.6768, edge_loss: 3.3471, dual_loss: 0.0031 2020-12-02 05:26:54 [INFO] [TRAIN] epoch=79, iter=29200/80000, loss=2.3801, lr=0.006645, batch_cost=1.4330, reader_cost=0.0002 | ETA 20:13:14 2020-12-02 05:29:11 [INFO] seg_loss:0.1141, att_loss: 0.5645, edge_loss: 1.1414, dual_loss: 0.0013 2020-12-02 05:29:12 [INFO] [TRAIN] epoch=79, iter=29300/80000, loss=2.4209, lr=0.006633, batch_cost=1.3862, reader_cost=0.0003 | ETA 19:31:18 2020-12-02 05:31:35 [INFO] seg_loss:0.1197, att_loss: 0.6276, edge_loss: 2.3633, dual_loss: 0.0018 2020-12-02 05:31:36 [INFO] [TRAIN] epoch=80, iter=29400/80000, loss=2.7484, lr=0.006622, batch_cost=1.4353, reader_cost=0.0393 | ETA 20:10:26 2020-12-02 05:33:55 [INFO] seg_loss:0.0464, att_loss: 0.3603, edge_loss: 1.2537, dual_loss: 0.0009 2020-12-02 05:33:56 [INFO] [TRAIN] epoch=80, iter=29500/80000, loss=2.6234, lr=0.006610, batch_cost=1.3968, reader_cost=0.0002 | ETA 19:35:37 2020-12-02 05:36:15 [INFO] seg_loss:0.1027, att_loss: 0.5808, edge_loss: 2.1927, dual_loss: 0.0017 2020-12-02 05:36:17 [INFO] [TRAIN] epoch=80, iter=29600/80000, loss=2.4128, lr=0.006598, batch_cost=1.4093, reader_cost=0.0004 | ETA 19:43:48 2020-12-02 05:38:37 [INFO] seg_loss:0.1132, att_loss: 0.6004, edge_loss: 2.8509, dual_loss: 0.0021 2020-12-02 05:38:38 [INFO] [TRAIN] epoch=80, iter=29700/80000, loss=2.6053, lr=0.006586, batch_cost=1.4114, reader_cost=0.0007 | ETA 19:43:11 2020-12-02 05:41:01 [INFO] seg_loss:0.0963, att_loss: 0.3725, edge_loss: 1.5642, dual_loss: 0.0015 2020-12-02 05:41:02 [INFO] [TRAIN] epoch=81, iter=29800/80000, loss=2.5968, lr=0.006574, batch_cost=1.4406, reader_cost=0.0405 | ETA 20:05:20 2020-12-02 05:43:21 [INFO] seg_loss:0.0641, att_loss: 0.5088, edge_loss: 1.7893, dual_loss: 0.0012 2020-12-02 05:43:22 [INFO] [TRAIN] epoch=81, iter=29900/80000, loss=2.5412, lr=0.006563, batch_cost=1.4037, reader_cost=0.0004 | ETA 19:32:05 2020-12-02 05:45:45 [INFO] seg_loss:0.0742, att_loss: 0.3879, edge_loss: 1.7381, dual_loss: 0.0013 2020-12-02 05:45:46 [INFO] [TRAIN] epoch=81, iter=30000/80000, loss=2.4752, lr=0.006551, batch_cost=1.4343, reader_cost=0.0002 | ETA 19:55:16 2020-12-02 05:45:46 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 05:46:49 [INFO] [EVAL] #Images=500 mIoU=0.7897 Acc=0.9626 Kappa=0.9514 2020-12-02 05:46:49 [INFO] [EVAL] Class IoU: [0.9812 0.8577 0.9297 0.4837 0.6104 0.6952 0.7414 0.8162 0.9256 0.6014 0.9504 0.8313 0.6012 0.9564 0.8212 0.9126 0.8279 0.6676 0.7929] 2020-12-02 05:46:49 [INFO] [EVAL] Class Acc: [0.9913 0.9111 0.9641 0.7903 0.8901 0.8252 0.8293 0.923 0.9493 0.8835 0.9653 0.874 0.8184 0.9744 0.8944 0.9637 0.9379 0.889 0.8699] 2020-12-02 05:46:55 [INFO] [EVAL] The model with the best validation mIoU (0.7897) was saved at iter 30000. 2020-12-02 05:49:15 [INFO] seg_loss:0.1061, att_loss: 0.5668, edge_loss: 2.4835, dual_loss: 0.0018 2020-12-02 05:49:16 [INFO] [TRAIN] epoch=81, iter=30100/80000, loss=2.6159, lr=0.006539, batch_cost=1.4029, reader_cost=0.0003 | ETA 19:26:44 2020-12-02 05:51:41 [INFO] seg_loss:0.1358, att_loss: 0.6651, edge_loss: 1.6064, dual_loss: 0.0019 2020-12-02 05:51:42 [INFO] [TRAIN] epoch=82, iter=30200/80000, loss=2.5724, lr=0.006527, batch_cost=1.4641, reader_cost=0.0393 | ETA 20:15:09 2020-12-02 05:54:00 [INFO] seg_loss:0.0809, att_loss: 0.4594, edge_loss: 2.3990, dual_loss: 0.0015 2020-12-02 05:54:01 [INFO] [TRAIN] epoch=82, iter=30300/80000, loss=2.5020, lr=0.006515, batch_cost=1.3838, reader_cost=0.0002 | ETA 19:06:15 2020-12-02 05:56:18 [INFO] seg_loss:0.0600, att_loss: 0.5243, edge_loss: 1.9927, dual_loss: 0.0011 2020-12-02 05:56:19 [INFO] [TRAIN] epoch=82, iter=30400/80000, loss=2.4903, lr=0.006504, batch_cost=1.3865, reader_cost=0.0002 | ETA 19:06:10 2020-12-02 05:58:37 [INFO] seg_loss:0.2171, att_loss: 0.5320, edge_loss: 1.5569, dual_loss: 0.0018 2020-12-02 05:58:38 [INFO] [TRAIN] epoch=82, iter=30500/80000, loss=2.5747, lr=0.006492, batch_cost=1.3916, reader_cost=0.0003 | ETA 19:08:02 2020-12-02 06:00:58 [INFO] seg_loss:0.1773, att_loss: 0.6270, edge_loss: 2.3224, dual_loss: 0.0024 2020-12-02 06:00:59 [INFO] [TRAIN] epoch=83, iter=30600/80000, loss=2.5977, lr=0.006480, batch_cost=1.4058, reader_cost=0.0436 | ETA 19:17:27 2020-12-02 06:03:18 [INFO] seg_loss:0.0375, att_loss: 0.3911, edge_loss: 1.0173, dual_loss: 0.0006 2020-12-02 06:03:19 [INFO] [TRAIN] epoch=83, iter=30700/80000, loss=2.4945, lr=0.006468, batch_cost=1.4032, reader_cost=0.0003 | ETA 19:12:56 2020-12-02 06:05:39 [INFO] seg_loss:0.0688, att_loss: 0.5266, edge_loss: 2.0166, dual_loss: 0.0014 2020-12-02 06:05:40 [INFO] [TRAIN] epoch=83, iter=30800/80000, loss=2.4256, lr=0.006456, batch_cost=1.4032, reader_cost=0.0002 | ETA 19:10:39 2020-12-02 06:08:07 [INFO] seg_loss:0.1141, att_loss: 0.5230, edge_loss: 2.4289, dual_loss: 0.0017 2020-12-02 06:08:08 [INFO] [TRAIN] epoch=84, iter=30900/80000, loss=2.7261, lr=0.006445, batch_cost=1.4796, reader_cost=0.0406 | ETA 20:10:49 2020-12-02 06:10:25 [INFO] seg_loss:0.1558, att_loss: 0.5391, edge_loss: 2.3573, dual_loss: 0.0020 2020-12-02 06:10:27 [INFO] [TRAIN] epoch=84, iter=31000/80000, loss=2.5553, lr=0.006433, batch_cost=1.3890, reader_cost=0.0003 | ETA 18:54:19 2020-12-02 06:12:47 [INFO] seg_loss:0.1516, att_loss: 0.6654, edge_loss: 2.6072, dual_loss: 0.0021 2020-12-02 06:12:48 [INFO] [TRAIN] epoch=84, iter=31100/80000, loss=2.4176, lr=0.006421, batch_cost=1.4160, reader_cost=0.0004 | ETA 19:14:00 2020-12-02 06:15:05 [INFO] seg_loss:0.0929, att_loss: 0.4266, edge_loss: 2.1947, dual_loss: 0.0017 2020-12-02 06:15:06 [INFO] [TRAIN] epoch=84, iter=31200/80000, loss=2.6135, lr=0.006409, batch_cost=1.3769, reader_cost=0.0002 | ETA 18:39:53 2020-12-02 06:17:30 [INFO] seg_loss:0.1791, att_loss: 0.5731, edge_loss: 3.3461, dual_loss: 0.0027 2020-12-02 06:17:31 [INFO] [TRAIN] epoch=85, iter=31300/80000, loss=2.6489, lr=0.006397, batch_cost=1.4561, reader_cost=0.0570 | ETA 19:41:51 2020-12-02 06:19:51 [INFO] seg_loss:0.1176, att_loss: 0.6317, edge_loss: 2.0258, dual_loss: 0.0022 2020-12-02 06:19:52 [INFO] [TRAIN] epoch=85, iter=31400/80000, loss=2.4996, lr=0.006386, batch_cost=1.4022, reader_cost=0.0002 | ETA 18:55:49 2020-12-02 06:22:11 [INFO] seg_loss:0.0952, att_loss: 0.5242, edge_loss: 2.1152, dual_loss: 0.0016 2020-12-02 06:22:12 [INFO] [TRAIN] epoch=85, iter=31500/80000, loss=2.5629, lr=0.006374, batch_cost=1.3987, reader_cost=0.0002 | ETA 18:50:36 2020-12-02 06:24:30 [INFO] seg_loss:0.0669, att_loss: 0.4884, edge_loss: 1.5204, dual_loss: 0.0011 2020-12-02 06:24:31 [INFO] [TRAIN] epoch=85, iter=31600/80000, loss=2.7130, lr=0.006362, batch_cost=1.3993, reader_cost=0.0003 | ETA 18:48:47 2020-12-02 06:26:56 [INFO] seg_loss:0.1371, att_loss: 0.5682, edge_loss: 2.1601, dual_loss: 0.0018 2020-12-02 06:26:57 [INFO] [TRAIN] epoch=86, iter=31700/80000, loss=2.4750, lr=0.006350, batch_cost=1.4570, reader_cost=0.0424 | ETA 19:32:54 2020-12-02 06:29:13 [INFO] seg_loss:0.0513, att_loss: 0.4441, edge_loss: 1.7563, dual_loss: 0.0011 2020-12-02 06:29:14 [INFO] [TRAIN] epoch=86, iter=31800/80000, loss=2.4989, lr=0.006338, batch_cost=1.3719, reader_cost=0.0006 | ETA 18:22:07 2020-12-02 06:31:33 [INFO] seg_loss:0.0703, att_loss: 0.4754, edge_loss: 2.5752, dual_loss: 0.0016 2020-12-02 06:31:34 [INFO] [TRAIN] epoch=86, iter=31900/80000, loss=2.4190, lr=0.006326, batch_cost=1.3966, reader_cost=0.0007 | ETA 18:39:37 2020-12-02 06:33:59 [INFO] seg_loss:0.0917, att_loss: 0.5357, edge_loss: 2.5651, dual_loss: 0.0016 2020-12-02 06:34:00 [INFO] [TRAIN] epoch=87, iter=32000/80000, loss=2.6612, lr=0.006315, batch_cost=1.4559, reader_cost=0.0467 | ETA 19:24:44 2020-12-02 06:34:00 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 06:35:03 [INFO] [EVAL] #Images=500 mIoU=0.7864 Acc=0.9614 Kappa=0.9498 2020-12-02 06:35:03 [INFO] [EVAL] Class IoU: [0.9785 0.844 0.9283 0.3866 0.6065 0.6927 0.7373 0.8025 0.9258 0.6497 0.9498 0.8498 0.675 0.9538 0.7804 0.8933 0.8089 0.6855 0.7932] 2020-12-02 06:35:03 [INFO] [EVAL] Class Acc: [0.9855 0.9325 0.9576 0.8552 0.8362 0.828 0.8456 0.8987 0.9522 0.8539 0.9673 0.9167 0.8115 0.9743 0.9522 0.9724 0.8862 0.8352 0.8733] 2020-12-02 06:35:08 [INFO] [EVAL] The model with the best validation mIoU (0.7897) was saved at iter 30000. 2020-12-02 06:37:25 [INFO] seg_loss:0.0830, att_loss: 0.5061, edge_loss: 1.6034, dual_loss: 0.0016 2020-12-02 06:37:26 [INFO] [TRAIN] epoch=87, iter=32100/80000, loss=2.5655, lr=0.006303, batch_cost=1.3840, reader_cost=0.0002 | ETA 18:24:53 2020-12-02 06:39:46 [INFO] seg_loss:0.1181, att_loss: 0.5922, edge_loss: 1.7151, dual_loss: 0.0014 2020-12-02 06:39:47 [INFO] [TRAIN] epoch=87, iter=32200/80000, loss=2.3879, lr=0.006291, batch_cost=1.4029, reader_cost=0.0002 | ETA 18:37:37 2020-12-02 06:42:03 [INFO] seg_loss:0.0845, att_loss: 0.5195, edge_loss: 2.3792, dual_loss: 0.0018 2020-12-02 06:42:04 [INFO] [TRAIN] epoch=87, iter=32300/80000, loss=2.4960, lr=0.006279, batch_cost=1.3774, reader_cost=0.0002 | ETA 18:15:01 2020-12-02 06:44:30 [INFO] seg_loss:0.0342, att_loss: 0.4229, edge_loss: 1.2068, dual_loss: 0.0007 2020-12-02 06:44:31 [INFO] [TRAIN] epoch=88, iter=32400/80000, loss=2.7399, lr=0.006267, batch_cost=1.4668, reader_cost=0.0715 | ETA 19:23:41 2020-12-02 06:46:54 [INFO] seg_loss:0.0590, att_loss: 0.5184, edge_loss: 1.8760, dual_loss: 0.0012 2020-12-02 06:46:55 [INFO] [TRAIN] epoch=88, iter=32500/80000, loss=2.5130, lr=0.006255, batch_cost=1.4356, reader_cost=0.0002 | ETA 18:56:29 2020-12-02 06:49:15 [INFO] seg_loss:0.0635, att_loss: 0.4992, edge_loss: 1.9119, dual_loss: 0.0014 2020-12-02 06:49:16 [INFO] [TRAIN] epoch=88, iter=32600/80000, loss=2.5788, lr=0.006243, batch_cost=1.4167, reader_cost=0.0007 | ETA 18:39:11 2020-12-02 06:51:34 [INFO] seg_loss:0.1031, att_loss: 0.5680, edge_loss: 2.1471, dual_loss: 0.0017 2020-12-02 06:51:35 [INFO] [TRAIN] epoch=88, iter=32700/80000, loss=2.6157, lr=0.006232, batch_cost=1.3868, reader_cost=0.0003 | ETA 18:13:14 2020-12-02 06:54:00 [INFO] seg_loss:0.2338, att_loss: 0.5478, edge_loss: 1.8829, dual_loss: 0.0025 2020-12-02 06:54:01 [INFO] [TRAIN] epoch=89, iter=32800/80000, loss=2.5280, lr=0.006220, batch_cost=1.4619, reader_cost=0.0396 | ETA 19:10:03 2020-12-02 06:56:18 [INFO] seg_loss:0.0353, att_loss: 0.4291, edge_loss: 1.6964, dual_loss: 0.0010 2020-12-02 06:56:20 [INFO] [TRAIN] epoch=89, iter=32900/80000, loss=2.5942, lr=0.006208, batch_cost=1.3835, reader_cost=0.0012 | ETA 18:06:00 2020-12-02 06:58:37 [INFO] seg_loss:0.1348, att_loss: 0.5694, edge_loss: 2.0406, dual_loss: 0.0019 2020-12-02 06:58:38 [INFO] [TRAIN] epoch=89, iter=33000/80000, loss=2.4930, lr=0.006196, batch_cost=1.3815, reader_cost=0.0002 | ETA 18:02:11 2020-12-02 07:00:55 [INFO] seg_loss:0.0812, att_loss: 0.4729, edge_loss: 2.2405, dual_loss: 0.0017 2020-12-02 07:00:56 [INFO] [TRAIN] epoch=89, iter=33100/80000, loss=2.6334, lr=0.006184, batch_cost=1.3786, reader_cost=0.0002 | ETA 17:57:35 2020-12-02 07:03:22 [INFO] seg_loss:0.0582, att_loss: 0.4181, edge_loss: 0.8927, dual_loss: 0.0010 2020-12-02 07:03:23 [INFO] [TRAIN] epoch=90, iter=33200/80000, loss=2.6562, lr=0.006172, batch_cost=1.4790, reader_cost=0.0723 | ETA 19:13:35 2020-12-02 07:05:41 [INFO] seg_loss:0.0409, att_loss: 0.4285, edge_loss: 1.4859, dual_loss: 0.0008 2020-12-02 07:05:42 [INFO] [TRAIN] epoch=90, iter=33300/80000, loss=2.4217, lr=0.006160, batch_cost=1.3847, reader_cost=0.0003 | ETA 17:57:47 2020-12-02 07:08:01 [INFO] seg_loss:0.0857, att_loss: 0.5441, edge_loss: 2.0105, dual_loss: 0.0017 2020-12-02 07:08:02 [INFO] [TRAIN] epoch=90, iter=33400/80000, loss=2.3887, lr=0.006149, batch_cost=1.4032, reader_cost=0.0003 | ETA 18:09:48 2020-12-02 07:10:24 [INFO] seg_loss:0.1051, att_loss: 0.6333, edge_loss: 2.9145, dual_loss: 0.0019 2020-12-02 07:10:25 [INFO] [TRAIN] epoch=91, iter=33500/80000, loss=2.7529, lr=0.006137, batch_cost=1.4318, reader_cost=0.0494 | ETA 18:29:38 2020-12-02 07:12:42 [INFO] seg_loss:0.0231, att_loss: 0.3488, edge_loss: 0.9989, dual_loss: 0.0006 2020-12-02 07:12:43 [INFO] [TRAIN] epoch=91, iter=33600/80000, loss=2.5483, lr=0.006125, batch_cost=1.3778, reader_cost=0.0004 | ETA 17:45:31 2020-12-02 07:15:00 [INFO] seg_loss:0.1116, att_loss: 0.5666, edge_loss: 2.7292, dual_loss: 0.0021 2020-12-02 07:15:01 [INFO] [TRAIN] epoch=91, iter=33700/80000, loss=2.4784, lr=0.006113, batch_cost=1.3769, reader_cost=0.0002 | ETA 17:42:29 2020-12-02 07:17:20 [INFO] seg_loss:0.1607, att_loss: 0.7289, edge_loss: 2.2629, dual_loss: 0.0024 2020-12-02 07:17:22 [INFO] [TRAIN] epoch=91, iter=33800/80000, loss=2.5691, lr=0.006101, batch_cost=1.4060, reader_cost=0.0003 | ETA 18:02:36 2020-12-02 07:19:47 [INFO] seg_loss:0.1644, att_loss: 0.5736, edge_loss: 1.4278, dual_loss: 0.0022 2020-12-02 07:19:48 [INFO] [TRAIN] epoch=92, iter=33900/80000, loss=2.5611, lr=0.006089, batch_cost=1.4637, reader_cost=0.0581 | ETA 18:44:34 2020-12-02 07:22:09 [INFO] seg_loss:0.0119, att_loss: 0.1819, edge_loss: 0.6294, dual_loss: 0.0003 2020-12-02 07:22:10 [INFO] [TRAIN] epoch=92, iter=34000/80000, loss=2.4632, lr=0.006077, batch_cost=1.4179, reader_cost=0.0009 | ETA 18:07:03 2020-12-02 07:22:10 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 07:23:13 [INFO] [EVAL] #Images=500 mIoU=0.7425 Acc=0.9606 Kappa=0.9489 2020-12-02 07:23:13 [INFO] [EVAL] Class IoU: [0.9797 0.8416 0.9292 0.4662 0.6228 0.6916 0.7294 0.8117 0.9267 0.6368 0.9492 0.838 0.6197 0.9555 0.7148 0.7295 0.2416 0.6304 0.7927] 2020-12-02 07:23:13 [INFO] [EVAL] Class Acc: [0.99 0.9061 0.9619 0.8306 0.8039 0.794 0.8052 0.888 0.9593 0.8536 0.9647 0.8923 0.8322 0.9804 0.8018 0.7526 0.8846 0.7189 0.8771] 2020-12-02 07:23:17 [INFO] [EVAL] The model with the best validation mIoU (0.7897) was saved at iter 30000. 2020-12-02 07:25:33 [INFO] seg_loss:0.0680, att_loss: 0.5130, edge_loss: 2.3010, dual_loss: 0.0014 2020-12-02 07:25:34 [INFO] [TRAIN] epoch=92, iter=34100/80000, loss=2.5068, lr=0.006065, batch_cost=1.3701, reader_cost=0.0003 | ETA 17:28:08 2020-12-02 07:27:50 [INFO] seg_loss:0.0908, att_loss: 0.5273, edge_loss: 2.0896, dual_loss: 0.0014 2020-12-02 07:27:51 [INFO] [TRAIN] epoch=92, iter=34200/80000, loss=2.6813, lr=0.006053, batch_cost=1.3711, reader_cost=0.0006 | ETA 17:26:36 2020-12-02 07:30:19 [INFO] seg_loss:0.1245, att_loss: 0.5706, edge_loss: 3.0028, dual_loss: 0.0023 2020-12-02 07:30:20 [INFO] [TRAIN] epoch=93, iter=34300/80000, loss=2.5379, lr=0.006042, batch_cost=1.4868, reader_cost=0.0395 | ETA 18:52:24 2020-12-02 07:32:38 [INFO] seg_loss:0.0375, att_loss: 0.4482, edge_loss: 1.3657, dual_loss: 0.0008 2020-12-02 07:32:39 [INFO] [TRAIN] epoch=93, iter=34400/80000, loss=2.4853, lr=0.006030, batch_cost=1.3858, reader_cost=0.0002 | ETA 17:33:10 2020-12-02 07:34:57 [INFO] seg_loss:0.0835, att_loss: 0.4673, edge_loss: 2.4431, dual_loss: 0.0017 2020-12-02 07:34:58 [INFO] [TRAIN] epoch=93, iter=34500/80000, loss=2.3933, lr=0.006018, batch_cost=1.3877, reader_cost=0.0002 | ETA 17:32:21 2020-12-02 07:37:23 [INFO] seg_loss:0.2770, att_loss: 0.5028, edge_loss: 2.1541, dual_loss: 0.0023 2020-12-02 07:37:24 [INFO] [TRAIN] epoch=94, iter=34600/80000, loss=2.6672, lr=0.006006, batch_cost=1.4682, reader_cost=0.0725 | ETA 18:30:56 2020-12-02 07:39:42 [INFO] seg_loss:0.1535, att_loss: 0.6131, edge_loss: 2.2410, dual_loss: 0.0019 2020-12-02 07:39:43 [INFO] [TRAIN] epoch=94, iter=34700/80000, loss=2.7263, lr=0.005994, batch_cost=1.3906, reader_cost=0.0002 | ETA 17:29:54 2020-12-02 07:42:02 [INFO] seg_loss:0.0672, att_loss: 0.4748, edge_loss: 1.4460, dual_loss: 0.0011 2020-12-02 07:42:03 [INFO] [TRAIN] epoch=94, iter=34800/80000, loss=2.4042, lr=0.005982, batch_cost=1.3976, reader_cost=0.0010 | ETA 17:32:51 2020-12-02 07:44:23 [INFO] seg_loss:0.1012, att_loss: 0.4889, edge_loss: 3.1425, dual_loss: 0.0018 2020-12-02 07:44:24 [INFO] [TRAIN] epoch=94, iter=34900/80000, loss=2.5522, lr=0.005970, batch_cost=1.4061, reader_cost=0.0002 | ETA 17:36:56 2020-12-02 07:46:50 [INFO] seg_loss:0.3495, att_loss: 0.8317, edge_loss: 2.6439, dual_loss: 0.0033 2020-12-02 07:46:51 [INFO] [TRAIN] epoch=95, iter=35000/80000, loss=2.7440, lr=0.005958, batch_cost=1.4743, reader_cost=0.0622 | ETA 18:25:42 2020-12-02 07:49:12 [INFO] seg_loss:0.0651, att_loss: 0.4463, edge_loss: 1.2445, dual_loss: 0.0010 2020-12-02 07:49:13 [INFO] [TRAIN] epoch=95, iter=35100/80000, loss=2.4731, lr=0.005946, batch_cost=1.4159, reader_cost=0.0003 | ETA 17:39:32 2020-12-02 07:51:33 [INFO] seg_loss:0.0845, att_loss: 0.4933, edge_loss: 1.7848, dual_loss: 0.0016 2020-12-02 07:51:34 [INFO] [TRAIN] epoch=95, iter=35200/80000, loss=2.4580, lr=0.005934, batch_cost=1.4113, reader_cost=0.0003 | ETA 17:33:45 2020-12-02 07:53:53 [INFO] seg_loss:0.2078, att_loss: 0.7021, edge_loss: 2.2270, dual_loss: 0.0024 2020-12-02 07:53:54 [INFO] [TRAIN] epoch=95, iter=35300/80000, loss=2.5540, lr=0.005922, batch_cost=1.4042, reader_cost=0.0002 | ETA 17:26:06 2020-12-02 07:56:18 [INFO] seg_loss:0.0418, att_loss: 0.3833, edge_loss: 1.4708, dual_loss: 0.0008 2020-12-02 07:56:19 [INFO] [TRAIN] epoch=96, iter=35400/80000, loss=2.4991, lr=0.005911, batch_cost=1.4421, reader_cost=0.0711 | ETA 17:51:55 2020-12-02 07:58:36 [INFO] seg_loss:0.0716, att_loss: 0.4912, edge_loss: 2.5299, dual_loss: 0.0016 2020-12-02 07:58:37 [INFO] [TRAIN] epoch=96, iter=35500/80000, loss=2.5334, lr=0.005899, batch_cost=1.3803, reader_cost=0.0004 | ETA 17:03:44 2020-12-02 08:00:56 [INFO] seg_loss:0.0288, att_loss: 0.3443, edge_loss: 0.9980, dual_loss: 0.0007 2020-12-02 08:00:57 [INFO] [TRAIN] epoch=96, iter=35600/80000, loss=2.5034, lr=0.005887, batch_cost=1.4074, reader_cost=0.0002 | ETA 17:21:28 2020-12-02 08:03:14 [INFO] seg_loss:0.0294, att_loss: 0.3017, edge_loss: 0.7708, dual_loss: 0.0005 2020-12-02 08:03:15 [INFO] [TRAIN] epoch=96, iter=35700/80000, loss=2.6366, lr=0.005875, batch_cost=1.3798, reader_cost=0.0006 | ETA 16:58:43 2020-12-02 08:05:40 [INFO] seg_loss:0.2107, att_loss: 0.7621, edge_loss: 1.5379, dual_loss: 0.0020 2020-12-02 08:05:41 [INFO] [TRAIN] epoch=97, iter=35800/80000, loss=2.5846, lr=0.005863, batch_cost=1.4581, reader_cost=0.0420 | ETA 17:54:09 2020-12-02 08:08:00 [INFO] seg_loss:0.1074, att_loss: 0.5711, edge_loss: 2.2984, dual_loss: 0.0016 2020-12-02 08:08:01 [INFO] [TRAIN] epoch=97, iter=35900/80000, loss=2.4663, lr=0.005851, batch_cost=1.3934, reader_cost=0.0010 | ETA 17:04:07 2020-12-02 08:10:22 [INFO] seg_loss:0.0828, att_loss: 0.4685, edge_loss: 1.8456, dual_loss: 0.0015 2020-12-02 08:10:23 [INFO] [TRAIN] epoch=97, iter=36000/80000, loss=2.3762, lr=0.005839, batch_cost=1.4250, reader_cost=0.0004 | ETA 17:24:59 2020-12-02 08:10:23 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 08:11:27 [INFO] [EVAL] #Images=500 mIoU=0.7857 Acc=0.9625 Kappa=0.9513 2020-12-02 08:11:27 [INFO] [EVAL] Class IoU: [0.9814 0.8534 0.9292 0.5014 0.6272 0.696 0.7394 0.8121 0.9259 0.6355 0.9517 0.8461 0.6651 0.9564 0.7915 0.8351 0.7082 0.679 0.7938] 2020-12-02 08:11:27 [INFO] [EVAL] Class Acc: [0.9909 0.9201 0.958 0.7587 0.819 0.8461 0.8488 0.9148 0.9567 0.819 0.969 0.9147 0.7918 0.9788 0.9031 0.931 0.7497 0.7888 0.8884] 2020-12-02 08:11:31 [INFO] [EVAL] The model with the best validation mIoU (0.7897) was saved at iter 30000. 