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
"""
K.set_image_dim_ordering('tf')
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'] = Convolution2D(64,
3,
3,
activation='relu',
border_mode='same',
name='conv1_1')(net['input'])
net['conv1_2'] = Convolution2D(64,
3,
3,
activation='relu',
border_mode='same',
name='conv1_2')(net['conv1_1'])
net['pool1'] = MaxPooling2D((2, 2),
strides=(2, 2),
border_mode='same',
name='pool1')(net['conv1_2'])
# Block 2
net['conv2_1'] = Convolution2D(128,
3,
3,
activation='relu',
border_mode='same',
name='conv2_1')(net['pool1'])
net['conv2_2'] = Convolution2D(128,
3,
3,
activation='relu',
border_mode='same',
name='conv2_2')(net['conv2_1'])
net['pool2'] = MaxPooling2D((2, 2),
strides=(2, 2),
border_mode='same',
name='pool2')(net['conv2_2'])
# Block 3
net['conv3_1'] = Convolution2D(256,
3,
3,
activation='relu',
border_mode='same',
name='conv3_1')(net['pool2'])
net['conv3_2'] = Convolution2D(256,
3,
3,
activation='relu',
border_mode='same',
name='conv3_2')(net['conv3_1'])
net['conv3_3'] = Convolution2D(256,
3,
3,
activation='relu',
border_mode='same',
name='conv3_3')(net['conv3_2'])
net['pool3'] = MaxPooling2D((2, 2),
strides=(2, 2),
border_mode='same',
name='pool3')(net['conv3_3'])
# Block 4
net['conv4_1'] = Convolution2D(512,
3,
3,
activation='relu',
border_mode='same',
name='conv4_1')(net['pool3'])
net['conv4_2'] = Convolution2D(512,
3,
3,
activation='relu',
border_mode='same',
name='conv4_2')(net['conv4_1'])
net['conv4_3'] = Convolution2D(512,
3,
3,
activation='relu',
border_mode='same',
name='conv4_3')(net['conv4_2'])
net['pool4'] = MaxPooling2D((2, 2),
strides=(2, 2),
border_mode='same',
name='pool4')(net['conv4_3'])
# Block 5
net['conv5_1'] = Convolution2D(512,
3,
3,
activation='relu',
border_mode='same',
name='conv5_1')(net['pool4'])
net['conv5_2'] = Convolution2D(512,
3,
3,
activation='relu',
border_mode='same',
name='conv5_2')(net['conv5_1'])
net['conv5_3'] = Convolution2D(512,
3,
3,
activation='relu',
border_mode='same',
name='conv5_3')(net['conv5_2'])
net['pool5'] = MaxPooling2D((3, 3),
strides=(1, 1),
border_mode='same',
name='pool5')(net['conv5_3'])
# FC6
net['fc6'] = AtrousConvolution2D(1024,
3,
3,
atrous_rate=(6, 6),
activation='relu',
border_mode='same',
name='fc6')(net['pool5'])
# x = Dropout(0.5, name='drop6')(x)
# FC7
net['fc7'] = Convolution2D(1024,
1,
1,
activation='relu',
border_mode='same',
name='fc7')(net['fc6'])
# x = Dropout(0.5, name='drop7')(x)
# Block 6
net['conv6_1'] = Convolution2D(256,
1,
1,
activation='relu',
border_mode='same',
name='conv6_1')(net['fc7'])
net['conv6_2'] = Convolution2D(512,
3,
3,
subsample=(2, 2),
activation='relu',
border_mode='same',
name='conv6_2')(net['conv6_1'])
# Block 7
net['conv7_1'] = Convolution2D(128,
1,
1,
activation='relu',
border_mode='same',
name='conv7_1')(net['conv6_2'])
net['conv7_2'] = ZeroPadding2D()(net['conv7_1'])
net['conv7_2'] = Convolution2D(256,
3,
3,
subsample=(2, 2),
activation='relu',
border_mode='valid',
name='conv7_2')(net['conv7_2'])
# Block 8
net['conv8_1'] = Convolution2D(128,
1,
1,
activation='relu',
border_mode='same',
name='conv8_1')(net['conv7_2'])
net['conv8_2'] = Convolution2D(256,
3,
3,
subsample=(2, 2),
activation='relu',
border_mode='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 = Convolution2D(num_priors * 4,
3,
3,
border_mode='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 = Convolution2D(num_priors * num_classes,
3,
3,
border_mode='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'] = Convolution2D(num_priors * 4,
