class RPNPose(object):
"""
The class for Pose Estimation. 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.val_loss_heatmap = AverageMeter()
self.val_loss_associate = AverageMeter()
self.pose_visualizer = PoseVisualizer(configer)
self.pose_loss_manager = PoseLossManager(configer)
self.pose_model_manager = PoseModelManager(configer)
self.pose_data_loader = PoseDataLoader(configer)
self.module_utilizer = ModuleUtilizer(configer)
self.optim_scheduler = OptimScheduler(configer)
self.pose_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
def init_model(self):
self.pose_net = self.pose_model_manager.multi_pose_detector()
self.pose_net = self.module_utilizer.load_net(self.pose_net)
self.optimizer, self.scheduler = self.optim_scheduler.init_optimizer(
self._get_parameters())
self.train_loader = self.pose_data_loader.get_trainloader()
self.val_loader = self.pose_data_loader.get_valloader()
self.mse_loss = self.pose_loss_manager.get_pose_loss('mse_loss')
self.embeding_loss = self.pose_loss_manager.get_pose_loss(
'embedding_loss')
def _get_parameters(self):
return self.pose_net.parameters()
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.pose_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, (inputs, label, heatmap, maskmap, vecmap, tagmap,
num_objects) in enumerate(self.train_loader):
self.data_time.update(time.time() - start_time)
# Change the data type.
inputs, label, heatmap, maskmap, vecmap, tagmap = self.module_utilizer.to_device(
inputs, label, heatmap, maskmap, vecmap, tagmap)
# Forward pass.
paf_out, heatmap_out, embed_out = self.pose_net(inputs)
# Compute the loss of the train batch & backward.
loss_label = self.mse_loss(embed_out.sum(1).squeeze(), label)
loss_heatmap = self.mse_loss(heatmap_out, heatmap, maskmap)
loss_paf = self.mse_loss(paf_out, vecmap, maskmap)
loss_associate = self.embeding_loss(embed_out, tagmap, num_objects)
loss = loss_label + loss_heatmap + loss_paf + loss_associate
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.pose_net.eval()
start_time = time.time()
with torch.no_grad():
for j, (inputs, label, heatmap, maskmap, vecmap, tagmap,
num_objects) in enumerate(self.val_loader):
# Change the data type.
inputs, label, heatmap, maskmap, vecmap, tagmap = self.module_utilizer.to_device(
inputs, label, heatmap, maskmap, vecmap, tagmap)
# Forward pass.
paf_out, heatmap_out, embed_out = self.pose_net(inputs)
# Compute the loss of the val batch.
loss_label = self.mse_loss(embed_out, label)
loss_heatmap = self.mse_loss(heatmap_out, heatmap, maskmap)
loss_paf = self.mse_loss(paf_out, vecmap, maskmap)
loss_associate = self.embeding_loss(embed_out, tagmap,
num_objects)
loss = loss_label + loss_heatmap + loss_paf + loss_associate
self.val_losses.update(loss.item(), inputs.size(0))
self.val_loss_heatmap.update(loss_heatmap.item(),
inputs.size(0))
self.val_loss_associate.update(loss_associate.item(),
inputs.size(0))
# 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.pose_net, metric='iters')
Log.info('Loss Heatmap:{}, Loss Asso: {}'.format(
self.val_loss_heatmap.avg, self.val_loss_associate.avg))
# 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))
self.batch_time.reset()
self.val_losses.reset()
self.pose_net.train()
def train(self):
cudnn.benchmark = True
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 = 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 ConvPoseMachine(object):
"""
The class for Pose Estimation. Include train, val, val & 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.pose_visualizer = PoseVisualizer(configer)
self.pose_loss_manager = PoseLossManager(configer)
self.pose_model_manager = PoseModelManager(configer)
self.pose_data_loader = PoseDataLoader(configer)
self.module_utilizer = ModuleUtilizer(configer)
self.optim_scheduler = OptimScheduler(configer)
self.data_transformer = DataTransformer(configer)
self.heatmap_generator = HeatmapGenerator(configer)
self.pose_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self._init_model()
def _init_model(self):
self.pose_net = self.pose_model_manager.single_pose_detector()
self.pose_net = self.module_utilizer.load_net(self.pose_net)
self.optimizer, self.scheduler = self.optim_scheduler.init_optimizer(self._get_parameters())
self.train_loader = self.pose_data_loader.get_trainloader()
self.val_loader = self.pose_data_loader.get_valloader()
self.mse_loss = self.pose_loss_manager.get_pose_loss('mse_loss')
def _get_parameters(self):
return self.pose_net.parameters()
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.pose_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, tagmap, num_objects)
for i, data_dict in enumerate(self.train_loader):
inputs = data_dict['img']
input_size = [inputs.size(3), inputs.size(2)]
heatmap = self.heatmap_generator(data_dict['kpts'], input_size)
self.data_time.update(time.time() - start_time)
