"""Keras implementation of SSD.""" import keras.backend as K from keras.layers import Activation from keras.layers import AtrousConv2D from keras.layers.convolutional import Conv2D from keras.layers import Dense from keras.layers import Flatten from keras.layers import GlobalAveragePooling2D from keras.layers import Input from keras.layers import MaxPooling2D from keras.layers import Concatenate from keras.layers import Reshape from keras.layers import ZeroPadding2D from keras.models import Model from keras.layers import GaussianNoise from ssd_tools.ssd_layers import Normalize from ssd_tools.ssd_layers import PriorBox def SSD300(input_shape, num_classes=21): """SSD300 architecture. # Arguments input_shape: Shape of the input image, expected to be either (300, 300, 3) or (3, 300, 300)(not tested). num_classes: Number of classes including background. # References https://arxiv.org/abs/1512.02325 """ net = {} # Block 1 input_tensor = input_tensor = Input(shape=input_shape) img_size = (input_shape[1], input_shape[0]) net['input'] = input_tensor net['conv1_1'] = Conv2D(64,(3, 3), activation='relu', padding='same', name='conv1_1')(net['input']) net['conv1_2'] = Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_2')(net['conv1_1']) net['pool1'] = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='pool1')(net['conv1_2']) # Block 2 net['conv2_1'] = Conv2D(128,(3, 3), activation='relu', padding='same', name='conv2_1')(net['pool1']) net['conv2_2'] = Conv2D(128,(3, 3), activation='relu', padding='same', name='conv2_2')(net['conv2_1']) net['pool2'] = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='pool2')(net['conv2_2']) # Block 3 net['conv3_1'] = Conv2D(256,(3, 3), activation='relu', padding='same', name='conv3_1')(net['pool2']) net['conv3_2'] = Conv2D(256,(3, 3), activation='relu', padding='same', name='conv3_2')(net['conv3_1']) net['conv3_3'] = Conv2D(256,(3, 3), activation='relu', padding='same', name='conv3_3')(net['conv3_2']) net['pool3'] = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='pool3')(net['conv3_3']) # Block 4 net['conv4_1'] = Conv2D(512,(3, 3), activation='relu', padding='same', name='conv4_1')(net['pool3']) net['conv4_2'] = Conv2D(512,(3, 3), activation='relu', padding='same', name='conv4_2')(net['conv4_1']) net['conv4_3'] = Conv2D(512,(3, 3), activation='relu', padding='same', name='conv4_3')(net['conv4_2']) net['pool4'] = MaxPooling2D((2, 2), strides=(2, 2), padding='same', name='pool4')(net['conv4_3']) # Block 5 net['conv5_1'] = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_1')(net['pool4']) net['conv5_2'] = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_2')(net['conv5_1']) net['conv5_3'] = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_3')(net['conv5_2']) net['pool5'] = MaxPooling2D((3, 3), strides=(1, 1), padding='same', name='pool5')(net['conv5_3']) # FC6 """net['fc6'] = AtrousConv2D(1024, 3, 3, atrous_rate=(6, 6), activation='relu', padding='same', name='fc6')(net['pool5'])""" net['fc6'] = Conv2D(1024, (3, 3), dilation_rate=(6, 6), activation='relu', padding='same', name='fc6')(net['pool5']) # x = Dropout(0.5, name='drop6')(x) # FC7 net['fc7'] = Conv2D(1024, (1, 1), activation='relu', padding='same', name='fc7')(net['fc6']) # x = Dropout(0.5, name='drop7')(x) # Block 6 net['conv6_1'] = Conv2D(256, (1, 1), activation='relu', padding='same', name='conv6_1')(net['fc7']) net['conv6_2'] = Conv2D(512, (3, 3), strides=(2, 2), activation='relu', padding='same', name='conv6_2')(net['conv6_1']) # Block 7 net['conv7_1'] = Conv2D(128, (1, 1), activation='relu', padding='same', name='conv7_1')(net['conv6_2']) net['conv7_2'] = ZeroPadding2D()(net['conv7_1']) net['conv7_2'] = Conv2D(256, (3, 