#!/usr/bin/env python3
"""Implement several encoder classes"""
from keras.applications.resnet50 import ResNet50, preprocess_input as preprocess_resnet, decode_predictions
from keras.applications.inception_v3 import InceptionV3, preprocess_input as preprocess_inception
from keras.applications.vgg16 import VGG16, preprocess_input as preprocess_vgg
from keras.preprocessing import image
from keras import backend as K
from keras.models import Model
from keras.layers import GlobalAveragePooling2D, Concatenate
import numpy as np
class ResNet:
__name__ = "ResNet"
def __init__(self, batch_size=32):
self.model = ResNet50(include_top=False, weights='imagenet', pooling="avg")
self.batch_size = batch_size
self.data_format = K.image_data_format()
def predict(self, x):
if self.data_format == "channels_first":
x = x.transpose(0, 3, 1, 2)
x = preprocess_resnet(x.astype(K.floatx()))
return self.model.predict(x, batch_size=self.batch_size)
class Inception:
__name__ = "Inception"
def __init__(self, batch_size=32):
self.model = InceptionV3(include_top=False, weights="imagenet", pooling="avg")
self.batch_size = batch_size
self.data_format = K.image_data_format()
def predict(self, x):
if self.data_format == "channels_first":
x = x.transpose(0, 3, 1, 2)
x = preprocess_inception(x.astype(K.floatx()))
return self.model.predict(x, batch_size=self.batch_size)
class VGG:
__name__ = "VGG"
def __init__(self, batch_size=32):
model = VGG16(include_top=False, weights="imagenet", pooling="avg")
x2 = GlobalAveragePooling2D()(model.get_layer("block2_conv2").output) # 128
x3 = GlobalAveragePooling2D()(model.get_layer("block3_conv3").output) # 256
x4 = GlobalAveragePooling2D()(model.get_layer("block4_conv3").output) # 512
x5 = GlobalAveragePooling2D()(model.get_layer("block5_conv3").output) # 512
x = Concatenate()([x2, x3, x4, x5])
self.model = Model(inputs=model.input, outputs=x)
self.batch_size = batch_size
self.data_format = K.image_data_format()
def predict(self, x):
if self.data_format == "channels_first":
x = x.transpose(0, 3, 1, 2)
x = preprocess_vgg(x.astype(K.floatx()))
return self.model.predict(x, batch_size=self.batch_size)
if __name__ == "__main__":
img_path = "data/elephant.jpg"
img = image.load_img(img_path, target_size=(299, 299))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_inception(x)
model = InceptionV3()
preds = model.predict(x)
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print("Predicted:", decode_predictions(preds, top=3)[0])
# Predicted: [('n02504013', 'Indian_elephant', 0.78864819), ('n01871265', 'tusker', 0.029346621), ('n02504458', 'African_elephant', 0.01768155)]
