def setup_train8(self, lr):
image_batch, annotation_batch = tf.train.shuffle_batch( [self.resized_image, self.resized_annotation],
batch_size=1,
capacity=3000,
num_threads=2,
min_after_dequeue=1000)
upsampled_logits_batch, fcn_8s_variables_mapping = FCN_8s(image_batch_tensor=image_batch,
number_of_classes=self.num_labels,
is_training=True)
valid_labels_batch_tensor, valid_logits_batch_tensor = get_valid_logits_and_labels(annotation_batch_tensor=annotation_batch,
logits_batch_tensor=upsampled_logits_batch,
class_labels=self.class_labels)
# Count true positives, true negatives, false positives and false negatives.
actual = tf.contrib.layers.flatten(tf.cast(annotation_batch, tf.int64))
self.predicted_img = tf.argmax(upsampled_logits_batch, axis=3)
cross_entropies = tf.nn.softmax_cross_entropy_with_logits(logits=valid_logits_batch_tensor,
labels=valid_labels_batch_tensor)
self.cross_entropy_sum = tf.reduce_mean(cross_entropies)
pred = tf.argmax(upsampled_logits_batch, dimension=3)
probabilities = tf.nn.softmax(upsampled_logits_batch)
with tf.variable_scope("adam_vars"):
self.train_step = tf.train.AdamOptimizer(learning_rate=lr).minimize(self.cross_entropy_sum)
# Variable's initialization functions
self.init_fn = slim.assign_from_checkpoint_fn(model_path=self.fcn_8s_checkpoint_path,
var_list=fcn_8s_variables_mapping)
global_vars_init_op = tf.global_variables_initializer()
self.merged_summary_op = tf.summary.merge_all()
self.summary_string_writer = tf.summary.FileWriter("./log_8_{}".format(lr))
if not os.path.exists(self.checkpointnew):
os.makedirs(self.checkpointnew)
#The op for initializing the variables.
local_vars_init_op = tf.local_variables_initializer()
self.combined_op = tf.group(local_vars_init_op, global_vars_init_op)
# We need this to save only model variables and omit
# optimization-related and other variables.
model_variables = slim.get_model_variables()
self.saver = tf.train.Saver(model_variables)
resized_annotation = tf.squeeze(resized_annotation)
image_batch, annotation_batch = tf.train.shuffle_batch( [resized_image, resized_annotation],
batch_size=1,
capacity=samples,
num_threads=2,
min_after_dequeue=70)
upsampled_logits_batch, vgg_16_variables_mapping = FCN_32s(image_batch_tensor=image_batch,
number_of_classes=number_of_classes,
is_training=True)
valid_labels_batch_tensor, valid_logits_batch_tensor = get_valid_logits_and_labels(annotation_batch_tensor=annotation_batch,
logits_batch_tensor=upsampled_logits_batch,
class_labels=class_labels)
cross_entropies = tf.nn.softmax_cross_entropy_with_logits(logits=valid_logits_batch_tensor,
labels=valid_labels_batch_tensor)
# Normalize the cross entropy -- the number of elements
# is different during each step due to mask out regions
cross_entropy_sum = tf.reduce_mean(cross_entropies)
pred = tf.argmax(upsampled_logits_batch, dimension=3)
probabilities = tf.nn.softmax(upsampled_logits_batch)
resized_annotation = tf.squeeze(resized_annotation)
image_batch, annotation_batch = tf.train.shuffle_batch( [resized_image, resized_annotation],
batch_size=1,
capacity=3000,
num_threads=2,
min_after_dequeue=1000)
upsampled_logits_batch, fcn_8s_variables_mapping = FCN_8s(image_batch_tensor=image_batch,
number_of_classes=number_of_classes,
is_training=True)
valid_labels_batch_tensor, valid_logits_batch_tensor = get_valid_logits_and_labels(annotation_batch_tensor=annotation_batch,
logits_batch_tensor=upsampled_logits_batch,
class_labels=class_labels)
actual = tf.contrib.layers.flatten(tf.cast(annotation_batch, tf.int64))
predicted_img = tf.argmax(upsampled_logits_batch, axis=3)
predicted = tf.contrib.layers.flatten(predicted_img)
#actual = tf.reshape(actual2, [1, 50176])
tp = tf.count_nonzero(predicted * actual)
tn = tf.count_nonzero((predicted - 1) * (actual - 1))
fp = tf.count_nonzero(predicted * (actual - 1))
fn = tf.count_nonzero((predicted - 1) * actual)
# Calculate accuracy, precision, recall and F1 score.
accuracy = tf.divide(tf.add(tp, tn), tf.add(tf.add(tp, fp), tf.add(fn, tn)))
def setup_train8(self, lr):
"""
Setups queues and model evaluation.
"""
image_batch, annotation_batch = tf.train.shuffle_batch(
[self.resized_image, self.resized_annotation],
batch_size=1,
capacity=3000,
num_threads=2,
min_after_dequeue=1000)
upsampled_logits_batch, fcn_8s_variables_mapping = FCN_8s(
image_batch_tensor=image_batch,
number_of_classes=self.num_labels,
is_training=True)
valid_labels_batch_tensor, valid_logits_batch_tensor = get_valid_logits_and_labels(
annotation_batch_tensor=annotation_batch,
logits_batch_tensor=upsampled_logits_batch,
class_labels=self.class_labels)
# Count true positives, true negatives, false positives and false negatives.
actual = tf.contrib.layers.flatten(tf.cast(annotation_batch, tf.int64))
self.predicted_img = tf.argmax(upsampled_logits_batch, axis=3)
cross_entropies = tf.nn.softmax_cross_entropy_with_logits(
logits=valid_logits_batch_tensor, labels=valid_labels_batch_tensor)
self.cross_entropy_sum = tf.reduce_mean(cross_entropies)
pred = tf.argmax(upsampled_logits_batch, dimension=3)
probabilities = tf.nn.softmax(upsampled_logits_batch)
with tf.variable_scope("adam_vars"):
self.train_step = tf.train.AdamOptimizer(
learning_rate=lr).minimize(self.cross_entropy_sum)
# Variable's initialization functions
self.init_fn = slim.assign_from_checkpoint_fn(
model_path=self.fcn_8s_checkpoint_path,
var_list=fcn_8s_variables_mapping)
global_vars_init_op = tf.global_variables_initializer()
self.merged_summary_op = tf.summary.merge_all()
self.summary_string_writer = tf.summary.FileWriter(
"./log_8_{}".format(lr))
if not os.path.exists(self.checkpointnew):
os.makedirs(self.checkpointnew)
#The op for initializing the variables.
local_vars_init_op = tf.local_variables_initializer()
self.combined_op = tf.group(local_vars_init_op, global_vars_init_op)
# We need this to save only model variables and omit
# optimization-related and other variables.
model_variables = slim.get_model_variables()
self.saver = tf.train.Saver(model_variables)
