-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathFCN-4s_Potsdam_submission.py
194 lines (162 loc) · 9.92 KB
/
FCN-4s_Potsdam_submission.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
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_potsdam import resnet101_net
from sys import argv
from os.path import join
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_integer("batch_size", "64", "batch size for training")
tf.flags.DEFINE_string("logs_dir", "../logs-resnet101_potsdam_submission/", "path to logs directory")
tf.flags.DEFINE_string("data_dir", "../ISPRS_semantic_labeling_Potsdam", "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(115200 + 1) # 25 epochs with batch size 64
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, 6))
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)) * 97.639895122076
mean_pixel[:, :, 4] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * 45.548982715963994
mean_pixel[:, :, 5] = np.ones((IMAGE_SIZE, IMAGE_SIZE)) * 37.69138
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 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, 6], name="input_image")
""" image2 = tf.map_fn(lambda img: tf.image.random_flip_left_right(img), image1, parallel_iterations=FLAGS.batch_size)
image = tf.map_fn(lambda img: tf.image.random_flip_up_down(img), image2, parallel_iterations=FLAGS.batch_size) """
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)
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)
# saver = tf.train.Saver()
train_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/train', sess.graph)
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
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 = build_session(argv[1])
print("Setting up image reader...")
train_records, valid_records = reader.read_dataset_potsdam_submission(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)
if FLAGS.mode == "train":
for itr in xrange(MAX_ITERATION):
train_images, train_annotations = train_dataset_reader.next_batch_potsdam(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)
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()