def get_data_queue(args, coord, is_training=True):
h, w = map(int, args.input_size.split(','))
input_size_img = (h, w)
input_size_label = (h / FEATSTRIDE, w / FEATSTRIDE)
# Load reader.
if is_training:
with tf.name_scope("create_train_inputs"):
reader_train = ImageReader(
args.data_dir,
args.data_train_list,
input_size_img,
input_size_label,
RANDOM_SCALE,
IMG_MEAN,
coord)
image_batch_train, label_batch_train = reader_train.dequeue(args.batch_size)
return image_batch_train, label_batch_train
else:
with tf.name_scope("create_val_inputs"):
reader_val = ImageReader(
args.data_dir,
args.data_val_list,
input_size_img,
input_size_label,
False,
IMG_MEAN,
coord)
image_batch_val, label_batch_val = reader_val.dequeue(args.batch_size, is_training=False)
return image_batch_val, label_batch_val
def train_setup(self):
tf.set_random_seed(self.conf.random_seed)
# Create queue coordinator.
self.coord = tf.train.Coordinator()
# Input size
input_size = (self.conf.input_height, self.conf.input_width)
# Load reader
with tf.name_scope("create_inputs"):
reader = ImageReader(self.conf.data_dir, self.conf.data_list,
input_size, self.conf.random_scale,
self.conf.random_mirror,
self.conf.ignore_label, IMG_MEAN, self.coord)
self.image_batch, self.label_batch = reader.dequeue(
self.conf.batch_size)
# Create network
if self.conf.encoder_name not in ['res101', 'res50', 'deeplab']:
print('encoder_name ERROR!')
print("Please input: res101, res50, or deeplab")
sys.exit(-1)
elif self.conf.encoder_name == 'deeplab':
net = Deeplab_v2(self.image_batch, self.conf.num_classes, True)
# Variables that load from pre-trained model.
restore_var = [
v for v in tf.global_variables() if 'fc' not in v.name
]
# Trainable Variables
all_trainable = tf.trainable_variables()
# Fine-tune part
encoder_trainable = [
v for v in all_trainable if 'fc' not in v.name
] # lr * 1.0
# Decoder part
decoder_trainable = [v for v in all_trainable if 'fc' in v.name]
else:
net = ResNet_segmentation(self.image_batch, self.conf.num_classes,
True, self.conf.encoder_name)
# Variables that load from pre-trained model.
restore_var = [
v for v in tf.global_variables() if 'resnet_v1' in v.name
]
# Trainable Variables
all_trainable = tf.trainable_variables()
# Fine-tune part
encoder_trainable = [
v for v in all_trainable if 'resnet_v1' in v.name
] # lr * 1.0
# Decoder part
decoder_trainable = [
v for v in all_trainable if 'decoder' in v.name
]
decoder_w_trainable = [
v for v in decoder_trainable
if 'weights' in v.name or 'gamma' in v.name
] # lr * 10.0
decoder_b_trainable = [
v for v in decoder_trainable
if 'biases' in v.name or 'beta' in v.name
] # lr * 20.0
# Check
assert (len(all_trainable) == len(decoder_trainable) +
len(encoder_trainable))
assert (len(decoder_trainable) == len(decoder_w_trainable) +
len(decoder_b_trainable))
# Network raw output
raw_output = net.outputs # [batch_size, h, w, 21]
# Output size
output_shape = tf.shape(raw_output)
output_size = (output_shape[1], output_shape[2])
# Groud Truth: ignoring all labels greater or equal than n_classes
label_proc = prepare_label(self.label_batch,
output_size,
num_classes=self.conf.num_classes,
one_hot=False)
raw_gt = tf.reshape(label_proc, [
-1,
])
indices = tf.squeeze(
tf.where(tf.less_equal(raw_gt, self.conf.num_classes - 1)), 1)
gt = tf.cast(tf.gather(raw_gt, indices), tf.int32)
raw_prediction = tf.reshape(raw_output, [-1, self.conf.num_classes])
prediction = tf.gather(raw_prediction, indices)
# Pixel-wise softmax_cross_entropy loss
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=prediction, labels=gt)
# L2 regularization
l2_losses = [
self.conf.weight_decay * tf.nn.l2_loss(v) for v in all_trainable
if 'weights' in v.name
]
# Loss function
self.reduced_loss = tf.reduce_mean(loss) + tf.add_n(l2_losses)
# Define optimizers
# 'poly' learning rate
base_lr = tf.constant(self.conf.learning_rate)
self.curr_step = tf.placeholder(dtype=tf.float32, shape=())
learning_rate = tf.scalar_mul(
base_lr,
tf.pow((1 - self.curr_step / self.conf.num_steps),
self.conf.power))
# We have several optimizers here in order to handle the different lr_mult
# which is a kind of parameters in Caffe. This controls the actual lr for each
# layer.
opt_encoder = tf.train.MomentumOptimizer(learning_rate,
self.conf.momentum)
opt_decoder_w = tf.train.MomentumOptimizer(learning_rate * 10.0,
self.conf.momentum)
opt_decoder_b = tf.train.MomentumOptimizer(learning_rate * 20.0,
self.conf.momentum)
# To make sure each layer gets updated by different lr's, we do not use 'minimize' here.
# Instead, we separate the steps compute_grads+update_params.
# Compute grads
grads = tf.gradients(
self.reduced_loss,
encoder_trainable + decoder_w_trainable + decoder_b_trainable)
grads_encoder = grads[:len(encoder_trainable)]
grads_decoder_w = grads[len(encoder_trainable):(
len(encoder_trainable) + len(decoder_w_trainable))]
grads_decoder_b = grads[(len(encoder_trainable) +
len(decoder_w_trainable)):]
# Update params
train_op_conv = opt_encoder.apply_gradients(
zip(grads_encoder, encoder_trainable))
train_op_fc_w = opt_decoder_w.apply_gradients(
zip(grads_decoder_w, decoder_w_trainable))
train_op_fc_b = opt_decoder_b.apply_gradients(
zip(grads_decoder_b, decoder_b_trainable))
# Finally, get the train_op!
update_ops = tf.get_collection(
tf.GraphKeys.UPDATE_OPS
) # for collecting moving_mean and moving_variance
with tf.control_dependencies(update_ops):
self.train_op = tf.group(train_op_conv, train_op_fc_w,
train_op_fc_b)
# Saver for storing checkpoints of the model
self.saver = tf.train.Saver(var_list=tf.global_variables(),
max_to_keep=0)
# Loader for loading the pre-trained model
self.loader = tf.train.Saver(var_list=restore_var)
# Training summary
# Processed predictions: for visualisation.
raw_output_up = tf.image.resize_bilinear(raw_output, input_size)
raw_output_up = tf.argmax(raw_output_up, axis=3)
self.pred = tf.expand_dims(raw_output_up, dim=3)
# Image summary.
images_summary = tf.py_func(inv_preprocess,
[self.image_batch, 2, IMG_MEAN], tf.uint8)
labels_summary = tf.py_func(
decode_labels, [self.label_batch, 2, self.conf.num_classes],
tf.uint8)
preds_summary = tf.py_func(decode_labels,
[self.pred, 2, self.conf.num_classes],
tf.uint8)
self.total_summary = tf.summary.image(
'images',
tf.concat(axis=2,
values=[images_summary, labels_summary, preds_summary]),
max_outputs=2) # Concatenate row-wise.
