vgg16bn.py

from __future__ import division, print_function

import os, json
from glob import glob
import numpy as np
from scipy import misc, ndimage
from scipy.ndimage.interpolation import zoom

from keras.utils.data_utils import get_file
from keras import backend as K
from keras.layers.normalization import BatchNormalization
from keras.utils.data_utils import get_file
from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout, Lambda
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.layers.pooling import GlobalAveragePooling2D
from keras.optimizers import SGD, RMSprop, Adam
from keras.preprocessing import image


vgg_mean = np.array([123.68, 116.779, 103.939], dtype=np.float32).reshape((3,1,1))
def vgg_preprocess(x):
    x = x - vgg_mean
    return x[:, ::-1] # reverse axis rgb->bgr


class Vgg16BN():
    """The VGG 16 Imagenet model with Batch Normalization for the Dense Layers"""


    def __init__(self, size=(224,224), include_top=True):
        self.FILE_PATH = 'http://www.platform.ai/models/'
        self.create(size, include_top)
        self.get_classes()


    def get_classes(self):
        fname = 'imagenet_class_index.json'
        fpath = get_file(fname, self.FILE_PATH+fname, cache_subdir='models')
        with open(fpath) as f:
            class_dict = json.load(f)
        self.classes = [class_dict[str(i)][1] for i in range(len(class_dict))]

    def predict(self, imgs, details=False):
        all_preds = self.model.predict(imgs)
        idxs = np.argmax(all_preds, axis=1)
        preds = [all_preds[i, idxs[i]] for i in range(len(idxs))]
        classes = [self.classes[idx] for idx in idxs]
        return np.array(preds), idxs, classes


    def ConvBlock(self, layers, filters):
        model = self.model
        for i in range(layers):
            model.add(ZeroPadding2D((1, 1)))
            model.add(Convolution2D(filters, 3, 3, activation='relu'))
        model.add(MaxPooling2D((2, 2), strides=(2, 2)))


    def FCBlock(self):
        model = self.model
        model.add(Dense(4096, activation='relu'))
        model.add(BatchNormalization())
        model.add(Dropout(0.5))


    def create(self, size, include_top):
        if size != (224,224):
            include_top=False

        model = self.model = Sequential()
        model.add(Lambda(vgg_preprocess, input_shape=(3,)+size))

        self.ConvBlock(2, 64)
        self.ConvBlock(2, 128)
        self.ConvBlock(3, 256)
        self.ConvBlock(3, 512)
        self.ConvBlock(3, 512)

        if not include_top:
            fname = 'vgg16_bn_conv.h5'
            model.load_weights(get_file(fname, self.FILE_PATH+fname, cache_subdir='models'))
            return

        model.add(Flatten())
        self.FCBlock()
        self.FCBlock()
        model.add(Dense(1000, activation='softmax'))

        fname = 'vgg16_bn.h5'
        model.load_weights(get_file(fname, self.FILE_PATH+fname, cache_subdir='models'))


    def get_batches(self, path, gen=image.ImageDataGenerator(), shuffle=True, batch_size=8, class_mode='categorical'):
        return gen.flow_from_directory(path, target_size=(224,224),
                class_mode=class_mode, shuffle=shuffle, batch_size=batch_size)


    def ft(self, num):
        model = self.model
        model.pop()
        for layer in model.layers: layer.trainable=False
        model.add(Dense(num, activation='softmax'))
        self.compile()

    def finetune(self, batches):
        model = self.model
        model.pop()
        for layer in model.layers: layer.trainable=False
        model.add(Dense(batches.nb_class, activation='softmax'))
        self.compile()


    def compile(self, lr=0.001):
        self.model.compile(optimizer=Adam(lr=lr),
                loss='categorical_crossentropy', metrics=['accuracy'])


    def fit_data(self, trn, labels,  val, val_labels,  nb_epoch=1, batch_size=64):
        self.model.fit(trn, labels, nb_epoch=nb_epoch,
                validation_data=(val, val_labels), batch_size=batch_size)


    def fit(self, batches, val_batches, nb_epoch=1):
        self.model.fit_generator(batches, samples_per_epoch=batches.nb_sample, nb_epoch=nb_epoch,
                validation_data=val_batches, nb_val_samples=val_batches.nb_sample)


    def test(self, path, batch_size=8):
        test_batches = self.get_batches(path, shuffle=False, batch_size=batch_size, class_mode=None)
        return test_batches, self.model.predict_generator(test_batches, test_batches.nb_sample)