class YOLOv3(object):
"""
The class for YOLO v3. Include train, val, test & predict.
"""
def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = AverageMeter()
self.val_losses = AverageMeter()
self.det_visualizer = DetVisualizer(configer)
self.det_loss_manager = DetLossManager(configer)
self.det_model_manager = DetModelManager(configer)
self.det_data_loader = DetDataLoader(configer)
self.yolo_detection_layer = YOLODetectionLayer(configer)
self.yolo_target_generator = YOLOTargetGenerator(configer)
self.det_running_score = DetRunningScore(configer)
self.module_utilizer = ModuleUtilizer(configer)
self.optim_scheduler = OptimScheduler(configer)
self.det_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self._init_model()
def _init_model(self):
self.det_net = self.det_model_manager.object_detector()
self.det_net = self.module_utilizer.load_net(self.det_net)
self.optimizer, self.scheduler = self.optim_scheduler.init_optimizer(
self._get_parameters())
self.train_loader = self.det_data_loader.get_trainloader()
self.val_loader = self.det_data_loader.get_valloader()
self.det_loss = self.det_loss_manager.get_det_loss('yolov3_loss')
def _get_parameters(self):
lr_1 = []
lr_10 = []
params_dict = dict(self.det_net.named_parameters())
for key, value in params_dict.items():
if 'backbone.' not in key:
lr_10.append(value)
else:
lr_1.append(value)
params = [{
'params': lr_1,
'lr': self.configer.get('lr', 'base_lr')
}, {
'params': lr_10,
'lr': self.configer.get('lr', 'base_lr') * 10.
}]
return params
def warm_lr(self, batch_len):
"""Sets the learning rate
# Adapted from PyTorch Imagenet example:
# https://github.com/pytorch/examples/blob/master/imagenet/main.py
"""
warm_iters = self.configer.get('lr', 'warm')['warm_epoch'] * batch_len
if self.configer.get('iters') < warm_iters:
lr_ratio = (self.configer.get('iters') + 1) / warm_iters
base_lr_list = self.scheduler.get_lr()
for param_group, base_lr in zip(self.optimizer.param_groups,
base_lr_list):
param_group['lr'] = base_lr * lr_ratio
if self.configer.get('iters') % self.configer.get(
'solver', 'display_iter') == 0:
Log.info('LR: {}'.format([
param_group['lr']
for param_group in self.optimizer.param_groups
]))
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.det_net.train()
start_time = time.time()
# Adjust the learning rate after every epoch.
self.configer.plus_one('epoch')
self.scheduler.step(self.configer.get('epoch'))
# data_tuple: (inputs, heatmap, maskmap, vecmap)
for i, data_dict in enumerate(self.train_loader):
if not self.configer.is_empty(
'lr', 'is_warm') and self.configer.get('lr', 'is_warm'):
self.warm_lr(len(self.train_loader))
inputs = data_dict['img']
batch_gt_bboxes = data_dict['bboxes']
batch_gt_labels = data_dict['labels']
input_size = [inputs.size(3), inputs.size(2)]
self.data_time.update(time.time() - start_time)
# Change the data type.
inputs = self.module_utilizer.to_device(inputs)
# Forward pass.
feat_list, predictions, _ = self.det_net(inputs)
targets, objmask, noobjmask = self.yolo_target_generator(
feat_list, batch_gt_bboxes, batch_gt_labels, input_size)
targets, objmask, noobjmask = self.module_utilizer.to_device(
targets, objmask, noobjmask)
# Compute the loss of the train batch & backward.
loss = self.det_loss(predictions, targets, objmask, noobjmask)
self.train_losses.update(loss.item(), inputs.size(0))
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# Update the vars of the train phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.configer.plus_one('iters')
# Print the log info & reset the states.
if self.configer.get('iters') % self.configer.get(
'solver', 'display_iter') == 0:
Log.info(
'Train Epoch: {0}\tTrain Iteration: {1}\t'
'Time {batch_time.sum:.3f}s / {2}iters, ({batch_time.avg:.3f})\t'
'Data load {data_time.sum:.3f}s / {2}iters, ({data_time.avg:3f})\n'
'Learning rate = {3}\tLoss = {loss.val:.8f} (ave = {loss.avg:.8f})\n'
.format(self.configer.get('epoch'),
self.configer.get('iters'),
self.configer.get('solver', 'display_iter'),
self.scheduler.get_lr(),
batch_time=self.batch_time,
data_time=self.data_time,
loss=self.train_losses))
self.batch_time.reset()
self.data_time.reset()
self.train_losses.reset()
# Check to val the current model.
if self.val_loader is not None and \
(self.configer.get('iters')) % self.configer.get('solver', 'test_interval') == 0:
self.__val()
def __val(self):
"""
Validation function during the train phase.
