longtensor and reciprocal
Loading pretrained weights from data/pretrained_model/resnet101_caffe.pth
/home/ztd/anaconda2/envs/py2.7_tf1.4_tff/lib/python2.7/site-packages/torch/nn/functional.py:1749: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
/test/frcnn_pytorch/fpn_torch_10_16/lib/model/rpn/rpn_fpn.py:79: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
rpn_cls_prob_reshape = F.softmax(rpn_cls_score_reshape)
Traceback (most recent call last):
File "/test/frcnn_pytorch/fpn_torch_10_16/trainval_net.py", line 330, in
Process finished with exit code 1
I dont know how to solve this
Did you solve it? I got the same issue. At the same time, I got this "Exception NameError: "global name 'FileNotFoundError' is not defined" in <bound method _DataLoaderIter.del of <torch.utils.data.dataloader._DataLoaderIter object at 0x7fe12c6e2b10>> ignored"
I solve this by change num_exmaples from type long to float before divide operation. Specifically, add one line in anchor_target_layer_fpn.py", line 136:
num_examples = torch.sum(labels[i] >= 0)
num_examples = num_examples.float()
positive_weights = 1.0 / num_examples
negative_weights = 1.0 / num_examples