def __init__(self, input_size, log_dir, model_name="model.ckpt"):
# 和保存模型相关的参数
self.log_dir = Tools.new_dir(log_dir)
self.model_name = model_name
self.checkpoint_path = os.path.join(self.log_dir, self.model_name)
# 和数据相关的参数
self.input_size = input_size
self.num_classes = 21
# 网络
self.image_placeholder = tf.placeholder(tf.float32,
shape=(None,
self.input_size[0],
self.input_size[1], 3))
# 网络
self.net = BAISNet(self.image_placeholder,
False,
num_classes=self.num_classes)
self.segments = self.net.build()
self.pred_segment = tf.cast(tf.argmax(self.segments[0], axis=-1),
dtype=tf.uint8)
# sess 和 saver
self.sess = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True)))
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(var_list=tf.global_variables(),
max_to_keep=10)
pass
class Inference(object):
def __init__(self, input_size, log_dir, model_name="model.ckpt"):
# 和保存模型相关的参数
self.log_dir = Tools.new_dir(log_dir)
self.model_name = model_name
self.checkpoint_path = os.path.join(self.log_dir, self.model_name)
# 和数据相关的参数
self.input_size = input_size
self.num_classes = 21
# 网络
self.image_placeholder = tf.placeholder(tf.float32,
shape=(None,
self.input_size[0],
self.input_size[1], 3))
# 网络
self.net = BAISNet(self.image_placeholder,
False,
num_classes=self.num_classes)
self.segments = self.net.build()
self.pred_segment = tf.cast(tf.argmax(self.segments[0], axis=-1),
dtype=tf.uint8)
# sess 和 saver
self.sess = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True)))
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(var_list=tf.global_variables(),
max_to_keep=10)
pass
def load_model(self):
# 加载模型
Tools.restore_if_y(self.sess, self.log_dir)
pass
def inference(self, image_path, save_path=None):
im_data = Data.load_data(image_path=image_path,
input_size=self.input_size)
im_data = np.expand_dims(im_data, axis=0)
pred_segment_r = self.sess.run(
self.pred_segment, feed_dict={self.image_placeholder: im_data})
s_image = Image.fromarray(
np.asarray(np.squeeze(pred_segment_r) * 255, dtype=np.uint8))
if save_path is None:
s_image.show()
else:
Tools.new_dir(save_path)
s_image.convert("L").save("{}/{}.bmp".format(
save_path,
os.path.splitext(os.path.basename(image_path))[0]))
pass
pass
def __init__(self, input_size, summary_dir, log_dir, model_name="model.ckpt"):
# 和保存模型相关的参数
self.log_dir = Tools.new_dir(log_dir)
self.model_name = model_name
self.checkpoint_path = os.path.join(self.log_dir, self.model_name)
# 和数据相关的参数
self.input_size = input_size
self.num_classes = 21
# 网络
self.image_placeholder = tf.placeholder(tf.float32, shape=(None, self.input_size[0], self.input_size[1], 3))
# 网络
self.net = BAISNet(self.image_placeholder, False, num_classes=self.num_classes)
self.segments, self.features = self.net.build()
self.pred_segment = tf.cast(tf.argmax(self.segments[0], axis=-1), dtype=tf.uint8)
with tf.name_scope("image"):
tf.summary.image("input", self.image_placeholder)
# segment
for segment_index, segment in enumerate(self.segments):
segment = tf.cast(tf.argmax(segment, axis=-1), dtype=tf.uint8)
tf.summary.image("predict-{}".format(segment_index), tf.expand_dims(segment * 255, axis=-1))
pass
pass
for key in list(self.features.keys()):
with tf.name_scope(key):
for feature_index, feature in enumerate(self.features[key]):
feature_split = tf.split(feature, num_or_size_splits=int(feature.shape[-1]), axis=-1)
for feature_one_index, feature_one in enumerate(feature_split):