"""
self.det_net.eval()
start_time = time.time()
with torch.no_grad():
for i, data_dict in enumerate(self.val_loader):
inputs = data_dict['img']
batch_gt_bboxes = data_dict['bboxes']
batch_gt_labels = data_dict['labels']
input_size = [inputs.size(3), inputs.size(2)]
# Forward pass.
inputs = self.module_utilizer.to_device(inputs)
feat_list, predictions, detections = self.det_net(inputs)
targets, objmask, noobjmask = self.yolo_target_generator(
feat_list, batch_gt_bboxes, batch_gt_labels, input_size)
targets, objmask, noobjmask = self.module_utilizer.to_device(
targets, objmask, noobjmask)
# Compute the loss of the val batch.
loss = self.det_loss(predictions, targets, objmask, noobjmask)
self.val_losses.update(loss.item(), inputs.size(0))
batch_detections = YOLOv3Test.decode(detections, self.configer)
batch_pred_bboxes = self.__get_object_list(
batch_detections, input_size)
self.det_running_score.update(batch_pred_bboxes,
batch_gt_bboxes, batch_gt_labels)
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.module_utilizer.save_net(self.det_net, save_mode='iters')
# Print the log info & reset the states.
Log.info(
'Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t'
'Loss {loss.avg:.8f}\n'.format(batch_time=self.batch_time,
loss=self.val_losses))
Log.info('Val mAP: {}'.format(self.det_running_score.get_mAP()))
self.det_running_score.reset()
self.batch_time.reset()
self.val_losses.reset()
self.det_net.train()
def __get_object_list(self, batch_detections, input_size):
batch_pred_bboxes = list()
for idx, detections in enumerate(batch_detections):
object_list = list()
if detections is not None:
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
xmin = x1.cpu().item() * input_size[0]
ymin = y1.cpu().item() * input_size[1]
xmax = x2.cpu().item() * input_size[0]
ymax = y2.cpu().item() * input_size[1]
cf = conf.cpu().item()
cls_pred = cls_pred.cpu().item()
object_list.append([
xmin, ymin, xmax, ymax,
int(cls_pred),
float('%.2f' % cf)
])
batch_pred_bboxes.append(object_list)
return batch_pred_bboxes
def train(self):
cudnn.benchmark = True
if self.configer.get('network',
'resume') is not None and self.configer.get(
'network', 'resume_val'):
self.__val()
while self.configer.get('epoch') < self.configer.get(
'solver', 'max_epoch'):
self.__train()
if self.configer.get('epoch') == self.configer.get(
'solver', 'max_epoch'):
break
class FasterRCNN(object):
"""
The class for Single Shot Detector. Include train, val, test & predict.
"""
def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = AverageMeter()
self.val_losses = AverageMeter()
self.det_visualizer = DetVisualizer(configer)
self.det_loss_manager = DetLossManager(configer)
self.det_model_manager = DetModelManager(configer)
self.det_data_loader = DetDataLoader(configer)
self.fr_priorbox_layer = FRPriorBoxLayer(configer)
self.rpn_target_generator = RPNTargetGenerator(configer)
self.det_running_score = DetRunningScore(configer)
self.module_utilizer = ModuleUtilizer(configer)
self.optim_scheduler = OptimScheduler(configer)
self.det_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self._init_model()
def _init_model(self):
self.det_net = self.det_model_manager.object_detector()
self.det_net = self.module_utilizer.load_net(self.det_net)
self.optimizer, self.scheduler = self.optim_scheduler.init_optimizer(
self._get_parameters())
self.train_loader = self.det_data_loader.get_trainloader()
self.val_loader = self.det_data_loader.get_valloader()
self.fr_loss = self.det_loss_manager.get_det_loss('fr_loss')
def _get_parameters(self):
lr_1 = []
lr_2 = []
params_dict = dict(self.det_net.named_parameters())
for key, value in params_dict.items():
if value.requires_grad:
if 'bias' in key:
lr_2.append(value)
else:
lr_1.append(value)
params = [{
'params': lr_1,
'lr': self.configer.get('lr', 'base_lr')
}, {
'params': lr_2,
'lr': self.configer.get('lr', 'base_lr') * 2.,
'weight_decay': 0
}]
return params
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.det_net.train()
start_time = time.time()
