Question about BN in nas_fpn

[ggjy]

Hi, thanks for your code.
I try to implement NAS-FPN in pytorch, but there seems to be some errors with my BN layer (Both 1-stage(RetinaNeet) / 2-stage). I freeze the BN layer in r50 backbone, fine-tune the BN in NAS-FPN (initialized with weight=1 bias=0), am I right?
The log is like this, training seems OK, but eval is always 0 (0.001).

Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
2019-08-08 01:31:15,314 - INFO - Epoch [1][7330/7330] lr: 0.02000, bbox_mAP: 0.0000, bbox_mAP_50: 0.0000, bbox_mAP_75: 0.0000, bbox_mAP_s: 0.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0000, bbox_mAP_copypaste: 0.000 0.000 0.000 0.000 0.000 0.000
2019-08-08 01:31:39,196 - INFO - Epoch [2][50/7330] lr: 0.02000, eta: 8:01:08, time: 0.477, data_time: 0.070, memory: 4057, loss_rpn_cls: 0.0640, loss_rpn_bbox: 0.0223, loss_cls: 0.1917, acc: 96.7520, loss_bbox: 0.0561, loss: 0.3341
2019-08-08 01:31:56,286 - INFO - Epoch [2][100/7330] lr: 0.02000, eta: 8:00:42, time: 0.342, data_time: 0.007, memory: 4057, loss_rpn_cls: 0.0718, loss_rpn_bbox: 0.0185, loss_cls: 0.2137, acc: 96.5977, loss_bbox: 0.0619, loss: 0.3659