vgg16_hybrid_places_1365.py

# -*- coding: utf-8 -*-
'''VGG-ImageNetPlaces365-hybrid model for Keras

1000 classes from the ImageNet and the 365 classes from
the Places365-Standard were merged to train a VGG16-based model (Hybrid1365-VGG)

# Reference:
- [Places: A 10 million Image Database for Scene Recognition](http://places2.csail.mit.edu/PAMI_places.pdf)
'''

from __future__ import division, print_function
import os

import warnings

from keras import backend as K
from keras.layers import Input
from keras.layers.core import Activation, Dense, Flatten
from keras.layers.pooling import MaxPooling2D
from keras.models import Model
from keras.layers import Conv2D
from keras.regularizers import l2
from keras.layers.core import Dropout
from keras.layers import GlobalAveragePooling2D
from keras.layers import GlobalMaxPooling2D
from keras.applications.imagenet_utils import _obtain_input_shape
from keras.engine.topology import get_source_inputs
from keras.utils.data_utils import get_file
from keras.utils import layer_utils


WEIGHTS_PATH = 'https://github.com/GKalliatakis/Keras-Application-Zoo/releases/download/0.1/vgg16-hybrid1365_weights_tf_dim_ordering_tf_kernels.h5'
WEIGHTS_PATH_NO_TOP = 'https://github.com/GKalliatakis/Keras-Application-Zoo/releases/download/0.1/vgg16-hybrid1365_weights_tf_dim_ordering_tf_kernels_notop.h5'

def VGG16_Hubrid_1365(include_top=True, weights='places',
                      input_tensor=None, input_shape=None,
                      pooling=None,
                      classes=1365):
    """Instantiates the VGG-ImageNetPlaces365-hybrid architecture.

    Optionally loads weights pre-trained
    on Places. Note that when using TensorFlow,
    for best performance you should set
    `image_data_format="channels_last"` in your Keras config
    at ~/.keras/keras.json.

    The model and the weights are compatible with both
    TensorFlow and Theano. The data format
    convention used by the model is the one
    specified in your Keras config file.

    # Arguments
        include_top: whether to include the 3 fully-connected
            layers at the top of the network.
        weights: one of `None` (random initialization),
                 'places' (pre-training on Places),
                 or the path to the weights file to be loaded.
        input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
            to use as image input for the model.
        input_shape: optional shape tuple, only to be specified
            if `include_top` is False (otherwise the input shape
            has to be `(224, 224, 3)` (with `channels_last` data format)
            or `(3, 224, 244)` (with `channels_first` data format).
            It should have exactly 3 inputs channels,
            and width and height should be no smaller than 48.
            E.g. `(200, 200, 3)` would be one valid value.
        pooling: Optional pooling mode for feature extraction
            when `include_top` is `False`.
            - `None` means that the output of the model will be
                the 4D tensor output of the
                last convolutional layer.
            - `avg` means that global average pooling
                will be applied to the output of the
                last convolutional layer, and thus
                the output of the model will be a 2D tensor.
            - `max` means that global max pooling will
                be applied.
        classes: optional number of classes to classify images
            into, only to be specified if `include_top` is True, and
            if no `weights` argument is specified.
    # Returns
        A Keras model instance.
    # Raises
        ValueError: in case of invalid argument for `weights`, or invalid input shape
        """

    if not (weights in {'places', None} or os.path.exists(weights)):
        raise ValueError('The `weights` argument should be either '
                         '`None` (random initialization), `places` '
                         '(pre-training on Places), '
                         'or the path to the weights file to be loaded.')

    if weights == 'places' and include_top and classes != 1365:
        raise ValueError('If using `weights` as places with `include_top`'
                         ' as true, `classes` should be 1365')


    # Determine proper input shape
    input_shape = _obtain_input_shape(input_shape,
                                      default_size=224,
                                      min_size=48,
                                      data_format=K.image_data_format(),
                                      require_flatten=include_top)


    if input_tensor is None:
        img_input = Input(shape=input_shape)
    else:
        if not K.is_keras_tensor(input_tensor):
            img_input = Input(tensor=input_tensor, shape=input_shape)
        else:
            img_input = input_tensor



    # Block 1
    x = Conv2D(filters=64, kernel_size=3, strides=(1, 1), padding='same',
                     kernel_regularizer=l2(0.0002),
                     activation='relu', name='block1_conv1')(img_input)

    x = Conv2D(filters=64, kernel_size=3, strides=(1, 1), padding='same',
                     kernel_regularizer=l2(0.0002),
                     activation='relu', name='block1_conv2')(x)

    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block1_pool", padding='valid')(x)



    # Block 2
    x = Conv2D(filters=128, kernel_size=3, strides=(1, 1), padding='same',
                     kernel_regularizer=l2(0.0002),
                     activation='relu', name='block2_conv1')(x)

    x = Conv2D(filters=128, kernel_size=3, strides=(1, 1), padding='same',
                     kernel_regularizer=l2(0.0002),
                     activation='relu', name='block2_conv2')(x)

    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block2_pool", padding='valid')(x)