tf.summary.image("{}-{}".format(feature_index, feature_one_index), feature_one)
pass
pass
self.summary_op = tf.summary.merge_all()
# sess 和 saver
self.sess = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True)))
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=10)
self.summary_writer = tf.summary.FileWriter(summary_dir, self.sess.graph)
pass
def __init__(self,
batch_size,
input_size,
log_dir,
data_root_path,
train_list,
data_path,
annotation_path,
class_path,
model_name="model.ckpt",
pretrain=None,
is_test=False):
# 和保存模型相关的参数
self.log_dir = Tools.new_dir(log_dir)
self.model_name = model_name
self.checkpoint_path = os.path.join(self.log_dir, self.model_name)
self.pretrain = pretrain
# 和数据相关的参数
self.input_size = input_size
self.batch_size = batch_size
self.num_classes = 21
# 和模型训练相关的参数
self.learning_rate = 5e-3
self.num_steps = 100001
self.print_step = 10 if is_test else 100
self.cal_step = 100 if is_test else 1000
# 读取数据
self.data_reader = Data(data_root_path=data_root_path,
data_list=train_list,
data_path=data_path,
annotation_path=annotation_path,
class_path=class_path,
batch_size=self.batch_size,
image_size=self.input_size,
is_test=is_test)
# 数据
self.image_placeholder = tf.placeholder(tf.float32,
shape=(None,
self.input_size[0],
self.input_size[1], 3))
self.label_seg_placeholder = tf.placeholder(
tf.int32, shape=(None, self.input_size[0], self.input_size[1], 1))
# 网络
self.net = BAISNet(self.image_placeholder,
True,
num_classes=self.num_classes)
self.segments, self.features = self.net.build()
# loss
self.loss, self.loss_segment_all, self.loss_segments = self.cal_loss(
self.segments, self.label_seg_placeholder)
with tf.name_scope("train"):
# 学习率策略
self.step_ph = tf.placeholder(dtype=tf.float32, shape=())
self.learning_rate = tf.scalar_mul(
tf.constant(self.learning_rate),
tf.pow((1 - self.step_ph / self.num_steps), 0.8))
self.train_op = tf.train.GradientDescentOptimizer(
self.learning_rate).minimize(self.loss)
# 单独训练最后的 segment_side
segment_side_trainable = [
v for v in tf.trainable_variables() if 'segment_side' in v.name
]
print(len(segment_side_trainable))
self.train_segment_side_op = tf.train.GradientDescentOptimizer(
self.learning_rate).minimize(self.loss,
var_list=segment_side_trainable)
pass
# summary 1
with tf.name_scope("loss"):
tf.summary.scalar("loss", self.loss)
tf.summary.scalar("loss_segment", self.loss_segment_all)
for loss_segment_index, loss_segment in enumerate(
self.loss_segments):
tf.summary.scalar("loss_segment_{}".format(loss_segment_index),
loss_segment)
pass
with tf.name_scope("label"):
tf.summary.image("1-image", self.image_placeholder)
tf.summary.image(
"2-segment",
tf.cast(self.label_seg_placeholder * 255 // self.num_classes,
dtype=tf.uint8))
pass
with tf.name_scope("result"):
# segment
for segment_index, segment in enumerate(self.segments):
segment = tf.cast(tf.argmax(segment, axis=-1), dtype=tf.uint8)
segment = tf.split(segment,
num_or_size_splits=self.batch_size,
axis=0)
ii = 3 if self.batch_size >= 3 else self.batch_size
for i in range(ii):
tf.summary.image(
"predict-{}-{}".format(segment_index, i),
tf.expand_dims(segment[i] * 255 // self.num_classes,
axis=-1))
pass
pass
pass
self.summary_op = tf.summary.merge_all()
# sess 和 saver
self.sess = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True)))
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(var_list=tf.global_variables(),