# Adjust the learning rate after every epoch.
self.configer.plus_one('epoch')
self.scheduler.step(self.configer.get('epoch'))
for i, data_dict in enumerate(self.train_loader):
inputs = data_dict['img']
img_scale = data_dict['imgscale']
batch_gt_bboxes = data_dict['bboxes']
batch_gt_labels = data_dict['labels']
self.data_time.update(time.time() - start_time)
# Change the data type.
gt_bboxes, gt_nums, gt_labels = self.__make_tensor(
batch_gt_bboxes, batch_gt_labels)
gt_bboxes, gt_num, gt_labels = self.module_utilizer.to_device(
gt_bboxes, gt_nums, gt_labels)
inputs = self.module_utilizer.to_device(inputs)
# Forward pass.
feat_list, train_group = self.det_net(inputs, gt_bboxes, gt_num,
gt_labels, img_scale)
gt_rpn_locs, gt_rpn_labels = self.rpn_target_generator(
feat_list, batch_gt_bboxes,
[inputs.size(3), inputs.size(2)])
gt_rpn_locs, gt_rpn_labels = self.module_utilizer.to_device(
gt_rpn_locs, gt_rpn_labels)
rpn_locs, rpn_scores, sample_roi_locs, sample_roi_scores, gt_roi_bboxes, gt_roi_labels = train_group
# Compute the loss of the train batch & backward.
loss = self.fr_loss(
[rpn_locs, rpn_scores, sample_roi_locs, sample_roi_scores],
[gt_rpn_locs, gt_rpn_labels, gt_roi_bboxes, gt_roi_labels])
self.train_losses.update(loss.item(), inputs.size(0))
self.optimizer.zero_grad()
loss.backward()
self.module_utilizer.clip_grad(self.det_net, 10.)
self.optimizer.step()
# Update the vars of the train phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.configer.plus_one('iters')
# Print the log info & reset the states.
if self.configer.get('iters') % self.configer.get(
'solver', 'display_iter') == 0:
Log.info(
'Train Epoch: {0}\tTrain Iteration: {1}\t'
'Time {batch_time.sum:.3f}s / {2}iters, ({batch_time.avg:.3f})\t'
'Data load {data_time.sum:.3f}s / {2}iters, ({data_time.avg:3f})\n'
'Learning rate = {3}\tLoss = {loss.val:.8f} (ave = {loss.avg:.8f})\n'
.format(self.configer.get('epoch'),
self.configer.get('iters'),
self.configer.get('solver', 'display_iter'),
self.scheduler.get_lr(),
batch_time=self.batch_time,
data_time=self.data_time,
loss=self.train_losses))
self.batch_time.reset()
self.data_time.reset()
self.train_losses.reset()
# Check to val the current model.
if self.val_loader is not None and \
(self.configer.get('iters')) % self.configer.get('solver', 'test_interval') == 0:
self.__val()
def __val(self):
"""
Validation function during the train phase.
"""
self.det_net.eval()
start_time = time.time()
with torch.no_grad():
for j, data_dict in enumerate(self.val_loader):
inputs = data_dict['img']
img_scale = data_dict['imgscale']
batch_gt_bboxes = data_dict['bboxes']
batch_gt_labels = data_dict['labels']
# Change the data type.
gt_bboxes, gt_nums, gt_labels = self.__make_tensor(
batch_gt_bboxes, batch_gt_labels)
gt_bboxes, gt_num, gt_labels = self.module_utilizer.to_device(
gt_bboxes, gt_nums, gt_labels)
inputs = self.module_utilizer.to_device(inputs)
