weights: output/yolov3_mobilenet_v1_270e_voc/model_final #将collate_batch设置为false,因为VOC数据集上需要真实信息, # 并且当批量大小大于1时,不应批量整理数据。 EvalReader: # 评估阅读 collate_batch: false # 自定义collate_fn,处理具有不同数量的关联对象注释(边界框)的批量图像 # collate_fn:如何取...
- PadBatch: {pad_to_stride: 32} # 训练时batch_size batch_size: 1 # 读取数据是是否乱序 shuffle: true # 是否丢弃最后不能完整组成batch的数据 drop_last: true # 表示reader是否对gt进行组batch的操作,在rcnn系列算法中设置为false,得到的gt格式为list[Tensor] collate_batch: false # 评估数据 EvalR...
paddle.io.DataLoader返回一个迭代器,该迭代器根据batch_sampler指定的顺序迭代返回dataset数据。异步加载数据。 batch_sampler:DataLoader通过 batch_sampler 产生的mini-batch索引列表来 dataset 中索引样本并组成mini-batch collate_fn:指定如何将样本列表组合为mini-batch数据。传给它参数需要是一个callable对象,需要实现...
# set collate_batch to false because ground-truth info is needed # on voc dataset and should not collate data in batch when batch size # is larger than 1. EvalReader: collate_batch: false 2.数据集配置 重点配置数据集类型的num_classes、数据路径等PaddleDetection/configs/datasets/voc.yml metric...
A DataCollator is a function that takes a list of samples from a Dataset and collate them into a batch, as a dictionary of PaddlePaddle tensors or NumPy arrays.""" DataCollator = NewType("DataCollator", Callable[[List[InputDataClass]], Dict[str, Any]]) class...
map(trans_funs), batch_sampler=batch_sample, collate_fn=batchify_fn, return_list=True ) In [7] for idx,batch in enumerate(train_data_loader): if idx<1: print(batch) [Tensor(shape=[32, 74], dtype=int64, place=CUDAPinnedPlace, stop_gradient=True, [[101 , 4958, 7313, ..., 0 ,...
batch_size=32, shuffle=False) test_batch_sampler = BatchSampler(test_dataset, batch_size=32, shuffle=False) train_loader = DataLoader(dataset=train_dataset, batch_sampler=train_batch_sampler, collate_fn=collate_fn) dev_loader = DataLoader(dataset=dev_dataset, batch_sampler=dev_batch_sampler, ...
batch_size: 16 shuffle: true drop_last: true use_shared_memory: true collate_batch: true EvalReader: sample_transforms: - Decode: {} - Resize: {target_size: *eval_size, keep_ratio: False, interp: 2} - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is...
io.DataLoader( dataset=test_ds, batch_size=batch_size, return_list=True, collate_fn=batchify_fn) 3. 辅助函数 3.1 评估函数 代码语言:javascript 复制 @paddle.no_grad() def evaluate(model, metric, data_loader): model.eval() metric.reset() for input_ids, seg_ids, lens, labels in data_...
train_set, batch_size=batch_size, shuffle=True) train_data_loader = paddle.io.DataLoader( dataset=train_set, batch_sampler=train_batch_sampler, collate_fn=batchify_fn, num_workers=num_workers, # batch_size=batch_size, return_list=True) ...