2020-12-02 08:13:54 [INFO] seg_loss:0.0509, att_loss: 0.4615, edge_loss: 2.1409, dual_loss: 0.0012 2020-12-02 08:13:55 [INFO] [TRAIN] epoch=98, iter=36100/80000, loss=2.7198, lr=0.005827, batch_cost=1.4361, reader_cost=0.0488 | ETA 17:30:46 2020-12-02 08:16:15 [INFO] seg_loss:0.0263, att_loss: 0.3088, edge_loss: 1.1602, dual_loss: 0.0007 2020-12-02 08:16:16 [INFO] [TRAIN] epoch=98, iter=36200/80000, loss=2.5651, lr=0.005815, batch_cost=1.4096, reader_cost=0.0002 | ETA 17:09:00 2020-12-02 08:18:35 [INFO] seg_loss:0.0475, att_loss: 0.4361, edge_loss: 1.8878, dual_loss: 0.0013 2020-12-02 08:18:36 [INFO] [TRAIN] epoch=98, iter=36300/80000, loss=2.4294, lr=0.005803, batch_cost=1.3966, reader_cost=0.0002 | ETA 16:57:09 2020-12-02 08:20:50 [INFO] seg_loss:0.5838, att_loss: 0.7550, edge_loss: 1.5562, dual_loss: 0.0022 2020-12-02 08:20:51 [INFO] [TRAIN] epoch=98, iter=36400/80000, loss=2.5186, lr=0.005791, batch_cost=1.3560, reader_cost=0.0010 | ETA 16:25:19 2020-12-02 08:23:14 [INFO] seg_loss:0.1227, att_loss: 0.6245, edge_loss: 2.0829, dual_loss: 0.0019 2020-12-02 08:23:15 [INFO] [TRAIN] epoch=99, iter=36500/80000, loss=2.5447, lr=0.005779, batch_cost=1.4334, reader_cost=0.0394 | ETA 17:19:10 2020-12-02 08:25:34 [INFO] seg_loss:0.1215, att_loss: 0.5556, edge_loss: 2.8042, dual_loss: 0.0020 2020-12-02 08:25:35 [INFO] [TRAIN] epoch=99, iter=36600/80000, loss=2.5174, lr=0.005767, batch_cost=1.4058, reader_cost=0.0014 | ETA 16:56:52 2020-12-02 08:27:54 [INFO] seg_loss:0.0573, att_loss: 0.4559, edge_loss: 1.4303, dual_loss: 0.0010 2020-12-02 08:27:55 [INFO] [TRAIN] epoch=99, iter=36700/80000, loss=2.3980, lr=0.005755, batch_cost=1.3992, reader_cost=0.0006 | ETA 16:49:47 2020-12-02 08:30:12 [INFO] seg_loss:0.2108, att_loss: 0.6862, edge_loss: 3.3623, dual_loss: 0.0027 2020-12-02 08:30:13 [INFO] [TRAIN] epoch=99, iter=36800/80000, loss=2.6158, lr=0.005743, batch_cost=1.3811, reader_cost=0.0002 | ETA 16:34:24 2020-12-02 08:32:38 [INFO] seg_loss:0.1010, att_loss: 0.5546, edge_loss: 1.5842, dual_loss: 0.0018 2020-12-02 08:32:39 [INFO] [TRAIN] epoch=100, iter=36900/80000, loss=2.4676, lr=0.005731, batch_cost=1.4540, reader_cost=0.0472 | ETA 17:24:28 2020-12-02 08:34:57 [INFO] seg_loss:0.1097, att_loss: 0.5839, edge_loss: 2.0460, dual_loss: 0.0017 2020-12-02 08:34:58 [INFO] [TRAIN] epoch=100, iter=37000/80000, loss=2.5443, lr=0.005719, batch_cost=1.3930, reader_cost=0.0002 | ETA 16:38:17 2020-12-02 08:37:17 [INFO] seg_loss:0.0724, att_loss: 0.5305, edge_loss: 1.3566, dual_loss: 0.0012 2020-12-02 08:37:18 [INFO] [TRAIN] epoch=100, iter=37100/80000, loss=2.4270, lr=0.005707, batch_cost=1.4001, reader_cost=0.0002 | ETA 16:41:02 2020-12-02 08:39:32 [INFO] seg_loss:0.1294, att_loss: 0.6160, edge_loss: 2.8970, dual_loss: 0.0021 2020-12-02 08:39:33 [INFO] [TRAIN] epoch=100, iter=37200/80000, loss=2.5835, lr=0.005695, batch_cost=1.3506, reader_cost=0.0005 | ETA 16:03:27 2020-12-02 08:41:59 [INFO] seg_loss:0.2514, att_loss: 0.7449, edge_loss: 2.8026, dual_loss: 0.0028 2020-12-02 08:42:00 [INFO] [TRAIN] epoch=101, iter=37300/80000, loss=2.6542, lr=0.005683, batch_cost=1.4742, reader_cost=0.0444 | ETA 17:29:10 2020-12-02 08:44:19 [INFO] seg_loss:0.1087, att_loss: 0.4934, edge_loss: 1.3959, dual_loss: 0.0015 2020-12-02 08:44:20 [INFO] [TRAIN] epoch=101, iter=37400/80000, loss=2.3407, lr=0.005671, batch_cost=1.3986, reader_cost=0.0003 | ETA 16:32:59 2020-12-02 08:46:40 [INFO] seg_loss:0.0311, att_loss: 0.3362, edge_loss: 1.4667, dual_loss: 0.0008 2020-12-02 08:46:41 [INFO] [TRAIN] epoch=101, iter=37500/80000, loss=2.3919, lr=0.005660, batch_cost=1.4022, reader_cost=0.0002 | ETA 16:33:13 2020-12-02 08:49:03 [INFO] seg_loss:0.0560, att_loss: 0.6537, edge_loss: 1.1780, dual_loss: 0.0008 2020-12-02 08:49:04 [INFO] [TRAIN] epoch=102, iter=37600/80000, loss=2.7845, lr=0.005648, batch_cost=1.4371, reader_cost=0.0532 | ETA 16:55:34 2020-12-02 08:51:22 [INFO] seg_loss:0.2236, att_loss: 0.5869, edge_loss: 1.8777, dual_loss: 0.0025 2020-12-02 08:51:23 [INFO] [TRAIN] epoch=102, iter=37700/80000, loss=2.6056, lr=0.005636, batch_cost=1.3907, reader_cost=0.0007 | ETA 16:20:25 2020-12-02 08:53:39 [INFO] seg_loss:0.0838, att_loss: 0.5526, edge_loss: 1.7596, dual_loss: 0.0014 2020-12-02 08:53:40 [INFO] [TRAIN] epoch=102, iter=37800/80000, loss=2.3632, lr=0.005624, batch_cost=1.3662, reader_cost=0.0006 | ETA 16:00:52 2020-12-02 08:56:00 [INFO] seg_loss:0.1584, att_loss: 0.7035, edge_loss: 2.4017, dual_loss: 0.0023 2020-12-02 08:56:01 [INFO] [TRAIN] epoch=102, iter=37900/80000, loss=2.5411, lr=0.005612, batch_cost=1.4065, reader_cost=0.0002 | ETA 16:26:54 2020-12-02 08:58:26 [INFO] seg_loss:0.0591, att_loss: 0.3893, edge_loss: 1.1183, dual_loss: 0.0010 2020-12-02 08:58:27 [INFO] [TRAIN] epoch=103, iter=38000/80000, loss=2.4974, lr=0.005600, batch_cost=1.4640, reader_cost=0.0462 | ETA 17:04:47 2020-12-02 08:58:27 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 08:59:31 [INFO] [EVAL] #Images=500 mIoU=0.7892 Acc=0.9616 Kappa=0.9502 2020-12-02 08:59:31 [INFO] [EVAL] Class IoU: [0.9808 0.8478 0.9295 0.4895 0.611 0.6915 0.7342 0.8209 0.9226 0.5926 0.9466 0.8432 0.6668 0.9546 0.8144 0.9056 0.826 0.6208 0.7958] 2020-12-02 08:59:31 [INFO] [EVAL] Class Acc: [0.9901 0.9058 0.9561 0.7212 0.8649 0.8481 0.8396 0.9268 0.9603 0.7977 0.9592 0.9271 0.8227 0.9772 0.8773 0.9748 0.908 0.8135 0.8693] 2020-12-02 08:59:36 [INFO] [EVAL] The model with the best validation mIoU (0.7897) was saved at iter 30000. 2020-12-02 09:01:55 [INFO] seg_loss:0.1010, att_loss: 0.5279, edge_loss: 2.7943, dual_loss: 0.0018 2020-12-02 09:01:56 [INFO] [TRAIN] epoch=103, iter=38100/80000, loss=2.5102, lr=0.005588, batch_cost=1.3990, reader_cost=0.0004 | ETA 16:16:57 2020-12-02 09:04:14 [INFO] seg_loss:0.0423, att_loss: 0.4011, edge_loss: 0.9022, dual_loss: 0.0009 2020-12-02 09:04:15 [INFO] [TRAIN] epoch=103, iter=38200/80000, loss=2.5717, lr=0.005576, batch_cost=1.3899, reader_cost=0.0002 | ETA 16:08:17 2020-12-02 09:06:32 [INFO] seg_loss:0.0823, att_loss: 0.5614, edge_loss: 1.1581, dual_loss: 0.0011 2020-12-02 09:06:33 [INFO] [TRAIN] epoch=103, iter=38300/80000, loss=2.6325, lr=0.005564, batch_cost=1.3860, reader_cost=0.0006 | ETA 16:03:17 2020-12-02 09:08:57 [INFO] seg_loss:0.1023, att_loss: 0.5096, edge_loss: 2.8787, dual_loss: 0.0018 2020-12-02 09:08:58 [INFO] [TRAIN] epoch=104, iter=38400/80000, loss=2.6472, lr=0.005552, batch_cost=1.4457, reader_cost=0.0386 | ETA 16:42:19 2020-12-02 09:11:15 [INFO] seg_loss:0.1779, att_loss: 0.6134, edge_loss: 2.9635, dual_loss: 0.0025 2020-12-02 09:11:16 [INFO] [TRAIN] epoch=104, iter=38500/80000, loss=2.4202, lr=0.005540, batch_cost=1.3762, reader_cost=0.0004 | ETA 15:51:54 2020-12-02 09:13:34 [INFO] seg_loss:0.0188, att_loss: 0.3302, edge_loss: 0.7013, dual_loss: 0.0004 2020-12-02 09:13:35 [INFO] [TRAIN] epoch=104, iter=38600/80000, loss=2.3279, lr=0.005528, batch_cost=1.3939, reader_cost=0.0007 | ETA 16:01:47 2020-12-02 09:16:00 [INFO] seg_loss:0.0371, att_loss: 0.3835, edge_loss: 1.4888, dual_loss: 0.0008 2020-12-02 09:16:01 [INFO] [TRAIN] epoch=105, iter=38700/80000, loss=2.6443, lr=0.005515, batch_cost=1.4617, reader_cost=0.0695 | ETA 16:46:09 2020-12-02 09:18:22 [INFO] seg_loss:0.0293, att_loss: 0.3820, edge_loss: 1.3914, dual_loss: 0.0007 2020-12-02 09:18:23 [INFO] [TRAIN] epoch=105, iter=38800/80000, loss=2.5704, lr=0.005503, batch_cost=1.4203, reader_cost=0.0003 | ETA 16:15:14 2020-12-02 09:20:42 [INFO] seg_loss:0.0696, att_loss: 0.4863, edge_loss: 2.2027, dual_loss: 0.0014 2020-12-02 09:20:43 [INFO] [TRAIN] epoch=105, iter=38900/80000, loss=2.3999, lr=0.005491, batch_cost=1.4027, reader_cost=0.0002 | ETA 16:00:52 2020-12-02 09:23:02 [INFO] seg_loss:0.1194, att_loss: 0.5567, edge_loss: 2.9479, dual_loss: 0.0022 2020-12-02 09:23:04 [INFO] [TRAIN] epoch=105, iter=39000/80000, loss=2.5320, lr=0.005479, batch_cost=1.4008, reader_cost=0.0002 | ETA 15:57:13 2020-12-02 09:25:27 [INFO] seg_loss:0.0935, att_loss: 0.5223, edge_loss: 1.3660, dual_loss: 0.0008 2020-12-02 09:25:28 [INFO] [TRAIN] epoch=106, iter=39100/80000, loss=2.5704, lr=0.005467, batch_cost=1.4467, reader_cost=0.0465 | ETA 16:26:08 2020-12-02 09:27:48 [INFO] seg_loss:0.0971, att_loss: 0.6177, edge_loss: 2.3661, dual_loss: 0.0016 2020-12-02 09:27:49 [INFO] [TRAIN] epoch=106, iter=39200/80000, loss=2.4990, lr=0.005455, batch_cost=1.4127, reader_cost=0.0002 | ETA 16:00:39 2020-12-02 09:30:10 [INFO] seg_loss:0.1411, att_loss: 0.6131, edge_loss: 2.4713, dual_loss: 0.0022 2020-12-02 09:30:11 [INFO] [TRAIN] epoch=106, iter=39300/80000, loss=2.5168, lr=0.005443, batch_cost=1.4200, reader_cost=0.0009 | ETA 16:03:15 2020-12-02 09:32:29 [INFO] seg_loss:0.1055, att_loss: 0.4963, edge_loss: 2.6073, dual_loss: 0.0019 2020-12-02 09:32:30 [INFO] [TRAIN] epoch=106, iter=39400/80000, loss=2.5747, lr=0.005431, batch_cost=1.3864, reader_cost=0.0015 | ETA 15:38:06 2020-12-02 09:34:53 [INFO] seg_loss:0.0598, att_loss: 0.3925, edge_loss: 0.5755, dual_loss: 0.0008 2020-12-02 09:34:54 [INFO] [TRAIN] epoch=107, iter=39500/80000, loss=2.5364, lr=0.005419, batch_cost=1.4353, reader_cost=0.0620 | ETA 16:08:49 2020-12-02 09:37:13 [INFO] seg_loss:0.0664, att_loss: 0.4669, edge_loss: 1.8748, dual_loss: 0.0012 2020-12-02 09:37:14 [INFO] [TRAIN] epoch=107, iter=39600/80000, loss=2.5542, lr=0.005407, batch_cost=1.4072, reader_cost=0.0005 | ETA 15:47:32 2020-12-02 09:39:34 [INFO] seg_loss:0.0678, att_loss: 0.5652, edge_loss: 1.6563, dual_loss: 0.0011 2020-12-02 09:39:35 [INFO] [TRAIN] epoch=107, iter=39700/80000, loss=2.4053, lr=0.005395, batch_cost=1.4084, reader_cost=0.0004 | ETA 15:45:59 2020-12-02 09:41:52 [INFO] seg_loss:0.0260, att_loss: 0.3600, edge_loss: 0.7286, dual_loss: 0.0006 2020-12-02 09:41:53 [INFO] [TRAIN] epoch=107, iter=39800/80000, loss=2.6115, lr=0.005383, batch_cost=1.3763, reader_cost=0.0010 | ETA 15:22:07 2020-12-02 09:44:16 [INFO] seg_loss:0.1401, att_loss: 0.6016, edge_loss: 2.3856, dual_loss: 0.0021 2020-12-02 09:44:17 [INFO] [TRAIN] epoch=108, iter=39900/80000, loss=2.5603, lr=0.005371, batch_cost=1.4392, reader_cost=0.0450 | ETA 16:01:52 2020-12-02 09:46:31 [INFO] seg_loss:0.2033, att_loss: 0.6190, edge_loss: 1.6334, dual_loss: 0.0018 2020-12-02 09:46:32 [INFO] [TRAIN] epoch=108, iter=40000/80000, loss=2.4168, lr=0.005359, batch_cost=1.3547, reader_cost=0.0003 | ETA 15:03:07 2020-12-02 09:46:32 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 09:47:36 [INFO] [EVAL] #Images=500 mIoU=0.7947 Acc=0.9639 Kappa=0.9532 2020-12-02 09:47:36 [INFO] [EVAL] Class IoU: [0.9815 0.8537 0.9347 0.5465 0.6285 0.7007 0.7487 0.8174 0.9279 0.6373 0.9474 0.8471 0.6534 0.9581 0.7112 0.9226 0.826 0.6565 0.7995] 2020-12-02 09:47:36 [INFO] [EVAL] Class Acc: [0.9908 0.9161 0.9617 0.8431 0.8499 0.8367 0.8593 0.9019 0.9596 0.8855 0.9579 0.9086 0.8101 0.9793 0.7413 0.9694 0.9028 0.8218 0.8792] 2020-12-02 09:47:42 [INFO] [EVAL] The model with the best validation mIoU (0.7947) was saved at iter 40000. 2020-12-02 09:49:57 [INFO] seg_loss:0.0422, att_loss: 0.4121, edge_loss: 1.9586, dual_loss: 0.0010 2020-12-02 09:49:58 [INFO] [TRAIN] epoch=108, iter=40100/80000, loss=2.3704, lr=0.005347, batch_cost=1.3545, reader_cost=0.0004 | ETA 15:00:46 2020-12-02 09:52:20 [INFO] seg_loss:0.0537, att_loss: 0.3994, edge_loss: 1.8976, dual_loss: 0.0011 2020-12-02 09:52:21 [INFO] [TRAIN] epoch=109, iter=40200/80000, loss=2.6926, lr=0.005335, batch_cost=1.4321, reader_cost=0.0454 | ETA 15:49:55 2020-12-02 09:54:38 [INFO] seg_loss:0.1658, att_loss: 0.5856, edge_loss: 2.6668, dual_loss: 0.0024 2020-12-02 09:54:39 [INFO] [TRAIN] epoch=109, iter=40300/80000, loss=2.4633, lr=0.005323, batch_cost=1.3816, reader_cost=0.0002 | ETA 15:14:11 2020-12-02 09:56:56 [INFO] seg_loss:0.0290, att_loss: 0.3624, edge_loss: 1.0791, dual_loss: 0.0006 2020-12-02 09:56:57 [INFO] [TRAIN] epoch=109, iter=40400/80000, loss=2.4181, lr=0.005311, batch_cost=1.3746, reader_cost=0.0004 | ETA 15:07:15 2020-12-02 09:59:16 [INFO] seg_loss:0.1018, att_loss: 0.4664, edge_loss: 1.9363, dual_loss: 0.0012 2020-12-02 09:59:17 [INFO] [TRAIN] epoch=109, iter=40500/80000, loss=2.5691, lr=0.005299, batch_cost=1.4010, reader_cost=0.0002 | ETA 15:22:18 2020-12-02 10:01:42 [INFO] seg_loss:0.1853, att_loss: 0.5402, edge_loss: 2.9787, dual_loss: 0.0022 2020-12-02 10:01:43 [INFO] [TRAIN] epoch=110, iter=40600/80000, loss=2.5232, lr=0.005287, batch_cost=1.4673, reader_cost=0.0449 | ETA 16:03:31 2020-12-02 10:04:01 [INFO] seg_loss:0.1269, att_loss: 0.7119, edge_loss: 2.0300, dual_loss: 0.0018 2020-12-02 10:04:02 [INFO] [TRAIN] epoch=110, iter=40700/80000, loss=2.5459, lr=0.005275, batch_cost=1.3881, reader_cost=0.0002 | ETA 15:09:12 2020-12-02 10:06:17 [INFO] seg_loss:0.3285, att_loss: 0.6722, edge_loss: 2.0573, dual_loss: 0.0018 2020-12-02 10:06:18 [INFO] [TRAIN] epoch=110, iter=40800/80000, loss=2.4442, lr=0.005262, batch_cost=1.3609, reader_cost=0.0002 | ETA 14:49:06 2020-12-02 10:08:35 [INFO] seg_loss:0.0652, att_loss: 0.4695, edge_loss: 1.8627, dual_loss: 0.0014 2020-12-02 10:08:36 [INFO] [TRAIN] epoch=110, iter=40900/80000, loss=2.6747, lr=0.005250, batch_cost=1.3728, reader_cost=0.0002 | ETA 14:54:36 2020-12-02 10:10:58 [INFO] seg_loss:0.1538, att_loss: 0.6582, edge_loss: 2.3348, dual_loss: 0.0022 2020-12-02 10:10:59 [INFO] [TRAIN] epoch=111, iter=41000/80000, loss=2.5209, lr=0.005238, batch_cost=1.4335, reader_cost=0.0367 | ETA 15:31:45 2020-12-02 10:13:17 [INFO] seg_loss:0.1220, att_loss: 0.4929, edge_loss: 1.5580, dual_loss: 0.0012 2020-12-02 10:13:18 [INFO] [TRAIN] epoch=111, iter=41100/80000, loss=2.5217, lr=0.005226, batch_cost=1.3928, reader_cost=0.0002 | ETA 15:02:59 2020-12-02 10:15:37 [INFO] seg_loss:0.0653, att_loss: 0.4498, edge_loss: 2.0492, dual_loss: 0.0012 2020-12-02 10:15:38 [INFO] [TRAIN] epoch=111, iter=41200/80000, loss=2.3550, lr=0.005214, batch_cost=1.3991, reader_cost=0.0005 | ETA 15:04:44 2020-12-02 10:17:58 [INFO] seg_loss:0.1088, att_loss: 0.5319, edge_loss: 2.8873, dual_loss: 0.0019 2020-12-02 10:17:59 [INFO] [TRAIN] epoch=112, iter=41300/80000, loss=2.7160, lr=0.005202, batch_cost=1.4048, reader_cost=0.0419 | ETA 15:06:03 2020-12-02 10:20:17 [INFO] seg_loss:0.0341, att_loss: 0.3708, edge_loss: 1.2866, dual_loss: 0.0008 2020-12-02 10:20:18 [INFO] [TRAIN] epoch=112, iter=41400/80000, loss=2.6444, lr=0.005190, batch_cost=1.3943, reader_cost=0.0008 | ETA 14:57:00 2020-12-02 10:22:34 [INFO] seg_loss:0.1712, att_loss: 0.6961, edge_loss: 1.8964, dual_loss: 0.0020 2020-12-02 10:22:35 [INFO] [TRAIN] epoch=112, iter=41500/80000, loss=2.4116, lr=0.005178, batch_cost=1.3728, reader_cost=0.0005 | ETA 14:40:52 2020-12-02 10:24:52 [INFO] seg_loss:0.0438, att_loss: 0.3523, edge_loss: 1.3691, dual_loss: 0.0008 2020-12-02 10:24:53 [INFO] [TRAIN] epoch=112, iter=41600/80000, loss=2.4665, lr=0.005166, batch_cost=1.3783, reader_cost=0.0003 | ETA 14:42:05 2020-12-02 10:27:17 [INFO] seg_loss:0.0302, att_loss: 0.4307, edge_loss: 1.3988, dual_loss: 0.0007 2020-12-02 10:27:18 [INFO] [TRAIN] epoch=113, iter=41700/80000, loss=2.7366, lr=0.005154, batch_cost=1.4497, reader_cost=0.0518 | ETA 15:25:22 2020-12-02 10:29:34 [INFO] seg_loss:0.0676, att_loss: 0.5524, edge_loss: 1.5802, dual_loss: 0.0010 2020-12-02 10:29:35 [INFO] [TRAIN] epoch=113, iter=41800/80000, loss=2.4176, lr=0.005141, batch_cost=1.3722, reader_cost=0.0002 | ETA 14:33:37 2020-12-02 10:31:52 [INFO] seg_loss:0.0719, att_loss: 0.4959, edge_loss: 2.0778, dual_loss: 0.0013 2020-12-02 10:31:53 [INFO] [TRAIN] epoch=113, iter=41900/80000, loss=2.3419, lr=0.005129, batch_cost=1.3789, reader_cost=0.0002 | ETA 14:35:36 2020-12-02 10:34:11 [INFO] seg_loss:0.0934, att_loss: 0.4975, edge_loss: 2.7240, dual_loss: 0.0017 2020-12-02 10:34:12 [INFO] [TRAIN] epoch=113, iter=42000/80000, loss=2.6750, lr=0.005117, batch_cost=1.3829, reader_cost=0.0003 | ETA 14:35:49 2020-12-02 10:34:12 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 10:35:15 [INFO] [EVAL] #Images=500 mIoU=0.7683 Acc=0.9617 Kappa=0.9502 2020-12-02 10:35:15 [INFO] [EVAL] Class IoU: [0.9815 0.8575 0.9291 0.3412 0.5952 0.6974 0.7435 0.814 0.925 0.6487 0.9504 0.8446 0.6725 0.9554 0.6701 0.8246 0.73 0.623 0.7936] 2020-12-02 10:35:15 [INFO] [EVAL] Class Acc: [0.9893 0.9256 0.9566 0.8389 0.8204 0.83 0.8535 0.9072 0.955 0.8089 0.9665 0.921 0.7817 0.9777 0.8632 0.8606 0.8688 0.8662 0.8759] 2020-12-02 10:35:19 [INFO] [EVAL] The model with the best validation mIoU (0.7947) was saved at iter 40000. 