max_to_keep=10)
# summary 2
self.summary_writer = tf.summary.FileWriter(self.log_dir,
self.sess.graph)
pass
class Train(object):
def __init__(self,
batch_size,
input_size,
log_dir,
data_root_path,
train_list,
data_path,
annotation_path,
class_path,
model_name="model.ckpt",
pretrain=None,
is_test=False):
# 和保存模型相关的参数
self.log_dir = Tools.new_dir(log_dir)
self.model_name = model_name
self.checkpoint_path = os.path.join(self.log_dir, self.model_name)
self.pretrain = pretrain
# 和数据相关的参数
self.input_size = input_size
self.batch_size = batch_size
self.num_classes = 21
# 和模型训练相关的参数
self.learning_rate = 5e-3
self.num_steps = 100001
self.print_step = 10 if is_test else 100
self.cal_step = 100 if is_test else 1000
# 读取数据
self.data_reader = Data(data_root_path=data_root_path,
data_list=train_list,
data_path=data_path,
annotation_path=annotation_path,
class_path=class_path,
batch_size=self.batch_size,
image_size=self.input_size,
is_test=is_test)
# 数据
self.image_placeholder = tf.placeholder(tf.float32,
shape=(None,
self.input_size[0],
self.input_size[1], 3))
self.label_seg_placeholder = tf.placeholder(
tf.int32, shape=(None, self.input_size[0], self.input_size[1], 1))
# 网络
self.net = BAISNet(self.image_placeholder,
True,
num_classes=self.num_classes)
self.segments, self.features = self.net.build()
# loss
self.loss, self.loss_segment_all, self.loss_segments = self.cal_loss(
self.segments, self.label_seg_placeholder)
with tf.name_scope("train"):
# 学习率策略
self.step_ph = tf.placeholder(dtype=tf.float32, shape=())
self.learning_rate = tf.scalar_mul(
tf.constant(self.learning_rate),
tf.pow((1 - self.step_ph / self.num_steps), 0.8))
self.train_op = tf.train.GradientDescentOptimizer(
self.learning_rate).minimize(self.loss)
# 单独训练最后的 segment_side
segment_side_trainable = [
v for v in tf.trainable_variables() if 'segment_side' in v.name
]
print(len(segment_side_trainable))
self.train_segment_side_op = tf.train.GradientDescentOptimizer(
self.learning_rate).minimize(self.loss,
var_list=segment_side_trainable)
pass
# summary 1
with tf.name_scope("loss"):
tf.summary.scalar("loss", self.loss)
tf.summary.scalar("loss_segment", self.loss_segment_all)
for loss_segment_index, loss_segment in enumerate(
self.loss_segments):
tf.summary.scalar("loss_segment_{}".format(loss_segment_index),
loss_segment)
pass
with tf.name_scope("label"):
tf.summary.image("1-image", self.image_placeholder)
tf.summary.image(
"2-segment",
tf.cast(self.label_seg_placeholder * 255 // self.num_classes,
dtype=tf.uint8))
pass
with tf.name_scope("result"):
# segment
for segment_index, segment in enumerate(self.segments):
segment = tf.cast(tf.argmax(segment, axis=-1), dtype=tf.uint8)
segment = tf.split(segment,
num_or_size_splits=self.batch_size,
axis=0)
ii = 3 if self.batch_size >= 3 else self.batch_size
for i in range(ii):
tf.summary.image(
"predict-{}-{}".format(segment_index, i),
tf.expand_dims(segment[i] * 255 // self.num_classes,
axis=-1))
pass
pass
pass
self.summary_op = tf.summary.merge_all()
# sess 和 saver
self.sess = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True)))
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(var_list=tf.global_variables(),
max_to_keep=10)
# summary 2
self.summary_writer = tf.summary.FileWriter(self.log_dir,
self.sess.graph)
pass
def cal_loss(self, segments, label_segment):
loss_segments = []
for segment_index, segment in enumerate(segments):
