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dilationv2_mobilenet.py
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dilationv2_mobilenet.py
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from models.basic.basic_model import BasicModel
from models.encoders.VGG import VGG16
from models.encoders.mobilenet import MobileNet
from layers.convolution import conv2d_transpose, conv2d, atrous_conv2d, depthwise_separable_conv2d
import numpy as np
import tensorflow as tf
from utils.misc import _debug
import pdb
class DilationV2MobileNet(BasicModel):
"""
FCN8s with MobileNet as an encoder Model Architecture
"""
def __init__(self, args):
super().__init__(args)
# init encoder
self.encoder = None
self.wd= self.args.weight_decay
# init network layers
self.upscore2 = None
self.score_feed1 = None
self.fuse_feed1 = None
self.upscore4 = None
self.score_feed2 = None
self.fuse_feed2 = None
self.upscore8 = None
self.targets_resize = self.args.targets_resize
def build(self):
print("\nBuilding the MODEL...")
self.init_input()
self.init_network()
self.init_output()
self.init_train()
self.init_summaries()
print("The Model is built successfully\n")
def init_input(self):
with tf.name_scope('input'):
self.x_pl = tf.placeholder(tf.float32,
[self.args.batch_size, self.params.img_height, self.params.img_width, 3])
self.y_pl = tf.placeholder(tf.int32, [self.args.batch_size, self.params.img_height//self.targets_resize,
self.params.img_width//self.targets_resize])
print('X_batch shape ', self.x_pl.get_shape().as_list(), ' ', self.y_pl.get_shape().as_list())
print('Afterwards: X_batch shape ', self.x_pl.get_shape().as_list(), ' ', self.y_pl.get_shape().as_list())
self.curr_learning_rate = tf.placeholder(tf.float32)
if self.params.weighted_loss:
self.wghts = np.zeros((self.args.batch_size, self.params.img_height, self.params.img_width),
dtype=np.float32)
self.is_training = tf.placeholder(tf.bool)
def init_network(self):
"""
Building the Network here
:return:
"""
# Init MobileNet as an encoder
self.encoder = MobileNet(x_input=self.x_pl, num_classes=self.params.num_classes,
pretrained_path=self.args.pretrained_path,
train_flag=self.is_training, width_multipler=1.0, weight_decay=self.args.weight_decay)
# Build Encoding part
self.encoder.build()
# Build Decoding part
with tf.name_scope('dilation_2'):
self.conv4_2 = atrous_conv2d('conv_ds_7_dil', self.encoder.conv4_1,
num_filters=512, kernel_size=(3, 3), padding='SAME',
activation=tf.nn.relu, dilation_rate=2,
batchnorm_enabled=True, is_training=self.is_training,
l2_strength=self.wd)
_debug(self.conv4_2)
self.conv5_1 = depthwise_separable_conv2d('conv_ds_8_dil', self.conv4_2,
width_multiplier=self.encoder.width_multiplier,
num_filters=512, kernel_size=(3, 3), padding='SAME',
stride=(1, 1), activation=tf.nn.relu,
batchnorm_enabled=True, is_training=self.is_training,
l2_strength=self.wd)
_debug(self.conv5_1)
self.conv5_2 = depthwise_separable_conv2d('conv_ds_9_dil', self.conv5_1,
width_multiplier=self.encoder.width_multiplier,
num_filters=512, kernel_size=(3, 3), padding='SAME',
stride=(1, 1), activation=tf.nn.relu,
batchnorm_enabled=True, is_training=self.is_training,
l2_strength=self.wd)
_debug(self.conv5_2)
self.conv5_3 = depthwise_separable_conv2d('conv_ds_10_dil', self.conv5_2,
width_multiplier=self.encoder.width_multiplier,
num_filters=512, kernel_size=(3, 3), padding='SAME',
stride=(1, 1), activation=tf.nn.relu,
batchnorm_enabled=True, is_training=self.is_training,
l2_strength=self.wd)
_debug(self.conv5_3)
self.conv5_4 = depthwise_separable_conv2d('conv_ds_11_dil', self.conv5_3,
width_multiplier=self.encoder.width_multiplier,
num_filters=512, kernel_size=(3, 3), padding='SAME',
stride=(1, 1), activation=tf.nn.relu,
batchnorm_enabled=True, is_training=self.is_training,
l2_strength=self.wd)
_debug(self.conv5_4)
self.conv5_5 = depthwise_separable_conv2d('conv_ds_12_dil', self.conv5_4,
width_multiplier=self.encoder.width_multiplier,
num_filters=512, kernel_size=(3, 3), padding='SAME',
stride=(1, 1), activation=tf.nn.relu,
batchnorm_enabled=True, is_training=self.is_training,
l2_strength=self.wd)
_debug(self.conv5_5)
self.conv5_6 = atrous_conv2d('conv_ds_13_dil', self.conv5_5,
num_filters=1024, kernel_size=(3, 3), padding='SAME',
activation=tf.nn.relu, dilation_rate=4,
batchnorm_enabled=True, is_training=self.is_training,
l2_strength=self.wd)
_debug(self.conv5_6)
self.conv6_1 = depthwise_separable_conv2d('conv_ds_14_dil', self.conv5_6,
width_multiplier=self.encoder.width_multiplier,
num_filters=1024, kernel_size=(3, 3), padding='SAME',
stride=(1, 1), activation=tf.nn.relu,
batchnorm_enabled=True, is_training=self.is_training,
l2_strength=self.wd)
_debug(self.conv6_1)
# Pooling is removed.
self.score_fr = conv2d('conv_1c_1x1_dil', self.conv6_1, num_filters=self.params.num_classes, l2_strength=self.wd,
batchnorm_enabled=True, is_training= self.is_training, kernel_size=(1, 1))
_debug(self.score_fr)
if self.targets_resize < 8:
self.targets_resize= 8 // self.targets_resize
self.upscore8 = conv2d_transpose('upscore8', x=self.score_fr,
output_shape=self.y_pl.shape.as_list()[0:3] + [self.params.num_classes],
kernel_size=(self.targets_resize*2, self.targets_resize*2),
stride=(self.targets_resize,self.targets_resize),
l2_strength=self.encoder.wd, is_training= self.is_training)
_debug(self.upscore8)
self.logits= self.upscore8
else:
self.logits= self.score_fr