2020-12-02 10:37:40 [INFO] seg_loss:0.0906, att_loss: 0.3993, edge_loss: 1.8422, dual_loss: 0.0016 2020-12-02 10:37:41 [INFO] [TRAIN] epoch=114, iter=42100/80000, loss=2.5333, lr=0.005105, batch_cost=1.4175, reader_cost=0.0393 | ETA 14:55:21 2020-12-02 10:39:58 [INFO] seg_loss:0.0430, att_loss: 0.3712, edge_loss: 1.4632, dual_loss: 0.0008 2020-12-02 10:39:59 [INFO] [TRAIN] epoch=114, iter=42200/80000, loss=2.4340, lr=0.005093, batch_cost=1.3855, reader_cost=0.0004 | ETA 14:32:50 2020-12-02 10:42:18 [INFO] seg_loss:0.1198, att_loss: 0.5403, edge_loss: 2.4683, dual_loss: 0.0020 2020-12-02 10:42:19 [INFO] [TRAIN] epoch=114, iter=42300/80000, loss=2.4357, lr=0.005081, batch_cost=1.3993, reader_cost=0.0002 | ETA 14:39:13 2020-12-02 10:44:37 [INFO] seg_loss:0.0439, att_loss: 0.4220, edge_loss: 1.8103, dual_loss: 0.0009 2020-12-02 10:44:38 [INFO] [TRAIN] epoch=114, iter=42400/80000, loss=2.5293, lr=0.005069, batch_cost=1.3921, reader_cost=0.0003 | ETA 14:32:23 2020-12-02 10:47:04 [INFO] seg_loss:0.0888, att_loss: 0.5195, edge_loss: 1.7353, dual_loss: 0.0015 2020-12-02 10:47:05 [INFO] [TRAIN] epoch=115, iter=42500/80000, loss=2.6288, lr=0.005057, batch_cost=1.4618, reader_cost=0.0421 | ETA 15:13:35 2020-12-02 10:49:25 [INFO] seg_loss:0.0965, att_loss: 0.6537, edge_loss: 1.6548, dual_loss: 0.0012 2020-12-02 10:49:26 [INFO] [TRAIN] epoch=115, iter=42600/80000, loss=2.3326, lr=0.005044, batch_cost=1.4184, reader_cost=0.0002 | ETA 14:44:06 2020-12-02 10:51:51 [INFO] seg_loss:0.2306, att_loss: 0.5730, edge_loss: 2.1466, dual_loss: 0.0021 2020-12-02 10:51:52 [INFO] [TRAIN] epoch=115, iter=42700/80000, loss=2.3292, lr=0.005032, batch_cost=1.4553, reader_cost=0.0009 | ETA 15:04:40 2020-12-02 10:54:16 [INFO] seg_loss:0.0410, att_loss: 0.4302, edge_loss: 1.9520, dual_loss: 0.0010 2020-12-02 10:54:17 [INFO] [TRAIN] epoch=116, iter=42800/80000, loss=2.7304, lr=0.005020, batch_cost=1.4501, reader_cost=0.0453 | ETA 14:59:03 2020-12-02 10:56:36 [INFO] seg_loss:0.1046, att_loss: 0.5525, edge_loss: 1.7680, dual_loss: 0.0014 2020-12-02 10:56:37 [INFO] [TRAIN] epoch=116, iter=42900/80000, loss=2.4737, lr=0.005008, batch_cost=1.4003, reader_cost=0.0002 | ETA 14:25:50 2020-12-02 10:58:59 [INFO] seg_loss:0.1108, att_loss: 0.5230, edge_loss: 2.3449, dual_loss: 0.0021 2020-12-02 10:59:00 [INFO] [TRAIN] epoch=116, iter=43000/80000, loss=2.3939, lr=0.004996, batch_cost=1.4326, reader_cost=0.0006 | ETA 14:43:24 2020-12-02 11:01:17 [INFO] seg_loss:0.1075, att_loss: 0.5687, edge_loss: 2.5707, dual_loss: 0.0019 2020-12-02 11:01:18 [INFO] [TRAIN] epoch=116, iter=43100/80000, loss=2.4621, lr=0.004984, batch_cost=1.3777, reader_cost=0.0002 | ETA 14:07:16 2020-12-02 11:03:41 [INFO] seg_loss:0.0609, att_loss: 0.3819, edge_loss: 1.4625, dual_loss: 0.0015 2020-12-02 11:03:42 [INFO] [TRAIN] epoch=117, iter=43200/80000, loss=2.5378, lr=0.004972, batch_cost=1.4399, reader_cost=0.0465 | ETA 14:43:09 2020-12-02 11:06:00 [INFO] seg_loss:0.0199, att_loss: 0.3219, edge_loss: 0.7355, dual_loss: 0.0004 2020-12-02 11:06:01 [INFO] [TRAIN] epoch=117, iter=43300/80000, loss=2.5510, lr=0.004959, batch_cost=1.3845, reader_cost=0.0004 | ETA 14:06:50 2020-12-02 11:08:19 [INFO] seg_loss:0.0505, att_loss: 0.3985, edge_loss: 2.1219, dual_loss: 0.0011 2020-12-02 11:08:20 [INFO] [TRAIN] epoch=117, iter=43400/80000, loss=2.5073, lr=0.004947, batch_cost=1.3947, reader_cost=0.0002 | ETA 14:10:46 2020-12-02 11:10:38 [INFO] seg_loss:0.0521, att_loss: 0.3954, edge_loss: 1.5732, dual_loss: 0.0009 2020-12-02 11:10:39 [INFO] [TRAIN] epoch=117, iter=43500/80000, loss=2.6962, lr=0.004935, batch_cost=1.3896, reader_cost=0.0004 | ETA 14:05:18 2020-12-02 11:13:03 [INFO] seg_loss:0.1138, att_loss: 0.5893, edge_loss: 2.7878, dual_loss: 0.0020 2020-12-02 11:13:04 [INFO] [TRAIN] epoch=118, iter=43600/80000, loss=2.5605, lr=0.004923, batch_cost=1.4530, reader_cost=0.0490 | ETA 14:41:28 2020-12-02 11:15:25 [INFO] seg_loss:0.0691, att_loss: 0.5287, edge_loss: 1.7986, dual_loss: 0.0015 2020-12-02 11:15:26 [INFO] [TRAIN] epoch=118, iter=43700/80000, loss=2.4626, lr=0.004911, batch_cost=1.4170, reader_cost=0.0002 | ETA 14:17:18 2020-12-02 11:17:44 [INFO] seg_loss:0.0749, att_loss: 0.4886, edge_loss: 3.0768, dual_loss: 0.0017 2020-12-02 11:17:45 [INFO] [TRAIN] epoch=118, iter=43800/80000, loss=2.3991, lr=0.004899, batch_cost=1.3892, reader_cost=0.0002 | ETA 13:58:07 2020-12-02 11:20:10 [INFO] seg_loss:0.0345, att_loss: 0.3480, edge_loss: 1.4944, dual_loss: 0.0009 2020-12-02 11:20:11 [INFO] [TRAIN] epoch=119, iter=43900/80000, loss=2.5438, lr=0.004886, batch_cost=1.4571, reader_cost=0.0420 | ETA 14:36:40 2020-12-02 11:22:31 [INFO] seg_loss:0.1583, att_loss: 0.6214, edge_loss: 2.7733, dual_loss: 0.0023 2020-12-02 11:22:32 [INFO] [TRAIN] epoch=119, iter=44000/80000, loss=2.6379, lr=0.004874, batch_cost=1.4166, reader_cost=0.0002 | ETA 14:09:56 2020-12-02 11:22:32 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 11:23:35 [INFO] [EVAL] #Images=500 mIoU=0.7787 Acc=0.9609 Kappa=0.9492 2020-12-02 11:23:35 [INFO] [EVAL] Class IoU: [0.9817 0.8545 0.9253 0.4024 0.6125 0.6837 0.7457 0.8085 0.9279 0.6407 0.9511 0.8474 0.6682 0.9391 0.6061 0.8809 0.8374 0.6873 0.7952] 2020-12-02 11:23:35 [INFO] [EVAL] Class Acc: [0.9907 0.9128 0.9528 0.8093 0.7868 0.8455 0.8504 0.9197 0.9598 0.8222 0.9661 0.9212 0.8012 0.9802 0.6342 0.9752 0.9143 0.8299 0.8827] 2020-12-02 11:23:40 [INFO] [EVAL] The model with the best validation mIoU (0.7947) was saved at iter 40000. 2020-12-02 11:26:00 [INFO] seg_loss:0.0982, att_loss: 0.6940, edge_loss: 1.8806, dual_loss: 0.0015 2020-12-02 11:26:01 [INFO] [TRAIN] epoch=119, iter=44100/80000, loss=2.3503, lr=0.004862, batch_cost=1.4103, reader_cost=0.0006 | ETA 14:03:50 2020-12-02 11:28:21 [INFO] seg_loss:0.0493, att_loss: 0.4133, edge_loss: 2.1259, dual_loss: 0.0013 2020-12-02 11:28:23 [INFO] [TRAIN] epoch=119, iter=44200/80000, loss=2.4654, lr=0.004850, batch_cost=1.4214, reader_cost=0.0002 | ETA 14:08:07 2020-12-02 11:30:49 [INFO] seg_loss:0.0872, att_loss: 0.5017, edge_loss: 2.1078, dual_loss: 0.0018 2020-12-02 11:30:50 [INFO] [TRAIN] epoch=120, iter=44300/80000, loss=2.6064, lr=0.004838, batch_cost=1.4696, reader_cost=0.0406 | ETA 14:34:25 2020-12-02 11:33:09 [INFO] seg_loss:0.0580, att_loss: 0.5059, edge_loss: 1.2206, dual_loss: 0.0011 2020-12-02 11:33:10 [INFO] [TRAIN] epoch=120, iter=44400/80000, loss=2.5303, lr=0.004825, batch_cost=1.4001, reader_cost=0.0002 | ETA 13:50:42 2020-12-02 11:35:32 [INFO] seg_loss:0.0417, att_loss: 0.4005, edge_loss: 1.7005, dual_loss: 0.0010 2020-12-02 11:35:33 [INFO] [TRAIN] epoch=120, iter=44500/80000, loss=2.4099, lr=0.004813, batch_cost=1.4323, reader_cost=0.0002 | ETA 14:07:27 2020-12-02 11:37:54 [INFO] seg_loss:0.1522, att_loss: 0.5850, edge_loss: 2.6122, dual_loss: 0.0023 2020-12-02 11:37:56 [INFO] [TRAIN] epoch=120, iter=44600/80000, loss=2.5183, lr=0.004801, batch_cost=1.4257, reader_cost=0.0002 | ETA 14:01:09 2020-12-02 11:40:21 [INFO] seg_loss:0.0360, att_loss: 0.2750, edge_loss: 1.0010, dual_loss: 0.0007 2020-12-02 11:40:22 [INFO] [TRAIN] epoch=121, iter=44700/80000, loss=2.4803, lr=0.004789, batch_cost=1.4605, reader_cost=0.0443 | ETA 14:19:14 2020-12-02 11:42:42 [INFO] seg_loss:0.1088, att_loss: 0.5810, edge_loss: 2.7565, dual_loss: 0.0018 2020-12-02 11:42:43 [INFO] [TRAIN] epoch=121, iter=44800/80000, loss=2.5065, lr=0.004777, batch_cost=1.4173, reader_cost=0.0002 | ETA 13:51:27 2020-12-02 11:45:02 [INFO] seg_loss:0.0558, att_loss: 0.4810, edge_loss: 1.2789, dual_loss: 0.0011 2020-12-02 11:45:03 [INFO] [TRAIN] epoch=121, iter=44900/80000, loss=2.4448, lr=0.004764, batch_cost=1.3940, reader_cost=0.0003 | ETA 13:35:30 2020-12-02 11:47:22 [INFO] seg_loss:0.0507, att_loss: 0.3358, edge_loss: 0.9666, dual_loss: 0.0010 2020-12-02 11:47:23 [INFO] [TRAIN] epoch=121, iter=45000/80000, loss=2.5268, lr=0.004752, batch_cost=1.4025, reader_cost=0.0003 | ETA 13:38:08 2020-12-02 11:49:49 [INFO] seg_loss:0.0650, att_loss: 0.4417, edge_loss: 1.3201, dual_loss: 0.0013 2020-12-02 11:49:50 [INFO] [TRAIN] epoch=122, iter=45100/80000, loss=2.5970, lr=0.004740, batch_cost=1.4685, reader_cost=0.0486 | ETA 14:14:11 2020-12-02 11:52:12 [INFO] seg_loss:0.0479, att_loss: 0.5333, edge_loss: 1.5085, dual_loss: 0.0008 2020-12-02 11:52:13 [INFO] [TRAIN] epoch=122, iter=45200/80000, loss=2.4457, lr=0.004728, batch_cost=1.4288, reader_cost=0.0003 | ETA 13:48:42 2020-12-02 11:54:33 [INFO] seg_loss:0.0935, att_loss: 0.6552, edge_loss: 1.8851, dual_loss: 0.0014 2020-12-02 11:54:34 [INFO] [TRAIN] epoch=122, iter=45300/80000, loss=2.3613, lr=0.004715, batch_cost=1.4132, reader_cost=0.0002 | ETA 13:37:19 2020-12-02 11:57:03 [INFO] seg_loss:0.1054, att_loss: 0.5608, edge_loss: 1.7147, dual_loss: 0.0013 2020-12-02 11:57:04 [INFO] [TRAIN] epoch=123, iter=45400/80000, loss=2.6573, lr=0.004703, batch_cost=1.4964, reader_cost=0.0445 | ETA 14:22:56 2020-12-02 11:59:22 [INFO] seg_loss:0.0558, att_loss: 0.4520, edge_loss: 1.4843, dual_loss: 0.0011 2020-12-02 11:59:23 [INFO] [TRAIN] epoch=123, iter=45500/80000, loss=2.5131, lr=0.004691, batch_cost=1.3919, reader_cost=0.0003 | ETA 13:20:21 2020-12-02 12:01:39 [INFO] seg_loss:0.0607, att_loss: 0.4904, edge_loss: 2.1520, dual_loss: 0.0013 2020-12-02 12:01:40 [INFO] [TRAIN] epoch=123, iter=45600/80000, loss=2.2958, lr=0.004679, batch_cost=1.3744, reader_cost=0.0002 | ETA 13:07:58 2020-12-02 12:03:58 [INFO] seg_loss:0.1377, att_loss: 0.4589, edge_loss: 2.4101, dual_loss: 0.0020 2020-12-02 12:03:59 [INFO] [TRAIN] epoch=123, iter=45700/80000, loss=2.4822, lr=0.004667, batch_cost=1.3824, reader_cost=0.0006 | ETA 13:10:17 2020-12-02 12:06:25 [INFO] seg_loss:0.1305, att_loss: 0.6712, edge_loss: 2.5287, dual_loss: 0.0020 2020-12-02 12:06:26 [INFO] [TRAIN] epoch=124, iter=45800/80000, loss=2.5687, lr=0.004654, batch_cost=1.4763, reader_cost=0.0484 | ETA 14:01:29 2020-12-02 12:08:45 [INFO] seg_loss:0.0766, att_loss: 0.5091, edge_loss: 2.2904, dual_loss: 0.0016 2020-12-02 12:08:46 [INFO] [TRAIN] epoch=124, iter=45900/80000, loss=2.4502, lr=0.004642, batch_cost=1.4013, reader_cost=0.0008 | ETA 13:16:25 2020-12-02 12:11:05 [INFO] seg_loss:0.0783, att_loss: 0.4994, edge_loss: 0.9417, dual_loss: 0.0009 2020-12-02 12:11:06 [INFO] [TRAIN] epoch=124, iter=46000/80000, loss=2.4125, lr=0.004630, batch_cost=1.3978, reader_cost=0.0002 | ETA 13:12:05 2020-12-02 12:11:06 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 12:12:09 [INFO] [EVAL] #Images=500 mIoU=0.7941 Acc=0.9633 Kappa=0.9523 2020-12-02 12:12:09 [INFO] [EVAL] Class IoU: [0.981 0.8596 0.9311 0.4383 0.6214 0.697 0.7538 0.8204 0.9261 0.6503 0.9485 0.8509 0.6769 0.9597 0.8329 0.8883 0.7757 0.6783 0.7983] 2020-12-02 12:12:09 [INFO] [EVAL] Class Acc: [0.9876 0.9433 0.9629 0.8285 0.8369 0.8255 0.8664 0.9185 0.9506 0.8399 0.9624 0.9093 0.7819 0.9806 0.9226 0.9704 0.8341 0.796 0.8842] 2020-12-02 12:12:13 [INFO] [EVAL] The model with the best validation mIoU (0.7947) was saved at iter 40000. 2020-12-02 12:14:32 [INFO] seg_loss:0.1379, att_loss: 0.5547, edge_loss: 2.9468, dual_loss: 0.0024 2020-12-02 12:14:33 [INFO] [TRAIN] epoch=124, iter=46100/80000, loss=2.6110, lr=0.004618, batch_cost=1.3981, reader_cost=0.0002 | ETA 13:09:56 2020-12-02 12:16:59 [INFO] seg_loss:0.1051, att_loss: 0.5427, edge_loss: 1.9820, dual_loss: 0.0019 2020-12-02 12:17:00 [INFO] [TRAIN] epoch=125, iter=46200/80000, loss=2.4325, lr=0.004605, batch_cost=1.4696, reader_cost=0.0423 | ETA 13:47:51 2020-12-02 12:19:18 [INFO] seg_loss:0.0592, att_loss: 0.4066, edge_loss: 1.4436, dual_loss: 0.0011 2020-12-02 12:19:19 [INFO] [TRAIN] epoch=125, iter=46300/80000, loss=2.4955, lr=0.004593, batch_cost=1.3911, reader_cost=0.0013 | ETA 13:01:19 2020-12-02 12:21:38 [INFO] seg_loss:0.0267, att_loss: 0.3596, edge_loss: 0.8956, dual_loss: 0.0006 2020-12-02 12:21:39 [INFO] [TRAIN] epoch=125, iter=46400/80000, loss=2.3239, lr=0.004581, batch_cost=1.3979, reader_cost=0.0013 | ETA 13:02:49 2020-12-02 12:23:57 [INFO] seg_loss:0.1047, att_loss: 0.5282, edge_loss: 3.3356, dual_loss: 0.0019 2020-12-02 12:23:58 [INFO] [TRAIN] epoch=125, iter=46500/80000, loss=2.5262, lr=0.004568, batch_cost=1.3864, reader_cost=0.0002 | ETA 12:54:05 2020-12-02 12:26:20 [INFO] seg_loss:0.1386, att_loss: 0.5839, edge_loss: 3.0071, dual_loss: 0.0024 2020-12-02 12:26:21 [INFO] [TRAIN] epoch=126, iter=46600/80000, loss=2.6554, lr=0.004556, batch_cost=1.4284, reader_cost=0.0410 | ETA 13:15:07 2020-12-02 12:28:42 [INFO] seg_loss:0.0706, att_loss: 0.8176, edge_loss: 0.7860, dual_loss: 0.0008 2020-12-02 12:28:43 [INFO] [TRAIN] epoch=126, iter=46700/80000, loss=2.3244, lr=0.004544, batch_cost=1.4242, reader_cost=0.0003 | ETA 13:10:26 2020-12-02 12:31:02 [INFO] seg_loss:0.0488, att_loss: 0.4300, edge_loss: 1.8604, dual_loss: 0.0011 2020-12-02 12:31:03 [INFO] [TRAIN] epoch=126, iter=46800/80000, loss=2.4604, lr=0.004532, batch_cost=1.4026, reader_cost=0.0002 | ETA 12:56:05 2020-12-02 12:33:27 [INFO] seg_loss:0.0334, att_loss: 0.3907, edge_loss: 0.8187, dual_loss: 0.0005 2020-12-02 12:33:28 [INFO] [TRAIN] epoch=127, iter=46900/80000, loss=2.6818, lr=0.004519, batch_cost=1.4454, reader_cost=0.0658 | ETA 13:17:21 2020-12-02 12:35:50 [INFO] seg_loss:0.0519, att_loss: 0.5214, edge_loss: 1.9321, dual_loss: 0.0010 2020-12-02 12:35:51 [INFO] [TRAIN] epoch=127, iter=47000/80000, loss=2.5576, lr=0.004507, batch_cost=1.4317, reader_cost=0.0002 | ETA 13:07:25 2020-12-02 12:38:09 [INFO] seg_loss:0.1001, att_loss: 0.6116, edge_loss: 2.0437, dual_loss: 0.0015 2020-12-02 12:38:11 [INFO] [TRAIN] epoch=127, iter=47100/80000, loss=2.4063, lr=0.004495, batch_cost=1.3936, reader_cost=0.0002 | ETA 12:44:10 2020-12-02 12:40:27 [INFO] seg_loss:0.2834, att_loss: 0.6360, edge_loss: 1.2544, dual_loss: 0.0018 2020-12-02 12:40:28 [INFO] [TRAIN] epoch=127, iter=47200/80000, loss=2.5201, lr=0.004482, batch_cost=1.3744, reader_cost=0.0002 | ETA 12:31:20 2020-12-02 12:42:52 [INFO] seg_loss:0.0784, att_loss: 0.4028, edge_loss: 1.8154, dual_loss: 0.0012 2020-12-02 12:42:53 [INFO] [TRAIN] epoch=128, iter=47300/80000, loss=2.5602, lr=0.004470, batch_cost=1.4460, reader_cost=0.0450 | ETA 13:08:03 2020-12-02 12:45:11 [INFO] seg_loss:0.0609, att_loss: 0.4234, edge_loss: 2.3399, dual_loss: 0.0012 2020-12-02 12:45:12 [INFO] [TRAIN] epoch=128, iter=47400/80000, loss=2.4018, lr=0.004458, batch_cost=1.3954, reader_cost=0.0006 | ETA 12:38:10 2020-12-02 12:47:31 [INFO] seg_loss:0.1413, att_loss: 0.4123, edge_loss: 0.7777, dual_loss: 0.0009 2020-12-02 12:47:32 [INFO] [TRAIN] epoch=128, iter=47500/80000, loss=2.3678, lr=0.004446, batch_cost=1.4012, reader_cost=0.0006 | ETA 12:38:58 2020-12-02 12:49:53 [INFO] seg_loss:0.1276, att_loss: 0.4106, edge_loss: 1.1282, dual_loss: 0.0011 2020-12-02 12:49:54 [INFO] [TRAIN] epoch=128, iter=47600/80000, loss=2.4791, lr=0.004433, batch_cost=1.4210, reader_cost=0.0003 | ETA 12:47:21 2020-12-02 12:52:20 [INFO] seg_loss:0.0722, att_loss: 0.3790, edge_loss: 2.5202, dual_loss: 0.0015 2020-12-02 12:52:21 [INFO] [TRAIN] epoch=129, iter=47700/80000, loss=2.5612, lr=0.004421, batch_cost=1.4634, reader_cost=0.0476 | ETA 13:07:48 2020-12-02 12:54:42 [INFO] seg_loss:0.1797, att_loss: 0.6429, edge_loss: 3.1136, dual_loss: 0.0027 2020-12-02 12:54:43 [INFO] [TRAIN] epoch=129, iter=47800/80000, loss=2.4354, lr=0.004409, batch_cost=1.4226, reader_cost=0.0004 | ETA 12:43:26 2020-12-02 12:57:03 [INFO] seg_loss:0.0713, att_loss: 0.4606, edge_loss: 1.1618, dual_loss: 0.0011 2020-12-02 12:57:04 [INFO] [TRAIN] epoch=129, iter=47900/80000, loss=2.3017, lr=0.004396, batch_cost=1.4093, reader_cost=0.0002 | ETA 12:33:58 2020-12-02 12:59:31 [INFO] seg_loss:0.1611, att_loss: 0.7149, edge_loss: 2.3225, dual_loss: 0.0021 2020-12-02 12:59:32 [INFO] [TRAIN] epoch=130, iter=48000/80000, loss=2.6091, lr=0.004384, batch_cost=1.4811, reader_cost=0.0481 | ETA 13:09:54 2020-12-02 12:59:32 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 13:00:37 [INFO] [EVAL] #Images=500 mIoU=0.7844 Acc=0.9632 Kappa=0.9522 2020-12-02 13:00:37 [INFO] [EVAL] Class IoU: [0.98 0.8599 0.9335 0.4214 0.6252 0.6945 0.7482 0.8184 0.9298 0.643 0.9509 0.8473 0.6716 0.9582 0.7345 0.8543 0.7342 0.7004 0.7985] 2020-12-02 13:00:37 [INFO] [EVAL] Class Acc: [0.9874 0.9247 0.961 0.8319 0.796 0.8364 0.8519 0.9229 0.9605 0.8521 0.9673 0.9181 0.7883 0.9782 0.8445 0.9399 0.7808 0.868 0.8749] 2020-12-02 13:00:41 [INFO] [EVAL] The model with the best validation mIoU (0.7947) was saved at iter 40000. 