# if "segment_side" not in segment.name:
# continue
now_label_segment = tf.image.resize_nearest_neighbor(
label_segment, tf.stack(segment.get_shape()[1:3]))
now_loss_segment = tf.reduce_mean(
tf.nn.weighted_cross_entropy_with_logits(
targets=tf.one_hot(tf.reshape(now_label_segment, [
-1,
]),
depth=self.num_classes),
logits=tf.reshape(segment, [-1, self.num_classes]),
pos_weight=1))
loss_segments.append(now_loss_segment)
pass
loss_segment_all = tf.add_n(loss_segments) / len(loss_segments)
# 总损失
loss = loss_segment_all
return loss, loss_segment_all, loss_segments
def train(self, save_pred_freq, begin_step=0):
# 加载模型
Tools.restore_if_y(self.sess, self.log_dir, pretrain=self.pretrain)
total_loss = 0.0
pre_avg_loss = 0.0
for step in range(begin_step, self.num_steps):
start_time = time.time()
batch_data, batch_segment = self.data_reader.next_batch_train()
train_op = self.train_op
# train_op = self.train_segment_side_op
if step % self.print_step == 0:
# summary 3
_, learning_rate_r, loss_segment_r, loss_r, summary_now = self.sess.run(
[
train_op, self.learning_rate, self.loss_segment_all,
self.loss, self.summary_op
],
feed_dict={
self.step_ph: step,
self.image_placeholder: batch_data,
self.label_seg_placeholder: batch_segment
})
self.summary_writer.add_summary(summary_now, global_step=step)
else:
_, learning_rate_r, loss_segment_r, loss_r = self.sess.run(
[
train_op, self.learning_rate, self.loss_segment_all,
self.loss
],
feed_dict={
self.step_ph: step,
self.image_placeholder: batch_data,
self.label_seg_placeholder: batch_segment
})
pass
if step % save_pred_freq == 0:
self.saver.save(self.sess,
self.checkpoint_path,
global_step=step)
Tools.print_info('The checkpoint has been created.')
pass
duration = time.time() - start_time
if step % self.cal_step == 0:
pre_avg_loss = total_loss / self.cal_step
total_loss = loss_r
else:
total_loss += loss_r
total_loss_step = ((step % self.cal_step) + 1)
if step % (self.cal_step // 10) == 0:
Tools.print_info(
'step {:d} pre_avg_loss={:.3f} avg_loss={:.3f} loss={:.3f} seg={:.3f} lr={:.6f} '
'({:.3f} s/step)'.format(step, pre_avg_loss,
total_loss / total_loss_step,
loss_r, loss_segment_r,
learning_rate_r, duration))
pass
if step % self.print_step == 0:
Tools.print_info("")
pass
pass
pass
class Inference(object):
def __init__(self, input_size, summary_dir, log_dir, model_name="model.ckpt"):
# 和保存模型相关的参数
self.log_dir = Tools.new_dir(log_dir)
self.model_name = model_name
self.checkpoint_path = os.path.join(self.log_dir, self.model_name)
# 和数据相关的参数
self.input_size = input_size
self.num_classes = 21
# 网络
self.image_placeholder = tf.placeholder(tf.float32, shape=(None, self.input_size[0], self.input_size[1], 3))
# 网络
self.net = BAISNet(self.image_placeholder, False, num_classes=self.num_classes)
self.segments, self.features = self.net.build()
self.pred_segment = tf.cast(tf.argmax(self.segments[0], axis=-1), dtype=tf.uint8)
with tf.name_scope("image"):
tf.summary.image("input", self.image_placeholder)
# segment
for segment_index, segment in enumerate(self.segments):
segment = tf.cast(tf.argmax(segment, axis=-1), dtype=tf.uint8)
tf.summary.image("predict-{}".format(segment_index), tf.expand_dims(segment * 255, axis=-1))
pass
pass
for key in list(self.features.keys()):
with tf.name_scope(key):
for feature_index, feature in enumerate(self.features[key]):
feature_split = tf.split(feature, num_or_size_splits=int(feature.shape[-1]), axis=-1)
