device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_dict = model.state_dict()
pretrained_dict = torch.load(model_path, map_location=device)
pretrained_dict = {k: v for k, v in pretrained_dict.items() if np.shape(model_dict[k]) == np.shape(v)}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
print('Finished!')
net = model.train()
if Cuda:
net = torch.nn.DataParallel(model)
cudnn.benchmark = True
net = net.cuda()
yolo_loss = YOLOLoss(np.reshape(anchors,[-1,2]), num_classes, (input_shape[1], input_shape[0]), smoooth_label, Cuda, normalize)
loss_history = LossHistory("logs/")
#----------------------------------------------------#
# 获得图片路径和标签
#----------------------------------------------------#
annotation_path = '2007_train.txt'
#----------------------------------------------------------------------#
# 验证集的划分在train.py代码里面进行
# 2007_test.txt和2007_val.txt里面没有内容是正常的。训练不会使用到。
# 当前划分方式下，验证集和训练集的比例为1:9
#----------------------------------------------------------------------#
val_split = 0.1
with open(annotation_path) as f:
lines = f.readlines()
np.random.seed(10101)
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
print('Finished!')
net = model.train()
if Cuda:
net = torch.nn.DataParallel(model)
cudnn.benchmark = True
# net = net.cuda()
net = net.cpu()
# 建立loss函数
yolo_losses = []
for i in range(3):
yolo_losses.append(YOLOLoss(np.reshape(Config["yolo"]["anchors"],[-1,2]),
Config["yolo"]["classes"], (Config["img_w"], Config["img_h"]), Cuda, normalize))
#----------------------------------------------------#
# 获得图片路径和标签
#----------------------------------------------------#
annotation_path = '2007_train.txt'
#----------------------------------------------------------------------#
# 验证集的划分在train.py代码里面进行
# 2007_test.txt和2007_val.txt里面没有内容是正常的。训练不会使用到。
# 当前划分方式下，验证集和训练集的比例为1:9
#----------------------------------------------------------------------#
val_split = 0.1
with open(annotation_path) as f:
lines = f.readlines()
np.random.seed(10101)
np.random.shuffle(lines)
model.load_state_dict(model_dict)
print('Finished!')
net = model.train()
if Cuda:
net = torch.nn.DataParallel(model)
cudnn.benchmark = True
net = net.cuda()
# 建立loss函数
yolo_losses = []
for i in range(3):
yolo_losses.append(
YOLOLoss(np.reshape(Config["yolo"]["anchors"],
[-1, 2]), Config["yolo"]["classes"],
(Config["img_w"], Config["img_h"]), Cuda))
# 0.1用于验证，0.9用于训练
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:
# 最开始使用1e-3的学习率可以收敛的更快
lr = 1e-3
}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
print('Finished!')
net = model.train()
if Cuda:
net = torch.nn.DataParallel(model)
cudnn.benchmark = True
net = net.cuda()
# 建立loss函数
yolo_losses = []
for i in range(3):
yolo_losses.append(YOLOLoss(np.reshape(anchors,[-1,2]),num_classes, \
(input_shape[1], input_shape[0]), smoooth_label, Cuda))
# 0.1用于验证，0.9用于训练
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-3
Batch_size = 2
Init_Epoch = 0
net = torch.nn.DataParallel(model)
# 在开始训练时花费一点额外时间，为整个网络的每个卷积层搜索最适合它的卷积实现算法
cudnn.benchmark = True
net = net.cuda()
#-----------------------------------#
# 建立loss函数
#-----------------------------------#
yolo_losses = []
# 3因为有13、26、52三种尺寸的输出
for i in range(3):
yolo_losses.append(
YOLOLoss(
# 把anchors调整为[w, h]的格式，倒序是因为13、26、52的feature map分别对应大、中、小的anchor
np.reshape(anchors, [-1, 2])[::-1, :],
num_classes,
input_shape,
smoooth_label,
Cuda))
#-----------------------------------#
# 第一阶段训练
#-----------------------------------#
# 初始学习率
lr = 1e-3
Batch_size = 4
Init_Epoch = 0
End_Epoch = 50
# 设置优化器
optimizer = optim.Adam(net.parameters(), lr, weight_decay=5e-4)
pretrained_dict = torch.load(model_path, map_location=device)
pretrained_dict = {
k: v
for k, v in pretrained_dict.items()
if np.shape(model_dict[k]) == np.shape(v)
}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model_train = model.train()
if Cuda:
model_train = torch.nn.DataParallel(model)
cudnn.benchmark = True
model_train = model_train.cuda()
yolo_loss = YOLOLoss(anchors, num_classes, input_shape, Cuda, anchors_mask,
label_smoothing)
loss_history = LossHistory("logs/")
#---------------------------#
# 读取数据集对应的txt
#---------------------------#
with open(train_annotation_path) as f:
train_lines = f.readlines()
with open(val_annotation_path) as f:
val_lines = f.readlines()
num_train = len(train_lines)
num_val = len(val_lines)
#------------------------------------------------------#
# 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。
#------------------------------------------------------#
print('Load weights {}.'.format(model_path))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_dict = model.state_dict()
pretrained_dict = torch.load(model_path, map_location = device)
pretrained_dict = {k: v for k, v in pretrained_dict.items() if np.shape(model_dict[k]) == np.shape(v)}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
model_train = model.train()
if Cuda:
model_train = torch.nn.DataParallel(model)
cudnn.benchmark = True
model_train = model_train.cuda()
yolo_loss = YOLOLoss(anchors, num_classes, input_shape, Cuda, anchors_mask)
loss_history = LossHistory("logs/")
#---------------------------#
# 读取数据集对应的txt
#---------------------------#
with open(train_annotation_path) as f:
train_lines = f.readlines()
with open(val_annotation_path) as f:
val_lines = f.readlines()
num_train = len(train_lines)
num_val = len(val_lines)
#------------------------------------------------------#
# 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。
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
print('Finished!')
net = model.train()
if Cuda:
net = torch.nn.DataParallel(model)
cudnn.benchmark = True
net = net.cuda()
# 建立loss函数
yolo_losses = []
for i in range(3):
yolo_losses.append(
YOLOLoss(np.reshape(anchors, [-1, 2]), num_classes,
(input_shape[1], input_shape[0]), smoooth_label, Cuda))
# 0.2用于验证，0.9用于训练
val_split = 0.2
with open(annotation_path) as f:
lines = f.readlines()
n = len(lines)
list = range(n)
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
num_val = int(n * val_split)
valid = random.sample(list, num_val)
# 将验证数据写入txt文件
