Freeze_epoch = 50
learning_rate_base = 1e-3
gen = Generator(training_dataset_path, img_dim, batch_size, bbox_util)
model.compile(loss={
'bbox_reg': box_smooth_l1(weights=cfg['loc_weight']),
'cls': conf_loss(),
'ldm_reg': ldm_smooth_l1()
},
optimizer=keras.optimizers.Adam(lr=learning_rate_base))
model.fit_generator(
gen,
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:
batch_size = 4
Freeze_epoch = 50
Epoch = 100
learning_rate_base = 1e-4
gen = Generator(training_dataset_path, img_dim, batch_size, bbox_util)
gen = Generator(training_dataset_path, img_dim, batch_size, bbox_util)
model.compile(loss={
'bbox_reg': box_smooth_l1(weights=cfg['loc_weight']),
'cls': conf_loss(),
'ldm_reg': ldm_smooth_l1()
},
optimizer=keras.optimizers.Adam(lr=learning_rate_base))
model.fit_generator(
gen,
steps_per_epoch=gen.get_len() // batch_size,
verbose=1,
epochs=Freeze_epoch,
initial_epoch=Init_epoch,
# 开启多线程可以加快数据读取的速度。
# workers=4,
# use_multiprocessing=True,
callbacks=[
logging, checkpoint, reduce_lr, early_stopping, loss_history
])
for i in range(freeze_layers):
model.layers[i].trainable = True
if True:
batch_size = 4
Freeze_epoch = 50
Epoch = 100
learning_rate_base = 1e-4
epoch_size, gen, Freeze_epoch, cfg)
else:
model.compile(
loss={
'bbox_reg': box_smooth_l1(weights=cfg['loc_weight']),
'cls': conf_loss(),
'ldm_reg': ldm_smooth_l1()
},
optimizer=keras.optimizers.Adam(lr=learning_rate_base))
model.fit_generator(
generator=gen,
steps_per_epoch=epoch_size,
epochs=Freeze_epoch,
initial_epoch=Init_epoch,
use_multiprocessing=True if num_workers > 1 else False,
workers=num_workers,
callbacks=[
logging, checkpoint, reduce_lr, early_stopping,
loss_history
])
if Freeze_Train:
for i in range(freeze_layers):
model.layers[i].trainable = True
if True:
#----------------------------------------------------#
# 解冻阶段训练参数
# 此时模型的主干不被冻结了，特征提取网络会发生改变
# 占用的显存较大，网络所有的参数都会发生改变