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使用Keras Tensorflow 2.0实现YOLO V1

本文使用Tensorflow 2.0复现论文<You Only Look Once: Unified, Real-Time Object Detection>的效果。

import tensorflow as tf
import matplotlib.pyplot as plt    # for plotting the images

1.数据预处理(Data Preprocessing)

使用VOC 2007(http://host.robots.ox.ac.uk /pascal/VOC/voc2007/)数据集进行神经网络训练。

​获取训练集、验证集和测试集数据。

!wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/
VOCtrainval_06-Nov-2007.tar
!wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/
VOCtest_06-Nov-2007.tar

!tar xvf VOCtrainval_06-Nov-2007.tar
!tar xvf VOCtest_06-Nov-2007.tar

!rm VOCtrainval_06-Nov-2007.tar
!rm VOCtest_06-Nov-2007.tar

预处理Annotation,将XML转换成txt文件,方便后续处理。

import argparse
import xml.etree.ElementTree as ET
import os

parser = argparse.ArgumentParser(description='Build Annotations.')
parser.add_argument('dir', default='..', help='Annotations.')

sets = [('2007', 'train'), ('2007', 'val'), ('2007', 'test')]

classes_num = {'aeroplane': 0, 'bicycle': 1, 'bird': 2, 'boat': 3, 'bottle': 4, 'bus': 5, 'car': 6, 'cat': 7, 'chair': 8, 'cow': 9, 'diningtable': 10, 'dog': 11, 'horse': 12, 'motorbike': 13, 'person': 14, 'pottedplant': 15, 'sheep': 16, 'sofa': 17, 'train': 18, 'tvmonitor': 19}


def convert_annotation(year, image_id, f):
    in_file = os.path.join('VOCdevkit/VOC%s/Annotations/%s.xml' % (year, image_id))
    tree = ET.parse(in_file)
    root = tree.getroot()

    for obj in root.iter('object'):
        difficult = obj.find('difficult').text
        cls = obj.find('name').text
        classes = list(classes_num.keys())
        if cls not in classes or int(difficult) == 1:
            continue
        cls_id = classes.index(cls)
        xmlbox = obj.find('bndbox')
        b = (int(xmlbox.find('xmin').text), int(xmlbox.find('ymin').text),
             int(xmlbox.find('xmax').text), int(xmlbox.find('ymax').text))
        f.write(' ' + ','.join([str(a) for a in b]) + ',' + str(cls_id))
for year, image_set in sets:
  print(year, image_set)
  with open(os.path.join('VOCdevkit/VOC%s/ImageSets/Main/%s.txt' % (year, image_set)), 'r') as f:
      image_ids = f.read().strip().split()
  with open(os.path.join("VOCdevkit", '%s_%s.txt' % (year, image_set)), 'w') as f:
      for image_id in image_ids:
          f.write('%s/VOC%s/JPEGImages/%s.jpg' % ("VOCdevkit", year, image_id))
          convert_annotation(year, image_id, f)
          f.write('\n')

转换后生成的文本如下:

./data/VOCtrainval_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/000012.jpg 156,97,351,270,6
 ./data/VOCtrainval_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/000017.jpg 185,62,279,199,14 90,78,403,336,12
 ./data/VOCtrainval_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/000023.jpg 9,230,245,500,1 230,220,334,500,1 2,1,117,369,14 3,2,243,462,14 225,1,334,486,14
 ./data/VOCtrainval_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/000026.jpg 90,125,337,212,6
 ./data/VOCtrainval_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/000032.jpg 104,78,375,183,0 133,88,197,123,0 195,180,213,229,14 26,189,44,238,14
 ./data/VOCtrainval_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/000033.jpg 9,107,499,263,0 421,200,482,226,0 325,188,411,223,0
 ......

