forked from mrq/DL-Art-School
46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
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# Defines the expected API for a step
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class SrGanGeneratorStep:
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def __init__(self, opt_step, opt, netsG, netsD):
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self.step_opt = opt_step
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self.opt = opt
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self.gen = netsG['base']
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self.disc = netsD['base']
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for loss in self.step_opt['losses']:
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# G pixel loss
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if train_opt['pixel_weight'] > 0:
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l_pix_type = train_opt['pixel_criterion']
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if l_pix_type == 'l1':
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self.cri_pix = nn.L1Loss().to(self.device)
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elif l_pix_type == 'l2':
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self.cri_pix = nn.MSELoss().to(self.device)
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else:
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raise NotImplementedError('Loss type [{:s}] not recognized.'.format(l_pix_type))
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self.l_pix_w = train_opt['pixel_weight']
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else:
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logger.info('Remove pixel loss.')
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self.cri_pix = None
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# Returns all optimizers used in this step.
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def get_optimizers(self):
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pass
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# Returns optimizers which are opting in for default LR scheduling.
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def get_optimizers_with_default_scheduler(self):
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pass
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# Returns the names of the networks this step will train. Other networks will be frozen.
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def get_networks_trained(self):
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pass
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# Performs all forward and backward passes for this step given an input state. All input states are lists or
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# chunked tensors. Use grad_accum_step to derefernce these steps. Return the state with any variables the step
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# exports (which may be used by subsequent steps)
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def do_forward_backward(self, state, grad_accum_step):
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return state
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# Performs the optimizer step after all gradient accumulation is completed.
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def do_step(self):
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pass
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