Saving / Restoring Weights

[mertmerci]

Hello,

Are the weights automatically saved in eachh epoch, or how frequently they are saved during training ? How can I restore them if I need to stop the training at some point and continue later?

Thank you for your support.

[Tramac]

1.The weights are saved in each epoch if no_val=True.

Fast-SCNN-pytorch/train.py

Line 150 in a197f5e

save_checkpoint(self.model, self.args, is_best=False)

2.resume the model.

Fast-SCNN-pytorch/train.py

Line 94 in a197f5e

# resume checkpoint if needed

[mertmerci]

Hello again,

I tried to restore the weights from the previous training with the starting command:

python train.py --model fast_scnn --dataset citys --resume ./weights/fast_scnn_citys.pth --epochs 600

but I get an error message, I directly paste the complete error below because it's very long.

Resuming training, loading ./weights/fast_scnn_citys.pth... Traceback (most recent call last): File "train.py", line 204, in <module> trainer = Trainer(args) File "train.py", line 110, in __init__ self.model.load_state_dict(torch.load(args.resume, map_location=lambda storage, loc: storage)) File "/home/gg392953/anaconda3/envs/myenv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 777, in load_state_dict self.__class__.__name__, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for FastSCNN: Missing key(s) in state_dict: "learning_to_downsample.conv.conv.0.weight", "learning_to_downsample.conv.conv.1.weight", "learning_to_downsample.conv.conv.1.bias", "learning_to_downsample.conv.conv.1.running_mean", "learning_to_downsample.conv.conv.1.running_var", "learning_to_downsample.dsconv1.conv.0.weight", "learning_to_downsample.dsconv1.conv.1.weight", "learning_to_downsample.dsconv1.conv.1.bias", "learning_to_downsample.dsconv1.conv.1.running_mean", "learning_to_downsample.dsconv1.conv.1.running_var", 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"global_feature_extractor.bottleneck1.0.block.0.conv.1.running_mean", "global_feature_extractor.bottleneck1.0.block.0.conv.1.running_var", "global_feature_extractor.bottleneck1.0.block.1.conv.0.weight", "global_feature_extractor.bottleneck1.0.block.1.conv.1.weight", "global_feature_extractor.bottleneck1.0.block.1.conv.1.bias", "global_feature_extractor.bottleneck1.0.block.1.conv.1.running_mean", "global_feature_extractor.bottleneck1.0.block.1.conv.1.running_var", "global_feature_extractor.bottleneck1.0.block.2.weight", "global_feature_extractor.bottleneck1.0.block.3.weight", "global_feature_extractor.bottleneck1.0.block.3.bias", "global_feature_extractor.bottleneck1.0.block.3.running_mean", "global_feature_extractor.bottleneck1.0.block.3.running_var", "global_feature_extractor.bottleneck1.1.block.0.conv.0.weight", "global_feature_extractor.bottleneck1.1.block.0.conv.1.weight", "global_feature_extractor.bottleneck1.1.block.0.conv.1.bias", 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"global_feature_extractor.bottleneck1.2.block.0.conv.1.running_mean", "global_feature_extractor.bottleneck1.2.block.0.conv.1.running_var", "global_feature_extractor.bottleneck1.2.block.1.conv.0.weight", "global_feature_extractor.bottleneck1.2.block.1.conv.1.weight", "global_feature_extractor.bottleneck1.2.block.1.conv.1.bias", "global_feature_extractor.bottleneck1.2.block.1.conv.1.running_mean", "global_feature_extractor.bottleneck1.2.block.1.conv.1.running_var", "global_feature_extractor.bottleneck1.2.block.2.weight", "global_feature_extractor.bottleneck1.2.block.3.weight", "global_feature_extractor.bottleneck1.2.block.3.bias", "global_feature_extractor.bottleneck1.2.block.3.running_mean", "global_feature_extractor.bottleneck1.2.block.3.running_var", "global_feature_extractor.bottleneck2.0.block.0.conv.0.weight", "global_feature_extractor.bottleneck2.0.block.0.conv.1.weight", "global_feature_extractor.bottleneck2.0.block.0.conv.1.bias", 