I’m pretty new to using PyTorch and trying to learn about extracting feature vectors. I have been able to piece together some code that will extract a feature vector from a single image. See below.
import torch
import torch.nn as nn
import torchvision
import torchvision.models as models
import torchvision.transforms as transforms
from torch.autograd import Variable
from PIL import Image
img=Image.open("Documents/01235.png")
# Load the pretrained model
model = models.resnet18(pretrained=True)
# Use the model object to select the desired layer
layer = model._modules.get('avgpool')
# Set model to evaluation mode
model.eval()
transformations = torchvision.transforms.Compose([
torchvision.transforms.Resize(256),
torchvision.transforms.CenterCrop(224),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
def get_vector(image):
# Create a PyTorch tensor with the transformed image
t_img = transformations(image)
# Create a vector of zeros that will hold our feature vector
# The 'avgpool' layer has an output size of 512
my_embedding = torch.zeros(512)
# Define a function that will copy the output of a layer
def copy_data(m, i, o):
my_embedding.copy_(o.flatten()) # <-- flatten
# Attach that function to our selected layer
h = layer.register_forward_hook(copy_data)
# Run the model on our transformed image
with torch.no_grad(): # <-- no_grad context
model(t_img.unsqueeze(0)) # <-- unsqueeze
# Detach our copy function from the layer
h.remove()
# Return the feature vector
return my_embedding
pic_vector = get_vector(img)
pic_vector.size()
What I’m curious about is if there is some documentation somewhere that would talk more about the various layers we are able to extract these vectors from. For example, if I wanted to switch to the Resnet 152 model, how could I learn what all the available layers are I could extract from/what the syntax is for each layer? I’ve been looking around for some more documentation on the “_modules.get” piece and I can’t seem to find what I’m looking for.