one problem in the network training

[WBinke]

When I'm training on the coco as the README declared,I meet this problem just like the blod log,and then the NMSLoss_pos and the NMSLoss_neg become nan,does anyone meet the same problem and give me some help?

('lr', 0.0005, 'lr_epoch_diff', [5.33], 'lr_iters', [625027])
Epoch[0] Batch [100] Speed: 5.08 samples/sec Train-RPNAcc=0.847250, RPNLogLoss=0.376764, RPNL1Loss=0.187504, RCNNAcc=0.801361, RCNNLogLoss=1.674762, RCNNL1Loss=0.311297, NMSLoss_pos=0.035744, NMSLoss_neg=0.016391, NMSAcc_pos=0.000000, NMSAcc_neg=1.000000,
Epoch[0] Batch [200] Speed: 5.10 samples/sec Train-RPNAcc=0.865089, RPNLogLoss=0.328289, RPNL1Loss=0.176516, RCNNAcc=0.811237, RCNNLogLoss=1.380794, RCNNL1Loss=0.316205, NMSLoss_pos=0.048681, NMSLoss_neg=0.013534, NMSAcc_pos=0.000000, NMSAcc_neg=1.000000,
Epoch[0] Batch [300] Speed: 5.11 samples/sec Train-RPNAcc=0.874916, RPNLogLoss=0.302038, RPNL1Loss=0.159570, RCNNAcc=0.802546, RCNNLogLoss=1.319950, RCNNL1Loss=0.352934, NMSLoss_pos=0.057433, NMSLoss_neg=0.013499, NMSAcc_pos=0.000000, NMSAcc_neg=1.000000,
experiments/relation_rcnn/../../relation_rcnn/../lib/bbox/bbox_transform.py:128: RuntimeWarning: overflow encountered in exp
pred_w = np.exp(dw) * widths[:, np.newaxis]
experiments/relation_rcnn/../../relation_rcnn/../lib/bbox/bbox_transform.py:129: RuntimeWarning: overflow encountered in exp
pred_h = np.exp(dh) * heights[:, np.newaxis]
experiments/relation_rcnn/../../relation_rcnn/../lib/bbox/bbox_transform.py:133: RuntimeWarning: invalid value encountered in subtract
pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * (pred_w - 1.0)
experiments/relation_rcnn/../../relation_rcnn/../lib/bbox/bbox_transform.py:135: RuntimeWarning: invalid value encountered in subtract
pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * (pred_h - 1.0)
experiments/relation_rcnn/../../relation_rcnn/../lib/bbox/bbox_transform.py:137: RuntimeWarning: invalid value encountered in add
pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * (pred_w - 1.0)
experiments/relation_rcnn/../../relation_rcnn/../lib/bbox/bbox_transform.py:139: RuntimeWarning: invalid value encountered in add
pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * (pred_h - 1.0)
experiments/relation_rcnn/../../relation_rcnn/operator_py/proposal.py:180: RuntimeWarning: invalid value encountered in greater_equal
keep = np.where((ws >= min_size) & (hs >= min_size))[0]
Epoch[0] Batch [400] Speed: 5.02 samples/sec Train-RPNAcc=0.871289, RPNLogLoss=nan, RPNL1Loss=nan, RCNNAcc=0.810123, RCNNLogLoss=1.576645, RCNNL1Loss=0.334166, NMSLoss_pos=0.054120, NMSLoss_neg=nan, NMSAcc_pos=0.000000, NMSAcc_neg=0.999650,
Epoch[0] Batch [500] Speed: 4.91 samples/sec Train-RPNAcc=0.859804, RPNLogLoss=nan, RPNL1Loss=nan, RCNNAcc=0.836702, RCNNLogLoss=1.888214, RCNNL1Loss=0.267614, NMSLoss_pos=nan, NMSLoss_neg=nan, NMSAcc_pos=0.000000, NMSAcc_neg=0.999720,
Epoch[0] Batch [600] Speed: 4.99 samples/sec Train-RPNAcc=0.850682, RPNLogLoss=nan, RPNL1Loss=nan, RCNNAcc=0.853031, RCNNLogLoss=1.725999, RCNNL1Loss=0.223882, NMSLoss_pos=nan, NMSLoss_neg=nan, NMSAcc_pos=0.000000, NMSAcc_neg=0.999767,
Epoch[0] Batch [700] Speed: 4.98 samples/sec Train-RPNAcc=0.844466, RPNLogLoss=nan, RPNL1Loss=nan, RCNNAcc=0.865544, RCNNLogLoss=1.547918, RCNNL1Loss=0.192278, NMSLoss_pos=nan, NMSLoss_neg=nan, NMSAcc_pos=0.000000, NMSAcc_neg=0.999800,

[ancientmooner]

If you encounter NaN, please try more times until there is no NaN. Some random initialization might cause divergence problem. If problem still exists, it might because the base lr is too large for your task. In this case, please use a smaller base lr.

[yafz]

Seconded. Either your data layer is incorrect or you need to alter the learning policy (use smaller base lr, try warmup, ...)