class Efficientdet(object):
_defaults = {
#--------------------------------------------------------------------------#
# 使用自己训练好的模型进行预测一定要修改model_path和classes_path！
# model_path指向logs文件夹下的权值文件，classes_path指向model_data下的txt
#
# 训练好后logs文件夹下存在多个权值文件，选择验证集损失较低的即可。
# 验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。
# 如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改
#--------------------------------------------------------------------------#
"model_path" : 'model_data/efficientdet-d0.pth',
"classes_path" : 'model_data/coco_classes.txt',
#---------------------------------------------------------------------#
# 用于选择所使用的模型的版本，0-7
#---------------------------------------------------------------------#
"phi" : 0,
#---------------------------------------------------------------------#
# 只有得分大于置信度的预测框会被保留下来
#---------------------------------------------------------------------#
"confidence" : 0.3,
#---------------------------------------------------------------------#
# 非极大抑制所用到的nms_iou大小
#---------------------------------------------------------------------#
"nms_iou" : 0.3,
#---------------------------------------------------------------------#
# 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize，
# 在多次测试后，发现关闭letterbox_image直接resize的效果更好
#---------------------------------------------------------------------#
"letterbox_image" : False,
#---------------------------------------------------------------------#
# 是否使用Cuda
# 没有GPU可以设置成False
#---------------------------------------------------------------------#
"cuda" : True
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化Efficientdet
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
for name, value in kwargs.items():
setattr(self, name, value)
self.input_shape = [image_sizes[self.phi], image_sizes[self.phi]]
#---------------------------------------------------#
# 计算总的类的数量
#---------------------------------------------------#
self.class_names, self.num_classes = get_classes(self.classes_path)
#---------------------------------------------------#
# 画框设置不同的颜色
#---------------------------------------------------#
hsv_tuples = [(x / self.num_classes, 1., 1.) for x in range(self.num_classes)]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors))
self.generate()
#---------------------------------------------------#
# 载入模型
#---------------------------------------------------#
def generate(self):
#----------------------------------------#
# 创建Efficientdet模型
#----------------------------------------#
self.net = EfficientDetBackbone(self.num_classes, self.phi)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.net.load_state_dict(torch.load(self.model_path, map_location=device))
self.net = self.net.eval()
print('{} model, anchors, and classes loaded.'.format(self.model_path))
if self.cuda:
self.net = nn.DataParallel(self.net)
self.net = self.net.cuda()
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image, crop = False):
#---------------------------------------------------#
# 计算输入图片的高和宽
#---------------------------------------------------#
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
#---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 传入网络当中进行预测
#---------------------------------------------------------#
_, regression, classification, anchors = self.net(images)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
outputs = decodebox(regression, anchors, self.input_shape)
results = non_max_suppression(torch.cat([outputs, classification], axis=-1), self.input_shape,
image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
if results[0] is None:
return image
top_label = np.array(results[0][:, 5], dtype = 'int32')
top_conf = results[0][:, 4]
top_boxes = results[0][:, :4]
#---------------------------------------------------------#
# 设置字体与边框厚度
#---------------------------------------------------------#
font = ImageFont.truetype(font='model_data/simhei.ttf', size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = int(max((image.size[0] + image.size[1]) // np.mean(self.input_shape), 1))
#---------------------------------------------------------#
# 是否进行目标的裁剪
#---------------------------------------------------------#
if crop:
for i, c in list(enumerate(top_label)):
top, left, bottom, right = top_boxes[i]
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
dir_save_path = "img_crop"
if not os.path.exists(dir_save_path):
os.makedirs(dir_save_path)
crop_image = image.crop([left, top, right, bottom])
crop_image.save(os.path.join(dir_save_path, "crop_" + str(i) + ".png"), quality=95, subsampling=0)
print("save crop_" + str(i) + ".png to " + dir_save_path)
#---------------------------------------------------------#
# 图像绘制
#---------------------------------------------------------#
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = top_conf[i]
top, left, bottom, right = box
top = max(0, np.floor(top).astype('int32'))
