in_features = model.roi_heads.box_predictor.cls_score.in_features model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes) # replace the pre-trained head with a new one model.to(device) # step 3: loss # in lib/python3.6/site-packages/torchvision/models/detection/roi_h...
类的初始化参数:in_features、out_features分别表示全连接前后的神经元数量,bias表示是否拟合偏置项; 类的输入输出形状,输入数据维度为(*, in_features),输出数据维度为(*, out_features),即保持前序的维度不变,仅将最后一个维度由in_features维度变换为out_features; 类的属性:weight,拟合的权重矩阵,维度为(out...
(in_features=96, out_features=1024, bias=True) # (relu1): ReLU(inplace=True) # (batchnorm1d_1): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) # (linear2): Linear(in_features=1024, out_features=6272, bias=True) # (relu2): ReLU(inplace=...
classtransformer_engine.pytorch.LayerNormLinear(in_features,out_features,eps=1e-5,bias=True,**kwargs)¶ Applies layer normalization followed by linear transformation to the incoming data. Parameters: in_features(int) – size of each input sample. ...
input_dim=trained_experts[0].layer1.in_features self.gating=Gating(input_dim,num_experts)defforward(self,x):# Get the weights from the gating network weights=self.gating(x)# Calculate the expert outputs outputs=torch.stack([expert(x)forexpertinself.experts],dim=2)# Adjust the weights tenso...
self.out_features=out_features self.weight=Parameter(torch.Tensor(out_features,in_features))ifbias:self.bias=Parameter(torch.Tensor(out_features))else:self.register_parameter('bias',None)self.reset_parameters()defreset_parameters(self):stdv=1./math.sqrt(self.weight.size(1))self.weight.data.uni...
forparaminmodel_conv.parameters(): # 将所有参数求导设为否 param.requires_grad=False # Parameters of newly constructed modules have requires_grad=True by default # 得到最后一个全连接的输入维度 num_ftrs=model_conv.fc.in_features # 将最后一个全连接由(512, 1000)改成(512, 2),取代最后一个全...
(1, 1), bias=False) (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU6(inplace=True) ) ) (classifier): Sequential( (0): Dropout(p=0.2, inplace=False) (1): Linear(in_features=1280, out_features=3, bias=True) ) ) /usr/...
(test_dataset, batch_size=batch_size)# 加载预训练的MobileNetV3-Large模型model = mobilenet_v3_large(pretrained=True)num_ftrs = model.classifier[3].in_featuresmodel.classifier[3] = nn.Linear(num_ftrs, 2) # 替换最后一层全连接层,以适应二分类问题device = torch.device("cuda" if torch.cuda....
y = features(x) viz.images(x, win='input') viz.images(y, win='output') 其中,使用了VGG16模型和一个输入数据x,将其输出到Visdom中。 4. 可视化损失函数和其他指标:可以使用以下代码将损失函数和其他指标输出到Visdom: import numpy as np from visdom import Visdom viz = Visdom() loss_win = viz...