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T19.py
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T19.py
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#coding=utf-8
import tensorflow as tf
import vgg19
#import IAF
import cv2
import numpy as np
from PIL import Image
import math
#alpha=0.1
#beta=1
img_size = 352
label_size = img_size
#img_t_mean=[85.971, 56.608, 151.944]
class Model:
def __init__(self):
#self.vgg_t = vgg16.Vgg16()
#self.input_holder_t = tf.placeholder(tf.float32, [1, img_size, img_size, 3])
#self.label_holder = tf.placeholder(tf.float32, [label_size*label_size, 2])
#self.contour_th = 1.5
#self.contour_weight = 0.0001
#self.sobel_fx, self.sobel_fy = self.sobel_filter()
self.data_dict = np.load('vgg19.npy', encoding='latin1').item()
print("T file loaded")
def build_model(self, input):
#build the VGG-16 model
#vgg_t = self.vgg_t
#vgg_t.build(self.input_holder_t)
self.conv1_1 = self._conv_layer(input, 1, "conv1_1")
self.conv1_2 = self._conv_layer(self.conv1_1, 1, "conv1_2")
self.pool1 = self._max_pool(self.conv1_2, 'pool1')
self.conv2_1 = self._conv_layer(self.pool1, 1, "conv2_1")
self.conv2_2 = self._conv_layer(self.conv2_1, 1,"conv2_2")
self.pool2 = self._max_pool(self.conv2_2, 'pool2')
self.conv3_1 = self._conv_layer(self.pool2, 1, "conv3_1")
self.conv3_2 = self._conv_layer(self.conv3_1, 1, "conv3_2")
self.conv3_3 = self._conv_layer(self.conv3_2, 1, "conv3_3")
self.conv3_4 = self._conv_layer(self.conv3_3, 1, "conv3_4")
self.pool3 = self._max_pool(self.conv3_4, 'pool3')
self.conv4_1 = self._conv_layer(self.pool3, 1, "conv4_1")
self.conv4_2 = self._conv_layer(self.conv4_1, 1, "conv4_2")
self.conv4_3 = self._conv_layer(self.conv4_2, 1, "conv4_3")
self.conv4_4 = self._conv_layer(self.conv4_3, 1, "conv4_4")
self.pool4 = self.s_max_pool(self.conv4_4, 'pool4')
self.conv5_1 = self._conv_layer(self.pool4, 2, "conv5_1")
self.conv5_2 = self._conv_layer(self.conv5_1, 2, "conv5_2")
self.conv5_3 = self._conv_layer(self.conv5_2, 2, "conv5_3")
self.conv5_4 = self._conv_layer(self.conv5_3, 2, "conv5_4")
self.pool5 = self.s_max_pool(self.conv5_4, 'pool5')
tconv2_3 = tf.nn.relu(self._dilated_conv2d(self.conv2_2, 3, 128, 1, 0.01, 'tconv2_3', biased = True))
#tconv2_3_ = tf.nn.relu(self.Conv_2d(tf.concat([tconv2_2_, tconv2_3], axis=3),
#[1, 1, 192, 128], 0.01, padding='SAME', name='tconv2_3_'))
tpool2_4 = tf.nn.max_pool(tconv2_3, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME', name='tpool2_4')
tconv2_4 = tf.nn.relu(self._dilated_conv2d(tpool2_4, 3, 128, 3, 0.01, 'tconv2_4', biased = True))
#rconv2_4_ = tf.nn.relu(self.Conv_2d(tf.concat([rconv2_3, rconv2_4], axis=3),
#[3, 3, 128, 64], 0.01, padding='SAME', name='rconv2_4_'))
tpool2_5 = tf.nn.max_pool(tconv2_4, ksize=[1, 5, 5, 1], strides=[1, 1, 1, 1], padding='SAME', name='tpool2_5')
tconv2_5 = tf.nn.relu(self._dilated_conv2d(tpool2_5,
3, 64, 5, 0.01, 'tconv2_5', biased = True))
