def train()

in torchnet/engine/engine.py [0:0]

• 33 lines of code
• 3 McCabe index (conditional complexity)

    def train(self, network, iterator, maxepoch, optimizer):
        state = {
            'network': network,
            'iterator': iterator,
            'maxepoch': maxepoch,
            'optimizer': optimizer,
            'epoch': 0,
            't': 0,
            'train': True,
        }

        self.hook('on_start', state)
        while state['epoch'] < state['maxepoch']:
            self.hook('on_start_epoch', state)
            for sample in state['iterator']:
                state['sample'] = sample
                self.hook('on_sample', state)

                def closure():
                    loss, output = state['network'](state['sample'])
                    state['output'] = output
                    state['loss'] = loss
                    loss.backward()
                    self.hook('on_forward', state)
                    # to free memory in save_for_backward
                    state['output'] = None
                    state['loss'] = None
                    return loss

                state['optimizer'].zero_grad()
                state['optimizer'].step(closure)
                self.hook('on_update', state)
                state['t'] += 1
            state['epoch'] += 1
            self.hook('on_end_epoch', state)
        self.hook('on_end', state)
        return state