train_dataset = DataLoad(img_dir,gt_dir,MAX_OBJS, INPUT_H, INPUT_W) train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) train_loader = torch.utils.data.DataLoader( train_dataset, batch_size=BATCH_SIZE, sampler=train_sampler, num_workers=NUM_WORKER, pin_memory=True...
clarify.DataConfig( s3_data_input_path=train_data_uri, s3_output_path=bias_report_output_uri, label="fraud", headers=train_df_cols, dataset_type="text/csv", ) # Model config model_config = sagemaker.clarify.ModelConfig( model_name=xgb_model_name, instance_type=train_instance_type, ...
DataLoader( val_subset, batch_size=int(config["batch_size"]), shuffle=True, num_workers=8) for epoch in range(10): # loop over the dataset multiple times running_loss = 0.0 epoch_steps = 0 for i, data in enumerate(trainloader, 0): # get the inputs; data is a list of [inputs...
get_dataset_size() // args.epochs if args.run_eval: from test import test eval_dataset = COCODataset( dataset_path=args.data.val_set, img_size=args.img_size, transforms_dict=args.data.test_transforms, is_training=False, augment=False, rect=args.rect, single_cls=args....
log_string("The number of training data is: %d"%len(TRAIN_DATASET)) log_string("The number of test data is: %d"%len(TEST_DATASET)) num_classes =16num_part =50'''MODEL LOADING'''MODEL = importlib.import_module(args.model) shutil.copy('models/%s.py'% args.model,str(exp_dir)) ...
@TrainerBase.register("default")classTrainer(TrainerBase):def__init__(self,model:Model,optimizer:torch.optim.Optimizer,iterator:DataIterator,train_dataset:Iterable[Instance],validation_dataset:Optional[Iterable[Instance]]=None,patience:Optional[int]=None,validation_metric:str="-loss",validation_iterator:...
imdb = get_imdb(imdb_name) ##重新获取imdb数据 print 'Loaded dataset `{:s}` for proposal generation'.format(imdb.name) # Load RPN and configure output directory rpn_net = caffe.Net(rpn_test_prototxt, rpn_model_path, caffe.TEST) ##加载rpn_test_prototxt= ##'py-faster-rcnn/models/...
然后设置随机种子,下一行的torch_distributed_zero_first(LOCAL_RANK)与分布式训练相关的,如果不进行分布式训练则不执行,利用check_dataset会进行数据集检查读取操作,获取训练集和测试集图片路径。接着利用nc获取数据集的种类,names会进行类的种数以及类的名称是否相同的判断,不相同会进行报错处理,然后保存类别数量以及类...
Firefly: 大模型训练工具,支持训练Qwen2、Yi1.5、Phi-3、Llama3、Gemma、MiniCPM、Yi、Deepseek、Orion、Xverse、Mixtral-8x7B、Zephyr、Mistral、Baichuan2、Llma2、Llama、Qwen、Baichuan、ChatGLM2、InternLM、Ziya2、Vicuna、Bloom等大模型 - Firefly/train.py at mast
['cls'] *= nc /80.# scale coco-tuned hyp['cls'] to current datasetmodel.nc = nc# attach number of classes to modelmodel.hyp = hyp# attach hyperparameters to modelmodel.gr =1.0# giou loss ratio (obj_loss = 1.0 or giou)model.class_weights = labels_to_class_weights(dataset.labels...