if not os.path.exists(self.conf.logdir):
os.makedirs(self.conf.logdir)
self.summary_writer = tf.summary.FileWriter(
self.conf.logdir, graph=tf.get_default_graph())
def train_setup(self):
tf.set_random_seed(self.conf.random_seed)
# Create queue coordinator.
self.coord = tf.train.Coordinator()
# Input size
input_size = (self.conf.input_height, self.conf.input_width)
# Load reader
with tf.name_scope("create_inputs"):
reader = ImageReader(
self.conf.data_dir,
self.conf.data_list,
input_size,
self.conf.random_scale,
self.conf.random_mirror,
self.conf.ignore_label,
IMG_MEAN,
self.coord)
self.image_batch, self.label_batch = reader.dequeue(self.conf.batch_size)
# Create network
if self.conf.encoder_name not in ['res101', 'res50', 'deeplab']:
print('encoder_name ERROR!')
print("Please input: res101, res50, or deeplab")
sys.exit(-1)
elif self.conf.encoder_name == 'deeplab':
net = Deeplab_v2(self.image_batch, self.conf.num_classes, True)
# Variables that load from pre-trained model.
restore_var = [v for v in tf.global_variables() if 'fc' not in v.name]
# Trainable Variables
all_trainable = tf.trainable_variables()
# Fine-tune part
encoder_trainable = [v for v in all_trainable if 'fc' not in v.name] # lr * 1.0
# Decoder part
decoder_trainable = [v for v in all_trainable if 'fc' in v.name]
else:
net = ResNet_segmentation(self.image_batch, self.conf.num_classes, True, self.conf.encoder_name)
# Variables that load from pre-trained model.
restore_var = [v for v in tf.global_variables() if 'resnet_v1' in v.name]
# Trainable Variables
all_trainable = tf.trainable_variables()
# Fine-tune part
encoder_trainable = [v for v in all_trainable if 'resnet_v1' in v.name] # lr * 1.0
# Decoder part
decoder_trainable = [v for v in all_trainable if 'decoder' in v.name]
decoder_w_trainable = [v for v in decoder_trainable if 'weights' in v.name or 'gamma' in v.name] # lr * 10.0
decoder_b_trainable = [v for v in decoder_trainable if 'biases' in v.name or 'beta' in v.name] # lr * 20.0
# Check
assert(len(all_trainable) == len(decoder_trainable) + len(encoder_trainable))
assert(len(decoder_trainable) == len(decoder_w_trainable) + len(decoder_b_trainable))
# Network raw output
raw_output = net.outputs # [batch_size, h, w, 21]
# Output size
output_shape = tf.shape(raw_output)
output_size = (output_shape[1], output_shape[2])
# Groud Truth: ignoring all labels greater or equal than n_classes
label_proc = prepare_label(self.label_batch, output_size, num_classes=self.conf.num_classes, one_hot=False)
raw_gt = tf.reshape(label_proc, [-1,])
indices = tf.squeeze(tf.where(tf.less_equal(raw_gt, self.conf.num_classes - 1)), 1)
gt = tf.cast(tf.gather(raw_gt, indices), tf.int32)
raw_prediction = tf.reshape(raw_output, [-1, self.conf.num_classes])
prediction = tf.gather(raw_prediction, indices)
# Pixel-wise softmax_cross_entropy loss
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction, labels=gt)
# L2 regularization
l2_losses = [self.conf.weight_decay * tf.nn.l2_loss(v) for v in all_trainable if 'weights' in v.name]
# Loss function
self.reduced_loss = tf.reduce_mean(loss) + tf.add_n(l2_losses)
# Define optimizers
# 'poly' learning rate
base_lr = tf.constant(self.conf.learning_rate)
self.curr_step = tf.placeholder(dtype=tf.float32, shape=())
learning_rate = tf.scalar_mul(base_lr, tf.pow((1 - self.curr_step / self.conf.num_steps), self.conf.power))
# We have several optimizers here in order to handle the different lr_mult
# which is a kind of parameters in Caffe. This controls the actual lr for each
# layer.
opt_encoder = tf.train.MomentumOptimizer(learning_rate, self.conf.momentum)
opt_decoder_w = tf.train.MomentumOptimizer(learning_rate * 10.0, self.conf.momentum)
opt_decoder_b = tf.train.MomentumOptimizer(learning_rate * 20.0, self.conf.momentum)
# To make sure each layer gets updated by different lr's, we do not use 'minimize' here.
# Instead, we separate the steps compute_grads+update_params.
# Compute grads
grads = tf.gradients(self.reduced_loss, encoder_trainable + decoder_w_trainable + decoder_b_trainable)
grads_encoder = grads[:len(encoder_trainable)]
grads_decoder_w = grads[len(encoder_trainable) : (len(encoder_trainable) + len(decoder_w_trainable))]
grads_decoder_b = grads[(len(encoder_trainable) + len(decoder_w_trainable)):]
# Update params
train_op_conv = opt_encoder.apply_gradients(zip(grads_encoder, encoder_trainable))
train_op_fc_w = opt_decoder_w.apply_gradients(zip(grads_decoder_w, decoder_w_trainable))
train_op_fc_b = opt_decoder_b.apply_gradients(zip(grads_decoder_b, decoder_b_trainable))
# Finally, get the train_op!
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) # for collecting moving_mean and moving_variance
with tf.control_dependencies(update_ops):
self.train_op = tf.group(train_op_conv, train_op_fc_w, train_op_fc_b)
# Saver for storing checkpoints of the model
self.saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=0)
# Loader for loading the pre-trained model
self.loader = tf.train.Saver(var_list=restore_var)
# Training summary
# Processed predictions: for visualisation.
raw_output_up = tf.image.resize_bilinear(raw_output, input_size)
raw_output_up = tf.argmax(raw_output_up, axis=3)
self.pred = tf.expand_dims(raw_output_up, dim=3)
# Image summary.
images_summary = tf.py_func(inv_preprocess, [self.image_batch, 2, IMG_MEAN], tf.uint8)
labels_summary = tf.py_func(decode_labels, [self.label_batch, 2, self.conf.num_classes], tf.uint8)
preds_summary = tf.py_func(decode_labels, [self.pred, 2, self.conf.num_classes], tf.uint8)
self.total_summary = tf.summary.image('images',
tf.concat(axis=2, values=[images_summary, labels_summary, preds_summary]),
max_outputs=2) # Concatenate row-wise.