2020-12-02 13:03:05 [INFO] seg_loss:0.0204, att_loss: 0.3224, edge_loss: 0.9642, dual_loss: 0.0004 2020-12-02 13:03:06 [INFO] [TRAIN] epoch=130, iter=48100/80000, loss=2.5389, lr=0.004372, batch_cost=1.4456, reader_cost=0.0002 | ETA 12:48:35 2020-12-02 13:05:31 [INFO] seg_loss:0.0748, att_loss: 0.4720, edge_loss: 2.2492, dual_loss: 0.0014 2020-12-02 13:05:32 [INFO] [TRAIN] epoch=130, iter=48200/80000, loss=2.3248, lr=0.004359, batch_cost=1.4667, reader_cost=0.0002 | ETA 12:57:21 2020-12-02 13:07:54 [INFO] seg_loss:0.1014, att_loss: 0.4982, edge_loss: 2.7764, dual_loss: 0.0018 2020-12-02 13:07:55 [INFO] [TRAIN] epoch=130, iter=48300/80000, loss=2.4863, lr=0.004347, batch_cost=1.4222, reader_cost=0.0003 | ETA 12:31:22 2020-12-02 13:10:21 [INFO] seg_loss:0.0168, att_loss: 0.2934, edge_loss: 0.9076, dual_loss: 0.0004 2020-12-02 13:10:22 [INFO] [TRAIN] epoch=131, iter=48400/80000, loss=2.6719, lr=0.004335, batch_cost=1.4769, reader_cost=0.0584 | ETA 12:57:48 2020-12-02 13:12:43 [INFO] seg_loss:0.1067, att_loss: 0.3086, edge_loss: 1.6287, dual_loss: 0.0011 2020-12-02 13:12:44 [INFO] [TRAIN] epoch=131, iter=48500/80000, loss=2.4467, lr=0.004322, batch_cost=1.4131, reader_cost=0.0002 | ETA 12:21:53 2020-12-02 13:15:09 [INFO] seg_loss:0.0917, att_loss: 0.5373, edge_loss: 2.4895, dual_loss: 0.0017 2020-12-02 13:15:10 [INFO] [TRAIN] epoch=131, iter=48600/80000, loss=2.3719, lr=0.004310, batch_cost=1.4638, reader_cost=0.0003 | ETA 12:46:02 2020-12-02 13:17:29 [INFO] seg_loss:0.1021, att_loss: 0.5180, edge_loss: 2.4468, dual_loss: 0.0017 2020-12-02 13:17:30 [INFO] [TRAIN] epoch=131, iter=48700/80000, loss=2.6468, lr=0.004298, batch_cost=1.4003, reader_cost=0.0005 | ETA 12:10:28 2020-12-02 13:19:55 [INFO] seg_loss:0.0787, att_loss: 0.4318, edge_loss: 1.1580, dual_loss: 0.0010 2020-12-02 13:19:56 [INFO] [TRAIN] epoch=132, iter=48800/80000, loss=2.5215, lr=0.004285, batch_cost=1.4596, reader_cost=0.0467 | ETA 12:39:00 2020-12-02 13:22:21 [INFO] seg_loss:0.2174, att_loss: 0.6709, edge_loss: 2.5859, dual_loss: 0.0021 2020-12-02 13:22:22 [INFO] [TRAIN] epoch=132, iter=48900/80000, loss=2.5044, lr=0.004273, batch_cost=1.4593, reader_cost=0.0004 | ETA 12:36:24 2020-12-02 13:24:43 [INFO] seg_loss:0.0398, att_loss: 0.3578, edge_loss: 1.2961, dual_loss: 0.0007 2020-12-02 13:24:44 [INFO] [TRAIN] epoch=132, iter=49000/80000, loss=2.4554, lr=0.004260, batch_cost=1.4211, reader_cost=0.0005 | ETA 12:14:13 2020-12-02 13:27:03 [INFO] seg_loss:0.1298, att_loss: 0.4161, edge_loss: 1.7798, dual_loss: 0.0015 2020-12-02 13:27:04 [INFO] [TRAIN] epoch=132, iter=49100/80000, loss=2.5729, lr=0.004248, batch_cost=1.3970, reader_cost=0.0003 | ETA 11:59:27 2020-12-02 13:29:33 [INFO] seg_loss:0.1277, att_loss: 0.5052, edge_loss: 2.0513, dual_loss: 0.0019 2020-12-02 13:29:34 [INFO] [TRAIN] epoch=133, iter=49200/80000, loss=2.6504, lr=0.004236, batch_cost=1.5040, reader_cost=0.0442 | ETA 12:52:04 2020-12-02 13:31:54 [INFO] seg_loss:0.0527, att_loss: 0.5086, edge_loss: 1.0589, dual_loss: 0.0008 2020-12-02 13:31:55 [INFO] [TRAIN] epoch=133, iter=49300/80000, loss=2.4543, lr=0.004223, batch_cost=1.4119, reader_cost=0.0004 | ETA 12:02:26 2020-12-02 13:34:15 [INFO] seg_loss:0.0367, att_loss: 0.4197, edge_loss: 1.6523, dual_loss: 0.0008 2020-12-02 13:34:16 [INFO] [TRAIN] epoch=133, iter=49400/80000, loss=2.3939, lr=0.004211, batch_cost=1.4050, reader_cost=0.0004 | ETA 11:56:31 2020-12-02 13:36:41 [INFO] seg_loss:0.0677, att_loss: 0.4492, edge_loss: 2.3490, dual_loss: 0.0014 2020-12-02 13:36:43 [INFO] [TRAIN] epoch=134, iter=49500/80000, loss=2.7676, lr=0.004199, batch_cost=1.4663, reader_cost=0.0640 | ETA 12:25:21 2020-12-02 13:39:05 [INFO] seg_loss:0.0886, att_loss: 0.4231, edge_loss: 2.2794, dual_loss: 0.0015 2020-12-02 13:39:06 [INFO] [TRAIN] epoch=134, iter=49600/80000, loss=2.4337, lr=0.004186, batch_cost=1.4362, reader_cost=0.0002 | ETA 12:07:40 2020-12-02 13:41:27 [INFO] seg_loss:0.1530, att_loss: 0.7173, edge_loss: 2.2014, dual_loss: 0.0018 2020-12-02 13:41:28 [INFO] [TRAIN] epoch=134, iter=49700/80000, loss=2.4059, lr=0.004174, batch_cost=1.4219, reader_cost=0.0002 | ETA 11:58:02 2020-12-02 13:43:49 [INFO] seg_loss:0.0728, att_loss: 0.4556, edge_loss: 2.3141, dual_loss: 0.0014 2020-12-02 13:43:50 [INFO] [TRAIN] epoch=134, iter=49800/80000, loss=2.6140, lr=0.004161, batch_cost=1.4212, reader_cost=0.0004 | ETA 11:55:20 2020-12-02 13:46:18 [INFO] seg_loss:0.1243, att_loss: 0.5251, edge_loss: 2.4666, dual_loss: 0.0019 2020-12-02 13:46:19 [INFO] [TRAIN] epoch=135, iter=49900/80000, loss=2.5367, lr=0.004149, batch_cost=1.4843, reader_cost=0.0387 | ETA 12:24:36 2020-12-02 13:48:39 [INFO] seg_loss:0.0886, att_loss: 0.5750, edge_loss: 1.6516, dual_loss: 0.0014 2020-12-02 13:48:40 [INFO] [TRAIN] epoch=135, iter=50000/80000, loss=2.4231, lr=0.004137, batch_cost=1.4080, reader_cost=0.0005 | ETA 11:44:00 2020-12-02 13:48:40 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 13:49:43 [INFO] [EVAL] #Images=500 mIoU=0.7961 Acc=0.9631 Kappa=0.9520 2020-12-02 13:49:43 [INFO] [EVAL] Class IoU: [0.9801 0.8473 0.9303 0.4367 0.6239 0.6985 0.7501 0.8156 0.9303 0.6522 0.9535 0.8472 0.6736 0.9577 0.8141 0.9133 0.8187 0.6836 0.7991] 2020-12-02 13:49:43 [INFO] [EVAL] Class Acc: [0.9893 0.9201 0.9571 0.8272 0.8373 0.8559 0.8554 0.9031 0.9575 0.8394 0.9729 0.9111 0.7794 0.9777 0.9331 0.9512 0.895 0.8093 0.8706] 2020-12-02 13:49:50 [INFO] [EVAL] The model with the best validation mIoU (0.7961) was saved at iter 50000. 2020-12-02 13:52:13 [INFO] seg_loss:0.0839, att_loss: 0.4345, edge_loss: 1.9401, dual_loss: 0.0014 2020-12-02 13:52:14 [INFO] [TRAIN] epoch=135, iter=50100/80000, loss=2.4813, lr=0.004124, batch_cost=1.4350, reader_cost=0.0004 | ETA 11:55:05 2020-12-02 13:54:35 [INFO] seg_loss:0.0975, att_loss: 0.5712, edge_loss: 1.5046, dual_loss: 0.0015 2020-12-02 13:54:36 [INFO] [TRAIN] epoch=135, iter=50200/80000, loss=2.6538, lr=0.004112, batch_cost=1.4286, reader_cost=0.0002 | ETA 11:49:32 2020-12-02 13:57:01 [INFO] seg_loss:0.0668, att_loss: 0.4281, edge_loss: 2.3245, dual_loss: 0.0015 2020-12-02 13:57:02 [INFO] [TRAIN] epoch=136, iter=50300/80000, loss=2.4769, lr=0.004099, batch_cost=1.4583, reader_cost=0.0435 | ETA 12:01:51 2020-12-02 13:59:21 [INFO] seg_loss:0.0409, att_loss: 0.3664, edge_loss: 1.6259, dual_loss: 0.0009 2020-12-02 13:59:22 [INFO] [TRAIN] epoch=136, iter=50400/80000, loss=2.3928, lr=0.004087, batch_cost=1.4015, reader_cost=0.0002 | ETA 11:31:24 2020-12-02 14:01:49 [INFO] seg_loss:0.0682, att_loss: 0.4786, edge_loss: 2.5623, dual_loss: 0.0016 2020-12-02 14:01:50 [INFO] [TRAIN] epoch=136, iter=50500/80000, loss=2.3955, lr=0.004074, batch_cost=1.4728, reader_cost=0.0006 | ETA 12:04:07 2020-12-02 14:04:17 [INFO] seg_loss:0.0746, att_loss: 0.4875, edge_loss: 2.9462, dual_loss: 0.0016 2020-12-02 14:04:18 [INFO] [TRAIN] epoch=137, iter=50600/80000, loss=2.5750, lr=0.004062, batch_cost=1.4800, reader_cost=0.0437 | ETA 12:05:11 2020-12-02 14:06:38 [INFO] seg_loss:0.1124, att_loss: 0.5299, edge_loss: 2.0799, dual_loss: 0.0019 2020-12-02 14:06:39 [INFO] [TRAIN] epoch=137, iter=50700/80000, loss=2.5954, lr=0.004050, batch_cost=1.4134, reader_cost=0.0003 | ETA 11:30:12 2020-12-02 14:09:04 [INFO] seg_loss:0.2364, att_loss: 1.1406, edge_loss: 1.3516, dual_loss: 0.0019 2020-12-02 14:09:05 [INFO] [TRAIN] epoch=137, iter=50800/80000, loss=2.3102, lr=0.004037, batch_cost=1.4641, reader_cost=0.0006 | ETA 11:52:31 2020-12-02 14:11:24 [INFO] seg_loss:0.2090, att_loss: 0.5522, edge_loss: 2.1717, dual_loss: 0.0016 2020-12-02 14:11:25 [INFO] [TRAIN] epoch=137, iter=50900/80000, loss=2.5029, lr=0.004025, batch_cost=1.3991, reader_cost=0.0002 | ETA 11:18:34 2020-12-02 14:13:49 [INFO] seg_loss:0.0298, att_loss: 0.4160, edge_loss: 1.1901, dual_loss: 0.0006 2020-12-02 14:13:50 [INFO] [TRAIN] epoch=138, iter=51000/80000, loss=2.5450, lr=0.004012, batch_cost=1.4488, reader_cost=0.0446 | ETA 11:40:14 2020-12-02 14:16:12 [INFO] seg_loss:0.1073, att_loss: 0.5618, edge_loss: 2.0268, dual_loss: 0.0013 2020-12-02 14:16:13 [INFO] [TRAIN] epoch=138, iter=51100/80000, loss=2.4125, lr=0.004000, batch_cost=1.4270, reader_cost=0.0003 | ETA 11:27:19 2020-12-02 14:18:34 [INFO] seg_loss:0.0854, att_loss: 0.5200, edge_loss: 2.3997, dual_loss: 0.0018 2020-12-02 14:18:35 [INFO] [TRAIN] epoch=138, iter=51200/80000, loss=2.4549, lr=0.003987, batch_cost=1.4172, reader_cost=0.0003 | ETA 11:20:15 2020-12-02 14:20:55 [INFO] seg_loss:0.0848, att_loss: 0.5078, edge_loss: 2.1613, dual_loss: 0.0016 2020-12-02 14:20:56 [INFO] [TRAIN] epoch=138, iter=51300/80000, loss=2.4937, lr=0.003975, batch_cost=1.4086, reader_cost=0.0005 | ETA 11:13:47 2020-12-02 14:23:23 [INFO] seg_loss:0.1262, att_loss: 0.5623, edge_loss: 2.8067, dual_loss: 0.0018 2020-12-02 14:23:24 [INFO] [TRAIN] epoch=139, iter=51400/80000, loss=2.5374, lr=0.003962, batch_cost=1.4808, reader_cost=0.0432 | ETA 11:45:50 2020-12-02 14:25:45 [INFO] seg_loss:0.0289, att_loss: 0.4068, edge_loss: 1.4381, dual_loss: 0.0008 2020-12-02 14:25:46 [INFO] [TRAIN] epoch=139, iter=51500/80000, loss=2.4766, lr=0.003950, batch_cost=1.4219, reader_cost=0.0003 | ETA 11:15:23 2020-12-02 14:28:06 [INFO] seg_loss:0.1978, att_loss: 0.5798, edge_loss: 2.1572, dual_loss: 0.0020 2020-12-02 14:28:07 [INFO] [TRAIN] epoch=139, iter=51600/80000, loss=2.4489, lr=0.003937, batch_cost=1.4123, reader_cost=0.0002 | ETA 11:08:28 2020-12-02 14:30:27 [INFO] seg_loss:0.2144, att_loss: 0.5296, edge_loss: 1.7484, dual_loss: 0.0016 2020-12-02 14:30:28 [INFO] [TRAIN] epoch=139, iter=51700/80000, loss=2.4703, lr=0.003925, batch_cost=1.4066, reader_cost=0.0014 | ETA 11:03:25 2020-12-02 14:32:52 [INFO] seg_loss:0.0622, att_loss: 0.4704, edge_loss: 1.4462, dual_loss: 0.0011 2020-12-02 14:32:53 [INFO] [TRAIN] epoch=140, iter=51800/80000, loss=2.5499, lr=0.003913, batch_cost=1.4567, reader_cost=0.0477 | ETA 11:24:38 2020-12-02 14:35:14 [INFO] seg_loss:0.0346, att_loss: 0.4563, edge_loss: 1.1879, dual_loss: 0.0006 2020-12-02 14:35:15 [INFO] [TRAIN] epoch=140, iter=51900/80000, loss=2.4503, lr=0.003900, batch_cost=1.4147, reader_cost=0.0002 | ETA 11:02:33 2020-12-02 14:37:35 [INFO] seg_loss:0.1009, att_loss: 0.6325, edge_loss: 1.9877, dual_loss: 0.0017 2020-12-02 14:37:36 [INFO] [TRAIN] epoch=140, iter=52000/80000, loss=2.4027, lr=0.003888, batch_cost=1.4130, reader_cost=0.0005 | ETA 10:59:23 2020-12-02 14:37:36 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 14:38:40 [INFO] [EVAL] #Images=500 mIoU=0.7738 Acc=0.9624 Kappa=0.9512 2020-12-02 14:38:40 [INFO] [EVAL] Class IoU: [0.9821 0.8607 0.9291 0.4199 0.6364 0.697 0.7456 0.8207 0.9257 0.6404 0.9481 0.8449 0.6555 0.9552 0.7385 0.9095 0.5209 0.6737 0.7983] 2020-12-02 14:38:40 [INFO] [EVAL] Class Acc: [0.9888 0.9351 0.9595 0.839 0.8079 0.8138 0.8598 0.905 0.9597 0.8337 0.9593 0.9154 0.8168 0.973 0.893 0.9605 0.5496 0.7956 0.884 ] 2020-12-02 14:38:46 [INFO] [EVAL] The model with the best validation mIoU (0.7961) was saved at iter 50000. 2020-12-02 14:41:09 [INFO] seg_loss:0.1129, att_loss: 0.5697, edge_loss: 2.2063, dual_loss: 0.0025 2020-12-02 14:41:10 [INFO] [TRAIN] epoch=141, iter=52100/80000, loss=2.6526, lr=0.003875, batch_cost=1.4415, reader_cost=0.0406 | ETA 11:10:17 2020-12-02 14:43:30 [INFO] seg_loss:0.0415, att_loss: 0.3587, edge_loss: 1.5395, dual_loss: 0.0008 2020-12-02 14:43:31 [INFO] [TRAIN] epoch=141, iter=52200/80000, loss=2.4102, lr=0.003863, batch_cost=1.4114, reader_cost=0.0005 | ETA 10:53:56 2020-12-02 14:45:53 [INFO] seg_loss:0.1205, att_loss: 0.5517, edge_loss: 3.1330, dual_loss: 0.0023 2020-12-02 14:45:54 [INFO] [TRAIN] epoch=141, iter=52300/80000, loss=2.4114, lr=0.003850, batch_cost=1.4265, reader_cost=0.0004 | ETA 10:58:34 2020-12-02 14:48:17 [INFO] seg_loss:0.0860, att_loss: 0.4607, edge_loss: 2.8896, dual_loss: 0.0019 2020-12-02 14:48:18 [INFO] [TRAIN] epoch=141, iter=52400/80000, loss=2.5034, lr=0.003838, batch_cost=1.4409, reader_cost=0.0005 | ETA 11:02:47 2020-12-02 14:50:44 [INFO] seg_loss:0.2397, att_loss: 0.6297, edge_loss: 2.3597, dual_loss: 0.0023 2020-12-02 14:50:45 [INFO] [TRAIN] epoch=142, iter=52500/80000, loss=2.5425, lr=0.003825, batch_cost=1.4670, reader_cost=0.0399 | ETA 11:12:21 2020-12-02 14:53:02 [INFO] seg_loss:0.0046, att_loss: 0.0868, edge_loss: 0.5799, dual_loss: 0.0003 2020-12-02 14:53:03 [INFO] [TRAIN] epoch=142, iter=52600/80000, loss=2.5300, lr=0.003812, batch_cost=1.3844, reader_cost=0.0007 | ETA 10:32:11 2020-12-02 14:55:26 [INFO] seg_loss:0.0736, att_loss: 0.5199, edge_loss: 2.4909, dual_loss: 0.0015 2020-12-02 14:55:27 [INFO] [TRAIN] epoch=142, iter=52700/80000, loss=2.5208, lr=0.003800, batch_cost=1.4354, reader_cost=0.0004 | ETA 10:53:07 2020-12-02 14:57:45 [INFO] seg_loss:0.0500, att_loss: 0.4114, edge_loss: 2.0408, dual_loss: 0.0010 2020-12-02 14:57:46 [INFO] [TRAIN] epoch=142, iter=52800/80000, loss=2.5926, lr=0.003787, batch_cost=1.3974, reader_cost=0.0003 | ETA 10:33:28 2020-12-02 15:00:12 [INFO] seg_loss:0.1006, att_loss: 0.4776, edge_loss: 2.9006, dual_loss: 0.0018 2020-12-02 15:00:13 [INFO] [TRAIN] epoch=143, iter=52900/80000, loss=2.3619, lr=0.003775, batch_cost=1.4645, reader_cost=0.0424 | ETA 11:01:28 2020-12-02 15:02:31 [INFO] seg_loss:0.0222, att_loss: 0.3544, edge_loss: 1.5606, dual_loss: 0.0007 2020-12-02 15:02:32 [INFO] [TRAIN] epoch=143, iter=53000/80000, loss=2.4423, lr=0.003762, batch_cost=1.3938, reader_cost=0.0003 | ETA 10:27:13 2020-12-02 15:04:52 [INFO] seg_loss:0.0807, att_loss: 0.5046, edge_loss: 2.4461, dual_loss: 0.0015 2020-12-02 15:04:53 [INFO] [TRAIN] epoch=143, iter=53100/80000, loss=2.4432, lr=0.003750, batch_cost=1.4073, reader_cost=0.0002 | ETA 10:30:55 2020-12-02 15:07:17 [INFO] seg_loss:0.2923, att_loss: 0.7866, edge_loss: 2.1485, dual_loss: 0.0017 2020-12-02 15:07:18 [INFO] [TRAIN] epoch=144, iter=53200/80000, loss=2.5491, lr=0.003737, batch_cost=1.4529, reader_cost=0.0413 | ETA 10:48:57 2020-12-02 15:09:38 [INFO] seg_loss:0.0875, att_loss: 0.5388, edge_loss: 2.3092, dual_loss: 0.0014 2020-12-02 15:09:39 [INFO] [TRAIN] epoch=144, iter=53300/80000, loss=2.6706, lr=0.003725, batch_cost=1.4088, reader_cost=0.0002 | ETA 10:26:54 2020-12-02 15:11:58 [INFO] seg_loss:0.0957, att_loss: 0.4505, edge_loss: 1.3878, dual_loss: 0.0015 2020-12-02 15:12:00 [INFO] [TRAIN] epoch=144, iter=53400/80000, loss=2.2514, lr=0.003712, batch_cost=1.4045, reader_cost=0.0002 | ETA 10:22:38 2020-12-02 15:14:15 [INFO] seg_loss:0.0963, att_loss: 0.4994, edge_loss: 2.8796, dual_loss: 0.0019 2020-12-02 15:14:16 [INFO] [TRAIN] epoch=144, iter=53500/80000, loss=2.4673, lr=0.003700, batch_cost=1.3683, reader_cost=0.0002 | ETA 10:04:19 2020-12-02 15:16:39 [INFO] seg_loss:0.3893, att_loss: 0.7772, edge_loss: 2.4581, dual_loss: 0.0030 2020-12-02 15:16:40 [INFO] [TRAIN] epoch=145, iter=53600/80000, loss=2.6212, lr=0.003687, batch_cost=1.4363, reader_cost=0.0430 | ETA 10:31:58 2020-12-02 15:18:57 [INFO] seg_loss:0.0303, att_loss: 0.3818, edge_loss: 0.9817, dual_loss: 0.0008 2020-12-02 15:18:58 [INFO] [TRAIN] epoch=145, iter=53700/80000, loss=2.4581, lr=0.003674, batch_cost=1.3761, reader_cost=0.0004 | ETA 10:03:11 2020-12-02 15:21:18 [INFO] seg_loss:0.0983, att_loss: 0.5005, edge_loss: 2.0965, dual_loss: 0.0017 2020-12-02 15:21:19 [INFO] [TRAIN] epoch=145, iter=53800/80000, loss=2.3967, lr=0.003662, batch_cost=1.4109, reader_cost=0.0007 | ETA 10:16:05 2020-12-02 15:23:42 [INFO] seg_loss:0.1262, att_loss: 0.6282, edge_loss: 2.2350, dual_loss: 0.0019 2020-12-02 15:23:43 [INFO] [TRAIN] epoch=145, iter=53900/80000, loss=2.5775, lr=0.003649, batch_cost=1.4437, reader_cost=0.0007 | ETA 10:28:01 2020-12-02 15:26:07 [INFO] seg_loss:0.0347, att_loss: 0.3747, edge_loss: 1.4110, dual_loss: 0.0007 2020-12-02 15:26:08 [INFO] [TRAIN] epoch=146, iter=54000/80000, loss=2.4634, lr=0.003637, batch_cost=1.4489, reader_cost=0.0562 | ETA 10:27:50 2020-12-02 15:26:08 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 15:27:11 [INFO] [EVAL] #Images=500 mIoU=0.8034 Acc=0.9646 Kappa=0.9540 2020-12-02 15:27:11 [INFO] [EVAL] Class IoU: [0.9824 0.8567 0.9336 0.5513 0.6173 0.7047 0.7489 0.8199 0.9295 0.6419 0.952 0.8506 0.6724 0.9588 0.8084 0.9252 0.8426 0.67 0.7978] 2020-12-02 15:27:11 [INFO] [EVAL] Class Acc: [0.9906 0.9263 0.9607 0.8188 0.862 0.849 0.8415 0.8918 0.9592 0.8183 0.9658 0.9246 0.8245 0.977 0.861 0.9756 0.9075 0.8419 0.8832] 2020-12-02 15:27:17 [INFO] [EVAL] The model with the best validation mIoU (0.8034) was saved at iter 54000. 