def __process_affine(self, image, target, theta, nopoints, aux_info=None):
image, target, theta = image.clone(), target.copy(), theta.clone()
(C, H, W), (height, width) = image.size(), self.shape
if nopoints: # do not have label
norm_trans_points = torch.zeros((3, self.NUM_PTS))
heatmaps = torch.zeros((self.NUM_PTS + 1, height // self.downsample, width // self.downsample))
mask = torch.ones((self.NUM_PTS + 1, 1, 1), dtype=torch.uint8)
transpose_theta = identity2affine(False)
else:
norm_trans_points = apply_affine2point(target.get_points(), theta, (H, W))
norm_trans_points = apply_boundary(norm_trans_points)
real_trans_points = norm_trans_points.clone()
real_trans_points[:2, :] = denormalize_points(self.shape, real_trans_points[:2, :])
heatmaps, mask = generate_label_map(real_trans_points.numpy(), height // self.downsample,
width // self.downsample, self.sigma, self.downsample, nopoints,
self.heatmap_type) # H*W*C
heatmaps = torch.from_numpy(heatmaps.transpose((2, 0, 1))).type(torch.FloatTensor)
mask = torch.from_numpy(mask.transpose((2, 0, 1))).type(torch.ByteTensor)
if self.mean_face is None:
# warnings.warn('In LandmarkDataset use identity2affine for transpose_theta because self.mean_face is None.')
transpose_theta = identity2affine(False)
else:
if torch.sum(norm_trans_points[2, :] == 1) < 3:
warnings.warn(
'In LandmarkDataset after transformation, no visiable point, using identity instead. Aux: {:}'.format(
aux_info))
transpose_theta = identity2affine(False)
else:
transpose_theta = solve2theta(norm_trans_points, self.mean_face.clone())
affineImage = affine2image(image, theta, self.shape)
if self.cutout is not None: affineImage = self.cutout(affineImage)
return affineImage, heatmaps, mask, norm_trans_points, theta, transpose_theta
def __process_affine(self, image, target, theta, nopoints, aux_info=None):
image, target, theta = image.clone(), target.copy(), theta.clone()
(C, H, W), (height, width) = image.size(), self.shape
if nopoints: # do not have label
norm_trans_points = torch.zeros((3, self.NUM_PTS))
heatmaps = torch.zeros(
(self.NUM_PTS + 1, height // self.downsample,
width // self.downsample))
mask = torch.ones((self.NUM_PTS + 1, 1, 1), dtype=torch.uint8)
else:
norm_trans_points = apply_affine2point(target.get_points(), theta,
(H, W))
norm_trans_points = apply_boundary(norm_trans_points)
real_trans_points = norm_trans_points.clone()
real_trans_points[:2, :] = denormalize_points(
self.shape, real_trans_points[:2, :])
heatmaps, mask = generate_label_map(real_trans_points.numpy(),
height // self.downsample,
width // self.downsample,
self.sigma, self.downsample,
nopoints,
self.heatmap_type) # H*W*C
heatmaps = torch.from_numpy(heatmaps.transpose(
(2, 0, 1))).type(torch.FloatTensor)
mask = torch.from_numpy(mask.transpose(
(2, 0, 1))).type(torch.ByteTensor)
affineImage = affine2image(image, theta, self.shape)
return affineImage, heatmaps, mask, norm_trans_points, theta
def eval_robust_heatmap(detector, xloader, print_freq, logger):
batch_time, NUM_PTS = AverageMeter(), xloader.dataset.NUM_PTS
Preds, GT_locs, Distances = [], [], []
eval_meta, end = Eval_Meta(), time.time()
with torch.no_grad():
detector.eval()
for i, (inputs, heatmaps, masks, norm_points, thetas, data_index,
nopoints, xshapes) in enumerate(xloader):
data_index = data_index.squeeze(1).tolist()
batch_size, iters, C, H, W = inputs.size()
for ibatch in range(batch_size):
xinputs, xpoints, xthetas = inputs[ibatch], norm_points[
ibatch].permute(0, 2, 1).contiguous(), thetas[ibatch]
batch_features, batch_heatmaps, batch_locs, batch_scos = detector(
xinputs.cuda(non_blocking=True))
batch_locs = batch_locs.cpu()[:, :-1]
all_locs = []
for _iter in range(iters):
_locs = normalize_points((H, W),
batch_locs[_iter].permute(1, 0))
xlocs = torch.cat((_locs, torch.ones(1, NUM_PTS)), dim=0)
nlocs = torch.mm(xthetas[_iter, :2], xlocs)
rlocs = denormalize_points(xshapes[ibatch].tolist(), nlocs)
rlocs = torch.cat(
(rlocs.permute(1, 0), xpoints[_iter, :, 2:]), dim=1)
all_locs.append(rlocs.clone())
GT_loc = xloader.dataset.labels[
data_index[ibatch]].get_points()
norm_distance = xloader.dataset.get_normalization_distance(
data_index[ibatch])
# save the results
eval_meta.append((sum(all_locs) / len(all_locs)).numpy().T,
GT_loc.numpy(),
xloader.dataset.datas[data_index[ibatch]],
norm_distance)
Distances.append(norm_distance)
Preds.append(all_locs)
GT_locs.append(GT_loc.permute(1, 0))
# compute time
batch_time.update(time.time() - end)
end = time.time()
if i % print_freq == 0 or i + 1 == len(xloader):
last_time = convert_secs2time(
batch_time.avg * (len(xloader) - i - 1), True)
logger.log(
' -->>[Robust HEATMAP-based Evaluation] [{:03d}/{:03d}] Time : {:}'
.format(i, len(xloader), last_time))
# evaluate the results
errors, valids = calculate_robust(Preds, GT_locs, Distances, NUM_PTS)
return errors, valids, eval_meta
def stm_main_heatmap(args, loader, net, criterion, optimizer, epoch_str,
logger, opt_config, stm_config, use_stm, mode):
assert mode == 'train' or mode == 'test', 'invalid mode : {:}'.format(mode)
args = copy.deepcopy(args)
batch_time, data_time, forward_time, eval_time = AverageMeter(
), AverageMeter(), AverageMeter(), AverageMeter()
visible_points, DetLosses, TemporalLosses, MultiviewLosses, TotalLosses = AverageMeter(
), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
alk_points, a3d_points = AverageMeter(), AverageMeter()
annotate_index = loader.dataset.video_L
eval_meta = Eval_Meta()
cpu = torch.device('cpu')
if args.debug:
save_dir = Path(
args.save_path) / 'DEBUG' / ('{:}-'.format(mode) + epoch_str)
else:
save_dir = None
# switch to train mode
if mode == 'train':
logger.log('STM-Main-REG : training : {:} .. STM = {:}'.format(
stm_config, use_stm))
print_freq = args.print_freq
net.train()
criterion.train()
else:
logger.log('STM-Main-REG : evaluation mode.')
