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| import numpy as np
import tensorflow as tfdef convolutional(input_layer, filters_shape, downsample=False, activate=True, bn=True):
'''卷积层'''
if downsample:
input_layer = tf.keras.layers.ZeroPadding2D(((1, 0), (1, 0)))(input_layer)
padding = 'valid'
strides = 2
else:
strides = 1
padding = 'same' conv = tf.keras.layers.Conv2D(filters=filters_shape[-1],
kernel_size=filters_shape[0],
strides=strides,
padding=padding,
use_bias=not bn,
kernel_regularizer=tf.keras.regularizers.l2(0.0005),
kernel_initializer=tf.random_normal_initializer(stddev=0.01),
bias_initializer=tf.constant_initializer(0.))(input_layer)
if bn:
conv = tf.keras.layers.BatchNormalization()(conv)
if activate:
conv = tf.keras.layers.LeakyReLU(alpha=0.1)(conv)
return conv def residual_block(input_layer, filters_num1, filters_num2):
"""
:param input_layer: 残差块的输入特征图
:param filters_num1: 第一个卷积层的卷积核数量
:param filters_num2: 第二个卷积层的卷积核数量
:return:
"""
'''残差块'''
short_cut = input_layer conv = convolutional(input_layer, filters_shape=(1, 1, input_layer.shape[-1], filters_num1))
conv = convolutional(conv, filters_shape=(3, 3, filters_num1, filters_num2))
residual_output = short_cut + conv return residual_output def darknet53(input_data):
"""
:param input_data:
:return: 阶段输出一, 阶段输出二, 最终输出
"""
'''Darknet-53主干网络'''
# input_data : (416, 416, 3)
input_data = convolutional(input_data, (3, 3, 3, 32)) # 第一个卷积块后，输出特征图形状为 (416, 416, 32)
input_data = convolutional(input_data, (3, 3, 32, 64), downsample=True) # 第二个卷积块后，输出特征图形状为 (208, 208, 64) # 残差块 1×
for i in range(1):
input_data = residual_block(input_data, 32, 64) # 第一个残差块后，输出特征图形状不变 (208, 208, 64) input_data = convolutional(input_data, (3, 3, 64, 128), downsample=True) # 第三个卷积块后，输出特征图形状为 (104, 104, 128) # 残差块 2×
for i in range(2):
input_data = residual_block(input_data, 64, 128) # 第二个残差块后，输出特征图形状不变 (104, 104, 128) input_data = convolutional(input_data, (3, 3, 128, 256), downsample=True) # 第四个卷积块后，输出特征图形状为 (52, 52, 256) # 残差块 8×
for i in range(8):
input_data = residual_block(input_data, 128, 256) # 第三个残差块后，输出特征图形状不变 (52, 52, 256) route_1 = input_data # 保存第一个特征图，形状不变 (52, 52, 256)
input_data = convolutional(input_data, (3, 3, 256, 512), downsample=True) # 第五个卷积块后，输出特征图形状为 (26, 26, 512) # 残差块 8×
for i in range(8):
input_data = residual_block(input_data, 256, 512) # 第四个残差块后，输出特征图形状不变 (26, 26, 512) route_2 = input_data # 保存第二个特征图，形状不变 (26, 26, 512)
input_data = convolutional(input_data, (3, 3, 512, 1024), downsample=True) # 第六个卷积块后，输出特征图形状为 (13, 13, 1024) # 残差块 4×
for i in range(4):
input_data = residual_block(input_data, 512, 1024) # 第五个残差块后，输出特征图形状不变 (13, 13, 1024) return route_1, route_2, input_data def yolo_convolutional(input_layer, filters_shape):
'''Yolo卷积层'''
conv = convolutional(input_layer, filters_shape)
conv = tf.keras.layers.Conv2D(filters=3 * (4 + 1 + 80),
kernel_size=(1, 1),
strides=(1, 1),
padding='same',
use_bias=True,
kernel_initializer=tf.random_normal_initializer(stddev=0.01),
bias_initializer=tf.constant_initializer(0.))(conv)
return conv def decode(conv_output, num_classes, anchors, i=0):
"""
return tensor of shape [batch_size, output_size, output_size, anchor_per_scale, 5 + num_classes]
contains (x, y, w, h, score, probability)
