Extract features from pre-trained model

[dineshbvadhia]

To extract features from an arbitrary layer with say, VGG19, given an input image "elephant.jpg", the code in keras+TF is simply:

from keras.applications.vgg19 import VGG19
from keras.preprocessing import image
from keras.applications.vgg19 import preprocess_input
from keras.models import Model
import numpy as np

base_model = VGG19(weights='imagenet')
model = Model(input=base_model.input, output=base_model.get_layer('block4_pool').output)

img_path = 'elephant.jpg'

img = image.load_img(img_path, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

block4_pool_features = model.predict(x)

This can be turned into a function to support multiple calls. How is this achieved in PyTorch?

[ATSEGYPT]

How to get the indices of the extracted "block4_pool_features"?