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yolo_net.py
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yolo_net.py
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import tensorflow as tf
import os
import numpy as np
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d, avg_pool_2d
from tflearn.layers.normalization import batch_normalization
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.merge_ops import merge
slim = tf.contrib.slim
import config as cfg
#同一个类内的函数之间的调用部分前后顺序
class YOLONet(object):
def __init__(self):
self.classes=cfg.classes_name
self.num_classes=len(self.classes)
self.image_size=cfg.image_size
self.cell_size=cfg.cell_size
self.boxes_per_cell=cfg.boxes_per_cell
self.output_size=(self.cell_size*self.cell_size)*(self.num_classes+self.boxes_per_cell*5)#7*7*(20+2*5)
self.boundary1=self.cell_size*self.cell_size*self.num_classes
self.boundary2=self.boundary1+self.cell_size*self.cell_size*self.boxes_per_cell
self.object_scale=cfg.object_scale
self.noobject_scale=cfg.noobject_scale
self.class_scale=cfg.class_scale
self.coord_scale=cfg.coord_scale
self.learning_rate=cfg.learning_rate
self.batch_size=cfg.batch_size
self.offset=np.transpose(np.reshape(np.array([np.arange(self.cell_size*self.cell_size*self.boxes_per_cell)]),
(self.boxes_per_cell,self.cell_size,self.cell_size)),(1,2,0))
self.images=tf.placeholder(tf.float32,[None,self.image_size,self.image_size,3],name='images')
self.logits=self.build_network(self.images,num_output=self.output_size)
# if is_training:
self.labels=tf.placeholder(tf.float32,[None,self.cell_size,self.cell_size,5+self.num_classes])
self.loss_layer(self.logits,self.labels)
self.total_loss=tf.losses.get_total_loss()
tf.summary.scalar('total loss',self.total_loss)
def build_network(self,images,num_output,scope='yolo'):
# with tf.variable_scope(scope):
# with slim.arg_scope([slim.conv2d, slim.fully_connected],
# weights_initializer=tf.truncated_normal_initializer(0.0, 0.001),
# weights_regularizer=slim.l2_regularizer(0.0005)):
#
# net = tf.pad(images, np.array([[0, 0], [3, 3], [3, 3], [0, 0]]), name='pad_1')
# net = slim.conv2d(net, 64, 7, 2, padding='VALID', scope='conv_2')
# net = slim.max_pool2d(net, 2, padding='SAME', scope='pool_3')
# net = slim.conv2d(net, 192, 3, scope='conv_4')
# net = slim.max_pool2d(net, 2, padding='SAME', scope='pool_5')
# net = slim.conv2d(net, 128, 1, scope='conv_6')
# net = slim.conv2d(net, 256, 3, scope='conv_7')
# net = slim.conv2d(net, 256, 1, scope='conv_8')
# net = slim.conv2d(net, 512, 3, scope='conv_9')
# net = slim.batch_norm(net)
# net = slim.max_pool2d(net, 2, padding='SAME', scope='pool_10')
# net = slim.conv2d(net, 256, 1, scope='conv_11')
# net = slim.conv2d(net, 512, 3, scope='conv_12')
# net = slim.conv2d(net, 256, 1, scope='conv_13')
# net = slim.conv2d(net, 512, 3, scope='conv_14')
# net = slim.conv2d(net, 256, 1, scope='conv_15')
# net = slim.conv2d(net, 512, 3, scope='conv_16')
# net = slim.conv2d(net, 256, 1, scope='conv_17')
# net = slim.conv2d(net, 512, 3, scope='conv_18')
# net = slim.conv2d(net, 512, 1, scope='conv_19')
# net = slim.conv2d(net, 1024, 3, scope='conv_20')
# net = slim.batch_norm(net)
# net = slim.max_pool2d(net, 2, padding='SAME', scope='pool_21')
# net = slim.conv2d(net, 512, 1, scope='conv_22')
# net = slim.conv2d(net, 1024, 3, scope='conv_23')
# net = slim.conv2d(net, 512, 1, scope='conv_24')
# net = slim.conv2d(net, 1024, 3, scope='conv_25')
# net = slim.conv2d(net, 1024, 3, scope='conv_26')
# net = slim.batch_norm(net)
# net = tf.pad(net, np.array([[0, 0], [1, 1], [1, 1], [0, 0]]), name='pad_27')
# net = slim.conv2d(net, 1024, 3, 2, padding='VALID', scope='conv_28')
