def__init__(self, data_dir, num_classes, device, Transform=None, sample=False, loss_weights=False, batch_size=16, lr=1e-4, stop_early=True, freeze_backbone=True) : ### # data_dir - directory with images in subfolders, subfolders name are categories # Transform - data augmentations ...
num_classes =2model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False, progress=True, num_classes=num_classes, pretrained_backbone=True)device = torch.device('cuda:0')model.to(device) 其中pretrained=False表示训练使用,num_classes 表示对象检测数据集的对象类别,这里只有dog跟cat...
self.softmax = nn.Softmax(dim=-1) #所有类的概率相加为1 self.detect = Detect(num_classes, 0, 200, 0.01, 0.45) 1. 2. 3. top_k, conf_thresh, nms_thresh分别表示 一张图片中,每一类的预测框数量 置信度阈值,低于该阈值的预测框会被舍弃 非极大值抑制,值越小表示要求的预测框重叠度越小,0.0...
self.relu=nn.ReLU()self.fc2=nn.Linear(hidden_dim,num_classes)defforward(self,x):attended_values=self.attention(x)x=attended_values.mean(dim=1)x=self.fc1(x)x=self.relu(x)x=self.fc2(x)returnxif__name__=='__main__':# att=Self_Attention(dim=2,dk=2,dv=3)model=SelfAttentionClass...
self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity() # Weight init 权重初始化 nn.init.trunc_normal_(self.pos_embed, std=0.02) if self.dist_token is not None: nn.init.trunc_normal_(self.dist_token, std=0.02) ...
numClasses = numel(unique(trainLabels)); options.maxIter = 100; softmaxModel = softmaxTrain(inputSize, numClasses, lambda, ... trainFeatures, trainLabels, options); %% --- %%=== %% STEP 5: Testing %% --- YOUR CODE HERE --- % Compute Predictions...
numClasses= numel(unique(trainLabels));%unique为找出向量中的非重复元素并进行排序% You need to compute softmaxModelusingsoftmaxTrain on trainFeatures and%trainLabels% You need to compute softmaxModelusingsoftmaxTrain on trainFeatures and%trainLabels ...
# 12-28行defspaco(...)=>num_classes=data.num_trainval_ids# 首先解释下几个参数吧。iter_step就是我们co-training交替迭代的次数。gamma比较重要,# 后面讲。train_ratio就是train/untrain中train的比例,以适应半监督学习。# 重点讲讲26行split_dataset的实现。实现比较特殊,划分train/untrain时,让每个人的...
I.L. Freire, New classes of nonlinearly self-adjoint evolution equations of third- and fifth-order, Commun. Nonlin. Sci. Num. Simul. 18 (2013) 493-499.I.L. Freire, New classes of nonlinearly self-adjoint evolution equations of third- and fifth-order, Commun. Nonlin. Sci. Numer. Simul...
nn.Linear(512, num_classes) ) def forward(self, x): bs = x.shape[0] x = self.feature(x) x1 = self.avg_pool(x) x2 = self.max_pool(x) x = torch.cat([x1, x2], dim=1) x = self.reduce_layer(x).view(bs, -1)