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ssd_model.py
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ssd_model.py
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"""Keras implementation of SSD."""
from keras.layers import Activation
from keras.layers import Conv2D
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Input
from keras.layers import MaxPool2D
from keras.layers import concatenate
from keras.layers import Reshape
from keras.layers import ZeroPadding2D
from keras.models import Model
from utils.layers import Normalize
from ssd_model_dense import dsod300_body, dsod512_body
from ssd_model_resnet import ssd512_resnet_body
def ssd300_body(x):
source_layers = []
# Block 1
x = Conv2D(64, 3, strides=1, padding='same', name='conv1_1', activation='relu')(x)
x = Conv2D(64, 3, strides=1, padding='same', name='conv1_2', activation='relu')(x)
x = MaxPool2D(pool_size=2, strides=2, padding='same', name='pool1')(x)
# Block 2
x = Conv2D(128, 3, strides=1, padding='same', name='conv2_1', activation='relu')(x)
x = Conv2D(128, 3, strides=1, padding='same', name='conv2_2', activation='relu')(x)
x = MaxPool2D(pool_size=2, strides=2, padding='same', name='pool2')(x)
# Block 3
x = Conv2D(256, 3, strides=1, padding='same', name='conv3_1', activation='relu')(x)
x = Conv2D(256, 3, strides=1, padding='same', name='conv3_2', activation='relu')(x)
x = Conv2D(256, 3, strides=1, padding='same', name='conv3_3', activation='relu')(x)
x = MaxPool2D(pool_size=2, strides=2, padding='same', name='pool3')(x)
# Block 4
x = Conv2D(512, 3, strides=1, padding='same', name='conv4_1', activation='relu')(x)
x = Conv2D(512, 3, strides=1, padding='same', name='conv4_2', activation='relu')(x)
x = Conv2D(512, 3, strides=1, padding='same', name='conv4_3', activation='relu')(x)
source_layers.append(x)
x = MaxPool2D(pool_size=2, strides=2, padding='same', name='pool4')(x)
# Block 5
x = Conv2D(512, 3, strides=1, padding='same', name='conv5_1', activation='relu')(x)
x = Conv2D(512, 3, strides=1, padding='same', name='conv5_2', activation='relu')(x)
x = Conv2D(512, 3, strides=1, padding='same', name='conv5_3', activation='relu')(x)
x = MaxPool2D(pool_size=3, strides=1, padding='same', name='pool5')(x)
# FC6
x = Conv2D(1024, 3, strides=1, dilation_rate=(6, 6), padding='same', name='fc6', activation='relu')(x)
# FC7
x = Conv2D(1024, 1, strides=1, padding='same', name='fc7', activation='relu')(x)
source_layers.append(x)
# Block 6
x = Conv2D(256, 1, strides=1, padding='same', name='conv6_1', activation='relu')(x)
x = ZeroPadding2D((1,1))(x)
x = Conv2D(512, 3, strides=2, padding='valid', name='conv6_2', activation='relu')(x)
source_layers.append(x)
# Block 7
x = Conv2D(128, 1, strides=1, padding='same', name='conv7_1', activation='relu')(x)
x = ZeroPadding2D((1,1))(x)
x = Conv2D(256, 3, strides=2, padding='valid', name='conv7_2', activation='relu')(x)
source_layers.append(x)
# Block 8
x = Conv2D(128, 1, strides=1, padding='same', name='conv8_1', activation='relu')(x)
x = Conv2D(256, 3, strides=1, padding='valid', name='conv8_2', activation='relu')(x)
source_layers.append(x)
# Block 9
x = Conv2D(128, 1, strides=1, padding='same', name='conv9_1', activation='relu')(x)
x = Conv2D(256, 3, strides=1, padding='valid', name='conv9_2', activation='relu')(x)
source_layers.append(x)
return source_layers
def ssd512_body(x):
source_layers = []
# Block 1
x = Conv2D(64, 3, strides=1, padding='same', name='conv1_1', activation='relu')(x)
x = Conv2D(64, 3, strides=1, padding='same', name='conv1_2', activation='relu')(x)
x = MaxPool2D(pool_size=2, strides=2, padding='same', name='pool1')(x)
# Block 2
x = Conv2D(128, 3, strides=1, padding='same', name='conv2_1', activation='relu')(x)
x = Conv2D(128, 3, strides=1, padding='same', name='conv2_2', activation='relu')(x)
x = MaxPool2D(pool_size=2, strides=2, padding='same', name='pool2')(x)
# Block 3
x = Conv2D(256, 3, strides=1, padding='same', name='conv3_1', activation='relu')(x)
x = Conv2D(256, 3, strides=1, padding='same', name='conv3_2', activation='relu')(x)
x = Conv2D(256, 3, strides=1, padding='same', name='conv3_3', activation='relu')(x)
x = MaxPool2D(pool_size=2, strides=2, padding='same', name='pool3')(x)
# Block 4
x = Conv2D(512, 3, strides=1, padding='same', name='conv4_1', activation='relu')(x)
