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shufflenetv2_centernet.py
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from shufflenetv2_layer_utils import *
import cfg
import loss
class ShuffleNetV2_centernet():
first_conv_channel = 24
def __init__(self, model_scale=1.0, shuffle_group=2):
self.inputs = tf.placeholder(shape=[None, cfg.input_image_size, cfg.input_image_size, 3], dtype=tf.float32, name='inputs')
self.is_training = tf.placeholder(dtype=tf.bool, name='is_training')
self.shuffle_group = shuffle_group
self.channel_sizes = self._select_channel_size(model_scale)
self.center_gt = tf.placeholder(shape=[None, cfg.featuremap_h, cfg.featuremap_w, cfg.num_classes],dtype=tf.float32)
self.offset_gt = tf.placeholder(shape=[None, cfg.featuremap_h, cfg.featuremap_w, 2], dtype=tf.float32)
self.size_gt = tf.placeholder(shape=[None, cfg.featuremap_h, cfg.featuremap_w, 2], dtype=tf.float32)
self.mask_gt = tf.placeholder(shape=[None, cfg.featuremap_h, cfg.featuremap_w], dtype=tf.float32)
with tf.variable_scope('shufflenet_centernet'):
with slim.arg_scope([slim.batch_norm], is_training=self.is_training):
self.pred_center,self.pred_offset,self.pred_size=self._build_model()
self.build_train()
self.merged_summay = tf.summary.merge_all()
def _select_channel_size(self, model_scale):
# [(out_channel, repeat_times), (out_channel, repeat_times), ...]
if model_scale == 0.5:
return [(48, 4), (96, 8), (192, 4), (1024, 1)]
elif model_scale == 1.0:
return [(116, 4), (232, 8), (464, 4), (1024, 1)]
elif model_scale == 1.5:
return [(176, 4), (352, 8), (704, 4), (1024, 1)]
elif model_scale == 2.0:
return [(244, 4), (488, 8), (976, 4), (2048, 1)]
else:
raise ValueError('Unsupported model size.')
def _build_model(self):
with tf.variable_scope('stage_4'):
out_2 = conv_bn_relu(self.inputs, self.first_conv_channel, 3, 2)#/2
out_4 = slim.max_pool2d(out_2, 3, 2 , padding='SAME')#/4
with tf.variable_scope('stage_8'):
out_channel, repeat = self.channel_sizes[0]
# First block is downsampling
out_8 = shufflenet_v2_block(out_4, out_channel, 3, 2, shuffle_group=self.shuffle_group)#/8
for i in range(repeat-1):
out_8 = shufflenet_v2_block(out_8, out_channel, 3, shuffle_group=self.shuffle_group)
with tf.variable_scope('stage_16'):
out_channel, repeat = self.channel_sizes[1]
# First block is downsampling
out_16 = shufflenet_v2_block(out_8, out_channel, 3, 2, shuffle_group=self.shuffle_group)#/16
for i in range(repeat - 1):
out_16 = shufflenet_v2_block(out_16, out_channel, 3, shuffle_group=self.shuffle_group)
with tf.variable_scope('stage_32'):
out_channel, repeat = self.channel_sizes[2]
# First block is downsampling
out_32 = shufflenet_v2_block(out_16, out_channel, 3, 2, shuffle_group=self.shuffle_group)#/32
for i in range(repeat - 1):
out_32 = shufflenet_v2_block(out_32, out_channel, 3, shuffle_group=self.shuffle_group)
with tf.variable_scope('feature_map_fuse'):
deconv1=deconv_bn_relu(out_32,cfg.feature_channels)
out_16=conv_bn_relu(out_16,cfg.feature_channels,1)
fuse1=deconv1+out_16
deconv2 = deconv_bn_relu(fuse1, cfg.feature_channels)
out_8 = conv_bn_relu(out_8, cfg.feature_channels, 1)
fuse2 = out_8 + deconv2
deconv3 = deconv_bn_relu(fuse2, cfg.feature_channels)
out_4 = conv_bn_relu(out_4, cfg.feature_channels, 1)
fuse3 = out_4 + deconv3
with tf.variable_scope('se_sa_module'):
features=se_unit(fuse3)
features=sa_unit(features)
with tf.variable_scope('detector'):
