self).__init__()self.fc1=nn.Linear(100,80)# 隐藏层1self.fc2=nn.Linear(80,60)# 隐藏层2self.fc3=nn.Linear(60,40)# 隐藏层3self.fc4=nn.Linear(40,10)# 输出层# 使用He初始化来初始化权重nn.init.kaiming_uniform_(self.fc1.weight,mode='fan_in',nonlinearity...
class ResNet(nn.Module): def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None): super(ResNet, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d self._n...
accelerator = Accelerator()除了提供要使用的主要对象之外，此行还将从环境中分析分布式训练运行的类型并执行必要的初始化。用户可以通过将 cpu = True 或 fp16 = True 传递给此 init 来强制进行 CPU 训练或混合精度训练。这两个选项都可以使用脚本的启动器进行设置。model, optim, data = accelerator.prepare(mod...
self).__init__() 4 self.conv1 = nn.Conv2d(inplanes, outplanes, stride=1, 5 kernel_size=3, padding=1, bias=False) 6 self.bn1 = nn.BatchNorm2d(outplanes) 7 8 for block in range(BLOCKS): 9 setattr(self, "res{}".format(block), \10 Basic...
flatten(x) logits = self.linear_relu_stack(x) return logits然后定义loss函数、计算输出和losses。loss_fn = CrossEntropyLoss()# Calculate lossesout = nn(t)loss = loss_fn(out, label)# Backward passnn.zero_grad()loss.backward()好了，如何使用koila来防止内存溢出？超级简单！只需在第一行...
(Y):"""Convert mask Y to a bounding box, assumes 0 as background nonzero object"""cols,rows=np.nonzero(Y)iflen(cols)==0:returnnp.zeros(4,dtype=np.float32)top_row=np.min(rows)left_col=np.min(cols)bottom_row=np.max(rows)right_col=np.max(cols)returnnp.array([left_col,top_...
class SinusoidalPosEmb(nn.Module):def __init__(self, dim, theta=10000):super().__init__()self.dim = dimself.theta = theta def forward(self, x):device = x.devicehalf_dim = self.dim // 2emb = math.log(self.theta) / (half_dim - 1)em...
PyTorch 为了实现量化,首先就得需要具备能够表示量化数据的 Tensor,这就是从 PyTorch 1.1 之后引入的 Quantized Tensor。Quantized Tensor 可以存储 int8/uint8/int32 类型的数据,并携带有 scale、zero_point 这些参数。把一个标准的 float Tensor 转换为量化 Tensor 的步骤如下: ...
class Optimizer(object):def zero_grad(self):for group in self.param_groups:for p in group['params']:if p.grad is not None:p.grad.detach_()p.grad.zero_() class Optimizer(object):def __init__(self, params, defaults):self.defaults = defaultsself.state = defaultdict(dict)self.param_...
zero_grad() # 清空过去的梯度 outputs = model(train_data) # 前向传播 loss = criterion(outputs, train_labels) # 计算损失 loss.backward() # 反向传播计算梯度 optimizer.step() # 更新权重 print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 100, loss.item())) # 打印当前轮次和损失...