2.准备训练输入输出数据

YOLO V1 输入是大小为448x448x3的图片,输出是大小为7x7x30的张量(Tensor)。

YOLO V1输入是大小为448x448x3的图片,我们把数据集的所有的图片大小缩放到448×448,然后将图片的所有像素值缩放到[0, 1]之间。

YOLO的输出是大小为7x7x30的张量(Tensor)。

其中,BoundingBox中的(x,y)是相对于Grid Cell左上角坐标的偏移量,并使用Grid Cell的宽高做归一化处理;(w,h)是相对于整个图片的宽和高的比例;(x,y,w,h)的数值都在[0,1]范围内。

import cv2 as cv
import numpy as np

def read(image_path, label):
    image = cv.imread(image_path)
    image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
    image_h, image_w = image.shape[0:2]
    image = cv.resize(image, (448, 448))
    image = image / 255.

    label_matrix = np.zeros([7, 7, 30])
    for l in label:
        l = l.split(',')
        l = np.array(l, dtype=np.int)
        xmin = l[0]
        ymin = l[1]
        xmax = l[2]
        ymax = l[3]
        cls = l[4]
        x = (xmin + xmax) / 2 / image_w
        y = (ymin + ymax) / 2 / image_h
        w = (xmax - xmin) / image_w
        h = (ymax - ymin) / image_h
        loc = [7 * x, 7 * y]
        loc_i = int(loc[1])
        loc_j = int(loc[0])
        y = loc[1] - loc_i
        x = loc[0] - loc_j

        if label_matrix[loc_i, loc_j, 24] == 0:
            label_matrix[loc_i, loc_j, cls] = 1
            label_matrix[loc_i, loc_j, 20:24] = [x, y, w, h]
            label_matrix[loc_i, loc_j, 24] = 1  # response

    return image, label_matrix

3. Training The Model

抽样训练集中的一张图片和标注数据。

image_path = "./data/VOCtrainval_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/000207.jpg"
label = "1,205,113,320,5"

l = label.split(',')
l = np.array(l, dtype=np.int)

img = cv.imread(image_path)
ptLeftTop = (l[0], l[1])
ptRightBottom = (l[2], l[3])
point_color = (0, 255, 0) # BGR
thickness = 1 
lineType = 4

cv.rectangle(img, ptLeftTop, ptRightBottom, point_color, thickness, lineType)

cv.namedWindow("YOLO V1")
cv.imshow('YOLO V1', img)

while(1):
    if (cv.waitKey(0) == 27):
        break

cv.destroyAllWindows()

​构造数据集读取的工具,用于在训练过程中为模型提供数据。

from tensorflow import keras

class My_Custom_Generator(keras.utils.Sequence) :
  
  def __init__(self, images, labels, batch_size) :
    self.images = images
    self.labels = labels
    self.batch_size = batch_size
    
    
  def __len__(self) :
    return (np.ceil(len(self.images) / float(self.batch_size))).astype(np.int)
  
  
  def __getitem__(self, idx) :
    batch_x = self.images[idx * self.batch_size : (idx+1) * self.batch_size]
    batch_y = self.labels[idx * self.batch_size : (idx+1) * self.batch_size]

    train_image = []
    train_label = []

    for i in range(0, len(batch_x)):
      img_path = batch_x[i]
      label = batch_y[i]
      image, label_matrix = read(img_path, label)
      train_image.append(image)
      train_label.append(label_matrix)
    return np.array(train_image), np.array(train_label)

将训练集和验证集数据加载到内存。

train_datasets = []
val_datasets = []

with open(os.path.join("VOCdevkit", '2007_train.txt'), 'r') as f:
    train_datasets = train_datasets + f.readlines()
with open(os.path.join("VOCdevkit", '2007_val.txt'), 'r') as f:
    val_datasets = val_datasets + f.readlines()

X_train = []
Y_train = []

X_val = []
Y_val = []

for item in train_datasets:
  item = item.replace("\n", "").split(" ")
  X_train.append(item[0])
  arr = []
  for i in range(1, len(item)):
    arr.append(item[i])
  Y_train.append(arr)

print("X_train sample:")
print(X_train[0:2])

print("Y_train sample:")
print(Y_train[0:2])

for item in val_datasets:
  item = item.replace("\n", "").split(" ")
  X_val.append(item[0])
  arr = []
  for i in range(1, len(item)):
    arr.append(item[i])
  Y_val.append(arr)

print("X_val sample:")
print(X_val[0:2])

print("Y_val sample:")
print(Y_val[0:2])

取出训练集和样本集的前两个元素,打印如下:

