yolov5 changes

yolov5_changes

 if any(freeze):
        for k, v in model.named_parameters():
            if "model.24" not in k:    # model.24 is the detect head module
                print('freezing %s' % k)
                v.requires_grad = False
            else:
                print("Not freezing %s" %k)
                v.requires_grad = True

    pg0, pg1, pg2 = [], [], []  # optimizer parameter groups

    for k, v in model.named_parameters():
        if "model.24" in k:
            v.requires_grad = True
            if '.bias' in k:
                pg2.append(v)  # biases
            elif '.weight' in k and '.bn' not in k:
                pg1.append(v)  # apply weight decay
            else:
                pg0.append(v)  # all else`

This would give error in 

if opt.adam:
    optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum


But this will give error because pg0 will be empty as it is filled only with models that 
had batch normalization layer weights e.g. model.23.conv.bn.weight etc. 
And pg0 cannot be empty as it is needed by the optimizer SGD/Adam 

To prevent that, I replaced following
    
if opt.adam:
   optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  
else:
    optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']})  # add pg1 with weight_decay                                           
optimizer.add_param_group({'params': pg2})  # add pg2 (biases)


with 


   if opt.adam:
        optimizer = optim.Adam(model.parameters(), lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum
    else:
        optimizer = optim.SGD(model.parameters(), lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)