Resnet50_counting_keras.py

from __future__ import print_function
from __future__ import absolute_import

import warnings
import keras
import keras.backend as K
from keras.layers import Input
from keras import layers
from keras.layers import Dense
from keras.layers import Activation
from keras.layers import Flatten
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import ZeroPadding2D
from keras.layers import AveragePooling2D
from keras.layers import GlobalAveragePooling2D
from keras.layers import GlobalMaxPooling2D
from keras.layers import BatchNormalization
from keras.models import Model
from keras import regularizers
from keras import backend as K
from keras.callbacks import EarlyStopping
from keras.optimizers import Adam
from keras.engine.topology import get_source_inputs
from keras.utils import layer_utils
from keras.utils.data_utils import get_file
from keras.applications.imagenet_utils import decode_predictions
from keras.applications.imagenet_utils import preprocess_input
from keras.applications.imagenet_utils import _obtain_input_shape


WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5'


def identity_block(input_tensor, kernel_size, filters, stage, block):
    filters1, filters2, filters3 = filters
    if K.image_data_format() == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters2, kernel_size,
               padding='same', name=conv_name_base + '2b')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)

    x = layers.add([x, input_tensor])
    x = Activation('relu')(x)
    return x


def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
    # A block that has a conv layer at the skip connection shortcut.
    
    filters1, filters2, filters3 = filters
    if K.image_data_format() == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    x = Conv2D(filters1, (1, 1), strides=strides,
               name=conv_name_base + '2a')(input_tensor)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters2, kernel_size, padding='same',
               name=conv_name_base + '2b')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
    x = Activation('relu')(x)

    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)

    shortcut = Conv2D(filters3, (1, 1), strides=strides,
                      name=conv_name_base + '1')(input_tensor)
    shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)

    x = layers.add([x, shortcut])
    x = Activation('relu')(x)
    # Returns Output tensor for the block.
    return x


def ResNet50(include_top=False, weights='imagenet',
             input_tensor=None, input_shape=None,
             pooling=None):
    #Instantiates the ResNet50 architecture.
   
    if weights not in {'imagenet', None}:
        raise ValueError('The `weights` argument should be either '
                         '`None` (random initialization) or `imagenet` '
                         '(pre-training on ImageNet).')

    # Determine proper input shape
    input_shape = _obtain_input_shape(input_shape,
                                      default_size=224,
                                      min_size=197,
                                      data_format=K.image_data_format(),
                                      include_top=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
    if K.image_data_format() == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1

    x = ZeroPadding2D((3, 3))(img_input)
    x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
    x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
    x = Activation('relu')(x)
    x = MaxPooling2D((3, 3), strides=(2, 2))(x)

    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
    x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')

    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
    x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')

    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
    x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')

    x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
    x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')

    x = AveragePooling2D((7, 7), name='avg_pool')(x)

    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='resnet50')

    # load weights
    if weights == 'imagenet':
        weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
                                WEIGHTS_PATH_NO_TOP,
                                cache_subdir='models',
                                md5_hash='a268eb855778b3df3c7506639542a6af')
        model.load_weights(weights_path)
        if K.backend() == 'theano':
            layer_utils.convert_all_kernels_in_model(model)
            if include_top:
                maxpool = model.get_layer(name='avg_pool')
                shape = maxpool.output_shape[1:]
                dense = model.get_layer(name='fc1000')
                layer_utils.convert_dense_weights_data_format(dense, shape, 'channels_first')

        if K.image_data_format() == 'channels_first' and 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.')
    return model


# Counter model with NO data augmentation.
def counter_model(x_train_all, x_val_all, y_train_all, y_val_all):
    

    res_model = ResNet50(weights='imagenet', include_top=False, input_shape=(320,320, 3))
    model = res_model.output
    model = Flatten(name='flatten')(model)
    model = Dense(1024, activation='relu')(model)
    model = Dense(512, activation='relu', activity_regularizer=regularizers.l2(0.2))(model)
    leaf_pred = Dense(1)(model)

    epoch = 50
    csv_logger = keras.callbacks.CSVLogger('training.log', separator=',')
    early_stop = EarlyStopping(monitor='val_loss', min_delta=0.03, mode='min', patience=8)

    model = Model(inputs = res_model.input, outputs = leaf_pred)
    model.compile(optimizer=Adam(lr=0.0001), loss= 'mse')
    fitted_model = model.fit(x_train_all, y_train_all, epochs=epoch, validation_data=(x_val_all, y_val_all), batch_size=16, callbacks= [csv_logger])
    

    return model

# Counter model with data augmentation. Images are rotated, width, height shifts, and veritical + horizontal flips.
def counter_model_augmentation(x_train_all, x_val_all, y_train_all, y_val_all):
    
    x_aug = ImageDataGenerator(
        rotation_range=180,
        width_shift_range=0.1,
        height_shift_range=0.1,
        horizontal_flip=True,
        vertical_flip=True,
        )

    x_aug.fit(x_train_all)


    res_model = ResNet50(weights='imagenet', include_top=False, input_shape=(320,320, 3))
    model = res_model.output
    model = Flatten(name='flatten')(model)
    model = Dense(1024, activation='relu')(model)
    model = Dense(512, activation='relu', activity_regularizer=regularizers.l2(0.2))(model)
    leaf_pred = Dense(1)(model)

    epoch = 50
    csv_logger = keras.callbacks.CSVLogger('training.log', separator=',')
    early_stop = EarlyStopping(monitor='val_loss', min_delta=0.03, mode='min', patience=8)

    model = Model(inputs = res_model.input, outputs = leaf_pred)

    model.compile(optimizer=Adam(lr=0.0001), loss= 'mse')
    fitted_model= model.fit_generator(x_aug.flow(x_train_all, y_train_all, batch_size=6), steps_per_epoch=len(x_train_all)*2,
                                                 epochs=epoch, validation_data=(x_val_all, y_val_all), callbacks= [csv_logger, early_stop])


    return model
    
    '''
    For using Resnet model with data augmentation use counter_model_augmentation(x_train_all,x_val_all,y_train_all,y_val_all)
    For using Resnet model with NO data augmentation use counter_model(x_train_all,x_val_all,y_train_all,y_val_all)

    You can take out the validation data if you don't use it.

    '''