def train_step(imgs, targets1, targets2, targets3, net, optimizer):
with tf.GradientTape() as tape:
# 计算loss
prediction = net(imgs, training=True)
loss_value1 = box_smooth_l1()(targets1, prediction[0])
loss_value2 = conf_loss()(targets2, prediction[1])
loss_value3 = ldm_smooth_l1()(targets3, prediction[2])
loss_value = loss_value1 + loss_value2 + loss_value3
grads = tape.gradient(loss_value, net.trainable_variables)
optimizer.apply_gradients(zip(grads, net.trainable_variables))
return loss_value
# Epoch总训练世代
#------------------------------------------------------#
if True:
Init_epoch = 0
Freeze_epoch = 50
# batch_size大小，每次喂入多少数据
batch_size = 8
# 最大学习率
learning_rate_base = 1e-3
gen = Generator(training_dataset_path,img_dim,batch_size,bbox_util)
model.compile(loss={
'bbox_reg' : box_smooth_l1(),
'cls' : conf_loss(),
'ldm_reg' : ldm_smooth_l1()
},optimizer=keras.optimizers.Adam(lr=learning_rate_base)
)
model.fit(gen.generate(False),
steps_per_epoch=gen.get_len()//batch_size,
verbose=1,
epochs=Freeze_epoch,
initial_epoch=Init_epoch,
callbacks=[logging, checkpoint, reduce_lr, early_stopping])
for i in range(freeze_layers): model.layers[i].trainable = True
if True:
Freeze_epoch = 50
Epoch = 100