optimizer=keras.optimizers.Adam(lr=lr))
model_all.compile(loss={
'classification':
cls_loss(),
'regression':
smooth_l1(),
'dense_class_{}'.format(NUM_CLASSES):
class_loss_cls,
'dense_regress_{}'.format(NUM_CLASSES):
class_loss_regr(NUM_CLASSES - 1)
},
optimizer=keras.optimizers.Adam(lr=lr))
gen = Generator(bbox_util,
lines[:num_train],
NUM_CLASSES,
Batch_size,
input_shape=[input_shape[0],
input_shape[1]]).generate()
gen_val = Generator(bbox_util,
lines[num_train:],
NUM_CLASSES,
Batch_size,
input_shape=[input_shape[0],
input_shape[1]]).generate()
epoch_size = num_train // Batch_size
epoch_size_val = num_val // Batch_size
if epoch_size == 0 or epoch_size_val == 0:
raise ValueError("数据集过小，无法进行训练，请扩充数据集。")
np.random.seed(10101)
np.random.shuffle(lines)
np.random.seed(None)
num_val = int(len(lines)*val_split)
num_train = len(lines) - num_val
if True:
lr = 1e-4
Init_Epoch = 0
Freeze_Epoch = 25
optimizer = optim.Adam(model.parameters(),lr)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer,step_size=1,gamma=0.9)
gen = Generator(lines[:num_train],(IMAGE_SHAPE[0],IMAGE_SHAPE[1])).generate()
gen_val = Generator(lines[num_train:],(IMAGE_SHAPE[0],IMAGE_SHAPE[1])).generate()
epoch_size = EPOCH_LENGTH
epoch_size_val = int(EPOCH_LENGTH/10)
# ------------------------------------#
# 冻结一定部分训练
# ------------------------------------#
for param in model.extractor.parameters():
param.requires_grad = False
for epoch in range(Init_Epoch,Freeze_Epoch):
fit_ont_epoch(model,epoch,epoch_size,epoch_size_val,gen,gen_val,Freeze_Epoch)
lr_scheduler.step()
if True:
model_all.summary()
model_rpn.load_weights(base_net_weights, by_name=True)
model_classifier.load_weights(base_net_weights, by_name=True)
with open(annotation_path) as f:
lines = f.readlines()
np.random.seed(10101)
np.random.shuffle(lines)
np.random.seed(None)
# 每个世代训练数据集长度的步数
# 根据数据集大小进行指定
EPOCH_LENGTH = len(lines)
gen = Generator(bbox_util, lines, NUM_CLASSES, solid=True)
rpn_train = gen.generate()
log_dir = "logs"
# 训练参数设置
logging = TensorBoard(log_dir=log_dir)
callback = logging
callback.set_model(model_all)
model_rpn.compile(loss={
'regression': smooth_l1(),
'classification': cls_loss()
},
optimizer=keras.optimizers.Adam(lr=Learning_rate))
model_classifier.compile(
loss=[class_loss_cls, class_loss_regr(NUM_CLASSES - 1)],
metrics={'dense_class_{}'.format(NUM_CLASSES): 'accuracy'},