# Forward pass.
feat_list, train_group, test_group = self.det_net(
inputs, gt_bboxes, gt_nums, gt_labels, img_scale)
rpn_locs, rpn_scores, sample_roi_locs, sample_roi_scores, gt_roi_bboxes, gt_roi_labels = train_group
gt_rpn_locs, gt_rpn_labels = self.rpn_target_generator(
feat_list, batch_gt_bboxes,
[inputs.size(3), inputs.size(2)])
gt_rpn_locs, gt_rpn_labels = self.module_utilizer.to_device(
gt_rpn_locs, gt_rpn_labels)
# Compute the loss of the train batch & backward.
loss = self.fr_loss(
[rpn_locs, rpn_scores, sample_roi_locs, sample_roi_scores],
[gt_rpn_locs, gt_rpn_labels, gt_roi_bboxes, gt_roi_labels])
self.val_losses.update(loss.item(), inputs.size(0))
test_indices_and_rois, test_roi_locs, test_roi_scores, test_rois_num = test_group
batch_detections = FastRCNNTest.decode(
test_roi_locs, test_roi_scores, test_indices_and_rois,
test_rois_num, self.configer,
[inputs.size(3), inputs.size(2)])
batch_pred_bboxes = self.__get_object_list(batch_detections)
self.det_running_score.update(batch_pred_bboxes,
batch_gt_bboxes, batch_gt_labels)
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.module_utilizer.save_net(self.det_net, save_mode='iters')
# Print the log info & reset the states.
Log.info(
'Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t'
'Loss {loss.avg:.8f}\n'.format(batch_time=self.batch_time,
loss=self.val_losses))
Log.info('Val mAP: {}\n'.format(self.det_running_score.get_mAP()))
self.det_running_score.reset()
self.batch_time.reset()
self.val_losses.reset()
self.det_net.train()
def __make_tensor(self, gt_bboxes, gt_labels):
len_arr = [gt_labels[i].numel() for i in range(len(gt_bboxes))]
batch_maxlen = max(max(len_arr), 1)
target_bboxes = torch.zeros((len(gt_bboxes), batch_maxlen, 4)).float()
target_labels = torch.zeros((len(gt_bboxes), batch_maxlen)).long()
for i in range(len(gt_bboxes)):
if len_arr[i] == 0:
continue
target_bboxes[i, :len_arr[i], :] = gt_bboxes[i].clone()
target_labels[i, :len_arr[i]] = gt_labels[i].clone()
target_bboxes_num = torch.Tensor(len_arr).long()
return target_bboxes, target_bboxes_num, target_labels
def __get_object_list(self, batch_detections):
batch_pred_bboxes = list()
for idx, detections in enumerate(batch_detections):
object_list = list()
if detections is not None:
for x1, y1, x2, y2, conf, cls_pred in detections:
xmin = x1.cpu().item()
ymin = y1.cpu().item()
xmax = x2.cpu().item()
ymax = y2.cpu().item()
cf = conf.cpu().item()
cls_pred = int(cls_pred.cpu().item()) - 1
object_list.append(
[xmin, ymin, xmax, ymax, cls_pred,
float('%.2f' % cf)])
batch_pred_bboxes.append(object_list)
return batch_pred_bboxes
def train(self):
cudnn.benchmark = True
if self.configer.get('network',
'resume') is not None and self.configer.get(
'network', 'resume_val'):
self.__val()
while self.configer.get('epoch') < self.configer.get(
'solver', 'max_epoch'):
self.__train()
if self.configer.get('epoch') == self.configer.get(
'solver', 'max_epoch'):
break
class YOLOv3(object):
"""
The class for YOLO v3. Include train, val, test & predict.
"""
def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = AverageMeter()
self.val_losses = AverageMeter()
self.det_visualizer = DetVisualizer(configer)
self.det_loss_manager = LossManager(configer)
self.det_model_manager = DetModelManager(configer)
self.det_data_loader = DataLoader(configer)
self.yolo_detection_layer = YOLODetectionLayer(configer)
self.yolo_target_generator = YOLOTargetGenerator(configer)
self.det_running_score = DetRunningScore(configer)
self.det_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self.runner_state = dict()
self._init_model()
def _init_model(self):
self.det_net = self.det_model_manager.object_detector()
self.det_net = RunnerHelper.load_net(self, self.det_net)
self.optimizer, self.scheduler = Trainer.init(self,
self._get_parameters())
self.train_loader = self.det_data_loader.get_trainloader()
self.val_loader = self.det_data_loader.get_valloader()
self.det_loss = self.det_loss_manager.get_det_loss()
def _get_parameters(self):
lr_1 = []
lr_10 = []
params_dict = dict(self.det_net.named_parameters())
for key, value in params_dict.items():
if 'backbone' not in key:
lr_10.append(value)
else:
lr_1.append(value)
params = [{
'params': lr_1,
'lr': self.configer.get('lr', 'base_lr')
}, {
'params': lr_10,
'lr': self.configer.get('lr', 'base_lr') * 10.
