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):
# Forward pass.
out_dict = self.cls_net(data_dict)
# Compute the loss of the val batch.
loss = self.ce_loss(out_dict, data_dict, gathered=self.configer.get('network', 'gathered'))
out_dict = RunnerHelper.gather(self, out_dict)
self.cls_running_score.update(out_dict['out'], DCHelper.tolist(data_dict['labels']))
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.cls_net, performance=self.cls_running_score.get_top1_acc())
self.runner_state['performance'] = self.cls_running_score.get_top1_acc()
# 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 val(self, data_loader=None):
"""
Validation function during the train phase.
"""
self.gan_net.eval()
start_time = time.time()
data_loader = self.val_loader if data_loader is None else data_loader
for j, data_dict in enumerate(data_loader):
inputs = data_dict['imgA']
with torch.no_grad():
# Forward pass.
out_dict = self.gan_net(data_dict)
# Compute the loss of the val batch.
self.val_losses.update(
out_dict['loss_G'].mean().item() +
out_dict['loss_D'].mean().item(), inputs.size(0))
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
RunnerHelper.save_net(self, self.gan_net, val_loss=self.val_losses.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.gan_net.train()
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.runner_state['epoch'] += 1
for i, data_dict in enumerate(self.train_loader):
Trainer.update(self)
inputs = data_dict['img']
labels = data_dict['label']
self.data_time.update(time.time() - start_time)
# Change the data type.
inputs, labels = RunnerHelper.to_device(self, inputs, labels)
# Forward pass.
outputs = self.cls_net(inputs)
# 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.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('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 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, ),
backbone_lr_list=(self.configer.get(
'solver', 'lr')['base_lr'], ),
solver_dict=self.configer.get('solver'))
self.data_time.update(time.time() - start_time)
# Forward pass.
out_dict = self.det_net(data_dict)
loss = out_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.pose_net.eval()
start_time = time.time()
with torch.no_grad():
for j, data_dict in enumerate(self.val_loader):
inputs = data_dict['img']
heatmap = data_dict['heatmap']
# Change the data type.
inputs, heatmap = RunnerHelper.to_device(self, 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()
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()
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()
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']
metas = data_dict['meta']
data_dict['bboxes'] = DCHelper.todc(
batch_gt_bboxes,
gpu_list=self.configer.get('gpu'),
cpu_only=True)
data_dict['labels'] = DCHelper.todc(
batch_gt_labels,
gpu_list=self.configer.get('gpu'),
cpu_only=True)
data_dict['meta'] = DCHelper.todc(
metas, gpu_list=self.configer.get('gpu'), cpu_only=True)
# Forward pass.
inputs = RunnerHelper.to_device(self, inputs)
loss, test_group = self.det_net(data_dict)
# Compute the loss of the train batch & backward.
loss = loss.mean()
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, metas)
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()
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 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 = RunnerHelper.to_device(self, inputs)
input_size = [inputs.size(3), inputs.size(2)]
# Forward pass.
outputs = self.det_net(inputs)
feat_list, loc, cls = RunnerHelper.gather(self, outputs)
bboxes, labels = self.ssd_target_generator(
feat_list, batch_gt_bboxes, batch_gt_labels, input_size)
bboxes, labels = RunnerHelper.to_device(bboxes, labels)
# Compute the loss of the val batch.
loss = self.det_loss(outputs,
bboxes,
labels,
gathered=self.configer.get(
'network', 'gathered'))
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()
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 val(self, data_loader=None):
"""
Validation function during the train phase.
"""
self.seg_net.eval()
start_time = time.time()
data_loader = self.val_loader if data_loader is None else data_loader
for j, data_dict in enumerate(data_loader):
inputs = data_dict['img']
targets = data_dict['labelmap']
with torch.no_grad():
# Change the data type.
inputs, targets = RunnerHelper.to_device(self, inputs, targets)
# Forward pass.
outputs = self.seg_net(inputs)
