mirror of
https://github.com/wassname/PSPNet-Keras-tensorflow.git
synced 2026-09-10 11:40:16 +08:00
Added support for pspnet100_cityscapes, alpha blending, keras saving & loading and much more
This commit is contained in:
Julian Tatsch
+109
-87
@@ -1,3 +1,5 @@
|
||||
from __future__ import print_function
|
||||
from math import ceil
|
||||
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
|
||||
from keras.layers import BatchNormalization, Activation, Input, Dropout, ZeroPadding2D, Lambda
|
||||
from keras.layers.merge import Concatenate, Add
|
||||
@@ -6,137 +8,153 @@ from keras.optimizers import SGD
|
||||
|
||||
import tensorflow as tf
|
||||
|
||||
learning_rate = 1e-3 # Layer specific learning rate
|
||||
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)
|
||||
|
||||
def Interp(x, shape):
|
||||
from keras.backend import tf as ktf
|
||||
new_height, new_width = shape
|
||||
resized = ktf.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)
|
||||
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 is False:
|
||||
prev = Conv2D(64 * level, (1, 1), strides=(1, 1), name=names[0], use_bias=False)(prev)
|
||||
elif modify_stride is 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 = 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 = 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"]
|
||||
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)
|
||||
if modify_stride is False:
|
||||
prev = Conv2D(256 * level, (1, 1), strides=(1, 1), name=names[0], use_bias=False)(prev)
|
||||
elif modify_stride is 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)
|
||||
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)
|
||||
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_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
|
||||
|
||||
def ResNet(inp, layers):
|
||||
# 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"]
|
||||
"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)
|
||||
# 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"
|
||||
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"
|
||||
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(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"
|
||||
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)
|
||||
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)
|
||||
# 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)
|
||||
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)
|
||||
# 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)
|
||||
res = residual_empty(res, 2, pad=1, lvl=3, sub_lvl=i+2)
|
||||
if layers is 50:
|
||||
# 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)
|
||||
elif layers is 101:
|
||||
# 4_1 - 4_23
|
||||
res = residual_short(res, 4, pad=2, lvl=4, sub_lvl=1)
|
||||
for i in range(22):
|
||||
res = residual_empty(res, 4, pad=2, lvl=4, sub_lvl=i+2)
|
||||
else:
|
||||
print("This ResNet is not implemented")
|
||||
|
||||
#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)
|
||||
# 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):
|
||||
|
||||
def interp_block(prev_layer, level, feature_map_shape, str_lvl=1, ):
|
||||
|
||||
str_lvl = str(str_lvl)
|
||||
|
||||
@@ -147,45 +165,49 @@ def interp_block(prev_layer, level, str_lvl=1):
|
||||
|
||||
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 = 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)
|
||||
prev_layer = Lambda(Interp, arguments={'shape': feature_map_shape})(prev_layer)
|
||||
return prev_layer
|
||||
|
||||
def PSPNet(res):
|
||||
|
||||
#---PSPNet concat layers with Interpolation
|
||||
def PSPNet(res, input_shape):
|
||||
"""Build the Pyramid Pooling Module."""
|
||||
# ---PSPNet concat layers with Interpolation
|
||||
feature_map_size = tuple(int(ceil(input_dim / 8.0)) for input_dim in input_shape)
|
||||
print("PSP module will interpolate to a final feature map size of %s" % (feature_map_size, ))
|
||||
|
||||
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)
|
||||
interp_block1 = interp_block(res, 6, feature_map_size, str_lvl=1)
|
||||
interp_block2 = interp_block(res, 3, feature_map_size, str_lvl=2)
|
||||
interp_block3 = interp_block(res, 2, feature_map_size, str_lvl=3)
|
||||
interp_block6 = interp_block(res, 1, feature_map_size, str_lvl=6)
|
||||
|
||||
#concat all these layers. resulted shape=(1,60,60,4096)
|
||||
# concat all these layers. resulted shape=(1,60,60,4096)
|
||||
res = Concatenate()([res,
|
||||
interp_block6,
|
||||
interp_block3,
|
||||
interp_block2,
|
||||
interp_block1])
|
||||
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)
|
||||
|
||||
def build_pspnet(nb_classes, resnet_layers, input_shape, activation='softmax'):
|
||||
"""Build PSPNet."""
|
||||
print("Building a PSPNet based on ResNet %i expecting inputs of shape %s predicting %i classes" % (resnet_layers, input_shape, nb_classes))
|
||||
|
||||
inp = Input((input_shape[0], input_shape[1], 3))
|
||||
res = ResNet(inp, layers=resnet_layers)
|
||||
psp = PSPNet(res, input_shape)
|
||||
|
||||
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 = Conv2D(nb_classes, (1, 1), strides=(1, 1), name="conv6")(x)
|
||||
x = Lambda(Interp, arguments={'shape': (input_shape[0], input_shape[1])})(x)
|
||||
x = Activation('softmax')(x)
|
||||
|
||||
model = Model(inputs=inp, outputs=x)
|
||||
@@ -193,6 +215,6 @@ def build_pspnet(activation='softmax'):
|
||||
# Solver
|
||||
sgd = SGD(lr=learning_rate, momentum=0.9, nesterov=True)
|
||||
model.compile(optimizer=sgd,
|
||||
loss='categorical_crossentropy',
|
||||
metrics=['accuracy'])
|
||||
loss='categorical_crossentropy',
|
||||
metrics=['accuracy'])
|
||||
return model
|
||||
|
||||
Reference in New Issue
Block a user