网站建设实训的意义,深圳网站建设制作,免费建站自助建站网站建设教程网站建设教程,余姚 网站建设pytorch的hook机制允许我们在不修改模型class的情况下#xff0c;去debug backward、查看forward的activations和修改梯度。hook是一个在forward和backward计算时可以被执行的函数。在pytorch中#xff0c;可以对Tensor和nn.Module添加hook。hook有两种类型#xff0c;forwa…pytorch的hook机制允许我们在不修改模型class的情况下去debug backward、查看forward的activations和修改梯度。hook是一个在forward和backward计算时可以被执行的函数。在pytorch中可以对Tensor和nn.Module添加hook。hook有两种类型forward hook和backward hook。
1. 对Tensors添加hook
对于Tensors来说只有backward hook没有forward hook。对于backward hook来说其函数输入输出形式是 hook(grad) - Tensor or None。其中grad是pytorch执行backward之后一个tensor的grad属性值。
例如
import torch
a torch.ones(5)
a.requires_grad Trueb 2*a
c b.mean()
c.backward()print(fa.grad {a.grad}, b.grad {b.grad})输出
a.grad tensor([0.4000, 0.4000, 0.4000, 0.4000, 0.4000]), b.grad None由于b不是叶子节点因此在计算完梯度后b的grad会被释放。因此b.gradNone。这里我们要显式的指定不释放掉非叶子节点的grad。代码改为下面这样
import torch
a torch.ones(5)
a.requires_grad Trueb 2*ab.retain_grad() # 让非叶子节点b的梯度保持
c b.mean()
c.backward()print(fa.grad {a.grad}, b.grad {b.grad})输出
a.grad tensor([0.4000, 0.4000, 0.4000, 0.4000, 0.4000]), b.grad tensor([0.2000, 0.2000, 0.2000, 0.2000, 0.2000])我们可以通过加print的方式来查看一个tensor的梯度值也可以通过加hook的方式来实现这点。
import torcha torch.ones(5)a.requires_grad Trueb 2*aa.register_hook(lambda x:print(fa.grad {x}))
b.register_hook(lambda x: print(fb.grad {x})) c b.mean()c.backward() 输出
b.grad tensor([0.2000, 0.2000, 0.2000, 0.2000, 0.2000])
a.grad tensor([0.4000, 0.4000, 0.4000, 0.4000, 0.4000])使用hook的一个好处是代码中的b.retain_grad() # 让非叶子节点b的梯度保持 这句可以删除掉同样可以记录到非叶子节点的值。对于不方便修改源码的程序可以通过对tensors添加hook查看梯度。同时.retain_grad()操作会增加显存的使用。
另外一点对Tensors使用hook的好处是可以对backward时的梯度进行修改。来看一个更加实际具体的例子
import torch
import torch.nn as nnclass myNet(nn.Module):def __init__(self):super().__init__()self.conv nn.Conv2d(3,10,2, stride 2)self.relu nn.ReLU()self.flatten lambda x: x.view(-1)self.fc1 nn.Linear(160,5)def forward(self, x):x self.relu(self.conv(x))# 修改反向传播时conv输出的梯度不小于0x.register_hook(lambda grad : torch.clamp(grad, min 0))# 打印确认是否有小于0的梯度x.register_hook(lambda grad: print(Gradients less than zero:, bool((grad 0).any()))) return self.fc1(self.flatten(x))net myNet()for name, param in net.named_parameters():# 使用named_parameters对fc和bias添加修改使其梯度全部为0if fc in name and bias in name:param.register_hook(lambda grad: torch.zeros(grad.shape))out net(torch.randn(1,3,8,8)) (1 - out).mean().backward()print(The biases are, net.fc1.bias.grad)输出为
Gradients less than zero: False
The biases are tensor([0., 0., 0., 0., 0.])2. 对nn.Module添加hook
对nn.Module添加hook的函数输入输出形式为
backward hookhook(module, grad_input, grad_output) - Tensor or None
forward hookhook(module, input, output) - None
对nn.Module添加backward hook非常容易造成困扰。看下面的例子
import torch
import torch.nn as nnclass myNet(nn.Module):def __init__(self):super().__init__()self.conv nn.Conv2d(3,10,2, stride 2)self.relu nn.ReLU()self.flatten lambda x: x.view(-1)self.fc1 nn.Linear(160,5)def forward(self, x):x self.relu(self.conv(x))return self.fc1(self.flatten(x))net myNet()def hook_fn(m, i, o):print(m)print(------------Input Grad------------)for grad in i:try:print(grad.shape)except AttributeError: print (None found for Gradient)print(------------Output Grad------------)for grad in o: try:print(grad.shape)except AttributeError: print (None found for Gradient)print(\n)net.conv.register_backward_hook(hook_fn)
net.fc1.register_backward_hook(hook_fn)
inp torch.randn(1,3,8,8)
out net(inp)(1 - out.mean()).backward()输出为
Linear(in_features160, out_features5, biasTrue)
------------Input Grad------------
torch.Size([5])
torch.Size([5])
------------Output Grad------------
torch.Size([5])Conv2d(3, 10, kernel_size(2, 2), stride(2, 2))
------------Input Grad------------
None found for Gradient
torch.Size([10, 3, 2, 2])
torch.Size([10])
------------Output Grad------------
torch.Size([1, 10, 4, 4])可以看到对nn.Module添加的backward hook对于Input Grad和Output Grad对于弄清其具体指代的梯度是比较难以搞清楚的。
对nn.Module添加forward hook对于我们查看每层的激活值输出activations是非常方便的。
import torch