3,
3,
border_mode='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'] = Convolution2D(num_priors * num_classes,
3,
3,
border_mode='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 = Convolution2D(num_priors * 4,
3,
3,
border_mode='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 = Convolution2D(num_priors * num_classes,
3,
3,
border_mode='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 = Convolution2D(num_priors * 4,
3,
3,
border_mode='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 = Convolution2D(num_priors * num_classes,
3,
3,
border_mode='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 = Convolution2D(num_priors * 4,
3,
3,
border_mode='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 = Convolution2D(num_priors * num_classes,
3,
3,
border_mode='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_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_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')
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')
model = Model(net['input'], net['predictions'])
return model
def build_ssd(input_shape, n_classes, base_network, priors):
"""input_shape: (h, w, c)
priors: list of priors created through create_priors() function.
extractors, and values being the priors (bboxes info) corresponding to each
layer.
"""
net = dict(base_network)
# Build the prediction layers on top of the base network
for p in priors:
base_layer = p['layer_name']
num_priors = p['n_boxes']
prior_min_size = p['min_size']
prior_max_size = p['max_size']
prior_aspect_ratios = p['aspect_ratios']
prior_variances = p['variances']
# Bounding box locations
loc_layer = base_layer + '_loc'
loc_layer_flat = base_layer + '_loc_flat'
net[loc_layer] = Convolution2D(num_priors * 4,
3,
3,
border_mode='same',
name=loc_layer)(net[base_layer])
net[loc_layer_flat] = Flatten(name=loc_layer_flat)(net[loc_layer])
# Class confidences for each bounding box
conf_layer = base_layer + '_conf'
conf_layer_flat = base_layer + '_conf_flat'
net[conf_layer] = Convolution2D(num_priors * n_classes,
3,
3,
border_mode='same',
name=conf_layer)(net[base_layer])
net[conf_layer_flat] = Flatten(name=conf_layer_flat)(net[conf_layer])
# Bounding box priors (one for each layer)
priors_layer = base_layer + '_priors'
net[priors_layer] = PriorBox([input_shape[1], input_shape[0]],
prior_min_size,
max_size=prior_max_size,
aspect_ratios=prior_aspect_ratios,
variances=prior_variances,
name=priors_layer)(net[base_layer])
# Build the top layer
feature_layers = [p['layer_name'] for p in priors]
loc_layers = [net[name + '_loc_flat'] for name in feature_layers]
conf_layers = [net[name + '_conf_flat'] for name in feature_layers]
priors_layers = [net[name + '_priors'] for name in feature_layers]
# TODO: this is theano-compatible?
net['pred_loc'] = merge(loc_layers,
mode='concat',
concat_axis=1,
name='pred_loc')
n_boxes = net['pred_loc']._keras_shape[-1] // 4
net['pred_loc'] = Reshape((n_boxes, 4),
name='pred_loc_resh')(net['pred_loc'])
net['pred_conf'] = merge(conf_layers,
mode='concat',
concat_axis=1,
name='pred_conf')
net['pred_conf'] = Reshape((n_boxes, n_classes),
name='pred_conf_resh')(net['pred_conf'])
net['pred_conf'] = Activation('softmax',
name='pred_conf_final')(net['pred_conf'])
net['pred_prior'] = merge(priors_layers,
mode='concat',
concat_axis=1,
name='pred_prior')
net['pred_all'] = merge(
[net['pred_loc'], net['pred_conf'], net['pred_prior']],
mode='concat',
concat_axis=2,
name='pred_all')
model = Model(net['input'], net['pred_all'])
return model