""" conv_shape = tf.shape(conv_output)
batch_size = conv_shape[0]
output_size = conv_shape[1] # 对 tensor 进行 reshape
conv_output = tf.reshape(conv_output, (batch_size, output_size, output_size, 3, 5 + num_classes)) # 按顺序提取[x, y, w, h, c]
conv_raw_dxdy = conv_output[:, :, :, :, 0:2] # 中心位置的偏移量
conv_raw_dwdh = conv_output[:, :, :, :, 2:4] # 预测框长宽的偏移量
conv_raw_conf = conv_output[:, :, :, :, 4:5] # 预测框的置信度
conv_raw_prob = conv_output[:, :, :, :, 5:] # 预测框的类别概率 # 好了，接下来是画网格。其中，output_size 等于 13、26 或者 52
y = tf.tile(tf.range(output_size, dtype=tf.int32)[:, tf.newaxis], [1, output_size])
x = tf.tile(tf.range(output_size, dtype=tf.int32)[tf.newaxis, :], [output_size, 1])
xy_grid = tf.concat([x[:, :, tf.newaxis], y[:, :, tf.newaxis]], axis=-1)
xy_grid = tf.tile(xy_grid[tf.newaxis, :, :, tf.newaxis, :], [batch_size, 1, 1, 3, 1])
xy_grid = tf.cast(xy_grid, tf.float32) # 计算网格左上角的位置，即cx cy的值 # 根据上图公式计算预测框的中心位置
# 这里的 i=0、1 或者 2， 以分别对应三种网格尺度
pred_xy = (tf.sigmoid(conv_raw_dxdy) + xy_grid) * 32.0 # 计算预测框在原图尺寸上的x y
pred_wh = (tf.exp(conv_raw_dwdh) * anchors[i]) * 32.0 # 计算预测框在原图尺寸上的w h
pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1) # 拼接起来
pred_conf = tf.sigmoid(conv_raw_conf) # 计算预测框里object的置信度
pred_prob = tf.sigmoid(conv_raw_prob) # 计算预测框里object的类别概率 return tf.concat([pred_xywh, pred_conf, pred_prob], axis=-1) def YoloV3(input_layer, NUM_CLASS):
"""
:param input_layer: 输入层[416, 416, 3]
:param NUM_CLASS: 类别数量
:return: YoloV3模型
"""
'''YoloV3模型'''
# input_layer : 输入张量的形状为 (416, 416, 3)
route_1, route_2, conv = darknet53(input_layer)
# route_1: (52, 52, 256)
# route_2: (26, 26, 512)
# conv: (13, 13, 1024) '''Convolution Set'''
conv = convolutional(conv, (1, 1, 1024, 512)) # 第一个卷积块后，输出特征图形状为 (13, 13, 512) (通道数减半为512)
conv = convolutional(conv, (3, 3, 512, 1024)) # 第二个卷积块后，输出特征图形状不变 (13, 13, 1024) (通道数翻倍为1024)
conv = convolutional(conv, (1, 1, 1024, 512)) # 第三个卷积块后，输出特征图形状不变 (13, 13, 512
conv = convolutional(conv, (3, 3, 512, 1024)) # 第四个卷积块后，输出特征图形状不变 (13, 13, 1024)
conv = convolutional(conv, (1, 1, 1024, 512)) # 第五个卷积块后，输出特征图形状不变 (13, 13, 512)
''' Convolution Set ''' conv_lobj_branch = convolutional(conv, (3, 3, 512, 1024)) # 输出特征图形状不变 (13, 13, 1024)
conv_lbbox = yolo_convolutional(conv_lobj_branch, (1, 1, 1024, 3 * (NUM_CLASS + 5)))# 输出特征图形状不变 (13, 13, 255)
# conv_lbbox: Predict One conv = convolutional(conv, (1, 1, 512, 256)) # 第一个卷积块后，输出特征图形状为 (13, 13, 256)
conv = tf.keras.layers.UpSampling2D(2)(conv) # 上采样后，输出特征图形状为 (26, 26, 256) conv = tf.concat([conv, route_2], axis=-1) # 在通道维度上连接特征图，输出特征图形状不变 (26, 26, 768)
''' Convolution Set '''
conv = convolutional(conv, (1, 1, 768, 256)) # 第二个卷积块后，输出特征图形状为 (26, 26, 256)
conv = convolutional(conv, (3, 3, 256, 512)) # 第三个卷积块后，输出特征图形状不变 (26, 26, 512)
conv = convolutional(conv, (1, 1, 512, 256)) # 第四个卷积块后，输出特征图形状不变 (26, 26, 256)
conv = convolutional(conv, (3, 3, 256, 512)) # 第五个卷积块后，输出特征图形状不变 (26, 26, 512)
conv = convolutional(conv, (1, 1, 512, 256)) # 第六个卷积块后，输出特征图形状不变 (26, 26, 256)
''' Convolution Set ''' conv_mobj_branch = convolutional(conv, (3, 3, 256, 512)) # 输出特征图形状不变 (26, 26, 512)
conv_mbbox = yolo_convolutional(conv_mobj_branch, (1, 1, 512, 3 * (NUM_CLASS + 5))) # 输出特征图形状不变 (26, 26, 255)
