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,
collate_fn=frcnn_dataset_collate)
gen_val = DataLoader(val_dataset,
batch_size=1,
num_workers=4,
pin_memory=True,
drop_last=True,
collate_fn=frcnn_dataset_collate)
else:
# 也可以在训练初期防止权值被破坏。
# Init_Epoch为起始世代
# Freeze_Epoch为冻结训练的世代
# Epoch总训练世代
# 提示OOM或者显存不足请调小Batch_size
#------------------------------------------------------#
if True:
lr = 1e-4
Batch_size = 2
Init_Epoch = 0
Freeze_Epoch = 50
optimizer = optim.Adam(net.parameters(), lr, weight_decay=5e-4)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer,step_size=1,gamma=0.95)
train_dataset = FRCNNDataset(lines[:num_train], (input_shape[0], input_shape[1]), is_train=True)
val_dataset = FRCNNDataset(lines[num_train:], (input_shape[0], input_shape[1]), is_train=False)
gen = DataLoader(train_dataset, shuffle=True, batch_size=Batch_size, num_workers=4, pin_memory=True,
drop_last=True, collate_fn=frcnn_dataset_collate)
gen_val = DataLoader(val_dataset, shuffle=True, batch_size=Batch_size, num_workers=4, pin_memory=True,
drop_last=True, collate_fn=frcnn_dataset_collate)
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。")
# ------------------------------------#
# 冻结一定部分训练
# ------------------------------------#
# Epoch总训练世代
# 提示OOM或者显存不足请调小Batch_size
#------------------------------------------------------#
if True:
lr = 1e-4
Batch_size = 2
Init_Epoch = 0
Freeze_Epoch = 50
optimizer = optim.Adam(net.parameters(), lr, weight_decay=5e-4)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=1,
gamma=0.95) #调整学习率机制
train_dataset = FRCNNDataset(lines[:num_train],
(input_shape[0], input_shape[1]),
is_train=True) #进行数据增强
val_dataset = FRCNNDataset(
lines[num_train:], (input_shape[0], input_shape[1]),
is_train=False) #不进行数据增强 图像等比例缩放,无图像区域用128填充
gen = DataLoader(train_dataset,
shuffle=True,
batch_size=Batch_size,
num_workers=0,
pin_memory=True,
drop_last=True,
collate_fn=frcnn_dataset_collate)
# pin_memory:会自动将获取的数据张量放入固定的内存中，从而更快地将数据传输到支持CUDA的GPU。
# collate_fn:用于合并样本列表以形成小批量的Tensor ,批量加载数据时使用
gen_val = DataLoader(val_dataset,
shuffle=True,