if not os.path.exists(self.conf.logdir):
os.makedirs(self.conf.logdir)
self.summary_writer = tf.summary.FileWriter(self.conf.logdir, graph=tf.get_default_graph())
def train_setup(self):
tf.set_random_seed(self.conf.random_seed)
# Create queue coordinator.
self.coord = tf.train.Coordinator()
# Input size
h, w = (self.conf.input_height, self.conf.input_width)
input_size = (h, w)
# Load reader
with tf.name_scope("create_inputs"):
reader = ImageReader(self.conf.data_dir, self.conf.data_list,
input_size, self.conf.random_scale,
self.conf.random_mirror,
self.conf.ignore_label, IMG_MEAN, self.coord)
self.image_batch, self.label_batch = reader.dequeue(
self.conf.batch_size)
image_batch_075 = tf.image.resize_images(
self.image_batch, [int(h * 0.75), int(w * 0.75)])
image_batch_05 = tf.image.resize_images(
self.image_batch, [int(h * 0.5), int(w * 0.5)])
# #testWWang
# image = self.image_batch[0]
# label = self.label_batch[0]
# utils.save_image(image, "/home/py36tf14/wangyichao/Deeplab-v2--ResNet-101--Tensorflow-master/images",
# name = image ,mean = IMG_MEAN)
# utils.save_image(label, "/home/py36tf14/wangyichao/Deeplab-v2--ResNet-101--Tensorflow-master/images",
# name=label, mean=IMG_MEAN)
#
# #end
# Create network
if self.conf.encoder_name not in ['res101', 'res50', 'deeplab']:
print('encoder_name ERROR!')
print("Please input: res101, res50, or deeplab")
sys.exit(-1)
elif self.conf.encoder_name == 'deeplab':
with tf.variable_scope('', reuse=False):
net = Deeplab_v2(self.image_batch, self.conf.num_classes, True)
with tf.variable_scope('', reuse=True):
net075 = Deeplab_v2(image_batch_075, self.conf.num_classes,
True)
with tf.variable_scope('', reuse=True):
net05 = Deeplab_v2(image_batch_05, self.conf.num_classes, True)
# Variables that load from pre-trained model.
restore_var = [
v for v in tf.global_variables() if 'fc' not in v.name
]
# Trainable Variables
all_trainable = tf.trainable_variables()
# Fine-tune part
encoder_trainable = [
v for v in all_trainable if 'fc' not in v.name
] # lr * 1.0
# Decoder part
decoder_trainable = [v for v in all_trainable if 'fc' in v.name]
else:
with tf.variable_scope('', reuse=False):
net = ResNet_segmentation(self.image_batch,
self.conf.num_classes, True,
self.conf.encoder_name)
with tf.variable_scope('', reuse=True):
net075 = ResNet_segmentation(image_batch_075,
self.conf.num_classes, True,
self.conf.encoder_name)
with tf.variable_scope('', reuse=True):
net05 = ResNet_segmentation(image_batch_05,
self.conf.num_classes, True,
self.conf.encoder_name)
# Variables that load from pre-trained model.
restore_var = [
v for v in tf.global_variables() if 'resnet_v1' in v.name
]
# Trainable Variables
all_trainable = tf.trainable_variables()
# Fine-tune part
encoder_trainable = [
v for v in all_trainable if 'resnet_v1' in v.name
] # lr * 1.0
# Decoder part
decoder_trainable = [
v for v in all_trainable if 'decoder' in v.name
]
decoder_w_trainable = [
v for v in decoder_trainable
if 'weights' in v.name or 'gamma' in v.name
] # lr * 10.0
decoder_b_trainable = [
v for v in decoder_trainable
if 'biases' in v.name or 'beta' in v.name
] # lr * 20.0
# Check
assert (len(all_trainable) == len(decoder_trainable) +
len(encoder_trainable))
assert (len(decoder_trainable) == len(decoder_w_trainable) +
len(decoder_b_trainable))
# Network raw output
raw_output100 = net.outputs
raw_output075 = net075.outputs
raw_output05 = net05.outputs
raw_output = tf.reduce_max(tf.stack([
raw_output100,
tf.image.resize_images(raw_output075,
tf.shape(raw_output100)[1:3, ]),
tf.image.resize_images(raw_output05,
tf.shape(raw_output100)[1:3, ])
]),
axis=0)
# Groud Truth: ignoring all labels greater or equal than n_classes
label_proc = prepare_label(self.label_batch,
tf.stack(raw_output.get_shape()[1:3]),
num_classes=self.conf.num_classes,
one_hot=False) # [batch_size, h, w]
label_proc075 = prepare_label(self.label_batch,
tf.stack(raw_output075.get_shape()[1:3]),
num_classes=self.conf.num_classes,
one_hot=False)
label_proc05 = prepare_label(self.label_batch,
tf.stack(raw_output05.get_shape()[1:3]),
num_classes=self.conf.num_classes,
one_hot=False)
raw_gt = tf.reshape(label_proc, [
-1,
])
raw_gt075 = tf.reshape(label_proc075, [
-1,
])
raw_gt05 = tf.reshape(label_proc05, [
-1,
])
indices = tf.squeeze(
tf.where(tf.less_equal(raw_gt, self.conf.num_classes - 1)), 1)
indices075 = tf.squeeze(
tf.where(tf.less_equal(raw_gt075, self.conf.num_classes - 1)), 1)
indices05 = tf.squeeze(
tf.where(tf.less_equal(raw_gt05, self.conf.num_classes - 1)), 1)
gt = tf.cast(tf.gather(raw_gt, indices), tf.int32)
gt075 = tf.cast(tf.gather(raw_gt075, indices075), tf.int32)
gt05 = tf.cast(tf.gather(raw_gt05, indices05), tf.int32)
raw_prediction = tf.reshape(raw_output, [-1, self.conf.num_classes])
raw_prediction100 = tf.reshape(raw_output100,
[-1, self.conf.num_classes])
raw_prediction075 = tf.reshape(raw_output075,
[-1, self.conf.num_classes])
raw_prediction05 = tf.reshape(raw_output05,
[-1, self.conf.num_classes])
prediction = tf.gather(raw_prediction, indices)
prediction100 = tf.gather(raw_prediction100, indices)
prediction075 = tf.gather(raw_prediction075, indices075)
prediction05 = tf.gather(raw_prediction05, indices05)
# Pixel-wise softmax_cross_entropy loss
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=prediction, labels=gt)
loss100 = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=prediction100, labels=gt)
loss075 = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=prediction075, labels=gt075)
loss05 = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=prediction05, labels=gt05)
# L2 regularization
l2_losses = [
self.conf.weight_decay * tf.nn.l2_loss(v) for v in all_trainable
if 'weights' in v.name
]
# Loss function
self.reduced_loss = tf.reduce_mean(loss) + tf.reduce_mean(
loss100) + tf.reduce_mean(loss075) + tf.reduce_mean(
loss05) + tf.add_n(l2_losses)
# Define optimizers
# 'poly' learning rate
base_lr = tf.constant(self.conf.learning_rate)
self.curr_step = tf.placeholder(dtype=tf.float32, shape=())
learning_rate = tf.scalar_mul(
base_lr,
tf.pow((1 - self.curr_step / self.conf.num_steps),
self.conf.power))
