Unable to pause and resume training (loading a model) without getting a small jump in training loss

I am not able to figure out the reason for jump in training loss that I get, after loading from the saved checkpoint. I am using Adam optimizer.

Model base - Load pretrained vgg16 model weights

def base_model_vgg16(num_freeze_top): 
    vgg16 = models.vgg16(pretrained=True)
    vgg_feature_extracter  = vgg16.features[:-1]
    # Freeze learning of top few conv layers
    for layer in vgg_feature_extracter[:num_freeze_top]:
        for param in layer.parameters():
            param.requires_grad = False
    return vgg_feature_extracter.to(device)

Actual Model - create new model

class YOLONetwork(nn.Module):
    def __init__(self, extractor):
        self.extractor = extractor
        self.conv1 = nn.Conv2d(512, 1024,3,1,1)
        self.pool1 = nn.MaxPool2d(2,2)
        self.conv2 = nn.Conv2d(1024, 1024,3,1,1)
        self.pool2 = nn.MaxPool2d(2,2)
        self.lin1 = nn.Flatten()
        self.drop1 = nn.Dropout(p=0.5)
        self.lin2 = nn.Linear(7*7*1024, 7*7*(num_classes + anchors_per_box*5))

    def forward(self,x):
        out = self.extractor(x)
        out = self.pool1(F.relu(self.conv1(out)))
        out = self.pool2(F.relu(self.conv2(out)))
        out = self.drop1(F.relu(self.lin1(out)))
        out = torch.sigmoid(self.lin2(out))

        num = out.shape[0]
        return out.contiguous().view(num,7,7,-1)

Creating new model and optimiser

extractor = base_model_vgg16(10)
net = YOLONetwork(extractor).to(device)
loss_hist = []
valid_hist = []
best_valid_loss = 100000
optimizer = optim.Adam(net.parameters(), lr=0.00001)
epoch_start = 0

Saving model

PATH = 'drive/My Drive/saved_models/current.pt'
        'net_state_dict': net.state_dict(),
        'optimizer_state_dict': optimizer.state_dict(),
      }, PATH)

Loading Model :

checkpoint = torch.load(load_model, map_location=device)
    loss_hist = checkpoint['loss_hist']
    valid_hist = checkpoint['valid_hist']
    best_valid_loss = checkpoint['best_valid_loss']
    epoch_start =checkpoint['epoch_start']


I am unable to figure out the reason for the training loss jump once the training is resumed from a checkpoint.

Thank you very much :slight_smile:

I am training the network on google colab GPU. If I interrupt the training and continue it by loading saved weights, I do not see this problem.

But, if I factory reset the runtime. Reload the weights, then I can see jump in training loss.

I am also setting the same global random seed in my colab notebook.

It seems strange that one work flow seems to work on Colab, while another fails.
Are you also restoring the optimizer.state_dict() in both use cases?

Could you run a small test and compare the output for a fixed input, e.g. torch.ones()?
If you get exactly the same outputs, I would guess that the way you are restoring the data loading pipeline might create the difference.

Thanks for your response.

Based on your suggestion, this is what I tried.

  1. Trained the network for 1 epoch. Saved network weights after the epoch. With the existing weights (without loading it from saved checkpoint), ran the network on torch.ones(). (setting network to net.train() before running)
First row of output - tensor([0.5039, 0.5054, 0.4700, 0.4680, 0.3996, 0.5174, 0.5194, 0.4724, 0.4739,
        0.4014, 0.4767, 0.4717, 0.4892, 0.4578, 0.4734, 0.4513, 0.4582, 0.4603],
       device='cuda:0', grad_fn=<SelectBackward>)
  1. Loaded the network weights from saved checkpoint. Set it on net.train(). Ran the network on torch.ones(). The output that I get is
First row of output - tensor([0.5027, 0.5097, 0.4698, 0.4635, 0.4030, 0.5184, 0.5228, 0.4709, 0.4676,
        0.4011, 0.4730, 0.4778, 0.4874, 0.4604, 0.4683, 0.4380, 0.4620, 0.4640],
       device='cuda:0', grad_fn=<SelectBackward>)

So, the values that I obtain as output in both these cases are different.

I take my last comment back. Since a dropout layer is used in the network, the output after net.train() will not be exactly the same.

Setting the net to net.eval() before running gives the same output in both cases. So, at least network weights loading is correct.

Need to check if the issue is happening due to optimizer loading or data loader change after the runtime is reset. Thanks

After spending few hours trying to figure out the problem, found the root cause. And it has nothing to do with model / optimizer loading and saving at all.

What I needed to change is

lab_to_val = {j:i for i,j in enumerate(training_classes)}


lab_to_val = {j:i for i,j in enumerate(sorted(training_classes))}

Since the label to value conversion was not happening on the sorted list, each time my runtime was reset, a single label would take different values. Ex - if person class takes value 1 for some run, it used to take value 4 after restarting the runtime and running the code.

After fixing this small error, I do not observe any jumps in training loss

I face the same problem. However, I don’t get your point here, where is lab_to_val = {j:i for i,j in enumerate(training_classes)}? I could not find it in your code.