print_freq = args.print_freq_eval
net.eval()
criterion.eval()
i_batch_size, v_batch_size, m_batch_size = args.i_batch_size, args.v_batch_size, args.m_batch_size
iv_size = i_batch_size + v_batch_size
end = time.time()
for i, (frames, Fflows, Bflows, targets, masks, normpoints, transthetas, MV_Tensors, MV_Thetas, MV_Shapes, MV_KRT, torch_is_3D, torch_is_images \
, image_index, nopoints, shapes, MultiViewPaths) in enumerate(loader):
# frames : IBatch+VBatch+MBatch, Frame, Channel, Height, Width
# Fflows : IBatch+VBatch+MBatch, Frame-1, Height, Width, 2
# Bflows : IBatch+VBatch+MBatch, Frame-1, Height, Width, 2
# information
MV_Mask = masks[iv_size:]
frames, Fflows, Bflows, targets, masks, normpoints, transthetas = frames[:
iv_size], Fflows[:
iv_size], Bflows[:
iv_size], targets[:
iv_size], masks[:
iv_size], normpoints[:
iv_size], transthetas[:
iv_size]
nopoints, shapes, torch_is_images = nopoints[:
iv_size], shapes[:
iv_size], torch_is_images[:
iv_size]
MV_Tensors, MV_Thetas, MV_Shapes, MV_KRT, torch_is_3D = \
MV_Tensors[iv_size:], MV_Thetas[iv_size:], MV_Shapes[iv_size:], MV_KRT[iv_size:], torch_is_3D[iv_size:]
assert torch.sum(torch_is_images[:i_batch_size]).item(
) == i_batch_size, 'Image Check Fail : {:} vs. {:}'.format(
torch_is_images[:i_batch_size], i_batch_size)
assert v_batch_size == 0 or torch.sum(
torch_is_images[i_batch_size:]).item(
) == 0, 'Video Check Fail : {:} vs. {:}'.format(
torch_is_images[i_batch_size:], v_batch_size)
assert torch_is_3D.sum().item(
) == m_batch_size, 'Multiview Check Fail : {:} vs. {:}'.format(
torch_is_3D, m_batch_size)
image_index = image_index.squeeze(1).tolist()
(batch_size, frame_length, C, H, W), num_pts, num_views = frames.size(
), args.num_pts, stm_config.max_views
visible_point_num = float(np.sum(
masks.numpy()[:, :-1, :, :])) / batch_size
visible_points.update(visible_point_num, batch_size)
normpoints = normpoints.permute(0, 2, 1)
target_heats = targets.cuda(non_blocking=True)
target_points = normpoints[:, :, :2].contiguous().cuda(
non_blocking=True)
target_scores = normpoints[:, :,
2:].contiguous().cuda(non_blocking=True)
det_masks = (1 - nopoints).view(batch_size, 1, 1, 1) * masks
have_det_loss = det_masks.sum().item() > 0
det_masks = det_masks.cuda(non_blocking=True)
nopoints = nopoints.squeeze(1).tolist()
# measure data loading time
data_time.update(time.time() - end)
# batch_heatmaps is a list for stage-predictions, each element should be [Batch, Sequence, PTS, H/Down, W/Down]
batch_heatmaps, batch_locs, batch_scos, batch_past2now, batch_future2now, batch_FBcheck, multiview_heatmaps, multiview_locs = net(
frames, Fflows, Bflows, MV_Tensors, torch_is_images)
annot_heatmaps = [x[:, annotate_index] for x in batch_heatmaps]
forward_time.update(time.time() - end)
# detection loss
if have_det_loss:
det_loss, each_stage_loss_value = compute_stage_loss(
criterion, target_heats, annot_heatmaps, det_masks)
DetLosses.update(det_loss.item(), batch_size)
each_stage_loss_value = show_stage_loss(each_stage_loss_value)
else:
det_loss, each_stage_loss_value = 0, 'no-det-loss'
# temporal loss
if use_stm[0]:
video_batch_locs = batch_locs[i_batch_size:, :, :num_pts]
video_past2now, video_future2now = batch_past2now[
i_batch_size:, :, :num_pts], batch_future2now[
