(self, opt, dataset_size)
| 11 | # Helper class that keeps track of training iterations |
| 12 | class IterationCounter(): |
| 13 | def __init__(self, opt, dataset_size): |
| 14 | self.opt = opt |
| 15 | self.dataset_size = dataset_size |
| 16 | |
| 17 | self.first_epoch = 1 |
| 18 | self.total_epochs = opt.niter + opt.niter_decay |
| 19 | self.epoch_iter = 0 # iter number within each epoch |
| 20 | self.iter_record_path = os.path.join(self.opt.checkpoints_dir, self.opt.name, 'iter.txt') |
| 21 | if opt.isTrain and opt.continue_train: |
| 22 | try: |
| 23 | self.first_epoch, self.epoch_iter = np.loadtxt( |
| 24 | self.iter_record_path, delimiter=',', dtype=int) |
| 25 | print('Resuming from epoch %d at iteration %d' % (self.first_epoch, self.epoch_iter)) |
| 26 | except: |
| 27 | print('Could not load iteration record at %s. Starting from beginning.' % |
| 28 | self.iter_record_path) |
| 29 | |
| 30 | self.total_steps_so_far = (self.first_epoch - 1) * dataset_size + self.epoch_iter |
| 31 | |
| 32 | # return the iterator of epochs for the training |
| 33 | def training_epochs(self): |
nothing calls this directly
no outgoing calls
no test coverage detected