}]
return params
def train(self):
"""
Train function of every epoch during train phase.
"""
self.det_net.train()
start_time = time.time()
# Adjust the learning rate after every epoch.
self.runner_state['epoch'] += 1
# data_tuple: (inputs, heatmap, maskmap, vecmap)
for i, data_dict in enumerate(self.train_loader):
Trainer.update(self, backbone_list=(0, ))
inputs = data_dict['img']
batch_gt_bboxes = data_dict['bboxes']
batch_gt_labels = data_dict['labels']
input_size = [inputs.size(3), inputs.size(2)]
self.data_time.update(time.time() - start_time)
# Change the data type.
inputs = RunnerHelper.to_device(self, inputs)
# Forward pass.
feat_list, predictions, _ = self.det_net(inputs)
targets, objmask, noobjmask = self.yolo_target_generator(
feat_list, batch_gt_bboxes, batch_gt_labels, input_size)
targets, objmask, noobjmask = RunnerHelper.to_device(
self, targets, objmask, noobjmask)
# Compute the loss of the train batch & backward.
loss = self.det_loss(predictions, targets, objmask, noobjmask)
self.train_losses.update(loss.item(), inputs.size(0))
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# Update the vars of the train phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.runner_state['iters'] += 1
# Print the log info & reset the states.
if self.configer.get('iters') % self.configer.get(
'solver', 'display_iter') == 0:
Log.info(
'Train Epoch: {0}\tTrain Iteration: {1}\t'
'Time {batch_time.sum:.3f}s / {2}iters, ({batch_time.avg:.3f})\t'
'Data load {data_time.sum:.3f}s / {2}iters, ({data_time.avg:3f})\n'
'Learning rate = {3}\tLoss = {loss.val:.8f} (ave = {loss.avg:.8f})\n'
.format(self.runner_state['epoch'],
self.runner_state['iters'],
self.configer.get('solver', 'display_iter'),
RunnerHelper.get_lr(self.optimizer),
batch_time=self.batch_time,
data_time=self.data_time,
loss=self.train_losses))
self.batch_time.reset()
self.data_time.reset()
self.train_losses.reset()
if self.configer.get('lr', 'metric') == 'iters' \
and self.runner_state['iters'] == self.configer.get('solver', 'max_iters'):
break
# Check to val the current model.
if self.runner_state['iters'] % self.configer.get(
'solver', 'test_interval') == 0:
self.val()
def val(self):
"""
Validation function during the train phase.
"""
self.det_net.eval()
start_time = time.time()
with torch.no_grad():
for i, data_dict in enumerate(self.val_loader):
inputs = data_dict['img']
batch_gt_bboxes = data_dict['bboxes']
batch_gt_labels = data_dict['labels']
input_size = [inputs.size(3), inputs.size(2)]
# Forward pass.
inputs = RunnerHelper.to_device(self, inputs)
feat_list, predictions, detections = self.det_net(inputs)
targets, objmask, noobjmask = self.yolo_target_generator(
feat_list, batch_gt_bboxes, batch_gt_labels, input_size)
targets, objmask, noobjmask = RunnerHelper.to_device(
self, targets, objmask, noobjmask)
# Compute the loss of the val batch.
loss = self.det_loss(predictions, targets, objmask, noobjmask)
self.val_losses.update(loss.item(), inputs.size(0))
batch_detections = YOLOv3Test.decode(detections, self.configer,
input_size)
batch_pred_bboxes = self.__get_object_list(
batch_detections, input_size)
self.det_running_score.update(batch_pred_bboxes,
batch_gt_bboxes, batch_gt_labels)
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
RunnerHelper.save_net(self,
self.det_net,
iters=self.runner_state['iters'])