i_batch_size:, :, :num_pts]
video_FBcheck = batch_FBcheck[i_batch_size:, :num_pts]
video_mask = masks[i_batch_size:, :num_pts].contiguous().cuda(
non_blocking=True)
video_heatmaps = [
x[i_batch_size:, :, :num_pts] for x in batch_heatmaps
]
sbr_loss, available_nums, loss_string = calculate_temporal_loss(
criterion, video_heatmaps, video_batch_locs, video_past2now,
video_future2now, video_FBcheck, video_mask, stm_config)
alk_points.update(
float(available_nums) / v_batch_size, v_batch_size)
if available_nums > stm_config.available_sbr_thresh:
TemporalLosses.update(sbr_loss.item(), v_batch_size)
else:
sbr_loss, sbr_loss_string = 0, 'non-sbr-loss'
else:
sbr_loss, sbr_loss_string = 0, 'non-sbr-loss'
# multiview loss
if use_stm[1]:
MV_Mask_G = MV_Mask[:, :-1].view(
m_batch_size, 1, -1, 1).contiguous().cuda(non_blocking=True)
MV_Thetas_G = MV_Thetas.to(multiview_locs.device)
MV_Shapes_G = MV_Shapes.to(multiview_locs.device).view(
m_batch_size, num_views, 1, 2)
MV_KRT_G = MV_KRT.to(multiview_locs.device)
mv_norm_locs_trs = torch.cat(
(multiview_locs[:, :, :num_pts].permute(0, 1, 3, 2),
torch.ones(m_batch_size,
num_views,
1,
num_pts,
device=multiview_locs.device)),
dim=2)
mv_norm_locs_ori = torch.matmul(MV_Thetas_G[:, :, :2],
mv_norm_locs_trs)
mv_norm_locs_ori = mv_norm_locs_ori.permute(0, 1, 3, 2)
mv_real_locs_ori = denormalize_L(mv_norm_locs_ori, MV_Shapes_G)
mv_3D_locs_ori = TriangulateDLT_BatchCam(MV_KRT_G,
mv_real_locs_ori)
mv_proj_locs_ori = ProjectKRT_Batch(
MV_KRT_G, mv_3D_locs_ori.view(m_batch_size, 1, num_pts, 3))
mv_pnorm_locs_ori = normalize_L(mv_proj_locs_ori, MV_Shapes_G)
mv_pnorm_locs_trs = convert_theta(mv_pnorm_locs_ori, MV_Thetas_G)
MV_locs = multiview_locs[:, :, :num_pts].contiguous()
MV_heatmaps = [x[:, :, :num_pts] for x in multiview_heatmaps]
if args.debug:
with torch.no_grad():
for ims in range(m_batch_size):
x_index = image_index[iv_size + ims]
x_paths = [
xlist[iv_size + ims] for xlist in MultiViewPaths
]
x_mv_locs, p_mv_locs = mv_real_locs_ori[
ims], mv_proj_locs_ori[ims]
multiview_debug_save(save_dir, '{:}'.format(x_index),
x_paths,
x_mv_locs.cpu().numpy(),
p_mv_locs.cpu().numpy())
y_mv_locs = denormalize_points_batch((H, W),
MV_locs[ims])
q_mv_locs = denormalize_points_batch(
(H, W), mv_pnorm_locs_trs[ims])
temp_tensors = MV_Tensors[ims]
temp_images = [
args.tensor2imageF(x) for x in temp_tensors
]
temp_names = [Path(x).name for x in x_paths]
multiview_debug_save_v2(save_dir,
'{:}'.format(x_index),
temp_names, temp_images,
y_mv_locs.cpu().numpy(),
q_mv_locs.cpu().numpy())
stm_loss, available_nums = calculate_multiview_loss(
criterion, MV_heatmaps, MV_locs, mv_pnorm_locs_trs, MV_Mask_G,
stm_config)
a3d_points.update(
float(available_nums) / m_batch_size, m_batch_size)
if available_nums > stm_config.available_stm_thresh:
MultiviewLosses.update(stm_loss.item(), m_batch_size)
else:
stm_loss = 0
else:
stm_loss = 0
# measure accuracy and record loss
if use_stm[0]:
total_loss = det_loss + sbr_loss * stm_config.sbr_weights + stm_loss * stm_config.stm_weights
else:
total_loss = det_loss + stm_loss * stm_config.stm_weights
if isinstance(total_loss, numbers.Number):
warnings.warn(
'The {:}-th iteration has no detection loss and no lk loss'.
