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bgsCNN_v3.py
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bgsCNN_v3.py
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from utilities import *
import numpy as np
import tensorflow as tf
from tensorflow.contrib.slim.nets import resnet_v2
from tensorflow.contrib import slim
class bgsCNN_v3:
def __init__(self,
train_file = "train.tfrecords", test_file = "test.tfrecords",
log_dir = "logs",
train_batch_size = 40, test_batch_size = 200,
max_iteration = 10000,
image_height = 321, image_width = 321, image_depth = 7):
self.train_file = train_file
self.test_file = test_file
self.log_dir = log_dir
self.train_batch_size = train_batch_size
self.test_batch_size = test_batch_size
self.max_iteration = max_iteration
self.image_height = image_height
self.image_width = image_width
self.image_depth = image_depth
self.build_inputs()
self.build_model()
self.build_loss()
self.build_optimizer()
self.build_summary()
def build_inputs(self):
with tf.name_scope("input_data"):
self.input_data = tf.placeholder(tf.float32, [None, self.image_height, self.image_height, 6])
self.gt = tf.placeholder(tf.float32, [None, self.image_height, self.image_height, 1])
self.learning_rate = tf.placeholder(tf.float32, [])
self.batch_size = tf.placeholder(tf.int32, [])
self.is_training = tf.placeholder(tf.bool, []) # not used
frame = tf.slice(self.input_data, [0,0,0,0], [-1,self.image_height, self.image_height, 3])
bg = tf.slice(self.input_data, [0,0,0,3], [-1,self.image_height, self.image_height, 3])
tf.summary.image("frame", frame, max_outputs=3)
tf.summary.image("background", bg, max_outputs=3)
tf.summary.image("groundtruth", self.gt, max_outputs=3)
def build_model(self):
with tf.name_scope("pre_conv"):
# shape: 321X321X3
W_pre = weight([1, 1, 6, 3], "weights")
pre_conv = conv2d(self.input_data, W_pre)
tf.summary.histogram("W_pre_conv", W_pre)
tf.summary.image("pre_conv_out", pre_conv, max_outputs=3)
with tf.name_scope("resnet_v2"):
# shape: 21X21X2048
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
net, end_points = resnet_v2.resnet_v2_50(
pre_conv,
num_classes = None,
is_training = True,
global_pool = False,
output_stride = 16)
with tf.name_scope("feature_reduction"):
# shape: 21X21X26
net = pool3d(net, [1,48,1,1,1], [1,25,1,1,1], 'avg')
net = pool3d(net, [1,6,1,1,1], [1,3,1,1,1], 'avg')
with tf.name_scope("deconv_1"):
# shape: 81X81X16
W_deconv1 = weight([1, 1, 16, 26], "weights")
deconv_1 = deconv2d(net, W_deconv1,
output_shape = [self.batch_size, 81, 81, 16], strides = [1, 4, 4, 1])
tf.summary.histogram("W_deconv1", W_deconv1)
tf.summary.image("channel1", tf.slice(deconv_1, [0,0,0,0],[-1,81,81,1]), max_outputs=3)
tf.summary.image("channel2", tf.slice(deconv_1, [0,0,0,1],[-1,81,81,1]), max_outputs=3)
tf.summary.image("channel3", tf.slice(deconv_1, [0,0,0,2],[-1,81,81,1]), max_outputs=3)
tf.summary.image("channel4", tf.slice(deconv_1, [0,0,0,3],[-1,81,81,1]), max_outputs=3)
with tf.name_scope("deconv_2"):
# shape: 165X165X8
W_deconv2 = weight([5, 5, 8, 16], "weights")
deconv_2 = deconv2d(deconv_1, W_deconv2,
output_shape = [self.batch_size, 165, 165, 8], strides = [1, 2, 2, 1])
tf.summary.histogram("W_deconv2", W_deconv2)
tf.summary.image("channel1", tf.slice(deconv_2, [0,0,0,0],[-1,165,165,1]), max_outputs=3)
tf.summary.image("channel2", tf.slice(deconv_2, [0,0,0,1],[-1,165,165,1]), max_outputs=3)
tf.summary.image("channel3", tf.slice(deconv_2, [0,0,0,2],[-1,165,165,1]), max_outputs=3)
tf.summary.image("channel4", tf.slice(deconv_2, [0,0,0,3],[-1,165,165,1]), max_outputs=3)
with tf.name_scope("deconv_3"):
# shape: 333X333X4
W_deconv3 = weight([5, 5, 4, 8], "weights")
deconv_3 = deconv2d(deconv_2, W_deconv3,
output_shape = [self.batch_size, 333, 333, 4], strides = [1, 2, 2, 1])
tf.summary.histogram("W_deconv3", W_deconv3)
tf.summary.image("channel1", tf.slice(deconv_3, [0,0,0,0],[-1,333,333,1]), max_outputs=3)