# Change the data type.
inputs, heatmap = self.module_utilizer.to_device(inputs, heatmap)
# self.pose_visualizer.vis_peaks(heatmap[0], inputs[0], name='cpm')
# Forward pass.
outputs = self.pose_net(inputs)
# Compute the loss of the train batch & backward.
loss = self.mse_loss(outputs, heatmap, maskmap)
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.pose_net.eval()
start_time = time.time()
with torch.no_grad():
for j, data_dict in enumerate(self.val_loader):
inputs = data_dict['img']
input_size = [inputs.size(3), inputs.size(2)]
heatmap = self.heatmap_generator(data_dict['kpts'], input_size)
# Change the data type.
inputs, heatmap = self.module_utilizer.to_device(inputs, heatmap)
# Forward pass.
outputs = self.pose_net(inputs)
# Compute the loss of the val batch.
loss = self.mse_loss(outputs[-1], heatmap)
self.val_losses.update(loss.item(), inputs.size(0))
# 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.pose_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))
self.batch_time.reset()
self.val_losses.reset()
self.pose_net.train()
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 FCClassifier(object):
"""
The class for the training phase of Image classification.
"""
def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = AverageMeter()
self.val_losses = AverageMeter()
self.cls_loss_manager = ClsLossManager(configer)
self.cls_model_manager = ClsModelManager(configer)
self.cls_data_loader = ClsDataLoader(configer)
self.module_utilizer = ModuleUtilizer(configer)
self.optim_scheduler = OptimScheduler(configer)
self.cls_running_score = ClsRunningScore(configer)
self.cls_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self._init_model()
def _init_model(self):
self.cls_net = self.cls_model_manager.image_classifier()
self.cls_net = self.module_utilizer.load_net(self.cls_net)
self.optimizer, self.scheduler = self.optim_scheduler.init_optimizer(
self._get_parameters())
self.train_loader = self.cls_data_loader.get_trainloader()
self.val_loader = self.cls_data_loader.get_valloader()
self.ce_loss = self.cls_loss_manager.get_cls_loss('cross_entropy_loss')
def _get_parameters(self):
return self.cls_net.parameters()
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.cls_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']
labels = data_dict['label']
self.data_time.update(time.time() - start_time)
# Change the data type.
inputs, labels = self.module_utilizer.to_device(inputs, labels)
# Forward pass.
outputs = self.cls_net(inputs)
outputs = self.module_utilizer.gather(outputs)
# Compute the loss of the train batch & backward.
loss = self.ce_loss(outputs, 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.cls_net.eval()
start_time = time.time()
with torch.no_grad():
for j, data_dict in enumerate(self.val_loader):
inputs = data_dict['img']
labels = data_dict['label']
# Change the data type.
inputs, labels = self.module_utilizer.to_device(inputs, labels)
# Forward pass.
outputs = self.cls_net(inputs)
outputs = self.module_utilizer.gather(outputs)
# Compute the loss of the val batch.
loss = self.ce_loss(outputs, labels)
self.cls_running_score.update(outputs, labels)
self.val_losses.update(loss.item(), inputs.size(0))
