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ssd_v2.py
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"""Keras implementation of SSD."""
import keras.backend as K
from keras.layers import Activation
from keras.layers 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 Reshape
from keras.layers import ZeroPadding2D
from keras.layers import concatenate
from keras.models import Model
from ssd_layers import Normalize
from ssd_layers import PriorBox
def SSD300v2(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
"""
input_layer = Input(shape=input_shape)
# Block 1
conv1_1 = Conv2D(64, (3, 3),
name='conv1_1',
padding='same',
activation='relu')(input_layer)
conv1_2 = Conv2D(64, (3, 3),
name='conv1_2',
padding='same',
activation='relu')(conv1_1)
pool1 = MaxPooling2D(name='pool1',
pool_size=(2, 2),
strides=(2, 2),
padding='same', )(conv1_2)
# Block 2
conv2_1 = Conv2D(128, (3, 3),
name='conv2_1',
padding='same',
activation='relu')(pool1)
conv2_2 = Conv2D(128, (3, 3),
name='conv2_2',
padding='same',
activation='relu')(conv2_1)
pool2 = MaxPooling2D(name='pool2',
pool_size=(2, 2),
strides=(2, 2),
padding='same')(conv2_2)
# Block 3
conv3_1 = Conv2D(256, (3, 3),
name='conv3_1',
padding='same',
activation='relu')(pool2)
conv3_2 = Conv2D(256, (3, 3),
name='conv3_2',
padding='same',
activation='relu')(conv3_1)
conv3_3 = Conv2D(256, (3, 3),
name='conv3_3',
padding='same',
activation='relu')(conv3_2)
pool3 = MaxPooling2D(name='pool3',
pool_size=(2, 2),
strides=(2, 2),
padding='same')(conv3_3)
# Block 4
conv4_1 = Conv2D(512, (3, 3),
name='conv4_1',
padding='same',
activation='relu')(pool3)
conv4_2 = Conv2D(512, (3, 3),
name='conv4_2',
padding='same',
activation='relu')(conv4_1)
conv4_3 = Conv2D(512, (3, 3),
name='conv4_3',
padding='same',
activation='relu')(conv4_2)
pool4 = MaxPooling2D(name='pool4',
pool_size=(2, 2),
strides=(2, 2),
padding='same')(conv4_3)
# Block 5
conv5_1 = Conv2D(512, (3, 3),
name='conv5_1',
padding='same',
activation='relu')(pool4)
conv5_2 = Conv2D(512, (3, 3),
name='conv5_2',
padding='same',
activation='relu')(conv5_1)
conv5_3 = Conv2D(512, (3, 3),
name='conv5_3',
padding='same',
activation='relu')(conv5_2)
pool5 = MaxPooling2D(name='pool5',
pool_size=(3, 3),
strides=(1, 1),
padding='same')(conv5_3)
# FC6
fc6 = Conv2D(1024, (3, 3),
name='fc6',
dilation_rate=(6, 6),
padding='same',
activation='relu'
)(pool5)
# x = Dropout(0.5, name='drop6')(x)
# FC7
fc7 = Conv2D(1024, (1, 1),
name='fc7',
padding='same',
activation='relu'
)(fc6)
# x = Dropout(0.5, name='drop7')(x)
# Block 6
conv6_1 = Conv2D(256, (1, 1),
name='conv6_1',
padding='same',
activation='relu')(fc7)
conv6_2 = Conv2D(512, (3, 3),
name='conv6_2',
strides=(2, 2),
padding='same',
activation='relu')(conv6_1)
# Block 7
conv7_1 = Conv2D(128, (1, 1),
name='conv7_1',
padding='same',
activation='relu')(conv6_2)
conv7_1z = ZeroPadding2D(name='conv7_1z')(conv7_1)
conv7_2 = Conv2D(256, (3, 3),
name='conv7_2',
padding='valid',
strides=(2, 2),
activation='relu')(conv7_1z)
# Block 8
conv8_1 = Conv2D(128, (1, 1),
name='conv8_1',
padding='same',
activation='relu')(conv7_2)
conv8_2 = Conv2D(256, (3, 3),
name='conv8_2',
padding='same',
strides=(2, 2),
activation='relu')(conv8_1)
# Last Pool
pool6 = GlobalAveragePooling2D(name='pool6')(conv8_2)
# Prediction from conv4_3
num_priors = 3
img_size = (input_shape[1], input_shape[0])
name = 'conv4_3_norm_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
conv4_3_norm = Normalize(20, name='conv4_3_norm')(conv4_3)
conv4_3_norm_mbox_loc = Conv2D(num_priors * 4, (3, 3),
name='conv4_3_norm_mbox_loc',
padding='same')(conv4_3_norm)
conv4_3_norm_mbox_loc_flat = Flatten(name='conv4_3_norm_mbox_loc_flat')(conv4_3_norm_mbox_loc)
conv4_3_norm_mbox_conf = Conv2D(num_priors * num_classes, (3, 3),
name=name,
padding='same')(conv4_3_norm)
conv4_3_norm_mbox_conf_flat = Flatten(name='conv4_3_norm_mbox_conf_flat')(conv4_3_norm_mbox_conf)
conv4_3_norm_mbox_priorbox = PriorBox(img_size, 30.0,
name='conv4_3_norm_mbox_priorbox',
