What if the model was not build with nn.Sequential? How can I extract a part of it? For instance, I have derived a class from nn.Module and implemented its forward() method as
def forward(self, x):
x = F.conv2d(input=x, weight=self.conv1_w, bias=self.conv1_b, padding=self.conv1_pad)
x = F.max_pool2d(F.relu(x), 2)
x = F.conv2d(input=x, weight=self.conv2_w, bias=self.conv2_b, padding=self.conv2_pad)
x = F.max_pool2d(F.relu(x), 2)
x = x.view(x.size(0), -1) # Flatten but keep the same batch size
x = F.linear(input=x, weight=self.fc1_w, bias=self.fc1_b)
x = F.relu(x)
x = F.linear(input=x, weight=self.fc2_w, bias=self.fc2_b)
return x
Can I extract the model until the second convolutional layer, to see its output? That is, without doing copy-and-paste of the code.
I am not sure I understand why the FeatureExtractor function by @woaichipingguo_TSH works for Resnet network that has residual connections and not just a sequential list of convolutions. It is not always the case the output of current layer is the input to the next one because of the residual connections. How does it handle that?
Hi, could you kindly point out, where to put x.view() in the above FeatureExtractor to avoid the mismatch error and the dimensions?
I am still a beginner to pytorch and I am trying to learn to perform feature extraction using pretrained models.
I want to know that Net().forward(x) is called, where x is the image, and then what is returned is the feature result that we want to extract from the layer, and this result exists as a list?So what do we say if we extract the results of each layer separately?Could you please write out the detailed process for me to see?thanks
../../../data.imagenet is the path to the ImageNet dataset. When the application is invoked this way, passing the dataset path doesnāt make sense, but due to development priority it is currently mandatory - sorry about that.
In case anyone is still interested in this topic: In Distiller weāve implemented a couple of classes that record activation statistics when you forward input images in TorchVisionās image classifiers.
The code is here, and it is long - sorry about that: itās part of a larger library. The complexity is also because we wanted a solution that can work with all of TorchVisionās image classifier models (present, and hopefully, future).
We collect both detailed records of activation statistics (e.g. min, max, mean, std for each activation), or summary data (e.g. average L1-norm of activations). We also collect statistics over sub-structures of activations (e.g. average L1-norm of activation channels), but thatās a post for a different time.
We also dump stats to Excel worksheets.
Earlier in this thread, I posted a pointer to some code which extracts fully-qualified layer (module) names, to enable use to create a dictionary of {module_name: module_activation_stats}.
Hope that helps someone, even though this is not a self-standing example
my_embedding = torch.zeros(512)
def fun(m, i, o): my_embedding.copy_(o.data)
h = avgpool_layer.register_forward_hook(fun)
h_x = resnet_18(x)
h.remove()
RuntimeError: expand(torch.FloatTensor{[1, 512, 1, 1]}, size=[512]): the number of sizes provided (1) must be greater or equal to the number of dimensions in the tensor (4)
from torchvision import models
from torchsummary import summary
import torch.nn as nn #feature = models.alexnet().features
feature = models.resnet18()
all=[]
count=1
for name,module in feature._modules.items():
for n,m in module._modules.items():
for nb,mb in m._modules.items():
if(nb==āreluā and count<=3):
all.append(nb)
count+=1
print(all," : ",count)
Extract only Relu Layer Feature in ResNet18
feature = models.resnet18()
all=[]
count=1
for name,module in feature._modules.items():
for n,m in module._modules.items():
for nb,mb in m._modules.items():
if(nb==āreluā and count<=3):
all.append(nb)
count+=1
print(all," : ",count)
I have a similar question on feature extraction from the pretrained model. My model structure is given below,
Demo(nn.Module):
def init(self):
super(Demo,self).init()
self.conv1d = nn.Conv2d( 200, 128, 3, 1, 0, bias=False)
self.conv2d = nn.Conv2d( 128, 256, 3, 1, 0, bias=False)
self.conv3d = nn.Conv2d( 256, 512, 3, 1, 0, bias=False)
self.bn1d = nn.BatchNorm2d(128)
self.bn2d = nn.BatchNorm2d(256)
self.bn3d = nn.BatchNorm2d(512)
self.fc = nn.Linear(4608,1)
self.relud = nn.LeakyReLU(0.2, inplace=True)
def forward(self,x):
batch_size = x.size()[0]
x = self.relud(self.bn1d(self.conv1d(x)))
x = self.relud(self.bn2d(self.conv2d(x)))
x = self.relud(self.bn3d(self.conv3d(x)))
x = x.view(batch_size,-1)
x = self.fc(x)
return x
Demo model is used for training.
Feature_Net(nn.Module):
def init(self,pretrained):
super(Feature_Net,self).init()
self.pretrained = pretrained
for para in self.pretrained.parameters():
para.requires_grad =False
self.network = []
self.model_layer = list(self.pretrained.children())
for i in range(len(self.model_layer)):
self.network.append(nn.Sequential(*self.model_layer[i]))
self.max = nn.MaxPool(5)
self.max = nn.MaxPool(3)
def forward(self,x):
batch_size = x.size()[0]
x = self.network7
x = self.network7
f_1 = self.max1(x)
x = self.network7
f_2 = self.max2(x)
x = torch.cat((f_1,f_2),1)
return x
To avoid this error, what you should do is the following:
class FeatureExtractor(nn.Module):
def __init__(self, submodule, extracted_layers):
super(FeatureExtractor,self).__init__()
self.submodule = submodule
self.extracted_layers= extracted_layers
def forward(self, x):
outputs = []
for name, module in self.submodule._modules.items():
if name == "fc":
x = x.view(x.size(0), -1)
x = module(x)
if name in self.extracted_layers:
outputs.append(x)
return outputs
Exactly what Iām looking for when trying to get feature from certain intermediate layer.