VGG16_Places_365 and VGG16_Hybrid_Places_1365 CNN models with theano backend
ephi...@yahoo.com
. I am testing version of a python script (see below) that I found at https://github.com/GKalliatakis/Keras-VGG16-places365 to predict the VGG places 365 CNN classes and probabilities for several test images.
> 1) I found that the image variable "x" has dimension 4 and shape (1L, 224L, 224L, 3L).
> 2) I found that the "preds" variable has dimension 1 and shape (365L,) so that np.sort(preds)[::-1][0:5] looked something like [ 0.03043453 0.02591384 0.02299733 0.01979745 0.01885794].
> *** Are these the predicted probabilities? if so, they are quite small. please advise.
> 3) I also found that "top_preds" variable has dimension 1 and shape (5L,) so that np.argsort(preds)[::-1][0:5] looked something like [236 99 21 317 77].
> *** These appear to be the top-5 predicted classes, wherein the SCENE CATEGORIES are: museum/indoor; coffee_shop; art_studio; staircase; and campus.
> HOWEVER, my image was image_path='tank_desert' and when I change the test image to 'tank_forest' I get similar classes, i.e., [99 236 21 77 362].
> *** I suspect this test script for the VGG16 Places 365 model is hardwired for the jpg image 'restaurant.jpg' as shown in the example on the github web page for the Places 365 CNN .
> *** Note that when I test the attached 'restaurant.jpg' the top-5 classes are [99, 236, 21, 77, 193] corresponding to the categories coffee_shop, museum/indoor, art_studio, campus, inn/outdoor.
> .
> Is there a way to change a configuration file and/or a part of a .py code to allow for new and different test images?
> Any comments or suggestions that you may offer would be helpful.
>
> Best Regards,
> Arnold
>
> PS: I downloaded the categories list locally to my notebook computer because I was having trouble accessing the file online as the code ran.
> .
> PSS: to follow up... I modified the script for VGG places 365 CNN to test the VGG16_hybrid_places_1365 CNN (see further below) and found that now the variable "x" has dimension 4 and shape 4 (1L, 224L, 224L, 3L). I also found that "preds" have dimension 3 and shape (7L, 7L, 512L) and that "top_preds" have shape (5, 7L, 512L) so that I get a very large array of top-5 class predictions.
>
> ***Why are the prediction arrays of a different shape in this case? I expected "preds" to have shape (1365L) and top_preds again to have shape (5L).
> Again, Any comments or suggestions that you may offer would be helpful.
> Best,
> Arnold
> ====================================================================
> #script for VGG places 365 CNN
> import keras
> import numpy as np
> import os
> from VGG16_places_365 import VGG16_Places365 from keras.preprocessing
> import image from places_utils import preprocess_input model =
> VGG16_Places365(weights='places')
>
> img_path = r'C:\Users\atunick\VGG16_hybrid_places_1365\tank_desert.jpg'
> #img_path = 'tank_desert.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) print x.ndim, x.shape, 'x'
> predictions_to_return = 5
> preds = model.predict(x)[0]
> print 'preds', preds.ndim, preds.shape, np.sort(preds)[::-1][0:5]
> top_preds = np.argsort(preds)[::-1][0:predictions_to_return]
> print 'top_preds', top_preds.ndim, top_preds.shape, top_preds # load
> the class label file_name =
> r'C:\Users\atunick\VGG16_hybrid_places_1365\categories_places365.txt'
> #if not os.access(file_name, os.W_OK):
> # synset_url = 'Caution-https://raw.githubusercontent.com/CSAILVision/places365/master/categories_places365.txt'
> # os.system('wget ' + synset_url)
> classes = list()
> with open(file_name) as class_file:
> for line in class_file:
> classes.append(line.strip().split(' ')[0][3:]) classes =
> tuple(classes) #print classes print('--SCENE CATEGORIES:') # output
> the prediction for i in range(0, 5):
> #print top_preds[i]
> print(classes[top_preds[i]])
> print 'completed'
>
>
> =============================================
> #script to test the VGG16_hybrid_places_1365 CNN
> import keras
> import numpy as np
> import os
> from VGG16_hybrid_places_1365 import VGG16_Hubrid_1365
> from keras.preprocessing import image
> from places_utils import preprocess_input
> model = VGG16_Hubrid_1365(weights='places', include_top=False)
>
> img_path = r'C:\Users\atunick\VGG16_hybrid_places_1365\tank_desert.jpg'
> #img_path = 'tank_desert.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)
> print x.ndim, x.shape
> predictions_to_return = 5
> preds = model.predict(x)[0]
> print 'preds', preds.ndim, preds.shape, np.sort(preds)[::-1][0:5]
> top_preds = np.argsort(preds)[::-1][0:predictions_to_return]
> print'top_preds', top_preds.ndim, top_preds.shape, top_preds
> print ' '
> # load the class label
> file_name = r'C:\Users\atunick\VGG16_hybrid_places_1365\categories_hybrid1365.txt'
> #if not os.access(file_name, os.W_OK):
> # synset_url = 'Caution-https://raw.githubusercontent.com/CSAILVision/places365/master/categories_hybrid1365.txt'
> # os.system('wget ' + synset_url)
> classes = list()
> with open(file_name) as class_file:
> for line in class_file:
> classes.append(line.strip().split(' ')[0][3:])
> classes = tuple(classes)
> print('--SCENE CATEGORIES:')
> # output the prediction
> for i in range(0, 5):
> print(classes[top_preds[i]])
> print 'completed'