tf.summary.image("channel2", tf.slice(deconv_3, [0,0,0,1],[-1,333,333,1]), max_outputs=3)
tf.summary.image("channel3", tf.slice(deconv_3, [0,0,0,2],[-1,333,333,1]), max_outputs=3)
tf.summary.image("channel4", tf.slice(deconv_3, [0,0,0,3],[-1,333,333,1]), max_outputs=3)
with tf.name_scope("conv_1"):
# shape: 327X327X1
W_conv1 = weight([7, 7, 4, 1], "weights")
conv_1 = conv2d(deconv_3, W_conv1)
tf.summary.histogram("W_conv1", W_conv1)
tf.summary.image("conv1_out", conv_1, max_outputs=3)
with tf.name_scope("conv_2"):
# shape: 321X321X1
W_conv2 = weight([7, 7, 1, 1], "weights")
conv_2 = conv2d(conv_1, W_conv2)
tf.summary.histogram("W_conv2", W_conv2)
tf.summary.image("conv2_out", conv_2, max_outputs=3)
with tf.name_scope("final_result"):
output = tf.nn.sigmoid(conv_2)
result = 255 * tf.cast(output + 0.5, tf.uint8)
tf.summary.image("sigmoid_out", output, max_outputs=3)
tf.summary.image("segmentation", result, max_outputs=3)
self.logits = conv_2
self.output = output
def build_loss(self):
with tf.name_scope("evaluation"):
self.cross_entropy = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels = self.gt, logits = self.logits))
tf.summary.scalar("loss", self.cross_entropy)
def build_optimizer(self):
with tf.name_scope('training_op'):
optimizer = tf.train.AdamOptimizer(learning_rate = self.learning_rate)
self.train_step = optimizer.minimize(self.cross_entropy)
def build_summary(self):
self.summary = tf.summary.merge_all()
def train(self):
img_size = [self.image_height, self.image_width, self.image_depth]
train_batch = tf.train.shuffle_batch([read_tfrecord(self.train_file, img_size)],
batch_size = self.train_batch_size,
capacity = 3000,
num_threads = 2,
min_after_dequeue = 1000)
test_batch = tf.train.shuffle_batch([read_tfrecord(self.test_file, img_size)],
batch_size = self.test_batch_size,
capacity = 500,
num_threads = 2,
min_after_dequeue = 300)
init = tf.global_variables_initializer()
init_fn = slim.assign_from_checkpoint_fn("resnet_v2_50.ckpt", slim.get_model_variables('resnet_v2'))
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
init_fn(sess)
train_writer = tf.summary.FileWriter(self.log_dir + "/train", sess.graph)
test_writer = tf.summary.FileWriter(self.log_dir + "/test", sess.graph)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
inputs_test, outputs_gt_test = build_img_pair(sess.run(test_batch))
for iter in range(self.max_iteration):
inputs_train, outputs_gt_train = build_img_pair(sess.run(train_batch))
# train with dynamic learning rate
if iter <= 500:
self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
self.learning_rate:1e-3, self.batch_size:self.train_batch_size})
elif iter <= self.max_iteration - 1000:
self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
self.learning_rate:0.5e-3, self.batch_size:self.train_batch_size})
else:
self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
self.learning_rate:1e-4, self.batch_size:self.train_batch_size})
# print training loss and test loss
if iter%10 == 0:
summary_train = sess.run(self.summary, {self.input_data:inputs_train, self.gt:outputs_gt_train,
self.batch_size:self.train_batch_size})
train_writer.add_summary(summary_train, iter)
train_writer.flush()
summary_test = sess.run(self.summary, {self.input_data:inputs_test, self.gt:outputs_gt_test,
self.batch_size:self.test_batch_size})
test_writer.add_summary(summary_test, iter)
test_writer.flush()
# record training loss and test loss
if iter%10 == 0:
train_loss = self.cross_entropy.eval({self.input_data:inputs_train, self.gt:outputs_gt_train,
self.batch_size:self.train_batch_size})
test_loss = self.cross_entropy.eval({self.input_data:inputs_test, self.gt:outputs_gt_test,
self.batch_size:self.test_batch_size})
print("iter step %d trainning batch loss %f"%(iter, train_loss))
print("iter step %d test loss %f\n"%(iter, test_loss))
# record model
if iter%100 == 0:
saver.save(sess, self.log_dir + "/model.ckpt", global_step=iter)
coord.request_stop()
coord.join(threads)