# We have several optimizers here in order to handle the different lr_mult
# which is a kind of parameters in Caffe. This controls the actual lr for each
# layer.
opt_encoder = tf.train.MomentumOptimizer(learning_rate,
self.conf.momentum)
opt_decoder_w = tf.train.MomentumOptimizer(learning_rate * 10.0,
self.conf.momentum)
opt_decoder_b = tf.train.MomentumOptimizer(learning_rate * 20.0,
self.conf.momentum)
# Gradient accumulation
# Define a variable to accumulate gradients.
accum_grads = [
tf.Variable(tf.zeros_like(v.initialized_value()), trainable=False)
for v in encoder_trainable + decoder_w_trainable +
decoder_b_trainable
]
# Define an operation to clear the accumulated gradients for next batch.
self.zero_op = [v.assign(tf.zeros_like(v)) for v in accum_grads]
# To make sure each layer gets updated by different lr's, we do not use 'minimize' here.
# Instead, we separate the steps compute_grads+update_params.
# Compute grads
grads = tf.gradients(
self.reduced_loss,
encoder_trainable + decoder_w_trainable + decoder_b_trainable)
# Accumulate and normalise the gradients.
self.accum_grads_op = [
accum_grads[i].assign_add(grad / self.conf.grad_update_every)
for i, grad in enumerate(grads)
]
grads = tf.gradients(
self.reduced_loss,
encoder_trainable + decoder_w_trainable + decoder_b_trainable)
grads_encoder = accum_grads[:len(encoder_trainable)]
grads_decoder_w = accum_grads[len(encoder_trainable):(
len(encoder_trainable) + len(decoder_w_trainable))]
grads_decoder_b = accum_grads[(len(encoder_trainable) +
len(decoder_w_trainable)):]
# Update params
train_op_conv = opt_encoder.apply_gradients(
zip(grads_encoder, encoder_trainable))
train_op_fc_w = opt_decoder_w.apply_gradients(
zip(grads_decoder_w, decoder_w_trainable))
train_op_fc_b = opt_decoder_b.apply_gradients(
zip(grads_decoder_b, decoder_b_trainable))
# Finally, get the train_op!
update_ops = tf.get_collection(
tf.GraphKeys.UPDATE_OPS
) # for collecting moving_mean and moving_variance
with tf.control_dependencies(update_ops):
self.train_op = tf.group(train_op_conv, train_op_fc_w,
train_op_fc_b)
# Saver for storing checkpoints of the model
self.saver = tf.train.Saver(var_list=tf.global_variables(),
max_to_keep=0)
# Loader for loading the pre-trained model
self.loader = tf.train.Saver(var_list=restore_var)
# Training summary
# Processed predictions: for visualisation.
raw_output_up = tf.image.resize_bilinear(raw_output, input_size)
raw_output_up = tf.argmax(raw_output_up, axis=3)
self.pred = tf.expand_dims(raw_output_up, dim=3)
# Image summary.
images_summary = tf.py_func(inv_preprocess,
[self.image_batch, 1, IMG_MEAN], tf.uint8)
labels_summary = tf.py_func(
decode_labels, [self.label_batch, 1, self.conf.num_classes],
tf.uint8)
preds_summary = tf.py_func(decode_labels,
[self.pred, 1, self.conf.num_classes],
tf.uint8)
self.total_summary = tf.summary.image(
'images',
tf.concat(axis=2,
values=[images_summary, labels_summary, preds_summary]),
max_outputs=20) # Concatenate row-wise.
if not os.path.exists(self.conf.logdir):
os.makedirs(self.conf.logdir)
self.summary_writer = tf.summary.FileWriter(
self.conf.logdir, graph=tf.get_default_graph())
def train_setup(self, reuse=False):
tf.set_random_seed(self.conf.random_seed)
num_layers = 50 #-----------------------------------------------------------------------------------------