left = max(0, np.floor(left).astype('int32'))
bottom = min(image.size[1], np.floor(bottom).astype('int32'))
right = min(image.size[0], np.floor(right).astype('int32'))
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label, top, left, bottom, right)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i], outline=self.colors[c])
draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[c])
draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
del draw
return image
def get_FPS(self, image, test_interval):
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
#---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 传入网络当中进行预测
#---------------------------------------------------------#
_, regression, classification, anchors = self.net(images)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
outputs = decodebox(regression, anchors, self.input_shape)
results = non_max_suppression(torch.cat([outputs, classification], axis=-1), self.input_shape,
image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
t1 = time.time()
for _ in range(test_interval):
with torch.no_grad():
#---------------------------------------------------------#
# 传入网络当中进行预测
#---------------------------------------------------------#
_, regression, classification, anchors = self.net(images)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
outputs = decodebox(regression, anchors, self.input_shape)
results = non_max_suppression(torch.cat([outputs, classification], axis=-1), self.input_shape,
image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def get_map_txt(self, image_id, image, class_names, map_out_path):
f = open(os.path.join(map_out_path, "detection-results/"+image_id+".txt"),"w")
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 在这里将图像转换成RGB图像，防止灰度图在预测时报错。
# 代码仅仅支持RGB图像的预测，所有其它类型的图像都会转化成RGB
#---------------------------------------------------------#
image = cvtColor(image)
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
image_data = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
#---------------------------------------------------------#
# 添加上batch_size维度，图片预处理，归一化。
#---------------------------------------------------------#
image_data = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
with torch.no_grad():
images = torch.from_numpy(image_data)
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 传入网络当中进行预测
#---------------------------------------------------------#
_, regression, classification, anchors = self.net(images)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
outputs = decodebox(regression, anchors, self.input_shape)
results = non_max_suppression(torch.cat([outputs, classification], axis=-1), self.input_shape,
image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
if results[0] is None:
return
top_label = np.array(results[0][:, 5], dtype = 'int32')
top_conf = results[0][:, 4]
top_boxes = results[0][:, :4]
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
box = top_boxes[i]
score = str(top_conf[i])
top, left, bottom, right = box
if predicted_class not in class_names:
continue
f.write("%s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)),str(int(bottom))))
f.close()
return
model = EfficientDetBackbone(num_classes, phi)
#------------------------------------------------------#
# 权值文件请看README，百度网盘下载
#------------------------------------------------------#
model_path = "model_data/efficientdet-d0.pth"
print('Loading weights into state dict...')
model_dict = model.state_dict()
pretrained_dict = torch.load(model_path)
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()
efficient_loss = FocalLoss()
#----------------------------------------------------#
# 获得图片路径和标签
#----------------------------------------------------#
annotation_path = '2007_train.txt'
class EfficientDet(object):
_defaults = {
"model_path": 'model_data/efficientdet-d0.pth',
"classes_path": 'model_data/coco_classes.txt',
"phi": 0,
"confidence": 0.3,
"cuda": True
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化Efficientdet
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
self.class_names = self._get_class()
self.generate()
#---------------------------------------------------#
# 获得所有的分类
#---------------------------------------------------#
def _get_class(self):
classes_path = os.path.expanduser(self.classes_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
#---------------------------------------------------#
# 获得所有的分类
#---------------------------------------------------#
def generate(self):
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
self.net = EfficientDetBackbone(len(self.class_names), self.phi).eval()
# 加快模型训练的效率
print('Loading weights into state dict...')
state_dict = torch.load(self.model_path)
self.net.load_state_dict(state_dict)
self.net = nn.DataParallel(self.net)
if self.cuda:
self.net = self.net.cuda()
print('Finished!')