# conv_mbbox: Predict Two conv = convolutional(conv, (1, 1, 256, 128)) # 第一个卷积块后，输出特征图形状为 (26, 26, 128)
conv = tf.keras.layers.UpSampling2D(2)(conv) # 上采样后，输出特征图形状为 (52, 52, 128) conv = tf.concat([conv, route_1], axis=-1) # 在通道维度上连接特征图，输出特征图形状不变 (52, 52, 384)
''' Convolution Set '''
conv = convolutional(conv, (1, 1, 384, 128)) # 第二个卷积块后，输出特征图形状为 (52, 52, 128)
conv = convolutional(conv, (3, 3, 128, 256)) # 第三个卷积块后，输出特征图形状不变 (52, 52, 256)
conv = convolutional(conv, (1, 1, 256, 128)) # 第四个卷积块后，输出特征图形状不变 (52, 52, 128)
conv = convolutional(conv, (3, 3, 128, 256)) # 第五个卷积块后，输出特征图形状不变 (52, 52, 256)
conv = convolutional(conv, (1, 1, 256, 128)) # 第六个卷积块后，输出特征图形状不变 (52, 52, 128)
''' Convolution Set ''' conv_sobj_branch = convolutional(conv, (3, 3, 128, 256)) # 输出特征图形状不变 (52, 52, 256)
conv_sbbox = yolo_convolutional(conv_sobj_branch, (1, 1, 256, 3 * (NUM_CLASS + 5))) # 输出特征图形状不变 (52, 52, 255)
# conv_sbbox: Predict Three # [Predict Three, Predict Two, Predict One]
# [(52, 52, 255), (26, 26, 255), (13, 13, 255)]
return [conv_sbbox, conv_mbbox, conv_lbbox] def YoloLoss(output_size, num_classes, anchors):
'''Yolo损失函数'''
def yolo_loss(y_true, y_pred):
pred_xywh = y_pred[..., :4]
pred_conf = y_pred[..., 4:5]
pred_prob = y_pred[..., 5:] true_xywh = y_true[..., :4]
true_conf = y_true[..., 4:5]
true_prob = y_true[..., 5:] true_xy = true_xywh[..., :2]
true_wh = true_xywh[..., 2:4] pred_xy = pred_xywh[..., :2]
pred_wh = pred_xywh[..., 2:4] intersect_wh = tf.maximum(tf.minimum(pred_xy + pred_wh / 2, true_xy + true_wh / 2) - tf.maximum(pred_xy - pred_wh / 2, true_xy - true_wh / 2), 0)
intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
true_area = true_wh[..., 0] * true_wh[..., 1]
pred_area = pred_wh[..., 0] * pred_wh[..., 1]
union_area = pred_area + true_area - intersect_area
iou = intersect_area / union_area best_iou = tf.reduce_max(iou, axis=-1)
mask = tf.cast(best_iou < 0.5, dtype=tf.float32) xy_loss = tf.reduce_sum(tf.square(true_xy - pred_xy) * true_conf * mask) / tf.reduce_sum(true_conf * mask + 1e-6) * 2.0
wh_loss = tf.reduce_sum(tf.square(true_wh - pred_wh) * true_conf * mask) / tf.reduce_sum(true_conf * mask + 1e-6) * 2.0 obj_loss = tf.reduce_sum(tf.square(true_conf - pred_conf) * true_conf * mask) / tf.reduce_sum(true_conf * mask + 1e-6)
no_obj_loss = tf.reduce_sum(tf.square(true_conf - pred_conf) * (1.0 - true_conf) * (1.0 - mask)) / tf.reduce_sum((1.0 - true_conf) * (1.0 - mask) + 1e-6) class_loss = tf.keras.losses.sparse_categorical_crossentropy(y_true[..., 5:], y_pred[..., 5:])
class_loss = tf.reduce_sum(class_loss * true_conf) / tf.reduce_sum(true_conf + 1e-6) return xy_loss + wh_loss + obj_loss + no_obj_loss + class_loss return yolo_loss # 设置anchors
anchors = np.array([(10, 13), (16, 30), (33, 23), (30, 61), (62, 45),
(59, 119), (116, 90), (156, 198), (373, 326)], np.float32) / 416.0 # 模型输入
input_layer = tf.keras.layers.Input([416, 416, 3])
num_classes = 13
# 创建YoloV3模型
output = YoloV3(input_layer, num_classes)
model = tf.keras.Model(input_layer, output) # 编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
loss=[YoloLoss(13, 80, anchors), YoloLoss(26, 80, anchors), YoloLoss(52, 80, anchors)]) # 打印模型结构
model.summary() # # 保存模型为SavedModel格式
# tf.saved_model.save(model, 'yolov3_saved_model') # 保存模型为.h5格式
model.save('yolov3_model.h5')
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