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_model_manager = ModelManager(configer)
self.det_data_loader = DataLoader(configer)
self.fr_priorbox_layer = FRPriorBoxLayer(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._get_parameters(), self.configer.get('solver'))
self.train_loader = self.det_data_loader.get_trainloader()
self.val_loader = self.det_data_loader.get_valloader()
self.det_loss = self.det_model_manager.get_det_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('solver', 'lr')['base_lr']},
{'params': lr_2, 'lr': self.configer.get('solver', '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.runner_state['epoch'] += 1
for i, data_dict in enumerate(self.train_loader):
Trainer.update(self, solver_dict=self.configer.get('solver'))
self.data_time.update(time.time() - start_time)
# Forward pass.
data_dict = RunnerHelper.to_device(self, data_dict)
out = self.det_net(data_dict)
loss_dict = self.det_loss(out)
loss = loss_dict['loss'].mean()
self.train_losses.update(loss.item(), len(DCHelper.tolist(data_dict['meta'])))
self.optimizer.zero_grad()
loss.backward()
RunnerHelper.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.runner_state['iters'] += 1
# Print the log info & reset the states.
if self.runner_state['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('solver', '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 j, data_dict in enumerate(self.val_loader):
# Forward pass.
data_dict = RunnerHelper.to_device(self, data_dict)
out = self.det_net(data_dict)
loss_dict = self.det_loss(out)
# Compute the loss of the train batch & backward.
loss = loss_dict['loss'].mean()
out_dict, _ = RunnerHelper.gather(self, out)
self.val_losses.update(loss.item(), len(DCHelper.tolist(data_dict['meta'])))
test_indices_and_rois, test_roi_locs, test_roi_scores, test_rois_num = out_dict['test_group']
batch_detections = FastRCNNTest.decode(test_roi_locs,
test_roi_scores,
test_indices_and_rois,
test_rois_num,
self.configer,
DCHelper.tolist(data_dict['meta']))
batch_pred_bboxes = self.__get_object_list(batch_detections)
self.det_running_score.update(batch_pred_bboxes,
[item['ori_bboxes'] for item in DCHelper.tolist(data_dict['meta'])],
[item['ori_labels'] for item in DCHelper.tolist(data_dict['meta'])])
# 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: {}\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 __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:
cf = float('%.2f' % conf.item())
cls_pred = int(cls_pred.cpu().item()) - 1
object_list.append([x1.item(), y1.item(), x2.item(), y2.item(), cls_pred, cf])
batch_pred_bboxes.append(object_list)
return batch_pred_bboxes
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