# net = slim.conv2d(net, 1024, 3, scope='conv_29')
# net = slim.conv2d(net, 1024, 3, scope='conv_30')
# net = tf.transpose(net, [0, 3, 1, 2], name='trans_31')
# net = slim.flatten(net, scope='flat_32')
# net = slim.fully_connected(net, 512, scope='fc_33')
# net = slim.fully_connected(net, 4096, scope='fc_34')
# # net = slim.dropout(net, keep_prob=keep_prob,
# # is_training=is_training, scope='dropout_35')
# net = batch_normalization(net)
# net = slim.fully_connected(net, num_output,
# activation_fn=None, scope='fc_36')
# net = tf.nn.relu(net)
net = conv_2d(images,64,7,strides=2,activation='relu',name='layer1')
net = max_pool_2d(net, 2, strides=2)
net = conv_2d(net, 192, 3, activation='relu', name='layer2')
net = max_pool_2d(net, 2, strides=2)
net = conv_2d(net, 128, 1, activation='relu', name='layer3')
net = conv_2d(net, 256, 3, activation='relu', name='layer4')
net = conv_2d(net, 256, 1, activation='relu', name='layer5')
net = conv_2d(net, 512, 3, activation='relu', name='layer6')
net = max_pool_2d(net, 2, strides=2)
net = batch_normalization(net)
net = conv_2d(net, 256, 1, activation='relu', name='layer7')
net = conv_2d(net, 512, 3, activation='relu', name='layer8')
net = conv_2d(net, 256, 1, activation='relu', name='layer9')
net = conv_2d(net, 512, 3, activation='relu', name='layer10')
net = conv_2d(net, 256, 1, activation='relu', name='layer11')
net = conv_2d(net, 512, 3, activation='relu', name='layer12')
net = conv_2d(net, 256, 1, activation='relu', name='layer13')
net = conv_2d(net, 512, 3, activation='relu', name='layer14')
net = conv_2d(net, 512, 1, activation='relu', name='layer15')
net = conv_2d(net, 1024, 3, activation='relu', name='layer16')
net = max_pool_2d(net, 2, strides=2)
net = batch_normalization(net)
net = conv_2d(net, 512, 1, activation='relu', name='layer17')
net = conv_2d(net, 1024, 3, activation='relu', name='layer18')
net = conv_2d(net, 512, 1, activation='relu', name='layer19')
net = conv_2d(net, 1024, 3, activation='relu', name='layer20')
net = conv_2d(net, 1024, 3, activation='relu', name='layer21')
net = conv_2d(net, 1024, 3, activation='relu', name='layer22')
net = conv_2d(net, 1024, 3, activation='relu', name='layer23')
net = conv_2d(net, 1024, 3, activation='relu', name='layer24')
net = batch_normalization(net)
net = fully_connected(net, 256, activation='relu')
net = fully_connected(net,4096,activation='relu')
net = fully_connected(net,1470,activation='relu')
return net
#计算IOU,即预测bounding box 与标签的bounding box 的重叠部分
def calc_iou(self,boxes1,boxes2,scope='iou'):
with tf.variable_scope(scope):
boxes1 = tf.stack([boxes1[:, :, :, :, 0] - boxes1[:, :, :, :, 2] / 2.0,
boxes1[:, :, :, :, 1] - boxes1[:, :, :, :, 3] / 2.0,
boxes1[:, :, :, :, 0] + boxes1[:, :, :, :, 2] / 2.0,
boxes1[:, :, :, :, 1] + boxes1[:, :, :, :, 3] / 2.0])
boxes1 = tf.transpose(boxes1, [1, 2, 3, 4, 0])
boxes2 = tf.stack([boxes2[:, :, :, :, 0] - boxes2[:, :, :, :, 2] / 2.0,
boxes2[:, :, :, :, 1] - boxes2[:, :, :, :, 3] / 2.0,
boxes2[:, :, :, :, 0] + boxes2[:, :, :, :, 2] / 2.0,
boxes2[:, :, :, :, 1] + boxes2[:, :, :, :, 3] / 2.0])
boxes2 = tf.transpose(boxes2, [1, 2, 3, 4, 0])
# calculate the left up point & right down point
lu = tf.maximum(boxes1[:, :, :, :, :2], boxes2[:, :, :, :, :2])
rd = tf.minimum(boxes1[:, :, :, :, 2:], boxes2[:, :, :, :, 2:])
# intersection
intersection = tf.maximum(0.0, rd - lu)
inter_square = intersection[:, :, :, :, 0] * intersection[:, :, :, :, 1]
# calculate the boxs1 square and boxs2 square
square1 = (boxes1[:, :, :, :, 2] - boxes1[:, :, :, :, 0]) * \
(boxes1[:, :, :, :, 3] - boxes1[:, :, :, :, 1])
square2 = (boxes2[:, :, :, :, 2] - boxes2[:, :, :, :, 0]) * \
