def train_first_stage(self):
"""
主干特征提取网络特征通用，冻结训练可以加快训练速度
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
:return:
"""
optimizer_stage1 = optim.Adam(self.net.parameters(), self.lr_first, weight_decay=self.opt_weight_decay)
if self.Cosine_lr:
lr_scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer_stage1, T_max=self.CosineAnnealingLR_T_max, eta_min=self.CosineAnnealingLR_eta_min)
else:
lr_scheduler = optim.lr_scheduler.StepLR(
optimizer_stage1, step_size=self.StepLR_step_size, gamma=self.StepLR_gamma)
if self.Use_Data_Loader:
train_dataset = EfficientdetDataset(self.lines[:self.num_train], (self.input_shape[0], self.input_shape[1]))
val_dataset = EfficientdetDataset(self.lines[self.num_train:], (self.input_shape[0], self.input_shape[1]))
gen = DataLoader(train_dataset, batch_size=self.Batch_size_first, num_workers=self.num_workers,
pin_memory=True,
drop_last=True, collate_fn=efficientdet_dataset_collate)
gen_val = DataLoader(val_dataset, batch_size=self.Batch_size_first, num_workers=self.num_workers,
pin_memory=True,
drop_last=True, collate_fn=efficientdet_dataset_collate)
else:
gen = Generator(self.Batch_size_first, self.lines[:self.num_train],
(self.input_shape[0], self.input_shape[1])).generate()
gen_val = Generator(self.Batch_size_first, self.lines[self.num_train:],
(self.input_shape[0], self.input_shape[1])).generate()
epoch_size = max(1, self.num_train // self.Batch_size_first)
epoch_size_val = self.num_val // self.Batch_size_first
# ------------------------------------#
# 冻结一定部分训练
# ------------------------------------#
for param in self.model.backbone_net.parameters():
param.requires_grad = False
for epoch in range(self.Init_Epoch, self.Freeze_Epoch):
self.fit_one_epoch(self.net, self.model, optimizer_stage1, self.efficient_loss, epoch, epoch_size,
epoch_size_val, gen, gen_val, self.Freeze_Epoch, self.Cuda)
lr_scheduler.step()
def train_second_stage(self):
"""
整个网络的参数一起更新
:return:
"""
optimizer_stage2 = optim.Adam(self.net.parameters(), self.lr_second, weight_decay=self.opt_weight_decay)
if self.Cosine_lr:
lr_scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer_stage2, T_max=self.CosineAnnealingLR_T_max, eta_min=self.CosineAnnealingLR_eta_min)
else:
lr_scheduler = optim.lr_scheduler.StepLR(
optimizer_stage2, step_size=self.StepLR_step_size, gamma=self.StepLR_gamma)
if self.Use_Data_Loader:
train_dataset = EfficientdetDataset(self.lines[:self.num_train], (self.input_shape[0], self.input_shape[1]))
val_dataset = EfficientdetDataset(self.lines[self.num_train:], (self.input_shape[0], self.input_shape[1]))
gen = DataLoader(train_dataset, batch_size=self.Batch_size_first, num_workers=self.num_workers,
pin_memory=True,
drop_last=True, collate_fn=efficientdet_dataset_collate)
gen_val = DataLoader(val_dataset, batch_size=self.Batch_size_first, num_workers=self.num_workers,
pin_memory=True,
drop_last=True, collate_fn=efficientdet_dataset_collate)
else:
gen = Generator(self.Batch_size_first, self.lines[:self.num_train],
(self.input_shape[0], self.input_shape[1])).generate()
gen_val = Generator(self.Batch_size_first, self.lines[self.num_train:],
(self.input_shape[0], self.input_shape[1])).generate()
epoch_size = max(1, self.num_train // self.Batch_size_second)
epoch_size_val = self.num_val // self.Batch_size_second
# ------------------------------------#
# 解冻后训练
# ------------------------------------#
for param in self.model.backbone_net.parameters():