# Compute the loss of the val batch.
loss = self.pixel_loss(outputs,
targets,
gathered=self.configer.get(
'network', 'gathered'))
outputs = RunnerHelper.gather(self, outputs)
self.val_losses.update(loss.item(), inputs.size(0))
self._update_running_score(outputs[-1], data_dict['meta'])
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.runner_state['performance'] = self.seg_running_score.get_mean_iou(
)
self.runner_state['val_loss'] = self.val_losses.avg
RunnerHelper.save_net(
self,
self.seg_net,
performance=self.seg_running_score.get_mean_iou(),
val_loss=self.val_losses.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))
Log.info('Mean IOU: {}\n'.format(
self.seg_running_score.get_mean_iou()))
Log.info('Pixel ACC: {}\n'.format(
self.seg_running_score.get_pixel_acc()))
self.batch_time.reset()
self.val_losses.reset()
self.seg_running_score.reset()
self.seg_net.train()
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 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']
heatmap = data_dict['heatmap']
vecmap = data_dict['vecmap']
# Change the data type.
inputs, heatmap, maskmap, vecmap = RunnerHelper.to_device(
self, 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 = 2.0 * 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.runner_state['val_loss'] = self.val_losses.avg
RunnerHelper.save_net(self,
self.pose_net,
val_loss=self.val_losses.avg)
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 val(self, data_loader=None):
"""
Validation function during the train phase.
"""
self.gan_net.eval()
start_time = time.time()
data_loader = self.val_loader if data_loader is None else data_loader
for j, data_dict in enumerate(data_loader):
inputs = data_dict['imgA']
with torch.no_grad():
# Forward pass.
out_dict = self.gan_net(data_dict)
# Compute the loss of the val batch.
self.val_losses.update(out_dict['loss'].mean().item(),
inputs.size(0))
meta_list = DCHelper.tolist(data_dict['meta'])
probe_features = []
gallery_features = []
probe_labels = []
gallery_labels = []
for idx in range(len(meta_list)):
gallery_features.append(out_dict['featB'][idx].cpu().numpy())
gallery_labels.append(meta_list[idx]['labelB'])
probe_features.append(out_dict['featA'][idx].cpu().numpy())
probe_labels.append(meta_list[idx]['labelA'])
rank_1, vr_far_001 = FaceGANTest.decode(probe_features,
gallery_features,
probe_labels,
gallery_labels)
Log.info('Rank1 accuracy is {}'.format(rank_1))
Log.info('VR@FAR=0.1% accuracy is {}'.format(vr_far_001))
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
RunnerHelper.save_net(self, self.gan_net, val_loss=self.val_losses.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.gan_net.train()
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 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.
out_dict = self.det_net(data_dict)
# Compute the loss of the train batch & backward.
loss = out_dict['loss'].mean()
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 _init_model(self):
self.cls_net = self.cls_model_manager.get_cls_model()
self.cls_net = RunnerHelper.load_net(self, self.cls_net)
self.optimizer, self.scheduler = Trainer.init(self._get_parameters(), self.configer.get('solver'))
self.train_loader = self.cls_data_loader.get_trainloader()
self.val_loader = self.cls_data_loader.get_valloader()
self.ce_loss = self.cls_model_manager.get_cls_loss()
def _init_model(self):
self.seg_net = self.seg_model_manager.semantic_segmentor()
self.seg_net = RunnerHelper.load_net(self, self.seg_net)
self.optimizer, self.scheduler = Trainer.init(self, 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()
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 = RunnerHelper.to_device(self, inputs, labels)
# Forward pass.
outputs = self.cls_net(inputs)
outputs = RunnerHelper.gather(self, 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()
RunnerHelper.save_net(
self,
self.cls_net,
performance=self.cls_running_score.get_top1_acc())
self.runner_state[
'performance'] = self.cls_running_score.get_top1_acc()
# 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 _init_model(self):
self.gan_net = self.model_manager.gan_model()
self.gan_net = RunnerHelper.load_net(self, self.gan_net)
self.optimizer_G, self.scheduler_G = Trainer.init(
self._get_parameters()[0], self.configer.get('solver'))
self.optimizer_D, self.scheduler_D = Trainer.init(
self._get_parameters()[1], self.configer.get('solver'))
self.train_loader = self.seg_data_loader.get_trainloader()
self.val_loader = self.seg_data_loader.get_valloader()
def _init_model(self):
self.pose_net = self.pose_model_manager.multi_pose_detector()
self.pose_net = RunnerHelper.load_net(self, self.pose_net)
self.optimizer, self.scheduler = Trainer.init(self,
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()
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)
batch_gt_bboxes = data_dict['bboxes']
batch_gt_labels = data_dict['labels']
metas = data_dict['meta']
data_dict['bboxes'] = DCHelper.todc(
batch_gt_bboxes,
gpu_list=self.configer.get('gpu'),
cpu_only=True)
data_dict['labels'] = DCHelper.todc(
batch_gt_labels,
gpu_list=self.configer.get('gpu'),
cpu_only=True)
data_dict['meta'] = DCHelper.todc(
metas, gpu_list=self.configer.get('gpu'), cpu_only=True)
self.data_time.update(time.time() - start_time)
