def main(argv=None):
# 1. input placeholders
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")
# global_step = tf.Variable(0, trainable=False, name='global_step')
# 2. construct inference network
pred_annotation, logits, net = unetinference(image, keep_probability)
tf.summary.image("input_image", image, max_outputs=3)
tf.summary.image(
"ground_truth",
tf.cast(
annotation,
tf.uint8),
max_outputs=3)
tf.summary.image(
"pred_annotation",
tf.cast(
pred_annotation,
tf.uint8),
max_outputs=3)
# 3. loss measure
loss = tf.reduce_mean(
(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits,
labels=tf.squeeze(
annotation,
squeeze_dims=[3]),
name="entropy")))
tf.summary.scalar("entropy", loss)
# 4. optimizing
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, net['global_step'])
print("Setting up summary op...")
summary_op = tf.summary.merge_all()
print("Setting up image reader from ", FLAGS.data_dir, "...")
print("data dir:", FLAGS.data_dir)
train_records, valid_records = fashion_parsing.read_dataset(FLAGS.data_dir)
test_records = None
if DATA_SET == "CFPD":
train_records, valid_records, test_records = ClothingParsing.read_dataset(
FLAGS.data_dir)
print("test_records length :", len(test_records))
if DATA_SET == "LIP":
train_records, valid_records = HumanParsing.read_dataset(
FLAGS.data_dir)
print("train_records length :", len(train_records))
print("valid_records length :", len(valid_records))
print("Setting up dataset reader")
train_dataset_reader = None
validation_dataset_reader = None
test_dataset_reader = None
image_options = {'resize': True, 'resize_size': IMAGE_SIZE}
if FLAGS.mode == 'train':
train_dataset_reader = DataSetReader.BatchDatset(
train_records, image_options)
validation_dataset_reader = DataSetReader.BatchDatset(
valid_records, image_options)
if DATA_SET == "CFPD":
test_dataset_reader = DataSetReader.BatchDatset(
test_records, image_options)
if FLAGS.mode == 'visualize':
validation_dataset_reader = DataSetReader.BatchDatset(
valid_records, image_options)
if FLAGS.mode == 'test' or FLAGS.mode == 'crftest' or FLAGS.mode == 'predonly' or FLAGS.mode == "fulltest":
if DATA_SET == "CFPD":
test_dataset_reader = DataSetReader.BatchDatset(
test_records, image_options)
else:
test_dataset_reader = DataSetReader.BatchDatset(
valid_records, image_options)
test_records = valid_records
sess = tf.Session()
print("Setting up Saver...")
saver = tf.train.Saver()
summary_writer = tf.summary.FileWriter(FLAGS.logs_dir, sess.graph)
# 5. parameter setup
# 5.1 init params
sess.run(tf.global_variables_initializer())
# 5.2 restore params if possible
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...")
# 6. train-mode
if FLAGS.mode == "train":
fd.mode_train(sess, FLAGS, net, train_dataset_reader, validation_dataset_reader, train_records,
pred_annotation,
image, annotation, keep_probability, logits, train_op, loss, summary_op, summary_writer,
saver, DISPLAY_STEP)
# test-random-validation-data mode
elif FLAGS.mode == "visualize":
fd.mode_visualize(sess, FLAGS, VIS_DIR, validation_dataset_reader,
pred_annotation, image, annotation, keep_probability, NUM_OF_CLASSES)
# test-full-validation-dataset mode
elif FLAGS.mode == "test":
fd.mode_new_test(sess, FLAGS, TEST_DIR, test_dataset_reader, test_records,
pred_annotation, image, annotation, keep_probability, logits, NUM_OF_CLASSES)
# fd.mode_test(sess, FLAGS, TEST_DIR, test_dataset_reader, test_records,
# pred_annotation, image, annotation, keep_probability, logits, NUM_OF_CLASSES)
elif FLAGS.mode == "crftest":
fd.mode_predonly(sess, FLAGS, TEST_DIR, test_dataset_reader, test_records,
pred_annotation, image, annotation, keep_probability, logits, NUM_OF_CLASSES)
elif FLAGS.mode == "predonly":
fd.mode_predonly(sess, FLAGS, TEST_DIR, test_dataset_reader, test_records,
pred_annotation, image, annotation, keep_probability, logits, NUM_OF_CLASSES)
elif FLAGS.mode == "fulltest":
fd.mode_full_test(sess, FLAGS, TEST_DIR, test_dataset_reader, test_records,
pred_annotation, image, annotation, keep_probability, logits, NUM_OF_CLASSES)
sess.close()
def main(argv=None):
# 1. input placeholders
keep_probability = tf.placeholder(tf.float32, name="keep_probability")
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")
# global_step = tf.Variable(0, trainable=False, name='global_step')
is_training = False
if FLAGS.mode == "train":
is_training = True
image075 = tf.image.resize_images(
image, [int(IMAGE_SIZE * 0.75),
int(IMAGE_SIZE * 0.75)])
image050 = tf.image.resize_images(
image,
[int(IMAGE_SIZE * 0.5), int(IMAGE_SIZE * 0.5)])
image125 = tf.image.resize_images(
image, [int(IMAGE_SIZE * 1.25),
int(IMAGE_SIZE * 1.25)])
annotation075 = tf.cast(
tf.image.resize_images(
annotation, [int(IMAGE_SIZE * 0.75),
int(IMAGE_SIZE * 0.75)]), tf.int32)
annotation050 = tf.cast(
tf.image.resize_images(annotation,
[int(IMAGE_SIZE * 0.5),
int(IMAGE_SIZE * 0.5)]), tf.int32)
annotation125 = tf.cast(
tf.image.resize_images(
annotation, [int(IMAGE_SIZE * 1.25),
int(IMAGE_SIZE * 1.25)]), tf.int32)
# 2. construct inference network
reuse1 = False
reuse2 = True
with tf.variable_scope('', reuse=reuse1):
pred_annotation100, logits100, net100, att100 = unetinference(
image, keep_probability, is_training=is_training)
with tf.variable_scope('', reuse=reuse2):
pred_annotation075, logits075, net075, att075 = unetinference(
image075, keep_probability, is_training=is_training)
with tf.variable_scope('', reuse=reuse2):
pred_annotation050, logits050, net050, att050 = unetinference(
image050, keep_probability, is_training=is_training)
with tf.variable_scope('', reuse=reuse2):
pred_annotation125, logits125, net125, att125 = unetinference(
image125, keep_probability, is_training=is_training)
# apply attention model - train
score_final_train = None
score_final_test = None
final_annotation_pred_train = None
final_annotation_pred_test = None
train_op = None
reduced_loss = None
if FLAGS.mode == "train":
attn_input = []
attn_input.append(att100)
attn_input.append(
tf.image.resize_images(att075,
tf.shape(att100)[1:3, ]))
attn_input.append(
tf.image.resize_images(att050,
tf.shape(att100)[1:3, ]))
attn_input_train = tf.concat(attn_input, axis=3)
attn_output_train = attention(attn_input_train, is_training)
scale_att_mask = tf.nn.softmax(attn_output_train)
score_att_x = tf.multiply(
logits100,
tf.image.resize_images(
tf.expand_dims(scale_att_mask[:, :, :, 0], axis=3),
tf.shape(logits100)[1:3, ]))
score_att_x_075 = tf.multiply(
tf.image.resize_images(logits075,
tf.shape(logits100)[1:3, ]),
tf.image.resize_images(
tf.expand_dims(scale_att_mask[:, :, :, 1], axis=3),
tf.shape(logits100)[1:3, ]))
score_att_x_050 = tf.multiply(
tf.image.resize_images(logits050,
tf.shape(logits100)[1:3, ]),
tf.image.resize_images(
tf.expand_dims(scale_att_mask[:, :, :, 2], axis=3),
tf.shape(logits100)[1:3, ]))
score_final_train = score_att_x + score_att_x_075 + score_att_x_050
final_annotation_pred = tf.expand_dims(tf.argmax(
score_final_train, dimension=3, name="final_prediction"),
dim=3)
final_annotation_pred_train = tf.reduce_mean(tf.stack([
tf.cast(final_annotation_pred, tf.float32),
tf.cast(pred_annotation100, tf.float32),
tf.image.resize_images(pred_annotation075,
tf.shape(pred_annotation100)[1:3, ]),
tf.image.resize_images(pred_annotation050,
tf.shape(pred_annotation100)[1:3, ])
]),
axis=0)
# 3. loss measure
loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=score_final_train,
labels=tf.squeeze(annotation, squeeze_dims=[3]),
name="entropy")))
tf.summary.scalar("entropy", loss)
loss100 = tf.reduce_mean(
(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits100,
labels=tf.squeeze(annotation, squeeze_dims=[3]),
name="entropy")))
tf.summary.scalar("entropy", loss100)
loss075 = tf.reduce_mean(
(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits075,
labels=tf.squeeze(annotation075, squeeze_dims=[3]),
name="entropy")))
tf.summary.scalar("entropy", loss075)
loss050 = tf.reduce_mean(
(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits050,
labels=tf.squeeze(annotation050, squeeze_dims=[3]),
name="entropy")))
tf.summary.scalar("entropy", loss050)
reduced_loss = loss + loss100 + loss075 + loss050
# 4. optimizing
trainable_var = tf.trainable_variables()
if FLAGS.debug:
for var in trainable_var:
Utils.add_to_regularization_and_summary(var)
train_op = train(reduced_loss, trainable_var, net100['global_step'])
else:
# apply attention model - test
attn_input = []
attn_input.append(att100)
attn_input.append(
tf.image.resize_images(att075,
tf.shape(att100)[1:3, ]))
attn_input.append(
tf.image.resize_images(att125,
tf.shape(att100)[1:3, ]))
attn_input_test = tf.concat(attn_input, axis=3)
attn_output_test = attention(attn_input_test, is_training)
scale_att_mask = tf.nn.softmax(attn_output_test)
score_att_x = tf.multiply(
logits100,
tf.image.resize_images(
tf.expand_dims(scale_att_mask[:, :, :, 0], axis=3),
tf.shape(logits100)[1:3, ]))
score_att_x_075 = tf.multiply(
tf.image.resize_images(logits075,
tf.shape(logits100)[1:3, ]),
tf.image.resize_images(
tf.expand_dims(scale_att_mask[:, :, :, 1], axis=3),
tf.shape(logits100)[1:3, ]))
score_att_x_125 = tf.multiply(
tf.image.resize_images(logits125,
tf.shape(logits100)[1:3, ]),
tf.image.resize_images(
tf.expand_dims(scale_att_mask[:, :, :, 2], axis=3),
tf.shape(logits100)[1:3, ]))
score_final_test = score_att_x + score_att_x_075 + score_att_x_125
final_annotation_pred = tf.expand_dims(tf.argmax(
score_final_test, dimension=3, name="final_prediction"),
dim=3)
final_annotation_pred_test = tf.reduce_mean(tf.stack([
tf.cast(final_annotation_pred, tf.float32),
tf.cast(pred_annotation100, tf.float32),
tf.image.resize_images(pred_annotation075,
tf.shape(pred_annotation100)[1:3, ]),
tf.image.resize_images(pred_annotation125,
tf.shape(pred_annotation100)[1:3, ])
]),
axis=0)
tf.summary.image("input_image", image, max_outputs=3)
tf.summary.image("ground_truth",
tf.cast(annotation, tf.uint8),
max_outputs=3)
tf.summary.image("pred_annotation",
tf.cast(pred_annotation100, tf.uint8),
max_outputs=3)
print("Setting up summary op...")