(boxes2[:, :, :, :, 3] - boxes2[:, :, :, :, 1])
union_square = tf.maximum(square1 + square2 - inter_square, 1e-10)
return tf.clip_by_value(inter_square / union_square, 0.0, 1.0)
#损失函数定义
def loss_layer(self,predict,labels,scope='loss_layer'):
with tf.variable_scope(scope):
predict_classes=tf.reshape(predict[:,:self.boundary1],[self.batch_size, self.cell_size, self.cell_size,
self.num_classes])
predict_scales=tf.reshape(predict[:,self.boundary1:self.boundary2],[self.batch_size,self.cell_size,
self.cell_size,self.boxes_per_cell])
predict_boxes=tf.reshape(predict[:,self.boundary2:],[self.batch_size,self.cell_size,
self.cell_size,self.boxes_per_cell,4])
response=tf.reshape(labels[...,0],[self.batch_size,self.cell_size,self.cell_size,1])
boxes=tf.reshape(labels[...,1:5],[self.batch_size,self.cell_size,self.cell_size,1,4])
boxes=tf.tile(boxes,[1,1,1,self.boxes_per_cell,1])/self.image_size
classes=labels[...,5:]
# 偏置的shape(7,7,2)
offset = tf.constant(self.offset, dtype=tf.float32)
# shape为(1,7,7,2)
offset = tf.reshape(offset, [1, self.cell_size, self.cell_size, self.boxes_per_cell])
# shape为(45,7,7,2)
offset = tf.tile(offset, [self.batch_size, 1, 1, 1])#对矩阵进行复制
# shape为(4,45,7,7,2)
predict_boxes_tran = tf.stack([(predict_boxes[:, :, :, :, 0] + offset) / self.cell_size,
(predict_boxes[:, :, :, :, 1] + tf.transpose(offset,
(0, 2, 1, 3))) / self.cell_size,
tf.square(predict_boxes[:, :, :, :, 2]),
tf.square(predict_boxes[:, :, :, :, 3])])
# shape为(45,7,7,2,4)
predict_boxes_tran = tf.transpose(predict_boxes_tran, [1, 2, 3, 4, 0])
iou_predict_truth = self.calc_iou(predict_boxes_tran, boxes)
# calculate I tensor [BATCH_SIZE, CELL_SIZE, CELL_SIZE, BOXES_PER_CELL]
object_mask = tf.reduce_max(iou_predict_truth, 3, keep_dims=True)
object_mask = tf.cast((iou_predict_truth >= object_mask), tf.float32) * response
# calculate no_I tensor [CELL_SIZE, CELL_SIZE, BOXES_PER_CELL]
noobject_mask = tf.ones_like(object_mask, dtype=tf.float32) - object_mask
boxes_tran = tf.stack([boxes[:, :, :, :, 0] * self.cell_size - offset,
boxes[:, :, :, :, 1] * self.cell_size - tf.transpose(offset, (0, 2, 1, 3)),
tf.sqrt(boxes[:, :, :, :, 2]),
tf.sqrt(boxes[:, :, :, :, 3])])
boxes_tran = tf.transpose(boxes_tran, [1, 2, 3, 4, 0])
# class_loss
class_delta = response * (predict_classes - classes)
class_loss = tf.reduce_mean(tf.reduce_sum(tf.square(class_delta), axis=[1, 2, 3]),
name='class_loss') * self.class_scale
# object_loss
object_delta = object_mask * (predict_scales - iou_predict_truth)
object_loss = tf.reduce_mean(tf.reduce_sum(tf.square(object_delta), axis=[1, 2, 3]),
name='object_loss') * self.object_scale
# noobject_loss
noobject_delta = noobject_mask * predict_scales
noobject_loss = tf.reduce_mean(tf.reduce_sum(tf.square(noobject_delta), axis=[1, 2, 3]),
name='noobject_loss') * self.noobject_scale
# coord_loss
coord_mask = tf.expand_dims(object_mask, 4)
boxes_delta = coord_mask * (predict_boxes - boxes_tran)
coord_loss = tf.reduce_mean(tf.reduce_sum(tf.square(boxes_delta), axis=[1, 2, 3, 4]),
name='coord_loss') * self.coord_scale
tf.losses.add_loss(class_loss)
tf.losses.add_loss(object_loss)
tf.losses.add_loss(noobject_loss)
tf.losses.add_loss(coord_loss)
tf.summary.scalar('class_loss', class_loss)
tf.summary.scalar('object_loss', object_loss)
tf.summary.scalar('noobject_loss', noobject_loss)
tf.summary.scalar('coord_loss', coord_loss)
tf.summary.histogram('boxes_delta_x', boxes_delta[:, :, :, :, 0])
tf.summary.histogram('boxes_delta_y', boxes_delta[:, :, :, :, 1])
tf.summary.histogram('boxes_delta_w', boxes_delta[:, :, :, :, 2])
tf.summary.histogram('boxes_delta_h', boxes_delta[:, :, :, :, 3])
tf.summary.histogram('iou', iou_predict_truth)