还可以用help(model.forward)对运行该模型所需参数有更深入的了解。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 >>>help(model.forward)>>>Help on method forwardinmodule pytorch_pretrained_bert.modeling:forward(input_ids,token_type_ids=None,attention_mask=None,masked_lm_labels=None)... 我自...
x=torch.rand(20,1)*10# xdata(tensor),shape=(20,1)y=2*x+(5+torch.randn(20,1))# ydata(tensor),shape=(20,1)# Build Linear Regression Parameters # Initialize w and b,where w is initialized to a normal distribution and b is initialized to0# Automatic differentiation is required,soset...
from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline tokenizer = BertTokenizer.from_pretrained("uer/gpt2-chinese-poem") model = GPT2LMHeadModel.from_pretrained("uer/gpt2-chinese-poem") text_generator = TextGenerationPipeline(model, tokenizer) result = text_generator("昨日...
preds = self.model(x, batched_timestamps) betas_t = extract(self.betas, batched_timestamps, x.shape)sqrt_recip_alphas_t = extract(self.sqrt_recip_alphas, batched_timestamps, x.shape)sqrt_one_minus_alphas_cumprod_t = extract(self.sqrt_one_min...
x = x.view(-1, int(x.nelement() / x.shape[0])) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x model = LeNet().to(device=device) model = LeNet() parameters_to_prune = ( (model.conv1, 'weight'), ...
torch.save(model_object, 'resnet.pth') model = torch.load('resnet.pth') 2. 分别加载网络的结构和参数 #将my_resnet模型字典储存为my_resnet.pth torch.save(my_resnet.state_dict(), "my_resnet.pth") # 加载resnet,模型存放在my_resnet.pth my_resnet.load_state_dict(torch.load("my_res...
#getthe"features"portionofVGG19(we will not need the"classifier"portion)vgg=models.vgg19(pretrained=True).features # freeze allVGGparameters since we're only optimizing the target imageforparaminvgg.parameters():param.requires_grad_(False)# move the model toGPU,ifavailable ...
利用FPN特征金字塔,我们可以获得三个加强特征,这三个加强特征的shape分别为(20,20,1024)、(40,40,512)、(80,80,256),然后我们利用这三个shape的特征层传入Yolo Head获得预测结果。 对于每一个特征层,我们可以获得利用一个卷积调整通道数,最终的通道数和需要区分的种类个数相关,在YoloV5里,每一个特征层上每一...
def forward(self, x):"""x_proj.shape = torch.Size([batch_size, seq_len, 2*d_model])x_conv.shape = torch.Size([batch_size, seq_len, 2*d_model])x_conv_act.shape = torch.Size([batch_size, seq_len, 2*d_model])"""# Refer...
2. 设置模型参数并转化为 OpenVINO™ 模型对象,由于 BERT 是一个多输入模型,这里额外添加了一个 input=input_info 参数,可以用来指定多输入模型中每一个 input 的 shape 以及数据类型。 3. 准备校验数据集,并启动量化,上一步中获得的 model 为 openvino.runtime.Model 类型,可以直接被 NNCF 工具加载 ...