这个SpamDataset类处理几个关键任务:它识别训练数据集中最长的序列,编码文本消息,并确保所有其他序列都用填充令牌填充,以匹配最长序列的长度。 importtorchfromtorch.utils.dataimportDatasetimportpandasaspdclassSpamDataset(Dataset):"""自定义 PyTorch Dataset 类,用于加载文本数据和标签。参数:csv_file (str): 包含数据...
from torch.utils.data import DataLoader num_workers = 0 batch_size = 8 torch.manual_seed(123) train_loader = DataLoader( dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, drop_last=True, ) val_loader = DataLoader( dataset=val_dataset, batch_size=batch_...
from torch.utils.data import DataLoader num_workers = 0 batch_size = 8 torch.manual_seed(123) train_loader = DataLoader( dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, drop_last=True, ) val_loader = DataLoader( dataset=val_dataset, batch_size=batch_...
import torch from accelerate import init_empty_weights from accelerate.utils.modeling import set_module_tensor_to_device from safetensors.torch import load_file, save_file from transformers import CLIPTextModel, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer from typing import List from diff...
当时接触到几个项目,都需要用YOLOv3,由于每次都需要转化,大概分别调用4、5个脚本吧,感觉很累,所以当时花了一段时间构建了一个一键从VOC转U版YOLOv3格式的脚本库: https://github.com/pprp/voc2007_for_yolo_torch。 到此时为止,我们项目就已经可以运行了,然后就是很多细节调整了。 2.1 修改Anchor 红外小目标的...
batch_size =8image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])forxin['train','valid']}#用datasets.ImageFolder读数据,传入路径和预处理方法,构建完数据集dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=Tr...
def __init__(self, cache_dir=DEFAULT_CACHE_DIR, verbose=False): from transformers import AutoModelForTokenClassification from transformers import AutoTokenizer # download the model or load the model path weights_path = download_model('bert.ner', cache_dir, process_func=_unzip_process_func, ver...
# 需要导入模块: from torch import hub [as 别名]# 或者: from torch.hub importload_state_dict_from_url[as 别名]defgsc_super_sparse_cnn(pretrained=False, progress=True):""" Super Sparse CNN model used to classify `Google Speech Commands` ...
Problem solution for: size mismatch for classifier.4.weight: copying a param with shape torch.Size([751, 256]) from checkpoint, the shape in current model is torch.Size([1, 256]). To resolve this issue, one potential solution is to modify the code ...
to load the model from the path you saved it to and then “Convert the model to onnx with and without quantization”. Next, run the section “Upload the onnx model and quantized onnx model to S3 Bucket” to upload both the onnx models (classifier.onnx and classifier_int8.onnx) that...