2020-12-02 15:29:37 [INFO] seg_loss:0.0813, att_loss: 0.4439, edge_loss: 1.9178, dual_loss: 0.0014 2020-12-02 15:29:38 [INFO] [TRAIN] epoch=146, iter=54100/80000, loss=2.4065, lr=0.003624, batch_cost=1.4122, reader_cost=0.0002 | ETA 10:09:36 2020-12-02 15:31:59 [INFO] seg_loss:0.0240, att_loss: 0.3479, edge_loss: 1.0419, dual_loss: 0.0006 2020-12-02 15:32:00 [INFO] [TRAIN] epoch=146, iter=54200/80000, loss=2.3767, lr=0.003612, batch_cost=1.4233, reader_cost=0.0005 | ETA 10:12:02 2020-12-02 15:34:21 [INFO] seg_loss:0.0359, att_loss: 0.3430, edge_loss: 0.8710, dual_loss: 0.0008 2020-12-02 15:34:22 [INFO] [TRAIN] epoch=146, iter=54300/80000, loss=2.6254, lr=0.003599, batch_cost=1.4119, reader_cost=0.0002 | ETA 10:04:44 2020-12-02 15:36:44 [INFO] seg_loss:0.0820, att_loss: 0.4989, edge_loss: 1.5370, dual_loss: 0.0013 2020-12-02 15:36:45 [INFO] [TRAIN] epoch=147, iter=54400/80000, loss=2.5441, lr=0.003586, batch_cost=1.4372, reader_cost=0.0437 | ETA 10:13:13 2020-12-02 15:39:03 [INFO] seg_loss:0.0694, att_loss: 0.5158, edge_loss: 1.4396, dual_loss: 0.0011 2020-12-02 15:39:04 [INFO] [TRAIN] epoch=147, iter=54500/80000, loss=2.3711, lr=0.003574, batch_cost=1.3886, reader_cost=0.0003 | ETA 09:50:09 2020-12-02 15:41:20 [INFO] seg_loss:0.0486, att_loss: 0.4746, edge_loss: 1.1022, dual_loss: 0.0008 2020-12-02 15:41:21 [INFO] [TRAIN] epoch=147, iter=54600/80000, loss=2.3149, lr=0.003561, batch_cost=1.3646, reader_cost=0.0003 | ETA 09:37:41 2020-12-02 15:43:45 [INFO] seg_loss:0.0558, att_loss: 0.4316, edge_loss: 2.2551, dual_loss: 0.0013 2020-12-02 15:43:46 [INFO] [TRAIN] epoch=148, iter=54700/80000, loss=2.5887, lr=0.003548, batch_cost=1.4507, reader_cost=0.0622 | ETA 10:11:42 2020-12-02 15:46:05 [INFO] seg_loss:0.0718, att_loss: 0.4697, edge_loss: 2.4103, dual_loss: 0.0016 2020-12-02 15:46:06 [INFO] [TRAIN] epoch=148, iter=54800/80000, loss=2.4815, lr=0.003536, batch_cost=1.4063, reader_cost=0.0002 | ETA 09:50:39 2020-12-02 15:48:28 [INFO] seg_loss:0.0711, att_loss: 0.4301, edge_loss: 2.2349, dual_loss: 0.0015 2020-12-02 15:48:29 [INFO] [TRAIN] epoch=148, iter=54900/80000, loss=2.2847, lr=0.003523, batch_cost=1.4260, reader_cost=0.0002 | ETA 09:56:32 2020-12-02 15:50:47 [INFO] seg_loss:0.0594, att_loss: 0.5285, edge_loss: 2.1720, dual_loss: 0.0013 2020-12-02 15:50:48 [INFO] [TRAIN] epoch=148, iter=55000/80000, loss=2.4719, lr=0.003511, batch_cost=1.3882, reader_cost=0.0002 | ETA 09:38:24 2020-12-02 15:53:09 [INFO] seg_loss:0.0539, att_loss: 0.4146, edge_loss: 1.5441, dual_loss: 0.0011 2020-12-02 15:53:10 [INFO] [TRAIN] epoch=149, iter=55100/80000, loss=2.5399, lr=0.003498, batch_cost=1.4190, reader_cost=0.0415 | ETA 09:48:53 2020-12-02 15:55:32 [INFO] seg_loss:0.0873, att_loss: 0.4755, edge_loss: 2.4223, dual_loss: 0.0016 2020-12-02 15:55:33 [INFO] [TRAIN] epoch=149, iter=55200/80000, loss=2.5780, lr=0.003485, batch_cost=1.4295, reader_cost=0.0004 | ETA 09:50:52 2020-12-02 15:57:49 [INFO] seg_loss:0.0171, att_loss: 0.3344, edge_loss: 0.7655, dual_loss: 0.0004 2020-12-02 15:57:50 [INFO] [TRAIN] epoch=149, iter=55300/80000, loss=2.4003, lr=0.003473, batch_cost=1.3725, reader_cost=0.0002 | ETA 09:25:01 2020-12-02 16:00:08 [INFO] seg_loss:0.2933, att_loss: 0.6242, edge_loss: 2.9581, dual_loss: 0.0024 2020-12-02 16:00:09 [INFO] [TRAIN] epoch=149, iter=55400/80000, loss=2.5630, lr=0.003460, batch_cost=1.3879, reader_cost=0.0009 | ETA 09:29:03 2020-12-02 16:02:32 [INFO] seg_loss:0.1966, att_loss: 0.7379, edge_loss: 2.1796, dual_loss: 0.0022 2020-12-02 16:02:33 [INFO] [TRAIN] epoch=150, iter=55500/80000, loss=2.2984, lr=0.003447, batch_cost=1.4415, reader_cost=0.0488 | ETA 09:48:35 2020-12-02 16:04:53 [INFO] seg_loss:0.1265, att_loss: 0.5245, edge_loss: 2.4667, dual_loss: 0.0019 2020-12-02 16:04:54 [INFO] [TRAIN] epoch=150, iter=55600/80000, loss=2.5033, lr=0.003435, batch_cost=1.4087, reader_cost=0.0002 | ETA 09:32:51 2020-12-02 16:07:12 [INFO] seg_loss:0.0567, att_loss: 0.5240, edge_loss: 1.3372, dual_loss: 0.0011 2020-12-02 16:07:13 [INFO] [TRAIN] epoch=150, iter=55700/80000, loss=2.3586, lr=0.003422, batch_cost=1.3953, reader_cost=0.0005 | ETA 09:25:06 2020-12-02 16:09:32 [INFO] seg_loss:0.1190, att_loss: 0.5830, edge_loss: 3.0636, dual_loss: 0.0020 2020-12-02 16:09:33 [INFO] [TRAIN] epoch=150, iter=55800/80000, loss=2.5431, lr=0.003409, batch_cost=1.3972, reader_cost=0.0003 | ETA 09:23:31 2020-12-02 16:12:00 [INFO] seg_loss:0.1059, att_loss: 0.4700, edge_loss: 1.9526, dual_loss: 0.0016 2020-12-02 16:12:01 [INFO] [TRAIN] epoch=151, iter=55900/80000, loss=2.6257, lr=0.003397, batch_cost=1.4830, reader_cost=0.0419 | ETA 09:55:40 2020-12-02 16:14:19 [INFO] seg_loss:0.0352, att_loss: 0.4968, edge_loss: 1.1705, dual_loss: 0.0007 2020-12-02 16:14:20 [INFO] [TRAIN] epoch=151, iter=56000/80000, loss=2.3044, lr=0.003384, batch_cost=1.3904, reader_cost=0.0002 | ETA 09:16:08 2020-12-02 16:14:21 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 16:15:24 [INFO] [EVAL] #Images=500 mIoU=0.7956 Acc=0.9645 Kappa=0.9539 2020-12-02 16:15:24 [INFO] [EVAL] Class IoU: [0.984 0.8726 0.9311 0.4735 0.6185 0.7009 0.75 0.8108 0.9285 0.6587 0.9525 0.8496 0.6669 0.9573 0.7991 0.8728 0.7947 0.6939 0.8004] 2020-12-02 16:15:24 [INFO] [EVAL] Class Acc: [0.9908 0.9436 0.9572 0.8575 0.8518 0.8434 0.8663 0.8927 0.9588 0.796 0.9699 0.9083 0.7846 0.9785 0.9103 0.9075 0.8835 0.8542 0.8859] 2020-12-02 16:15:28 [INFO] [EVAL] The model with the best validation mIoU (0.8034) was saved at iter 54000. 2020-12-02 16:17:47 [INFO] seg_loss:0.1173, att_loss: 0.3801, edge_loss: 1.6074, dual_loss: 0.0011 2020-12-02 16:17:48 [INFO] [TRAIN] epoch=151, iter=56100/80000, loss=2.3640, lr=0.003371, batch_cost=1.4026, reader_cost=0.0003 | ETA 09:18:42 2020-12-02 16:20:12 [INFO] seg_loss:0.1098, att_loss: 1.0686, edge_loss: 0.8264, dual_loss: 0.0007 2020-12-02 16:20:13 [INFO] [TRAIN] epoch=152, iter=56200/80000, loss=2.6903, lr=0.003359, batch_cost=1.4441, reader_cost=0.0432 | ETA 09:32:50 2020-12-02 16:22:36 [INFO] seg_loss:0.0664, att_loss: 0.4533, edge_loss: 2.1537, dual_loss: 0.0015 2020-12-02 16:22:37 [INFO] [TRAIN] epoch=152, iter=56300/80000, loss=2.5418, lr=0.003346, batch_cost=1.4438, reader_cost=0.0004 | ETA 09:30:16 2020-12-02 16:24:55 [INFO] seg_loss:0.0332, att_loss: 0.4099, edge_loss: 1.3691, dual_loss: 0.0007 2020-12-02 16:24:56 [INFO] [TRAIN] epoch=152, iter=56400/80000, loss=2.3825, lr=0.003333, batch_cost=1.3840, reader_cost=0.0010 | ETA 09:04:22 2020-12-02 16:27:15 [INFO] seg_loss:0.0751, att_loss: 0.4071, edge_loss: 1.3588, dual_loss: 0.0013 2020-12-02 16:27:16 [INFO] [TRAIN] epoch=152, iter=56500/80000, loss=2.5455, lr=0.003320, batch_cost=1.3999, reader_cost=0.0003 | ETA 09:08:18 2020-12-02 16:29:40 [INFO] seg_loss:0.0812, att_loss: 0.4496, edge_loss: 2.0089, dual_loss: 0.0014 2020-12-02 16:29:41 [INFO] [TRAIN] epoch=153, iter=56600/80000, loss=2.5693, lr=0.003308, batch_cost=1.4573, reader_cost=0.0397 | ETA 09:28:21 2020-12-02 16:31:59 [INFO] seg_loss:0.0769, att_loss: 0.4999, edge_loss: 2.6585, dual_loss: 0.0016 2020-12-02 16:32:00 [INFO] [TRAIN] epoch=153, iter=56700/80000, loss=2.3300, lr=0.003295, batch_cost=1.3906, reader_cost=0.0002 | ETA 09:00:00 2020-12-02 16:34:18 [INFO] seg_loss:0.0122, att_loss: 0.3366, edge_loss: 0.8719, dual_loss: 0.0004 2020-12-02 16:34:19 [INFO] [TRAIN] epoch=153, iter=56800/80000, loss=2.4719, lr=0.003282, batch_cost=1.3820, reader_cost=0.0002 | ETA 08:54:23 2020-12-02 16:36:37 [INFO] seg_loss:0.0375, att_loss: 0.3409, edge_loss: 1.0341, dual_loss: 0.0008 2020-12-02 16:36:38 [INFO] [TRAIN] epoch=153, iter=56900/80000, loss=2.4966, lr=0.003270, batch_cost=1.3922, reader_cost=0.0005 | ETA 08:56:00 2020-12-02 16:39:04 [INFO] seg_loss:0.0672, att_loss: 0.4987, edge_loss: 2.4427, dual_loss: 0.0014 2020-12-02 16:39:05 [INFO] [TRAIN] epoch=154, iter=57000/80000, loss=2.4435, lr=0.003257, batch_cost=1.4713, reader_cost=0.0489 | ETA 09:24:00 2020-12-02 16:41:25 [INFO] seg_loss:0.1349, att_loss: 0.6293, edge_loss: 2.4999, dual_loss: 0.0019 2020-12-02 16:41:26 [INFO] [TRAIN] epoch=154, iter=57100/80000, loss=2.4346, lr=0.003244, batch_cost=1.4143, reader_cost=0.0002 | ETA 08:59:46 2020-12-02 16:43:44 [INFO] seg_loss:0.0870, att_loss: 0.5785, edge_loss: 1.2842, dual_loss: 0.0009 2020-12-02 16:43:45 [INFO] [TRAIN] epoch=154, iter=57200/80000, loss=2.3288, lr=0.003231, batch_cost=1.3857, reader_cost=0.0003 | ETA 08:46:34 2020-12-02 16:46:11 [INFO] seg_loss:0.0980, att_loss: 0.5089, edge_loss: 2.0349, dual_loss: 0.0015 2020-12-02 16:46:12 [INFO] [TRAIN] epoch=155, iter=57300/80000, loss=2.6061, lr=0.003219, batch_cost=1.4698, reader_cost=0.0424 | ETA 09:16:05 2020-12-02 16:48:31 [INFO] seg_loss:0.0482, att_loss: 0.4529, edge_loss: 1.4079, dual_loss: 0.0008 2020-12-02 16:48:32 [INFO] [TRAIN] epoch=155, iter=57400/80000, loss=2.5693, lr=0.003206, batch_cost=1.4025, reader_cost=0.0005 | ETA 08:48:16 2020-12-02 16:50:50 [INFO] seg_loss:0.0429, att_loss: 0.3865, edge_loss: 1.4565, dual_loss: 0.0009 2020-12-02 16:50:51 [INFO] [TRAIN] epoch=155, iter=57500/80000, loss=2.3185, lr=0.003193, batch_cost=1.3878, reader_cost=0.0003 | ETA 08:40:25 2020-12-02 16:53:10 [INFO] seg_loss:0.0535, att_loss: 0.4261, edge_loss: 2.2969, dual_loss: 0.0013 2020-12-02 16:53:11 [INFO] [TRAIN] epoch=155, iter=57600/80000, loss=2.4464, lr=0.003180, batch_cost=1.4024, reader_cost=0.0009 | ETA 08:43:33 2020-12-02 16:55:36 [INFO] seg_loss:0.0396, att_loss: 0.4005, edge_loss: 1.2522, dual_loss: 0.0008 2020-12-02 16:55:37 [INFO] [TRAIN] epoch=156, iter=57700/80000, loss=2.5873, lr=0.003167, batch_cost=1.4580, reader_cost=0.0614 | ETA 09:01:53 2020-12-02 16:57:58 [INFO] seg_loss:0.2126, att_loss: 0.4102, edge_loss: 1.8046, dual_loss: 0.0015 2020-12-02 16:57:59 [INFO] [TRAIN] epoch=156, iter=57800/80000, loss=2.5524, lr=0.003155, batch_cost=1.4235, reader_cost=0.0009 | ETA 08:46:40 2020-12-02 17:00:18 [INFO] seg_loss:0.0314, att_loss: 0.3281, edge_loss: 1.8649, dual_loss: 0.0009 2020-12-02 17:00:19 [INFO] [TRAIN] epoch=156, iter=57900/80000, loss=2.4487, lr=0.003142, batch_cost=1.3991, reader_cost=0.0003 | ETA 08:35:20 2020-12-02 17:02:38 [INFO] seg_loss:0.1174, att_loss: 0.5695, edge_loss: 3.2831, dual_loss: 0.0021 2020-12-02 17:02:39 [INFO] [TRAIN] epoch=156, iter=58000/80000, loss=2.5150, lr=0.003129, batch_cost=1.3995, reader_cost=0.0004 | ETA 08:33:08 2020-12-02 17:02:39 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 17:03:42 [INFO] [EVAL] #Images=500 mIoU=0.7976 Acc=0.9648 Kappa=0.9543 2020-12-02 17:03:42 [INFO] [EVAL] Class IoU: [0.9843 0.8721 0.9316 0.4523 0.6397 0.7055 0.7526 0.8215 0.9301 0.6381 0.952 0.8484 0.6589 0.9569 0.787 0.9115 0.8514 0.6621 0.7985] 2020-12-02 17:03:42 [INFO] [EVAL] Class Acc: [0.9917 0.9343 0.9594 0.8357 0.819 0.8439 0.8597 0.9049 0.9576 0.8653 0.9662 0.9049 0.7977 0.9762 0.8794 0.9783 0.9272 0.822 0.8759] 2020-12-02 17:03:47 [INFO] [EVAL] The model with the best validation mIoU (0.8034) was saved at iter 54000. 2020-12-02 17:06:12 [INFO] seg_loss:0.0603, att_loss: 0.4508, edge_loss: 1.1741, dual_loss: 0.0011 2020-12-02 17:06:13 [INFO] [TRAIN] epoch=157, iter=58100/80000, loss=2.3958, lr=0.003116, batch_cost=1.4637, reader_cost=0.0463 | ETA 08:54:14 2020-12-02 17:08:32 [INFO] seg_loss:0.0828, att_loss: 0.5108, edge_loss: 1.8041, dual_loss: 0.0015 2020-12-02 17:08:33 [INFO] [TRAIN] epoch=157, iter=58200/80000, loss=2.3789, lr=0.003103, batch_cost=1.4019, reader_cost=0.0002 | ETA 08:29:21 2020-12-02 17:10:51 [INFO] seg_loss:0.0525, att_loss: 0.4964, edge_loss: 1.8741, dual_loss: 0.0010 2020-12-02 17:10:52 [INFO] [TRAIN] epoch=157, iter=58300/80000, loss=2.3715, lr=0.003091, batch_cost=1.3865, reader_cost=0.0007 | ETA 08:21:27 2020-12-02 17:13:09 [INFO] seg_loss:0.0601, att_loss: 0.4930, edge_loss: 1.3826, dual_loss: 0.0011 2020-12-02 17:13:10 [INFO] [TRAIN] epoch=157, iter=58400/80000, loss=2.5482, lr=0.003078, batch_cost=1.3813, reader_cost=0.0002 | ETA 08:17:15 2020-12-02 17:15:32 [INFO] seg_loss:0.0985, att_loss: 0.5276, edge_loss: 2.3846, dual_loss: 0.0019 2020-12-02 17:15:33 [INFO] [TRAIN] epoch=158, iter=58500/80000, loss=2.5000, lr=0.003065, batch_cost=1.4328, reader_cost=0.0405 | ETA 08:33:24 2020-12-02 17:17:53 [INFO] seg_loss:0.1353, att_loss: 0.4970, edge_loss: 1.3427, dual_loss: 0.0016 2020-12-02 17:17:54 [INFO] [TRAIN] epoch=158, iter=58600/80000, loss=2.3957, lr=0.003052, batch_cost=1.4091, reader_cost=0.0002 | ETA 08:22:35 2020-12-02 17:20:13 [INFO] seg_loss:0.0317, att_loss: 0.4231, edge_loss: 1.7581, dual_loss: 0.0008 2020-12-02 17:20:14 [INFO] [TRAIN] epoch=158, iter=58700/80000, loss=2.3516, lr=0.003039, batch_cost=1.4025, reader_cost=0.0002 | ETA 08:17:53 2020-12-02 17:22:40 [INFO] seg_loss:0.1007, att_loss: 0.4486, edge_loss: 2.9722, dual_loss: 0.0021 2020-12-02 17:22:41 [INFO] [TRAIN] epoch=159, iter=58800/80000, loss=2.5914, lr=0.003026, batch_cost=1.4674, reader_cost=0.0429 | ETA 08:38:29 2020-12-02 17:25:00 [INFO] seg_loss:0.3522, att_loss: 0.7629, edge_loss: 2.3986, dual_loss: 0.0024 2020-12-02 17:25:01 [INFO] [TRAIN] epoch=159, iter=58900/80000, loss=2.3790, lr=0.003014, batch_cost=1.4025, reader_cost=0.0002 | ETA 08:13:13 2020-12-02 17:27:19 [INFO] seg_loss:0.0961, att_loss: 0.5593, edge_loss: 1.9677, dual_loss: 0.0013 2020-12-02 17:27:21 [INFO] [TRAIN] epoch=159, iter=59000/80000, loss=2.3434, lr=0.003001, batch_cost=1.3911, reader_cost=0.0002 | ETA 08:06:52 2020-12-02 17:29:42 [INFO] seg_loss:0.0752, att_loss: 0.3981, edge_loss: 2.2931, dual_loss: 0.0014 2020-12-02 17:29:43 [INFO] [TRAIN] epoch=159, iter=59100/80000, loss=2.4635, lr=0.002988, batch_cost=1.4254, reader_cost=0.0002 | ETA 08:16:31 2020-12-02 17:32:07 [INFO] seg_loss:0.0466, att_loss: 0.3005, edge_loss: 1.5680, dual_loss: 0.0010 2020-12-02 17:32:08 [INFO] [TRAIN] epoch=160, iter=59200/80000, loss=2.4191, lr=0.002975, batch_cost=1.4500, reader_cost=0.0418 | ETA 08:22:40 2020-12-02 17:34:28 [INFO] seg_loss:0.0618, att_loss: 0.4802, edge_loss: 1.8582, dual_loss: 0.0013 2020-12-02 17:34:29 [INFO] [TRAIN] epoch=160, iter=59300/80000, loss=2.4313, lr=0.002962, batch_cost=1.4134, reader_cost=0.0003 | ETA 08:07:36 2020-12-02 17:36:48 [INFO] seg_loss:0.1112, att_loss: 0.5199, edge_loss: 2.4585, dual_loss: 0.0016 2020-12-02 17:36:49 [INFO] [TRAIN] epoch=160, iter=59400/80000, loss=2.4145, lr=0.002949, batch_cost=1.3919, reader_cost=0.0013 | ETA 07:57:53 2020-12-02 17:39:06 [INFO] seg_loss:0.0302, att_loss: 0.4289, edge_loss: 0.9239, dual_loss: 0.0005 2020-12-02 17:39:07 [INFO] [TRAIN] epoch=160, iter=59500/80000, loss=2.4831, lr=0.002936, batch_cost=1.3807, reader_cost=0.0004 | ETA 07:51:44 2020-12-02 17:41:32 [INFO] seg_loss:0.0602, att_loss: 0.4771, edge_loss: 2.1417, dual_loss: 0.0014 2020-12-02 17:41:33 [INFO] [TRAIN] epoch=161, iter=59600/80000, loss=2.5081, lr=0.002924, batch_cost=1.4603, reader_cost=0.0448 | ETA 08:16:29 2020-12-02 17:43:52 [INFO] seg_loss:0.0279, att_loss: 0.3227, edge_loss: 1.3443, dual_loss: 0.0007 2020-12-02 17:43:53 [INFO] [TRAIN] epoch=161, iter=59700/80000, loss=2.4429, lr=0.002911, batch_cost=1.3988, reader_cost=0.0004 | ETA 07:53:15 2020-12-02 17:46:11 [INFO] seg_loss:0.1124, att_loss: 0.5380, edge_loss: 3.2190, dual_loss: 0.0021 2020-12-02 17:46:12 [INFO] [TRAIN] epoch=161, iter=59800/80000, loss=2.3843, lr=0.002898, batch_cost=1.3966, reader_cost=0.0003 | ETA 07:50:11 2020-12-02 17:48:35 [INFO] seg_loss:0.0655, att_loss: 0.4376, edge_loss: 2.6736, dual_loss: 0.0015 2020-12-02 17:48:36 [INFO] [TRAIN] epoch=162, iter=59900/80000, loss=2.5332, lr=0.002885, batch_cost=1.4325, reader_cost=0.0439 | ETA 07:59:53 2020-12-02 17:50:54 [INFO] seg_loss:0.0993, att_loss: 0.4092, edge_loss: 2.0554, dual_loss: 0.0016 2020-12-02 17:50:55 [INFO] [TRAIN] epoch=162, iter=60000/80000, loss=2.5432, lr=0.002872, batch_cost=1.3938, reader_cost=0.0002 | ETA 07:44:35 2020-12-02 17:50:55 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 17:51:59 [INFO] [EVAL] #Images=500 mIoU=0.8032 Acc=0.9648 Kappa=0.9543 2020-12-02 17:51:59 [INFO] [EVAL] Class IoU: [0.984 0.8725 0.9328 0.4824 0.638 0.7046 0.7538 0.8144 0.9279 0.6523 0.95 0.8512 0.6737 0.959 0.8135 0.9168 0.8398 0.6923 0.8014] 2020-12-02 17:51:59 [INFO] [EVAL] Class Acc: [0.9916 0.9354 0.9619 0.8231 0.8132 0.836 0.8669 0.9103 0.9609 0.7675 0.9624 0.9111 0.7982 0.9784 0.9041 0.9595 0.8997 0.8626 0.8815] 2020-12-02 17:52:03 [INFO] [EVAL] The model with the best validation mIoU (0.8034) was saved at iter 54000. 