module has parameters that were not used in producing loss

[ZhenkaiGao]

Dear author,

I am getting the following error when running Fine-tuning:

RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by making sure all `forward` function outputs participate in calculating loss.

If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable). Parameters which did not receive grad for rank 1: roi_heads.box_predictor.conv_cor.weight, roi_heads.box_predictor.cls_score_pr.bias, roi_heads.box_predictor.cls_score_pr.weight, roi_heads.box_predictor.conv_3.weight, roi_heads.box_predictor.conv_2.weight, roi_heads.box_predictor.conv_1.weight, roi_heads.box_predictor.locator.loc_fc.bias, roi_heads.box_predictor.locator.loc_fc.weight, roi_heads.box_predictor.locator.mlp.2.bias, roi_heads.box_predictor.locator.mlp.2.weight, roi_heads.box_predictor.locator.mlp.0.bias, roi_heads.box_predictor.locator.mlp.0.weight, roi_heads.box_predictor.locator.conv1.1.bias, roi_heads.box_predictor.locator.conv1.1.weight, roi_heads.box_predictor.locator.conv1.0.bias, roi_heads.box_predictor.locator.conv1.0.weight, roi_heads.box_predictor.locator.support_fc.1.bias, roi_heads.box_predictor.locator.support_fc.1.weight, roi_heads.box_predictor.locator.support_fc.0.bias, roi_heads.box_predictor.locator.support_fc.0.weight, proposal_generator.rpn_head.anchor_deltas.bias, proposal_generator.rpn_head.anchor_deltas.weight, proposal_generator.rpn_head.objectness_logits.bias, proposal_generator.rpn_head.objectness_logits.weight, proposal_generator.rpn_head.conv.weight, proposal_generator.rpn_head.conv.bias, roi_heads.res5.0.shortcut.weight, roi_heads.res5.0.conv1.weight, roi_heads.res5.0.conv2.weight, roi_heads.res5.0.conv3.weight, roi_heads.res5.1.conv1.weight, roi_heads.res5.1.conv2.weight, roi_heads.res5.1.conv3.weight, roi_heads.res5.2.conv1.weight, roi_heads.res5.2.conv2.weight, roi_heads.res5.2.conv3.weight, roi_heads.box_predictor.cls_score_cor.weight, roi_heads.box_predictor.cls_score_cor.bias, roi_heads.box_predictor.fc_1.weight, roi_heads.box_predictor.fc_1.bias, roi_heads.box_predictor.fc_2.weight, roi_heads.box_predictor.fc_2.bias, roi_heads.box_predictor.cls_score_fc.weight, roi_heads.box_predictor.cls_score_fc.bias, fuser.conv1.0.weight, fuser.conv1.0.bias, fuser.conv1.1.weight, fuser.conv1.1.bias, fuser.conv2.0.weight, fuser.conv2.0.bias, fuser.conv2.1.weight, fuser.conv2.1.bias, fuser.dw_conv.0.weight, fuser.dw_conv.0.bias, fuser.dw_conv.1.weight, fuser.dw_conv.1.bias, fuser.mlp.0.weight, fuser.mlp.0.bias, fuser.mlp.2.weight, fuser.mlp.2.bias, apn.v_2, apn.r1.conv.0.weight, apn.r1.conv.0.bias, apn.r1.conv.1.weight, apn.r1.conv.1.bias, apn.gconv1.conv_g.0.weight, apn.gconv1.conv_g.0.bias, apn.gconv1.conv_g.1.weight, apn.gconv1.conv_g.1.bias, apn.gconv1.conv.0.weight, apn.gconv1.conv.0.bias, apn.gconv1.conv.1.weight, apn.gconv1.conv.1.bias, apn.r3.conv.0.weight, apn.r3.conv.0.bias, apn.r3.conv.1.weight, apn.r3.conv.1.bias Parameter indices which did not receive grad for rank 1: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76

This seems to be because some parameters are not involved in the calculation of the loss. Any help on this would be greatly appreciated.

Best regards

[ImNotPrepared]

looking into it, will get back