FastAI v1: Extracting Feature vectors for test images from Last Layer Resnet50 (looking for feedback)

This post is to get feedback to ascertain that the steps I have taken to extract activations are correct.

Problem Statement - To extract activations for the test images passed into a learner object trained using resnet50.
FastAI Version - 1.0.39
Here are the codes -

from fastai.metrics import fbeta, accuracy_thresh 
from import create_cnn, models 
from import ImageItemList, imagenet_stats
from fastai.widgets import DatasetFormatter
from fastai.callbacks.hooks import hook_output

# get_data function contains ImageItemList object 
data = get_data('Classifier_df.csv',root_path,train_folder='train',

#Defining the learner object
learn = create_cnn(data, models.resnet50, metrics=[acc_02,fbeta_score])
# loading the object
# Indexing the last layer of CNN of resnet50
layer_ls=[0, 7, 2]
hook = hook_output(learn.model[layer_ls[0]][layer_ls[1]][layer_ls[2]])
test_activations = DatasetFormatter.get_actns(learn,hook=hook, 
                                              dl = data.test_dl, pool_dim=8)

Output would look something like-
test_activations = tensor([[1.4966, 1.4966, 0.6284,  ..., 0.2086, 0.0875, 0.0611],
            [0.4432, 0.6602, 0.6602,  ..., 0.0588, 0.1247, 0.1628]])

The output is 512*512(262,144) long row vector for each image.

I m not sure whether I m doing it correctly.
Here are my concerns -

  1. Are these indexes correct for resnet50 last CNN layer, layer_ls=[0, 7, 2]?
  2. Vector size is 262,144 for every image. I m also unsure about this one?

Any help, suggestions highly appreciated!!