# 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.cls_net, save_mode='iters')
# Print the log info & reset the states.
Log.info('Test Time {batch_time.sum:.3f}s'.format(
batch_time=self.batch_time))
Log.info('TestLoss = {loss.avg:.8f}'.format(loss=self.val_losses))
Log.info('Top1 ACC = {}'.format(
self.cls_running_score.get_top1_acc()))
Log.info('Top5 ACC = {}'.format(
self.cls_running_score.get_top5_acc()))
self.batch_time.reset()
self.val_losses.reset()
self.cls_running_score.reset()
self.cls_net.train()
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 OpenPose(object):
"""
The class for Pose Estimation. 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.train_loss_heatmap = AverageMeter()
self.train_loss_associate = AverageMeter()
self.val_losses = AverageMeter()
self.val_loss_heatmap = AverageMeter()
self.val_loss_associate = AverageMeter()
self.pose_visualizer = PoseVisualizer(configer)
self.pose_loss_manager = PoseLossManager(configer)
self.pose_model_manager = PoseModelManager(configer)
self.pose_data_loader = PoseDataLoader(configer)
self.module_utilizer = ModuleUtilizer(configer)
self.optim_scheduler = OptimScheduler(configer)
self.heatmap_generator = HeatmapGenerator(configer)
self.paf_generator = PafGenerator(configer)
self.data_transformer = DataTransformer(configer)
self.pose_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self._init_model()
def _init_model(self):
self.pose_net = self.pose_model_manager.multi_pose_detector()
self.pose_net = self.module_utilizer.load_net(self.pose_net)
self.optimizer, self.scheduler = self.optim_scheduler.init_optimizer(
self._get_parameters())
self.train_loader = self.pose_data_loader.get_trainloader()
self.val_loader = self.pose_data_loader.get_valloader()
self.weights = self.configer.get('network', 'loss_weights')
self.mse_loss = self.pose_loss_manager.get_pose_loss('mse_loss')
def _get_parameters(self):
lr_1 = []
lr_2 = []
lr_4 = []
lr_8 = []
params_dict = dict(self.pose_net.named_parameters())
for key, value in params_dict.items():
if ('model1_' not in key) and ('model0.'
not in key) and ('backbone.'
not in key):
if key[-4:] == 'bias':
lr_8.append(value)
else:
lr_4.append(value)
elif key[-4:] == 'bias':
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': 0.0
}, {
'params': lr_4,
'lr': self.configer.get('lr', 'base_lr') * 4.
}, {
'params': lr_8,
'lr': self.configer.get('lr', 'base_lr') * 8.,
'weight_decay': 0.0
}]
return params
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.pose_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):
inputs = data_dict['img']
maskmap = data_dict['maskmap']
input_size = [inputs.size(3), inputs.size(2)]
heatmap = self.heatmap_generator(data_dict['kpts'],
input_size,
maskmap=maskmap)
vecmap = self.paf_generator(data_dict['kpts'],
input_size,
maskmap=maskmap)
self.data_time.update(time.time() - start_time)
# Change the data type.
inputs, heatmap, maskmap, vecmap = self.module_utilizer.to_device(
inputs, heatmap, maskmap, vecmap)
# Forward pass.
paf_out, heatmap_out = self.pose_net(inputs)
# Compute the loss of the train batch & backward.
loss_heatmap = self.mse_loss(heatmap_out,
heatmap,
mask=maskmap,
weights=self.weights)
loss_associate = self.mse_loss(paf_out,
vecmap,
mask=maskmap,
weights=self.weights)
loss = loss_heatmap + loss_associate
self.train_losses.update(loss.item(), inputs.size(0))
self.train_loss_heatmap.update(loss_heatmap.item(), inputs.size(0))
self.train_loss_associate.update(loss_associate.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('Loss Heatmap:{}, Loss Asso: {}'.format(
self.train_loss_heatmap.avg,
self.train_loss_associate.avg))
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()
self.train_loss_heatmap.reset()
self.train_loss_associate.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.pose_net.eval()
start_time = time.time()
with torch.no_grad():
for i, data_dict in enumerate(self.val_loader):
inputs = data_dict['img']
maskmap = data_dict['maskmap']
input_size = [inputs.size(3), inputs.size(2)]
heatmap = self.heatmap_generator(data_dict['kpts'],
input_size,
maskmap=maskmap)
vecmap = self.paf_generator(data_dict['kpts'],
input_size,
maskmap=maskmap)
# Change the data type.
inputs, heatmap, maskmap, vecmap = self.module_utilizer.to_device(
inputs, heatmap, maskmap, vecmap)
# Forward pass.
paf_out, heatmap_out = self.pose_net(inputs)