2020-12-02 17:54:21 [INFO] seg_loss:0.0780, att_loss: 0.5425, edge_loss: 1.6049, dual_loss: 0.0013 2020-12-02 17:54:22 [INFO] [TRAIN] epoch=162, iter=60100/80000, loss=2.3405, lr=0.002859, batch_cost=1.3882, reader_cost=0.0002 | ETA 07:40:24 2020-12-02 17:56:43 [INFO] seg_loss:0.0295, att_loss: 0.2683, edge_loss: 1.5565, dual_loss: 0.0010 2020-12-02 17:56:44 [INFO] [TRAIN] epoch=162, iter=60200/80000, loss=2.4291, lr=0.002846, batch_cost=1.4145, reader_cost=0.0004 | ETA 07:46:48 2020-12-02 17:59:07 [INFO] seg_loss:0.0222, att_loss: 0.4004, edge_loss: 0.9444, dual_loss: 0.0005 2020-12-02 17:59:08 [INFO] [TRAIN] epoch=163, iter=60300/80000, loss=2.6041, lr=0.002833, batch_cost=1.4437, reader_cost=0.0391 | ETA 07:54:01 2020-12-02 18:01:26 [INFO] seg_loss:0.1331, att_loss: 0.5564, edge_loss: 2.1507, dual_loss: 0.0016 2020-12-02 18:01:27 [INFO] [TRAIN] epoch=163, iter=60400/80000, loss=2.3825, lr=0.002820, batch_cost=1.3903, reader_cost=0.0006 | ETA 07:34:10 2020-12-02 18:03:46 [INFO] seg_loss:0.0528, att_loss: 0.4767, edge_loss: 2.2194, dual_loss: 0.0012 2020-12-02 18:03:47 [INFO] [TRAIN] epoch=163, iter=60500/80000, loss=2.4048, lr=0.002807, batch_cost=1.4026, reader_cost=0.0002 | ETA 07:35:50 2020-12-02 18:06:08 [INFO] seg_loss:0.0810, att_loss: 0.4284, edge_loss: 2.5155, dual_loss: 0.0014 2020-12-02 18:06:09 [INFO] [TRAIN] epoch=163, iter=60600/80000, loss=2.4972, lr=0.002794, batch_cost=1.4117, reader_cost=0.0003 | ETA 07:36:26 2020-12-02 18:08:36 [INFO] seg_loss:0.0893, att_loss: 0.4986, edge_loss: 2.3698, dual_loss: 0.0017 2020-12-02 18:08:37 [INFO] [TRAIN] epoch=164, iter=60700/80000, loss=2.4536, lr=0.002781, batch_cost=1.4833, reader_cost=0.0459 | ETA 07:57:07 2020-12-02 18:11:00 [INFO] seg_loss:0.0462, att_loss: 0.3558, edge_loss: 2.0025, dual_loss: 0.0012 2020-12-02 18:11:01 [INFO] [TRAIN] epoch=164, iter=60800/80000, loss=2.5257, lr=0.002768, batch_cost=1.4388, reader_cost=0.0002 | ETA 07:40:24 2020-12-02 18:13:21 [INFO] seg_loss:0.1490, att_loss: 0.6616, edge_loss: 2.4457, dual_loss: 0.0020 2020-12-02 18:13:22 [INFO] [TRAIN] epoch=164, iter=60900/80000, loss=2.4264, lr=0.002755, batch_cost=1.4118, reader_cost=0.0005 | ETA 07:29:24 2020-12-02 18:15:41 [INFO] seg_loss:0.0746, att_loss: 0.4495, edge_loss: 2.0668, dual_loss: 0.0014 2020-12-02 18:15:42 [INFO] [TRAIN] epoch=164, iter=61000/80000, loss=2.5361, lr=0.002742, batch_cost=1.4019, reader_cost=0.0002 | ETA 07:23:55 2020-12-02 18:18:05 [INFO] seg_loss:0.0830, att_loss: 0.4951, edge_loss: 1.1666, dual_loss: 0.0012 2020-12-02 18:18:06 [INFO] [TRAIN] epoch=165, iter=61100/80000, loss=2.5082, lr=0.002729, batch_cost=1.4342, reader_cost=0.0399 | ETA 07:31:46 2020-12-02 18:20:26 [INFO] seg_loss:0.0355, att_loss: 0.3534, edge_loss: 1.6027, dual_loss: 0.0008 2020-12-02 18:20:27 [INFO] [TRAIN] epoch=165, iter=61200/80000, loss=2.3837, lr=0.002716, batch_cost=1.4118, reader_cost=0.0002 | ETA 07:22:21 2020-12-02 18:22:45 [INFO] seg_loss:0.1528, att_loss: 0.6067, edge_loss: 2.6501, dual_loss: 0.0024 2020-12-02 18:22:46 [INFO] [TRAIN] epoch=165, iter=61300/80000, loss=2.3982, lr=0.002703, batch_cost=1.3888, reader_cost=0.0002 | ETA 07:12:50 2020-12-02 18:25:10 [INFO] seg_loss:0.0517, att_loss: 0.4424, edge_loss: 2.2525, dual_loss: 0.0012 2020-12-02 18:25:11 [INFO] [TRAIN] epoch=166, iter=61400/80000, loss=2.6522, lr=0.002690, batch_cost=1.4506, reader_cost=0.0484 | ETA 07:29:41 2020-12-02 18:27:30 [INFO] seg_loss:0.0374, att_loss: 0.2163, edge_loss: 0.8035, dual_loss: 0.0005 2020-12-02 18:27:31 [INFO] [TRAIN] epoch=166, iter=61500/80000, loss=2.4028, lr=0.002677, batch_cost=1.4005, reader_cost=0.0002 | ETA 07:11:49 2020-12-02 18:29:51 [INFO] seg_loss:0.2120, att_loss: 0.4388, edge_loss: 1.7114, dual_loss: 0.0023 2020-12-02 18:29:52 [INFO] [TRAIN] epoch=166, iter=61600/80000, loss=2.4590, lr=0.002664, batch_cost=1.4077, reader_cost=0.0003 | ETA 07:11:40 2020-12-02 18:32:10 [INFO] seg_loss:0.1123, att_loss: 0.5192, edge_loss: 2.2709, dual_loss: 0.0015 2020-12-02 18:32:11 [INFO] [TRAIN] epoch=166, iter=61700/80000, loss=2.4599, lr=0.002651, batch_cost=1.3918, reader_cost=0.0002 | ETA 07:04:29 2020-12-02 18:34:37 [INFO] seg_loss:0.1295, att_loss: 0.6260, edge_loss: 2.2138, dual_loss: 0.0021 2020-12-02 18:34:38 [INFO] [TRAIN] epoch=167, iter=61800/80000, loss=2.4000, lr=0.002638, batch_cost=1.4740, reader_cost=0.0423 | ETA 07:27:07 2020-12-02 18:36:59 [INFO] seg_loss:0.0159, att_loss: 0.2999, edge_loss: 0.9137, dual_loss: 0.0005 2020-12-02 18:37:00 [INFO] [TRAIN] epoch=167, iter=61900/80000, loss=2.4030, lr=0.002625, batch_cost=1.4133, reader_cost=0.0004 | ETA 07:06:21 2020-12-02 18:39:22 [INFO] seg_loss:0.0442, att_loss: 0.3866, edge_loss: 1.6602, dual_loss: 0.0009 2020-12-02 18:39:24 [INFO] [TRAIN] epoch=167, iter=62000/80000, loss=2.4071, lr=0.002612, batch_cost=1.4398, reader_cost=0.0003 | ETA 07:11:56 2020-12-02 18:39:24 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 18:40:28 [INFO] [EVAL] #Images=500 mIoU=0.8037 Acc=0.9643 Kappa=0.9536 2020-12-02 18:40:28 [INFO] [EVAL] Class IoU: [0.9819 0.856 0.9319 0.5151 0.6284 0.7083 0.7536 0.8188 0.9307 0.6528 0.9512 0.8482 0.6649 0.9581 0.8112 0.9092 0.8615 0.6874 0.8008] 2020-12-02 18:40:28 [INFO] [EVAL] Class Acc: [0.9919 0.9203 0.9597 0.859 0.8507 0.827 0.8721 0.8982 0.9604 0.8279 0.9667 0.9004 0.8167 0.9758 0.9051 0.9605 0.9366 0.8446 0.8728] 2020-12-02 18:40:40 [INFO] [EVAL] The model with the best validation mIoU (0.8037) was saved at iter 62000. 2020-12-02 18:43:01 [INFO] seg_loss:0.1079, att_loss: 0.4364, edge_loss: 1.9318, dual_loss: 0.0012 2020-12-02 18:43:02 [INFO] [TRAIN] epoch=167, iter=62100/80000, loss=2.6088, lr=0.002599, batch_cost=1.4171, reader_cost=0.0004 | ETA 07:02:45 2020-12-02 18:45:27 [INFO] seg_loss:0.0763, att_loss: 0.4664, edge_loss: 2.4620, dual_loss: 0.0016 2020-12-02 18:45:28 [INFO] [TRAIN] epoch=168, iter=62200/80000, loss=2.4856, lr=0.002586, batch_cost=1.4547, reader_cost=0.0424 | ETA 07:11:32 2020-12-02 18:47:46 [INFO] seg_loss:0.0340, att_loss: 0.4388, edge_loss: 1.8060, dual_loss: 0.0009 2020-12-02 18:47:47 [INFO] [TRAIN] epoch=168, iter=62300/80000, loss=2.3981, lr=0.002573, batch_cost=1.3944, reader_cost=0.0004 | ETA 06:51:21 2020-12-02 18:50:05 [INFO] seg_loss:0.0690, att_loss: 0.4474, edge_loss: 2.5769, dual_loss: 0.0015 2020-12-02 18:50:06 [INFO] [TRAIN] epoch=168, iter=62400/80000, loss=2.3337, lr=0.002560, batch_cost=1.3909, reader_cost=0.0005 | ETA 06:47:59 2020-12-02 18:52:31 [INFO] seg_loss:0.1498, att_loss: 0.4257, edge_loss: 2.0876, dual_loss: 0.0020 2020-12-02 18:52:32 [INFO] [TRAIN] epoch=169, iter=62500/80000, loss=2.5859, lr=0.002547, batch_cost=1.4569, reader_cost=0.0462 | ETA 07:04:56 2020-12-02 18:54:54 [INFO] seg_loss:0.1492, att_loss: 0.5489, edge_loss: 1.8602, dual_loss: 0.0016 2020-12-02 18:54:55 [INFO] [TRAIN] epoch=169, iter=62600/80000, loss=2.6557, lr=0.002534, batch_cost=1.4358, reader_cost=0.0006 | ETA 06:56:23 2020-12-02 18:57:12 [INFO] seg_loss:0.0640, att_loss: 0.5200, edge_loss: 1.7643, dual_loss: 0.0013 2020-12-02 18:57:13 [INFO] [TRAIN] epoch=169, iter=62700/80000, loss=2.2500, lr=0.002520, batch_cost=1.3782, reader_cost=0.0005 | ETA 06:37:22 2020-12-02 18:59:33 [INFO] seg_loss:0.0535, att_loss: 0.4008, edge_loss: 2.2821, dual_loss: 0.0013 2020-12-02 18:59:34 [INFO] [TRAIN] epoch=169, iter=62800/80000, loss=2.4512, lr=0.002507, batch_cost=1.4120, reader_cost=0.0005 | ETA 06:44:46 2020-12-02 19:02:02 [INFO] seg_loss:0.1752, att_loss: 0.4531, edge_loss: 1.4302, dual_loss: 0.0016 2020-12-02 19:02:03 [INFO] [TRAIN] epoch=170, iter=62900/80000, loss=2.5530, lr=0.002494, batch_cost=1.4904, reader_cost=0.0403 | ETA 07:04:45 2020-12-02 19:04:25 [INFO] seg_loss:0.0338, att_loss: 0.5315, edge_loss: 0.9027, dual_loss: 0.0006 2020-12-02 19:04:26 [INFO] [TRAIN] epoch=170, iter=63000/80000, loss=2.4366, lr=0.002481, batch_cost=1.4216, reader_cost=0.0004 | ETA 06:42:47 2020-12-02 19:06:47 [INFO] seg_loss:0.0880, att_loss: 0.4821, edge_loss: 1.9590, dual_loss: 0.0013 2020-12-02 19:06:49 [INFO] [TRAIN] epoch=170, iter=63100/80000, loss=2.3106, lr=0.002468, batch_cost=1.4287, reader_cost=0.0002 | ETA 06:42:24 2020-12-02 19:09:12 [INFO] seg_loss:0.0672, att_loss: 0.4781, edge_loss: 1.7875, dual_loss: 0.0012 2020-12-02 19:09:13 [INFO] [TRAIN] epoch=170, iter=63200/80000, loss=2.4682, lr=0.002455, batch_cost=1.4478, reader_cost=0.0002 | ETA 06:45:22 2020-12-02 19:11:41 [INFO] seg_loss:0.0841, att_loss: 0.5419, edge_loss: 1.7772, dual_loss: 0.0011 2020-12-02 19:11:42 [INFO] [TRAIN] epoch=171, iter=63300/80000, loss=2.5715, lr=0.002442, batch_cost=1.4888, reader_cost=0.0793 | ETA 06:54:23 2020-12-02 19:14:02 [INFO] seg_loss:0.0784, att_loss: 0.4139, edge_loss: 2.2987, dual_loss: 0.0015 2020-12-02 19:14:03 [INFO] [TRAIN] epoch=171, iter=63400/80000, loss=2.4189, lr=0.002429, batch_cost=1.4118, reader_cost=0.0002 | ETA 06:30:35 2020-12-02 19:16:24 [INFO] seg_loss:0.0157, att_loss: 0.3092, edge_loss: 0.7564, dual_loss: 0.0004 2020-12-02 19:16:26 [INFO] [TRAIN] epoch=171, iter=63500/80000, loss=2.3213, lr=0.002415, batch_cost=1.4212, reader_cost=0.0004 | ETA 06:30:49 2020-12-02 19:18:44 [INFO] seg_loss:0.0487, att_loss: 0.4899, edge_loss: 1.0780, dual_loss: 0.0009 2020-12-02 19:18:45 [INFO] [TRAIN] epoch=171, iter=63600/80000, loss=2.5668, lr=0.002402, batch_cost=1.3935, reader_cost=0.0002 | ETA 06:20:52 2020-12-02 19:21:13 [INFO] seg_loss:0.0677, att_loss: 0.5281, edge_loss: 1.3407, dual_loss: 0.0010 2020-12-02 19:21:14 [INFO] [TRAIN] epoch=172, iter=63700/80000, loss=2.4903, lr=0.002389, batch_cost=1.4886, reader_cost=0.0659 | ETA 06:44:23 2020-12-02 19:23:32 [INFO] seg_loss:0.0284, att_loss: 0.3592, edge_loss: 1.0409, dual_loss: 0.0005 2020-12-02 19:23:33 [INFO] [TRAIN] epoch=172, iter=63800/80000, loss=2.3079, lr=0.002376, batch_cost=1.3935, reader_cost=0.0002 | ETA 06:16:14 2020-12-02 19:25:52 [INFO] seg_loss:0.1084, att_loss: 0.5779, edge_loss: 1.8215, dual_loss: 0.0014 2020-12-02 19:25:53 [INFO] [TRAIN] epoch=172, iter=63900/80000, loss=2.3590, lr=0.002363, batch_cost=1.3996, reader_cost=0.0003 | ETA 06:15:33 2020-12-02 19:28:20 [INFO] seg_loss:0.0431, att_loss: 0.3669, edge_loss: 2.0587, dual_loss: 0.0011 2020-12-02 19:28:21 [INFO] [TRAIN] epoch=173, iter=64000/80000, loss=2.6514, lr=0.002349, batch_cost=1.4827, reader_cost=0.0446 | ETA 06:35:22 2020-12-02 19:28:21 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 19:29:24 [INFO] [EVAL] #Images=500 mIoU=0.8022 Acc=0.9647 Kappa=0.9542 2020-12-02 19:29:25 [INFO] [EVAL] Class IoU: [0.9824 0.8639 0.9344 0.4946 0.6335 0.7068 0.7541 0.8155 0.9288 0.6495 0.9508 0.8491 0.6704 0.9593 0.8056 0.9147 0.8295 0.6972 0.8014] 2020-12-02 19:29:25 [INFO] [EVAL] Class Acc: [0.9898 0.9366 0.9648 0.8471 0.819 0.832 0.8575 0.9042 0.9546 0.8435 0.9663 0.9049 0.815 0.9788 0.8952 0.9582 0.9072 0.8615 0.8756] 2020-12-02 19:29:29 [INFO] [EVAL] The model with the best validation mIoU (0.8037) was saved at iter 62000. 2020-12-02 19:31:49 [INFO] seg_loss:0.0597, att_loss: 0.4417, edge_loss: 1.7979, dual_loss: 0.0011 2020-12-02 19:31:50 [INFO] [TRAIN] epoch=173, iter=64100/80000, loss=2.5305, lr=0.002336, batch_cost=1.4071, reader_cost=0.0005 | ETA 06:12:53 2020-12-02 19:34:12 [INFO] seg_loss:0.0614, att_loss: 0.3802, edge_loss: 2.1741, dual_loss: 0.0015 2020-12-02 19:34:13 [INFO] [TRAIN] epoch=173, iter=64200/80000, loss=2.3797, lr=0.002323, batch_cost=1.4329, reader_cost=0.0002 | ETA 06:17:20 2020-12-02 19:36:29 [INFO] seg_loss:0.1238, att_loss: 0.4802, edge_loss: 2.4229, dual_loss: 0.0023 2020-12-02 19:36:31 [INFO] [TRAIN] epoch=173, iter=64300/80000, loss=2.4506, lr=0.002310, batch_cost=1.3761, reader_cost=0.0004 | ETA 06:00:05 2020-12-02 19:38:56 [INFO] seg_loss:0.0371, att_loss: 0.3964, edge_loss: 1.4324, dual_loss: 0.0009 2020-12-02 19:38:57 [INFO] [TRAIN] epoch=174, iter=64400/80000, loss=2.4221, lr=0.002296, batch_cost=1.4663, reader_cost=0.0407 | ETA 06:21:13 2020-12-02 19:41:13 [INFO] seg_loss:0.0556, att_loss: 0.4335, edge_loss: 2.1407, dual_loss: 0.0012 2020-12-02 19:41:14 [INFO] [TRAIN] epoch=174, iter=64500/80000, loss=2.4299, lr=0.002283, batch_cost=1.3662, reader_cost=0.0004 | ETA 05:52:55 2020-12-02 19:43:31 [INFO] seg_loss:0.0172, att_loss: 0.2394, edge_loss: 0.9021, dual_loss: 0.0004 2020-12-02 19:43:32 [INFO] [TRAIN] epoch=174, iter=64600/80000, loss=2.3260, lr=0.002270, batch_cost=1.3783, reader_cost=0.0002 | ETA 05:53:46 2020-12-02 19:45:54 [INFO] seg_loss:0.0918, att_loss: 0.4861, edge_loss: 1.8807, dual_loss: 0.0014 2020-12-02 19:45:56 [INFO] [TRAIN] epoch=174, iter=64700/80000, loss=2.5527, lr=0.002257, batch_cost=1.4388, reader_cost=0.0006 | ETA 06:06:53 2020-12-02 19:48:22 [INFO] seg_loss:0.0606, att_loss: 0.4291, edge_loss: 1.8519, dual_loss: 0.0014 2020-12-02 19:48:23 [INFO] [TRAIN] epoch=175, iter=64800/80000, loss=2.4013, lr=0.002243, batch_cost=1.4716, reader_cost=0.0435 | ETA 06:12:48 2020-12-02 19:50:43 [INFO] seg_loss:0.0398, att_loss: 0.4161, edge_loss: 1.6194, dual_loss: 0.0008 2020-12-02 19:50:44 [INFO] [TRAIN] epoch=175, iter=64900/80000, loss=2.5079, lr=0.002230, batch_cost=1.4146, reader_cost=0.0004 | ETA 05:56:00 2020-12-02 19:53:02 [INFO] seg_loss:0.0401, att_loss: 0.3649, edge_loss: 1.2933, dual_loss: 0.0008 2020-12-02 19:53:03 [INFO] [TRAIN] epoch=175, iter=65000/80000, loss=2.3081, lr=0.002217, batch_cost=1.3899, reader_cost=0.0004 | ETA 05:47:29 2020-12-02 19:55:24 [INFO] seg_loss:0.0299, att_loss: 0.3490, edge_loss: 1.6907, dual_loss: 0.0009 2020-12-02 19:55:25 [INFO] [TRAIN] epoch=175, iter=65100/80000, loss=2.5160, lr=0.002203, batch_cost=1.4196, reader_cost=0.0002 | ETA 05:52:32 2020-12-02 19:57:55 [INFO] seg_loss:0.0999, att_loss: 0.5994, edge_loss: 1.6372, dual_loss: 0.0014 2020-12-02 19:57:57 [INFO] [TRAIN] epoch=176, iter=65200/80000, loss=2.4810, lr=0.002190, batch_cost=1.5140, reader_cost=0.0441 | ETA 06:13:27 2020-12-02 20:00:16 [INFO] seg_loss:0.1152, att_loss: 0.5123, edge_loss: 1.2211, dual_loss: 0.0013 2020-12-02 20:00:17 [INFO] [TRAIN] epoch=176, iter=65300/80000, loss=2.2659, lr=0.002177, batch_cost=1.4048, reader_cost=0.0005 | ETA 05:44:09 2020-12-02 20:02:38 [INFO] seg_loss:0.0365, att_loss: 0.3483, edge_loss: 1.5615, dual_loss: 0.0010 2020-12-02 20:02:39 [INFO] [TRAIN] epoch=176, iter=65400/80000, loss=2.4038, lr=0.002164, batch_cost=1.4185, reader_cost=0.0002 | ETA 05:45:09 2020-12-02 20:05:05 [INFO] seg_loss:0.0192, att_loss: 0.3977, edge_loss: 0.6779, dual_loss: 0.0005 2020-12-02 20:05:06 [INFO] [TRAIN] epoch=177, iter=65500/80000, loss=2.6187, lr=0.002150, batch_cost=1.4746, reader_cost=0.0425 | ETA 05:56:22 2020-12-02 20:07:28 [INFO] seg_loss:0.0365, att_loss: 0.4239, edge_loss: 1.7770, dual_loss: 0.0009 2020-12-02 20:07:29 [INFO] [TRAIN] epoch=177, iter=65600/80000, loss=2.3795, lr=0.002137, batch_cost=1.4219, reader_cost=0.0002 | ETA 05:41:15 2020-12-02 20:09:50 [INFO] seg_loss:0.0497, att_loss: 0.4353, edge_loss: 1.5509, dual_loss: 0.0009 2020-12-02 20:09:51 [INFO] [TRAIN] epoch=177, iter=65700/80000, loss=2.3146, lr=0.002123, batch_cost=1.4199, reader_cost=0.0003 | ETA 05:38:24 2020-12-02 20:12:10 [INFO] seg_loss:0.1500, att_loss: 0.5778, edge_loss: 1.8115, dual_loss: 0.0019 2020-12-02 20:12:11 [INFO] [TRAIN] epoch=177, iter=65800/80000, loss=2.5660, lr=0.002110, batch_cost=1.4011, reader_cost=0.0002 | ETA 05:31:35 2020-12-02 20:14:37 [INFO] seg_loss:0.0471, att_loss: 0.3628, edge_loss: 1.6625, dual_loss: 0.0008 2020-12-02 20:14:38 [INFO] [TRAIN] epoch=178, iter=65900/80000, loss=2.5251, lr=0.002097, batch_cost=1.4772, reader_cost=0.0494 | ETA 05:47:08 2020-12-02 20:16:58 [INFO] seg_loss:0.0778, att_loss: 0.4569, edge_loss: 2.6963, dual_loss: 0.0016 2020-12-02 20:16:59 [INFO] [TRAIN] epoch=178, iter=66000/80000, loss=2.5006, lr=0.002083, batch_cost=1.4024, reader_cost=0.0006 | ETA 05:27:13 2020-12-02 20:16:59 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 20:18:02 [INFO] [EVAL] #Images=500 mIoU=0.8067 Acc=0.9650 Kappa=0.9546 2020-12-02 20:18:02 [INFO] [EVAL] Class IoU: [0.983 0.8659 0.933 0.5239 0.6306 0.7073 0.7536 0.8214 0.9296 0.6439 0.9534 0.8529 0.6774 0.9597 0.8457 0.92 0.8405 0.6887 0.7972] 2020-12-02 20:18:02 [INFO] [EVAL] Class Acc: [0.9897 0.9406 0.9612 0.8455 0.8355 0.8397 0.8429 0.8937 0.9592 0.8099 0.9706 0.9143 0.8029 0.9796 0.9227 0.9665 0.9445 0.8599 0.8617] 2020-12-02 20:18:09 [INFO] [EVAL] The model with the best validation mIoU (0.8067) was saved at iter 66000. 2020-12-02 20:20:26 [INFO] seg_loss:0.0183, att_loss: 0.4325, edge_loss: 0.8728, dual_loss: 0.0005 2020-12-02 20:20:27 [INFO] [TRAIN] epoch=178, iter=66100/80000, loss=2.4010, lr=0.002070, batch_cost=1.3832, reader_cost=0.0002 | ETA 05:20:26 2020-12-02 20:22:44 [INFO] seg_loss:0.0645, att_loss: 0.4378, edge_loss: 1.1344, dual_loss: 0.0009 2020-12-02 20:22:45 [INFO] [TRAIN] epoch=178, iter=66200/80000, loss=2.5176, lr=0.002057, batch_cost=1.3834, reader_cost=0.0003 | ETA 05:18:10 2020-12-02 20:25:14 [INFO] seg_loss:0.1083, att_loss: 0.5136, edge_loss: 2.6893, dual_loss: 0.0019 2020-12-02 20:25:15 [INFO] [TRAIN] epoch=179, iter=66300/80000, loss=2.5585, lr=0.002043, batch_cost=1.4934, reader_cost=0.0399 | ETA 05:40:59 2020-12-02 20:27:33 [INFO] seg_loss:0.1164, att_loss: 0.5137, edge_loss: 2.3199, dual_loss: 0.0020 2020-12-02 20:27:34 [INFO] [TRAIN] epoch=179, iter=66400/80000, loss=2.4350, lr=0.002030, batch_cost=1.3951, reader_cost=0.0002 | ETA 05:16:13 2020-12-02 20:29:55 [INFO] seg_loss:0.1955, att_loss: 0.6710, edge_loss: 1.7933, dual_loss: 0.0021 2020-12-02 20:29:56 [INFO] [TRAIN] epoch=179, iter=66500/80000, loss=2.3578, lr=0.002016, batch_cost=1.4160, reader_cost=0.0002 | ETA 05:18:36 2020-12-02 20:32:19 [INFO] seg_loss:0.1142, att_loss: 0.6603, edge_loss: 2.2986, dual_loss: 0.0018 2020-12-02 20:32:20 [INFO] [TRAIN] epoch=180, iter=66600/80000, loss=2.6460, lr=0.002003, batch_cost=1.4458, reader_cost=0.0455 | ETA 05:22:53 2020-12-02 20:34:40 [INFO] seg_loss:0.0242, att_loss: 0.3523, edge_loss: 1.3914, dual_loss: 0.0006 2020-12-02 20:34:41 [INFO] [TRAIN] epoch=180, iter=66700/80000, loss=2.5223, lr=0.001989, batch_cost=1.4007, reader_cost=0.0002 | ETA 05:10:29 2020-12-02 20:37:01 [INFO] seg_loss:0.0958, att_loss: 0.4440, edge_loss: 2.2165, dual_loss: 0.0015 2020-12-02 20:37:02 [INFO] [TRAIN] epoch=180, iter=66800/80000, loss=2.3308, lr=0.001976, batch_cost=1.4097, reader_cost=0.0004 | ETA 05:10:08 2020-12-02 20:39:21 [INFO] seg_loss:0.0787, att_loss: 0.4727, edge_loss: 2.5959, dual_loss: 0.0016 2020-12-02 20:39:22 [INFO] [TRAIN] epoch=180, iter=66900/80000, loss=2.5070, lr=0.001962, batch_cost=1.4026, reader_cost=0.0002 | ETA 05:06:14 2020-12-02 20:41:50 [INFO] seg_loss:0.0364, att_loss: 0.3379, edge_loss: 1.3021, dual_loss: 0.0006 2020-12-02 20:41:51 [INFO] [TRAIN] epoch=181, iter=67000/80000, loss=2.5081, lr=0.001949, batch_cost=1.4957, reader_cost=0.0636 | ETA 05:24:04 2020-12-02 20:44:13 [INFO] seg_loss:0.0232, att_loss: 0.2760, edge_loss: 1.2790, dual_loss: 0.0006 2020-12-02 20:44:14 [INFO] [TRAIN] epoch=181, iter=67100/80000, loss=2.3890, lr=0.001935, batch_cost=1.4227, reader_cost=0.0003 | ETA 05:05:52 2020-12-02 20:46:33 [INFO] seg_loss:0.1224, att_loss: 0.5092, edge_loss: 2.8305, dual_loss: 0.0022 2020-12-02 20:46:34 [INFO] [TRAIN] epoch=181, iter=67200/80000, loss=2.4235, lr=0.001922, batch_cost=1.3999, reader_cost=0.0007 | ETA 04:58:38 2020-12-02 20:48:54 [INFO] seg_loss:0.1583, att_loss: 0.6035, edge_loss: 2.9520, dual_loss: 0.0023 2020-12-02 20:48:55 [INFO] [TRAIN] epoch=181, iter=67300/80000, loss=2.4699, lr=0.001908, batch_cost=1.4132, reader_cost=0.0002 | ETA 04:59:07 2020-12-02 20:51:20 [INFO] seg_loss:0.0432, att_loss: 0.4582, edge_loss: 1.3542, dual_loss: 0.0009 2020-12-02 20:51:21 [INFO] [TRAIN] epoch=182, iter=67400/80000, loss=2.3941, lr=0.001895, batch_cost=1.4605, reader_cost=0.0779 | ETA 05:06:42 2020-12-02 20:53:41 [INFO] seg_loss:0.1094, att_loss: 0.6314, edge_loss: 2.3439, dual_loss: 0.0016 2020-12-02 20:53:42 [INFO] [TRAIN] epoch=182, iter=67500/80000, loss=2.4069, lr=0.001881, batch_cost=1.4127, reader_cost=0.0009 | ETA 04:54:18 2020-12-02 20:56:06 [INFO] seg_loss:0.0559, att_loss: 0.5184, edge_loss: 1.6443, dual_loss: 0.0010 2020-12-02 20:56:07 [INFO] [TRAIN] epoch=182, iter=67600/80000, loss=2.3159, lr=0.001868, batch_cost=1.4419, reader_cost=0.0002 | ETA 04:57:58 2020-12-02 20:58:27 [INFO] seg_loss:0.0190, att_loss: 0.3670, edge_loss: 0.8750, dual_loss: 0.0005 2020-12-02 20:58:28 [INFO] [TRAIN] epoch=182, iter=67700/80000, loss=2.4665, lr=0.001854, batch_cost=1.4127, reader_cost=0.0002 | ETA 04:49:36 2020-12-02 21:00:56 [INFO] seg_loss:0.1445, att_loss: 0.6886, edge_loss: 2.6282, dual_loss: 0.0022 2020-12-02 21:00:57 [INFO] [TRAIN] epoch=183, iter=67800/80000, loss=2.4858, lr=0.001841, batch_cost=1.4894, reader_cost=0.0747 | ETA 05:02:51 2020-12-02 21:03:14 [INFO] seg_loss:0.0322, att_loss: 0.4180, edge_loss: 1.0911, dual_loss: 0.0006 2020-12-02 21:03:15 [INFO] [TRAIN] epoch=183, iter=67900/80000, loss=2.4246, lr=0.001827, batch_cost=1.3864, reader_cost=0.0002 | ETA 04:39:35 2020-12-02 21:05:34 [INFO] seg_loss:0.0641, att_loss: 0.3788, edge_loss: 1.9593, dual_loss: 0.0014 2020-12-02 21:05:35 [INFO] [TRAIN] epoch=183, iter=68000/80000, loss=2.4484, lr=0.001813, batch_cost=1.3916, reader_cost=0.0002 | ETA 04:38:19 2020-12-02 21:05:35 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 21:06:37 [INFO] [EVAL] #Images=500 mIoU=0.8054 Acc=0.9655 Kappa=0.9552 2020-12-02 21:06:37 [INFO] [EVAL] Class IoU: [0.9842 0.8719 0.9339 0.5321 0.6339 0.7036 0.7525 0.8186 0.93 0.664 0.9539 0.8498 0.6651 0.9583 0.8034 0.9109 0.8442 0.692 0.8009] 2020-12-02 21:06:37 [INFO] [EVAL] Class Acc: [0.9915 0.9414 0.9647 0.8591 0.8055 0.8267 0.8583 0.9122 0.9561 0.829 0.9698 0.9068 0.8158 0.9757 0.9069 0.9605 0.9092 0.8494 0.8766] 2020-12-02 21:06:41 [INFO] [EVAL] The model with the best validation mIoU (0.8067) was saved at iter 66000. 