# Print the log info & reset the states.
Log.info(
'Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t'
'Loss {loss.avg:.8f}\n'.format(batch_time=self.batch_time,
loss=self.val_losses))
Log.info('Val mAP: {}'.format(self.det_running_score.get_mAP()))
self.det_running_score.reset()
self.batch_time.reset()
self.val_losses.reset()
self.det_net.train()
def __get_object_list(self, batch_detections, input_size):
batch_pred_bboxes = list()
for idx, detections in enumerate(batch_detections):
object_list = list()
if detections is not None:
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
xmin = x1.cpu().item()
ymin = y1.cpu().item()
xmax = x2.cpu().item()
ymax = y2.cpu().item()
cf = conf.cpu().item()
cls_pred = cls_pred.cpu().item()
object_list.append([
xmin, ymin, xmax, ymax,
int(cls_pred),
float('%.2f' % cf)
])
batch_pred_bboxes.append(object_list)
return batch_pred_bboxes
class SingleShotDetector(object):
"""
The class for Single Shot Detector. Include train, val, test & predict.
"""
def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = AverageMeter()
self.val_losses = AverageMeter()
self.det_visualizer = DetVisualizer(configer)
self.det_loss_manager = DetLossManager(configer)
self.det_model_manager = DetModelManager(configer)
self.det_data_loader = DetDataLoader(configer)
self.ssd_target_generator = SSDTargetGenerator(configer)
self.ssd_priorbox_layer = SSDPriorBoxLayer(configer)
self.det_running_score = DetRunningScore(configer)
self.module_utilizer = ModuleUtilizer(configer)
self.optim_scheduler = OptimScheduler(configer)
self.data_transformer = DataTransformer(configer)
self.det_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self._init_model()
def _init_model(self):
self.det_net = self.det_model_manager.object_detector()
self.det_net = self.module_utilizer.load_net(self.det_net)
self.optimizer, self.scheduler = self.optim_scheduler.init_optimizer(
self._get_parameters())
self.train_loader = self.det_data_loader.get_trainloader()
self.val_loader = self.det_data_loader.get_valloader()
self.det_loss = self.det_loss_manager.get_det_loss('ssd_multibox_loss')
def _get_parameters(self):
return self.det_net.parameters()
def warm_lr(self, batch_len):
"""Sets the learning rate
# Adapted from PyTorch Imagenet example:
# https://github.com/pytorch/examples/blob/master/imagenet/main.py
"""
warm_iters = self.configer.get('lr', 'warm')['warm_epoch'] * batch_len
warm_lr = self.configer.get('lr', 'warm')['warm_lr']
if self.configer.get('iters') < warm_iters:
lr_delta = (self.configer.get('lr', 'base_lr') -
warm_lr) * self.configer.get('iters') / warm_iters
lr = warm_lr + lr_delta
for param_group in self.optimizer.param_groups:
param_group['lr'] = lr
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.det_net.train()
start_time = time.time()
# Adjust the learning rate after every epoch.
self.configer.plus_one('epoch')
self.scheduler.step(self.configer.get('epoch'))
# data_tuple: (inputs, heatmap, maskmap, vecmap)
for i, data_dict in enumerate(self.train_loader):
if not self.configer.is_empty(
'lr', 'is_warm') and self.configer.get('lr', 'is_warm'):
self.warm_lr(len(self.train_loader))
inputs = data_dict['img']
batch_gt_bboxes = data_dict['bboxes']
batch_gt_labels = data_dict['labels']
# Change the data type.
inputs = self.module_utilizer.to_device(inputs)
self.data_time.update(time.time() - start_time)
# Forward pass.
feat_list, loc, cls = self.det_net(inputs)
bboxes, labels = self.ssd_target_generator(
feat_list, batch_gt_bboxes, batch_gt_labels,
[inputs.size(3), inputs.size(2)])
bboxes, labels = self.module_utilizer.to_device(bboxes, labels)
# Compute the loss of the train batch & backward.
loss = self.det_loss(loc, bboxes, cls, labels)
self.train_losses.update(loss.item(), inputs.size(0))
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# Update the vars of the train phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.configer.plus_one('iters')