# Forward pass.
loss = self.det_net(data_dict)
loss = loss.mean()
self.train_losses.update(loss.item(), data_dict['img'].size(0))
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('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 train(self):
"""
Train function of every epoch during train phase.
"""
self.gan_net.train()
start_time = time.time()
# Adjust the learning rate after every epoch.
self.scheduler_G.step(self.runner_state['epoch'])
self.scheduler_D.step(self.runner_state['epoch'])
for i, data_dict in enumerate(self.train_loader):
inputs = data_dict['imgA']
self.data_time.update(time.time() - start_time)
# Forward pass.
out_dict = self.gan_net(data_dict)
# outputs = self.module_utilizer.gather(outputs)
self.optimizer_G.zero_grad()
loss_G = out_dict['loss_G'].mean()
loss_G.backward()
self.optimizer_G.step()
self.optimizer_D.zero_grad()
loss_D = out_dict['loss_D'].mean()
loss_D.backward()
self.optimizer_D.step()
loss = loss_G + loss_D
self.train_losses.update(loss.item(), inputs.size(0))
# 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_G),
RunnerHelper.get_lr(self.optimizer_D)
],
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()
self.runner_state['epoch'] += 1
def _init_model(self):
self.pose_net = self.pose_model_manager.multi_pose_detector()
self.pose_net = RunnerHelper.load_net(self, self.pose_net)
self.pose_net.eval()
def _init_model(self):
self.cls_net = self.cls_model_manager.image_classifier()
self.cls_net = RunnerHelper.load_net(self, self.cls_net)
self.cls_net.eval()
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
self.scheduler.step(self.train_schedule_loss.avg,
epoch=self.configer.get('epoch'))
self.train_schedule_loss.reset()
# data_tuple: (inputs, heatmap, maskmap, vecmap)
for i, data_dict in enumerate(self.train_loader):
inputs = data_dict['img']
maskmap = data_dict['maskmap']
heatmap = data_dict['heatmap']
vecmap = data_dict['vecmap']
self.data_time.update(time.time() - start_time)
# Change the data type.
inputs, heatmap, maskmap, vecmap = RunnerHelper.to_device(
self, 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 = 2.0 * loss_heatmap + loss_associate
self.train_losses.update(loss.item(), inputs.size(0))
self.train_schedule_loss.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.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('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.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()
self.train_loss_heatmap.reset()
self.train_loss_associate.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 _init_model(self):
self.gan_net = self.model_manager.gan_model()
self.gan_net = RunnerHelper.load_net(self, self.gan_net)
self.gan_net.eval()
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 _init_model(self):
self.seg_net = self.seg_model_manager.semantic_segmentor()
self.seg_net = RunnerHelper.load_net(self, self.seg_net)
self.seg_net.eval()
def _init_model(self):
self.seg_net = self.seg_model_manager.get_seg_model()
self.seg_net = RunnerHelper.load_net(self, self.seg_net)
self.seg_net.eval()
def _init_model(self):
self.det_net = self.det_model_manager.object_detector()
self.det_net = RunnerHelper.load_net(self, self.det_net)
self.det_net.eval()
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,
backbone_list=(0, ),
solver_dict=self.configer.get('solver'))
self.data_time.update(time.time() - start_time)
# Forward pass.
out_dict = self.pose_net(data_dict)
# Compute the loss of the train batch & backward.
loss_dict = self.mse_loss(out_dict,
data_dict,
gathered=self.configer.get(
'network', 'gathered'))
loss = loss_dict['loss']
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('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.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()
self.train_loss_heatmap.reset()
self.train_loss_associate.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()
