| 85 | |
| 86 | |
| 87 | class ClassificationTrainer(object): |
| 88 | def __init__(self, label_map, logger, evaluator, conf, loss_fn): |
| 89 | self.label_map = label_map |
| 90 | self.logger = logger |
| 91 | self.evaluator = evaluator |
| 92 | self.conf = conf |
| 93 | self.loss_fn = loss_fn |
| 94 | if self.conf.task_info.hierarchical: |
| 95 | self.hierar_relations = get_hierar_relations( |
| 96 | self.conf.task_info.hierar_taxonomy, label_map) |
| 97 | |
| 98 | def train(self, data_loader, model, optimizer, stage, epoch): |
| 99 | model.update_lr(optimizer, epoch) |
| 100 | model.train() |
| 101 | return self.run(data_loader, model, optimizer, stage, epoch, |
| 102 | ModeType.TRAIN) |
| 103 | |
| 104 | def eval(self, data_loader, model, optimizer, stage, epoch): |
| 105 | model.eval() |
| 106 | return self.run(data_loader, model, optimizer, stage, epoch) |
| 107 | |
| 108 | def run(self, data_loader, model, optimizer, stage, |
| 109 | epoch, mode=ModeType.EVAL): |
| 110 | is_multi = False |
| 111 | # multi-label classifcation |
| 112 | if self.conf.task_info.label_type == ClassificationType.MULTI_LABEL: |
| 113 | is_multi = True |
| 114 | predict_probs = [] |
| 115 | standard_labels = [] |
| 116 | num_batch = data_loader.__len__() |
| 117 | total_loss = 0. |
| 118 | for batch in data_loader: |
| 119 | # hierarchical classification using hierarchy penalty loss |
| 120 | if self.conf.task_info.hierarchical: |
| 121 | logits = model(batch) |
| 122 | linear_paras = model.linear.weight |
| 123 | is_hierar = True |
| 124 | used_argvs = (self.conf.task_info.hierar_penalty, linear_paras, self.hierar_relations) |
| 125 | loss = self.loss_fn( |
| 126 | logits, |
| 127 | batch[ClassificationDataset.DOC_LABEL].to(self.conf.device), |
| 128 | is_hierar, |
| 129 | is_multi, |
| 130 | *used_argvs) |
| 131 | # hierarchical classification with HMCN |
| 132 | elif self.conf.model_name == "HMCN": |
| 133 | (global_logits, local_logits, logits) = model(batch) |
| 134 | loss = self.loss_fn( |
| 135 | global_logits, |
| 136 | batch[ClassificationDataset.DOC_LABEL].to(self.conf.device), |
| 137 | False, |
| 138 | is_multi) |
| 139 | loss += self.loss_fn( |
| 140 | local_logits, |
| 141 | batch[ClassificationDataset.DOC_LABEL].to(self.conf.device), |
| 142 | False, |
| 143 | is_multi) |
| 144 | # flat classificaiton |
nothing calls this directly
no outgoing calls
no test coverage detected