tpool2_6 = tf.nn.max_pool(tconv2_5, ksize=[1, 7, 7, 1], strides=[1, 1, 1, 1], padding='SAME', name='tpool2_6')
tconv2_6 = tf.nn.relu(self._dilated_conv2d(tpool2_6, 3, 64, 7, 0.01, 'tconv2_6', biased = True))
self.tconv2_5_ = tf.nn.relu(self.Conv_2d(tf.concat([tconv2_3, tconv2_4, tconv2_5, tconv2_6], axis=3),
[3, 3, 384, 128], 0.01, padding='SAME', name='tconv2_5_'))
tconv3_4 = tf.nn.relu(self._dilated_conv2d(self.conv3_4, 3, 128, 1, 0.01, 'tconv3_4', biased = True))
#tconv3_4_ = tf.nn.relu(self.Conv_2d(tf.concat([tconv3_3_ , tconv3_4], axis=3),
#[1, 1, 192, 128], 0.01, padding='SAME', name='tconv3_4_'))
tpool3_5 = tf.nn.max_pool(tconv3_4, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME', name='tpool3_5')
tconv3_5 = tf.nn.relu(self._dilated_conv2d(tpool3_5, 3, 128, 3, 0.01, 'tconv3_5', biased = True))
#conv3_5_ = tf.nn.relu(self.Conv_2d(tf.concat([conv3_4, conv3_5], axis=3),
#[1, 1, 128, 64], 0.01, padding='SAME', name='conv3_5_'))
tpool3_6 = tf.nn.max_pool(tconv3_5, ksize=[1, 5, 5, 1], strides=[1, 1, 1, 1], padding='SAME', name='tpool3_6')
tconv3_6 = tf.nn.relu(self._dilated_conv2d(tpool3_6,
3, 64, 5, 0.01, 'tconv3_6', biased = True))
tpool3_7 = tf.nn.max_pool(tconv3_6, ksize=[1, 7, 7, 1], strides=[1, 1, 1, 1], padding='SAME', name='tpool3_7')
tconv3_7 = tf.nn.relu(self._dilated_conv2d(tpool3_7,
3, 64, 7, 0.01, 'tconv3_7', biased = True))
self.tconv3_6_ = tf.nn.relu(self.Conv_2d(tf.concat([tconv3_4, tconv3_5, tconv3_6, tconv3_7], axis=3),
[3, 3, 384, 128], 0.01, padding='SAME', name='tconv3_6_'))
#pool5_ = self._max_pool(pool4_s, 'pool5_')
#pool5_s = tf.nn.relu(self.Conv_2d(pool5_, [3, 3, 512, 512], 0.01, padding='SAME', name='pool5_s'))
#pool5_1 = tf.nn.relu(self.Conv_2d(pool5_s, [5, 5, 512, 128], 0.01, padding='SAME', name='pool5_1'))
#pool5_up = self.upsampling_2d(pool5_1, name='pool5_up', size = (4, 4))
self.conv5_3_t = tf.nn.relu(self.Conv_2d(self.pool5, [3, 3, 512, 256], 0.01, padding='SAME', name='conv5_3_t'))
#conv5_3_ = tf.nn.relu(self.Conv_2d(tf.concat([conv5_3_r, pool4_up], axis=3),
#[1, 1, 256, 128], 0.01, padding='SAME', name='conv5_3_'))
#conv6_1 = tf.nn.relu(self._dilated_conv2d(conv5_3_r, 3, 128, 1, 0.01, 'conv6_1', biased = True))
#conv6_1_ = tf.nn.relu(self.Conv_2d(tf.concat([conv5_3_r, conv6_1], axis=3),
#[1, 1, 192, 128], 0.01, padding='SAME', name='conv6_1_'))
#pool6_2 = tf.nn.max_pool(conv5_3_r, ksize=[1, 2, 2, 1], strides=[1, 1, 1, 1],
#padding='SAME', name='pool6_2')
tconv6_2 = tf.nn.relu(self._dilated_conv2d(self.conv5_3_t, 3, 128, 2, 0.01, 'tconv6_2', biased = True))
#conv6_2_ = tf.nn.relu(self.Conv_2d(tf.concat([self.conv5_3_t, conv6_2], axis=3),
#[3, 3, 384, 256], 0.01, padding='SAME', name='conv6_2_'))
#pool6_3 = tf.nn.avg_pool(conv6_2, ksize=[1, 5, 5, 1], strides=[1, 1, 1, 1], padding='SAME')
pool6_3 = tf.nn.max_pool(tconv6_2, ksize=[1, 5, 5, 1], strides=[1, 1, 1, 1], padding='SAME', name='pool6_3')
tconv6_3 = tf.nn.relu(self._dilated_conv2d(pool6_3, 3, 128, 5, 0.01, 'tconv6_3', biased = True))
