def drop_path(x, drop_prob: float = 0., training: bool = False): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ if drop_prob == 0. or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) random_tensor.floor_() # binarize output = x.div(keep_prob) * random_tensor return output 详细讲解一下
时间: 2023-06-06 18:04:51 浏览: 63
这是一个函数,用于在残差块的主路径上每个样本中丢弃路径(随机深度)。如果在EfficientNet等网络中使用,则与我创建的DropConnect实现相同,但原始名称会导致误解,因为“Drop Connect”是另一篇论文中不同形式的丢失连接。详见讨论:https://github.com/tensorflow/tpu/issues/494. 其中,x为输入的张量,drop_prob为float类型,表示丢弃概率,默认值为0,training为布尔类型,表示是否训练,默认为False。