x = Conv2D(512, 3, strides=1, padding='same', name='conv4_2', activation='relu')(x)
x = Conv2D(512, 3, strides=1, padding='same', name='conv4_3', activation='relu')(x)
source_layers.append(x)
x = MaxPool2D(pool_size=2, strides=2, padding='same', name='pool4')(x)
# Block 5
x = Conv2D(512, 3, strides=1, padding='same', name='conv5_1', activation='relu')(x)
x = Conv2D(512, 3, strides=1, padding='same', name='conv5_2', activation='relu')(x)
x = Conv2D(512, 3, strides=1, padding='same', name='conv5_3', activation='relu')(x)
x = MaxPool2D(pool_size=3, strides=1, padding='same', name='pool5')(x)
# FC6
x = Conv2D(1024, 3, strides=1, dilation_rate=(6, 6), padding='same', name='fc6', activation='relu')(x)
# FC7
x = Conv2D(1024, 1, strides=1, padding='same', name='fc7', activation='relu')(x)
source_layers.append(x)
# Block 6
x = Conv2D(256, 1, strides=1, padding='same', name='conv6_1', activation='relu')(x)
x = ZeroPadding2D((1,1))(x)
x = Conv2D(512, 3, strides=2, padding='valid', name='conv6_2', activation='relu')(x)
source_layers.append(x)
# Block 7
x = Conv2D(128, 1, strides=1, padding='same', name='conv7_1', activation='relu')(x)
x = ZeroPadding2D((1,1))(x)
x = Conv2D(256, 3, strides=2, padding='valid', name='conv7_2', activation='relu')(x)
source_layers.append(x)
# Block 8
x = Conv2D(128, 1, strides=1, padding='same', name='conv8_1', activation='relu')(x)
x = ZeroPadding2D((1,1))(x)
x = Conv2D(256, 3, strides=2, padding='valid', name='conv8_2', activation='relu')(x)
source_layers.append(x)
# Block 9
x = Conv2D(128, 1, strides=1, padding='same', name='conv9_1', activation='relu')(x)
x = ZeroPadding2D((1,1))(x)
x = Conv2D(256, 3, strides=2, padding='valid', name='conv9_2', activation='relu')(x)
source_layers.append(x)
# Block 10
x = Conv2D(128, 1, strides=1, padding='same', name='conv10_1', activation='relu')(x)
x = ZeroPadding2D((1,1))(x)
x = Conv2D(256, 4, strides=2, padding='valid', name='conv10_2', activation='relu')(x)
source_layers.append(x)
return source_layers
def multibox_head(source_layers, num_priors, num_classes, normalizations=None, softmax=True):
class_activation = 'softmax' if softmax else 'sigmoid'
mbox_conf = []
mbox_loc = []
for i in range(len(source_layers)):
x = source_layers[i]
name = x.name.split('/')[0]
# normalize
if normalizations is not None and normalizations[i] > 0:
name = name + '_norm'
x = Normalize(normalizations[i], name=name)(x)
# confidence
name1 = name + '_mbox_conf'
x1 = Conv2D(num_priors[i] * num_classes, 3, padding='same', name=name1)(x)
x1 = Flatten(name=name1+'_flat')(x1)
mbox_conf.append(x1)
# location
name2 = name + '_mbox_loc'
x2 = Conv2D(num_priors[i] * 4, 3, padding='same', name=name2)(x)
x2 = Flatten(name=name2+'_flat')(x2)
mbox_loc.append(x2)
mbox_loc = concatenate(mbox_loc, axis=1, name='mbox_loc')
mbox_loc = Reshape((-1, 4), name='mbox_loc_final')(mbox_loc)
mbox_conf = concatenate(mbox_conf, axis=1, name='mbox_conf')
mbox_conf = Reshape((-1, num_classes), name='mbox_conf_logits')(mbox_conf)
mbox_conf = Activation(class_activation, name='mbox_conf_final')(mbox_conf)
predictions = concatenate([mbox_loc, mbox_conf], axis=2, name='predictions')
return predictions
def SSD300(input_shape=(300, 300, 3), num_classes=21, softmax=True):
"""SSD300 architecture.
# Arguments
input_shape: Shape of the input image.
num_classes: Number of classes including background.
# Notes
In order to stay compatible with pre-trained models, the parameters
were chosen as in the caffee implementation.
# References
https://arxiv.org/abs/1512.02325
"""
x = input_tensor = Input(shape=input_shape)
source_layers = ssd300_body(x)
# Add multibox head for classification and regression
num_priors = [4, 6, 6, 6, 4, 4]
normalizations = [20, -1, -1, -1, -1, -1]
output_tensor = multibox_head(source_layers, num_priors, num_classes, normalizations, softmax)
model = Model(input_tensor, output_tensor)
model.num_classes = num_classes
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
# stay compatible with caffe models
model.aspect_ratios = [[1,2,1/2], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2], [1,2,1/2]]
model.minmax_sizes = [(30, 60), (60, 111), (111, 162), (162, 213), (213, 264), (264, 315)]
model.steps = [8, 16, 32, 64, 100, 300]
model.special_ssd_boxes = True
return model
def SSD512(input_shape=(512, 512, 3), num_classes=21, softmax=True):
"""SSD512 architecture.