# Compute the loss of the val batch.
loss_heatmap = self.mse_loss(heatmap_out[-1], heatmap, maskmap)
loss_associate = self.mse_loss(paf_out[-1], vecmap, maskmap)
loss = loss_heatmap + loss_associate
self.val_losses.update(loss.item(), inputs.size(0))
self.val_loss_heatmap.update(loss_heatmap.item(),
inputs.size(0))
self.val_loss_associate.update(loss_associate.item(),
inputs.size(0))
# 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.pose_net, save_mode='iters')
Log.info('Loss Heatmap:{}, Loss Asso: {}'.format(
self.val_loss_heatmap.avg, self.val_loss_associate.avg))
# 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))
self.batch_time.reset()
self.val_losses.reset()
self.val_loss_heatmap.reset()
self.val_loss_associate.reset()
self.pose_net.train()
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 FCNSegmentor(object):
"""
The class for Pose Estimation. Include train, val, val & 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.seg_running_score = SegRunningScore(configer)
self.seg_visualizer = SegVisualizer(configer)
self.seg_loss_manager = SegLossManager(configer)
self.module_utilizer = ModuleUtilizer(configer)
self.data_transformer = DataTransformer(configer)
self.seg_model_manager = SegModelManager(configer)
self.seg_data_loader = SegDataLoader(configer)
self.optim_scheduler = OptimScheduler(configer)
self.seg_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self._init_model()
def _init_model(self):
self.seg_net = self.seg_model_manager.semantic_segmentor()
self.seg_net = self.module_utilizer.load_net(self.seg_net)
self.optimizer, self.scheduler = self.optim_scheduler.init_optimizer(
self._get_parameters())
self.train_loader = self.seg_data_loader.get_trainloader()
self.val_loader = self.seg_data_loader.get_valloader()
self.pixel_loss = self.seg_loss_manager.get_seg_loss('fcn_seg_loss')
if self.configer.get('network', 'bn_type') == 'syncbn':
self.pixel_loss = DataParallelCriterion(self.pixel_loss).cuda()
def _get_parameters(self):
lr_1 = []
lr_10 = []
params_dict = dict(self.seg_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') * 1.0
}]
return params
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.seg_net.train()
start_time = time.time()
# Adjust the learning rate after every epoch.
self.scheduler.step(self.configer.get('epoch'))
for i, data_dict in enumerate(self.train_loader):
inputs = data_dict['img']
targets = data_dict['labelmap']
self.data_time.update(time.time() - start_time)
# Change the data type.
inputs, targets = self.module_utilizer.to_device(inputs, targets)
# Forward pass.
outputs = self.seg_net(inputs)
# Compute the loss of the train batch & backward.
loss = self.pixel_loss(outputs, targets)
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()
self.configer.plus_one('epoch')
def __val(self):
"""
Validation function during the train phase.
"""
self.seg_net.eval()
start_time = time.time()
for j, data_dict in enumerate(self.val_loader):
inputs = data_dict['img']
targets = data_dict['labelmap']
with torch.no_grad():
# Change the data type.
inputs, targets = self.module_utilizer.to_device(
inputs, targets)
# Forward pass.
outputs = self.seg_net(inputs)
# Compute the loss of the val batch.
loss = self.pixel_loss(outputs, targets)
outputs = self.module_utilizer.gather(outputs)
pred = outputs[0]
self.val_losses.update(loss.item(), inputs.size(0))
self.seg_running_score.update(
pred.max(1)[1].cpu().numpy(),
targets.cpu().numpy())
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.configer.update_value(['performance'],
self.seg_running_score.get_mean_iou())
self.configer.update_value(['val_loss'], self.val_losses.avg)
self.module_utilizer.save_net(self.seg_net, save_mode='performance')
self.module_utilizer.save_net(self.seg_net, save_mode='val_loss')
# 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('Mean IOU: {}\n'.format(
self.seg_running_score.get_mean_iou()))
self.batch_time.reset()
self.val_losses.reset()
self.seg_running_score.reset()
self.seg_net.train()
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 FCNSegmentor(object):
"""
The class for Pose Estimation. Include train, val, val & 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.seg_visualizer = SegVisualizer(configer)
self.seg_loss_manager = SegLossManager(configer)
self.module_utilizer = ModuleUtilizer(configer)
self.seg_model_manager = SegModelManager(configer)
self.seg_data_loader = SegDataLoader(configer)
self.seg_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.lr = None
self.iters = None
def init_model(self):
self.seg_net = self.seg_model_manager.seg_net()
self.iters = 0
self.seg_net, _ = self.module_utilizer.load_net(self.seg_net)
self.optimizer, self.lr = self.module_utilizer.update_optimizer(self.seg_net, self.iters)
if self.configer.get('dataset') == 'cityscape':
self.train_loader = self.seg_data_loader.get_trainloader(FSCityScapeLoader)
self.val_loader = self.seg_data_loader.get_valloader(FSCityScapeLoader)
else:
Log.error('Dataset: {} is not valid!'.format(self.configer.get('dataset')))
exit(1)
self.pixel_loss = self.seg_loss_manager.get_seg_loss('cross_entropy_loss')
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.seg_net.train()
start_time = time.time()
# data_tuple: (inputs, heatmap, maskmap, tagmap, num_objects)
for i, data_tuple in enumerate(self.train_loader):
self.data_time.update(time.time() - start_time)
# Change the data type.
if len(data_tuple) < 2:
Log.error('Train Loader Error!')