# Print the log info & reset the states.
if self.configer.get('iters') % self.configer.get(
'solver', 'display_iter') == 0:
Log.info(
'Train Epoch: {0}\tTrain Iteration: {1}\t'
'Time {batch_time.sum:.3f}s / {2}iters, ({batch_time.avg:.3f})\t'
'Data load {data_time.sum:.3f}s / {2}iters, ({data_time.avg:3f})\n'
'Learning rate = {3}\tLoss = {loss.val:.8f} (ave = {loss.avg:.8f})\n'
.format(self.configer.get('epoch'),
self.configer.get('iters'),
self.configer.get('solver', 'display_iter'),
self.scheduler.get_lr(),
batch_time=self.batch_time,
data_time=self.data_time,
loss=self.train_losses))
self.batch_time.reset()
self.data_time.reset()
self.train_losses.reset()
# Check to val the current model.
if self.val_loader is not None and \
(self.configer.get('iters')) % self.configer.get('solver', 'test_interval') == 0:
self.__val()
def __val(self):
"""
Validation function during the train phase.
"""
self.det_net.eval()
start_time = time.time()
with torch.no_grad():
for j, data_dict in enumerate(self.val_loader):
inputs = data_dict['img']
batch_gt_bboxes = data_dict['bboxes']
batch_gt_labels = data_dict['labels']
inputs = self.module_utilizer.to_device(inputs)
input_size = [inputs.size(3), inputs.size(2)]
# Forward pass.
feat_list, loc, cls = self.det_net(inputs)
bboxes, labels = self.ssd_target_generator(
feat_list, batch_gt_bboxes, batch_gt_labels, input_size)
bboxes, labels = self.module_utilizer.to_device(bboxes, labels)
# Compute the loss of the val batch.
loss = self.det_loss(loc, bboxes, cls, labels)
self.val_losses.update(loss.item(), inputs.size(0))
batch_detections = SingleShotDetectorTest.decode(
loc, cls, self.ssd_priorbox_layer(feat_list, input_size),
self.configer, input_size)
batch_pred_bboxes = self.__get_object_list(batch_detections)
# batch_pred_bboxes = self._get_gt_object_list(batch_gt_bboxes, batch_gt_labels)
self.det_running_score.update(batch_pred_bboxes,
batch_gt_bboxes, batch_gt_labels)
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.module_utilizer.save_net(self.det_net, save_mode='iters')
# Print the log info & reset the states.
Log.info(
'Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t'
'Loss {loss.avg:.8f}\n'.format(batch_time=self.batch_time,
loss=self.val_losses))
Log.info('Val mAP: {}'.format(self.det_running_score.get_mAP()))
self.det_running_score.reset()
self.batch_time.reset()
self.val_losses.reset()
self.det_net.train()
def _get_gt_object_list(self, batch_gt_bboxes, batch_gt_labels):
batch_pred_bboxes = list()
for i in range(len(batch_gt_bboxes)):
object_list = list()
if batch_gt_bboxes[i].numel() > 0:
for j in range(batch_gt_bboxes[i].size(0)):
object_list.append([
batch_gt_bboxes[i][j][0].item(),
batch_gt_bboxes[i][j][1].item(),
batch_gt_bboxes[i][j][2].item(),
batch_gt_bboxes[i][j][3].item(),
batch_gt_labels[i][j].item(), 1.0
])
batch_pred_bboxes.append(object_list)
return batch_pred_bboxes
def __get_object_list(self, batch_detections):
batch_pred_bboxes = list()
for idx, detections in enumerate(batch_detections):
object_list = list()
if detections is not None:
for x1, y1, x2, y2, conf, cls_pred in detections:
xmin = x1.cpu().item()
ymin = y1.cpu().item()
xmax = x2.cpu().item()
ymax = y2.cpu().item()
cf = conf.cpu().item()
cls_pred = cls_pred.cpu().item() - 1
object_list.append([
xmin, ymin, xmax, ymax,
int(cls_pred),
float('%.2f' % cf)
])
batch_pred_bboxes.append(object_list)
return batch_pred_bboxes
def train(self):
cudnn.benchmark = True
if self.configer.get('network',
'resume') is not None and self.configer.get(
'network', 'resume_val'):
self.__val()
while self.configer.get('epoch') < self.configer.get(
'solver', 'max_epoch'):
self.__train()
if self.configer.get('epoch') == self.configer.get(
'solver', 'max_epoch'):
break