149 lines
5.5 KiB
Python
149 lines
5.5 KiB
Python
import os
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import torch
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from tqdm import tqdm
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from utils.utils import get_lr
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def fit_one_epoch(model_train, model, ema, yolo_loss, loss_history, eval_callback, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, Epoch, cuda, fp16, scaler, save_period, save_dir, local_rank=0):
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loss = 0
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val_loss = 0
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if local_rank == 0:
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print('Start Train')
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pbar = tqdm(total=epoch_step,desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3)
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model_train.train()
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for iteration, batch in enumerate(gen):
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if iteration >= epoch_step:
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break
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images, targets, y_trues = batch[0], batch[1], batch[2]
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with torch.no_grad():
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if cuda:
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images = images.cuda(local_rank)
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targets = [ann.cuda(local_rank) for ann in targets]
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y_trues = [ann.cuda(local_rank) for ann in y_trues]
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#----------------------#
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# 清零梯度
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#----------------------#
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optimizer.zero_grad()
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if not fp16:
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#----------------------#
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# 前向传播
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#----------------------#
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outputs = model_train(images)
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loss_value_all = 0
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#----------------------#
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# 计算损失
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#----------------------#
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for l in range(len(outputs)):
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loss_item = yolo_loss(l, outputs[l], targets, y_trues[l])
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loss_value_all += loss_item
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loss_value = loss_value_all
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#----------------------#
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# 反向传播
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#----------------------#
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loss_value.backward()
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optimizer.step()
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else:
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from torch.cuda.amp import autocast
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with autocast():
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#----------------------#
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# 前向传播
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#----------------------#
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outputs = model_train(images)
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loss_value_all = 0
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#----------------------#
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# 计算损失
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#----------------------#
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for l in range(len(outputs)):
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loss_item = yolo_loss(l, outputs[l], targets, y_trues[l])
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loss_value_all += loss_item
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loss_value = loss_value_all
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#----------------------#
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# 反向传播
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#----------------------#
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scaler.scale(loss_value).backward()
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scaler.step(optimizer)
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scaler.update()
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if ema:
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ema.update(model_train)
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loss += loss_value.item()
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if local_rank == 0:
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pbar.set_postfix(**{'loss' : loss / (iteration + 1),
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'lr' : get_lr(optimizer)})
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pbar.update(1)
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if local_rank == 0:
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pbar.close()
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print('Finish Train')
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print('Start Validation')
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pbar = tqdm(total=epoch_step_val, desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3)
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if ema:
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model_train_eval = ema.ema
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else:
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model_train_eval = model_train.eval()
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for iteration, batch in enumerate(gen_val):
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if iteration >= epoch_step_val:
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break
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images, targets, y_trues = batch[0], batch[1], batch[2]
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with torch.no_grad():
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if cuda:
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images = images.cuda(local_rank)
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targets = [ann.cuda(local_rank) for ann in targets]
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y_trues = [ann.cuda(local_rank) for ann in y_trues]
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#----------------------#
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# 清零梯度
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#----------------------#
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optimizer.zero_grad()
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#----------------------#
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# 前向传播
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#----------------------#
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outputs = model_train_eval(images)
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loss_value_all = 0
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#----------------------#
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# 计算损失
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#----------------------#
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for l in range(len(outputs)):
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loss_item = yolo_loss(l, outputs[l], targets, y_trues[l])
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loss_value_all += loss_item
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loss_value = loss_value_all
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val_loss += loss_value.item()
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if local_rank == 0:
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pbar.set_postfix(**{'val_loss': val_loss / (iteration + 1)})
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pbar.update(1)
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if local_rank == 0:
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pbar.close()
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print('Finish Validation')
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loss_history.append_loss(epoch + 1, loss / epoch_step, val_loss / epoch_step_val)
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eval_callback.on_epoch_end(epoch + 1, model_train_eval)
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print('Epoch:'+ str(epoch + 1) + '/' + str(Epoch))
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print('Total Loss: %.3f || Val Loss: %.3f ' % (loss / epoch_step, val_loss / epoch_step_val))
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#-----------------------------------------------#
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# 保存权值
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#-----------------------------------------------#
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if ema:
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save_state_dict = ema.ema.state_dict()
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else:
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save_state_dict = model.state_dict()
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if (epoch + 1) % save_period == 0 or epoch + 1 == Epoch:
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torch.save(save_state_dict, os.path.join(save_dir, "ep%03d-loss%.3f-val_loss%.3f.pth" % (epoch + 1, loss / epoch_step, val_loss / epoch_step_val)))
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if len(loss_history.val_loss) <= 1 or (val_loss / epoch_step_val) <= min(loss_history.val_loss):
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print('Save best model to best_epoch_weights.pth')
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torch.save(save_state_dict, os.path.join(save_dir, "best_epoch_weights.pth"))
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torch.save(save_state_dict, os.path.join(save_dir, "last_epoch_weights.pth")) |