for feature_one_index, feature_one in enumerate(feature_split):
tf.summary.image("{}-{}".format(feature_index, feature_one_index), feature_one)
pass
pass
self.summary_op = tf.summary.merge_all()
# sess 和 saver
self.sess = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True)))
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=10)
self.summary_writer = tf.summary.FileWriter(summary_dir, self.sess.graph)
pass
def load_model(self):
# 加载模型
Tools.restore_if_y(self.sess, self.log_dir)
pass
def inference(self, image_path, image_index, save_path=None):
im_data = Data.load_data(image_path=image_path, input_size=self.input_size)
im_data = np.expand_dims(im_data, axis=0)
pred_segment_r, summary_now = self.sess.run([self.pred_segment, self.summary_op],
feed_dict={self.image_placeholder: im_data})
self.summary_writer.add_summary(summary_now, global_step=image_index)
s_image = Image.fromarray(np.asarray(np.squeeze(pred_segment_r) * 255, dtype=np.uint8))
if save_path is None:
s_image.show()
else:
Tools.new_dir(save_path)
s_image.convert("L").save("{}/{}.bmp".format(save_path, os.path.splitext(os.path.basename(image_path))[0]))
pass
pass
class Train(object):
def __init__(self, batch_size, input_size, log_dir, data_root_path, train_list, data_path,
annotation_path, class_path, model_name="model.ckpt", pretrain=None, is_test=False):
# 和保存模型相关的参数
self.log_dir = Tools.new_dir(log_dir)
self.model_name = model_name
self.checkpoint_path = os.path.join(self.log_dir, self.model_name)
self.pretrain = pretrain
# 和数据相关的参数
self.input_size = input_size
self.batch_size = batch_size
self.num_classes = 21
# 和模型训练相关的参数
self.learning_rate = 5e-4
self.num_steps = 500001
self.print_step = 10 if is_test else 100
self.cal_step = 100 if is_test else 1000
# 读取数据
self.data_reader = Data(data_root_path=data_root_path, data_list=train_list,
data_path=data_path, annotation_path=annotation_path, class_path=class_path,
batch_size=self.batch_size, image_size=self.input_size, is_test=is_test)
# 数据
self.image_placeholder = tf.placeholder(tf.float32, shape=(None, self.input_size[0], self.input_size[1], 3))
self.mask_placeholder = tf.placeholder(tf.float32, shape=(None, self.input_size[0], self.input_size[1], 1))
self.label_seg_placeholder = tf.placeholder(tf.int32, shape=(None, self.input_size[0], self.input_size[1], 1))
self.label_cls_placeholder = tf.placeholder(tf.int32, shape=(None,))
# 网络
self.net = BAISNet(self.image_placeholder, self.mask_placeholder, True, num_classes=self.num_classes)
self.segments, self.attentions, self.classes = self.net.build()
# loss
self.loss, self.loss_segment_all, self.loss_class_all, self.loss_segments, self.loss_classes = self.cal_loss(
self.segments, self.attentions, self.classes, self.label_seg_placeholder, self.label_cls_placeholder)
# 当前批次的准确率：accuracy
self.pred_classes = tf.cast(tf.argmax(self.classes[0], axis=-1), tf.int32)
self.accuracy_classes = tcm.accuracy(self.pred_classes, self.label_cls_placeholder)
with tf.name_scope("train"):
# 学习率策略
self.step_ph = tf.placeholder(dtype=tf.float32, shape=())
self.learning_rate = tf.scalar_mul(tf.constant(self.learning_rate),
tf.pow((1 - self.step_ph / self.num_steps), 0.9))
self.train_op = tf.train.GradientDescentOptimizer(self.learning_rate).minimize(self.loss)
# 单独训练最后的attention
attention_trainable = [v for v in tf.trainable_variables()
if 'attention' in v.name or "class_attention" in v.name]