exit(0)
inputs = Variable(data_tuple[0].cuda(async=True))
targets = Variable(data_tuple[1].cuda(async=True))
# Forward pass.
outputs = self.seg_net(inputs)
# Compute the loss of the train batch & backward.
loss_pixel = self.pixel_loss(outputs, targets)
loss = loss_pixel
self.train_losses.update(loss.data[0], 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.iters += 1
# Print the log info & reset the states.
if self.iters % self.configer.get('solver', 'display_iter') == 0:
Log.info('Train Iteration: {0}\t'
'Time {batch_time.sum:.3f}s / {1}iters, ({batch_time.avg:.3f})\t'
'Data load {data_time.sum:.3f}s / {1}iters, ({data_time.avg:3f})\n'
'Learning rate = {2}\n'
'Loss = {loss.val:.8f} (ave = {loss.avg:.8f})\n'.format(
self.iters, self.configer.get('solver', 'display_iter'),
self.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.iters % self.configer.get('solver', 'test_interval') == 0:
self.__val()
self.optimizer, self.lr = self.module_utilizer.update_optimizer(self.seg_net, self.iters)
def __val(self):
"""
Validation function during the train phase.
"""
self.seg_net.eval()
start_time = time.time()
for j, data_tuple in enumerate(self.val_loader):
# Change the data type.
inputs = Variable(data_tuple[0].cuda(async=True), volatile=True)
targets = Variable(data_tuple[1].cuda(async=True), volatile=True)
# Forward pass.
outputs = self.seg_net(inputs)
# Compute the loss of the val batch.
loss_pixel = self.pixel_loss(outputs, targets)
loss = loss_pixel
self.val_losses.update(loss.data[0], inputs.size(0))
# 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.seg_net, self.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))
self.batch_time.reset()
self.val_losses.reset()
self.seg_net.train()
def train(self):
cudnn.benchmark = True
while self.iters < self.configer.get('solver', 'max_iter'):
self.__train()
if self.iters == self.configer.get('solver', 'max_iter'):
break
class FCNSegmentor(object):
"""
The class for Pose Estimation. Include train, val, val & 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.seg_visualizer = SegVisualizer(configer)
self.seg_loss_manager = SegLossManager(configer)
self.module_utilizer = ModuleUtilizer(configer)
self.seg_model_manager = SegModelManager(configer)
self.seg_data_loader = SegDataLoader(configer)
self.seg_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.lr = None
self.iters = None
def init_model(self):
self.seg_net = self.seg_model_manager.seg_net()
self.iters = 0
self.seg_net, _ = self.module_utilizer.load_net(self.seg_net)
self.optimizer, self.lr = self.module_utilizer.update_optimizer(
self.seg_net, self.iters)
if self.configer.get('dataset') == 'cityscape':
self.train_loader = self.seg_data_loader.get_trainloader(
FSCityScapeLoader)
self.val_loader = self.seg_data_loader.get_valloader(
FSCityScapeLoader)
else:
Log.error('Dataset: {} is not valid!'.format(
self.configer.get('dataset')))
exit(1)
self.pixel_loss = self.seg_loss_manager.get_seg_loss(
'cross_entropy_loss')
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.seg_net.train()
start_time = time.time()
# data_tuple: (inputs, heatmap, maskmap, tagmap, num_objects)
for i, data_tuple in enumerate(self.train_loader):
self.data_time.update(time.time() - start_time)
# Change the data type.
if len(data_tuple) < 2:
Log.error('Train Loader Error!')
exit(0)
inputs = Variable(data_tuple[0].cuda(async=True))
targets = Variable(data_tuple[1].cuda(async=True))
# Forward pass.
outputs = self.seg_net(inputs)
# Compute the loss of the train batch & backward.
loss_pixel = self.pixel_loss(outputs, targets)
loss = loss_pixel
self.train_losses.update(loss.data[0], 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.iters += 1
# Print the log info & reset the states.
if self.iters % self.configer.get('solver', 'display_iter') == 0:
Log.info(
'Train Iteration: {0}\t'
'Time {batch_time.sum:.3f}s / {1}iters, ({batch_time.avg:.3f})\t'
'Data load {data_time.sum:.3f}s / {1}iters, ({data_time.avg:3f})\n'
'Learning rate = {2}\n'
'Loss = {loss.val:.8f} (ave = {loss.avg:.8f})\n'.format(
self.iters,
self.configer.get('solver', 'display_iter'),
self.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.iters % self.configer.get('solver', 'test_interval') == 0:
self.__val()
self.optimizer, self.lr = self.module_utilizer.update_optimizer(
self.seg_net, self.iters)
def __val(self):
"""
Validation function during the train phase.