X_train sample:
 ['./data/VOCtrainval_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/000012.jpg', './data/VOCtrainval_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/000017.jpg']
 Y_train sample:
 [['156,97,351,270,6'], ['185,62,279,199,14', '90,78,403,336,12']]
 X_val sample:
 ['./data/VOCtrainval_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/000005.jpg', './data/VOCtrainval_06-Nov-2007/VOCdevkit/VOC2007/JPEGImages/000007.jpg']
 Y_val sample:
 [['263,211,324,339,8', '165,264,253,372,8', '241,194,295,299,8'], ['141,50,500,330,6']]
batch_size = 4
my_training_batch_generator = My_Custom_Generator(X_train, Y_train, batch_size)

my_validation_batch_generator = My_Custom_Generator(X_val, Y_val, batch_size)

x_train, y_train = my_training_batch_generator.__getitem__(0)
x_val, y_val = my_training_batch_generator.__getitem__(0)

print(x_train.shape)
print(y_train.shape)

print(x_val.shape)
print(y_val.shape)
(4, 448, 448, 3)
(4, 7, 7, 30)
(4, 448, 448, 3)
(4, 7, 7, 30)

4.定义模型的输出层

from tensorflow import keras
import keras.backend as K

class Yolo_Reshape(tf.keras.layers.Layer):
  def __init__(self, target_shape):
    super(Yolo_Reshape, self).__init__()
    self.target_shape = tuple(target_shape)

  def get_config(self):
    config = super().get_config().copy()
    config.update({
        'target_shape': self.target_shape
    })
    return config

  def call(self, input):
    # grids 7x7
    S = [self.target_shape[0], self.target_shape[1]]
    # classes
    C = 20
    # no of bounding boxes per grid
    B = 2

    idx1 = S[0] * S[1] * C
    idx2 = idx1 + S[0] * S[1] * B
    
    # class probabilities
    class_probs = K.reshape(input[:, :idx1], (K.shape(input)[0],) + tuple([S[0], S[1], C]))
    class_probs = K.softmax(class_probs)

    #confidence
    confs = K.reshape(input[:, idx1:idx2], (K.shape(input)[0],) + tuple([S[0], S[1], B]))
    confs = K.sigmoid(confs)

    # boxes
    boxes = K.reshape(input[:, idx2:], (K.shape(input)[0],) + tuple([S[0], S[1], B * 4]))
    boxes = K.sigmoid(boxes)

    outputs = K.concatenate([class_probs, confs, boxes])
    return outputs

5. 定义YOLO模型

YOLO模型的定义与论文中一致。

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, InputLayer, Dropout, Flatten, Reshape
from tensorflow.keras.layers import Conv2D, MaxPooling2D, GlobalMaxPooling2D
from tensorflow.keras.regularizers import l2

lrelu = tf.keras.layers.LeakyReLU(alpha=0.1)

nb_boxes=1
grid_w=7
grid_h=7
cell_w=64
cell_h=64
img_w=grid_w*cell_w
img_h=grid_h*cell_h

model = Sequential()
model.add(Conv2D(filters=64, kernel_size= (7, 7), strides=(1, 1), input_shape =(img_h, img_w, 3), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding = 'same'))

model.add(Conv2D(filters=192, kernel_size= (3, 3), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding = 'same'))

model.add(Conv2D(filters=128, kernel_size= (1, 1), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=256, kernel_size= (3, 3), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=256, kernel_size= (1, 1), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=512, kernel_size= (3, 3), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding = 'same'))

model.add(Conv2D(filters=256, kernel_size= (1, 1), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=512, kernel_size= (3, 3), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=256, kernel_size= (1, 1), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=512, kernel_size= (3, 3), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=256, kernel_size= (1, 1), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=512, kernel_size= (3, 3), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=256, kernel_size= (1, 1), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=512, kernel_size= (3, 3), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=512, kernel_size= (1, 1), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=1024, kernel_size= (3, 3), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding = 'same'))

model.add(Conv2D(filters=512, kernel_size= (1, 1), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=1024, kernel_size= (3, 3), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=512, kernel_size= (1, 1), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=1024, kernel_size= (3, 3), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=1024, kernel_size= (3, 3), padding = 'same', activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=1024, kernel_size= (3, 3), strides=(2, 2), padding = 'same'))

model.add(Conv2D(filters=1024, kernel_size= (3, 3), activation=lrelu, kernel_regularizer=l2(5e-4)))
model.add(Conv2D(filters=1024, kernel_size= (3, 3), activation=lrelu, kernel_regularizer=l2(5e-4)))

model.add(Flatten())
model.add(Dense(512))
model.add(Dense(1024))
model.add(Dropout(0.5))
model.add(Dense(1470, activation='sigmoid'))
model.add(Yolo_Reshape(target_shape=(7,7,30)))
model.summary()
 Layer (type)                 Output Shape              Param #   
 conv2d (Conv2D)              (None, 448, 448, 64)      9472      
 
 max_pooling2d (MaxPooling2D) (None, 224, 224, 64)      0         
 
 conv2d_1 (Conv2D)            (None, 224, 224, 192)     110784    
 
 max_pooling2d_1 (MaxPooling2 (None, 112, 112, 192)     0         
 
 conv2d_2 (Conv2D)            (None, 112, 112, 128)     24704     
 
 conv2d_3 (Conv2D)            (None, 112, 112, 256)     295168    
 
 conv2d_4 (Conv2D)            (None, 112, 112, 256)     65792     
 
 conv2d_5 (Conv2D)            (None, 112, 112, 512)     1180160   
 
 max_pooling2d_2 (MaxPooling2 (None, 56, 56, 512)       0         
 
 conv2d_6 (Conv2D)            (None, 56, 56, 256)       131328    
 
 conv2d_7 (Conv2D)            (None, 56, 56, 512)       1180160   
 
 conv2d_8 (Conv2D)            (None, 56, 56, 256)       131328    
 
 conv2d_9 (Conv2D)            (None, 56, 56, 512)       1180160   
 
 conv2d_10 (Conv2D)           (None, 56, 56, 256)       131328    
 
 conv2d_11 (Conv2D)           (None, 56, 56, 512)       1180160   
 
 conv2d_12 (Conv2D)           (None, 56, 56, 256)       131328    
 
 conv2d_13 (Conv2D)           (None, 56, 56, 512)       1180160   
 
 conv2d_14 (Conv2D)           (None, 56, 56, 512)       262656    
 
 conv2d_15 (Conv2D)           (None, 56, 56, 1024)      4719616   
 
 max_pooling2d_3 (MaxPooling2 (None, 28, 28, 1024)      0         
 
 conv2d_16 (Conv2D)           (None, 28, 28, 512)       524800    
 
 conv2d_17 (Conv2D)           (None, 28, 28, 1024)      4719616   
 
 conv2d_18 (Conv2D)           (None, 28, 28, 512)       524800    
 
 conv2d_19 (Conv2D)           (None, 28, 28, 1024)      4719616   
 
 conv2d_20 (Conv2D)           (None, 28, 28, 1024)      9438208   
 
 conv2d_21 (Conv2D)           (None, 14, 14, 1024)      9438208   
 
 conv2d_22 (Conv2D)           (None, 12, 12, 1024)      9438208   
 
 conv2d_23 (Conv2D)           (None, 10, 10, 1024)      9438208   
 
 flatten (Flatten)            (None, 102400)            0         
 
 dense (Dense)                (None, 512)               52429312  
 
 dense_1 (Dense)              (None, 1024)              525312    
 
 dropout (Dropout)            (None, 1024)              0         
 
 dense_2 (Dense)              (None, 1470)              1506750   
 
 yolo__reshape (Yolo_Reshape) (None, 7, 7, 30)          0         
 Total params: 114,617,342
 Trainable params: 114,617,342
 Non-trainable params: 0

6.  定义Learning Rate

整个训练过程包含135个Epoch,其中前75个Epoch,learning rate设置为0.01;75~105个Epoch,learning rate设置为0.001;105个Epoch的learning rate设置为0.0001。

from tensorflow import keras

class CustomLearningRateScheduler(keras.callbacks.Callback):
    """Learning rate scheduler which sets the learning rate according to schedule.

  Arguments:
      schedule: a function that takes an epoch index
          (integer, indexed from 0) and current learning rate
          as inputs and returns a new learning rate as output (float).
  """

    def __init__(self, schedule):
        super(CustomLearningRateScheduler, self).__init__()
        self.schedule = schedule

    def on_epoch_begin(self, epoch, logs=None):
        if not hasattr(self.model.optimizer, "lr"):
            raise ValueError('Optimizer must have a "lr" attribute.')
        # Get the current learning rate from model's optimizer.
        lr = float(tf.keras.backend.get_value(self.model.optimizer.learning_rate))
        # Call schedule function to get the scheduled learning rate.
        scheduled_lr = self.schedule(epoch, lr)
        # Set the value back to the optimizer before this epoch starts
        tf.keras.backend.set_value(self.model.optimizer.lr, scheduled_lr)
        print("\nEpoch %05d: Learning rate is %6.4f." % (epoch, scheduled_lr))


LR_SCHEDULE = [
    # (epoch to start, learning rate) tuples
    (0, 0.01),
    (75, 0.001),
    (105, 0.0001),
]


def lr_schedule(epoch, lr):
    """Helper function to retrieve the scheduled learning rate based on epoch."""
    if epoch < LR_SCHEDULE[0][0] or epoch > LR_SCHEDULE[-1][0]:
        return lr
    for i in range(len(LR_SCHEDULE)):
        if epoch == LR_SCHEDULE[i][0]:
            return LR_SCHEDULE[i][1]
    return lr

7. 定义损失函数

YOLO的损失函数包含三个部分:位置损失、置信度损失和分类损失。

实现代码如下:

import keras.backend as K

def xywh2minmax(xy, wh):
    xy_min = xy - wh / 2
    xy_max = xy + wh / 2

    return xy_min, xy_max

def iou(pred_mins, pred_maxes, true_mins, true_maxes):
    intersect_mins = K.maximum(pred_mins, true_mins)
    intersect_maxes = K.minimum(pred_maxes, true_maxes)
    intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)
    intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]

    pred_wh = pred_maxes - pred_mins
    true_wh = true_maxes - true_mins
    pred_areas = pred_wh[..., 0] * pred_wh[..., 1]
    true_areas = true_wh[..., 0] * true_wh[..., 1]

    union_areas = pred_areas + true_areas - intersect_areas
    iou_scores = intersect_areas / union_areas

    return iou_scores


def yolo_head(feats):
    # Dynamic implementation of conv dims for fully convolutional model.
    conv_dims = K.shape(feats)[1:3]  # assuming channels last
    # In YOLO the height index is the inner most iteration.
    conv_height_index = K.arange(0, stop=conv_dims[0])
    conv_width_index = K.arange(0, stop=conv_dims[1])
    conv_height_index = K.tile(conv_height_index, [conv_dims[1]])

    # TODO: Repeat_elements and tf.split doesn't support dynamic splits.
    # conv_width_index = K.repeat_elements(conv_width_index, conv_dims[1], axis=0)
    conv_width_index = K.tile(
        K.expand_dims(conv_width_index, 0), [conv_dims[0], 1])
    conv_width_index = K.flatten(K.transpose(conv_width_index))
    conv_index = K.transpose(K.stack([conv_height_index, conv_width_index]))
    conv_index = K.reshape(conv_index, [1, conv_dims[0], conv_dims[1], 1, 2])
    conv_index = K.cast(conv_index, K.dtype(feats))

    conv_dims = K.cast(K.reshape(conv_dims, [1, 1, 1, 1, 2]), K.dtype(feats))

    box_xy = (feats[..., :2] + conv_index) / conv_dims * 448
    box_wh = feats[..., 2:4] * 448

    return box_xy, box_wh


def yolo_loss(y_true, y_pred):
    label_class = y_true[..., :20]  # ? * 7 * 7 * 20
    label_box = y_true[..., 20:24]  # ? * 7 * 7 * 4
    response_mask = y_true[..., 24]  # ? * 7 * 7
    response_mask = K.expand_dims(response_mask)  # ? * 7 * 7 * 1

    predict_class = y_pred[..., :20]  # ? * 7 * 7 * 20
    predict_trust = y_pred[..., 20:22]  # ? * 7 * 7 * 2
    predict_box = y_pred[..., 22:]  # ? * 7 * 7 * 8

    _label_box = K.reshape(label_box, [-1, 7, 7, 1, 4])
    _predict_box = K.reshape(predict_box, [-1, 7, 7, 2, 4])

    label_xy, label_wh = yolo_head(_label_box)  # ? * 7 * 7 * 1 * 2, ? * 7 * 7 * 1 * 2
    label_xy = K.expand_dims(label_xy, 3)  # ? * 7 * 7 * 1 * 1 * 2
    label_wh = K.expand_dims(label_wh, 3)  # ? * 7 * 7 * 1 * 1 * 2
    label_xy_min, label_xy_max = xywh2minmax(label_xy, label_wh)  # ? * 7 * 7 * 1 * 1 * 2, ? * 7 * 7 * 1 * 1 * 2

    predict_xy, predict_wh = yolo_head(_predict_box)  # ? * 7 * 7 * 2 * 2, ? * 7 * 7 * 2 * 2
    predict_xy = K.expand_dims(predict_xy, 4)  # ? * 7 * 7 * 2 * 1 * 2
    predict_wh = K.expand_dims(predict_wh, 4)  # ? * 7 * 7 * 2 * 1 * 2
    predict_xy_min, predict_xy_max = xywh2minmax(predict_xy, predict_wh)  # ? * 7 * 7 * 2 * 1 * 2, ? * 7 * 7 * 2 * 1 * 2

    iou_scores = iou(predict_xy_min, predict_xy_max, label_xy_min, label_xy_max)  # ? * 7 * 7 * 2 * 1
    best_ious = K.max(iou_scores, axis=4)  # ? * 7 * 7 * 2
    best_box = K.max(best_ious, axis=3, keepdims=True)  # ? * 7 * 7 * 1

    box_mask = K.cast(best_ious >= best_box, K.dtype(best_ious))  # ? * 7 * 7 * 2

    no_object_loss = 0.5 * (1 - box_mask * response_mask) * K.square(0 - predict_trust)
    object_loss = box_mask * response_mask * K.square(1 - predict_trust)
    confidence_loss = no_object_loss + object_loss
    confidence_loss = K.sum(confidence_loss)

    class_loss = response_mask * K.square(label_class - predict_class)
    class_loss = K.sum(class_loss)

    _label_box = K.reshape(label_box, [-1, 7, 7, 1, 4])
    _predict_box = K.reshape(predict_box, [-1, 7, 7, 2, 4])

    label_xy, label_wh = yolo_head(_label_box)  # ? * 7 * 7 * 1 * 2, ? * 7 * 7 * 1 * 2
    predict_xy, predict_wh = yolo_head(_predict_box)  # ? * 7 * 7 * 2 * 2, ? * 7 * 7 * 2 * 2

    box_mask = K.expand_dims(box_mask)
    response_mask = K.expand_dims(response_mask)

    box_loss = 5 * box_mask * response_mask * K.square((label_xy - predict_xy) / 448)
    box_loss += 5 * box_mask * response_mask * K.square((K.sqrt(label_wh) - K.sqrt(predict_wh)) / 448)
    box_loss = K.sum(box_loss)

    loss = confidence_loss + class_loss + box_loss

    return loss

8. 保存模型训练权重

# defining a function to save the weights of best model
from tensorflow.keras.callbacks import ModelCheckpoint

mcp_save = ModelCheckpoint('weight.hdf5', save_best_only=True, monitor='val_loss', mode='min')

9. 模型编译

from tensorflow import keras

model.compile(loss=yolo_loss ,optimizer='adam')

10. 模型训练

model.fit(x=my_training_batch_generator,
          steps_per_epoch = int(len(X_train) // batch_size),
          epochs = 135,
          verbose = 1,
          workers= 4,
          validation_data = my_validation_batch_generator,
          validation_steps = int(len(X_val) // batch_size),
           callbacks=[
              CustomLearningRateScheduler(lr_schedule),
              mcp_save
          ])
Epoch 00000: Learning rate is 0.0100. 
Epoch 1/135
625/625 [==============================] - 195s 311ms/step - loss: 88.0331 - val_loss: 245.3397
 
Epoch 00001: Learning rate is 0.0100. 
Epoch 2/135 
625/625 [==============================] - 194s 310ms/step - loss: 140.9500 - val_loss: 116.6240 

Epoch 00002: Learning rate is 0.0100. 
Epoch 3/135 
625/625 [==============================] - 194s 310ms/step - loss: 114.1760 - val_loss: 113.2524 

Epoch 00003: Learning rate is 0.0100. 
Epoch 4/135 
625/625 [==============================] - 194s 310ms/step - loss: 113.0043 - val_loss: 112.8592 

Epoch 00004: Learning rate is 0.0100. 
Epoch 5/135 
625/625 [==============================] - 189s 303ms/step - loss: 112.9847 - val_loss: 113.3475 

Epoch 00005: Learning rate is 0.0100. 
Epoch 6/135 
625/625 [==============================] - 194s 310ms/step - loss: 113.0094 - val_loss: 112.7520 

Epoch 00006: Learning rate is 0.0100. 
Epoch 7/135 
625/625 [==============================] - 194s 310ms/step - loss: 71.0617 - val_loss: 61.3470

参考材料

https://www.maskaravivek.com/post/yolov1/

https://hackernoon.com/understanding-yolo-f5a74bbc7967

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