param.requires_grad = True
for epoch in range(self.Freeze_Epoch, self.Unfreeze_Epoch):
self.fit_one_epoch(self.net, self.model, optimizer_stage2, self.efficient_loss, epoch, epoch_size,
epoch_size_val, gen, gen_val, self.Unfreeze_Epoch, self.Cuda)
lr_scheduler.step()
Batch_size = 4
Init_Epoch = 0
Freeze_Epoch = 50
optimizer = optim.Adam(net.parameters(), lr, weight_decay=5e-4)
lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=2, verbose=True)
if Use_Data_Loader:
train_dataset = EfficientdetDataset(lines[:num_train], (input_shape[0], input_shape[1]))
val_dataset = EfficientdetDataset(lines[num_train:], (input_shape[0], input_shape[1]))
gen = DataLoader(train_dataset, shuffle=True, batch_size=Batch_size, num_workers=4, pin_memory=True,
drop_last=True, collate_fn=efficientdet_dataset_collate)
gen_val = DataLoader(val_dataset, shuffle=True, batch_size=Batch_size, num_workers=4, pin_memory=True,
drop_last=True, collate_fn=efficientdet_dataset_collate)
else:
gen = Generator(Batch_size, lines[:num_train],
(input_shape[0], input_shape[1])).generate()
gen_val = Generator(Batch_size, lines[num_train:],
(input_shape[0], input_shape[1])).generate()
epoch_size = num_train // Batch_size
epoch_size_val = num_val // Batch_size
# ------------------------------------#
# 冻结一定部分训练
# ------------------------------------#
for param in model.backbone_net.parameters():
param.requires_grad = False
for epoch in range(Init_Epoch, Freeze_Epoch):
val_loss = fit_one_epoch(net, efficient_loss, epoch, epoch_size, epoch_size_val, gen, gen_val, Freeze_Epoch,
Cuda)
lr_scheduler.step(val_loss)
# Freeze_Epoch为冻结训练的世代
# Epoch总训练世代
#------------------------------------------------------#
for i in range(freeze_layers[phi]):
model.layers[i].trainable = False
if True:
#--------------------------------------------#
# BATCH_SIZE不要太小，不然训练效果很差
#--------------------------------------------#
BATCH_SIZE = 4
Lr = 1e-3
Init_Epoch = 0
Freeze_Epoch = 50
gen = Generator(bbox_util, BATCH_SIZE, lines[:num_train],
lines[num_train:],
(image_sizes[phi], image_sizes[phi]), NUM_CLASSES)
model.compile(loss={
'regression': smooth_l1(),
'classification': focal()
},
optimizer=keras.optimizers.Adam(Lr))
print('Train on {} samples, val on {} samples, with batch size {}.'.
format(num_train, num_val, BATCH_SIZE))
model.fit(gen.generate(True, eager=False),
steps_per_epoch=max(1, num_train // BATCH_SIZE),
validation_data=gen.generate(False, eager=False),
validation_steps=max(1, num_val // BATCH_SIZE),
epochs=Freeze_Epoch,
verbose=1,
initial_epoch=Init_Epoch,
# Epoch总训练世代
#------------------------------------------------------#
for i in range(freeze_layers[phi]):
model.layers[i].trainable = False
if True:
#--------------------------------------------#
# Batch_size不要太小，不然训练效果很差
#--------------------------------------------#
Batch_size = 8
Lr = 1e-3
Init_Epoch = 0
Freeze_Epoch = 50
gen = Generator(bbox_util, Batch_size, lines[:num_train],
lines[num_train:],
(image_sizes[phi], image_sizes[phi]), num_classes)
model.compile(loss={
'regression': smooth_l1(),
'classification': focal()
},
optimizer=keras.optimizers.Adam(Lr))
print('Train on {} samples, val on {} samples, with batch size {}.'.
format(num_train, num_val, Batch_size))
model.fit(gen.generate(True),
steps_per_epoch=max(1, num_train // Batch_size),
validation_data=gen.generate(False),
validation_steps=max(1, num_val // Batch_size),
epochs=Freeze_Epoch,
verbose=1,
initial_epoch=Init_Epoch,