import torch.nn as nnclass myNet(nn.Module):def __init__(self):super().__init__()self.conv nn.Conv2d(3,10,2, stride 2)self.relu nn.ReLU()self.flatten lambda x: x.view(-1)self.fc1 nn.Linear(160,5)self.seq nn.Sequential(nn.Linear(5,3), nn.Linear(3,2))def forward(self, x):x self.relu(self.conv(x))x self.fc1(self.flatten(x))x self.seq(x)net myNet()
visualisation {}def hook_fn(m, i, o):visualisation[m] o def get_all_layers(net):for name, layer in net._modules.items():#If it is a sequential, dont register a hook on it# but recursively register hook on all its module childrenif isinstance(layer, nn.Sequential):get_all_layers(layer)else:# its a non sequential. Register a hooklayer.register_forward_hook(hook_fn)get_all_layers(net)out net(torch.randn(1,3,8,8))# Just to check whether we got all layers
print(visualisation.keys()) #output includes sequential layers
print(visualisation)输出为
dict_keys([Conv2d(3, 10, kernel_size(2, 2), stride(2, 2)), ReLU(), Linear(in_features160, out_features5, biasTrue), Linear(in_features5, out_features3, biasTrue), Linear(in_features3, out_features2, biasTrue)]){Conv2d(3, 10, kernel_size(2, 2), stride(2, 2)): tensor([[[[ 0.8381, 0.3751, 0.0268, -0.1155],[-0.2221, 1.1316, 1.1800, -0.1370],[ 1.1750, -0.6800, -0.1855, 0.3174],[-0.3929, 0.1941, 0.8611, -0.4447]],[[ 0.2377, 0.5215, 1.2715, -0.1600],[-0.7852, -0.2954, -0.0898, 0.0045],[-0.6077, -0.0088, -0.0572, -0.4161],[-0.6604, 0.7242, -0.7878, 0.0525]],[[-0.7283, -0.2644, -1.0609, 0.4960],[ 0.7989, -1.2582, -0.4996, 0.4377],[ 0.0798, 1.3804, -0.2886, -0.1540],[ 1.4034, -0.6836, -0.0658, 0.5268]],[[-0.6073, -0.3875, -0.3015, 0.7174],[-1.2842, 0.7734, -0.6014, 0.4114],[-0.3582, -1.4564, -0.6590, -1.0223],[-0.7667, 0.6816, 0.0602, -0.2622]],[[-0.6175, -0.3179, -1.2208, -0.8645],[ 1.1918, -0.3578, -0.7223, -1.1834],[ 0.1654, -0.1522, 0.0066, 0.0934],[ 0.7423, -0.7827, 0.2465, 0.4299]],...[0.5625, 0.4753, 0.0000, 0.0000],[0.6904, 0.1533, 0.6416, 0.0000]]]], grad_fnReluBackward0),Linear(in_features160, out_features5, biasTrue): tensor([-0.0816, -0.1588, -0.0201, -0.4695, 0.2911], grad_fnAddBackward0),Linear(in_features5, out_features3, biasTrue): tensor([-0.3199, 0.0220, -0.3564], grad_fnAddBackward0),Linear(in_features3, out_features2, biasTrue): tensor([ 0.5371, -0.5260], grad_fnAddBackward0)}下面通过一个例子来展示forward hook以及对hook出的activation进行可视化。
import torch
from torchvision.models import resnet34
from PIL import Image
from torchvision import transforms as T
import matplotlib.pyplot as pltdevice torch.device(cuda) if torch.cuda.is_available() else torch.device(cpu)model resnet34(pretrainedTrue)
model model.to(device)# 定义hook
class SaveOutput:def __init__(self):self.outputs []def __call__(self, module, module_in, module_out):self.outputs.append(module_out)def clear(self):self.outputs []# 对Conv2d注册hook
save_output SaveOutput()
hook_handles []
for layer in model.modules():if isinstance(layer, torch.nn.modules.conv.Conv2d):handle layer.register_forward_hook(save_output)hook_handles.append(handle)image Image.open(cat.jpg)
transform T.Compose([T.Resize((224, 224)), T.ToTensor()])
X transform(image).unsqueeze(dim0).to(device)out model(X)print(len(save_output.outputs)) # 输出应该是36def module_output_to_numpy(tensor):return tensor.detach().to(cpu).numpy() images module_output_to_numpy(save_output.outputs[0])with plt.style.context(seaborn-white):plt.figure(figsize(20, 20), frameonFalse)for idx in range(64): # 这里根据输出通道数不止可以索引到64可以通过打印images的channels来查看最大的输出通道数plt.subplot(8, 8, idx1)plt.imshow(images[0, idx])plt.setp(plt.gcf().get_axes(), xticks[], yticks[]);matplotlib画出第一层的activation为 我们修改代码如下来查看比较靠后层的activation
images module_output_to_numpy(save_output.outputs[30]) # 将此处的索引改为30查看第30层的activationwith plt.style.context(seaborn-white):plt.figure(figsize(20, 20), frameonFalse)for idx in range(64): # 这里根据输出通道数不止可以索引到64可以通过打印images的channels来查看最大的输出通道数plt.subplot(8, 8, idx1)plt.imshow(images[0, idx])plt.setp(plt.gcf().get_axes(), xticks[], yticks[]);我们同样查看中间层例如第15层的activation。 可以看到随着网络层的加深activation越来越抽象。
除了上述的对forward加hook查看activation、对backward加hook、对Tensors加hook进行梯度相关的操作外还可以参考kaggle的文章进行一些更深层次的理解比如对backward过程的详细解释以及配合backward hook使用GRAD-CAM来查看网络等方法。
