mirror of
https://github.com/wassname/PSPNet-Keras-tensorflow.git
synced 2026-08-27 11:40:48 +08:00
199 lines
6.9 KiB
Python
199 lines
6.9 KiB
Python
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
|
|
from keras.layers import BatchNormalization, Activation, Input, Dropout, ZeroPadding2D, Lambda
|
|
from keras.layers.merge import Concatenate, Add
|
|
from keras.models import Model
|
|
from keras.optimizers import SGD
|
|
|
|
import tensorflow as tf
|
|
|
|
learning_rate = 1e-3 # Layer specific learning rate
|
|
# Weight decay not implemented
|
|
|
|
def BN(name=""):
|
|
return BatchNormalization(momentum=0.95, name=name, epsilon=1e-5)
|
|
|
|
def Interp(x, shape=(60,60)):
|
|
new_height,new_width = shape
|
|
resized = tf.image.resize_images(x, [new_height, new_width], align_corners=True)
|
|
return resized
|
|
|
|
def residual_conv(prev, level, pad=1, lvl=1, sub_lvl=1, modify_stride=False):
|
|
lvl = str(lvl)
|
|
sub_lvl = str(sub_lvl)
|
|
names = ["conv"+lvl+"_"+ sub_lvl +"_1x1_reduce" ,
|
|
"conv"+lvl+"_"+ sub_lvl +"_1x1_reduce_bn",
|
|
"conv"+lvl+"_"+ sub_lvl +"_3x3",
|
|
"conv"+lvl+"_"+ sub_lvl +"_3x3_bn",
|
|
"conv"+lvl+"_"+ sub_lvl +"_1x1_increase",
|
|
"conv"+lvl+"_"+ sub_lvl +"_1x1_increase_bn"]
|
|
if modify_stride == False:
|
|
prev = Conv2D(64 * level, (1,1), strides=(1,1), name=names[0], use_bias=False)(prev)
|
|
elif modify_stride == True:
|
|
prev = Conv2D(64 * level, (1,1), strides=(2,2), name=names[0], use_bias=False)(prev)
|
|
|
|
prev = BN(name=names[1])(prev)
|
|
prev = Activation('relu')(prev)
|
|
|
|
prev = ZeroPadding2D(padding=(pad,pad))(prev)
|
|
prev = Conv2D(64 * level, (3,3), strides=(1,1), dilation_rate=pad, name=names[2], use_bias=False)(prev)
|
|
|
|
prev = BN(name=names[3])(prev)
|
|
prev = Activation('relu')(prev)
|
|
prev = Conv2D(256 * level, (1,1), strides=(1,1), name=names[4], use_bias=False)(prev)
|
|
prev = BN(name=names[5])(prev)
|
|
return prev
|
|
|
|
def short_convolution_branch(prev, level, lvl=1, sub_lvl=1, modify_stride=False):
|
|
lvl = str(lvl)
|
|
sub_lvl = str(sub_lvl)
|
|
names = ["conv"+lvl+"_"+ sub_lvl +"_1x1_proj",
|
|
"conv"+lvl+"_"+ sub_lvl +"_1x1_proj_bn"]
|
|
|
|
if modify_stride == False:
|
|
prev = Conv2D(256 * level ,(1,1), strides=(1,1), name=names[0], use_bias=False)(prev)
|
|
elif modify_stride == True:
|
|
prev = Conv2D(256 * level, (1,1), strides=(2,2), name=names[0], use_bias=False)(prev)
|
|
|
|
prev = BN(name=names[1])(prev)
|
|
return prev
|
|
|
|
def empty_branch(prev):
|
|
return prev
|
|
|
|
def residual_short(prev_layer, level, pad=1, lvl=1, sub_lvl=1, modify_stride=False):
|
|
prev_layer = Activation('relu')(prev_layer)
|
|
block_1 = residual_conv(prev_layer, level,
|
|
pad=pad, lvl=lvl, sub_lvl=sub_lvl,
|
|
modify_stride=modify_stride)
|
|
|
|
block_2 = short_convolution_branch(prev_layer, level,
|
|
lvl=lvl, sub_lvl=sub_lvl,
|
|
modify_stride=modify_stride)
|
|
added = Add()([block_1, block_2])
|
|
return added
|
|
|
|
def residual_empty(prev_layer, level, pad=1, lvl=1, sub_lvl=1):
|
|
prev_layer = Activation('relu')(prev_layer)
|
|
|
|
block_1 = residual_conv(prev_layer, level,
|
|
pad=pad, lvl=lvl, sub_lvl=sub_lvl)
|
|
block_2 = empty_branch(prev_layer)
|
|
added = Add()([block_1, block_2])
|
|
return added
|
|
|
|
def ResNet(inp):
|
|
#Names for the first couple layers of model
|
|
names = ["conv1_1_3x3_s2",
|
|
"conv1_1_3x3_s2_bn",
|
|
"conv1_2_3x3",
|
|
"conv1_2_3x3_bn",
|
|
"conv1_3_3x3",
|
|
"conv1_3_3x3_bn"]
|
|
|
|
#---Short branch(only start of network)
|
|
|
|
cnv1 = Conv2D(64, (3, 3), strides=(2, 2), padding='same', name=names[0], use_bias=False)(inp) # "conv1_1_3x3_s2"
|
|
bn1 = BN(name=names[1])(cnv1) # "conv1_1_3x3_s2/bn"
|
|
relu1 = Activation('relu')(bn1) #"conv1_1_3x3_s2/relu"
|
|
|
|
cnv1 = Conv2D(64, (3, 3), strides=(1, 1), padding='same', name=names[2], use_bias=False)(relu1) #"conv1_2_3x3"
|
|
bn1 = BN(name=names[3])(cnv1) #"conv1_2_3x3/bn"
|
|
relu1 = Activation('relu')(bn1) #"conv1_2_3x3/relu"
|
|
|
|
cnv1 = Conv2D(128, (3, 3), strides=(1, 1), padding='same', name=names[4], use_bias=False)(relu1) #"conv1_3_3x3"
|
|
bn1 = BN(name=names[5])(cnv1) #"conv1_3_3x3/bn"
|
|
relu1 = Activation('relu')(bn1) #"conv1_3_3x3/relu"
|
|
|
|
res = MaxPooling2D(pool_size=(3,3), padding='same', strides=(2,2))(relu1) #"pool1_3x3_s2"
|
|
|
|
#---Residual layers(body of network)
|
|
|
|
"""
|
|
Modify_stride --Used only once in first 3_1 convolutions block.
