Skip to content

Instantly share code, notes, and snippets.

@sidgan

sidgan/ssd_keras2.py

Created August 23, 2017 23:32
Show Gist options
  • Save sidgan/672f5816c1d274883c1057eeacaca59d to your computer and use it in GitHub Desktop.
Save sidgan/672f5816c1d274883c1057eeacaca59d to your computer and use it in GitHub Desktop.
Download ZIP
ssd.py in for keras 2.0
Raw
ssd_keras2.py
"""Keras implementation of SSD."""
import keras.backend as K
from keras.layers import Activation
from keras.layers import AtrousConv2D
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 merge
from keras.layers import Reshape
from keras.layers import ZeroPadding2D
from keras.models import Model
#import ssd_layers as ssd_layers
from ssd_keras import ssd_layers
from ssd_keras.ssd_layers import Normalize
from ssd_keras.ssd_layers import PriorBox
#from ssd_layers import Normalize
#from ssd_layers import PriorBox
def SSD300(input_shape, num_classes=21):
#print(input_shape)
#print(num_classes)
#print('here')
"""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
https://github.com/fchollet/keras/issues/6006
https://github.com/fchollet/keras/wiki/Keras-2.0-release-notes
"""
net = {}
# Block 1
input_tensor = input_tensor = Input(shape=input_shape)
img_size = (input_shape[1], input_shape[0])
net['inputs'] = 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
'''
The AtrousConvolution1D and AtrousConvolution2D layer have been deprecated.
Their functionality is instead supported via the dilation_rate argument in
Convolution1D and Convolution2D layers.
'''
#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), subsample=(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), subsample=(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), subsample=(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
#print(net)
flatten = Flatten(name='conv4_3_norm_mbox_loc_flat')
#print(type(net['conv4_3_norm_mbox_loc'])) # is a conv2d <class 'keras.layers.convolutional.Conv2D'>
#print(net['conv4_3_norm_mbox_loc'].output)
#inputs should be a symbolic tensor
'''
https://github.com/fchollet/keras/blob/master/keras/backend/tensorflow_backend.py
line 370
'''
net['conv4_3_norm_mbox_loc_flat'] = flatten(inputs=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_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
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment