def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# Prepare image.
img = tf.image.decode_jpeg(tf.read_file(args.img_path), channels=3)
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(value=img, num_or_size_splits=3, axis=2)
img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]),
dtype=tf.float32)
# Extract mean.
img -= IMG_MEAN
# Create network.
net = DeepLabLFOVModel()
# Which variables to load.
trainable = tf.trainable_variables()
# Predictions.
pred = net.preds(tf.expand_dims(img, dim=0))
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.initialize_all_variables()
sess.run(init)
# Load weights.
saver = tf.train.Saver(var_list=trainable)
load(saver, sess, args.model_weights)
# Perform inference.
preds = sess.run([pred])
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
msk = decode_labels(np.array(preds)[0, 0, :, :, 0])
im = Image.fromarray(msk)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
im.save(args.save_dir + 'mask.png')
print('The output file has been saved to {}'.format(args.save_dir +
'mask.png'))
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# Prepare image.
img = tf.image.decode_jpeg(tf.read_file(args.img_path), channels=3)
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(split_dim=2, num_split=3, value=img)
img = tf.cast(tf.concat(2, [img_b, img_g, img_r]), dtype=tf.float32)
# Extract mean.
img -= IMG_MEAN
# Create network.
net = DeepLabLFOVModel()
# Which variables to load.
trainable = tf.trainable_variables()
# Predictions.
pred = net.preds(tf.expand_dims(img, dim=0))
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.initialize_all_variables()
sess.run(init)
# Load weights.
saver = tf.train.Saver(var_list=trainable)
load(saver, sess, args.model_weights)
# Perform inference.
preds = sess.run([pred])
msk = decode_labels(np.array(preds)[0, 0, :, :, 0])
im = Image.fromarray(msk)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
im.save(args.save_dir + 'mask.png')
print('The output file has been saved to {}'.format(args.save_dir + 'mask.png'))
def main():
"""Create the model and start the training."""
args = get_arguments()
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(args.data_dir, args.data_list, input_size,
RANDOM_SCALE, coord)
image_batch, label_batch = reader.dequeue(args.batch_size)
# Create network.
net = DeepLabLFOVModel(args.weights_path)
# Define the loss and optimisation parameters.
loss = net.loss(image_batch, label_batch)
optimiser = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
trainable = tf.trainable_variables()
optim = optimiser.minimize(loss, var_list=trainable)
tf.summary.scalar("lfov_loss", loss)
pred = net.preds(image_batch)
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.initialize_all_variables()
sess.run(init)
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(var_list=trainable, max_to_keep=40)
if args.restore_from is not None:
load(saver, sess, args.restore_from)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
summary_merged = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(args.snapshot_dir,
graph=tf.get_default_graph())
# Iterate over training steps.
for step in range(args.num_steps):
start_time = time.time()
if step % args.save_pred_every == 0:
loss_value, images, labels, preds, _ = sess.run(
[loss, image_batch, label_batch, pred, optim])
fig, axes = plt.subplots(args.save_num_images, 3, figsize=(16, 12))
for i in range(args.save_num_images):
axes.flat[i * 3].set_title('data')
axes.flat[i * 3].imshow(
(images[i] + IMG_MEAN)[:, :, ::-1].astype(np.uint8))
axes.flat[i * 3 + 1].set_title('mask')
axes.flat[i * 3 + 1].imshow(decode_labels(labels[i, :, :, 0]))
axes.flat[i * 3 + 2].set_title('pred')
axes.flat[i * 3 + 2].imshow(decode_labels(preds[i, :, :, 0]))
plt.savefig(args.save_dir + str(start_time) + ".png")
plt.close(fig)
save(saver, sess, args.snapshot_dir, step)
else:
loss_value, summary, _ = sess.run([loss, summary_merged, optim])
summary_writer.add_summary(summary, step)
duration = time.time() - start_time
print('step {:d} \t loss = {:.3f}, ({:.3f} sec/step)'.format(
step, loss_value, duration))
coord.request_stop()
coord.join(threads)
def main():
"""Create the model and start the training."""
args = get_arguments()
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(
args.data_dir,
args.data_list,
input_size,
RANDOM_SCALE,
coord)
image_batch, label_batch = reader.dequeue(args.batch_size)
# Create network.
net = DeepLabLFOVModel(args.weights_path)
# Define the loss and optimisation parameters.
loss = net.loss(image_batch, label_batch)
optimiser = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
trainable = tf.trainable_variables()
optim = optimiser.minimize(loss, var_list=trainable)
tf.summary.scalar("lfov_loss",loss)
pred = net.preds(image_batch)
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.initialize_all_variables()
sess.run(init)
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(var_list=trainable, max_to_keep=40)
if args.restore_from is not None:
load(saver, sess, args.restore_from)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
summary_merged = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(args.snapshot_dir,graph=tf.get_default_graph())
# Iterate over training steps.
for step in range(args.num_steps):
start_time = time.time()
if step % args.save_pred_every == 0:
loss_value, images, labels, preds, _ = sess.run([loss, image_batch, label_batch, pred, optim])
fig, axes = plt.subplots(args.save_num_images, 3, figsize = (16, 12))
for i in range(args.save_num_images):
axes.flat[i * 3].set_title('data')
axes.flat[i * 3].imshow((images[i] + IMG_MEAN)[:, :, ::-1].astype(np.uint8))
axes.flat[i * 3 + 1].set_title('mask')
axes.flat[i * 3 + 1].imshow(decode_labels(labels[i, :, :, 0]))
axes.flat[i * 3 + 2].set_title('pred')
axes.flat[i * 3 + 2].imshow(decode_labels(preds[i, :, :, 0]))
plt.savefig(args.save_dir + str(start_time) + ".png")
plt.close(fig)
save(saver, sess, args.snapshot_dir, step)
else:
loss_value, summary , _ = sess.run([loss,summary_merged, optim])
summary_writer.add_summary(summary,step)
duration = time.time() - start_time
print('step {:d} \t loss = {:.3f}, ({:.3f} sec/step)'.format(step, loss_value, duration))
coord.request_stop()
coord.join(threads)
def main():
"""Create the model and start the training."""
args = get_arguments()
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_train_inputs"):
reader_train = ImageReader(
args.data_dir,
args.data_train_list,
input_size,
RANDOM_SCALE,
coord)
image_batch_train, label_batch_train = reader_train.dequeue(args.batch_size)
with tf.name_scope("create_val_inputs"):
reader_val = ImageReader(
args.data_dir,
args.data_val_list,
input_size,
False,
coord)
image_batch_val, label_batch_val = reader_val.dequeue(args.batch_size)
is_training = tf.placeholder(tf.bool,shape = [],name = 'stauts')
image_batch,label_batch = tf.cond(is_training,lambda: (image_batch_train,label_batch_train),lambda: (image_batch_val,label_batch_val))
# Create network.
net = DeepLabLFOVModel(args.weights_path)
# Define the loss and optimisation parameters.
global_step = tf.Variable(0, trainable=False)
step_thre=tf.constant(args.step_thre)
loss,recall,precision = net.loss(image_batch, label_batch,global_step,step_thre)
optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
trainable = tf.trainable_variables()
optim = optimizer.minimize(loss, var_list=trainable,global_step=global_step)
pred = net.preds(image_batch)
# mIoU
mIoU, update_op = tf.metrics.mean_iou(label_batch, pred, num_classes=2)
mIoU_vali,update_op_vali=tf.metrics.mean_iou(label_batch, pred, num_classes=2)
merged = tf.summary.merge_all()
if os.path.exists(args.log_dir):
shutil.rmtree(args.log_dir)
summary_writer = tf.summary.FileWriter(args.log_dir,graph = tf.get_default_graph())
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = False
# config.gpu_options.per_process_gpu_memory_fraction = 0.8
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
sess.run(tf.local_variables_initializer())
# Saver for storing checkpoints of the model.
saver = tf.train.Saver(var_list=trainable, max_to_keep=40)
if args.restore_from is not None:
load(saver, sess, args.restore_from)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
if os.path.exists(args.save_dir):
shutil.rmtree(args.save_dir)
os.makedirs(args.save_dir)
if os.path.exists(args.snapshot_dir):
shutil.rmtree(args.snapshot_dir)
# Iterate over training steps.
for step in range(args.num_steps):
start_time = time.time()
if step % args.save_pred_every == 0:
loss_value,images, labels, preds, _ = sess.run([loss,image_batch, label_batch, pred, optim],feed_dict={is_training:True})
fig, axes = plt.subplots(args.save_num_images, 3, figsize = (16, 12))
for i in xrange(args.save_num_images):
axes.flat[i * 3].set_title('data')
axes.flat[i * 3].imshow((images[i] + IMG_MEAN)[:, :, ::-1].astype(np.uint8))
axes.flat[i * 3 + 1].set_title('mask')
axes.flat[i * 3 + 1].imshow(decode_labels(labels[i, :, :, 0]))
axes.flat[i * 3 + 2].set_title('pred')
axes.flat[i * 3 + 2].imshow(decode_labels(preds[i, :, :, 0]))
plt.savefig(args.save_dir + str(start_time) + ".png")
plt.close(fig)
save(saver, sess, args.snapshot_dir, step)
#validation
for i in range(VAL_LOOP):
start_time_vali=time.time()
loss_vali,images, labels, preds,recall_vali,precision_vali,mIoU_vali_value,_ = sess.run(\
[loss,image_batch, label_batch, pred,recall,precision,mIoU_vali,update_op_vali],feed_dict={is_training:False})
duration_vali=time.time()-start_time_vali
print('validation step {:<5d}\tbatch:{:<3d}, recall: {:.3f}, precision: {:.3f}, mIoU: {:.3f}'.format(\
step,i,recall_vali,precision_vali,mIoU_vali_value))
print('validation step {:<5d}\tbatch:{:<3d}, loss = {:.5f}, ({:.5f} sec/batch)'.format(step,i,loss_vali,duration_vali))
for j in range(BATCH_SIZE):
fig, axes = plt.subplots(1, 3, figsize = (16, 12))
axes.flat[0].set_title('data')
axes.flat[0].imshow((images[j] + IMG_MEAN)[:, :, ::-1].astype(np.uint8))
axes.flat[1].set_title('mask')
axes.flat[1].imshow(decode_labels(labels[j, :, :, 0]))
axes.flat[2].set_title('pred')
axes.flat[2].imshow(decode_labels(preds[j, :, :, 0]))
plt.savefig(args.save_dir + str(start_time) +'_'+str(i)+"test.png")
plt.close(fig)
else:
loss_value, _ ,summary,recall_value,precision_value,mIoU_value,_= sess.run(\
[loss,optim,merged,recall,precision,mIoU,update_op],feed_dict={is_training:True})
print('step {:<5d}\trecall: {:.3f}, precision: {:.3f}, mIoU: {:.3f}'.format(step,recall_value,precision_value,mIoU_value))
summary_writer.add_summary(summary,step)
duration = time.time() - start_time
print('step {:<5d}\tloss = {:.5f}, ({:.5f} sec/step)'.format(step,loss_value,duration))
coord.request_stop()
coord.join(threads)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(
args.data_dir,
args.data_list,
input_size=None,
random_scale=False,
coord=coord)
image, label = reader.image, reader.label
image_batch, label_batch = tf.expand_dims(image, dim=0), tf.expand_dims(label, dim=0) # Add the batch dimension.
# Create network.
net = DeepLabLFOVModel(args.weights_path)
# Which variables to load.
trainable = tf.trainable_variables()
# Predictions.
pred = net.preds(image_batch)
# mIoU
mIoU, update_op = tf.contrib.metrics.streaming_mean_iou(pred, label_batch, num_classes=21)
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.initialize_all_variables()
sess.run(init)
sess.run(tf.initialize_local_variables())
# Load weights.
saver = tf.train.Saver(var_list=trainable)
if args.restore_from is not None:
load(saver, sess, args.restore_from)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
# Iterate over images.
for step in range(args.num_steps):
#mIoU_value = sess.run([mIoU])
#_ = update_op.eval(session=sess)
preds, _ = sess.run([pred, update_op])
if args.save_dir is not None:
img = decode_labels(preds[0, :, :, 0])
im = Image.fromarray(img)
im.save(args.save_dir + str(step) + '.png')
if step % 100 == 0:
print('step {:d} \t'.format(step))
print('Mean IoU: {:.3f}'.format(mIoU.eval(session=sess)))
coord.request_stop()
coord.join(threads)
def main():
"""Create the model and start the training."""
args = get_arguments()
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# Create queue coordinator.
coord = tf.train.Coordinator()
# Load reader.
with tf.name_scope("create_inputs"):
reader = ImageReader(args.data_dir, args.data_list, input_size,
RANDOM_SCALE, coord)
image_batch, label_batch = reader.dequeue(args.batch_size)
# Create network.
net = DeepLabLFOVModel()
# Define the loss and optimisation parameters.
main_loss_1,attention_loss_1, pre_upscaled_1, output_attention_map_1,attention_map_1_predicted,predict_3d_1,\
main_loss_2,attention_loss_2, pre_upscaled_2,output_attention_map_2,attention_map_2_predicted,predict_3d_2, \
main_loss_3,attention_loss_3, pre_upscaled_3, output_attention_map_3 ,attention_map_3_predicted,predict_3d_3\
= net.loss(image_batch, label_batch)
main_loss = main_loss_1 + main_loss_2 + main_loss_3
attention_loss = attention_loss_1 + attention_loss_2 + attention_loss_3
learning_rate = tf.placeholder(tf.float32, shape=[])
att_learning_rate = tf.placeholder(tf.float32, shape=[])
trainable = tf.trainable_variables()
frozen_trainalbe = [u'conv1', u'conv2', u'conv3', u'conv4']
final_trainable = trainable
for f in frozen_trainalbe:
final_trainable = [x for x in final_trainable if f not in x.name]
print("====final_trainable shape check====")
for v in final_trainable:
print("{}: {}".format(v.name, v.get_shape()))
#optim = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9).minimize(main_loss, var_list=trainable)
optim = tf.train.MomentumOptimizer(learning_rate=learning_rate,
momentum=0.9)
att_optim = tf.train.MomentumOptimizer(learning_rate=att_learning_rate,
momentum=0.9).minimize(
attention_loss,
var_list=final_trainable)
grads_and_vars_tf_style = optim.compute_gradients(main_loss,
tf.trainable_variables())
train_tf_style = optim.apply_gradients(grads_and_vars_tf_style)
pred_result = net.preds(image_batch)
def convert(image):
return tf.image.convert_image_dtype(image,
dtype=tf.uint8,
saturate=True)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name + "/values", var)
images_summary = tf.py_func(inv_preprocess, [image_batch, SAVE_NUM_IMAGES],
tf.uint8)
labels_summary = tf.py_func(decode_labels_by_batch,
[label_batch, SAVE_NUM_IMAGES], tf.uint8)
preds_1_summary = tf.py_func(decode_labels_by_batch,
[pre_upscaled_1, SAVE_NUM_IMAGES], tf.uint8)
preds_2_summary = tf.py_func(decode_labels_by_batch,
[pre_upscaled_2, SAVE_NUM_IMAGES], tf.uint8)
preds_3_summary = tf.py_func(decode_labels_by_batch,
[pre_upscaled_3, SAVE_NUM_IMAGES], tf.uint8)
att_1_summary = tf.py_func(single_channel_process,
[output_attention_map_1, SAVE_NUM_IMAGES],
tf.uint8)
att_2_summary = tf.py_func(single_channel_process,
[output_attention_map_2, SAVE_NUM_IMAGES],
tf.uint8)