print(len(attention_trainable))
self.train_attention_op = tf.train.GradientDescentOptimizer(
self.learning_rate).minimize(self.loss, var_list=attention_trainable)
pass
# summary 1
with tf.name_scope("loss"):
tf.summary.scalar("loss", self.loss)
tf.summary.scalar("loss_segment", self.loss_segment_all)
tf.summary.scalar("loss_class", self.loss_class_all)
for loss_segment_index, loss_segment in enumerate(self.loss_segments):
tf.summary.scalar("loss_segment_{}".format(loss_segment_index), loss_segment)
for loss_class_index, loss_class in enumerate(self.loss_classes):
tf.summary.scalar("loss_class_{}".format(loss_class_index), loss_class)
pass
with tf.name_scope("accuracy"):
tf.summary.scalar("accuracy_classes", self.accuracy_classes)
pass
with tf.name_scope("label"):
tf.summary.image("1-image", self.image_placeholder)
tf.summary.image("2-segment", tf.cast(self.label_seg_placeholder * 255, dtype=tf.uint8))
tf.summary.image("3-mask", tf.cast(self.mask_placeholder * 255, dtype=tf.uint8))
pass
with tf.name_scope("result"):
# segment
for segment_index, segment in enumerate(self.segments):
segment = tf.split(segment, num_or_size_splits=2, axis=3)
# tf.summary.image("predict-{}-0".format(segment_index), tf.nn.sigmoid(segment[0]))
tf.summary.image("predict-{}-1".format(segment_index), tf.nn.sigmoid(segment[1]))
# attention
for attention_index, attention in enumerate(self.attentions):
attention = tf.split(attention, num_or_size_splits=2, axis=3)
# tf.summary.image("attention-{}-0".format(attention_index), tf.nn.sigmoid(attention[0]))
tf.summary.image("attention-{}-1".format(attention_index), tf.nn.sigmoid(attention[1]))
pass
pass
self.summary_op = tf.summary.merge_all()
# sess 和 saver
self.sess = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True)))
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=10)
# summary 2
self.summary_writer = tf.summary.FileWriter(self.log_dir, self.sess.graph)
pass
@staticmethod
def cal_loss(segments, attentions, classes, label_segment, label_classes):
# loss_segments = []
# for segment_index, segment in enumerate(segments):
# now_label_segment = tf.image.resize_nearest_neighbor(label_segment, tf.stack(segment.get_shape()[1:3]))
# now_loss_segment = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
# labels=tf.reshape(now_label_segment, [-1, ]),
# logits=tf.reshape(segment, [-1, 2])))
# loss_segments.append(now_loss_segment)
# pass
loss_attentions = []
for attention_index, attention in enumerate(attentions):
now_label_segment = tf.image.resize_nearest_neighbor(label_segment, tf.stack(attention.get_shape()[1:3]))
# now_loss_attention = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
# labels=tf.reshape(now_label_segment, [-1, ]),
# logits=tf.reshape(attention, [-1, 2])))
now_loss_attention = tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(
targets=tf.one_hot(tf.reshape(now_label_segment, [-1, ]), depth=2),
logits=tf.reshape(attention, [-1, 2]), pos_weight=3))
loss_attentions.append(now_loss_attention)
pass
loss_classes = []
for class_one in classes:
loss_classes.append(tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(labels=label_classes, logits=class_one)))
pass
# loss_segment_all = tf.add_n(loss_segments) / len(loss_segments)
loss_attention_all = tf.add_n(loss_attentions) / len(loss_attentions)
loss_class_all = tf.add_n(loss_classes) / len(loss_classes)
