class PoseEstimator(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 = DictAverageMeter()
self.val_losses = DictAverageMeter()
self.pose_visualizer = PoseVisualizer(configer)
self.pose_model_manager = ModelManager(configer)
self.pose_data_loader = DataLoader(configer)
self.pose_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.pose_net = self.pose_model_manager.get_pose_model()
self.pose_net = RunnerHelper.load_net(self, self.pose_net)
self.optimizer, self.scheduler = Trainer.init(self._get_parameters(), self.configer.get('solver'))
self.train_loader = self.pose_data_loader.get_trainloader()
self.val_loader = self.pose_data_loader.get_valloader()
self.pose_loss = self.pose_model_manager.get_pose_loss()
def _get_parameters(self):
lr_1 = []
lr_2 = []
params_dict = dict(self.pose_net.named_parameters())
for key, value in params_dict.items():
if 'backbone' not in key:
lr_2.append(value)
else:
lr_1.append(value)
params = [{'params': lr_1, 'lr': self.configer.get('solver', 'lr')['base_lr'], 'weight_decay': 0.0},
{'params': lr_2, 'lr': self.configer.get('solver', 'lr')['base_lr'], '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.runner_state['epoch'] += 1
for i, data_dict in enumerate(self.train_loader):
Trainer.update(self, warm_list=(0,), solver_dict=self.configer.get('solver'))
self.data_time.update(time.time() - start_time)
# Forward pass.
out = self.pose_net(data_dict)
# Compute the loss of the train batch & backward.
loss_dict = self.pose_loss(out)
loss = loss_dict['loss']
self.train_losses.update({key: loss.item() for key, loss in loss_dict.items()}, data_dict['img'].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.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 = {4}\tLoss = {3}\n'.format(
self.runner_state['epoch'], self.runner_state['iters'],
self.configer.get('solver', 'display_iter'), self.train_losses.info(),
RunnerHelper.get_lr(self.optimizer), batch_time=self.batch_time, data_time=self.data_time))
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.pose_net.eval()
start_time = time.time()
with torch.no_grad():
for i, data_dict in enumerate(self.val_loader):
# Forward pass.
out = self.pose_net(data_dict)
# Compute the loss of the val batch.
loss_dict = self.pose_loss(out)
self.val_losses.update({key: loss.item() for key, loss in loss_dict.items()}, data_dict['img'].size(0))
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.runner_state['val_loss'] = self.val_losses.avg['loss']
RunnerHelper.save_net(self, self.pose_net, val_loss=self.val_losses.avg['loss'])
# Print the log info & reset the states.
Log.info(
'Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t'
'Loss {0}\n'.format(self.val_losses.info(), batch_time=self.batch_time))
self.batch_time.reset()
self.val_losses.reset()
self.pose_net.train()
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_model_manager = ModelManager(configer)
self.pose_data_loader = DataLoader(configer)
self.pose_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.pose_net = self.pose_model_manager.get_single_pose_model()
self.pose_net = RunnerHelper.load_net(self, self.pose_net)
self.optimizer, self.scheduler = Trainer.init(
self._get_parameters(), self.configer.get('solver'))
self.train_loader = self.pose_data_loader.get_trainloader()
self.val_loader = self.pose_data_loader.get_valloader()
self.cpm_loss = self.pose_model_manager.get_pose_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.runner_state['epoch'] += 1
# data_tuple: (inputs, heatmap, maskmap, tagmap, num_objects)
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)
# Change the data type.
# Forward pass.
out_dict = self.pose_net(data_dict)
# Compute the loss of the train batch & backward.
loss = self.cpm_loss(out_dict,
data_dict,
gathered=self.configer.get(
'network', 'gathered'))
self.train_losses.update(loss.item(),
len(DCHelper.tolist(data_dict['meta'])))
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.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.pose_net.eval()
start_time = time.time()
with torch.no_grad():
for j, data_dict in enumerate(self.val_loader):
# Forward pass.
out_dict = self.pose_net(data_dict)
# Compute the loss of the val batch.
loss = self.cpm_loss(out_dict,
data_dict,
gathered=self.configer.get(
'network', 'gathered'))
self.val_losses.update(loss.item(),
len(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.pose_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))
self.batch_time.reset()
self.val_losses.reset()
self.pose_net.train()