fromptflopsimportget_model_complexity_info 导入ptflops macs,params=get_model_complexity_info(model1,(3,352,352),as_strings=True,print_per_layer_stat=False,verbose=True)print('{:<30}{:<8}'.format('Computational complexity: ',macs))print('{:<30}{:<8}'.format('Number of parameters: ',...
factor = 0.5, # The number we multiply learning rate until the milestone. total_iters = 8) # The number of steps that the scheduler decays the learning rate 如果起始因子小于1，那么学习率调度器在训练过程中会提高学习率，而不是
# calculating the dot product of all node embeddings# and first half the attention vector parameters (corresponding to neighbor messages)source_scores = torch.matmul(h_transformed, self.a[:out_feature, :]) # calculating the dot product of all n...
get_perf(no_vmap_timer, "without vmap", with_vmap_timer, "vmap") Performance delta: 69.4681 percent improvement with vmap 此外,很容易将问题转换过来,说我们想要计算模型参数(权重、偏置)的雅可比矩阵,而不是输入的雅可比矩阵 # note the change in input via ``argnums`` parameters of 0,1 to ...
torch.nonzero(tensor) # index of non-zero elementstorch.nonzero(tensor==0) # index of zero elementstorch.nonzero(tensor).size(0) # number of non-zero elementstorch.nonzero(tensor == 0).size(0) # number of zero elements 判断两个张量相等 ...
(data).local_value()# Need to move targets to the device where the output of the# pipeline resides.loss = criterion(output.view(-1, ntokens), targets.cuda(2 * rank + 1))loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)optimizer.step()total_loss += loss.item...
fromSimNetimportsimNet#导入模型model=simNet()#定义模型total=sum([param.nelement()forparaminmodel.parameters()])#计算总参数量print("Number of parameter:%.6f"%(total))#输出 调用thop模块中的profile包进行计算 这里需要使用包进行计算,调用方式也很简单,原理是初始化一个图片丢进去计算,当然这个初始化的图...
defstep(self):self.base_lrs=[group['initial_lr']forgroupinoptimizer.param_groups]self.last_epoch+=1lrs=self.get_lr()forparam,lrinzip(self.optimizer.param_groups,lrs):param['lr']=lr optimizer2=torch.optim.SGD(model.parameters(),lr=1e-3)scheduler2=CustomLambdaLR(optimizer2,lr_lambda,las...
specifies the name this value will take on.targetis similarly the name of the argument.argsholds either: 1) nothing, or 2) a single argument denoting the default parameter of the function input.kwargsis don’t-care. Placeholders correspond to the function parameters (e.g.x) in the graph ...
device parameters have been replaced with npu in the function below: torch.logspace, torch.randint torch.hann_window, torch.rand, torch.full_like, torch.ones_like, torch.rand_like, torch.randperm, torch.arange, torch.frombuffer, torch.normal, torch._empty_per_channel_affine_quantized, torch....