# 总损失
# loss = loss_segment_all + loss_attention_all + 0.5 * loss_class_all
# return loss, loss_segment_all, loss_class_all, loss_segments, loss_classes
loss = loss_attention_all + loss_class_all
return loss, loss_attention_all, loss_class_all, loss_attentions, loss_classes
def train(self, save_pred_freq, begin_step=0):
# 加载模型
Tools.restore_if_y(self.sess, self.log_dir, pretrain=self.pretrain)
total_loss = 0.0
pre_avg_loss = 0.0
total_acc = 0.0
pre_avg_acc = 0.0
for step in range(begin_step, self.num_steps):
start_time = time.time()
batch_data, batch_mask, batch_attention, batch_class = self.data_reader.next_batch_train()
# train_op = self.train_attention_op
train_op = self.train_op
if step % self.print_step == 0:
# summary 3
(accuracy_classes_r, _, learning_rate_r,
loss_segment_r, loss_classes_r, loss_r,
pred_classes_r, summary_now) = self.sess.run(
[self.accuracy_classes, train_op, self.learning_rate,
self.loss_segment_all, self.loss_class_all, self.loss,
self.pred_classes, self.summary_op],
feed_dict={self.step_ph: step, self.image_placeholder: batch_data,
self.mask_placeholder: batch_mask,
self.label_seg_placeholder: batch_attention,
self.label_cls_placeholder: batch_class})
self.summary_writer.add_summary(summary_now, global_step=step)
else:
(accuracy_classes_r, _, learning_rate_r,
loss_segment_r, loss_classes_r, loss_r, pred_classes_r) = self.sess.run(
[self.accuracy_classes, train_op, self.learning_rate,
self.loss_segment_all, self.loss_class_all, self.loss, self.pred_classes],
feed_dict={self.step_ph: step, self.image_placeholder: batch_data,
self.mask_placeholder: batch_mask,
self.label_seg_placeholder: batch_attention,
self.label_cls_placeholder: batch_class})
pass
if step % save_pred_freq == 0:
self.saver.save(self.sess, self.checkpoint_path, global_step=step)
Tools.print_info('The checkpoint has been created.')
pass
duration = time.time() - start_time
if step % self.cal_step == 0:
pre_avg_loss = total_loss / self.cal_step
pre_avg_acc = total_acc / self.cal_step
total_loss = loss_r
total_acc = accuracy_classes_r
else:
total_loss += loss_r
total_acc += accuracy_classes_r
total_loss_step = ((step % self.cal_step) + 1)
if step % (self.cal_step // 10) == 0:
Tools.print_info(
'step {:d} pre_avg_loss={:.3f} avg_loss={:.3f} pre_avg_acc={:.3f} avg_acc={:.3f} '
'loss={:.3f} seg={:.3f} class={:.3f} acc_class={:.3f} '
'lr={:.6f} ({:.3f} s/step) {} {}'.format(
step, pre_avg_loss, total_loss / total_loss_step, pre_avg_acc, total_acc / total_loss_step,
loss_r, loss_segment_r, loss_classes_r, accuracy_classes_r,
learning_rate_r, duration, list(batch_class), list(pred_classes_r)))
pass
if step % self.print_step == 0:
Tools.print_info("")
pass
pass
pass
def __init__(self, batch_size, input_size, log_dir, data_root_path, train_list, data_path,
annotation_path, class_path, model_name="model.ckpt", pretrain=None, is_test=False):
# 和保存模型相关的参数
self.log_dir = Tools.new_dir(log_dir)
self.model_name = model_name
self.checkpoint_path = os.path.join(self.log_dir, self.model_name)
self.pretrain = pretrain
# 和数据相关的参数
self.input_size = input_size
self.batch_size = batch_size
self.num_classes = 21
# 和模型训练相关的参数
self.learning_rate = 5e-4
self.num_steps = 500001
self.print_step = 10 if is_test else 100
self.cal_step = 100 if is_test else 1000
# 读取数据
self.data_reader = Data(data_root_path=data_root_path, data_list=train_list,
data_path=data_path, annotation_path=annotation_path, class_path=class_path,