    # Block 3
    x = Conv2D(filters=256, kernel_size=3, strides=(1, 1), padding='same',
                     kernel_regularizer=l2(0.0002),
                     activation='relu', name='block3_conv1')(x)

    x = Conv2D(filters=256, kernel_size=3, strides=(1, 1), padding='same',
                     kernel_regularizer=l2(0.0002),
                     activation='relu', name='block3_conv2')(x)

    x = Conv2D(filters=256, kernel_size=3, strides=(1, 1), padding='same',
                     kernel_regularizer=l2(0.0002),
                     activation='relu', name='block3_conv3')(x)

    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block3_pool", padding='valid')(x)



    # Block 4
    x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
                     kernel_regularizer=l2(0.0002),
                     activation='relu', name='block4_conv1')(x)

    x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
                     kernel_regularizer=l2(0.0002),
                     activation='relu', name='block4_conv2')(x)

    x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
                     kernel_regularizer=l2(0.0002),
                     activation='relu', name='block4_conv3')(x)

    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block4_pool", padding='valid')(x)



    # Block 5
    x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
                     kernel_regularizer=l2(0.0002),
                     activation='relu', name='block5_conv1')(x)

    x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
                     kernel_regularizer=l2(0.0002),
                     activation='relu', name='block5_conv2')(x)

    x = Conv2D(filters=512, kernel_size=3, strides=(1, 1), padding='same',
                     kernel_regularizer=l2(0.0002),
                     activation='relu', name='block5_conv3')(x)

    x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), name="block5_pool", padding='valid')(x)



    if include_top:
        # Classification block
        x = Flatten(name='flatten')(x)
        x = Dense(4096, activation='relu', name='fc1')(x)
        x = Dropout(0.5, name='drop_fc1')(x)

        x = Dense(4096, activation='relu', name='fc2')(x)
        x = Dropout(0.5, name='drop_fc2')(x)

        x = Dense(1365, activation='softmax', name='predictions')(x)

    else:
        if pooling == 'avg':
            x = GlobalAveragePooling2D()(x)
        elif pooling == 'max':
            x = GlobalMaxPooling2D()(x)

    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = get_source_inputs(input_tensor)
    else:
        inputs = img_input


    # Create model.
    model = Model(inputs, x, name='vgg16-hybrid1365')

    # load weights
    if weights == 'places':
        if include_top:
            weights_path = get_file('vgg16-hybrid1365_weights_tf_dim_ordering_tf_kernels.h5',
                                    WEIGHTS_PATH,
                                    cache_subdir='models')
        else:
            weights_path = get_file('vgg16-hybrid1365_weights_tf_dim_ordering_tf_kernels_notop.h5',
                                    WEIGHTS_PATH_NO_TOP,
                                    cache_subdir='models')

        model.load_weights(weights_path)

        if K.backend() == 'theano':
            layer_utils.convert_all_kernels_in_model(model)

        if K.image_data_format() == 'channels_first':
            if include_top:
                maxpool = model.get_layer(name='block5_pool')
                shape = maxpool.output_shape[1:]
                dense = model.get_layer(name='fc1')
                layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')

            if K.backend() == 'tensorflow':
                warnings.warn('You are using the TensorFlow backend, yet you '
                              'are using the Theano '
                              'image data format convention '
                              '(`image_data_format="channels_first"`). '
                              'For best performance, set '
                              '`image_data_format="channels_last"` in '
                              'your Keras config '
                              'at ~/.keras/keras.json.')


    elif weights is not None:
        model.load_weights(weights)



    return model


if __name__ == '__main__':
    import urllib2
    import numpy as np
    from PIL import Image
    from cv2 import resize

    TEST_IMAGE_URL = 'http://places2.csail.mit.edu/imgs/demo/6.jpg'

    image = Image.open(urllib2.urlopen(TEST_IMAGE_URL))
    image = np.array(image, dtype=np.uint8)
    image = resize(image, (224, 224))
    image = np.expand_dims(image, 0)

    model = VGG16_Hubrid_1365(weights='places')
    predictions_to_return = 5
    preds = model.predict(image)[0]
    top_preds = np.argsort(preds)[::-1][0:predictions_to_return]

    # load the class label
    file_name = 'categories_hybrid1365.txt'
    if not os.access(file_name, os.W_OK):
        synset_url = 'https://raw.githubusercontent.com/csailvision/places365/master/categories_hybrid1365.txt'
        os.system('wget ' + synset_url)
    classes = list()
    counter = 0
    with open(file_name) as class_file:
        for line in class_file:
            if counter <=999:
                tmp = line[9:]

                if 0 <= counter <= 9:
                    tmp = tmp[:-2]
                elif 10 <= counter <= 99:
                    tmp = tmp[:-3]
                elif 100 <= counter <= 999:
                    tmp = tmp[:-4]

                classes.append(tmp)

            else:
                classes.append(line.strip().split(' ')[0][3:])

            counter +=1
    classes = tuple(classes)


    print('--SCENE CATEGORIES:')
    # output the prediction
    for i in range(0, 5):
        print(classes[top_preds[i]])


    # --PREDICTED SCENE CATEGORIES:
    # restaurant, eating
    # house, eating
    # place, eatery
    # folding
    # chair
    # patio, terrace
    # food_court
    # cafeteria