(self, args, logger=None)
| 71 | self.scheduler = scheduler |
| 72 | |
| 73 | def train(self, args, logger=None): |
| 74 | |
| 75 | ngpus_per_node = torch.cuda.device_count() |
| 76 | |
| 77 | # EMA init |
| 78 | self.model.train() |
| 79 | self.ema = EMA(self.model, self.ema_m) |
| 80 | self.ema.register() |
| 81 | if args.resume == True: |
| 82 | self.ema.load(self.ema_model) |
| 83 | |
| 84 | # for gpu profiling |
| 85 | start_batch = torch.cuda.Event(enable_timing=True) |
| 86 | end_batch = torch.cuda.Event(enable_timing=True) |
| 87 | start_run = torch.cuda.Event(enable_timing=True) |
| 88 | end_run = torch.cuda.Event(enable_timing=True) |
| 89 | |
| 90 | start_batch.record() |
| 91 | best_eval_acc, best_it = 0.0, 0 |
| 92 | |
| 93 | scaler = GradScaler() |
| 94 | amp_cm = autocast if args.amp else contextlib.nullcontext |
| 95 | |
| 96 | # eval once to verify if the checkpoint is loaded correctly |
| 97 | if args.resume == True: |
| 98 | eval_dict = self.evaluate(args=args) |
| 99 | print(eval_dict) |
| 100 | |
| 101 | for (_, x_lb, y_lb), (_, x_ulb_w1, x_ulb_w2) in zip(self.loader_dict['train_lb'], |
| 102 | self.loader_dict['train_ulb']): |
| 103 | |
| 104 | # prevent the training iterations exceed args.num_train_iter |
| 105 | if self.it > args.num_train_iter: |
| 106 | break |
| 107 | |
| 108 | end_batch.record() |
| 109 | torch.cuda.synchronize() |
| 110 | start_run.record() |
| 111 | |
| 112 | num_lb = x_lb.shape[0] |
| 113 | num_ulb = x_ulb_w1.shape[0] |
| 114 | assert num_ulb == x_ulb_w2.shape[0] |
| 115 | |
| 116 | x_lb, x_ulb_w1, x_ulb_w2 = x_lb.cuda(args.gpu), x_ulb_w1.cuda(args.gpu), x_ulb_w2.cuda(args.gpu) |
| 117 | y_lb = y_lb.cuda(args.gpu) |
| 118 | |
| 119 | # inference and calculate sup/unsup losses |
| 120 | with amp_cm(): |
| 121 | with torch.no_grad(): |
| 122 | self.bn_controller.freeze_bn(self.model) |
| 123 | logits_x_ulb_w1 = self.model(x_ulb_w1) |
| 124 | logits_x_ulb_w2 = self.model(x_ulb_w2) |
| 125 | self.bn_controller.unfreeze_bn(self.model) |
| 126 | # Temperature sharpening |
| 127 | T = self.t_fn(self.it) |
| 128 | # avg |
| 129 | avg_prob_x_ulb = (torch.softmax(logits_x_ulb_w1, dim=1) + torch.softmax(logits_x_ulb_w2, dim=1)) / 2 |
| 130 | avg_prob_x_ulb = (avg_prob_x_ulb / avg_prob_x_ulb.sum(dim=-1, keepdim=True)) |
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