2020-12-02 21:09:06 [INFO] seg_loss:0.0261, att_loss: 0.2188, edge_loss: 1.6487, dual_loss: 0.0008 2020-12-02 21:09:07 [INFO] [TRAIN] epoch=184, iter=68100/80000, loss=2.6152, lr=0.001800, batch_cost=1.4506, reader_cost=0.0552 | ETA 04:47:41 2020-12-02 21:11:25 [INFO] seg_loss:0.1876, att_loss: 0.5188, edge_loss: 1.9797, dual_loss: 0.0017 2020-12-02 21:11:26 [INFO] [TRAIN] epoch=184, iter=68200/80000, loss=2.3291, lr=0.001786, batch_cost=1.3965, reader_cost=0.0002 | ETA 04:34:38 2020-12-02 21:13:46 [INFO] seg_loss:0.2496, att_loss: 0.5956, edge_loss: 1.8635, dual_loss: 0.0015 2020-12-02 21:13:47 [INFO] [TRAIN] epoch=184, iter=68300/80000, loss=2.3216, lr=0.001773, batch_cost=1.4053, reader_cost=0.0002 | ETA 04:34:01 2020-12-02 21:16:07 [INFO] seg_loss:0.0756, att_loss: 0.3958, edge_loss: 2.1491, dual_loss: 0.0014 2020-12-02 21:16:08 [INFO] [TRAIN] epoch=184, iter=68400/80000, loss=2.4601, lr=0.001759, batch_cost=1.4152, reader_cost=0.0002 | ETA 04:33:36 2020-12-02 21:18:35 [INFO] seg_loss:0.0869, att_loss: 0.4246, edge_loss: 2.5803, dual_loss: 0.0016 2020-12-02 21:18:36 [INFO] [TRAIN] epoch=185, iter=68500/80000, loss=2.4487, lr=0.001745, batch_cost=1.4729, reader_cost=0.0409 | ETA 04:42:18 2020-12-02 21:20:58 [INFO] seg_loss:0.0632, att_loss: 0.4106, edge_loss: 1.4165, dual_loss: 0.0012 2020-12-02 21:20:59 [INFO] [TRAIN] epoch=185, iter=68600/80000, loss=2.3090, lr=0.001732, batch_cost=1.4346, reader_cost=0.0002 | ETA 04:32:34 2020-12-02 21:23:21 [INFO] seg_loss:0.1116, att_loss: 0.5974, edge_loss: 2.0322, dual_loss: 0.0017 2020-12-02 21:23:22 [INFO] [TRAIN] epoch=185, iter=68700/80000, loss=2.4331, lr=0.001718, batch_cost=1.4306, reader_cost=0.0003 | ETA 04:29:26 2020-12-02 21:25:40 [INFO] seg_loss:0.0877, att_loss: 0.4138, edge_loss: 1.8400, dual_loss: 0.0014 2020-12-02 21:25:41 [INFO] [TRAIN] epoch=185, iter=68800/80000, loss=2.5955, lr=0.001704, batch_cost=1.3901, reader_cost=0.0002 | ETA 04:19:28 2020-12-02 21:28:07 [INFO] seg_loss:0.0833, att_loss: 0.4664, edge_loss: 2.6927, dual_loss: 0.0017 2020-12-02 21:28:08 [INFO] [TRAIN] epoch=186, iter=68900/80000, loss=2.4335, lr=0.001691, batch_cost=1.4697, reader_cost=0.0413 | ETA 04:31:53 2020-12-02 21:30:30 [INFO] seg_loss:0.0382, att_loss: 0.4590, edge_loss: 1.9091, dual_loss: 0.0010 2020-12-02 21:30:31 [INFO] [TRAIN] epoch=186, iter=69000/80000, loss=2.4199, lr=0.001677, batch_cost=1.4283, reader_cost=0.0003 | ETA 04:21:51 2020-12-02 21:32:52 [INFO] seg_loss:0.1102, att_loss: 0.5168, edge_loss: 2.9398, dual_loss: 0.0019 2020-12-02 21:32:53 [INFO] [TRAIN] epoch=186, iter=69100/80000, loss=2.3906, lr=0.001663, batch_cost=1.4228, reader_cost=0.0002 | ETA 04:18:28 2020-12-02 21:35:19 [INFO] seg_loss:0.1234, att_loss: 0.5173, edge_loss: 3.2340, dual_loss: 0.0020 2020-12-02 21:35:20 [INFO] [TRAIN] epoch=187, iter=69200/80000, loss=2.5582, lr=0.001649, batch_cost=1.4632, reader_cost=0.0434 | ETA 04:23:22 2020-12-02 21:37:39 [INFO] seg_loss:0.1986, att_loss: 0.4627, edge_loss: 1.8333, dual_loss: 0.0014 2020-12-02 21:37:40 [INFO] [TRAIN] epoch=187, iter=69300/80000, loss=2.5734, lr=0.001636, batch_cost=1.4010, reader_cost=0.0002 | ETA 04:09:51 2020-12-02 21:40:00 [INFO] seg_loss:0.0521, att_loss: 0.4513, edge_loss: 1.4263, dual_loss: 0.0011 2020-12-02 21:40:01 [INFO] [TRAIN] epoch=187, iter=69400/80000, loss=2.2695, lr=0.001622, batch_cost=1.4113, reader_cost=0.0003 | ETA 04:09:19 2020-12-02 21:42:22 [INFO] seg_loss:0.0670, att_loss: 0.4863, edge_loss: 2.4334, dual_loss: 0.0015 2020-12-02 21:42:23 [INFO] [TRAIN] epoch=187, iter=69500/80000, loss=2.4351, lr=0.001608, batch_cost=1.4257, reader_cost=0.0002 | ETA 04:09:30 2020-12-02 21:44:47 [INFO] seg_loss:0.0401, att_loss: 0.5036, edge_loss: 1.2777, dual_loss: 0.0007 2020-12-02 21:44:48 [INFO] [TRAIN] epoch=188, iter=69600/80000, loss=2.5273, lr=0.001594, batch_cost=1.4429, reader_cost=0.0489 | ETA 04:10:06 2020-12-02 21:47:07 [INFO] seg_loss:0.0914, att_loss: 0.3941, edge_loss: 2.2778, dual_loss: 0.0015 2020-12-02 21:47:08 [INFO] [TRAIN] epoch=188, iter=69700/80000, loss=2.4916, lr=0.001581, batch_cost=1.4050, reader_cost=0.0003 | ETA 04:01:11 2020-12-02 21:49:28 [INFO] seg_loss:0.1446, att_loss: 0.5419, edge_loss: 2.9223, dual_loss: 0.0021 2020-12-02 21:49:29 [INFO] [TRAIN] epoch=188, iter=69800/80000, loss=2.3932, lr=0.001567, batch_cost=1.4058, reader_cost=0.0004 | ETA 03:58:59 2020-12-02 21:51:46 [INFO] seg_loss:0.0965, att_loss: 0.5019, edge_loss: 2.8437, dual_loss: 0.0018 2020-12-02 21:51:47 [INFO] [TRAIN] epoch=188, iter=69900/80000, loss=2.5168, lr=0.001553, batch_cost=1.3864, reader_cost=0.0002 | ETA 03:53:22 2020-12-02 21:54:12 [INFO] seg_loss:0.0295, att_loss: 0.3027, edge_loss: 1.3763, dual_loss: 0.0008 2020-12-02 21:54:13 [INFO] [TRAIN] epoch=189, iter=70000/80000, loss=2.5012, lr=0.001539, batch_cost=1.4551, reader_cost=0.0563 | ETA 04:02:31 2020-12-02 21:54:13 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 21:55:16 [INFO] [EVAL] #Images=500 mIoU=0.8015 Acc=0.9640 Kappa=0.9532 2020-12-02 21:55:16 [INFO] [EVAL] Class IoU: [0.9819 0.8571 0.9302 0.4445 0.6276 0.7075 0.7523 0.8192 0.9303 0.6617 0.9542 0.8515 0.6702 0.9592 0.8307 0.9131 0.8462 0.6925 0.7993] 2020-12-02 21:55:16 [INFO] [EVAL] Class Acc: [0.9894 0.9386 0.9594 0.8553 0.8447 0.8183 0.8382 0.8904 0.9593 0.8073 0.9706 0.9105 0.8247 0.9773 0.9242 0.962 0.9165 0.8217 0.8634] 2020-12-02 21:55:21 [INFO] [EVAL] The model with the best validation mIoU (0.8067) was saved at iter 66000. 2020-12-02 21:57:41 [INFO] seg_loss:0.0551, att_loss: 0.4534, edge_loss: 1.6930, dual_loss: 0.0009 2020-12-02 21:57:42 [INFO] [TRAIN] epoch=189, iter=70100/80000, loss=2.3240, lr=0.001525, batch_cost=1.4165, reader_cost=0.0002 | ETA 03:53:43 2020-12-02 22:00:02 [INFO] seg_loss:0.2351, att_loss: 0.6501, edge_loss: 2.9446, dual_loss: 0.0026 2020-12-02 22:00:03 [INFO] [TRAIN] epoch=189, iter=70200/80000, loss=2.3261, lr=0.001511, batch_cost=1.4076, reader_cost=0.0005 | ETA 03:49:54 2020-12-02 22:02:23 [INFO] seg_loss:0.0654, att_loss: 0.4198, edge_loss: 1.9116, dual_loss: 0.0012 2020-12-02 22:02:24 [INFO] [TRAIN] epoch=189, iter=70300/80000, loss=2.5270, lr=0.001497, batch_cost=1.4102, reader_cost=0.0003 | ETA 03:47:59 2020-12-02 22:04:50 [INFO] seg_loss:0.0520, att_loss: 0.4716, edge_loss: 1.2307, dual_loss: 0.0009 2020-12-02 22:04:51 [INFO] [TRAIN] epoch=190, iter=70400/80000, loss=2.5016, lr=0.001484, batch_cost=1.4702, reader_cost=0.0424 | ETA 03:55:13 2020-12-02 22:07:14 [INFO] seg_loss:0.0522, att_loss: 0.3943, edge_loss: 1.3319, dual_loss: 0.0007 2020-12-02 22:07:16 [INFO] [TRAIN] epoch=190, iter=70500/80000, loss=2.3584, lr=0.001470, batch_cost=1.4442, reader_cost=0.0002 | ETA 03:48:40 2020-12-02 22:09:36 [INFO] seg_loss:0.0697, att_loss: 0.4159, edge_loss: 2.0480, dual_loss: 0.0014 2020-12-02 22:09:37 [INFO] [TRAIN] epoch=190, iter=70600/80000, loss=2.2773, lr=0.001456, batch_cost=1.4170, reader_cost=0.0002 | ETA 03:41:59 2020-12-02 22:12:01 [INFO] seg_loss:0.0761, att_loss: 0.4279, edge_loss: 2.7686, dual_loss: 0.0016 2020-12-02 22:12:02 [INFO] [TRAIN] epoch=191, iter=70700/80000, loss=2.6358, lr=0.001442, batch_cost=1.4474, reader_cost=0.0446 | ETA 03:44:21 2020-12-02 22:14:19 [INFO] seg_loss:0.1875, att_loss: 0.4989, edge_loss: 1.7328, dual_loss: 0.0017 2020-12-02 22:14:20 [INFO] [TRAIN] epoch=191, iter=70800/80000, loss=2.4686, lr=0.001428, batch_cost=1.3771, reader_cost=0.0012 | ETA 03:31:08 2020-12-02 22:16:38 [INFO] seg_loss:0.0658, att_loss: 0.4547, edge_loss: 2.2819, dual_loss: 0.0016 2020-12-02 22:16:39 [INFO] [TRAIN] epoch=191, iter=70900/80000, loss=2.3816, lr=0.001414, batch_cost=1.3965, reader_cost=0.0003 | ETA 03:31:48 2020-12-02 22:19:00 [INFO] seg_loss:0.1163, att_loss: 0.5595, edge_loss: 2.9142, dual_loss: 0.0019 2020-12-02 22:19:01 [INFO] [TRAIN] epoch=191, iter=71000/80000, loss=2.4978, lr=0.001400, batch_cost=1.4140, reader_cost=0.0005 | ETA 03:32:06 2020-12-02 22:21:26 [INFO] seg_loss:0.0851, att_loss: 0.4986, edge_loss: 1.8057, dual_loss: 0.0015 2020-12-02 22:21:27 [INFO] [TRAIN] epoch=192, iter=71100/80000, loss=2.5669, lr=0.001386, batch_cost=1.4657, reader_cost=0.0630 | ETA 03:37:24 2020-12-02 22:23:46 [INFO] seg_loss:0.0099, att_loss: 0.1819, edge_loss: 0.6410, dual_loss: 0.0003 2020-12-02 22:23:47 [INFO] [TRAIN] epoch=192, iter=71200/80000, loss=2.3907, lr=0.001372, batch_cost=1.3948, reader_cost=0.0002 | ETA 03:24:34 2020-12-02 22:26:05 [INFO] seg_loss:0.1090, att_loss: 0.4972, edge_loss: 2.7762, dual_loss: 0.0019 2020-12-02 22:26:06 [INFO] [TRAIN] epoch=192, iter=71300/80000, loss=2.4076, lr=0.001358, batch_cost=1.3961, reader_cost=0.0003 | ETA 03:22:26 2020-12-02 22:28:29 [INFO] seg_loss:0.0635, att_loss: 0.4555, edge_loss: 1.7267, dual_loss: 0.0011 2020-12-02 22:28:30 [INFO] [TRAIN] epoch=192, iter=71400/80000, loss=2.5288, lr=0.001344, batch_cost=1.4348, reader_cost=0.0002 | ETA 03:25:39 2020-12-02 22:30:58 [INFO] seg_loss:0.0534, att_loss: 0.4053, edge_loss: 2.0801, dual_loss: 0.0011 2020-12-02 22:30:59 [INFO] [TRAIN] epoch=193, iter=71500/80000, loss=2.3775, lr=0.001330, batch_cost=1.4869, reader_cost=0.0412 | ETA 03:30:38 2020-12-02 22:33:19 [INFO] seg_loss:0.0282, att_loss: 0.4007, edge_loss: 1.7379, dual_loss: 0.0008 2020-12-02 22:33:20 [INFO] [TRAIN] epoch=193, iter=71600/80000, loss=2.4250, lr=0.001316, batch_cost=1.4174, reader_cost=0.0002 | ETA 03:18:26 2020-12-02 22:35:40 [INFO] seg_loss:0.1000, att_loss: 0.5962, edge_loss: 2.8892, dual_loss: 0.0019 2020-12-02 22:35:41 [INFO] [TRAIN] epoch=193, iter=71700/80000, loss=2.3321, lr=0.001301, batch_cost=1.4079, reader_cost=0.0006 | ETA 03:14:45 2020-12-02 22:38:07 [INFO] seg_loss:0.1833, att_loss: 0.5455, edge_loss: 2.5997, dual_loss: 0.0024 2020-12-02 22:38:08 [INFO] [TRAIN] epoch=194, iter=71800/80000, loss=2.5493, lr=0.001287, batch_cost=1.4677, reader_cost=0.0681 | ETA 03:20:35 2020-12-02 22:40:31 [INFO] seg_loss:0.0710, att_loss: 0.4512, edge_loss: 2.1680, dual_loss: 0.0013 2020-12-02 22:40:32 [INFO] [TRAIN] epoch=194, iter=71900/80000, loss=2.5559, lr=0.001273, batch_cost=1.4378, reader_cost=0.0002 | ETA 03:14:06 2020-12-02 22:42:49 [INFO] seg_loss:0.0389, att_loss: 0.2769, edge_loss: 1.7385, dual_loss: 0.0010 2020-12-02 22:42:50 [INFO] [TRAIN] epoch=194, iter=72000/80000, loss=2.2016, lr=0.001259, batch_cost=1.3823, reader_cost=0.0004 | ETA 03:04:18 2020-12-02 22:42:50 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 22:43:54 [INFO] [EVAL] #Images=500 mIoU=0.8041 Acc=0.9647 Kappa=0.9542 2020-12-02 22:43:54 [INFO] [EVAL] Class IoU: [0.9827 0.8649 0.9323 0.4761 0.6273 0.7066 0.7568 0.8199 0.9294 0.6575 0.9519 0.8518 0.6749 0.9594 0.831 0.9134 0.8455 0.6943 0.8023] 2020-12-02 22:43:54 [INFO] [EVAL] Class Acc: [0.9906 0.9364 0.9621 0.8518 0.8478 0.8237 0.8564 0.9054 0.9542 0.869 0.9691 0.904 0.8091 0.9772 0.9334 0.9585 0.9237 0.845 0.87 ] 2020-12-02 22:43:58 [INFO] [EVAL] The model with the best validation mIoU (0.8067) was saved at iter 66000. 2020-12-02 22:46:14 [INFO] seg_loss:0.0768, att_loss: 0.4841, edge_loss: 2.6674, dual_loss: 0.0016 2020-12-02 22:46:15 [INFO] [TRAIN] epoch=194, iter=72100/80000, loss=2.4521, lr=0.001245, batch_cost=1.3676, reader_cost=0.0002 | ETA 03:00:04 2020-12-02 22:48:44 [INFO] seg_loss:0.1517, att_loss: 0.5013, edge_loss: 2.0450, dual_loss: 0.0020 2020-12-02 22:48:45 [INFO] [TRAIN] epoch=195, iter=72200/80000, loss=2.6201, lr=0.001231, batch_cost=1.5014, reader_cost=0.0431 | ETA 03:15:11 2020-12-02 22:51:05 [INFO] seg_loss:0.0105, att_loss: 0.2483, edge_loss: 0.7705, dual_loss: 0.0003 2020-12-02 22:51:06 [INFO] [TRAIN] epoch=195, iter=72300/80000, loss=2.3857, lr=0.001216, batch_cost=1.4098, reader_cost=0.0002 | ETA 03:00:55 2020-12-02 22:53:24 [INFO] seg_loss:0.1463, att_loss: 0.6284, edge_loss: 2.7102, dual_loss: 0.0022 2020-12-02 22:53:25 [INFO] [TRAIN] epoch=195, iter=72400/80000, loss=2.3167, lr=0.001202, batch_cost=1.3901, reader_cost=0.0002 | ETA 02:56:04 2020-12-02 22:55:44 [INFO] seg_loss:0.1581, att_loss: 0.5590, edge_loss: 1.9751, dual_loss: 0.0017 2020-12-02 22:55:45 [INFO] [TRAIN] epoch=195, iter=72500/80000, loss=2.4812, lr=0.001188, batch_cost=1.3995, reader_cost=0.0002 | ETA 02:54:56 2020-12-02 22:58:09 [INFO] seg_loss:0.0704, att_loss: 0.3894, edge_loss: 2.0287, dual_loss: 0.0010 2020-12-02 22:58:10 [INFO] [TRAIN] epoch=196, iter=72600/80000, loss=2.4679, lr=0.001174, batch_cost=1.4543, reader_cost=0.0642 | ETA 02:59:21 2020-12-02 23:00:29 [INFO] seg_loss:0.0966, att_loss: 0.4687, edge_loss: 3.0042, dual_loss: 0.0018 2020-12-02 23:00:30 [INFO] [TRAIN] epoch=196, iter=72700/80000, loss=2.3588, lr=0.001159, batch_cost=1.3970, reader_cost=0.0002 | ETA 02:49:58 2020-12-02 23:02:51 [INFO] seg_loss:0.0197, att_loss: 0.2406, edge_loss: 1.0982, dual_loss: 0.0006 2020-12-02 23:02:52 [INFO] [TRAIN] epoch=196, iter=72800/80000, loss=2.2824, lr=0.001145, batch_cost=1.4229, reader_cost=0.0002 | ETA 02:50:45 2020-12-02 23:05:13 [INFO] seg_loss:0.0278, att_loss: 0.3130, edge_loss: 1.0145, dual_loss: 0.0007 2020-12-02 23:05:14 [INFO] [TRAIN] epoch=196, iter=72900/80000, loss=2.4635, lr=0.001131, batch_cost=1.4137, reader_cost=0.0002 | ETA 02:47:17 2020-12-02 23:07:38 [INFO] seg_loss:0.0888, att_loss: 0.5968, edge_loss: 1.1291, dual_loss: 0.0008 2020-12-02 23:07:39 [INFO] [TRAIN] epoch=197, iter=73000/80000, loss=2.5363, lr=0.001117, batch_cost=1.4582, reader_cost=0.0395 | ETA 02:50:07 2020-12-02 23:09:58 [INFO] seg_loss:0.0858, att_loss: 0.4966, edge_loss: 2.5092, dual_loss: 0.0014 2020-12-02 23:09:59 [INFO] [TRAIN] epoch=197, iter=73100/80000, loss=2.4097, lr=0.001102, batch_cost=1.3950, reader_cost=0.0002 | ETA 02:40:25 2020-12-02 23:12:14 [INFO] seg_loss:0.1361, att_loss: 0.6088, edge_loss: 1.6035, dual_loss: 0.0013 2020-12-02 23:12:15 [INFO] [TRAIN] epoch=197, iter=73200/80000, loss=2.2361, lr=0.001088, batch_cost=1.3631, reader_cost=0.0003 | ETA 02:34:28 2020-12-02 23:14:40 [INFO] seg_loss:0.0783, att_loss: 0.4987, edge_loss: 2.7061, dual_loss: 0.0016 2020-12-02 23:14:41 [INFO] [TRAIN] epoch=198, iter=73300/80000, loss=2.5611, lr=0.001073, batch_cost=1.4607, reader_cost=0.0414 | ETA 02:43:06 2020-12-02 23:17:00 [INFO] seg_loss:0.0914, att_loss: 0.5597, edge_loss: 1.9557, dual_loss: 0.0015 2020-12-02 23:17:01 [INFO] [TRAIN] epoch=198, iter=73400/80000, loss=2.4402, lr=0.001059, batch_cost=1.3972, reader_cost=0.0004 | ETA 02:33:41 2020-12-02 23:19:19 [INFO] seg_loss:0.0320, att_loss: 0.3349, edge_loss: 1.3622, dual_loss: 0.0007 2020-12-02 23:19:20 [INFO] [TRAIN] epoch=198, iter=73500/80000, loss=2.2921, lr=0.001044, batch_cost=1.3899, reader_cost=0.0013 | ETA 02:30:34 2020-12-02 23:21:41 [INFO] seg_loss:0.0572, att_loss: 0.4439, edge_loss: 2.0748, dual_loss: 0.0013 2020-12-02 23:21:42 [INFO] [TRAIN] epoch=198, iter=73600/80000, loss=2.4210, lr=0.001030, batch_cost=1.4151, reader_cost=0.0005 | ETA 02:30:56 2020-12-02 23:24:05 [INFO] seg_loss:0.0474, att_loss: 0.4400, edge_loss: 1.4847, dual_loss: 0.0008 2020-12-02 23:24:06 [INFO] [TRAIN] epoch=199, iter=73700/80000, loss=2.5185, lr=0.001016, batch_cost=1.4478, reader_cost=0.0808 | ETA 02:32:01 2020-12-02 23:26:27 [INFO] seg_loss:0.0651, att_loss: 0.4216, edge_loss: 1.9784, dual_loss: 0.0011 2020-12-02 23:26:28 [INFO] [TRAIN] epoch=199, iter=73800/80000, loss=2.3436, lr=0.001001, batch_cost=1.4143, reader_cost=0.0003 | ETA 02:26:08 2020-12-02 23:28:46 [INFO] seg_loss:0.0260, att_loss: 0.4128, edge_loss: 1.0151, dual_loss: 0.0005 2020-12-02 23:28:47 [INFO] [TRAIN] epoch=199, iter=73900/80000, loss=2.2888, lr=0.000986, batch_cost=1.3880, reader_cost=0.0012 | ETA 02:21:06 2020-12-02 23:31:06 [INFO] seg_loss:0.0553, att_loss: 0.4191, edge_loss: 2.1301, dual_loss: 0.0013 2020-12-02 23:31:07 [INFO] [TRAIN] epoch=199, iter=74000/80000, loss=2.5110, lr=0.000972, batch_cost=1.4020, reader_cost=0.0002 | ETA 02:20:12 2020-12-02 23:31:07 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-02 23:32:11 [INFO] [EVAL] #Images=500 mIoU=0.8023 Acc=0.9650 Kappa=0.9545 2020-12-02 23:32:11 [INFO] [EVAL] Class IoU: [0.9836 0.8686 0.9326 0.4682 0.6442 0.7054 0.7514 0.8194 0.9302 0.6557 0.9532 0.8486 0.6642 0.9588 0.8098 0.9126 0.8463 0.6887 0.802 ] 2020-12-02 23:32:11 [INFO] [EVAL] Class Acc: [0.9911 0.9356 0.9627 0.8579 0.8372 0.808 0.8677 0.9132 0.958 0.8292 0.9691 0.8985 0.8226 0.9777 0.9178 0.9628 0.9174 0.8585 0.8713] 2020-12-02 23:32:15 [INFO] [EVAL] The model with the best validation mIoU (0.8067) was saved at iter 66000. 