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fully_conv_resnet_15_channels_3skips.py
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fully_conv_resnet_15_channels_3skips.py
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from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
from six.moves import xrange
import datetime
import Batch_manager_5channels as dataset
import data_reader_5channels as reader
import tensor_utils_5_channels as utils
from build_resnet_graph_15channels import resnet101_net
from sys import argv
from os.path import join
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_integer("batch_size", "32", "batch size for training")
tf.flags.DEFINE_string("logs_dir", "../logs-resnet101_15channels_3skips/", "path to logs directory")
tf.flags.DEFINE_string("data_dir", "../ISPRS_semantic_labeling_Vaihingen", "path to dataset")
tf.flags.DEFINE_float("learning_rate", "1e-4", "Learning rate for Adam Optimizer")
tf.flags.DEFINE_string("model_dir", "../pretrained_models/imagenet-resnet-101-dag.mat", "Path to model mat")
tf.flags.DEFINE_bool('debug', "False", "Debug mode: True/ False")
tf.flags.DEFINE_string('mode', "train", "Mode train/ test/ visualize")
MODEL_URL = 'http://www.vlfeat.org/matconvnet/models/imagenet-resnet-101-dag.mat'
MAX_ITERATION = int(153600 + 1) # 25 epochs
# MAX_ITERATION = int(76800 + 1) # 25 epochs
NUM_OF_CLASSES = 6
IMAGE_SIZE = 224
def inference(image, keep_prob, is_training):
"""
Semantic segmentation network definition
:param image: input image. Should have values in range 0-255
:param keep_prob:
:return:
"""
print(">> Setting up resnet-101 pretrained layers ...")
resnet101_model = utils.get_model_data(FLAGS.model_dir)
weights = np.squeeze(resnet101_model['params'])
mean_pixel_init = resnet101_model['meta'][0][0][2][0][0][2]
mean_pixel = np.zeros((IMAGE_SIZE, IMAGE_SIZE, 15))
mean_pixel[:, :, 0] = mean_pixel_init[:, :, 0]
mean_pixel[:, :, 1] = mean_pixel_init[:, :, 1]
mean_pixel[:, :, 2] = mean_pixel_init[:, :, 2]
mean_pixel[:, :, 3] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * 30.69861307993539 # nDSM
mean_pixel[:, :, 4] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * 284.9702 # DSM
mean_pixel[:, :, 5] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * 136.495572072 # A
mean_pixel[:, :, 6] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * -0.0827414 # azi
mean_pixel[:, :, 7] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * 106.472206683 # B
mean_pixel[:, :, 8] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * 1.12959 # ele
mean_pixel[:, :, 9] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * 1.74663508206 # entpy
mean_pixel[:, :, 10] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * 2.01737815343 # entpy2
mean_pixel[:, :, 11] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * 91.7477946018 # L
mean_pixel[:, :, 12] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * 4.82043402578 # ndvi
mean_pixel[:, :, 13] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * 79.7208191052 # sat
mean_pixel[:, :, 14] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * 25.6281567428 # texton
normalised_img = utils.process_image(image, mean_pixel)
with tf.variable_scope("inference"):
net = resnet101_net(normalised_img, weights, keep_prob, is_training)
last_layer = net["res5c_relu"]
fc_filter = utils.weight_variable([1, 1, 2048, NUM_OF_CLASSES], name="fc_filter")
fc_bias = utils.bias_variable([NUM_OF_CLASSES], name="fc_bias")
fc = tf.nn.bias_add(tf.nn.conv2d(last_layer, fc_filter, strides=[1, 1, 1, 1], padding="SAME"), fc_bias, name='fc')
# now to upscale to actual image size
deconv_shape1 = net["res4b22_relu"].get_shape()
W_t1 = utils.weight_variable([4, 4, deconv_shape1[3].value, NUM_OF_CLASSES], name="W_t1")
b_t1 = utils.bias_variable([deconv_shape1[3].value], name="b_t1")
conv_t1 = utils.conv2d_transpose_strided(fc, W_t1, b_t1, output_shape=tf.shape(net["res4b22_relu"]))
fuse_1 = tf.add(conv_t1, net["res4b22_relu"], name="fuse_1")
deconv_shape2 = net["res3b3_relu"].get_shape()
W_t2 = utils.weight_variable([4, 4, deconv_shape2[3].value, deconv_shape1[3].value], name="W_t2")
b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2")
conv_t2 = utils.conv2d_transpose_strided(fuse_1, W_t2, b_t2, output_shape=tf.shape(net["res3b3_relu"]))
fuse_2 = tf.add(conv_t2, net["res3b3_relu"], name="fuse_2")
deconv_shape3 = net["res2c_relu"].get_shape()
W_t3 = utils.weight_variable([4, 4, deconv_shape3[3].value, deconv_shape2[3].value], name="W_t3")
b_t3 = utils.bias_variable([deconv_shape3[3].value], name="b_t3")
conv_t3 = utils.conv2d_transpose_strided(fuse_2, W_t3, b_t3, output_shape=tf.shape(net["res2c_relu"]))
fuse_3 = tf.add(conv_t3, net["res2c_relu"], name="fuse_3")
shape = tf.shape(image)
deconv_shape4 = tf.stack([shape[0], shape[1], shape[2], NUM_OF_CLASSES])
W_t4 = utils.weight_variable([8, 8, NUM_OF_CLASSES, deconv_shape3[3].value], name="W_t4")
b_t4 = utils.bias_variable([NUM_OF_CLASSES], name="b_t4")
conv_t4 = utils.conv2d_transpose_strided(fuse_3, W_t4, b_t4, output_shape=deconv_shape4, stride=4)
annotation_pred = tf.argmax(conv_t4, axis=3, name="prediction")
return tf.expand_dims(annotation_pred, dim=3), conv_t4
def train(loss_val, var_list):
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
if FLAGS.debug:
# print(len(var_list))
for grad, var in grads:
utils.add_gradient_summary(grad, var)
return optimizer.apply_gradients(grads)
def _decay(weight_decay):
"""L2 weight decay loss."""