# Create queue coordinator.
self.coord = tf.train.Coordinator()
self.n_gpu = self.conf.n_gpu
# Input size
self.input_size = (self.conf.input_height, self.conf.input_width)
j_step = 0
with tf.name_scope("create_inputs"):
reader = ImageReader(self.conf.data_dir, self.conf.data_list,
self.input_size, self.conf.random_scale,
self.conf.random_mirror,
self.conf.ignore_label, IMG_MEAN, self.coord)
# print "1"*22
# print reader
image_data, image_label = reader.dequeue(self.conf.batch_size)
self.image_data = image_data
if tf.__version__.startswith('1.'):
split_train_data_node = tf.split(image_data, self.n_gpu)
split_train_labels_node = tf.split(image_label, self.n_gpu)
else:
split_train_data_node = tf.split(0, self.n_gpu, image_data)
split_train_labels_node = tf.split(0, self.n_gpu, image_label)
with tf.variable_scope(tf.get_variable_scope()):
all_loss = []
for device_index, (i, self.image_batch,
self.label_batch) in enumerate(
zip([1], split_train_data_node,
split_train_labels_node)):
with tf.device('/gpu:%d' % i):
#print i
with tf.name_scope('%s_%d' % ("gpu", i)) as scope:
if j_step == 0:
j_step = 1
pass
else:
reuse = True
# net = DeepLab_v2_Network(self.image_batch, num_classes=self.conf.num_classes,
# is_training=self.conf.is_training ,reuse=reuse)
net, end_points = deeplabv3(
self.image_batch,
num_classes=self.conf.num_classes,
depth=num_layers,
is_training=True,
reuse=reuse)
self.raw_output = end_points[
'gpu_{}/resnet{}/logits'.format(i, num_layers)]
# Network raw output
# [batch_size, 41, 41, 21]
output_size = (self.raw_output.shape[1].value,
self.raw_output.shape[2].value)
label_proc = prepare_label(
self.label_batch,
output_size,
num_classes=self.conf.num_classes,
one_hot=False) # [batch_size, 41, 41]
raw_gt = tf.reshape(label_proc, [
-1,
])
indices = tf.squeeze(
tf.where(
tf.less_equal(raw_gt,
self.conf.num_classes - 1)), 1)
gt = tf.cast(tf.gather(raw_gt, indices), tf.int32)
raw_prediction = tf.reshape(
self.raw_output, [-1, self.conf.num_classes])
# print raw_prediction
# print gt
prediction = raw_prediction
# prediction = tf.expand_dims(raw_prediction, 3)
# prediction = tl.act.pixel_wise_softmax(prediction)
# print prediction
# print label_proc
# loss = 1 - tl.cost.dice_coe(prediction, label_proc, axis=[1, 2, 3, 4])
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=prediction, labels=gt)
l2_losses = [
self.conf.weight_decay * tf.nn.l2_loss(v)
for v in tf.trainable_variables()
if 'weights' in v.name
]
# Loss function
all_loss.append(
tf.reduce_mean(loss) + tf.add_n(l2_losses))
tf.get_variable_scope().reuse_variables()
# Output size
#output_size = (self.raw_output.shape[1].value, self.raw_output.shape[2].value)
# Variables that load from pre-trained model.
# For training, last few layers should not be loaded.
if self.conf.pretrain_file is not None:
restore_var = [
v for v in tf.global_variables() if 'fc' not in v.name
]
original_step = int(self.conf.pretrain_file.split("-")[-1])
else:
original_step = 0
num_steps = self.conf.num_steps + original_step
# Trainable Variables
# Note that is_training=False still updates BN parameters gamma (scale) and beta (offset)
# if they are presented in var_list of the optimiser definition.
# So we remove them from the list.
all_trainable = [
v for v in tf.trainable_variables()
if 'beta' not in v.name and 'gamma' not in v.name
]
# Fine-tune part
conv_trainable = [v for v in all_trainable
if 'fc' not in v.name] # lr * 1.0
# ASPP part
fc_trainable = [v for v in all_trainable if 'fc' in v.name]
# fc_w_trainable = [v for v in fc_trainable if 'weights' in v.name] # lr * 10.0
# fc_b_trainable = [v for v in fc_trainable if 'biases' in v.name] # lr * 20.0
# check
#assert (len(all_trainable) == len(fc_trainable) + len(conv_trainable))
#assert (len(fc_trainable) == len(fc_w_trainable) + len(fc_b_trainable))
# Groud Truth: ignoring all labels greater or equal than n_classes
#label_proc = prepare_label(self.label_batch, output_size, num_classes=self.conf.num_classes,
#one_hot=False) # [batch_size, 41, 41]
#raw_gt = tf.reshape(label_proc, [-1, ])
#indices = tf.squeeze(tf.where(tf.less_equal(raw_gt, self.conf.num_classes - 1)), 1)
#gt = tf.cast(tf.gather(raw_gt, indices), tf.int32)
#raw_prediction = tf.reshape(self.raw_output, [-1, self.conf.num_classes])
#prediction = tf.gather(raw_prediction, indices)
# Pixel-wise softmax_cross_entropy loss
#loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction, labels=gt)
# L2 regularization
#l2_losses = [self.conf.weight_decay * tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'weights' in v.name]
# Loss function
self.reduced_loss = tf.add_n(all_loss) / self.n_gpu
#self.reduced_loss = tf.reduce_mean(loss) + tf.add_n(l2_losses)
# Define optimizers
# 'poly' learning rate
base_lr = tf.constant(self.conf.learning_rate)
self.curr_step = tf.placeholder(dtype=tf.float32, shape=())
self.loss_trans = tf.placeholder(dtype=tf.float32, shape=())
self.final_loss = (self.reduced_loss + self.loss_trans) / 2
learning_rate = tf.scalar_mul(
base_lr, tf.pow((1 - self.curr_step / num_steps), self.conf.power))
#print self.conf.power
self.learning_rate = learning_rate
#print learning_rate
# We have several optimizers here in order to handle the different lr_mult
# which is a kind of parameters in Caffe. This controls the actual lr for each
# layer.
opt = tf.train.AdamOptimizer(learning_rate, self.conf.momentum, 0.98)
#opt= tf.train.MomentumOptimizer(learning_rate, self.conf.momentum)
#opt_fc_w = tf.train.AdamOptimizer(learning_rate , self.conf.momentum,0.98)
#opt_fc_b = tf.train.AdamOptimizer(learning_rate , self.conf.momentum,0.98)
#opt_conv = tf.train.MomentumOptimizer(learning_rate, self.conf.momentum)
#opt_fc_w = tf.train.MomentumOptimizer(learning_rate * 10.0, self.conf.momentum)
#opt_fc_b = tf.train.MomentumOptimizer(learning_rate * 20.0, self.conf.momentum)