print('{} model, anchors, and classes loaded.'.format(self.model_path))
# 画框设置不同的颜色
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image):
image_shape = np.array(np.shape(image)[0:2])
crop_img = np.array(
letterbox_image(image,
(image_sizes[self.phi], image_sizes[self.phi])))
photo = np.array(crop_img, dtype=np.float32)
photo = np.transpose(preprocess_input(photo), (2, 0, 1))
images = []
images.append(photo)
images = np.asarray(images)
with torch.no_grad():
images = torch.from_numpy(images)
if self.cuda:
images = images.cuda()
_, regression, classification, anchors = self.net(images)
regression = decodebox(regression, anchors, images)
detection = torch.cat([regression, classification], axis=-1)
batch_detections = non_max_suppression(detection,
len(self.class_names),
conf_thres=self.confidence,
nms_thres=0.2)
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image
top_index = batch_detections[:, 4] > self.confidence
top_conf = batch_detections[top_index, 4]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2], -1), np.expand_dims(top_bboxes[:, 3], -1)
# 去掉灰条
boxes = efficientdet_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([image_sizes[self.phi], image_sizes[self.phi]]),
image_shape)
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * np.shape(image)[1] +
0.5).astype('int32'))
thickness = (np.shape(image)[0] +
np.shape(image)[1]) // image_sizes[self.phi]
for i, c in enumerate(top_label):
predicted_class = self.class_names[c]
score = top_conf[i]
top, left, bottom, right = boxes[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(
np.shape(image)[0],
np.floor(bottom + 0.5).astype('int32'))
right = min(
np.shape(image)[1],
np.floor(right + 0.5).astype('int32'))
# 画框框
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[self.class_names.index(
predicted_class)])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[self.class_names.index(predicted_class)])
draw.text(text_origin,
str(label, 'UTF-8'),
fill=(0, 0, 0),
font=font)
del draw
return image
class train_model(object):
def __init__(self):
cfg = configs.parse_config()
self.input_shape = (cfg.input_sizes[cfg.phi], cfg.input_sizes[cfg.phi])
self.lr_first = cfg.learning_rate_first_stage
self.Batch_size_first = cfg.Batch_size_first_stage
self.Init_Epoch = cfg.Init_Epoch
self.Freeze_Epoch = cfg.Freeze_Epoch
self.opt_weight_decay = cfg.opt_weight_decay
self.CosineAnnealingLR_T_max = cfg.CosineAnnealingLR_T_max
self.CosineAnnealingLR_eta_min = cfg.CosineAnnealingLR_eta_min
self.StepLR_step_size = cfg.StepLR_step_size
self.StepLR_gamma = cfg.StepLR_gamma
self.num_workers = cfg.num_workers
self.Save_num_epoch = cfg.Save_num_epoch
self.lr_second = cfg.learning_rate_second_stage
self.Batch_size_second = cfg.Batch_size_second_stage
self.Unfreeze_Epoch = cfg.Unfreeze_Epoch
# TODO:tricks的使用设置
self.Cosine_lr, self.mosaic = cfg.Cosine_lr, cfg.use_mosaic
self.Cuda = torch.cuda.is_available()
self.smoooth_label = cfg.smoooth_label
self.Use_Data_Loader, self.annotation_path = cfg.Use_Data_Loader, cfg.train_annotation_path
# TODO:获得类
self.classes_path = cfg.classes_path
self.class_names = self.get_classes(self.classes_path)
self.num_classes = len(self.class_names)
# TODO:创建模型
self.model = EfficientDetBackbone(self.num_classes, cfg.phi)
pretrain_weight_name = os.listdir(cfg.pretrain_dir)
index = [item for item in pretrain_weight_name if str(cfg.phi) in item][0]
# 加快模型训练的效率
print('Loading pretrain_weights into state dict...')
model_dict = self.model.state_dict()
pretrained_dict = torch.load(cfg.pretrain_dir + index)
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)
self.model.load_state_dict(model_dict)
print('Finished!')