3), strides=(2, 2), activation='relu', padding='valid', name='conv7_2')(net['conv7_2']) # Block 8 net['conv8_1'] = Conv2D(128, (1, 1), activation='relu', padding='same', name='conv8_1')(net['conv7_2']) net['conv8_2'] = Conv2D(256, (3, 3), strides=(2, 2), activation='relu', padding='same', name='conv8_2')(net['conv8_1']) # Last Pool net['pool6'] = GlobalAveragePooling2D(name='pool6')(net['conv8_2']) # Prediction from conv4_3 net['conv4_3_norm'] = Normalize(20, name='conv4_3_norm')(net['conv4_3']) num_priors = 3 x = Conv2D(num_priors * 4, (3, 3), padding='same', name='conv4_3_norm_mbox_loc')(net['conv4_3_norm']) net['conv4_3_norm_mbox_loc'] = x flatten = Flatten(name='conv4_3_norm_mbox_loc_flat') net['conv4_3_norm_mbox_loc_flat'] = flatten(net['conv4_3_norm_mbox_loc']) name = 'conv4_3_norm_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) x = Conv2D(num_priors * num_classes,(3, 3), padding='same', name=name)(net['conv4_3_norm']) net['conv4_3_norm_mbox_conf'] = x flatten = Flatten(name='conv4_3_norm_mbox_conf_flat') net['conv4_3_norm_mbox_conf_flat'] = flatten(net['conv4_3_norm_mbox_conf']) priorbox = PriorBox(img_size, 30.0, aspect_ratios=[2], variances=[0.1, 0.1, 0.2, 0.2], name='conv4_3_norm_mbox_priorbox') net['conv4_3_norm_mbox_priorbox'] = priorbox(net['conv4_3_norm']) # Prediction from fc7 num_priors = 6 net['fc7_mbox_loc'] = Conv2D(num_priors * 4,(3, 3), padding='same', name='fc7_mbox_loc')(net['fc7']) flatten = Flatten(name='fc7_mbox_loc_flat') net['fc7_mbox_loc_flat'] = flatten(net['fc7_mbox_loc']) name = 'fc7_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) net['fc7_mbox_conf'] = Conv2D(num_priors * num_classes, (3, 3), padding='same', name=name)(net['fc7']) flatten = Flatten(name='fc7_mbox_conf_flat') net['fc7_mbox_conf_flat'] = flatten(net['fc7_mbox_conf']) priorbox = PriorBox(img_size, 60.0, max_size=114.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='fc7_mbox_priorbox') net['fc7_mbox_priorbox'] = priorbox(net['fc7']) # Prediction from conv6_2 num_priors = 6 x = Conv2D(num_priors * 4, (3, 3), padding='same', name='conv6_2_mbox_loc')(net['conv6_2']) net['conv6_2_mbox_loc'] = x flatten = Flatten(name='conv6_2_mbox_loc_flat') net['conv6_2_mbox_loc_flat'] = flatten(net['conv6_2_mbox_loc']) name = 'conv6_2_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) x = Conv2D(num_priors * num_classes, (3, 3), padding='same', name=name)(net['conv6_2']) net['conv6_2_mbox_conf'] = x flatten = Flatten(name='conv6_2_mbox_conf_flat') net['conv6_2_mbox_conf_flat'] = flatten(net['conv6_2_mbox_conf']) priorbox = PriorBox(img_size, 114.0, max_size=168.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='conv6_2_mbox_priorbox') net['conv6_2_mbox_priorbox'] = priorbox(net['conv6_2']) # Prediction from conv7_2 num_priors = 6 x = Conv2D(num_priors * 4, (3, 3), padding='same', name='conv7_2_mbox_loc')(net['conv7_2']) net['conv7_2_mbox_loc'] = x flatten = Flatten(name='conv7_2_mbox_loc_flat') net['conv7_2_mbox_loc_flat'] = flatten(net['conv7_2_mbox_loc']) name = 'conv7_2_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) x = Conv2D(num_priors * num_classes, (3, 3), padding='same', name=name)(net['conv7_2']) net['conv7_2_mbox_conf'] = x flatten = Flatten(name='conv7_2_mbox_conf_flat') net['conv7_2_mbox_conf_flat'] = flatten(net['conv7_2_mbox_conf']) priorbox = PriorBox(img_size, 168.0, max_size=222.