def backward(self, dout): dout = dout.transpose(0, 2, 3, 1) pool_size = self.pool_h * self.pool_w dmax = np.zeros((dout.size, pool_size)) dmax[np.arange(self.arg_max.size), self.arg_max.flatten()] = dout.flatten() dmax = dmax.reshape(dout.shape + (pool_size,)) dcol = dmax.reshape(dmax.shape[0] * dmax.shape[1] * dmax.shape[2], -1) dx = col2im(dcol, self.x.shape, self.pool_h, self.pool_w, self.stride, self.pad) return dx
时间: 2024-04-03 20:35:53 浏览: 15
这段代码是池化层的反向传播函数实现。在前向传播中,我们已经得到了池化层的输出结果out和最大值的索引arg_max,现在需要根据输出结果和arg_max求出输入数据的梯度,即dout/dx。具体实现流程如下:
1. 将输出结果的维度转置为(N, out_h, out_w, C)。
2. 计算每个池化窗口内最大值的位置,根据arg_max和dout求出dmax,即每个最大值的梯度。
3. 将dmax重构为四维数组,形状为(N, C, out_h, out_w, pool_size)。
4. 将dmax转换为二维数组dcol,方便后续的矩阵计算。
5. 通过col2im函数将dcol转换为输入数据的梯度dx。
6. 返回dx。
以上就是该函数的具体实现流程。
相关问题
class Pooling: def init(self, pool_h, pool_w, stride=1, pad=0): self.pool_h = pool_h self.pool_w = pool_w self.stride = stride self.pad = pad self.x = None self.arg_max = None def forward(self, x): N, C, H, W = x.shape out_h = int(1 + (H - self.pool_h) / self.stride) out_w = int(1 + (W - self.pool_w) / self.stride) col = im2col(x, self.pool_h, self.pool_w, self.stride, self.pad) col = col.reshape(-1, self.pool_h*self.pool_w) arg_max = np.argmax(col, axis=1) out = np.max(col, axis=1) out = out.reshape(N, out_h, out_w, C).transpose(0, 3, 1, 2) self.x = x self.arg_max = arg_max return out def backward(self, dout): dout = dout.transpose(0, 2, 3, 1) pool_size = self.pool_h * self.pool_w dmax = np.zeros((dout.size, pool_size)) dmax[np.arange(self.arg_max.size), self.arg_max.flatten()] = dout.flatten() dmax = dmax.reshape(dout.shape + (pool_size,)) dcol = dmax.reshape(dmax.shape[0] * dmax.shape[1] * dmax.shape[2], -1) dx = col2im(dcol, self.x.shape, self.pool_h, self.pool_w, self.stride, self.pad) return dx
这段代码实现了一个池化层的类,包括构造函数和前向传播函数、反向传播函数。构造函数用于初始化池化层的超参数,包括池化窗口的高度和宽度、步长、填充等,并初始化一些变量用于后续的前向计算和反向传播。前向传播函数实现了池化层的前向计算,将输入数据进行池化操作,得到输出结果。反向传播函数实现了池化层的反向传播,根据输出结果和最大值的索引求出输入数据的梯度。该类可以被其他神经网络层调用,用于构建具有池化功能的神经网络。
运行class GuidedBackpropReLUModel: def __init__(self, model, use_cuda): self.model = model self.model.eval() self.cuda = use_cuda if self.cuda: self.model = model.cuda() for idx, module in self.model.features._modules.items(): if module.__class__.__name__ == 'ReLU': self.model.features._modules[idx] = GuidedBackpropReLU() def forward(self, input): return self.model(input) def forward_static(self, input): if self.cuda: output = self.forward(input.cuda()) else: output = self.forward(input) return output def __call__(self, input, index = None): output = self.forward_static(input) if index == None: index = np.argmax(output.cpu().data.numpy()) one_hot = np.zeros((1, output.size()[-1]), dtype = np.float32) one_hot[0][index] = 1 one_hot = Variable(torch.from_numpy(one_hot), requires_grad = True) if self.cuda: one_hot = torch.sum(one_hot.cuda() * output) else: one_hot = torch.sum(one_hot * output) one_hot.backward() output = input.grad.cpu().data.numpy() output = output[0,:,:,:] return output报错Legacy autograd function with non-static forward method is deprecated. Please use new-style autograd function with static forward method. 如何修改代码
这个错误提示是因为代码中使用了旧版本的autograd函数,需要使用新版本的autograd函数。可以使用`@staticmethod`修饰`GuidedBackpropReLU`类中的`forward`方法,将其转换为静态方法。修改后的代码如下:
```
class GuidedBackpropReLU(nn.ReLU):
@staticmethod
def forward(input):
output = torch.clamp(input, min=0.0)
grad = torch.zeros_like(output)
grad[output > 0] = 1.0
output = output * grad
return output
class GuidedBackpropReLUModel:
def __init__(self, model, use_cuda):
self.model = model
self.model.eval()
self.cuda = use_cuda
if self.cuda:
self.model = model.cuda()
for idx, module in self.model.features._modules.items():
if module.__class__.__name__ == 'ReLU':
self.model.features._modules[idx] = GuidedBackpropReLU()
def forward(self, input):
return self.model(input)
def forward_static(self, input):
if self.cuda:
output = self.forward(input.cuda())
else:
output = self.forward(input)
return output
def __call__(self, input, index=None):
output = self.forward_static(input)
if index is None:
index = np.argmax(output.cpu().data.numpy())
one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32)
one_hot[0][index] = 1
one_hot = Variable(torch.from_numpy(one_hot), requires_grad=True)
if self.cuda:
one_hot = torch.sum(one_hot.cuda() * output)
else:
one_hot = torch.sum(one_hot * output)
one_hot.backward()
output = input.grad.cpu().data.numpy()
output = output[0, :, :, :]
return output
```