2020-12-02 23:34:41 [INFO] seg_loss:0.1086, att_loss: 0.4579, edge_loss: 1.8116, dual_loss: 0.0018 2020-12-02 23:34:42 [INFO] [TRAIN] epoch=200, iter=74100/80000, loss=2.3253, lr=0.000957, batch_cost=1.4722, reader_cost=0.0512 | ETA 02:24:46 2020-12-02 23:36:59 [INFO] seg_loss:0.1062, att_loss: 0.5326, edge_loss: 2.5649, dual_loss: 0.0018 2020-12-02 23:37:00 [INFO] [TRAIN] epoch=200, iter=74200/80000, loss=2.4509, lr=0.000943, batch_cost=1.3853, reader_cost=0.0002 | ETA 02:13:54 2020-12-02 23:39:19 [INFO] seg_loss:0.0398, att_loss: 0.4228, edge_loss: 1.4575, dual_loss: 0.0010 2020-12-02 23:39:20 [INFO] [TRAIN] epoch=200, iter=74300/80000, loss=2.3475, lr=0.000928, batch_cost=1.3969, reader_cost=0.0003 | ETA 02:12:42 2020-12-02 23:41:41 [INFO] seg_loss:0.0595, att_loss: 0.4017, edge_loss: 2.7495, dual_loss: 0.0015 2020-12-02 23:41:42 [INFO] [TRAIN] epoch=200, iter=74400/80000, loss=2.4452, lr=0.000913, batch_cost=1.4180, reader_cost=0.0002 | ETA 02:12:20 2020-12-02 23:44:07 [INFO] seg_loss:0.1102, att_loss: 0.5753, edge_loss: 2.1610, dual_loss: 0.0019 2020-12-02 23:44:08 [INFO] [TRAIN] epoch=201, iter=74500/80000, loss=2.5490, lr=0.000899, batch_cost=1.4591, reader_cost=0.0422 | ETA 02:13:45 2020-12-02 23:46:26 [INFO] seg_loss:0.0208, att_loss: 0.3244, edge_loss: 0.9855, dual_loss: 0.0005 2020-12-02 23:46:27 [INFO] [TRAIN] epoch=201, iter=74600/80000, loss=2.3288, lr=0.000884, batch_cost=1.3873, reader_cost=0.0002 | ETA 02:04:51 2020-12-02 23:48:44 [INFO] seg_loss:0.0196, att_loss: 0.2793, edge_loss: 1.5148, dual_loss: 0.0006 2020-12-02 23:48:45 [INFO] [TRAIN] epoch=201, iter=74700/80000, loss=2.4284, lr=0.000869, batch_cost=1.3824, reader_cost=0.0002 | ETA 02:02:06 2020-12-02 23:51:13 [INFO] seg_loss:0.0137, att_loss: 0.4246, edge_loss: 0.6409, dual_loss: 0.0003 2020-12-02 23:51:14 [INFO] [TRAIN] epoch=202, iter=74800/80000, loss=2.6675, lr=0.000854, batch_cost=1.4871, reader_cost=0.0416 | ETA 02:08:52 2020-12-02 23:53:32 [INFO] seg_loss:0.0532, att_loss: 0.4577, edge_loss: 2.0782, dual_loss: 0.0011 2020-12-02 23:53:33 [INFO] [TRAIN] epoch=202, iter=74900/80000, loss=2.4260, lr=0.000840, batch_cost=1.3926, reader_cost=0.0005 | ETA 01:58:22 2020-12-02 23:55:52 [INFO] seg_loss:0.0450, att_loss: 0.3881, edge_loss: 1.6054, dual_loss: 0.0010 2020-12-02 23:55:53 [INFO] [TRAIN] epoch=202, iter=75000/80000, loss=2.2710, lr=0.000825, batch_cost=1.4034, reader_cost=0.0003 | ETA 01:56:57 2020-12-02 23:58:11 [INFO] seg_loss:0.1071, att_loss: 0.4667, edge_loss: 1.0433, dual_loss: 0.0010 2020-12-02 23:58:12 [INFO] [TRAIN] epoch=202, iter=75100/80000, loss=2.5443, lr=0.000810, batch_cost=1.3872, reader_cost=0.0007 | ETA 01:53:17 2020-12-03 00:00:36 [INFO] seg_loss:0.0844, att_loss: 0.3352, edge_loss: 1.3725, dual_loss: 0.0010 2020-12-03 00:00:37 [INFO] [TRAIN] epoch=203, iter=75200/80000, loss=2.4904, lr=0.000795, batch_cost=1.4512, reader_cost=0.0438 | ETA 01:56:05 2020-12-03 00:02:54 [INFO] seg_loss:0.0847, att_loss: 0.5378, edge_loss: 2.9472, dual_loss: 0.0017 2020-12-03 00:02:55 [INFO] [TRAIN] epoch=203, iter=75300/80000, loss=2.3230, lr=0.000780, batch_cost=1.3778, reader_cost=0.0002 | ETA 01:47:55 2020-12-03 00:05:14 [INFO] seg_loss:0.0173, att_loss: 0.2770, edge_loss: 0.8152, dual_loss: 0.0005 2020-12-03 00:05:15 [INFO] [TRAIN] epoch=203, iter=75400/80000, loss=2.4554, lr=0.000765, batch_cost=1.4030, reader_cost=0.0007 | ETA 01:47:33 2020-12-03 00:07:37 [INFO] seg_loss:0.1734, att_loss: 0.4170, edge_loss: 1.2582, dual_loss: 0.0017 2020-12-03 00:07:38 [INFO] [TRAIN] epoch=203, iter=75500/80000, loss=2.5581, lr=0.000750, batch_cost=1.4234, reader_cost=0.0002 | ETA 01:46:45 2020-12-03 00:10:02 [INFO] seg_loss:0.0589, att_loss: 0.3865, edge_loss: 2.5388, dual_loss: 0.0014 2020-12-03 00:10:03 [INFO] [TRAIN] epoch=204, iter=75600/80000, loss=2.4087, lr=0.000735, batch_cost=1.4579, reader_cost=0.0440 | ETA 01:46:54 2020-12-03 00:12:21 [INFO] seg_loss:0.0509, att_loss: 0.3646, edge_loss: 1.8566, dual_loss: 0.0011 2020-12-03 00:12:22 [INFO] [TRAIN] epoch=204, iter=75700/80000, loss=2.3847, lr=0.000720, batch_cost=1.3866, reader_cost=0.0007 | ETA 01:39:22 2020-12-03 00:14:41 [INFO] seg_loss:0.1815, att_loss: 0.5798, edge_loss: 1.5402, dual_loss: 0.0017 2020-12-03 00:14:42 [INFO] [TRAIN] epoch=204, iter=75800/80000, loss=2.1683, lr=0.000705, batch_cost=1.4007, reader_cost=0.0006 | ETA 01:38:03 2020-12-03 00:17:07 [INFO] seg_loss:0.1491, att_loss: 0.5881, edge_loss: 3.0402, dual_loss: 0.0022 2020-12-03 00:17:08 [INFO] [TRAIN] epoch=205, iter=75900/80000, loss=2.5684, lr=0.000690, batch_cost=1.4586, reader_cost=0.0427 | ETA 01:39:40 2020-12-03 00:19:26 [INFO] seg_loss:0.0725, att_loss: 0.3511, edge_loss: 1.2606, dual_loss: 0.0011 2020-12-03 00:19:27 [INFO] [TRAIN] epoch=205, iter=76000/80000, loss=2.4728, lr=0.000675, batch_cost=1.3923, reader_cost=0.0004 | ETA 01:32:49 2020-12-03 00:19:27 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-03 00:20:30 [INFO] [EVAL] #Images=500 mIoU=0.8039 Acc=0.9656 Kappa=0.9553 2020-12-03 00:20:30 [INFO] [EVAL] Class IoU: [0.9838 0.8711 0.9342 0.5051 0.6378 0.7107 0.7551 0.8185 0.9302 0.6576 0.9532 0.8518 0.6726 0.9587 0.7987 0.9106 0.8286 0.6928 0.8028] 2020-12-03 00:20:30 [INFO] [EVAL] Class Acc: [0.9913 0.938 0.9621 0.8667 0.8405 0.8379 0.8504 0.9017 0.957 0.8533 0.9702 0.9033 0.8192 0.9771 0.9071 0.9486 0.8934 0.8621 0.8725] 2020-12-03 00:20:35 [INFO] [EVAL] The model with the best validation mIoU (0.8067) was saved at iter 66000. 2020-12-03 00:22:55 [INFO] seg_loss:0.0930, att_loss: 0.5366, edge_loss: 2.8355, dual_loss: 0.0018 2020-12-03 00:22:56 [INFO] [TRAIN] epoch=205, iter=76100/80000, loss=2.2522, lr=0.000660, batch_cost=1.4158, reader_cost=0.0008 | ETA 01:32:01 2020-12-03 00:25:17 [INFO] seg_loss:0.0716, att_loss: 0.4228, edge_loss: 2.5806, dual_loss: 0.0015 2020-12-03 00:25:18 [INFO] [TRAIN] epoch=205, iter=76200/80000, loss=2.4400, lr=0.000644, batch_cost=1.4167, reader_cost=0.0010 | ETA 01:29:43 2020-12-03 00:27:44 [INFO] seg_loss:0.0540, att_loss: 0.3112, edge_loss: 0.9499, dual_loss: 0.0005 2020-12-03 00:27:45 [INFO] [TRAIN] epoch=206, iter=76300/80000, loss=2.4790, lr=0.000629, batch_cost=1.4700, reader_cost=0.0396 | ETA 01:30:39 2020-12-03 00:30:01 [INFO] seg_loss:0.1104, att_loss: 0.5079, edge_loss: 2.7337, dual_loss: 0.0017 2020-12-03 00:30:02 [INFO] [TRAIN] epoch=206, iter=76400/80000, loss=2.5272, lr=0.000614, batch_cost=1.3699, reader_cost=0.0002 | ETA 01:22:11 2020-12-03 00:32:22 [INFO] seg_loss:0.1037, att_loss: 0.5367, edge_loss: 2.7018, dual_loss: 0.0019 2020-12-03 00:32:23 [INFO] [TRAIN] epoch=206, iter=76500/80000, loss=2.3963, lr=0.000598, batch_cost=1.4153, reader_cost=0.0002 | ETA 01:22:33 2020-12-03 00:34:43 [INFO] seg_loss:0.0678, att_loss: 0.4377, edge_loss: 2.0934, dual_loss: 0.0013 2020-12-03 00:34:44 [INFO] [TRAIN] epoch=206, iter=76600/80000, loss=2.4909, lr=0.000583, batch_cost=1.4040, reader_cost=0.0004 | ETA 01:19:33 2020-12-03 00:37:11 [INFO] seg_loss:0.0771, att_loss: 0.3555, edge_loss: 1.5307, dual_loss: 0.0016 2020-12-03 00:37:12 [INFO] [TRAIN] epoch=207, iter=76700/80000, loss=2.3846, lr=0.000568, batch_cost=1.4865, reader_cost=0.0640 | ETA 01:21:45 2020-12-03 00:39:31 [INFO] seg_loss:0.0556, att_loss: 0.4335, edge_loss: 2.2425, dual_loss: 0.0013 2020-12-03 00:39:32 [INFO] [TRAIN] epoch=207, iter=76800/80000, loss=2.4183, lr=0.000552, batch_cost=1.3914, reader_cost=0.0002 | ETA 01:14:12 2020-12-03 00:41:52 [INFO] seg_loss:0.0187, att_loss: 0.2358, edge_loss: 1.0532, dual_loss: 0.0004 2020-12-03 00:41:53 [INFO] [TRAIN] epoch=207, iter=76900/80000, loss=2.3626, lr=0.000537, batch_cost=1.4095, reader_cost=0.0002 | ETA 01:12:49 2020-12-03 00:44:13 [INFO] seg_loss:0.0220, att_loss: 0.3612, edge_loss: 0.7737, dual_loss: 0.0005 2020-12-03 00:44:14 [INFO] [TRAIN] epoch=207, iter=77000/80000, loss=2.5086, lr=0.000521, batch_cost=1.4141, reader_cost=0.0002 | ETA 01:10:42 2020-12-03 00:46:37 [INFO] seg_loss:0.1500, att_loss: 0.5699, edge_loss: 2.3929, dual_loss: 0.0021 2020-12-03 00:46:38 [INFO] [TRAIN] epoch=208, iter=77100/80000, loss=2.4806, lr=0.000505, batch_cost=1.4435, reader_cost=0.0440 | ETA 01:09:46 2020-12-03 00:48:58 [INFO] seg_loss:0.0764, att_loss: 0.4450, edge_loss: 1.3120, dual_loss: 0.0010 2020-12-03 00:48:59 [INFO] [TRAIN] epoch=208, iter=77200/80000, loss=2.3791, lr=0.000490, batch_cost=1.4046, reader_cost=0.0003 | ETA 01:05:32 2020-12-03 00:51:17 [INFO] seg_loss:0.0159, att_loss: 0.2236, edge_loss: 1.5039, dual_loss: 0.0007 2020-12-03 00:51:18 [INFO] [TRAIN] epoch=208, iter=77300/80000, loss=2.4221, lr=0.000474, batch_cost=1.3900, reader_cost=0.0002 | ETA 01:02:32 2020-12-03 00:53:40 [INFO] seg_loss:0.1023, att_loss: 0.5299, edge_loss: 2.5894, dual_loss: 0.0017 2020-12-03 00:53:41 [INFO] [TRAIN] epoch=209, iter=77400/80000, loss=2.5185, lr=0.000458, batch_cost=1.4349, reader_cost=0.0417 | ETA 01:02:10 2020-12-03 00:56:02 [INFO] seg_loss:0.1030, att_loss: 0.4723, edge_loss: 2.2780, dual_loss: 0.0013 2020-12-03 00:56:03 [INFO] [TRAIN] epoch=209, iter=77500/80000, loss=2.3776, lr=0.000442, batch_cost=1.4216, reader_cost=0.0002 | ETA 00:59:14 2020-12-03 00:58:25 [INFO] seg_loss:0.0841, att_loss: 0.4862, edge_loss: 2.3508, dual_loss: 0.0016 2020-12-03 00:58:26 [INFO] [TRAIN] epoch=209, iter=77600/80000, loss=2.3760, lr=0.000426, batch_cost=1.4300, reader_cost=0.0003 | ETA 00:57:11 2020-12-03 01:00:45 [INFO] seg_loss:0.1011, att_loss: 0.5637, edge_loss: 2.8888, dual_loss: 0.0018 2020-12-03 01:00:46 [INFO] [TRAIN] epoch=209, iter=77700/80000, loss=2.4375, lr=0.000410, batch_cost=1.3987, reader_cost=0.0004 | ETA 00:53:36 2020-12-03 01:03:09 [INFO] seg_loss:0.0385, att_loss: 0.3053, edge_loss: 1.5452, dual_loss: 0.0009 2020-12-03 01:03:10 [INFO] [TRAIN] epoch=210, iter=77800/80000, loss=2.3550, lr=0.000394, batch_cost=1.4378, reader_cost=0.0425 | ETA 00:52:43 2020-12-03 01:05:30 [INFO] seg_loss:0.1319, att_loss: 0.6354, edge_loss: 2.3564, dual_loss: 0.0022 2020-12-03 01:05:31 [INFO] [TRAIN] epoch=210, iter=77900/80000, loss=2.3173, lr=0.000378, batch_cost=1.4106, reader_cost=0.0002 | ETA 00:49:22 2020-12-03 01:07:49 [INFO] seg_loss:0.0430, att_loss: 0.3920, edge_loss: 1.6120, dual_loss: 0.0009 2020-12-03 01:07:50 [INFO] [TRAIN] epoch=210, iter=78000/80000, loss=2.4521, lr=0.000362, batch_cost=1.3926, reader_cost=0.0004 | ETA 00:46:25 2020-12-03 01:07:50 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-03 01:08:52 [INFO] [EVAL] #Images=500 mIoU=0.8047 Acc=0.9655 Kappa=0.9553 2020-12-03 01:08:52 [INFO] [EVAL] Class IoU: [0.9836 0.8692 0.9343 0.5136 0.6403 0.7113 0.7553 0.8195 0.9307 0.6612 0.9528 0.8524 0.6737 0.9589 0.7834 0.9129 0.849 0.684 0.8038] 2020-12-03 01:08:52 [INFO] [EVAL] Class Acc: [0.9909 0.9373 0.9633 0.8555 0.8439 0.8286 0.8613 0.9048 0.9575 0.8534 0.9682 0.9074 0.8092 0.9771 0.8879 0.9579 0.9201 0.8654 0.8751] 2020-12-03 01:08:57 [INFO] [EVAL] The model with the best validation mIoU (0.8067) was saved at iter 66000. 2020-12-03 01:11:23 [INFO] seg_loss:0.0530, att_loss: 0.4698, edge_loss: 1.4643, dual_loss: 0.0010 2020-12-03 01:11:24 [INFO] [TRAIN] epoch=210, iter=78100/80000, loss=2.4695, lr=0.000345, batch_cost=1.4737, reader_cost=0.0003 | ETA 00:46:39 2020-12-03 01:13:50 [INFO] seg_loss:0.0863, att_loss: 0.5023, edge_loss: 2.7784, dual_loss: 0.0016 2020-12-03 01:13:51 [INFO] [TRAIN] epoch=211, iter=78200/80000, loss=2.3498, lr=0.000329, batch_cost=1.4676, reader_cost=0.0608 | ETA 00:44:01 2020-12-03 01:16:10 [INFO] seg_loss:0.0222, att_loss: 0.2698, edge_loss: 1.5838, dual_loss: 0.0007 2020-12-03 01:16:11 [INFO] [TRAIN] epoch=211, iter=78300/80000, loss=2.3975, lr=0.000313, batch_cost=1.3976, reader_cost=0.0004 | ETA 00:39:35 2020-12-03 01:18:29 [INFO] seg_loss:0.1057, att_loss: 0.4828, edge_loss: 2.7597, dual_loss: 0.0016 2020-12-03 01:18:30 [INFO] [TRAIN] epoch=211, iter=78400/80000, loss=2.3500, lr=0.000296, batch_cost=1.3933, reader_cost=0.0013 | ETA 00:37:09 2020-12-03 01:20:56 [INFO] seg_loss:0.0721, att_loss: 0.4453, edge_loss: 2.8115, dual_loss: 0.0016 2020-12-03 01:20:57 [INFO] [TRAIN] epoch=212, iter=78500/80000, loss=2.4585, lr=0.000279, batch_cost=1.4737, reader_cost=0.0390 | ETA 00:36:50 2020-12-03 01:23:14 [INFO] seg_loss:0.0904, att_loss: 0.4727, edge_loss: 2.0060, dual_loss: 0.0017 2020-12-03 01:23:15 [INFO] [TRAIN] epoch=212, iter=78600/80000, loss=2.5734, lr=0.000262, batch_cost=1.3760, reader_cost=0.0005 | ETA 00:32:06 2020-12-03 01:25:36 [INFO] seg_loss:0.0494, att_loss: 0.4605, edge_loss: 1.3644, dual_loss: 0.0009 2020-12-03 01:25:37 [INFO] [TRAIN] epoch=212, iter=78700/80000, loss=2.2704, lr=0.000246, batch_cost=1.4177, reader_cost=0.0002 | ETA 00:30:42 2020-12-03 01:27:55 [INFO] seg_loss:0.0582, att_loss: 0.3508, edge_loss: 2.1842, dual_loss: 0.0012 2020-12-03 01:27:56 [INFO] [TRAIN] epoch=212, iter=78800/80000, loss=2.4296, lr=0.000228, batch_cost=1.3964, reader_cost=0.0002 | ETA 00:27:55 2020-12-03 01:30:17 [INFO] seg_loss:0.0813, att_loss: 0.5010, edge_loss: 1.6391, dual_loss: 0.0010 2020-12-03 01:30:18 [INFO] [TRAIN] epoch=213, iter=78900/80000, loss=2.5967, lr=0.000211, batch_cost=1.4175, reader_cost=0.0400 | ETA 00:25:59 2020-12-03 01:32:39 [INFO] seg_loss:0.0592, att_loss: 0.4654, edge_loss: 1.9249, dual_loss: 0.0011 2020-12-03 01:32:40 [INFO] [TRAIN] epoch=213, iter=79000/80000, loss=2.4302, lr=0.000194, batch_cost=1.4177, reader_cost=0.0002 | ETA 00:23:37 2020-12-03 01:35:09 [INFO] seg_loss:0.0315, att_loss: 0.3297, edge_loss: 1.6182, dual_loss: 0.0010 2020-12-03 01:35:10 [INFO] [TRAIN] epoch=213, iter=79100/80000, loss=2.3503, lr=0.000176, batch_cost=1.5008, reader_cost=0.0025 | ETA 00:22:30 2020-12-03 01:37:26 [INFO] seg_loss:0.1499, att_loss: 0.5358, edge_loss: 2.7387, dual_loss: 0.0023 2020-12-03 01:37:27 [INFO] [TRAIN] epoch=213, iter=79200/80000, loss=2.4699, lr=0.000159, batch_cost=1.3724, reader_cost=0.0004 | ETA 00:18:17 2020-12-03 01:39:50 [INFO] seg_loss:0.0772, att_loss: 0.4742, edge_loss: 2.0152, dual_loss: 0.0013 2020-12-03 01:39:51 [INFO] [TRAIN] epoch=214, iter=79300/80000, loss=2.5612, lr=0.000141, batch_cost=1.4405, reader_cost=0.0402 | ETA 00:16:48 2020-12-03 01:42:08 [INFO] seg_loss:0.0125, att_loss: 0.2362, edge_loss: 1.2332, dual_loss: 0.0005 2020-12-03 01:42:09 [INFO] [TRAIN] epoch=214, iter=79400/80000, loss=2.3188, lr=0.000123, batch_cost=1.3807, reader_cost=0.0002 | ETA 00:13:48 2020-12-03 01:44:24 [INFO] seg_loss:0.0812, att_loss: 0.4522, edge_loss: 2.4316, dual_loss: 0.0016 2020-12-03 01:44:25 [INFO] [TRAIN] epoch=214, iter=79500/80000, loss=2.4122, lr=0.000104, batch_cost=1.3594, reader_cost=0.0005 | ETA 00:11:19 2020-12-03 01:46:44 [INFO] seg_loss:0.0563, att_loss: 0.4565, edge_loss: 1.3772, dual_loss: 0.0011 2020-12-03 01:46:45 [INFO] [TRAIN] epoch=214, iter=79600/80000, loss=2.4293, lr=0.000085, batch_cost=1.4007, reader_cost=0.0002 | ETA 00:09:20 2020-12-03 01:49:14 [INFO] seg_loss:0.0916, att_loss: 0.5482, edge_loss: 1.2474, dual_loss: 0.0009 2020-12-03 01:49:15 [INFO] [TRAIN] epoch=215, iter=79700/80000, loss=2.4433, lr=0.000066, batch_cost=1.5003, reader_cost=0.0388 | ETA 00:07:30 2020-12-03 01:51:33 [INFO] seg_loss:0.0247, att_loss: 0.3437, edge_loss: 1.1643, dual_loss: 0.0006 2020-12-03 01:51:34 [INFO] [TRAIN] epoch=215, iter=79800/80000, loss=2.3238, lr=0.000046, batch_cost=1.3847, reader_cost=0.0008 | ETA 00:04:36 2020-12-03 01:53:53 [INFO] seg_loss:0.1234, att_loss: 0.5635, edge_loss: 2.7056, dual_loss: 0.0021 2020-12-03 01:53:54 [INFO] [TRAIN] epoch=215, iter=79900/80000, loss=2.3605, lr=0.000025, batch_cost=1.4042, reader_cost=0.0005 | ETA 00:02:20 2020-12-03 01:56:24 [INFO] seg_loss:0.0766, att_loss: 0.4997, edge_loss: 2.7162, dual_loss: 0.0015 2020-12-03 01:56:25 [INFO] [TRAIN] epoch=216, iter=80000/80000, loss=2.6098, lr=0.000000, batch_cost=1.5108, reader_cost=0.0646 | ETA 00:00:00 2020-12-03 01:56:25 [INFO] Start evaluating (total_samples=500, total_iters=125)... 2020-12-03 01:57:27 [INFO] [EVAL] #Images=500 mIoU=0.8057 Acc=0.9654 Kappa=0.9551 2020-12-03 01:57:27 [INFO] [EVAL] Class IoU: [0.9837 0.8686 0.9339 0.5234 0.6414 0.7087 0.7557 0.8186 0.9303 0.6495 0.9529 0.8514 0.6758 0.9583 0.8027 0.9112 0.8502 0.6905 0.8018] 2020-12-03 01:57:27 [INFO] [EVAL] Class Acc: [0.9909 0.9371 0.9636 0.8653 0.8437 0.8245 0.8584 0.9025 0.9562 0.8504 0.9719 0.9037 0.8047 0.9763 0.9284 0.963 0.9181 0.8458 0.8724] 2020-12-03 01:57:32 [INFO] [EVAL] The model with the best validation mIoU (0.8067) was saved at iter 66000.