att_3_summary = tf.py_func(single_channel_process,
[output_attention_map_3, SAVE_NUM_IMAGES],
tf.uint8)
predict_3d_1_summary = tf.py_func(single_channel_process,
[predict_3d_1, SAVE_NUM_IMAGES],
tf.uint8)
predict_3d_2_summary = tf.py_func(single_channel_process,
[predict_3d_2, SAVE_NUM_IMAGES],
tf.uint8)
predict_3d_3_summary = tf.py_func(single_channel_process,
[predict_3d_3, SAVE_NUM_IMAGES],
tf.uint8)
attention_map_1_predicted_summary = tf.py_func(
single_channel_process, [attention_map_1_predicted, SAVE_NUM_IMAGES],
tf.uint8)
attention_map_2_predicted_summary = tf.py_func(
single_channel_process, [attention_map_2_predicted, SAVE_NUM_IMAGES],
tf.uint8)
attention_map_3_predicted_summary = tf.py_func(
single_channel_process, [attention_map_3_predicted, SAVE_NUM_IMAGES],
tf.uint8)
# define Summary
summary_list = []
for var in tf.trainable_variables():
summary_list.append(tf.summary.histogram(var.op.name + "/values", var))
#summary
with tf.name_scope("loss_summary"):
summary_list.append(tf.summary.scalar("main_loss", main_loss))
summary_list.append(tf.summary.scalar("attention_loss",
attention_loss))
summary_list.append(tf.summary.scalar("loss_1", main_loss_1))
summary_list.append(tf.summary.scalar("loss_2", main_loss_2))
summary_list.append(tf.summary.scalar("loss_3", main_loss_3))
with tf.name_scope("image_summary"):
#origin_summary = tf.summary.image("origin", images_summary)
#label_summary = tf.summary.image("label", labels_summary)
summary_list.append(
tf.summary.image(
'total_image',
tf.concat([
images_summary, labels_summary, preds_1_summary,
att_1_summary, preds_2_summary, att_2_summary,
preds_3_summary, att_3_summary
], 2),
max_outputs=SAVE_NUM_IMAGES))
summary_list.append(
tf.summary.image(
'attention_image',
tf.concat([
images_summary, labels_summary, att_1_summary,
attention_map_1_predicted_summary, att_2_summary,
attention_map_2_predicted_summary, att_3_summary,
attention_map_3_predicted_summary
], 2),
max_outputs=SAVE_NUM_IMAGES))
summary_list.append(
tf.summary.image(
'confidence_map',
tf.concat([
images_summary, labels_summary, predict_3d_1_summary,
predict_3d_2_summary, predict_3d_3_summary
], 2),
max_outputs=SAVE_NUM_IMAGES))
merged_summary_op = tf.summary.merge(summary_list)
summary_writer = tf.summary.FileWriter(args.summay_dir, sess.graph)
# check the shape
print("====global_variables shape check====")
for v in tf.global_variables():
print("{}: {}".format(v.name, v.get_shape()))
print("====trainable_variables shape check====")
for v in tf.trainable_variables():
print("{}: {}".format(v.name, v.get_shape()))
# don't need initiate "filter_of_attention_map"!!!
var_to_be_restored = [
x for x in trainable if u'filter_of_attention_map' not in x.name
]
var_to_be_restored = [
x for x in var_to_be_restored if u'aggregated_feat' not in x.name
]
uninitialized_vars = []
uninitialized_vars.extend([
x for x in tf.global_variables()
if u'filter_of_attention_map' in x.name
])
uninitialized_vars.extend(
[x for x in tf.global_variables() if u'aggregated_feat' in x.name])
uninitialized_vars.extend(
[x for x in tf.global_variables() if u'Variable' in x.name])
uninitialized_vars.extend(
[x for x in tf.global_variables() if u'Momentum' in x.name])
# Saver for storing checkpoints of the model.
print("====var_to_be_restored shape check====")
for tmp in var_to_be_restored:
print("variable name: {},shape: {}, type: {}".format(
tmp.name, tmp.get_shape(), type(tmp.name)))
# Saver for storing checkpoints of the model.
print("====uninitialized_vars shape check====")
for tmp in uninitialized_vars:
print("variable name: {},shape: {}, type: {}".format(
tmp.name, tmp.get_shape(), type(tmp.name)))
readSaver = tf.train.Saver(var_list=var_to_be_restored)
writeSaver = tf.train.Saver(max_to_keep=40)
if args.restore_from is not None:
load(readSaver, sess, args.restore_from)