"""
self.seg_net.eval()
start_time = time.time()
for j, data_tuple in enumerate(self.val_loader):
# Change the data type.
inputs = Variable(data_tuple[0].cuda(async=True), volatile=True)
targets = Variable(data_tuple[1].cuda(async=True), volatile=True)
# Forward pass.
outputs = self.seg_net(inputs)
# Compute the loss of the val batch.
loss_pixel = self.pixel_loss(outputs, targets)
loss = loss_pixel
self.val_losses.update(loss.data[0], inputs.size(0))
# 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.seg_net, self.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))
self.batch_time.reset()
self.val_losses.reset()
self.seg_net.train()
def train(self):
cudnn.benchmark = True
while self.iters < self.configer.get('solver', 'max_iter'):
self.__train()
if self.iters == self.configer.get('solver', 'max_iter'):
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 OpenPose(object):
"""
The class for Pose Estimation. 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.vis = PoseVisualizer(configer)
self.loss_manager = PoseLossManager(configer)
self.model_manager = PoseModelManager(configer)
self.data_loader = PoseDataLoader(configer)
self.module_utilizer = ModuleUtilizer(configer)
self.pose_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.lr = None
self.iters = None
def init_model(self):
self.pose_net = self.model_manager.pose_detector()
self.iters = 0
self.pose_net, _ = self.module_utilizer.load_net(self.pose_net)
self.optimizer, self.lr = self.module_utilizer.update_optimizer(
self.pose_net, self.iters)
if self.configer.get('dataset') == 'coco':
self.train_loader = self.data_loader.get_trainloader(OPCocoLoader)
self.val_loader = self.data_loader.get_valloader(OPCocoLoader)
else:
Log.error('Dataset: {} is not valid!'.format(
self.configer.get('dataset')))
exit(1)
self.mse_loss = self.loss_manager.get_pose_loss('mse_loss')
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.pose_net.train()
start_time = time.time()
# data_tuple: (inputs, heatmap, maskmap, vecmap)
for i, data_tuple in enumerate(self.train_loader):
self.data_time.update(time.time() - start_time)
# Change the data type.
if len(data_tuple) < 2:
Log.error('Train Loader Error!')
exit(0)
inputs = Variable(data_tuple[0].cuda(async=True))
heatmap = Variable(data_tuple[1].cuda(async=True))
maskmap = None
if len(data_tuple) > 2:
maskmap = Variable(data_tuple[2].cuda(async=True))
# Forward pass.
paf_out, heatmap_out = self.pose_net(inputs)
self.vis.vis_paf(paf_out,
inputs.data.cpu().squeeze().numpy().transpose(
1, 2, 0),
name='paf_out')
# Compute the loss of the train batch & backward.
loss_heatmap = self.mse_loss(heatmap_out, heatmap, maskmap)
loss = loss_heatmap
if len(data_tuple) > 3:
vecmap = Variable(data_tuple[3].cuda(async=True))
self.vis.vis_paf(vecmap,
inputs.data.cpu().squeeze().numpy().transpose(
1, 2, 0),
name='paf')
loss_associate = self.mse_loss(paf_out, vecmap, maskmap)
loss += loss_associate
self.train_losses.update(loss.data[0], 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.iters += 1
# Print the log info & reset the states.
if self.iters % self.configer.get('solver', 'display_iter') == 0:
Log.info(
'Train Iteration: {0}\t'
'Time {batch_time.sum:.3f}s / {1}iters, ({batch_time.avg:.3f})\t'
'Data load {data_time.sum:.3f}s / {1}iters, ({data_time.avg:3f})\n'
'Learning rate = {2}\n'
'Loss = {loss.val:.8f} (ave = {loss.avg:.8f})\n'.format(
self.iters,
self.configer.get('solver', 'display_iter'),
self.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.iters % self.configer.get('solver', 'test_interval') == 0:
self.__val()
# Adjust the learning rate after every iteration.
self.optimizer, self.lr = self.module_utilizer.update_optimizer(
self.pose_net, self.iters)
def __val(self):
"""
Validation function during the train phase.