summary_op = tf.summary.merge_all()
print("Setting up image reader from ", FLAGS.data_dir, "...")
print("data dir:", FLAGS.data_dir)
train_records, valid_records = fashion_parsing.read_dataset(FLAGS.data_dir)
test_records = None
if DATA_SET == "CFPD":
train_records, valid_records, test_records = ClothingParsing.read_dataset(
FLAGS.data_dir)
print("test_records length :", len(test_records))
if DATA_SET == "LIP":
train_records, valid_records = HumanParsing.read_dataset(
FLAGS.data_dir)
print("train_records length :", len(train_records))
print("valid_records length :", len(valid_records))
print("Setting up dataset reader")
train_dataset_reader = None
validation_dataset_reader = None
test_dataset_reader = None
image_options = {'resize': True, 'resize_size': IMAGE_SIZE}
if FLAGS.mode == 'train':
train_dataset_reader = DataSetReader.BatchDatset(
train_records, image_options)
validation_dataset_reader = DataSetReader.BatchDatset(
valid_records, image_options)
if DATA_SET == "CFPD":
test_dataset_reader = DataSetReader.BatchDatset(
test_records, image_options)
if FLAGS.mode == 'visualize':
validation_dataset_reader = DataSetReader.BatchDatset(
valid_records, image_options)
if FLAGS.mode == 'test' or FLAGS.mode == 'crftest' or FLAGS.mode == 'predonly' or FLAGS.mode == "fulltest":
if DATA_SET == "CFPD":
test_dataset_reader = DataSetReader.BatchDatset(
test_records, image_options)
else:
test_dataset_reader = DataSetReader.BatchDatset(
valid_records, image_options)
test_records = valid_records
sess = tf.Session()
print("Setting up Saver...")
saver = tf.train.Saver()
summary_writer = tf.summary.FileWriter(FLAGS.logs_dir, sess.graph)
# 5. parameter setup
# 5.1 init params
sess.run(tf.global_variables_initializer())
# 5.2 restore params if possible
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...")
# 6. train-mode
if FLAGS.mode == "train":
fd.mode_train(sess, FLAGS, net100, train_dataset_reader,
validation_dataset_reader, train_records,
final_annotation_pred_train, image, annotation,
keep_probability, score_final_train, train_op,
reduced_loss, summary_op, summary_writer, saver,
DISPLAY_STEP)
# test-random-validation-data mode
elif FLAGS.mode == "visualize":
fd.mode_visualize(sess, FLAGS, VIS_DIR, validation_dataset_reader,
final_annotation_pred_test, image, annotation,
keep_probability, NUM_OF_CLASSES)
# test-full-validation-dataset mode
elif FLAGS.mode == "test":
fd.mode_new_test(sess, FLAGS, TEST_DIR, test_dataset_reader,
test_records, final_annotation_pred_test, image,
annotation, keep_probability, score_final_test,
NUM_OF_CLASSES)
# fd.mode_test(sess, FLAGS, TEST_DIR, test_dataset_reader, test_records,
# pred_annotation, image, annotation, keep_probability, logits, NUM_OF_CLASSES)
elif FLAGS.mode == "crftest":
fd.mode_predonly(sess, FLAGS, TEST_DIR, test_dataset_reader,
test_records, final_annotation_pred_test, image,
annotation, keep_probability, score_final_test,
NUM_OF_CLASSES)
elif FLAGS.mode == "predonly":
fd.mode_predonly(sess, FLAGS, TEST_DIR, test_dataset_reader,
test_records, final_annotation_pred_test, image,
annotation, keep_probability, score_final_test,
NUM_OF_CLASSES)
elif FLAGS.mode == "fulltest":
fd.mode_full_test(sess, FLAGS, TEST_DIR, test_dataset_reader,
test_records, final_annotation_pred_test, image,
annotation, keep_probability, score_final_test,
NUM_OF_CLASSES)
sess.close()