train_dataloader.batch_size = Unfreeze_batch_size val_dataloader.batch_size = Unfreeze_batch_size

这句话是在设置训练集和验证集的 batch size，其中 train_dataloader.batch_size 和 val_dataloader.batch_size 都被设置为 Unfreeze_batch_size。这个 batch size 是在模型 fine-tuning 时解冻所有层进行训练时使用的 batch size。

相关问题

lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) model.Unfreeze_backbone() epoch_step = num_train // batch_size epoch_step_val = num_val // batch_size if epoch_step == 0 or epoch_step_val == 0: raise ValueError("数据集过小，无法继续进行训练，请扩充数据集。") if distributed: batch_size = batch_size // ngpus_per_node gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=train_sampler) gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=val_sampler) UnFreeze_flag = True if distributed: train_sampler.set_epoch(epoch) set_optimizer_lr(optimizer, lr_scheduler_func, epoch) fit_one_epoch(model_train, model, loss_history, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir, local_rank) if local_rank == 0: loss_history.writer.close() 转为伪代码

lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch)
model.Unfreeze_backbone()

epoch_step = num_train // batch_size
epoch_step_val = num_val // batch_size

if epoch_step == 0 or epoch_step_val == 0:
raise ValueError("数据集过小，无法继续进行训练，请扩充数据集。")

if distributed:
batch_size = batch_size // ngpus_per_node
gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=train_sampler)
gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=val_sampler)

UnFreeze_flag = True

if distributed:
train_sampler.set_epoch(epoch)
set_optimizer_lr(optimizer, lr_scheduler_func, epoch)
fit_one_epoch(model_train, model, loss_history, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir, local_rank)

if local_rank == 0:
loss_history.writer.close()

伪代码并不是一种具体的编程语言，而是一种算法描述语言，因此将上述代码转换为伪代码就是将其转换为类似于自然语言的算法描述。在这个过程中，可以将代码中的特定语法和语言结构替换为通用的算法表达方式，以便更清晰地表达算法的逻辑和流程。

用伪代码书写以下代码 r_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch) model.Unfreeze_backbone() epoch_step = num_train // batch_size epoch_step_val = num_val // batch_size if epoch_step == 0 or epoch_step_val == 0: raise ValueError("数据集过小，无法继续进行训练，请扩充数据集。") if distributed: batch_size = batch_size // ngpus_per_node gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=train_sampler) gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=val_sampler) UnFreeze_flag = True if distributed: train_sampler.set_epoch(epoch) set_optimizer_lr(optimizer, lr_scheduler_func, epoch) fit_one_epoch(model_train, model, loss_history, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir, local_rank) if local_rank == 0: loss_history.writer.close()

r_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch)
model.Unfreeze_backbone()

epoch_step = num_train // batch_size
epoch_step_val = num_val // batch_size

if epoch_step equals to 0 or epoch_step_val equals to 0:
raise ValueError("数据集过小，无法继续进行训练，请扩充数据集。")

if distributed equals to true:
batch_size = batch_size // ngpus_per_node
gen = DataLoader(train_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=train_sampler)
gen_val = DataLoader(val_dataset, shuffle=shuffle, batch_size=batch_size, num_workers=num_workers, pin_memory=True, drop_last=True, collate_fn=detection_collate, sampler=val_sampler)

UnFreeze_flag = True

if distributed equals to true:
train_sampler.set_epoch(epoch)
set_optimizer_lr(optimizer, lr_scheduler_func, epoch)
fit_one_epoch(model_train, model, loss_history, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir, local_rank)

if local_rank equals to 0:
loss_history.writer.close()