# To make sure each layer gets updated by different lr's, we do not use 'minimize' here.
# Instead, we separate the steps compute_grads+update_params.
# Compute grads
grads_conv = tf.gradients(self.final_loss, conv_trainable)
# train_op = opt.apply_gradients(zip(grads_conv, conv_trainable))
#grads = tf.gradients(self.reduced_loss, conv_trainable + fc_w_trainable + fc_b_trainable)
grads_conv = grads_conv[:len(conv_trainable)]
# grads_fc_w = grads[len(conv_trainable): (len(conv_trainable) + len(fc_w_trainable))]
# grads_fc_b = grads[(len(conv_trainable) + len(fc_w_trainable)):]
# Update params
train_op_conv = opt.apply_gradients(zip(grads_conv, conv_trainable))
# train_op_conv = opt_conv.apply_gradients(zip(grads_conv, conv_trainable))
# train_op_fc_w = opt_fc_w.apply_gradients(zip(grads_fc_w, fc_w_trainable))
# train_op_fc_b = opt_fc_b.apply_gradients(zip(grads_fc_b, fc_b_trainable))
# Finally, get the train_op!
self.train_op = train_op_conv
# Saver for storing checkpoints of the model
self.saver = tf.train.Saver(var_list=tf.global_variables(),
max_to_keep=0)
# Loader for loading the pre-trained model
if self.conf.pretrain_file is not None:
self.loader = tf.train.Saver(var_list=restore_var)
def train_setup(self):
tf.set_random_seed(self.conf.random_seed)
# Create queue coordinator.
self.coord = tf.train.Coordinator()
# Input size
self.input_size = (self.conf.input_height, self.conf.input_width)
# Load reader
with tf.name_scope("create_inputs"):
reader = ImageReader(
self.conf.data_dir,
self.conf.data_list,
self.input_size,
self.conf.random_scale,
self.conf.random_mirror,
self.conf.ignore_label,
IMG_MEAN,
self.coord)
self.image_batch, self.label_batch = reader.dequeue(self.conf.batch_size)
# Create network
net = DeepLab_v2_Network(self.image_batch, num_classes=self.conf.num_classes,
is_training=self.conf.is_training)
#net = DeepLabVGGModel(self.image_batch, num_classes=self.conf.num_classes,
# is_training=self.conf.is_training)
# Network raw output
self.raw_output = net.o # [batch_size, 41, 41, 21]
self.raw_output=tf.image.resize_bilinear(self.raw_output, [350,350])
print(tf.shape(self.image_batch))
# Output size
output_size = (self.raw_output.shape[1].value, self.raw_output.shape[2].value)
# Variables that load from pre-trained model.
# For training, last few layers should not be loaded.
#restore_var = [v for v in tf.global_variables() if 'fc' not in v.name] #这个是对INIT初始化模型用的
restore_var = [v for v in tf.global_variables() ] #恢复所有的参数。
# Trainable Variables
# Note that is_training=False still updates BN parameters gamma (scale) and beta (offset)
# if they are presented in var_list of the optimiser definition.
# So we remove them from the list.
all_trainable = [v for v in tf.trainable_variables() if 'beta' not in v.name and 'gamma' not in v.name]
# Fine-tune part
conv_trainable = [v for v in all_trainable if 'fc' not in v.name] # lr * 1.0
# ASPP part
fc_trainable = [v for v in all_trainable if 'fc' in v.name]
fc_w_trainable = [v for v in fc_trainable if 'weights' in v.name] # lr * 10.0
fc_b_trainable = [v for v in fc_trainable if 'biases' in v.name] # lr * 20.0
# check
print(len(fc_trainable))
print(len(fc_w_trainable) + len(fc_b_trainable))
assert(len(all_trainable) == len(fc_trainable) + len(conv_trainable))
assert(len(fc_trainable) == len(fc_w_trainable) + len(fc_b_trainable))
# Groud Truth: ignoring all labels greater or equal than n_classes
label_proc = prepare_label(self.label_batch, output_size, num_classes=self.conf.num_classes, one_hot=False) # [batch_size, 41, 41]
raw_gt = tf.reshape(label_proc, [-1,])
indices = tf.squeeze(tf.where(tf.less_equal(raw_gt, self.conf.num_classes - 1)), 1)
gt = tf.cast(tf.gather(raw_gt, indices), tf.int32)
raw_prediction = tf.reshape(self.raw_output, [-1, self.conf.num_classes])
prediction = tf.gather(raw_prediction, indices)
# Pixel-wise softmax_cross_entropy loss
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction, labels=gt)
# L2 regularization
l2_losses = [self.conf.weight_decay * tf.nn.l2_loss(v) for v in tf.trainable_variables() if 'weights' in v.name]
# Loss function
self.reduced_loss = tf.reduce_mean(loss) + tf.add_n(l2_losses)
# Define optimizers
# 'poly' learning rate
base_lr = tf.constant(self.conf.learning_rate)
self.curr_step = tf.placeholder(dtype=tf.float32, shape=())
#learning_rate = tf.scalar_mul(base_lr, tf.pow((1 - (15000+self.curr_step) /(15000+ self.conf.num_steps)), self.conf.power))
learning_rate = tf.scalar_mul(base_lr, tf.pow((1 - (self.curr_step) /(self.conf.num_steps)), self.conf.power))
# We have several optimizers here in order to handle the different lr_mult
# which is a kind of parameters in Caffe. This controls the actual lr for each
# layer.
opt_conv = tf.train.MomentumOptimizer(learning_rate, self.conf.momentum)
opt_fc_w = tf.train.MomentumOptimizer(learning_rate * 10.0, self.conf.momentum)
opt_fc_b = tf.train.MomentumOptimizer(learning_rate * 20.0, self.conf.momentum)
# To make sure each layer gets updated by different lr's, we do not use 'minimize' here.
# Instead, we separate the steps compute_grads+update_params.
# Compute grads
grads = tf.gradients(self.reduced_loss, conv_trainable + fc_w_trainable + fc_b_trainable)
grads_conv = grads[:len(conv_trainable)]
grads_fc_w = grads[len(conv_trainable) : (len(conv_trainable) + len(fc_w_trainable))]
grads_fc_b = grads[(len(conv_trainable) + len(fc_w_trainable)):]
# Update params
train_op_conv = opt_conv.apply_gradients(zip(grads_conv, conv_trainable))
train_op_fc_w = opt_fc_w.apply_gradients(zip(grads_fc_w, fc_w_trainable))
train_op_fc_b = opt_fc_b.apply_gradients(zip(grads_fc_b, fc_b_trainable))
# Finally, get the train_op!
self.train_op = tf.group(train_op_conv, train_op_fc_w, train_op_fc_b)
#self.train_op = tf.group(train_op_fc_w, train_op_fc_b) #只优化全连接部分
# Saver for storing checkpoints of the model
self.saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=5)
# Loader for loading the pre-trained model
self.loader = tf.train.Saver(var_list=restore_var)
def train_setup(self):
tf.set_random_seed(self.conf.random_seed)