batch_size=self.batch_size, image_size=self.input_size, is_test=is_test)
# 数据
self.image_placeholder = tf.placeholder(tf.float32, shape=(None, self.input_size[0], self.input_size[1], 3))
self.mask_placeholder = tf.placeholder(tf.float32, shape=(None, self.input_size[0], self.input_size[1], 1))
self.label_seg_placeholder = tf.placeholder(tf.int32, shape=(None, self.input_size[0], self.input_size[1], 1))
self.label_cls_placeholder = tf.placeholder(tf.int32, shape=(None,))
# 网络
self.net = BAISNet(self.image_placeholder, self.mask_placeholder, True, num_classes=self.num_classes)
self.segments, self.attentions, self.classes = self.net.build()
# loss
self.loss, self.loss_segment_all, self.loss_class_all, self.loss_segments, self.loss_classes = self.cal_loss(
self.segments, self.attentions, self.classes, self.label_seg_placeholder, self.label_cls_placeholder)
# 当前批次的准确率：accuracy
self.pred_classes = tf.cast(tf.argmax(self.classes[0], axis=-1), tf.int32)
self.accuracy_classes = tcm.accuracy(self.pred_classes, self.label_cls_placeholder)
with tf.name_scope("train"):
# 学习率策略
self.step_ph = tf.placeholder(dtype=tf.float32, shape=())
self.learning_rate = tf.scalar_mul(tf.constant(self.learning_rate),
tf.pow((1 - self.step_ph / self.num_steps), 0.9))
self.train_op = tf.train.GradientDescentOptimizer(self.learning_rate).minimize(self.loss)
# 单独训练最后的attention
attention_trainable = [v for v in tf.trainable_variables()
if 'attention' in v.name or "class_attention" in v.name]
print(len(attention_trainable))
self.train_attention_op = tf.train.GradientDescentOptimizer(
self.learning_rate).minimize(self.loss, var_list=attention_trainable)
pass
# summary 1
with tf.name_scope("loss"):
tf.summary.scalar("loss", self.loss)
tf.summary.scalar("loss_segment", self.loss_segment_all)
tf.summary.scalar("loss_class", self.loss_class_all)
for loss_segment_index, loss_segment in enumerate(self.loss_segments):
tf.summary.scalar("loss_segment_{}".format(loss_segment_index), loss_segment)
for loss_class_index, loss_class in enumerate(self.loss_classes):
tf.summary.scalar("loss_class_{}".format(loss_class_index), loss_class)
pass
with tf.name_scope("accuracy"):
tf.summary.scalar("accuracy_classes", self.accuracy_classes)
pass
with tf.name_scope("label"):
tf.summary.image("1-image", self.image_placeholder)
tf.summary.image("2-segment", tf.cast(self.label_seg_placeholder * 255, dtype=tf.uint8))
tf.summary.image("3-mask", tf.cast(self.mask_placeholder * 255, dtype=tf.uint8))
pass
with tf.name_scope("result"):
# segment
for segment_index, segment in enumerate(self.segments):
segment = tf.split(segment, num_or_size_splits=2, axis=3)
# tf.summary.image("predict-{}-0".format(segment_index), tf.nn.sigmoid(segment[0]))
tf.summary.image("predict-{}-1".format(segment_index), tf.nn.sigmoid(segment[1]))
# attention
for attention_index, attention in enumerate(self.attentions):
attention = tf.split(attention, num_or_size_splits=2, axis=3)
# tf.summary.image("attention-{}-0".format(attention_index), tf.nn.sigmoid(attention[0]))
tf.summary.image("attention-{}-1".format(attention_index), tf.nn.sigmoid(attention[1]))
pass
pass
self.summary_op = tf.summary.merge_all()
# sess 和 saver
self.sess = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True)))
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=10)
# summary 2
self.summary_writer = tf.summary.FileWriter(self.log_dir, self.sess.graph)
pass