test_inference.py

"""Test ImageNet pretrained DenseNet"""

import cv2
import numpy as np
from keras.optimizers import SGD
import keras.backend as K

# We only test DenseNet-121 in this script for demo purpose
from densenet121 import DenseNet 

im = cv2.resize(cv2.imread('resources/cat.jpg'), (224, 224)).astype(np.float32)
#im = cv2.resize(cv2.imread('shark.jpg'), (224, 224)).astype(np.float32)

# Subtract mean pixel and multiple by scaling constant 
# Reference: https://github.com/shicai/DenseNet-Caffe
im[:,:,0] = (im[:,:,0] - 103.94) * 0.017
im[:,:,1] = (im[:,:,1] - 116.78) * 0.017
im[:,:,2] = (im[:,:,2] - 123.68) * 0.017

if K.image_dim_ordering() == 'th':
  # Transpose image dimensions (Theano uses the channels as the 1st dimension)
  im = im.transpose((2,0,1))

  # Use pre-trained weights for Theano backend
  weights_path = 'imagenet_models/densenet121_weights_th.h5'
else:
  # Use pre-trained weights for Tensorflow backend
  weights_path = 'imagenet_models/densenet121_weights_tf.h5'

# Insert a new dimension for the batch_size
im = np.expand_dims(im, axis=0)

# Test pretrained model
model = DenseNet(reduction=0.5, classes=1000, weights_path=weights_path)

sgd = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])

out = model.predict(im)

# Load ImageNet classes file
classes = []
with open('resources/classes.txt', 'r') as list_:
    for line in list_:
        classes.append(line.rstrip('\n'))

print 'Prediction: '+str(classes[np.argmax(out)])