# Create queue coordinator.
self.coord = tf.train.Coordinator()
# Input size
h, w = (self.conf.input_height, self.conf.input_width)
input_size = (h, w)
# Devices
gpu_list = get_available_gpus()
zip_encoder, zip_decoder_b, zip_decoder_w, zip_crf = [], [], [], []
previous_crf_names = []
restore_vars = []
self.loaders = []
self.im_list = []
for i in range(len(gpu_list)):
with tf.device(gpu_list[i]):
# Load reader
with tf.name_scope("create_inputs"):
reader = ImageReader(self.conf.data_dir,
self.conf.data_list, input_size,
self.conf.random_scale,
self.conf.random_mirror,
self.conf.ignore_label, IMG_MEAN,
self.coord)
self.image_batch, self.label_batch = reader.dequeue(
self.conf.batch_size)
self.im_list.append(self.image_batch)
image_batch_075 = tf.image.resize_images(
self.image_batch,
[int(h * 0.75), int(w * 0.75)])
image_batch_05 = tf.image.resize_images(
self.image_batch,
[int(h * 0.5), int(w * 0.5)])
# Create network
with tf.variable_scope('', reuse=False):
net = Deeplab_v2(self.image_batch,
self.conf.num_classes,
True,
rescale075=False,
rescale05=False,
crf_type=self.conf.crf_type)
with tf.variable_scope('', reuse=True):
net075 = Deeplab_v2(image_batch_075,
self.conf.num_classes,
True,
rescale075=True,
rescale05=False,
crf_type=self.conf.crf_type)
with tf.variable_scope('', reuse=True):
net05 = Deeplab_v2(image_batch_05,
self.conf.num_classes,
True,
rescale075=False,
rescale05=True,
crf_type=self.conf.crf_type)
# Variables that load from pre-trained model.
restore_var = [
v for v in tf.global_variables()
if ('fc' not in v.name and 'crfrnn' not in v.name)
]
restore_vars.append(restore_var)
# Trainable Variables
all_trainable = tf.trainable_variables()
# Fine-tune part
for name in previous_crf_names:
for v in all_trainable:
if v.name == name:
all_trainable.remove(v)
crf_trainable = [
v for v in all_trainable
if ('crfrnn' in v.name and v.name not in previous_crf_names
)
]
previous_crf_names.extend(v.name for v in crf_trainable)
encoder_trainable = [
v for v in all_trainable
if 'fc' not in v.name and 'crfrnn' not in v.name
] # lr * 1.0
# Remove encoder_trainable from all_trainable
#all_trainable = [v for v in all_trainable if v not in encoder_trainable]
# Decoder part
decoder_trainable = [
v for v in all_trainable
if 'fc' in v.name and 'crfrnn' not in v.name
]
decoder_w_trainable = [
v for v in decoder_trainable
if ('weights' in v.name or 'gamma' in v.name)
and 'crfrnn' not in v.name
] # lr * 10.0
decoder_b_trainable = [
v for v in decoder_trainable
if ('biases' in v.name or 'beta' in v.name)
and 'crfrnn' not in v.name
] # lr * 20.0
# Check
assert (len(all_trainable) == len(decoder_trainable) +
len(crf_trainable)) + len(encoder_trainable)
assert (len(decoder_trainable) == len(decoder_w_trainable) +
len(decoder_b_trainable))
# Network raw output
raw_output100 = net.outputs
raw_output075 = net075.outputs
raw_output05 = net05.outputs
raw_output = tf.reduce_max(tf.stack([
raw_output100,
tf.image.resize_images(raw_output075,
tf.shape(raw_output100)[1:3, ]),
tf.image.resize_images(raw_output05,
tf.shape(raw_output100)[1:3, ])
]),
axis=0)
# Ground Truth: ignoring all labels greater or equal than n_classes
label_proc = prepare_label(self.label_batch,
tf.stack(
raw_output.get_shape()[1:3]),
num_classes=self.conf.num_classes,
one_hot=True) # [batch_size, h, w]
label_proc075 = prepare_label(
self.label_batch,
tf.stack(raw_output075.get_shape()[1:3]),
num_classes=self.conf.num_classes,
one_hot=True)
label_proc05 = prepare_label(
self.label_batch,
tf.stack(raw_output05.get_shape()[1:3]),
num_classes=self.conf.num_classes,
one_hot=True)
raw_gt = tf.reshape(label_proc, [
-1,
])
raw_gt075 = tf.reshape(label_proc075, [
-1,
])
raw_gt05 = tf.reshape(label_proc05, [
-1,
])
indices = tf.squeeze(
tf.where(tf.less_equal(raw_gt, self.conf.num_classes - 1)),
1)
indices075 = tf.squeeze(
tf.where(
tf.less_equal(raw_gt075, self.conf.num_classes - 1)),
1)
indices05 = tf.squeeze(
tf.where(tf.less_equal(raw_gt05,
self.conf.num_classes - 1)), 1)
gt = tf.cast(tf.gather(raw_gt, indices), tf.int32)
gt075 = tf.cast(tf.gather(raw_gt075, indices075), tf.int32)
gt05 = tf.cast(tf.gather(raw_gt05, indices05), tf.int32)
raw_prediction = tf.reshape(raw_output,
[-1, self.conf.num_classes])
raw_prediction100 = tf.reshape(raw_output100,
[-1, self.conf.num_classes])
raw_prediction075 = tf.reshape(raw_output075,
[-1, self.conf.num_classes])
raw_prediction05 = tf.reshape(raw_output05,
[-1, self.conf.num_classes])
prediction = tf.gather(raw_prediction, indices)
prediction100 = tf.gather(raw_prediction100, indices)
prediction075 = tf.gather(raw_prediction075, indices075)
prediction05 = tf.gather(raw_prediction05, indices05)
# Pixel-wise softmax_cross_entropy loss
#loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction, labels=gt)
loss = tf.nn.softmax_cross_entropy_with_logits_v2(
logits=raw_prediction,
labels=tf.reshape(label_proc[0],
(h * w, self.conf.num_classes)))
'''
coefficients = [0.01460247, 1.25147725, 2.88479363, 1.20348121, 1.65261654, 1.67514772,
0.62338799, 0.7729363, 0.42038501, 0.98557268, 1.31867536, 0.85313332,
0.67227604, 1.21317965, 1. , 0.24263748, 1.80877607, 1.3082213,
0.79664027, 0.72543945, 1.27823374]
'''
#loss = weighted_loss(self.conf.num_classes, coefficients, labels=tf.reshape(label_proc[0], (h*w, self.conf.num_classes)), logits=raw_prediction)
#loss100 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction100, labels=gt)
loss100 = tf.nn.softmax_cross_entropy_with_logits_v2(
logits=raw_prediction100,
labels=tf.reshape(label_proc[0],
(h * w, self.conf.num_classes)))
#loss100 = weighted_loss(self.conf.num_classes, coefficients, labels=tf.reshape(label_proc[0], (h*w, self.conf.num_classes)), logits=raw_prediction100)
#loss075 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction075, labels=gt075)
loss075 = tf.nn.softmax_cross_entropy_with_logits_v2(
logits=raw_prediction075,
labels=tf.reshape(label_proc075[0],
(int(h * 0.75) * int(w * 0.75),
self.conf.num_classes)))
#loss075 = weighted_loss(self.conf.num_classes, coefficients, labels=tf.reshape(label_proc075[0], (int(h * 0.75) * int(w * 0.75), self.conf.num_classes)), logits=raw_prediction075)
#loss05 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction05, labels=gt05)
loss05 = tf.nn.softmax_cross_entropy_with_logits_v2(
logits=raw_prediction05,
labels=tf.reshape(
label_proc05[0],
(int(h * 0.5) * int(w * 0.5), self.conf.num_classes)))
#loss05 = weighted_loss(self.conf.num_classes, coefficients, labels=tf.reshape(label_proc05[0], (int(h * 0.5) * int(w * 0.5), self.conf.num_classes)), logits=raw_prediction05)
# L2 regularization
l2_losses = [
self.conf.weight_decay * tf.nn.l2_loss(v)
for v in all_trainable if 'weights' in v.name
]
# Loss function
self.reduced_loss = tf.reduce_mean(loss) + tf.reduce_mean(
loss100) + tf.reduce_mean(loss075) + tf.reduce_mean(
loss05) + tf.add_n(l2_losses)
# Define optimizers
# 'poly' learning rate
base_lr = tf.constant(self.conf.learning_rate)
self.curr_step = tf.placeholder(dtype=tf.float32, shape=())
learning_rate = tf.scalar_mul(
base_lr,
tf.pow((1 - self.curr_step / self.conf.num_steps),
self.conf.power))