# Arguments
input_shape: Shape of the input image.
num_classes: Number of classes including background.
# Notes
In order to stay compatible with pre-trained models, the parameters
were chosen as in the caffee implementation.
# References
https://arxiv.org/abs/1512.02325
"""
x = input_tensor = Input(shape=input_shape)
source_layers = ssd512_body(x)
# Add multibox head for classification and regression
num_priors = [4, 6, 6, 6, 6, 4, 4]
normalizations = [20, -1, -1, -1, -1, -1, -1]
output_tensor = multibox_head(source_layers, num_priors, num_classes, normalizations, softmax)
model = Model(input_tensor, output_tensor)
model.num_classes = num_classes
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
# stay compatible with caffe models
model.aspect_ratios = [[1,2,1/2], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2], [1,2,1/2]]
#model.minmax_sizes = [(35, 76), (76, 153), (153, 230), (230, 307), (307, 384), (384, 460), (460, 537)]
model.minmax_sizes = [(20.48, 51.2), (51.2, 133.12), (133.12, 215.04), (215.04, 296.96), (296.96, 378.88), (378.88, 460.8), (460.8, 542.72)]
model.steps = [8, 16, 32, 64, 128, 256, 512]
model.special_ssd_boxes = True
return model
def DSOD300(input_shape=(300, 300, 3), num_classes=21, activation='relu', softmax=True):
"""DSOD, DenseNet based SSD300 architecture.
# Arguments
input_shape: Shape of the input image.
num_classes: Number of classes including background.
activation: Type of activation functions.
# References
https://arxiv.org/abs/1708.01241
"""
x = input_tensor = Input(shape=input_shape)
source_layers = dsod300_body(x, activation=activation)
num_priors = [4, 6, 6, 6, 4, 4]
normalizations = [20, 20, 20, 20, 20, 20]
output_tensor = multibox_head(source_layers, num_priors, num_classes, normalizations, softmax)
model = Model(input_tensor, output_tensor)
model.num_classes = num_classes
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
model.aspect_ratios = [[1,2,1/2], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2], [1,2,1/2]]
model.minmax_sizes = [(30, 60), (60, 111), (111, 162), (162, 213), (213, 264), (264, 315)]
model.steps = [8, 16, 32, 64, 100, 300]
model.special_ssd_boxes = True
return model
SSD300_dense = DSOD300
def DSOD512(input_shape=(512, 512, 3), num_classes=21, activation='relu', softmax=True):
"""DSOD, DenseNet based SSD512 architecture.
# Arguments
input_shape: Shape of the input image.
num_classes: Number of classes including background.
activation: Type of activation functions.
# References
https://arxiv.org/abs/1708.01241
"""
x = input_tensor = Input(shape=input_shape)
source_layers = dsod512_body(x, activation=activation)
num_priors = [4, 6, 6, 6, 6, 4, 4]
normalizations = [20, 20, 20, 20, 20, 20, 20]
output_tensor = multibox_head(source_layers, num_priors, num_classes, normalizations, softmax)
model = Model(input_tensor, output_tensor)
model.num_classes = num_classes
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
model.aspect_ratios = [[1,2,1/2], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2], [1,2,1/2]]
model.minmax_sizes = [(35, 76), (76, 153), (153, 230), (230, 307), (307, 384), (384, 460), (460, 537)]
model.steps = [8, 16, 32, 64, 128, 256, 512]
model.special_ssd_boxes = True
return model
SSD512_dense = DSOD512
def SSD512_resnet(input_shape=(512, 512, 3), num_classes=21, softmax=True):
# TODO: it does not converge!
x = input_tensor = Input(shape=input_shape)
source_layers = ssd512_resnet_body(x)
# Add multibox head for classification and regression
num_priors = [4, 6, 6, 6, 6, 4, 4]
normalizations = [20, 20, 20, 20, 20, 20, 20]
output_tensor = multibox_head(source_layers, num_priors, num_classes, normalizations, softmax)
model = Model(input_tensor, output_tensor)
model.num_classes = num_classes
# parameters for prior boxes
model.image_size = input_shape[:2]
model.source_layers = source_layers
# stay compatible with caffe models
model.aspect_ratios = [[1,2,1/2], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2,3,1/3], [1,2,1/2], [1,2,1/2]]
model.minmax_sizes = [(35, 76), (76, 153), (153, 230), (230, 307), (307, 384), (384, 460), (460, 537)]
model.steps = [8, 16, 32, 64, 128, 256, 512]
model.special_ssd_boxes = True
return model