importtorchimporttorch.fxclassMyModule(torch.nn.Module):def__init__(self):super().__init__()self.param=torch.nn.Parameter(torch.rand(3,4))self.linear=torch.nn.Linear(4,5)defforward(self,x):returntorch.topk(torch.sum(self.linear(x+self.linear.weight).relu(),dim=-1),3)m=MyModule(...
'''latent = torch.sum(memory, dim=1)# (batch_size, d_model)logit = self.decode(latent.unsqueeze(1), tgt, tgt_mask)# (batch_size, max_tgt_seq, d_model)# logit,_=self.gru_decoder(logit)prob = self.generator(logit)# (batch_size, max_seq, vocab_size)returnlatent, prob 开发者ID...
momentum– the value used for the running_mean and running_var computation. Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1 affine– a boolean value that when set to True, this module has learnable affine parameters. Default: True track_running_stats– ...
1.2.2 add_param_group 该方法在初始化函数中用到,主要用来向self.param_groups添加不同分组的模型参数 defadd_param_group(self,param_group):r"""Add a param group to the :class:`Optimizer` s `param_groups`.This can be useful when fine tuning a pre-trained network as frozen layers can be m...
大写的时候,传入的是,size的张量 5.dim=2 6.dim=3 均匀分布 7.mixed 8.程序 importtorchimportnumpy as np"""数据类型"""#常见的类型判断a = torch.randn(2,3)#正态分布print(a.type())#torch.FloatTensorprint(type(a))#<class 'torch.Tensor'>, python自带print(isinstance(a, torch.FloatTensor))...
input_size=[input_size] # batch_size of 2 for batchnorm x=[torch.rand(2,*in_size).type(dtype).to(device=device) forin_size,dtypeinzip(input_size,dtypes)] # create properties summary=OrderedDict() hooks=[] # register hook model.apply(register_hook) ...
batch_loss = -alpha * (torch.pow((1- probs), self.gamma)) * log_pifself.size_average: loss = batch_loss.mean()else: loss = batch_loss.sum()returnloss 開發者ID:ouyanghuiyu,項目名稱:RefinedetLite.pytorch,代碼行數:26,代碼來源:refinedet_multibox_loss.py ...
('sum', gradients, scale=1.0/xm.xrt_world_size())ifconfig.TRAIN.CLIP_GRAD: grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.TRAIN.CLIP_GRAD)else: grad_norm = get_grad_norm(model.parameters()) scaler.step(optimizer) scaler.update()else: loss.backward()ifconf...
("Create Resnet")model=models.resnet18().cuda()print("Create optimizer")optimizer=torch.optim.SGD(model.parameters(),lr=0.01)x=torch.randn(16,3,224,224).cuda()optimizer.zero_grad()print("Forward pass")out=model(x)print("Backward pass")out.sum().backward()print("Step optimizer")...
# torch.zeros(size)# torch.zeros_like(input,dtype)# torch.ones(size)# torch.ones_like(input,dtype)# torch.eye(size)x=torch.zeros(5,3)print('torch.zeros=',x)y=torch.tensor([[1,2,3,4],[5,6,7,8]])x=torch.zeros_like(y)print('torch.zeros_like=',x)x=torch.ones(5,3)print...