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"global_feature_extractor.bottleneck3.0.block.0.conv.1.running_mean", "global_feature_extractor.bottleneck3.0.block.0.conv.1.running_var", "global_feature_extractor.bottleneck3.0.block.1.conv.0.weight", "global_feature_extractor.bottleneck3.0.block.1.conv.1.weight", "global_feature_extractor.bottleneck3.0.block.1.conv.1.bias", "global_feature_extractor.bottleneck3.0.block.1.conv.1.running_mean", "global_feature_extractor.bottleneck3.0.block.1.conv.1.running_var", "global_feature_extractor.bottleneck3.0.block.2.weight", "global_feature_extractor.bottleneck3.0.block.3.weight", "global_feature_extractor.bottleneck3.0.block.3.bias", "global_feature_extractor.bottleneck3.0.block.3.running_mean", "global_feature_extractor.bottleneck3.0.block.3.running_var", "global_feature_extractor.bottleneck3.1.block.0.conv.0.weight", "global_feature_extractor.bottleneck3.1.block.0.conv.1.weight", "global_feature_extractor.bottleneck3.1.block.0.conv.1.bias", "global_feature_extractor.bottleneck3.1.block.0.conv.1.running_mean", "global_feature_extractor.bottleneck3.1.block.0.conv.1.running_var", "global_feature_extractor.bottleneck3.1.block.1.conv.0.weight", "global_feature_extractor.bottleneck3.1.block.1.conv.1.weight", "global_feature_extractor.bottleneck3.1.block.1.conv.1.bias", "global_feature_extractor.bottleneck3.1.block.1.conv.1.running_mean", "global_feature_extractor.bottleneck3.1.block.1.conv.1.running_var", "global_feature_extractor.bottleneck3.1.block.2.weight", "global_feature_extractor.bottleneck3.1.block.3.weight", "global_feature_extractor.bottleneck3.1.block.3.bias", "global_feature_extractor.bottleneck3.1.block.3.running_mean", "global_feature_extractor.bottleneck3.1.block.3.running_var", "global_feature_extractor.bottleneck3.2.block.0.conv.0.weight", "global_feature_extractor.bottleneck3.2.block.0.conv.1.weight", "global_feature_extractor.bottleneck3.2.block.0.conv.1.bias", "global_feature_extractor.bottleneck3.2.block.0.conv.1.running_mean", "global_feature_extractor.bottleneck3.2.block.0.conv.1.running_var", "global_feature_extractor.bottleneck3.2.block.1.conv.0.weight", "global_feature_extractor.bottleneck3.2.block.1.conv.1.weight", "global_feature_extractor.bottleneck3.2.block.1.conv.1.bias", "global_feature_extractor.bottleneck3.2.block.1.conv.1.running_mean", "global_feature_extractor.bottleneck3.2.block.1.conv.1.running_var", "global_feature_extractor.bottleneck3.2.block.2.weight", "global_feature_extractor.bottleneck3.2.block.3.weight", "global_feature_extractor.bottleneck3.2.block.3.bias", "global_feature_extractor.bottleneck3.2.block.3.running_mean", "global_feature_extractor.bottleneck3.2.block.3.running_var", "global_feature_extractor.ppm.conv1.conv.0.weight", "global_feature_extractor.ppm.conv1.conv.1.weight", "global_feature_extractor.ppm.conv1.conv.1.bias", "global_feature_extractor.ppm.conv1.conv.1.running_mean", "global_feature_extractor.ppm.conv1.conv.1.running_var", "global_feature_extractor.ppm.conv2.conv.0.weight", "global_feature_extractor.ppm.conv2.conv.1.weight", "global_feature_extractor.ppm.conv2.conv.1.bias", "global_feature_extractor.ppm.conv2.conv.1.running_mean", "global_feature_extractor.ppm.conv2.conv.1.running_var", "global_feature_extractor.ppm.conv3.conv.0.weight", "global_feature_extractor.ppm.conv3.conv.1.weight", "global_feature_extractor.ppm.conv3.conv.1.bias", "global_feature_extractor.ppm.conv3.conv.1.running_mean", "global_feature_extractor.ppm.conv3.conv.1.running_var", "global_feature_extractor.ppm.conv4.conv.0.weight", "global_feature_extractor.ppm.conv4.conv.1.weight", "global_feature_extractor.ppm.conv4.conv.1.bias", "global_feature_extractor.ppm.conv4.conv.1.running_mean", "global_feature_extractor.ppm.conv4.conv.1.running_var", "global_feature_extractor.ppm.out.conv.0.weight", "global_feature_extractor.ppm.out.conv.1.weight", "global_feature_extractor.ppm.out.conv.1.bias", "global_feature_extractor.ppm.out.conv.1.running_mean", "global_feature_extractor.ppm.out.conv.1.running_var", "feature_fusion.dwconv.conv.0.weight", "feature_fusion.dwconv.conv.1.weight", "feature_fusion.dwconv.conv.1.bias", "feature_fusion.dwconv.conv.1.running_mean", "feature_fusion.dwconv.conv.1.running_var", "feature_fusion.conv_lower_res.0.weight", "feature_fusion.conv_lower_res.0.bias", "feature_fusion.conv_lower_res.1.weight", "feature_fusion.conv_lower_res.1.bias", "feature_fusion.conv_lower_res.1.running_mean", "feature_fusion.conv_lower_res.1.running_var", "feature_fusion.conv_higher_res.0.weight", "feature_fusion.conv_higher_res.0.bias", "feature_fusion.conv_higher_res.1.weight", "feature_fusion.conv_higher_res.1.bias", "feature_fusion.conv_higher_res.1.running_mean", "feature_fusion.conv_higher_res.1.running_var", "classifier.dsconv1.conv.0.weight", "classifier.dsconv1.conv.1.weight", "classifier.dsconv1.conv.1.bias", "classifier.dsconv1.conv.1.running_mean", "classifier.dsconv1.conv.1.running_var", "classifier.dsconv1.conv.3.weight", "classifier.dsconv1.conv.4.weight", "classifier.dsconv1.conv.4.bias", "classifier.dsconv1.conv.4.running_mean", "classifier.dsconv1.conv.4.running_var", "classifier.dsconv2.conv.0.weight", "classifier.dsconv2.conv.1.weight", "classifier.dsconv2.conv.1.bias", "classifier.dsconv2.conv.1.running_mean", "classifier.dsconv2.conv.1.running_var", "classifier.dsconv2.conv.3.weight", "classifier.dsconv2.conv.4.weight", "classifier.dsconv2.conv.4.bias", "classifier.dsconv2.conv.4.running_mean", "classifier.dsconv2.conv.4.running_var", "classifier.conv.1.weight", "classifier.conv.1.bias". Unexpected key(s) in state_dict: "module.learning_to_downsample.conv.conv.0.weight", "module.learning_to_downsample.conv.conv.1.weight", "module.learning_to_downsample.conv.conv.1.bias", "module.learning_to_downsample.conv.conv.1.running_mean", "module.learning_to_downsample.conv.conv.1.running_var", "module.learning_to_downsample.conv.conv.1.num_batches_tracked", "module.learning_to_downsample.dsconv1.conv.0.weight", "module.learning_to_downsample.dsconv1.conv.1.weight", "module.learning_to_downsample.dsconv1.conv.1.bias", "module.learning_to_downsample.dsconv1.conv.1.running_mean", "module.learning_to_downsample.dsconv1.conv.1.running_var", "module.learning_to_downsample.dsconv1.conv.1.num_batches_tracked", "module.learning_to_downsample.dsconv1.conv.3.weight", "module.learning_to_downsample.dsconv1.conv.4.weight", "module.learning_to_downsample.dsconv1.conv.4.bias", "module.learning_to_downsample.dsconv1.conv.4.running_mean", "module.learning_to_downsample.dsconv1.conv.4.running_var", "module.learning_to_downsample.dsconv1.conv.4.num_batches_tracked", "module.learning_to_downsample.dsconv2.conv.0.weight", "module.learning_to_downsample.dsconv2.conv.1.weight", "module.learning_to_downsample.dsconv2.conv.1.bias", "module.learning_to_downsample.dsconv2.conv.1.running_mean", "module.learning_to_downsample.dsconv2.conv.1.running_var", "module.learning_to_downsample.dsconv2.conv.1.num_batches_tracked", "module.learning_to_downsample.dsconv2.conv.3.weight", "module.learning_to_downsample.dsconv2.conv.4.weight", "module.learning_to_downsample.dsconv2.conv.4.bias", "module.learning_to_downsample.dsconv2.conv.4.running_mean", "module.learning_to_downsample.dsconv2.conv.4.running_var", "module.learning_to_downsample.dsconv2.conv.4.num_batches_tracked", "module.global_feature_extractor.bottleneck1.0.block.0.conv.0.weight", "module.global_feature_extractor.bottleneck1.0.block.0.conv.1.weight", "module.global_feature_extractor.bottleneck1.0.block.0.conv.1.bias", "module.global_feature_extractor.bottleneck1.0.block.0.conv.1.running_mean", "module.global_feature_extractor.bottleneck1.0.block.0.conv.1.running_var", "module.global_feature_extractor.bottleneck1.0.block.0.conv.1.num_batches_tracked", "module.global_feature_extractor.bottleneck1.0.block.1.conv.0.weight", "module.global_feature_extractor.bottleneck1.0.block.1.conv.1.weight", "module.global_feature_extractor.bottleneck1.0.block.1.conv.1.bias", "module.global_feature_extractor.bottleneck1.0.block.1.conv.1.running_mean", "module.global_feature_extractor.bottleneck1.0.block.1.conv.1.running_var", "module.global_feature_extractor.bottleneck1.0.block.1.conv.1.num_batches_tracked", "module.global_feature_extractor.bottleneck1.0.block.2.weight", "module.global_feature_extractor.bottleneck1.0.block.3.weight", "module.global_feature_extractor.bottleneck1.0.block.3.bias", "module.global_feature_extractor.bottleneck1.0.block.3.running_mean", "module.global_feature_extractor.bottleneck1.0.block.3.running_var", "module.global_feature_extractor.bottleneck1.0.block.3.num_batches_tracked", "module.global_feature_extractor.bottleneck1.1.block.0.conv.0.weight", "module.global_feature_extractor.bottleneck1.1.block.0.conv.1.weight", "module.global_feature_extractor.bottleneck1.1.block.0.conv.1.bias", "module.global_feature_extractor.bottleneck1.1.block.0.conv.1.running_mean", "module.global_feature_extractor.bottleneck1.1.block.0.conv.1.running_var", "module.global_feature_extractor.bottleneck1.1.block.0.conv.1.num_batches_tracked", "module.global_feature_extractor.bottleneck1.1.block.1.conv.0.weight", "module.global_feature_extractor.bottleneck1.1.block.1.conv.1.weight", "module.global_feature_extractor.bottleneck1.1.block.1.conv.1.bias", "module.global_feature_extractor.bottleneck1.1.block.1.conv.1.running_mean", "module.global_feature_extractor.bottleneck1.1.block.1.conv.1.running_var", "module.global_feature_extractor.bottleneck1.1.block.1.conv.1.num_batches_tracked", "module.global_feature_extractor.bottleneck1.1.block.2.weight", "module.global_feature_extractor.bottleneck1.1.block.3.weight", "module.global_feature_extractor.bottleneck1.1.block.3.bias", "module.global_feature_extractor.bottleneck1.1.block.3.running_mean", "module.global_feature_extractor.bottleneck1.1.block.3.running_var", "module.global_feature_extractor.bottleneck1.1.block.3.num_batches_tracked", "module.global_feature_extractor.bottleneck1.2.block.0.conv.0.weight", "module.global_feature_extractor.bottleneck1.2.block.0.conv.1.weight", "module.global_feature_extractor.bottleneck1.2.block.0.conv.1.bias", "module.global_feature_extractor.bottleneck1.2.block.0.conv.1.running_mean", "module.global_feature_extractor.bottleneck1.2.block.0.conv.1.running_var", 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[mertmerci]

I'm trying to evaluate the network with early weights, for example, the weights on the 120th epoch. When I run the eval.py file I obtain the RuntimeError: Error(s) in loading state_dict for FastSCNN: Missing key(s) in state_dict: error. How can I solve the problem and do the evaluation?

[Tramac]

Maybe you should evaluate the network in parallel mode.

[mertmerci]

How should I do it? The training of network is finished and when I try to run eval.py and demo.py, I see the same error message.

[mertmerci]

I noticed that the eval.py is expecting to get some parse input, how does it work? How eval.py knows where the network and the weights are?

[Tramac]

The eval.py and train.py share the parse input.

[mertmerci]

Okay I understood. Maybe there is a problem about the path of the model and state dictionary that results the mentioned error above. How should I check that and do you have any other suggestions?

[Tramac]

These errors may occur because the eval.py was not run in parallel mode.

[mertmerci]

How should I run eval.py in parallel mode?

[Tramac]

Please try to add self.model = nn.DataParallel(self.model) here.