format(i))
else:
TotalLosses.update(total_loss.item(), batch_size)
# compute gradient and do SGD step
if mode == 'train': # training mode
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
eval_time.update(time.time() - end)
with torch.no_grad():
batch_locs = batch_locs.detach().to(cpu)[:,
annotate_index, :num_pts]
batch_scos = batch_scos.detach().to(cpu)[:,
annotate_index, :num_pts]
# evaluate the training data
for ibatch in range(iv_size):
imgidx, nopoint = image_index[ibatch], nopoints[ibatch]
if nopoint == 1: continue
norm_locs = torch.cat(
(batch_locs[ibatch].permute(1, 0), torch.ones(1, num_pts)),
dim=0)
transtheta = transthetas[ibatch][:2, :]
norm_locs = torch.mm(transtheta, norm_locs)
real_locs = denormalize_points(shapes[ibatch].tolist(),
norm_locs)
real_locs = torch.cat(
(real_locs, batch_scos[ibatch].view(1, num_pts)), dim=0)
image_path = loader.dataset.datas[imgidx][annotate_index]
normDistce = loader.dataset.NormDistances[imgidx]
xpoints = loader.dataset.labels[imgidx].get_points()
eval_meta.append(real_locs.numpy(), xpoints.numpy(),
image_path, normDistce)
if save_dir:
pro_debug_save(save_dir,
Path(image_path).name,
frames[ibatch,
annotate_index], targets[ibatch],
normpoints[ibatch], meanthetas[ibatch],
batch_heatmaps[-1][ibatch, annotate_index],
args.tensor2imageF)
# measure elapsed time
batch_time.update(time.time() - end)
last_time = convert_secs2time(batch_time.avg * (len(loader) - i - 1),
True)
end = time.time()
if i % print_freq == 0 or i + 1 == len(loader):
logger.log(' -->>[{:}]: [{:}][{:03d}/{:03d}] '
'Time {batch_time.val:4.2f} ({batch_time.avg:4.2f}) '
'Data {data_time.val:4.2f} ({data_time.avg:4.2f}) '
'F-time {forward_time.val:4.2f} ({forward_time.avg:4.2f}) '
'Det {dloss.val:7.4f} ({dloss.avg:7.4f}) '
'SBR {sloss.val:7.6f} ({sloss.avg:7.6f}) '
'STM {mloss.val:7.6f} ({mloss.avg:7.6f}) '
'Loss {loss.val:7.4f} ({loss.avg:7.4f}) '.format(
mode, epoch_str, i, len(loader), batch_time=batch_time,
data_time=data_time, forward_time=forward_time, \
dloss=DetLosses, sloss=TemporalLosses, mloss=MultiviewLosses, loss=TotalLosses)
+ last_time + each_stage_loss_value \
+ ' I={:}'.format(list(frames.size())) \
+ ' Vis-PTS : {:2d} ({:.1f})'.format(int(visible_points.val), visible_points.avg) \
+ ' Ava-PTS : {:.1f} ({:.1f})'.format(alk_points.val, alk_points.avg) \
+ ' A3D-PTS : {:.1f} ({:.1f})'.format(a3d_points.val, a3d_points.avg) )
if args.debug:
logger.log(' -->>Indexes : {:}'.format(image_index))
nme, _, _ = eval_meta.compute_mse(loader.dataset.dataset_name, logger)
return TotalLosses.avg, nme
def x_sbr_main_regression(args, loader, teacher, net, criterion, optimizer, epoch_str, logger, opt_config, sbr_config, use_sbr, mode):
assert mode == 'train' or mode == 'test', 'invalid mode : {:}'.format(mode)
args = copy.deepcopy(args)
batch_time, data_time, forward_time, eval_time = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
visible_points, DetLosses, TotalLosses, TemporalLosses = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
alk_points = AverageMeter()
annotate_index = loader.dataset.video_L
eval_meta = Eval_Meta()
cpu = torch.device('cpu')
if args.debug: save_dir = Path(args.save_path) / 'DEBUG' / ('{:}-'.format(mode) + epoch_str)
else : save_dir = None
# switch to train mode
if mode == 'train':
logger.log('Temporal-Main-Regression: training : {:} .. SBR={:}'.format(sbr_config, use_sbr))
print_freq = args.print_freq
net.train() ; criterion.train()
else:
logger.log('Temporal-Main-Regression : evaluation mode.')
print_freq = args.print_freq_eval
net.eval() ; criterion.eval()
teacher.eval()
i_batch_size, v_batch_size = args.i_batch_size, args.v_batch_size
end = time.time()
for i, (frames, Fflows, Bflows, targets, masks, normpoints, transthetas, meanthetas, image_index, nopoints, shapes, is_images) in enumerate(loader):
# frames : IBatch+VBatch, Frame, Channel, Height, Width
# Fflows : IBatch+VBatch, Frame-1, Height, Width, 2
# Bflows : IBatch+VBatch, Frame-1, Height, Width, 2
# information
image_index = image_index.squeeze(1).tolist()
(batch_size, frame_length, C, H, W), num_pts = frames.size(), args.num_pts
visible_point_num = float(np.sum(masks.numpy()[:,:-1,:,:])) / batch_size
visible_points.update(visible_point_num, batch_size)
assert is_images[:i_batch_size].sum().item() == i_batch_size, '{:} vs. {:}'.format(is_images, i_batch_size)
assert is_images[i_batch_size:].sum().item() == 0, '{:} vs. {:}'.format(is_images, v_batch_size)
normpoints = normpoints.permute(0, 2, 1)
target_points = normpoints[:, :, :2].contiguous().cuda(non_blocking=True)
target_scores = normpoints[:, :, 2:].contiguous().cuda(non_blocking=True)
det_masks = (1-nopoints).view(batch_size, 1, 1) * masks[:, :num_pts].contiguous().view(batch_size, num_pts, 1)
have_det_loss = det_masks.sum().item() > 0
det_masks = det_masks.cuda(non_blocking=True)
nopoints = nopoints.squeeze(1).tolist()
# measure data loading time
data_time.update(time.time() - end)
# batch_heatmaps is a list for stage-predictions, each element should be [Batch, Sequence, PTS, H/Down, W/Down]
batch_locs, batch_past2now, batch_future2now, batch_FBcheck = net(frames, Fflows, Bflows, is_images)
forward_time.update(time.time() - end)
# detection loss
if have_det_loss:
with torch.no_grad():
sotf_targets = teacher(frames)
det_loss = criterion(batch_locs, sotf_targets, None)
DetLosses.update(det_loss.item(), batch_size)
else:
det_loss = 0
# temporal loss
if use_sbr:
video_batch_locs = batch_locs[i_batch_size:, :]
video_past2now, video_future2now, video_FBcheck = batch_past2now[i_batch_size:], batch_future2now[i_batch_size:], batch_FBcheck[i_batch_size:]
video_mask = masks[i_batch_size:, :-1].contiguous().cuda(non_blocking=True)
sbr_loss, available_nums = calculate_temporal_loss(criterion, video_batch_locs, video_past2now, video_future2now, video_FBcheck, video_mask, sbr_config)
alk_points.update(float(available_nums)/v_batch_size, v_batch_size)
if available_nums > sbr_config.available_thresh:
TemporalLosses.update(sbr_loss.item(), v_batch_size)
else:
sbr_loss = 0
else:
sbr_loss = 0
# measure accuracy and record loss
#if sbr_config.weight != 0: total_loss = det_loss + sbr_loss * sbr_config.weight
#else : total_loss = det_loss
if use_sbr: total_loss = det_loss + sbr_loss * sbr_config.weight
else : total_loss = det_loss
if isinstance(total_loss, numbers.Number):
warnings.warn('The {:}-th iteration has no detection loss and no lk loss'.format(i))
else:
TotalLosses.update(total_loss.item(), batch_size)
# compute gradient and do SGD step
if mode == 'train': # training mode
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
eval_time.update(time.time() - end)
with torch.no_grad():
batch_locs = batch_locs.detach().to(cpu)[:, annotate_index]
# evaluate the training data
for ibatch, (imgidx, nopoint) in enumerate(zip(image_index, nopoints)):
if nopoint == 1: continue
norm_locs = torch.cat((batch_locs[ibatch].permute(1,0), torch.ones(1, num_pts)), dim=0)
transtheta = transthetas[ibatch][:2,:]
norm_locs = torch.mm(transtheta, norm_locs)
real_locs = denormalize_points(shapes[ibatch].tolist(), norm_locs)
real_locs = torch.cat((real_locs, torch.ones(1, num_pts)), dim=0)
image_path = loader.dataset.datas[imgidx][annotate_index]
normDistce = loader.dataset.NormDistances[imgidx]
xpoints = loader.dataset.labels[imgidx].get_points()
eval_meta.append(real_locs.numpy(), xpoints.numpy(), image_path, normDistce)
if save_dir:
pro_debug_save(save_dir, Path(image_path).name, frames[ibatch, annotate_index], targets[ibatch], normpoints[ibatch], meanthetas[ibatch], batch_heatmaps[-1][ibatch, annotate_index], args.tensor2imageF)
# measure elapsed time
batch_time.update(time.time() - end)
last_time = convert_secs2time(batch_time.avg * (len(loader)-i-1), True)
end = time.time()
if i % print_freq == 0 or i+1 == len(loader):
logger.log(' -->>[{:}]: [{:}][{:03d}/{:03d}] '
'Time {batch_time.val:4.2f} ({batch_time.avg:4.2f}) '
'Data {data_time.val:4.2f} ({data_time.avg:4.2f}) '
'F-time {forward_time.val:4.2f} ({forward_time.avg:4.2f}) '
'Det {dloss.val:7.4f} ({dloss.avg:7.4f}) '
'SBR {sloss.val:7.4f} ({sloss.avg:7.4f}) '
'Loss {loss.val:7.4f} ({loss.avg:7.4f}) '.format(
mode, epoch_str, i, len(loader), batch_time=batch_time,
data_time=data_time, forward_time=forward_time, \
dloss=DetLosses, sloss=TemporalLosses, loss=TotalLosses)
+ last_time \
+ ' I={:}'.format(list(frames.size())) \
+ ' Vis-PTS : {:2d} ({:.1f})'.format(int(visible_points.val), visible_points.avg) \
+ ' Ava-PTS : {:.1f} ({:.1f})'.format(alk_points.val, alk_points.avg))
if args.debug:
logger.log(' -->>Indexes : {:}'.format(image_index))
nme, _, _ = eval_meta.compute_mse(loader.dataset.dataset_name, logger)
return TotalLosses.avg, nme
def evaluate(args):
if args.cuda:
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
else:
print('Use the CPU mode')
print('The image is {:}'.format(args.image))
print('The model is {:}'.format(args.model))
last_info = Path(args.model)
assert last_info.exists(), 'The model path {:} does not exist'.format(
last_info)
last_info = torch.load(last_info, map_location=torch.device('cpu'))
snapshot = last_info['last_checkpoint']
assert snapshot.exists(), 'The model path {:} does not exist'.format(
snapshot)
print('The face bounding box is {:}'.format(args.face))
assert len(args.face) == 4, 'Invalid face input : {:}'.format(args.face)
snapshot = torch.load(snapshot, map_location=torch.device('cpu'))
param = snapshot['args']
# General Data Argumentation
if param.use_gray == False:
mean_fill = tuple([int(x * 255) for x in [0.485, 0.456, 0.406]])
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
else:
mean_fill = (0.5, )
normalize = transforms.Normalize(mean=[mean_fill[0]], std=[0.5])
eval_transform = transforms.Compose2V([transforms.ToTensor(), normalize, \
transforms.PreCrop(param.pre_crop_expand), \
transforms.CenterCrop(param.crop_max)])
model_config = load_configure(param.model_config, None)
dataset = Dataset(eval_transform, param.sigma, model_config.downsample,
param.heatmap_type, (120, 96), param.use_gray, None,
param.data_indicator)
#dataset = Dataset(eval_transform, param.sigma, model_config.downsample, param.heatmap_type, (param.height,param.width), param.use_gray, None, param.data_indicator)
dataset.reset(param.num_pts)
net = obtain_pro_model(model_config, param.num_pts + 1, param.sigma,
param.use_gray)
net.load_state_dict(remove_module_dict(snapshot['state_dict']))
if args.cuda: net = net.cuda()
print('Processing the input face image.')