aspect_ratios=[2],
variances=[0.1, 0.1, 0.2, 0.2])(conv4_3_norm)
# Prediction from fc7
num_priors = 6
name = 'fc7_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
fc7_mbox_conf = Conv2D(num_priors * num_classes, (3, 3),
padding='same',
name=name)(fc7)
fc7_mbox_conf_flat = Flatten(name='fc7_mbox_conf_flat')(fc7_mbox_conf)
fc7_mbox_loc = Conv2D(num_priors * 4, (3, 3),
name='fc7_mbox_loc',
padding='same')(fc7)
fc7_mbox_loc_flat = Flatten(name='fc7_mbox_loc_flat')(fc7_mbox_loc)
fc7_mbox_priorbox = PriorBox(img_size, 60.0,
name='fc7_mbox_priorbox',
max_size=114.0,
aspect_ratios=[2, 3],
variances=[0.1, 0.1, 0.2, 0.2]
)(fc7)
# Prediction from conv6_2
num_priors = 6
name = 'conv6_2_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
conv6_2_mbox_conf = Conv2D(num_priors * num_classes, (3, 3),
padding='same',
name=name)(conv6_2)
conv6_2_mbox_conf_flat = Flatten(name='conv6_2_mbox_conf_flat')(conv6_2_mbox_conf)
conv6_2_mbox_loc = Conv2D(num_priors * 4, (3, 3,),
name='conv6_2_mbox_loc',
padding='same')(conv6_2)
conv6_2_mbox_loc_flat = Flatten(name='conv6_2_mbox_loc_flat')(conv6_2_mbox_loc)
conv6_2_mbox_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')(conv6_2)
# Prediction from conv7_2
num_priors = 6
name = 'conv7_2_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
conv7_2_mbox_conf = Conv2D(num_priors * num_classes, (3, 3),
padding='same',
name=name)(conv7_2)
conv7_2_mbox_conf_flat = Flatten(name='conv7_2_mbox_conf_flat')(conv7_2_mbox_conf)
conv7_2_mbox_loc = Conv2D(num_priors * 4, (3, 3),
padding='same',
name='conv7_2_mbox_loc')(conv7_2)
conv7_2_mbox_loc_flat = Flatten(name='conv7_2_mbox_loc_flat')(conv7_2_mbox_loc)
conv7_2_mbox_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')(conv7_2)
# Prediction from conv8_2
num_priors = 6
name = 'conv8_2_mbox_conf'
if num_classes != 21:
name += '_{}'.format(num_classes)
conv8_2_mbox_conf = Conv2D(num_priors * num_classes, (3, 3),
padding='same',
name=name)(conv8_2)
conv8_2_mbox_conf_flat = Flatten(name='conv8_2_mbox_conf_flat')(conv8_2_mbox_conf)
conv8_2_mbox_loc = Conv2D(num_priors * 4, (3, 3),
padding='same',
name='conv8_2_mbox_loc')(conv8_2)
conv8_2_mbox_loc_flat = Flatten(name='conv8_2_mbox_loc_flat')(conv8_2_mbox_loc)
conv8_2_mbox_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')(conv8_2)
# Prediction from pool6
num_priors = 6
name = 'pool6_mbox_conf_flat'
if num_classes != 21:
name += '_{}'.format(num_classes)
if K.image_dim_ordering() == 'tf':
target_shape = (1, 1, 256)
else:
target_shape = (256, 1, 1)
pool6_mbox_loc_flat = Dense(num_priors * 4, name='pool6_mbox_loc_flat')(pool6)
pool6_mbox_conf_flat = Dense(num_priors * num_classes, name=name)(pool6)
pool6_reshaped = Reshape(target_shape,
name='pool6_reshaped')(pool6)
pool6_mbox_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')(pool6_reshaped)
# Gather all predictions
mbox_loc = concatenate([conv4_3_norm_mbox_loc_flat,
fc7_mbox_loc_flat,
conv6_2_mbox_loc_flat,
conv7_2_mbox_loc_flat,
conv8_2_mbox_loc_flat,
pool6_mbox_loc_flat],
axis=1,
name='mbox_loc')
mbox_conf = concatenate([conv4_3_norm_mbox_conf_flat,
fc7_mbox_conf_flat,
conv6_2_mbox_conf_flat,
conv7_2_mbox_conf_flat,
conv8_2_mbox_conf_flat,
pool6_mbox_conf_flat],
axis=1,
name='mbox_conf')
mbox_priorbox = concatenate([conv4_3_norm_mbox_priorbox,
fc7_mbox_priorbox,
conv6_2_mbox_priorbox,
conv7_2_mbox_priorbox,
conv8_2_mbox_priorbox,
pool6_mbox_priorbox],
axis=1,
name='mbox_priorbox')
if hasattr(mbox_loc, '_keras_shape'):
num_boxes = mbox_loc._keras_shape[-1] // 4
elif hasattr(mbox_loc, 'int_shape'):
num_boxes = K.int_shape(mbox_loc)[-1] // 4
mbox_loc = Reshape((num_boxes, 4),
name='mbox_loc_final')(mbox_loc)
mbox_conf = Reshape((num_boxes, num_classes),
name='mbox_conf_logits')(mbox_conf)
mbox_conf = Activation('softmax',
name='mbox_conf_final')(mbox_conf)
predictions = concatenate([mbox_loc,
mbox_conf,
mbox_priorbox],
axis=2,
name='predictions')
model = Model(inputs=input_layer, outputs=predictions)
return model