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fully_conv_densenet_potsdam.py
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fully_conv_densenet_potsdam.py
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from __future__ import division
from __future__ import print_function
from six.moves import xrange
from layers_fc_densenet import *
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_float("learning_rate", "5e-4", "Learning rate for Adam Optimizer")
tf.flags.DEFINE_integer("batch_size", "5", "batch size for training")
tf.flags.DEFINE_string("logs_dir", "../logs-dense/", "path to logs directory")
MAX_ITERATION = int(1e7 + 1)
NUM_OF_CLASSESS = 6
IMAGE_SIZE = 224
tf_records_filename = 'Potsdam.tfrecords'
def inference(image, keep_prob):
n_filters_first_conv = 48
n_pool = 5
growth_rate = 16
n_layers_per_block = [4, 5, 7, 10, 12, 15, 12, 10, 7, 5, 4]
n_classes = 6
mean_pixel = np.array([97.6398951221, 86.5517502156, 92.5452277039, 85.9159648918, 45.548982716, 31.4374])
processed_image = utils.process_image(image, mean_pixel)
print(np.shape(processed_image))
W_first = utils.weight_variable([3,3,processed_image.get_shape().as_list()[3],n_filters_first_conv], name='W_first')
b_first = utils.bias_variable([n_filters_first_conv], name= 'b_first')
conv_first = utils.conv2d_basic(processed_image, W_first, b_first)
stack = tf.nn.relu(conv_first)
n_filters = n_filters_first_conv
print("Before Downsample")
print(np.shape(stack))
#####################
# Downsampling path #
#####################
skip_connection_list = []
for i in range(n_pool):
# Dense Block
for j in range(n_layers_per_block[i]):
l = BN_ReLU_Conv(inputs=stack,n_filters= growth_rate,keep_prob=keep_prob, name="downsample_"+str(i)+"_"+str(j))
stack = tf.concat([stack,l], axis=3)
n_filters += growth_rate
skip_connection_list.append(stack)
stack = Transition_Down(inputs=stack, n_filters=n_filters, keep_prob=keep_prob, name='downsample_stack_'+str(i))
skip_connection_list = skip_connection_list[::-1]
#####################
# Bottleneck #
#####################
block_to_upsample = []
for j in range(n_layers_per_block[n_pool]):
l = BN_ReLU_Conv(inputs=stack, n_filters=growth_rate, keep_prob= keep_prob, name="bottleneck_"+str(j))
block_to_upsample.append(l)
stack = tf.concat([stack,l], axis=3)
#######################
# Upsampling path #
#######################
for i in range(n_pool):
n_filters_keep = growth_rate * n_layers_per_block[n_pool + i]
stack = Transition_Up(skip_connection=skip_connection_list[i], block_to_upsample=block_to_upsample, n_filters_keep = n_filters_keep, name="upsample_stack_"+str(i))
# Dense Block
block_to_upsample = []
for j in range(n_layers_per_block[n_pool + i + 1]):
l = BN_ReLU_Conv(inputs=stack, n_filters=growth_rate, keep_prob=keep_prob, name="upsample_"+str(i)+"_"+str(j))
block_to_upsample.append(l)
stack = tf.concat([stack, l], axis=3)
W_last = utils.weight_variable([1,1,stack.get_shape().as_list()[3],n_classes], name="W_last")
b_last = utils.bias_variable([n_classes], name="b_last")
conv_last = utils.conv2d_basic(stack,W_last,b_last)
print("Conv_last")
print(np.shape(conv_last))
annotation_pred = tf.argmax(conv_last, dimension=3, name="prediction")
return tf.expand_dims(annotation_pred, dim=3), conv_last
def train(loss_val, var_list):
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
return optimizer.apply_gradients(grads)
def read_and_decode(filename_queue):
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
features={
'image_raw': tf.FixedLenFeature([], tf.string),
'annotation_raw': tf.FixedLenFeature([], tf.string)
})
image = tf.decode_raw(features['image_raw'], tf.float16)
annotation = tf.decode_raw(features['annotation_raw'], tf.uint8)
image = tf.reshape(image, [224, 224, 6])
annotation = tf.reshape(annotation, [224, 224, 1])
min_after_deque = 1000
batch_size = 5
num_thread = 20
capacity = min_after_deque + (num_thread + 1) * batch_size
images, annotations = tf.train.shuffle_batch([image, annotation], batch_size=batch_size, num_threads=num_thread,
min_after_dequeue=min_after_deque, capacity=capacity)
return images, annotations
def main(argv=None):
filename_queue = tf.train.string_input_producer([tf_records_filename])
image, annotation = read_and_decode(filename_queue)
image = tf.cast(image, dtype=tf.float32)
annotation = tf.cast(annotation, dtype=tf.int32)
keep_probability = tf.placeholder(tf.float32, name="keep_probability")
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)
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)
acc_summary = tf.summary.scalar("accuracy", acc)
trainable_var = tf.trainable_variables()
train_op = train(loss, trainable_var)
sess = tf.Session()
print("Setting up Saver...")
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...")
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
for itr in xrange(MAX_ITERATION):
feed_dict = {keep_probability: 0.8}
sess.run(train_op, feed_dict=feed_dict)
if itr % 50 == 0:
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))
train_writer.add_summary(summary_loss, itr)
train_writer.add_summary(summary_acc, itr)
if itr % 500 == 0:
saver.save(sess, FLAGS.logs_dir + "model.ckpt", itr)
coord.request_stop()
coord.join(threads)
if __name__=='__main__':
tf.app.run()