tconv6_3_ = tf.nn.relu(self.Conv_2d(tf.concat([self.conv5_3_t, tconv6_3], axis=3), [1, 1, 384, 256], 0.01, padding='SAME', name='tconv6_3_'))
#conv6_3_ = tf.nn.relu(self.Conv_2d(tf.concat([conv6_1, conv6_2, conv6_3], axis=3),
#[1, 1, 256, 128], 0.01, padding='SAME', name='conv6_3_'))
#pool6_4 = tf.nn.avg_pool(conv6_3, ksize=[1, 9, 9, 1], strides=[1, 1, 1, 1], padding='SAME'
pool6_4 = tf.nn.max_pool(tconv6_3_, ksize=[1, 9, 9, 1], strides=[1, 1, 1, 1], padding='SAME', name='pool6_4')
tconv6_4 = tf.nn.relu(self._dilated_conv2d(pool6_4, 3, 128, 9, 0.01, 'tconv6_4', biased = True))
#conv6_4_ = tf.nn.relu(self.Conv_2d(conv6_4,
#[1, 1, 128, 64], 0.01, padding='SAME', name='conv6_4_'))
#pool6_5 = tf.nn.avg_pool(conv6_4, ksize=[1, 15, 15, 1], strides=[1, 1, 1, 1], padding='SAME')
pool6_5 = tf.nn.max_pool(tconv6_4, ksize=[1, 15, 15, 1], strides=[1, 1, 1, 1], padding='SAME', name='pool6_5')
tconv6_5 = tf.nn.relu(self._dilated_conv2d(pool6_5, 3, 128, 15, 0.01, 'tconv6_5', biased = True))
#conv6_5_ = tf.nn.relu(self.Conv_2d(conv6_5,
#[1, 1, 128, 64], 0.01, padding='SAME', name='conv6_5_'))
self.tconv6_6 = tf.nn.relu(self.Conv_2d(tf.concat([self.conv5_3_t, tconv6_2, tconv6_3, tconv6_4, tconv6_5], axis=3),
[1, 1, 768, 128], 0.01, padding='SAME', name='tconv6_6'))
#self.score3 = self.Conv_2d(conv6_5_ ,
#[1, 1, 128, 2], 0.01, padding='SAME', name = 'score3' )
#conv6_up = self.upsampling_2d(conv6_5_, name='conv6_up', size = (2, 2))
#conv6_up2 = self.upsampling_2d(conv6_5_, name='conv6_up2', size = (4, 4))
#decoder1 = conv6_up + conv3
#decoder1 = self.Conv_2d(tf.concat([conv6_up, conv3], axis=3),
#[1, 1, 320, 128], 0.01, padding='SAME', name='decoder1')
#self.decoder1_ = tf.nn.relu(self.Conv_2d(decoder1, [3, 3, 128, 128], 0.01, padding='SAME', name='decoder1_'))
#decoder1_up = self.Conv_2d(decoder1_, [1, 1, 128, 256], 0.01, padding='SAME', name='decoder1_up')
#self.score2 = self.Conv_2d(decoder1_,
#[3, 3, 128, 2], 0.01, padding='SAME', name = 'score2' )
#decoder_up = self.upsampling_2d(self.decoder1_, name='decoder_up', size = (2, 2))
#decoder2 = decoder_up + conv2 + conv6_up2 #tf.nn.relu()
#decoder2 = self.Conv_2d(tf.concat([conv6_up, conv2], axis=3),
#[1, 1, 256, 128], 0.01, padding='SAME', name='decoder2')
#self.decoder2_ = tf.nn.relu(self.Conv_2d(decoder2, [3, 3, 128, 128], 0.01, padding='SAME', name='decoder2_'))
#decoder2_up = self.Conv_2d(decoder2_, [1, 1, 128, 256], 0.01, padding='SAME', name='decoder2_up')
#conv6_5 = tf.nn.relu(self._dilated_conv2d(conv6_4_ , 3, 64, 13, 0.01, 'conv6_5', biased = True))
#conv6_5_ = self.Conv_2d(tf.concat([conv5_3_r, conv6_1, conv6_2, conv6_3, conv6_4, conv6_5], axis=3),
#[1, 1, 448, 128], 0.01, padding='SAME', name='conv6_5_')
#self.decoder3 = self.Conv_2d(self.conv6_5_, [3, 3, 128, 128], 0.01, padding='SAME', name='decoder3')#self.decoder2_ + conv6_up2
# self.score_t = self.Conv_2d(self.conv6_6,
#[3, 3, 128, 2], 0.01, padding='SAME', name = 'score_t' )
