if p.grad is not None: 改成 if p.grad is not None (p.grad == 0).all(): 或者在每次调用optim.step()之前,加一句: for p in model.parameters(): if p.grad is not None and (p.grad == 0).all(): p.grad = None DDP的梯度汇总使用的是avg,因
类似NAS这种动态子图,且你的优化器设置了momentum等除了grad以外其他需要参与梯度更新的参数时需要特别注意:在pytorch中,required_grad=False的参数在进行参数更新的时候,grad为None,所以torch中优化器的step中有一个p.grad is not None的判断用来跳过这些参数: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 for...
non_final_mask = torch.tensor(tuple(map(lambda s: s is not None, batch.next_state)), device=device, dtype=torch.bool) non_final_next_states = torch.cat([s for s in batch.next_state if s is not None]) state_batch = torch.cat(batch.state) action_batch = torch.cat(batch.action)...
if batch_sampler is not None: if batch_size > 1 or shuffle or sampler is not None or drop_last: raise ValueError('batch_sampler is mutually exclusive with ' 'batch_size, shuffle, sampler, and drop_last') if sampler is not None and shuffle: raise ValueError('sampler is mutually exclusiv...
(layer.bias, val=0.0)elif isinstance(layer, torch.nn.BatchNorm2d):torch.nn.init.constant_(layer.weight, val=1.0)torch.nn.init.constant_(layer.bias, val=0.0)elif isinstance(layer, torch.nn.Linear):torch.nn.init.xavier_normal_(layer.weight)if layer.bias is...
def _init_weights(self):for m in self.modules():if type(m) in {nn.Linear,nn.Conv3d,}:nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_out', nonlinearity='relu',)if m.bias is not None:fan_in, fan_out = \nn.init._calculate_fan_in_and_fan_out(m.weight.data)bound ...
if DEBUGGING_IS_ON:for name, parameter in model.named_parameters():if parameter.grad is not None:print(f"{name} gradient: {parameter.grad.data.norm(2)}")else:print(f"{name} has no gradient") if USE_MAMBA and DIFFERENT_H_STATES_RECU...
if self.num_samples is not None and replacement is False: raise ValueError("With replacement=False, num_samples should not be specified, " "since a random permute will be performed.") if self.num_samples is None: self.num_samples = len(self.data_source) ...
if self.target_transform is not None: target = self.target_transform(target) return img, target def __len__(self): return len(self.imgs) def __repr__(self): fmt_str = 'Dataset ' + self.__class__.__name__ + '\n' fmt_str += ' Number of datapoints: {}\n'.format(self._...
if target is not None: loss = self.loss(output, target.long()) return loss return output 通过这样做，会大大减少 RAM 的需求。对于上面的示例。用于高效存储数据表示的内存使用量将为每批 33Mb，而之前是 167Mb，减少为原来的五分之一。当然，这需要模型中添加额外的步骤来标准化数据或将数据转换为合适...