face_meta = PointMeta(dataset.NUM_PTS, None, args.face, args.image,
'BASE-EVAL')
face_img = pil_loader(args.image, dataset.use_gray)
affineImage, heatmaps, mask, norm_trans_points, transthetas, _, _, _, shape = dataset._process_(
face_img, face_meta, -1)
#import cv2; cv2.imwrite('temp.png', transforms.ToPILImage(normalize, False)(affineImage))
# network forward
with torch.no_grad():
if args.cuda: inputs = affineImage.unsqueeze(0).cuda()
else: inputs = affineImage.unsqueeze(0)
_, _, batch_locs, batch_scos = net(inputs)
batch_locs, batch_scos = batch_locs.cpu(), batch_scos.cpu()
(batch_size, C, H, W), num_pts = inputs.size(), param.num_pts
locations, scores = batch_locs[0, :-1, :], batch_scos[:, :-1]
norm_locs = normalize_points((H, W), locations.transpose(1, 0))
norm_locs = torch.cat((norm_locs, torch.ones(1, num_pts)), dim=0)
transtheta = transthetas[:2, :]
norm_locs = torch.mm(transtheta, norm_locs)
real_locs = denormalize_points(shape.tolist(), norm_locs)
real_locs = torch.cat((real_locs, scores), dim=0)
print('the coordinates for {:} facial landmarks:'.format(param.num_pts))
for i in range(param.num_pts):
point = real_locs[:, i]
print(
'the {:02d}/{:02d}-th landmark : ({:.1f}, {:.1f}), score = {:.2f}'.
format(i, param.num_pts, float(point[0]), float(point[1]),
float(point[2])))
if args.save:
resize = 512
image = draw_image_by_points(args.image, real_locs, 2, (255, 0, 0),
args.face, resize)
image.save(args.save)
print('save the visualization results into {:}'.format(args.save))
else:
print('ignore the visualization procedure')
def basic_main_heatmap(args, loader, net, criterion, optimizer, epoch_str,
logger, opt_config, mode):
assert mode == 'train' or mode == 'test', 'invalid mode : {:}'.format(mode)
args = copy.deepcopy(args)
batch_time, data_time, forward_time, eval_time = AverageMeter(
), AverageMeter(), AverageMeter(), AverageMeter()
visible_points, losses = AverageMeter(), AverageMeter()
eval_meta = Eval_Meta()
cpu = torch.device('cpu')
if args.debug:
save_dir = Path(
args.save_path) / 'DEBUG' / ('{:}-'.format(mode) + epoch_str)
else:
save_dir = None
# switch to train mode
if mode == 'train':
logger.log('basic-main-V2 : training mode.')
print_freq = args.print_freq
net.train()
criterion.train()
else:
logger.log('basic-main-V2 : evaluation mode.')
