@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:...
def __init__(self,dataset,batch_size,collate_fn,shuffle = True,drop_last = False): self.dataset = dataset self.sampler =torch.utils.data.RandomSampler if shuffle else \ torch.utils.data.SequentialSampler self.batch_sampler = torch.utils.data.BatchSampler self.sample_iter = self.batch_sample...
this.score());}publicvoidsetBackpropGradientsViewArray(INDArraygradients)//异步线程获取数据iter=newAsyncDataSetIterator(iterator,Math.min(Nd4j.getAffinityManager().getNumberOfDevices()*2,2),true
class IterableDataset(): # 可迭代数据集 # 可以通过迭代的方式逐步获取数据样本 def __init__(self, data, label): '''init the class object to hold the data''' self.data = data self.label = label self.start = 0 self.end = len(data) def __iter__(self): self.start = 0 self.end...
pcdet/datasets/kitti/kitti_dataset.py 从这进的 dataloader_iter = iter(train_loader) ### 这里进getitem 全大写的参数在配置文件中定义 yaml def __getitem__(self, index): # index = 4 if self._merge_all_iters_to_one_epoch: index = index % len(self.kitti_infos) ...
final_kmeans <- kmeans(seeds_features, centers =3, nstart =100, iter.max =1000) # Add cluster prediction to the data set results_kmeans <- augment(final_kmeans, seeds_features) %>% # Bind pca_data - features_2d bind_cols(features_2d) ...
DATASET: Market1501 ENABLED: False NUM_ITER: 300 RERANK: ENABLED: False K1: 20 K2: 6 LAMBDA: 0.3 Could you help me to train VeRi in a right way! Thanks a lot for your assisstance! Thanks for your attention to our repo! I find that you use 2 GPUs to train VeRi, I advice you ...
deftrain(net,loss,train_dataloader,valid_dataloader,device,batch_size,num_epoch,lr,lr_min,optim='...
sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True) return sortedClassCount[0][0] 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 这部分是定义已知分类的数据集,即训练集 def createDataSet(): ...
Unrelated: I was getting mAP as 0 as well until I increased cfg.SOVLER.MAX_ITER in the config file I was using inside Mask2Former. This was because I was using a very small custom dataset, so it could possibly have something to do with the size of your dataset aadityaks commented Ap...