load(pretrained, map_location="cpu") model.load_state_dict(state_dict) return model def qint8edsr(block=QuantizableResBlock, pretrained=None, quantize=False): model = QuantizableEDSR(block=block) _replace_relu(model) if quantize: backend = 'fbgemm' quantize_model(model, backend) else: ...
pooled_output: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a classifier pretrained on top of the hidden state associated to the first character of the input (CLF) to train on the Next-Sentence task (see BERT's paper). ...
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True)+ model = accelerator.prepare(model)optimizer = torch.optim.AdamW(params=model.parameters(), lr=lr)- model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(model,- optimiz...
import torchvision.models as models resnet34 = models.resnet34(pretrained=True) # 默认为False 2. 只加载网络结构, 不加载训练的参数 import torchvision.models as models resnet18 = models.resnet18(pretrained= False) 参数修改:resnet网络的最后一层对应1000个类别, 如果我们自己的数据只有10个类别, 那...
ViT-PyTorch is a PyTorch re-implementation of ViT. It is consistent with theoriginal Jax implementation, so that it's easy to load Jax-pretrained weights. At the same time, we aim to make our PyTorch implementation as simple, flexible, and extensible as possible. ...
T5_VARIANT = 't5-small't5_model = T5ForConditionalGeneration.from_pretrained(T5_VARIANT)tokenizer = T5Tokenizer.from_pretrained(T5_VARIANT)config = T5Config(T5_VARIANT)接下来，将模型转换为经过优化的TensorRT执行引擎。不过，在将T5模型转换为TensorRT引擎之前，需要将PyTorch模型转换为一种中间通用格式：ONN...
import torchvision.models as models_torchimport flowvision.models as models_flowresnet101_torch = models_torch.resnet101(pretrained=True)resnet101_flow = models_flow.resnet101()state_dict_torch = resnet101_torch.state_dict()state_dict_numpy = {key: value.detach().cpu().numpy() for key, ...
def resnet50(pretrained=True, requires_grad=False): model = models.resnet50(progress=True, pretrained=pretrained) # either freeze or train the hidden layer parameters if requires_grad == False: for param in model.parameters(): param.requires_grad = False ...
resnet50(pretrained=True) nn.init.xavier_uniform_(resnet.weight) 需要注意的是,上述代码示例中直接对resnet.weight进行了初始化,但在实际应用中,我们可能需要更细致地选择需要初始化的参数,因为模型通常包含多种类型的参数(如权重和偏置)。 在上述代码中,nn.init.kaiming_normal_()函数用于对网络权重进行He...
在上面的例子中,pretrained=True 和 useGPU=False 都被赋予不同的预训练模型。 探索已加载模型 当我们从 PyTorch Hub 中加载了模型时,我们能从以下工作流探索可用的方法,并更好地理解运行它们需要什么样的参数。 dir(model) 方法可以查看模型的所有方法,下面我们可以看看 bertForMaskedLM 模型的可用方法。 help(...