"""
self.pose_net.eval()
start_time = time.time()
for j, data_tuple in enumerate(self.val_loader):
# Change the data type.
inputs = Variable(data_tuple[0].cuda(async=True), volatile=True)
heatmap = Variable(data_tuple[1].cuda(async=True), volatile=True)
maskmap = None
if len(data_tuple) > 2:
maskmap = Variable(data_tuple[2].cuda(async=True),
volatile=True)
# Forward pass.
paf_out, heatmap_out = self.pose_net(inputs)
# Compute the loss of the val batch.
loss_heatmap = self.mse_loss(heatmap_out, heatmap, maskmap)
loss = loss_heatmap
if len(data_tuple) > 3:
vecmap = Variable(data_tuple[3].cuda(async=True),
volatile=True)
loss_associate = self.mse_loss(paf_out, vecmap, maskmap)
loss = loss_heatmap + loss_associate
self.val_losses.update(loss.data[0], inputs.size(0))
# 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.pose_net, self.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))
self.batch_time.reset()
self.val_losses.reset()
self.pose_net.train()
def train(self):
cudnn.benchmark = True
while self.iters < self.configer.get('solver', 'max_iter'):
self.__train()
if self.iters == self.configer.get('solver', 'max_iter'):
break
class FCNSegmentor(object):
"""
The class for Pose Estimation. Include train, val, val & 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.seg_running_score = SegRunningScore(configer)
self.seg_visualizer = SegVisualizer(configer)
self.seg_loss_manager = SegLossManager(configer)
self.module_utilizer = ModuleUtilizer(configer)
self.seg_model_manager = SegModelManager(configer)
self.seg_data_loader = SegDataLoader(configer)
self.optim_scheduler = OptimScheduler(configer)
self.seg_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
def init_model(self):
self.seg_net = self.seg_model_manager.semantic_segmentor()
self.seg_net = self.module_utilizer.load_net(self.seg_net)
self.optimizer, self.scheduler = self.optim_scheduler.init_optimizer(
self._get_parameters())
self.train_loader = self.seg_data_loader.get_trainloader()
self.val_loader = self.seg_data_loader.get_valloader()
self.pixel_loss = self.seg_loss_manager.get_seg_loss(
'cross_entropy_loss')
def _get_parameters(self):
return self.seg_net.parameters()
def __train(self):
"""
Train function of every epoch during train phase.
"""
if self.configer.get(
'network',
'resume') is not None and self.configer.get('iters') == 0:
self.__val()
self.seg_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, (inputs, targets) in enumerate(self.train_loader):
self.data_time.update(time.time() - start_time)
# Change the data type.
inputs, targets = self.module_utilizer.to_device(inputs, targets)
# Forward pass.
outputs = self.seg_net(inputs)
# Compute the loss of the train batch & backward.
loss = self.pixel_loss(outputs, targets)
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.seg_net.eval()
start_time = time.time()
with torch.no_grad():
for j, (inputs, targets) in enumerate(self.val_loader):
# Change the data type.
inputs, targets = self.module_utilizer.to_device(
inputs, targets)
# Forward pass.
outputs = self.seg_net(inputs)
# Compute the loss of the val batch.
loss = self.pixel_loss(outputs, targets)
self.val_losses.update(loss.item(), inputs.size(0))
self.seg_running_score.update(
outputs.max(1)[1].unsqueeze(1).data, targets.data)
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.configer.update_value(['performace'],
self.seg_running_score.get_mean_iou())
self.configer.update_value(['val_loss'], self.val_losses.avg)
self.module_utilizer.save_net(self.seg_net, metric='performance')
self.module_utilizer.save_net(self.seg_net, metric='val_loss')
# 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('Mean IOU: {}'.format(
self.seg_running_score.get_mean_iou()))
self.batch_time.reset()
self.val_losses.reset()
self.seg_running_score.reset()
self.seg_net.train()
def train(self):
cudnn.benchmark = True
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 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
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.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
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('multibox_loss')
def _get_parameters(self):
return self.det_net.parameters()
def __train(self):
"""
Train function of every epoch during train phase.
"""
if self.configer.get(
'network',
'resume') is not None and self.configer.get('iters') == 0:
self.__val()
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, (inputs, bboxes, labels) in enumerate(self.train_loader):
self.data_time.update(time.time() - start_time)
# Change the data type.
inputs, bboxes, labels = self.module_utilizer.to_device(
inputs, bboxes, labels)
# Forward pass.
loc, cls = self.det_net(inputs)
# 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, (inputs, bboxes, labels) in enumerate(self.val_loader):
# Change the data type.
inputs, bboxes, labels = self.module_utilizer.to_device(
inputs, bboxes, labels)
# Forward pass.
loc, cls = self.det_net(inputs)