# We have several optimizers here in order to handle the different lr_mult
# which is a kind of parameters in Caffe. This controls the actual lr for each
# layer.
opt_encoder = tf.train.MomentumOptimizer(
learning_rate, self.conf.momentum)
opt_decoder_w = tf.train.MomentumOptimizer(
learning_rate * 10.0, self.conf.momentum)
opt_decoder_b = tf.train.MomentumOptimizer(
learning_rate * 20.0, self.conf.momentum)
opt_crf = tf.train.MomentumOptimizer(learning_rate,
self.conf.momentum)
# Gradient accumulation
# Define a variable to accumulate gradients.
accum_grads = [
tf.Variable(tf.zeros_like(v.initialized_value()),
trainable=False) for v in encoder_trainable +
decoder_w_trainable + decoder_b_trainable + crf_trainable
]
# Define an operation to clear the accumulated gradients for next batch.
self.zero_op = [
v.assign(tf.zeros_like(v)) for v in accum_grads
]
# To make sure each layer gets updated by different lr's, we do not use 'minimize' here.
# Instead, we separate the steps compute_grads+update_params.
# Compute grads
grads = tf.gradients(
self.reduced_loss, encoder_trainable +
decoder_w_trainable + decoder_b_trainable + crf_trainable)
# Accumulate and normalise the gradients.
self.accum_grads_op = [
accum_grads[i].assign_add(grad /
self.conf.grad_update_every)
for i, grad in enumerate(grads)
]
grads_encoder = accum_grads[:len(encoder_trainable)]
grads_decoder_w = accum_grads[len(encoder_trainable
):len(encoder_trainable) +
len(decoder_w_trainable)]
grads_decoder_b = accum_grads[(
len(encoder_trainable) +
len(decoder_w_trainable)):(len(encoder_trainable) +
len(decoder_w_trainable) +
len(decoder_b_trainable))]
grads_crf = accum_grads[
len(encoder_trainable) + len(decoder_w_trainable) +
len(decoder_b_trainable
):] # assuming crf gradients are appended to the end
zip_encoder.append(list(zip(grads_encoder, encoder_trainable)))
zip_decoder_b.append(
list(zip(grads_decoder_b, decoder_b_trainable)))
zip_decoder_w.append(
list(zip(grads_decoder_w, decoder_w_trainable)))
zip_crf.append(list(zip(grads_crf, crf_trainable)))
avg_grads_encoder = average_gradients(zip_encoder)
avg_grads_decoder_w = average_gradients(zip_decoder_w)
avg_grads_decoder_b = average_gradients(zip_decoder_b)
avg_grads_crf = average_gradients(zip_crf)
for i in range(len(gpu_list)):
with tf.device(gpu_list[i]):
# Update params
train_op_conv = opt_encoder.apply_gradients(avg_grads_encoder)
train_op_fc_w = opt_decoder_w.apply_gradients(
avg_grads_decoder_w)
train_op_fc_b = opt_decoder_b.apply_gradients(
avg_grads_decoder_b)
train_op_crf = opt_crf.apply_gradients(avg_grads_crf)
# Finally, get the train_op!
update_ops = tf.get_collection(
tf.GraphKeys.UPDATE_OPS
) # for collecting moving_mean and moving_variance
with tf.control_dependencies(update_ops):
self.train_op = tf.group(train_op_fc_w, train_op_fc_b,
train_op_crf) # train_op_conv
# Saver for storing checkpoints of the model
self.saver = tf.train.Saver(var_list=tf.global_variables(),
max_to_keep=0)
# Loader for loading the pre-trained model
for i in range(len(gpu_list)):
with tf.device(gpu_list[i]):
self.loaders.append(tf.train.Saver(var_list=restore_vars[i]))
#self.loaders.append(tf.train.Saver(var_list=tf.global_variables()))
# Training summary
# Processed predictions: for visualisation.
raw_output_up = tf.image.resize_bilinear(raw_output, input_size)
raw_output_up = tf.argmax(raw_output_up, axis=3)
self.pred = tf.expand_dims(raw_output_up, axis=3)
# Image summary.
images_summary = tf.py_func(inv_preprocess,
[self.image_batch, 1, IMG_MEAN], tf.uint8)
labels_summary = tf.py_func(
decode_labels, [self.label_batch, 1, self.conf.num_classes],
tf.uint8)
preds_summary = tf.py_func(decode_labels,
[self.pred, 1, self.conf.num_classes],
tf.uint8)
self.total_summary = tf.summary.image(
'images',
tf.concat(axis=2,
values=[images_summary, labels_summary, preds_summary]),
max_outputs=1) # Concatenate row-wise.
if not os.path.exists(self.conf.logdir):
os.makedirs(self.conf.logdir)
self.summary_writer = tf.summary.FileWriter(
self.conf.logdir, graph=tf.get_default_graph())