|
|
changes stride of first convolution from 1 -> 2
|
|
"""
|
|
|
|
#2_1- 2_3
|
|
res = residual_short(res, 1, pad=1, lvl=2, sub_lvl=1)
|
|
for i in range(2):
|
|
res = residual_empty(res, 1, pad=1, lvl=2, sub_lvl=i+2)
|
|
|
|
#3_1 - 3_3
|
|
res = residual_short(res, 2, pad=1, lvl=3, sub_lvl=1, modify_stride=True)
|
|
for i in range(3):
|
|
res = residual_empty(res, 2, pad=1, lvl=3, sub_lvl=i+2)
|
|
|
|
#4_1 - 4_6
|
|
res = residual_short(res, 4, pad=2, lvl=4, sub_lvl=1)
|
|
for i in range(5):
|
|
res = residual_empty(res, 4, pad=2, lvl=4, sub_lvl=i+2)
|
|
|
|
#5_1 - 5_3
|
|
res = residual_short(res, 8, pad=4, lvl=5, sub_lvl=1)
|
|
for i in range(2):
|
|
res = residual_empty(res, 8, pad=4, lvl=5, sub_lvl=i+2)
|
|
|
|
res = Activation('relu')(res)
|
|
return res
|
|
|
|
def interp_block(prev_layer, level, str_lvl=1):
|
|
|
|
str_lvl = str(str_lvl)
|
|
|
|
names = [
|
|
"conv5_3_pool"+str_lvl+"_conv",
|
|
"conv5_3_pool"+str_lvl+"_conv_bn"
|
|
]
|
|
|
|
kernel = (10*level, 10*level)
|
|
strides = (10*level, 10*level)
|
|
prev_layer = AveragePooling2D(kernel,strides=strides)(prev_layer)
|
|
prev_layer = Conv2D(512, (1,1), strides=(1,1), name=names[0], use_bias=False)(prev_layer)
|
|
prev_layer = BN(name=names[1])(prev_layer)
|
|
prev_layer = Activation('relu')(prev_layer)
|
|
prev_layer = Lambda(Interp)(prev_layer)
|
|
return prev_layer
|
|
|
|
def PSPNet(res):
|
|
|
|
#---PSPNet concat layers with Interpolation
|
|
|
|
interp_block1 = interp_block(res, 6, str_lvl=1)
|
|
interp_block2 = interp_block(res, 3, str_lvl=2)
|
|
interp_block3 = interp_block(res, 2, str_lvl=3)
|
|
interp_block6 = interp_block(res, 1, str_lvl=6)
|
|
|
|
#concat all these layers. resulted shape=(1,60,60,4096)
|
|
res = Concatenate()([res,
|
|
interp_block6,
|
|
interp_block3,
|
|
interp_block2,
|
|
interp_block1])
|
|
return res
|
|
|
|
def build_pspnet(activation='softmax'):
|
|
'''
|
|
Normal PSPNet.
|
|
'''
|
|
inp = Input((473,473,3))
|
|
res = ResNet(inp)
|
|
psp = PSPNet(res)
|
|
|
|
x = Conv2D(512, (3, 3), strides=(1, 1), padding="same", name="conv5_4", use_bias=False)(psp)
|
|
x = BN(name="conv5_4_bn")(x)
|
|
x = Activation('relu')(x)
|
|
x = Dropout(0.1)(x)
|
|
|
|
x = Conv2D(150, (1, 1), strides=(1, 1), name="conv6")(x)
|
|
x = Lambda(Interp, arguments={'shape': (473,473)})(x)
|
|
x = Activation('softmax')(x)
|
|
|
|
model = Model(inputs=inp, outputs=x)
|
|
|
|
# Solver
|
|
sgd = SGD(lr=learning_rate, momentum=0.9, nesterov=True)
|
|
model.compile(optimizer=sgd,
|
|
loss='categorical_crossentropy',
|
|
metrics=['accuracy'])
|
|
return model
|