Hi,
when I'm trying to run Open Images Dataset demo (with handguns), I get the following error:
Traceback (most recent call last): File "run_ssd_example.py", line 22, in <module> net.load(model_path) File "/home/bustardeuhedral/pytorch-ssd/vision/ssd/ssd.py", line 119, in load self.load_state_dict(torch.load(model, map_location=lambda storage, loc: storage)) File "/home/bustardeuhedral/anaconda3/envs/py36/lib/python3.6/site-packages/torch/nn/modules/module.py", line 719 , in load_state_dict self.__class__.__name__, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for SSD: Missing key(s) in state_dict: "base_net.0.weight", "base_net.0.bias", "base_net.2.weight", "base_net.2.bias ", "base_net.5.weight", "base_net.5.bias", "base_net.7.weight", "base_net.7.bias", "base_net.10.weight", "base_net. 10.bias", "base_net.12.weight", "base_net.12.bias", "base_net.14.weight", "base_net.14.bias", "base_net.17.weight", "base_net.17.bias", "base_net.19.weight", "base_net.19.bias", "base_net.21.weight", "base_net.21.bias", "base_net. 24.weight", "base_net.24.bias", "base_net.26.weight", "base_net.26.bias", "base_net.28.weight", "base_net.28.bias", "base_net.31.weight", "base_net.31.bias", "base_net.33.weight", "base_net.33.bias", "source_layer_add_ons.0.weight ", "source_layer_add_ons.0.bias", "source_layer_add_ons.0.running_mean", "source_layer_add_ons.0.running_var". Unexpected key(s) in state_dict: "base_net.0.0.weight", "base_net.0.1.weight", "base_net.0.1.bias", "base_n et.0.1.running_mean", "base_net.0.1.running_var", "base_net.1.0.weight", "base_net.1.1.weight", "base_net.1.1.bias" , "base_net.1.1.running_mean", "base_net.1.1.running_var", "base_net.1.3.weight", "base_net.1.4.weight", "base_net. 1.4.bias", "base_net.1.4.running_mean", "base_net.1.4.running_var", "base_net.2.0.weight", "base_net.2.1.weight", " base_net.2.1.bias", "base_net.2.1.running_mean", "base_net.2.1.running_var", "base_net.2.3.weight", "base_net.2.4.w eight", "base_net.2.4.bias", "base_net.2.4.running_mean", "base_net.2.4.running_var", "base_net.3.0.weight", "base_ net.3.1.weight", "base_net.3.1.bias", "base_net.3.1.running_mean", "base_net.3.1.running_var", "base_net.3.3.weight ", "base_net.3.4.weight", "base_net.3.4.bias", "base_net.3.4.running_mean", "base_net.3.4.running_var", "base_net.4 .0.weight", "base_net.4.1.weight", "base_net.4.1.bias", "base_net.4.1.running_mean", "base_net.4.1.running_var", "b ase_net.4.3.weight", "base_net.4.4.weight", "base_net.4.4.bias", "base_net.4.4.running_mean", "base_net.4.4.running _var", "base_net.5.0.weight", "base_net.5.1.weight", "base_net.5.1.bias", "base_net.5.1.running_mean", "base_net.5. 1.running_var", "base_net.5.3.weight", "base_net.5.4.weight", "base_net.5.4.bias", "base_net.5.4.running_mean", "ba se_net.5.4.running_var", "base_net.6.0.weight", "base_net.6.1.weight", "base_net.6.1.bias", "base_net.6.1.running_m ean", "base_net.6.1.running_var", "base_net.6.3.weight", "base_net.6.4.weight", "base_net.6.4.bias", "base_net.6.4. running_mean", "base_net.6.4.running_var", "base_net.7.0.weight", "base_net.7.1.weight", "base_net.7.1.bias", "base _net.7.1.running_mean", "base_net.7.1.running_var", "base_net.7.3.weight", "base_net.7.4.weight", "base_net.7.4.bia s", "base_net.7.4.running_mean", "base_net.7.4.running_var", "base_net.8.0.weight", "base_net.8.1.weight", "base_ne t.8.1.bias", "base_net.8.1.running_mean", "base_net.8.1.running_var", "base_net.8.3.weight", "base_net.8.4.weight", "base_net.8.4.bias", "base_net.8.4.running_mean", "base_net.8.4.running_var", "base_net.9.0.weight", "base_net.9.1 .weight", "base_net.9.1.bias", "base_net.9.1.running_mean", "base_net.9.1.running_var", "base_net.9.3.weight", "bas e_net.9.4.weight", "base_net.9.4.bias", "base_net.9.4.running_mean", "base_net.9.4.running_var", "base_net.10.0.wei ght", "base_net.10.1.weight", "base_net.10.1.bias", "base_net.10.1.running_mean", "base_net.10.1.running_var", "bas e_net.10.3.weight", "base_net.10.4.weight", "base_net.10.4.bias", "base_net.10.4.running_mean", "base_net.10.4.runn ing_var", "base_net.11.0.weight", "base_net.11.1.weight", "base_net.11.1.bias", "base_net.11.1.running_mean", "base _net.11.1.running_var", "base_net.11.3.weight", "base_net.11.4.weight", "base_net.11.4.bias", "base_net.11.4.running_mean", "base_net.11.4.running_var", "base_net.12.0.weight", "base_net.12.1.weight", "base_net.12.1.bias", "base_net.12.1.running_mean", "base_net.12.1.running_var", "base_net.12.3.weight", "base_net.12.4.weight", "base_net.12.4.bias", "base_net.12.4.running_mean", "base_net.12.4.running_var", "base_net.13.0.weight", "base_net.13.1.weight", "base_net.13.1.bias", "base_net.13.1.running_mean", "base_net.13.1.running_var", "base_net.13.3.weight", "base_net.13.4.weight", "base_net.13.4.bias", "base_net.13.4.running_mean", "base_net.13.4.running_var". size mismatch for classification_headers.0.weight: copying a param of torch.Size([12, 512, 3, 3]) from checkpoint, where the shape is torch.Size([18, 512, 3, 3]) in current model. size mismatch for classification_headers.0.bias: copying a param of torch.Size([12]) from checkpoint, where the shape is torch.Size([18]) in current model. size mismatch for classification_headers.4.weight: copying a param of torch.Size([12, 256, 3, 3]) from checkpoint, where the shape is torch.Size([18, 256, 3, 3]) in current model. size mismatch for classification_headers.4.bias: copying a param of torch.Size([12]) from checkpoint, where the shape is torch.Size([18]) in current model. size mismatch for classification_headers.5.weight: copying a param of torch.Size([12, 256, 3, 3]) from checkpoint, where the shape is torch.Size([18, 256, 3, 3]) in current model. size mismatch for classification_headers.5.bias: copying a param of torch.Size([12]) from checkpoint, where the shape is torch.Size([18]) in current model. size mismatch for regression_headers.0.weight: copying a param of torch.Size([16, 512, 3, 3]) from checkpoint, where the shape is torch.Size([24, 512, 3, 3]) in current model. size mismatch for regression_headers.0.bias: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([24]) in current model. size mismatch for regression_headers.4.weight: copying a param of torch.Size([16, 256, 3, 3]) from checkpoint, where the shape is torch.Size([24, 256, 3, 3]) in current model. size mismatch for regression_headers.4.bias: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([24]) in current model. size mismatch for regression_headers.5.weight: copying a param of torch.Size([16, 256, 3, 3]) from checkpoint, where the shape is torch.Size([24, 256, 3, 3]) in current model. size mismatch for regression_headers.5.bias: copying a param of torch.Size([16]) from checkpoint, where the shape is torch.Size([24]) in current model.
The same error after training on Open Images Dataset (from scratch and using pretrained mobilenet-v1-ssd-mp-0_675.pth
).
I am using python3.6.7 with pytorch 0.4.1