model_args.evaluate_during_training_steps = 1 model_args.use_multiprocessing = False #False model_args.fp16 = False model_args.save_steps = -1 model_args.save_eval_checkpoints = True #False model_args.no_cache = True model_args.reprocess_input_data = True model_args.overwrite_output_dir...
use process-based threading. If unspecified,use_multiprocessingwill default toFalse. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes. ...
( train_generator, steps_per_epoch=args.steps, epochs=args.epochs, verbose=1, callbacks=callbacks, workers=args.workers, use_multiprocessing=args.multiprocessing, max_queue_size=args.max_queue_size, validation_data=validation_generator, initial_epoch=args.initial_epoch ) training_model.save('my_...
use_multiprocessing: Boolean. IfTrue, use process-based threading. If unspecified,use_multiprocessingwill default toFalse. Note that because this implementation relies on multiprocessing, you should not pass non-picklable arguments to the generator as they can't be passed easily to children processes....
import torch torch.multiprocessing.set_start_method('spawn', force=True) 如果你使用了 DataLoader 加载数据,在创建 DataLoader 对象之前,将 num_workers 设置为 0,禁用多进程加载数据: data_loader = DataLoader(dataset, num_workers=0, ...) 如果你是在 Jupyter Notebook 中执行训练过程,尝试在启动 Jupyter...
import torch.multiprocessing as mp import torch.distributed as dist import sys sys.path.append('/home/ma-user/work/ASGNet-main') from model import * from util import dataset from util import transform, config from util.util import AverageMeter, poly_learning_rate, intersectionAndUnion...
out = model.fit_generator(gen_data(4), steps_per_epoch=10, use_multiprocessing=True, workers=2) warning_raised = any(['Sequence'instr(w_.message)forw_inw])assertwarning_raised,'No warning raised when using generator with processes.'withpytest.warns(None)asw: ...
# 需要导入模块: from model import model [as 别名]# 或者: from model.model importModel[as 别名]defmain():torch.set_num_threads(multiprocessing.cpu_count()) args = parse_args()ifargs.set =='gta':frommodel.modelimportModelelifargs.set =='kitti':frommodel.model_cenimportModelelse:raiseValue...
WARNING: Setting args.overlap_p2p_comm to False since non-interleaved schedule does not support overlapping p2p communication using torch.float16 for parameters ... --- arguments --- accumulate_allreduce_grads_in_fp32 ... False adam_beta1 ......
engine/training.pyinfit(self,x,y,batch_size,epochs,verbose,callbacks,validation_split,validation_data,shuffle,class_weight,sample_weight,initial_epoch,steps_per_epoch,validation_steps,validation_batch_size,validation_freq,max_queue_size,workers,use_multiprocessing)1061use_multiprocessing=use_multiprocessing,...