self.net = self.model.train()
if self.Cuda:
self.net = torch.nn.DataParallel(self.model) # 多GPU进行训练，但这个设置有问题
cudnn.benchmark = True
self.net = self.net.cuda()
# TODO:建立loss函数
self.efficient_loss = FocalLoss()
# cfg.val_split用于验证，1-cfg.val_split用于训练
val_split = cfg.val_split
with open(self.annotation_path) as f:
self.lines = f.readlines()
np.random.seed(101)
np.random.shuffle(self.lines)
np.random.seed(None)
self.num_val = int(len(self.lines) * val_split)
self.num_train = len(self.lines) - self.num_val
self.train_first_stage()
self.train_second_stage()
def get_classes(self, classes_path):
"""
loads the classes name
:param classes_path:
:return:
"""
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def get_lr(self, optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
def fit_one_epoch(self, net, model, optimizer, focal_loss, epoch, epoch_size, epoch_size_val, gen, genval, Epoch,
cuda):
"""
:param net: 网络
:param yolo_losses: yolo损失类
:param epoch: 第几个epoch
:param epoch_size: train中每个epoch中有多少个数据
:param epoch_size_val: valid中每个epoch里面的数据
:param gen: train DataLoader
:param genval: valid DataLoader
:param Epoch: 截至epoch
:param cuda:
:return:
"""
total_r_loss = 0
total_c_loss = 0
total_loss = 0
val_loss = 0
start_time = time.time()
with tqdm(total=epoch_size, desc=f'Epoch {epoch + 1}/{Epoch}', postfix=dict, mininterval=0.3) as pbar:
for iteration, batch in enumerate(gen):
if iteration >= epoch_size:
break
images, targets = batch[0], batch[1]
with torch.no_grad():
if cuda:
images = Variable(torch.from_numpy(images).type(torch.FloatTensor)).cuda()
targets = [Variable(torch.from_numpy(ann).type(torch.FloatTensor)).cuda() for ann in targets]
else:
images = Variable(torch.from_numpy(images).type(torch.FloatTensor))
targets = [Variable(torch.from_numpy(ann).type(torch.FloatTensor)) for ann in targets]
optimizer.zero_grad()
# regression shape is (batch_size, H*W*9+……, 4), classification shape is (batch_size, H*W*9+……, num_classes)
_, regression, classification, anchors = net(images) # anchors先验框
loss, c_loss, r_loss = focal_loss(classification, regression, anchors, targets, cuda=cuda)
loss.backward()
optimizer.step()
total_loss += loss.detach().item()
total_r_loss += r_loss.detach().item()
total_c_loss += c_loss.detach().item()
waste_time = time.time() - start_time
pbar.set_postfix(**{'Conf Loss': total_c_loss / (iteration + 1),
'Regression Loss': total_r_loss / (iteration + 1),
'lr': self.get_lr(optimizer),
'step/s': waste_time})
pbar.update(1)
start_time = time.time()
print('Start Validation')
with tqdm(total=epoch_size_val, desc=f'Epoch {epoch + 1}/{Epoch}', postfix=dict, mininterval=0.3) as pbar:
for iteration, batch in enumerate(genval):
if iteration >= epoch_size_val:
break
images_val, targets_val = batch[0], batch[1]
with torch.no_grad():
if cuda:
images_val = Variable(torch.from_numpy(images_val).type(torch.FloatTensor)).cuda()
targets_val = [Variable(torch.from_numpy(ann).type(torch.FloatTensor)).cuda() for ann in
targets_val]
else:
images_val = Variable(torch.from_numpy(images_val).type(torch.FloatTensor))
targets_val = [Variable(torch.from_numpy(ann).type(torch.FloatTensor)) for ann in targets_val]
optimizer.zero_grad()
_, regression, classification, anchors = net(images_val)
loss, c_loss, r_loss = focal_loss(classification, regression, anchors, targets_val, cuda=cuda)
val_loss += loss.detach().item()
pbar.set_postfix(**{'total_loss': val_loss / (iteration + 1)})
pbar.update(1)
print('Finish Validation')
print('Epoch:' + str(epoch + 1) + '/' + str(Epoch))
print('Total Loss: %.4f || Val Loss: %.4f ' % (total_loss / (epoch_size + 1), val_loss / (epoch_size_val + 1)))
if (epoch + 1) % self.Save_num_epoch == 0:
print('Saving state, iter:', str(epoch + 1))
torch.save(model.state_dict(), 'model_weight/Epoch%d-Total_Loss%.4f-Val_Loss%.4f.pth' % (
(epoch + 1), total_loss / (epoch_size + 1), val_loss / (epoch_size_val + 1)))
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()