# Start queue threads.
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
init_new_vars_op = tf.variables_initializer(uninitialized_vars)
sess.run(init_new_vars_op)
print("====varaible initialize status check====")
init_flag = sess.run(
tf.stack(
[tf.is_variable_initialized(v) for v in tf.global_variables()]))
for v, flag in zip(tf.global_variables(), init_flag):
if not flag:
print("====warning====")
print("name: {}, shape: {}, is_variable_initialized:{}".format(
v.name, v.get_shape(), flag))
with tf.name_scope("parameter_count"):
parameter_count = tf.reduce_sum(
[tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()])
print("parameter_count =", sess.run(parameter_count))
summary_str = sess.run(merged_summary_op)
summary_writer.add_summary(summary_str)
# Iterate over training steps.
for step in range(1, args.num_steps):
start_time = time.time()
# get learning rate
lr_scale = math.floor(step / 2000)
cur_lr = args.learning_rate / math.pow(2, lr_scale)
print("current learning rate: {}".format(cur_lr))
_att_optim,_attention_loss,_attention_loss_1,_attention_loss_2,_attention_loss_3,_main_loss,\
_main_loss_1, _pre_upscaled_1, _output_attention_map_1,\
_main_loss_2, _pre_upscaled_2,_output_attention_map_2,\
_main_loss_3, _pre_upscaled_3, _output_attention_map_3 = sess.run([att_optim,attention_loss,attention_loss_1,attention_loss_2,attention_loss_3,main_loss,main_loss_1, pre_upscaled_1, output_attention_map_1, main_loss_2, pre_upscaled_2,\
output_attention_map_2, main_loss_3, pre_upscaled_3, output_attention_map_3],feed_dict={learning_rate:cur_lr,att_learning_rate:cur_lr})
print(
'step {:d}, main_loss: {:.5f}, loss 1: {:.5f}, loss 2: {:.5f}, loss 3: {:.5f}'
.format(step, _main_loss, _main_loss_1, _main_loss_2,
_main_loss_3))
print(
'step {:d}, attention_loss: {:.5f}, att_loss 1: {:.5f}, att_loss 2: {:.5f}, att_loss 3: {:.5f}'
.format(step, _attention_loss, _attention_loss_1,
_attention_loss_2, _attention_loss_3))
if step % args.summary_freq == 0:
print("write summay...")
# generate summary for tensorboard
summary_str = sess.run(merged_summary_op)
summary_writer.add_summary(summary_str, step)
if step % args.save_pred_every == 0:
print("save a predict as picture...")
#do predict
preds_result_value, images, labels = sess.run(
[pred_result, image_batch, label_batch])
fig, axes = plt.subplots(args.save_num_images, 3, figsize=(16, 12))
print("images type: {}".format(type(images)))
print("labels type: {}".format(type(labels)))
#print("preds_result_value type: {},shape {}".format(type(preds_result_value[0]),(preds_result_value[0]).get_shape()))
print("preds_result shape: {}".format(preds_result_value.shape))
for i in xrange(args.save_num_images):
axes.flat[i * 3].set_title('data')
axes.flat[i * 3].imshow(
(images[i] + IMG_MEAN)[:, :, ::-1].astype(np.uint8))
axes.flat[i * 3 + 1].set_title('mask')
axes.flat[i * 3 + 1].imshow(decode_labels(labels[i, :, :, 0]))
axes.flat[i * 3 + 2].set_title('pred')
axes.flat[i * 3 + 2].imshow(
decode_labels(preds_result_value[i, :, :, 0]))
plt.savefig(args.save_dir + str(start_time) + ".png")
plt.close(fig)
save(writeSaver, sess, args.snapshot_dir, step)
duration = time.time() - start_time
print('step {:d} \t ({:.3f} sec/step)'.format(step, duration))
coord.request_stop()
coord.join(threads)