costs = []
for var in tf.trainable_variables():
if var.op.name.find(r'W') > 0 or var.op.name.find(r'w') > 0 or var.op.name.find(r'filter') > 0:
costs.append(tf.nn.l2_loss(var))
tf.summary.histogram(var.op.name, var)
return tf.multiply(weight_decay, tf.add_n(costs))
def build_session(cuda_device):
os.environ["CUDA_VISIBLE_DEVICES"] = cuda_device
keep_probability = tf.placeholder(tf.float32, name="keep_probabilty")
image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 15], name="input_image")
annotation = tf.placeholder(tf.int32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 1], name="annotation")
is_training = tf.placeholder(tf.bool, name="is_training")
pred_annotation, logits = inference(image, keep_probability, is_training)
annotation_64 = tf.cast(annotation, dtype=tf.int64)
# calculate accuracy for batch.
cal_acc = tf.equal(pred_annotation, annotation_64)
cal_acc = tf.cast(cal_acc, dtype=tf.int8)
acc = tf.count_nonzero(cal_acc) / (FLAGS.batch_size * IMAGE_SIZE * IMAGE_SIZE)
tf.summary.image("input_image", image, max_outputs=2)
tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2)
tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2)
""" weight_decay = 1e-4
l2_loss = _decay(weight_decay)
cross_entropy = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=tf.squeeze(annotation, squeeze_dims=[3]), name="entropy")))
loss = l2_loss + cross_entropy
loss_summary = tf.summary.scalar("entropy_with_l2", loss) """
loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=tf.squeeze(annotation, squeeze_dims=[3]), name="entropy")))
loss_summary = tf.summary.scalar("entropy", loss)
# summary accuracy in tensorboard
acc_summary = tf.summary.scalar("accuracy", acc)
trainable_var = tf.trainable_variables()
if FLAGS.debug:
for var in trainable_var:
utils.add_to_regularization_and_summary(var)
train_op = train(loss, trainable_var)
print("Setting up summary op...")
summary_op = tf.summary.merge_all()
sess = tf.Session()
print("Setting up Saver...")
saver = tf.train.Saver(max_to_keep=15)
train_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/train', sess.graph)
validation_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/validation')
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored...")
return image, logits, is_training, keep_probability, sess, annotation, train_op, loss, acc, loss_summary, acc_summary, saver, pred_annotation, train_writer, validation_writer
def main(argv=None):
np.random.seed(3796)
image, logits, is_training, keep_probability, sess, annotation, train_op, loss, acc, loss_summary, acc_summary, saver, pred_annotation, train_writer, validation_writer = build_session(argv[1])
print("Setting up image reader...")
