Custom Checkpoint callback for multiple models
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What is your question?
I am looking to write my own callback for checkpointing for a list of models I initialize in init().
Code
I created 10 timeseries models and 1 image model lets say. Each model inherits Lightningmodule.
So LITFusionExp has 11 models.
When I save the checkpoint I can only see cnn_model's checkpoint and not ts_models.
However, I can see that trainer updates my ts_models.
The problem thus is when I reload the checkpoint all the ts_models are just randomly initialized. How to save ts_models too?
Thanks for the help
  
class LITFusionExp(LightningModule):
    def __init__(self,hparams):
        
        super().__init__()
        
       
        self.ts_models = [ Conv1dmultivariate(input_channels=10).cuda() for _ in range(10)]                
    
        self.cnn_model = LITConvAEexp(hparams)
trainer.fit(LITFusionExp())
trainer .save('mypath.ckpt')
###
my_ckpt= torch.load( 'mypath.ckpt')
#my_ckpt['state_dict'] has only keys with respect to CNN model

What's your environment?

OS:  Linux
Packaging [e.g. conda]
Version [e.g. 0.8.5]