print_freq = args.print_freq_eval
net.eval()
criterion.eval()
end = time.time()
for i, (inputs, targets, masks, normpoints, transthetas, meanthetas,
image_index, nopoints, shapes) in enumerate(loader):
# inputs : Batch, Channel, Height, Width
# information
image_index = image_index.squeeze(1).tolist()
(batch_size, C, H, W), num_pts = inputs.size(), args.num_pts
visible_point_num = float(np.sum(
masks.numpy()[:, :-1, :, :])) / batch_size
visible_points.update(visible_point_num, batch_size)
annotated_num = batch_size - sum(nopoints)
det_masks = (1 - nopoints).view(batch_size, 1, 1, 1) * masks
det_masks = det_masks.cuda(non_blocking=True)
nopoints = nopoints.squeeze(1).tolist()
targets = targets.cuda(non_blocking=True)
# measure data loading time
data_time.update(time.time() - end)
# batch_heatmaps is a list for stage-predictions, each element should be [Batch, C, H, W]
batch_features, batch_heatmaps, batch_locs, batch_scos = net(inputs)
forward_time.update(time.time() - end)
loss, each_stage_loss_value = compute_stage_loss(
criterion, targets, batch_heatmaps, det_masks)
# measure accuracy and record loss
losses.update(loss.item(), batch_size)
# compute gradient and do SGD step
if mode == 'train': # training mode
optimizer.zero_grad()
loss.backward()
optimizer.step()
eval_time.update(time.time() - end)
with torch.no_grad():
batch_locs, batch_scos = batch_locs.detach().to(
cpu), batch_scos.detach().to(cpu)
# evaluate the training data
for ibatch, (imgidx,
nopoint) in enumerate(zip(image_index, nopoints)):
locations = batch_locs[ibatch, :-1, :]
norm_locs = normalize_points((H, W), locations.transpose(1, 0))
norm_locs = torch.cat((norm_locs, torch.ones(1, num_pts)),
dim=0)
transtheta = transthetas[ibatch][:2, :]
norm_locs = torch.mm(transtheta, norm_locs)
real_locs = denormalize_points(shapes[ibatch].tolist(),
norm_locs)
real_locs = torch.cat(
(real_locs, batch_scos[ibatch, :-1].view(1, -1)), dim=0)
#real_locs = torch.cat((real_locs, torch.ones(1, num_pts)), dim=0)
image_path = loader.dataset.datas[imgidx]
normDistce = loader.dataset.NormDistances[imgidx]
if nopoint == 1: xpoints = None
else:
xpoints = loader.dataset.labels[imgidx].get_points().numpy(
)
eval_meta.append(real_locs.numpy(), xpoints, image_path,
normDistce)
if save_dir:
pro_debug_save(save_dir,
Path(image_path).name, inputs[ibatch],
targets[ibatch], normpoints[ibatch],
meanthetas[ibatch],
batch_heatmaps[-1][ibatch],
args.tensor2imageF)
# measure elapsed time
batch_time.update(time.time() - end)
last_time = convert_secs2time(batch_time.avg * (len(loader) - i - 1),
True)
end = time.time()
if i % print_freq == 0 or i + 1 == len(loader):
logger.log(' -->>[{:}]: [{:}][{:03d}/{:03d}] '
'Time {batch_time.val:4.2f} ({batch_time.avg:4.2f}) '
'Data {data_time.val:4.2f} ({data_time.avg:4.2f}) '
'Forward {forward_time.val:4.2f} ({forward_time.avg:4.2f}) '
'Loss {loss.val:7.4f} ({loss.avg:7.4f}) '.format(
mode, epoch_str, i, len(loader), batch_time=batch_time,
data_time=data_time, forward_time=forward_time, loss=losses)
+ last_time + show_stage_loss(each_stage_loss_value) \
+ ' In={:} Tar={:}'.format(list(inputs.size()), list(targets.size())) \
+ ' Vis-PTS : {:2d} ({:.1f})'.format(int(visible_points.val), visible_points.avg))
nme, _, _ = eval_meta.compute_mse(loader.dataset.dataset_name, logger)
return losses.avg, eval_meta, nme
def main(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.set_num_threads(args.workers)
print('Training Base Detector : prepare_seed : {:}'.format(args.rand_seed))
prepare_seed(args.rand_seed)
temporal_main, eval_all = procedures['{:}-train'.format(
args.procedure)], procedures['{:}-test'.format(args.procedure)]
logger = prepare_logger(args)
# General Data Argumentation
normalize, train_transform, eval_transform, robust_transform = prepare_data_augmentation(
transforms, args)
recover = transforms.ToPILImage(normalize)
args.tensor2imageF = recover
assert (args.scale_min +
args.scale_max) / 2 == 1, 'The scale is not ok : {:} ~ {:}'.format(
args.scale_min, args.scale_max)
# Model Configure Load
model_config = load_configure(args.model_config, logger)
sbr_config = load_configure(args.sbr_config, logger)
shape = (args.height, args.width)
logger.log('--> {:}\n--> Sigma : {:}, Shape : {:}'.format(
model_config, args.sigma, shape))
logger.log('--> SBR Configuration : {:}\n'.format(sbr_config))
# Training Dataset
train_data = VDataset(eval_transform, args.sigma, model_config.downsample, args.heatmap_type, shape, args.use_gray, args.mean_point, \
args.data_indicator, sbr_config, transforms.ToPILImage(normalize, 'cv2gray'))
train_data.load_list(args.train_lists, args.num_pts, args.boxindicator,
args.normalizeL, True)
if args.x68to49:
assert args.num_pts == 68, 'args.num_pts is not 68 vs. {:}'.format(
args.num_pts)
if train_data is not None: train_data = convert68to49(train_data)
args.num_pts = 49
# define the temporal model (accelerated SBR)
net = obtain_pro_temporal(model_config, sbr_config, args.num_pts,
args.sigma, args.use_gray)
assert model_config.downsample == net.downsample, 'downsample is not correct : {:} vs {:}'.format(
model_config.downsample, net.downsample)
logger.log("=> network :\n {}".format(net))
logger.log('Training-data : {:}'.format(train_data))
logger.log('arguments : {:}'.format(args))