train_records, valid_records = reader.read_dataset_15_test(FLAGS.data_dir)
print(len(train_records))
print(len(valid_records))
print("Setting up dataset reader")
image_options = {'resize': False, 'resize_size': IMAGE_SIZE}
if FLAGS.mode == 'train':
train_dataset_reader = dataset.Batch_manager(train_records, image_options)
validation_dataset_reader = dataset.Batch_manager(valid_records, image_options)
""" os.environ["CUDA_VISIBLE_DEVICES"] = argv[1]
keep_probability = tf.placeholder(tf.float32, name="keep_probabilty")
image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="input_image")
annotation = tf.placeholder(tf.int32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 1], name="annotation")
pred_annotation, logits = inference(image, keep_probability)
annotation_64 = tf.cast(annotation, dtype=tf.int64)
# calculate accuracy for batch.
cal_acc = tf.equal(pred_annotation, annotation_64)
cal_acc = tf.cast(cal_acc, dtype=tf.int8)
acc = tf.count_nonzero(cal_acc) / (FLAGS.batch_size * IMAGE_SIZE * IMAGE_SIZE)
tf.summary.image("input_image", image, max_outputs=2)
tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2)
tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2)
loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=tf.squeeze(annotation,
squeeze_dims=[3]),
name="entropy")))
loss_summary=tf.summary.scalar("entropy", loss)
# summary accuracy in tensorboard
acc_summary=tf.summary.scalar("accuracy", acc)
trainable_var = tf.trainable_variables()
if FLAGS.debug:
for var in trainable_var:
utils.add_to_regularization_and_summary(var)
train_op = train(loss, trainable_var)
print("Setting up summary op...")
summary_op = tf.summary.merge_all()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
print("Setting up Saver...")
saver = tf.train.Saver()
# train_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/train', sess.graph)
# validation_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/validation')
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored...") """
if FLAGS.mode == "train":
for itr in xrange(MAX_ITERATION):
train_images, train_annotations = train_dataset_reader.next_batch_15(saver, FLAGS.batch_size, image, logits, keep_probability, sess, is_training, FLAGS.logs_dir)
feed_dict = {image: train_images, annotation: train_annotations, keep_probability: 0.5, is_training: True}
tf.set_random_seed(3796 + itr) # get deterministicly random dropouts
sess.run(train_op, feed_dict=feed_dict)
if itr % 50 == 0:
feed_dict = {image: train_images, annotation: train_annotations, keep_probability: 1.0, is_training: False}
train_loss, train_acc, summary_loss, summary_acc = sess.run([loss, acc, loss_summary, acc_summary], feed_dict=feed_dict)
print("Step: %d, Train_loss: %g, Train_acc: %g" % (itr, train_loss, train_acc))
with open(join(FLAGS.logs_dir, 'iter_train_loss.csv'), 'a') as f:
f.write(str(itr) + ',' + str(train_loss) + '\n')
with open(join(FLAGS.logs_dir, 'iter_train_acc.csv'), 'a') as f:
f.write(str(itr) + ',' + str(train_acc) + '\n')
train_writer.add_summary(summary_loss, itr)
train_writer.add_summary(summary_acc, itr)
if itr % 600 == 0:
valid_images, valid_annotations = validation_dataset_reader.next_batch_15(saver, FLAGS.batch_size, image, logits, keep_probability, sess, is_training, FLAGS.logs_dir, is_validation=True)
valid_loss, valid_acc, summary_loss, summary_acc = sess.run([loss, acc, loss_summary, acc_summary],
feed_dict={image: valid_images, annotation: valid_annotations,
keep_probability: 1.0, is_training: False})
validation_writer.add_summary(summary_loss, itr)
validation_writer.add_summary(summary_acc, itr)
print("%s ---> Validation_loss: %g , Validation Accuracy: %g" % (datetime.datetime.now(), valid_loss, valid_acc))
with open(join(FLAGS.logs_dir, 'iter_val_loss.csv'), 'a') as f:
f.write(str(itr) + ',' + str(valid_loss) + '\n')
with open(join(FLAGS.logs_dir, 'iter_val_acc.csv'), 'a') as f:
f.write(str(itr) + ',' + str(valid_acc) + '\n')
# saver.save(sess, FLAGS.logs_dir + "model.ckpt", itr)
elif FLAGS.mode == "visualize":
valid_images, valid_annotations = validation_dataset_reader.get_random_batch(FLAGS.batch_size)
pred = sess.run(pred_annotation, feed_dict={image: valid_images, annotation: valid_annotations,
keep_probability: 1.0, is_training: False})
valid_annotations = np.squeeze(valid_annotations, axis=3)
pred = np.squeeze(pred, axis=3)
for itr in range(FLAGS.batch_size):
print(valid_images[itr].astype(np.uint8).shape)
utils.save_image(valid_images[itr, :, :, :3].astype(np.uint8), FLAGS.logs_dir, name="inp_" + str(itr))
print(valid_annotations[itr].astype(np.uint8).shape)
utils.save_image(valid_annotations[itr].astype(np.uint8), FLAGS.logs_dir, name="gt_" + str(itr))
print(pred[itr].astype(np.uint8).shape)
utils.save_image(pred[itr].astype(np.uint8), FLAGS.logs_dir, name="pred_" + str(itr))
print("Saved image: %d" % itr)
if __name__ == "__main__":
tf.app.run()