#self.score_t = tf.nn.relu(self.Deconv_2d(self.score_t_up, [1, 128, 128, 2], 4, 4, name='score_t'))
#self.score_t = self.upsampling_2d(self.score_t_up, name='score_t', size = (4, 4))
# self.Score = self.upsampling_2d(self.score_t, name='Score') #, size = (2, 2)
#self.Score = tf.reshape(self.Score, [-1,2])
#self.Prob = tf.nn.softmax(self.Score)
#Get the contour term
#self.Prob_C = tf.reshape(self.Prob, [1, 256, 256, 2])
#self.Prob_Grad = tf.tanh(self.im_gradient(self.Prob_C))
#self.Prob_Grad = tf.tanh(tf.reduce_sum(self.im_gradient(self.Prob_C), reduction_indices=3, keep_dims=True))
#self.label_C = tf.reshape(self.label_holder, [1, 256, 256, 2])
#self.label_Grad = tf.cast(tf.greater(self.im_gradient(self.label_C), self.contour_th), tf.float32)
#self.label_Grad = tf.cast(tf.greater(tf.reduce_sum(self.im_gradient(self.label_C),
#reduction_indices=3, keep_dims=True),
#self.contour_th), tf.float32)
#self.C_IoU_LOSS = self.Loss_IoU(self.Prob_Grad, self.label_Grad)
#self.Loss_Mean = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.Score,
#labels=self.label_holder))
#self.Loss = self.Loss_Mean + self.C_IoU_LOSS
#
#self.Score = tf.reshape(self.Score, [-1,2])
#self.correct_prediction = tf.equal(tf.argmax(self.Score,1), tf.argmax(self.label_holder, 1))
#self.accuracy = tf.reduce_mean(tf.cast(self.correct_prediction, tf.float32))
def Conv_2d(self, input_, shape, stddev, name, padding='SAME'):
with tf.variable_scope(name) as scope:
#W = tf.Variable(self.data_dict[name + '/W'], name="weights")#self.get_conv_filter(name)
W = tf.get_variable('W',
shape=shape,
initializer=tf.truncated_normal_initializer(stddev=stddev))
conv = tf.nn.conv2d(input_, W, [1, 1, 1, 1], padding=padding)
b = tf.Variable(tf.constant(0.0, shape=[shape[3]]), name='b')
#b = tf.Variable(self.data_dict[name + '/b'], name="biases")#self.get_bias(name)
conv = tf.nn.bias_add(conv, b)
return conv
def Conv_2d_down(self, input_, shape, stddev, name, padding='SAME'):
with tf.variable_scope(name) as scope:
W = tf.get_variable('W',
shape=shape,
initializer=tf.truncated_normal_initializer(stddev=stddev))
conv = tf.nn.conv2d(input_, W, [1, 2, 2, 1], padding=padding)
b = tf.Variable(tf.constant(0.0, shape=[shape[3]]), name='b')
conv = tf.nn.bias_add(conv, b)
return conv
def Deconv_2d(self, input_, output_shape,
k_s=3, st_s=2, stddev=0.01, padding='SAME', name="deconv2d"):
with tf.variable_scope(name):
W = tf.get_variable('W',
shape=[k_s, k_s, output_shape[3], input_.get_shape()[3]],
initializer=tf.random_normal_initializer(stddev=stddev))
deconv = tf.nn.conv2d_transpose(input_, W, output_shape=output_shape,
strides=[1, st_s, st_s, 1], padding=padding)
b = tf.get_variable('b', [output_shape[3]], initializer=tf.constant_initializer(0.0))
deconv = tf.nn.bias_add(deconv, b)
return deconv
def _dilated_conv2d(self, x, kernel_size, num_o, dilation_factor, stddev ,name, biased=False):
"""
Dilated conv2d without BN or relu.