# Compute the loss of the val batch.
loss = self.det_loss(loc, bboxes, cls, labels)
self.val_losses.update(loss.item(), inputs.size(0))
# 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, metric='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))
self.batch_time.reset()
self.val_losses.reset()
self.det_net.train()
def train(self):
cudnn.benchmark = True
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 OpenPose(object):
"""
The class for Pose Estimation. 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.vis = PoseVisualizer(configer)
self.loss_manager = PoseLossManager(configer)
self.model_manager = PoseModelManager(configer)
self.data_loader = PoseDataLoader(configer)
self.module_utilizer = ModuleUtilizer(configer)
self.pose_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.lr = None
self.iters = None
def init_model(self):
self.pose_net = self.model_manager.pose_detector()
self.iters = 0
self.pose_net, _ = self.module_utilizer.load_net(self.pose_net)
self.optimizer, self.lr = self.module_utilizer.update_optimizer(self.pose_net, self.iters)
if self.configer.get('dataset') == 'coco':
self.train_loader = self.data_loader.get_trainloader(OPCocoLoader)
self.val_loader = self.data_loader.get_valloader(OPCocoLoader)
else:
Log.error('Dataset: {} is not valid!'.format(self.configer.get('dataset')))
exit(1)
self.mse_loss = self.loss_manager.get_pose_loss('mse_loss')
def __train(self):
"""
Train function of every epoch during train phase.
"""
self.pose_net.train()
start_time = time.time()
# data_tuple: (inputs, heatmap, maskmap, vecmap)
for i, data_tuple in enumerate(self.train_loader):
self.data_time.update(time.time() - start_time)
# Change the data type.
if len(data_tuple) < 2:
Log.error('Train Loader Error!')
exit(0)
inputs = Variable(data_tuple[0].cuda(async=True))
heatmap = Variable(data_tuple[1].cuda(async=True))
maskmap = None
if len(data_tuple) > 2:
maskmap = Variable(data_tuple[2].cuda(async=True))
# Forward pass.
paf_out, heatmap_out = self.pose_net(inputs)
self.vis.vis_paf(paf_out, inputs.data.cpu().squeeze().numpy().transpose(1, 2, 0), name='paf_out')
# Compute the loss of the train batch & backward.
loss_heatmap = self.mse_loss(heatmap_out, heatmap, maskmap)
loss = loss_heatmap
if len(data_tuple) > 3:
vecmap = Variable(data_tuple[3].cuda(async=True))
self.vis.vis_paf(vecmap, inputs.data.cpu().squeeze().numpy().transpose(1, 2, 0), name='paf')
loss_associate = self.mse_loss(paf_out, vecmap, maskmap)
loss += loss_associate
self.train_losses.update(loss.data[0], 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.iters += 1
# Print the log info & reset the states.
if self.iters % self.configer.get('solver', 'display_iter') == 0:
Log.info('Train Iteration: {0}\t'
'Time {batch_time.sum:.3f}s / {1}iters, ({batch_time.avg:.3f})\t'
'Data load {data_time.sum:.3f}s / {1}iters, ({data_time.avg:3f})\n'
'Learning rate = {2}\n'
'Loss = {loss.val:.8f} (ave = {loss.avg:.8f})\n'.format(
self.iters, self.configer.get('solver', 'display_iter'), self.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.iters % self.configer.get('solver', 'test_interval') == 0:
self.__val()
# Adjust the learning rate after every iteration.
self.optimizer, self.lr = self.module_utilizer.update_optimizer(self.pose_net, self.iters)
def __val(self):
"""
Validation function during the train phase.
"""
self.pose_net.eval()
start_time = time.time()
for j, data_tuple in enumerate(self.val_loader):
# Change the data type.
inputs = Variable(data_tuple[0].cuda(async=True), volatile=True)
heatmap = Variable(data_tuple[1].cuda(async=True), volatile=True)
maskmap = None
if len(data_tuple) > 2:
maskmap = Variable(data_tuple[2].cuda(async=True), volatile=True)
# Forward pass.
paf_out, heatmap_out = self.pose_net(inputs)
# Compute the loss of the val batch.
loss_heatmap = self.mse_loss(heatmap_out, heatmap, maskmap)
loss = loss_heatmap
if len(data_tuple) > 3:
vecmap = Variable(data_tuple[3].cuda(async=True), volatile=True)
loss_associate = self.mse_loss(paf_out, vecmap, maskmap)
loss = loss_heatmap + loss_associate
self.val_losses.update(loss.data[0], inputs.size(0))
# 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.pose_net, self.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))
self.batch_time.reset()
self.val_losses.reset()
self.pose_net.train()
def train(self):
cudnn.benchmark = True
while self.iters < self.configer.get('solver', 'max_iter'):
self.__train()
if self.iters == self.configer.get('solver', 'max_iter'):
break
