if isinstance(m, nn.Conv2d): #isinstance:m类型判断 若当前组件为 conv n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) #正太分布初始化 elif isinstance(m, nn.BatchNorm2d): #若为batchnorm m.weight.data.fill_(1) #weight为...
# Common practise for initialization.for layer in model.modules():if isinstance(layer, torch.nn.Conv2d):torch.nn.init.kaiming_normal_(layer.weight, mode='fan_out',nonlinearity='relu')if layer.bias is not None:torch.nn.init.constant_(layer.bias, val=0.0)el...
同样的,conv.py模块的Conv2d层,它的基类是_ConvNd,而_ConvNd的权重初始化方法和Class Linear的一模一样,也是kaiming均匀分布。(代码和linear.py中的reset_parameters完全一致,就不粘贴了。) 关于kaiming均匀分布kaiming均匀分布可在文档[1]中找到,接口是: ...
importosimportnumpyasnpimportmatplotlib.pyplotaspltimporttensorflowastf# ===# Check Tensorflow Initialization (conv2d / linear / lstm).# ===print(tf.__version__)os.environ['CUDA_VISIBLE_DEVICES']='7'# ---# 2.0. PyTorch GRUCell# ---w_2d=tf.get_variable('w_2d',...
class Net(nn.Module):def __init__(self, l1=120, l2=84):super(Net, self).__init__()self.conv1 = nn.Conv2d(3, 6, 5)self.pool = nn.MaxPool2d(2, 2)self.conv2 = nn.Conv2d(6, 16, 5)self.fc1 = nn.Linear(16 * 5 * 5, l1)self.fc2 = nn.Linear(l1, l2)self.fc3 ...
复制 Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1)) 修剪通过从参数中删除weight并用一个名为weight_orig的新参数替换它(即将初始参数name附加"_orig")。weight_orig存储张量的未修剪版本。bias没有被修剪,因此它将保持不变。 代码语言:javascript 代码运行次数:0 运行 复制 print(list(module.named...
(self.last_channel, num_classes), ) # weight initialization for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: nn.init.zeros_(m.bias) elif isinstance(m, nn.BatchNorm2d): nn.init.ones_(m.weight) ...
然而,如果后端扩展者想要覆盖 PyTorch 提供的默认内核,他们仍然可以将他们定制的内核注册到他们的后端,调度器将仅在您的后端中使用它。例如,PyTorch 的max_pool2d的当前实现返回indices作为前向输出的一部分,这在 torch_xla 中创建了开销,因此 torch_xla 为max_pool2d注册了自己的内核。
self.t2=nn.Sequential(nn.Conv2d(in_channels=64,out_channels=128,kernel_size=(4,4),stride=2,padding=1),nn.BatchNorm2d(128),nn.LeakyReLU(0.2,in_place=True))self.t3=nn.Sequential(nn.Conv2d(in_channels=128,out_channels=256,kernel_size=(4,4),stride=2,padding=1),nn.BatchNorm2d(256)...
Changetorch.Tensor.new_tensor()to be on the given Tensor's device by default (#144958) This function was always creating the new Tensor on the "cpu" device and will now use the same device as the current Tensor object. This behavior is now consistent with other.new_*methods. ...