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='conv7_2_mbox_priorbox') net['conv7_2_mbox_priorbox'] = priorbox(net['conv7_2']) # Prediction from conv8_2 num_priors = 6 x = Conv2D(num_priors * 4, (3, 3), padding='same', name='conv8_2_mbox_loc')(net['conv8_2']) net['conv8_2_mbox_loc'] = x flatten = Flatten(name='conv8_2_mbox_loc_flat') net['conv8_2_mbox_loc_flat'] = flatten(net['conv8_2_mbox_loc']) name = 'conv8_2_mbox_conf' if num_classes != 21: name += '_{}'.format(num_classes) x = Conv2D(num_priors * num_classes, (3, 3), padding='same', name=name)(net['conv8_2']) net['conv8_2_mbox_conf'] = x flatten = Flatten(name='conv8_2_mbox_conf_flat') net['conv8_2_mbox_conf_flat'] = flatten(net['conv8_2_mbox_conf']) priorbox = PriorBox(img_size, 222.0, max_size=276.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='conv8_2_mbox_priorbox') net['conv8_2_mbox_priorbox'] = priorbox(net['conv8_2']) # Prediction from pool6 num_priors = 6 x = Dense(num_priors * 4, name='pool6_mbox_loc_flat')(net['pool6']) net['pool6_mbox_loc_flat'] = x name = 'pool6_mbox_conf_flat' if num_classes != 21: name += '_{}'.format(num_classes) x = Dense(num_priors * num_classes, name=name)(net['pool6']) net['pool6_mbox_conf_flat'] = x priorbox = PriorBox(img_size, 276.0, max_size=330.0, aspect_ratios=[2, 3], variances=[0.1, 0.1, 0.2, 0.2], name='pool6_mbox_priorbox') if K.image_dim_ordering() == 'tf': target_shape = (1, 1, 256) else: target_shape = (256, 1, 1) net['pool6_reshaped'] = Reshape(target_shape, name='pool6_reshaped')(net['pool6']) net['pool6_mbox_priorbox'] = priorbox(net['pool6_reshaped']) # Gather all predictions """net['mbox_loc'] = merge([net['conv4_3_norm_mbox_loc_flat'], net['fc7_mbox_loc_flat'], net['conv6_2_mbox_loc_flat'], net['conv7_2_mbox_loc_flat'], net['conv8_2_mbox_loc_flat'], net['pool6_mbox_loc_flat']], mode='concat', concat_axis=1, name='mbox_loc')""" net['mbox_loc'] = Concatenate(axis=1,name='mbox_loc')([ net['conv4_3_norm_mbox_loc_flat'], net['fc7_mbox_loc_flat'], net['conv6_2_mbox_loc_flat'], net['conv7_2_mbox_loc_flat'], net['conv8_2_mbox_loc_flat'], net['pool6_mbox_loc_flat'] ]) """net['mbox_conf'] = merge([net['conv4_3_norm_mbox_conf_flat'], net['fc7_mbox_conf_flat'], net['conv6_2_mbox_conf_flat'], net['conv7_2_mbox_conf_flat'], net['conv8_2_mbox_conf_flat'], net['pool6_mbox_conf_flat']], mode='concat', concat_axis=1, name='mbox_conf')""" net['mbox_conf'] = Concatenate(axis=1,name='mbox_conf')([ net['conv4_3_norm_mbox_conf_flat'], net['fc7_mbox_conf_flat'], net['conv6_2_mbox_conf_flat'], net['conv7_2_mbox_conf_flat'], net['conv8_2_mbox_conf_flat'], net['pool6_mbox_conf_flat'] ]) """net['mbox_priorbox'] = merge([net['conv4_3_norm_mbox_priorbox'], net['fc7_mbox_priorbox'], net['conv6_2_mbox_priorbox'], net['conv7_2_mbox_priorbox'], net['conv8_2_mbox_priorbox'], net['pool6_mbox_priorbox']], mode='concat', concat_axis=1, name='mbox_priorbox')""" net['mbox_priorbox'] = Concatenate(axis=1,name='mbox_priorbox')([ net['conv4_3_norm_mbox_priorbox'], net['fc7_mbox_priorbox'], net['conv6_2_mbox_priorbox'], net['conv7_2_mbox_priorbox'], net['conv8_2_mbox_priorbox'], net['pool6_mbox_priorbox'] ]) if hasattr(net['mbox_loc'], '_keras_shape'): num_boxes = net['mbox_loc']._keras_shape[-1] // 4 elif hasattr(net['mbox_loc'], 'int_shape'): num_boxes = K.int_shape(net['mbox_loc'])[-1] // 4 net['mbox_loc'] = Reshape((num_boxes, 4), name='mbox_loc_final')(net['mbox_loc']) net['mbox_conf'] = Reshape((num_boxes, num_classes), name='mbox_conf_logits')(net['mbox_conf']) net['mbox_conf'] = Activation('softmax', name='mbox_conf_final')(net['mbox_conf']) """net['predictions'] = merge([net['mbox_loc'], net['mbox_conf'], net['mbox_priorbox']], mode='concat', concat_axis=2, name='predictions')""" net['predictions'] = Concatenate(axis=2,name='predictions')([ net['mbox_loc'], net['mbox_conf'], net['mbox_priorbox'] ]) model = Model(net['input'], net['predictions']) return model