val_split = 0.1
with open(annotation_path) as f:
lines = f.readlines()
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)
num_train = len(lines) - num_val
'''
一些建议的参数设置：
VGG：SGD优化器，冻结时学习率1e-3，解冻时学习率1e-4
nets.rpn中ProposalCreator的n_train_post_nms=2000；
utils.utils中ProposalTargetCreator的pos_ratio=0.25；
RESNET50：Adam优化器，冻结时学习率1e-4，解冻时学习率1e-5
nets.rpn中ProposalCreator的n_train_post_nms=300；
utils.utils中ProposalTargetCreator的pos_ratio=0.5;
'''
if True:
lr = 1e-4
Init_Epoch = 0
Freeze_Epoch = 25
optimizer = optim.Adam(model.parameters(), lr, weight_decay=5e-4)
# optimizer = optim.SGD(model.parameters(),lr,weight_decay=5e-4,momentum=0.9)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=1,
gamma=0.95)
if Use_Data_Loader:
train_dataset = FRCNNDataset(lines[:num_train],
(IMAGE_SHAPE[0], IMAGE_SHAPE[1]))
val_dataset = FRCNNDataset(lines[num_train:],
(IMAGE_SHAPE[0], IMAGE_SHAPE[1]))
gen = DataLoader(train_dataset,
batch_size=1,
num_workers=4,
pin_memory=True,
drop_last=True,