"""
num_x = x.shape[3].value
with tf.variable_scope(name) as scope:
#w = tf.Variable(self.data_dict[name + '/weights'], name="weights")#self.get_conv_filter(name)
w = tf.get_variable('weights', shape=[kernel_size, kernel_size, num_x, num_o],
initializer=tf.truncated_normal_initializer(stddev=stddev))
o = tf.nn.atrous_conv2d(x, w, dilation_factor, padding='SAME')
if biased:
#b = tf.Variable(self.data_dict[name + '/biases'], name="biases")#self.get_bias(name)
b = tf.get_variable('biases', shape=[num_o], initializer=tf.constant_initializer(0.0))
o = tf.nn.bias_add(o, b)
return o
def upsampling_2d(self, tensor, name, size=(8,8)):
h_,w_,c_ = tensor.get_shape().as_list()[1:]
h_multi,w_multi = size
h = h_multi * h_
w = w_multi * w_
target = tf.image.resize_bilinear(tensor,size=(h,w),name='deconv_{}'.format(name))
return target
def sobel_filter(self):
fx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]).astype(np.float32)
fy = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]]).astype(np.float32)
fx = np.stack((fx, fx), axis=2)
fy = np.stack((fy, fy), axis=2)
fx = np.reshape(fx, (3, 3, 2, 1))
fy = np.reshape(fy, (3, 3, 2, 1))
tf_fx = tf.Variable(tf.constant(fx))
tf_fy = tf.Variable(tf.constant(fy))
return tf_fx, tf_fy
def im_gradient(self, im):
gx = tf.nn.depthwise_conv2d(tf.pad(im, [[0, 0], [1, 1], [1, 1], [0, 0]], 'SYMMETRIC'),
self.sobel_fx, [1, 1, 1, 1], padding='VALID')
gy = tf.nn.depthwise_conv2d(tf.pad(im, [[0, 0], [1, 1], [1, 1], [0, 0]], 'SYMMETRIC'),
self.sobel_fy, [1, 1, 1, 1], padding='VALID')
return tf.sqrt(tf.add(tf.square(gx), tf.square(gy)))
def Loss_IoU(self, pred, gt):
inter = tf.reduce_sum(tf.multiply(pred, gt))
union = tf.add(tf.reduce_sum(tf.square(pred)), tf.reduce_sum(tf.square(gt)))
if inter == 0:
return 0
else:
return 1 - (2*(inter+1)/(union + 1))
def L2(self, tensor, wd=0.0005):
return tf.mul(tf.nn.l2_loss(tensor), wd, name='L2-Loss')
def _max_pool(self, bottom, name):
return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
padding='SAME', name=name)
def s_max_pool(self, bottom, name):
return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 1, 1, 1],
padding='SAME', name=name)
def _conv_layer(self, bottom, rate, name):
with tf.variable_scope(name) as scope:
filt = self.get_conv_filter(name)#tf.Variable(self.data_dict[name + '/filter'], name="weights")#
#conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')
conv = tf.nn.atrous_conv2d(bottom, filt, rate, padding = 'SAME')
conv_biases = self.get_bias(name)#tf.Variable(self.data_dict[name + '/biases'], name="biases")#
bias = tf.nn.bias_add(conv, conv_biases)
relu = tf.nn.relu(bias)
return relu
def get_conv_filter(self, name):
return tf.Variable(self.data_dict[name][0], name="filter")
def get_bias(self, name):
return tf.Variable(self.data_dict[name][1], name="biases")