center = tf.layers.conv2d(features, cfg.feature_channels, 3, 1, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.01))
center = tf.nn.relu(center)
center = tf.layers.conv2d(center, cfg.num_classes, 1, 1, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.01))
center = tf.nn.sigmoid(center, name='center')
offset = tf.layers.conv2d(features, cfg.feature_channels, 3, 1, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.01))
offset = tf.nn.relu(offset)
offset = tf.layers.conv2d(offset, 2, 1, 1, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.01))
offset = tf.nn.sigmoid(offset, name='offset')
# size = conv_bn_activation(features,256,3,1)
size = tf.layers.conv2d(features, cfg.feature_channels, 3, 1, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.01))
size = tf.nn.relu(size)
size = tf.layers.conv2d(size, 2, 1, 1, padding='same',
kernel_initializer=tf.random_normal_initializer(stddev=0.01))
size = tf.nn.relu(size, name='size')
return center, offset, size
def compute_loss(self):
self.cls_loss=loss.focal_loss(self.pred_center,self.center_gt)
self.size_loss=loss._reg_l1loss(self.pred_size,self.size_gt,self.mask_gt)
self.offset_loss = loss._reg_l1loss(self.pred_offset, self.offset_gt, self.mask_gt)
# self.regular_loss=cfg.weight_decay * tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()])
self.total_loss=self.cls_loss+cfg.lambda_size*self.size_loss+cfg.lambda_offset*self.offset_loss#+self.regular_loss
def build_train(self):
with tf.variable_scope("loss","loss"):
self.compute_loss()
self.global_step = tf.Variable(0, trainable=False)
self.lr=cfg.lr
if cfg.lr_type=="exponential":
self.lr = tf.train.exponential_decay(cfg.lr_value,
self.global_step,
cfg.lr_decay_steps,
cfg.lr_decay_rate,
staircase=True)#staircase=True,globstep/decaystep=整数，代表lr突变的，阶梯状
elif cfg.lr_type=="fixed":
self.lr = tf.constant(cfg.lr, dtype=tf.float32)
elif cfg.lr_type=="piecewise":
self.lr = tf.train.piecewise_constant(self.global_step, cfg.lr_boundaries, cfg.lr_values)
if cfg.optimizer == 'adam':
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.lr)
elif cfg.optimizer == 'rmsprop':
self.optimizer = tf.train.RMSPropOptimizer(learning_rate=self.lr,
momentum=cfg.momentum)
elif cfg.optimizer == 'adadelta':
self.optimizer = tf.train.AdadeltaOptimizer(learning_rate=self.lr)
elif cfg.optimizer == 'momentum':
self.optimizer = tf.train.MomentumOptimizer(learning_rate=self.lr,
momentum=cfg.momentum)
elif cfg.optimizer=="sgd":
self.optimizer=tf.train.GradientDescentOptimizer(learning_rate=self.lr)
elif cfg.optimizer == "ftr":
self.optimizer = tf.train.FtrlOptimizer(learning_rate=self.lr)
elif cfg.optimizer == "adagradDA":
self.optimizer = tf.train.AdagradDAOptimizer(learning_rate=self.lr, global_step=self.global_step)
elif cfg.optimizer == "adagrad":
self.optimizer = tf.train.AdagradOptimizer(learning_rate=self.lr)
elif cfg.optimizer == "ProximalAdagrad":
self.optimizer = tf.train.ProximalAdagradOptimizer(learning_rate=self.lr)
elif cfg.optimizer == "ProximalGrad":
self.optimizer = tf.train.ProximalGradientDescentOptimizer(learning_rate=self.lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
self.train_op = self.optimizer.minimize(self.total_loss, global_step=self.global_step)
tf.summary.scalar('total_loss', self.total_loss)
tf.summary.scalar('cls_loss', self.cls_loss)
tf.summary.scalar('offset_loss', self.offset_loss)
tf.summary.scalar('size_loss', self.size_loss)
tf.summary.scalar("learning_rate", self.lr)