Pytorch | yolov3原理及代码详解（三）

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Pytorch | yolov3原理及代码详解（一）

https://blog.youkuaiyun.com/qq_24739717/article/details/92399359

Pytorch | yolov3原理及代码详解（二）

https://blog.youkuaiyun.com/qq_24739717/article/details/96705055

上接“Pytorch | yolov3原理及代码详解（二）”的内容

2.3 查看训练指标并评估（train.py——part3）

这段完整代码如下：

    for epoch in range(opt.epochs):
        model.train()
        start_time = time.time()
        #print("len(dataloader):\n",len(dataloader))
        for batch_i, (_, imgs, targets) in enumerate(dataloader):
            batches_done = len(dataloader) * epoch + batch_i
            imgs = Variable(imgs.to(device))
            targets = Variable(targets.to(device), requires_grad=False)
            print("targets.shape:\n",targets.shape)
            loss, outputs = model(imgs, targets)                      
            loss.backward()
            if batches_done % opt.gradient_accumulations:
                # Accumulates gradient before each step
                optimizer.step()
                optimizer.zero_grad()

            # ----------------
            #   Log progress
            # ----------------

            log_str = "\n---- [Epoch %d/%d, Batch %d/%d] ----\n" % (epoch, opt.epochs, batch_i, len(dataloader))

            metric_table = [["Metrics", *[f"YOLO Layer {i}" for i in range(len(model.yolo_layers))]]]

            # Log metrics at each YOLO layer
            for i, metric in enumerate(metrics):
                formats = {m: "%.6f" for m in metrics}
                formats["grid_size"] = "%2d"
                formats["cls_acc"] = "%.2f%%"
                row_metrics = [formats[metric] % yolo.metrics.get(metric, 0) for yolo in model.yolo_layers]
                metric_table += [[metric, *row_metrics]]

                # Tensorboard logging
                tensorboard_log = []
                for j, yolo in enumerate(model.yolo_layers):
                    for name, metric in yolo.metrics.items():
                        if name != "grid_size":
                            tensorboard_log += [(f"{name}_{j+1}", metric)]
                tensorboard_log += [("loss", loss.item())]
                logger.list_of_scalars_summary(tensorboard_log, batches_done)

            log_str += AsciiTable(metric_table).table
            log_str += f"\nTotal loss {loss.item()}"

            # Determine approximate time left for epoch
            epoch_batches_left = len(dataloader) - (batch_i + 1)
            time_left = datetime.timedelta(seconds=epoch_batches_left * (time.time() - start_time) / (batch_i + 1))
            log_str += f"\n---- ETA {time_left}"

            print(log_str)

            model.seen += imgs.size(0)

        if epoch % opt.evaluation_interval == 0:
            print("\n---- Evaluati