opt_config = load_configure(args.opt_config, logger)
optimizer, scheduler, criterion = obtain_optimizer(net.parameters(),
opt_config, logger)
logger.log('criterion : {:}'.format(criterion))
net, criterion = net.cuda(), criterion.cuda()
net = torch.nn.DataParallel(net)
last_info = logger.last_info()
try:
last_checkpoint = load_checkpoint(args.init_model)
checkpoint = remove_module_dict(last_checkpoint['state_dict'], False)
net.module.detector.load_state_dict(checkpoint)
except:
last_checkpoint = load_checkpoint(args.init_model)
net.load_state_dict(last_checkpoint['state_dict'])
detector = torch.nn.DataParallel(net.module.detector)
logger.log("=> initialize the detector : {:}".format(args.init_model))
net.eval()
detector.eval()
logger.log('SBR Config : {:}'.format(sbr_config))
save_xdir = logger.path('meta')
random.seed(111)
index_list = list(range(len(train_data)))
random.shuffle(index_list)
#selected_list = index_list[: min(200, len(index_list))]
#selected_list = [7260, 11506, 39952, 75196, 51614, 41061, 37747, 41355]
#for iidx, i in enumerate(selected_list):
index_list.remove(47875)
selected_list = [47875] + index_list
save_xdir = logger.path('meta')
type_error_1, type_error_2, type_error, misses = 0, 0, 0, 0
type_error_pts, total_pts = 0, 0
for iidx, i in enumerate(selected_list):
frames, Fflows, Bflows, targets, masks, normpoints, transthetas, meanthetas, image_index, nopoints, shapes, is_images = train_data[
i]
frames, Fflows, Bflows, is_images = frames.unsqueeze(
0), Fflows.unsqueeze(0), Bflows.unsqueeze(0), is_images.unsqueeze(
0)
# batch_heatmaps is a list for stage-predictions, each element should be [Batch, Sequence, PTS, H/Down, W/Down]
with torch.no_grad():
if args.procedure == 'heatmap':
batch_heatmaps, batch_locs, batch_scos, batch_past2now, batch_future2now, batch_FBcheck = net(
frames, Fflows, Bflows, is_images)
else:
batch_locs, batch_past2now, batch_future2now, batch_FBcheck = net(
frames, Fflows, Bflows, is_images)
(batch_size, frame_length, C, H,
W), num_pts, annotate_index = frames.size(
), args.num_pts, train_data.video_L
batch_locs = batch_locs.cpu()[:, :, :num_pts]
video_mask = masks.unsqueeze(0)[:, :num_pts]
batch_past2now = batch_past2now.cpu()[:, :, :num_pts]
batch_future2now = batch_future2now.cpu()[:, :, :num_pts]
batch_FBcheck = batch_FBcheck[:, :num_pts].cpu()
FB_check_oks = FB_communication(criterion, batch_locs, batch_past2now,
batch_future2now, batch_FBcheck,
video_mask, sbr_config)
# locations
norm_past_det_locs = torch.cat(
(batch_locs[0, annotate_index - 1, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_noww_det_locs = torch.cat(
(batch_locs[0, annotate_index, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_next_det_locs = torch.cat(
(batch_locs[0, annotate_index + 1, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_next_locs = torch.cat(
(batch_past2now[0, annotate_index, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_past_locs = torch.cat(
(batch_future2now[0, annotate_index - 1, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
transtheta = transthetas[:2, :]
norm_past_det_locs = torch.mm(transtheta, norm_past_det_locs)
norm_noww_det_locs = torch.mm(transtheta, norm_noww_det_locs)
norm_next_det_locs = torch.mm(transtheta, norm_next_det_locs)
norm_next_locs = torch.mm(transtheta, norm_next_locs)
norm_past_locs = torch.mm(transtheta, norm_past_locs)
real_past_det_locs = denormalize_points(shapes.tolist(),
norm_past_det_locs)
real_noww_det_locs = denormalize_points(shapes.tolist(),
norm_noww_det_locs)
real_next_det_locs = denormalize_points(shapes.tolist(),
norm_next_det_locs)
real_next_locs = denormalize_points(shapes.tolist(), norm_next_locs)
real_past_locs = denormalize_points(shapes.tolist(), norm_past_locs)
gt_noww_points = train_data.labels[image_index.item()].get_points()
gt_past_points = train_data.find_index(
train_data.datas[image_index.item()][annotate_index - 1])
gt_next_points = train_data.find_index(
train_data.datas[image_index.item()][annotate_index + 1])
FB_check_oks = FB_check_oks[:num_pts].squeeze()
#import pdb; pdb.set_trace()
if FB_check_oks.sum().item() > 2:
# type 1 error : detection at both (t) and (t-1) is wrong, while pass the check
is_type_1, (T_wrong, T_total) = check_is_1st_error(
[real_past_det_locs, real_noww_det_locs, real_next_det_locs],
[gt_past_points, gt_noww_points, gt_next_points], FB_check_oks,
shapes)
# type 2 error : detection at frame t is ok, while tracking are wrong and frame at (t-1) is wrong:
spec_index, is_type_2 = check_is_2nd_error(
real_noww_det_locs, gt_noww_points,
[real_past_locs, real_next_locs],
[gt_past_points, gt_next_points], FB_check_oks, shapes)
type_error_1 += is_type_1
type_error_2 += is_type_2
type_error += is_type_1 or is_type_2
type_error_pts, total_pts = type_error_pts + T_wrong, total_pts + T_total
if is_type_2:
RED, GREEN, BLUE = (255, 0, 0), (0, 255, 0), (0, 0, 255)
[image_past, image_noww,
image_next] = train_data.datas[image_index.item()]
crop_box = train_data.labels[
image_index.item()].get_box().tolist()
point_index = FB_check_oks.nonzero().squeeze().tolist()
colors = [
GREEN if _i in point_index else RED
for _i in range(num_pts)
] + [BLUE for _i in range(num_pts)]
I_past_det = draw_image_by_points(
image_past,
torch.cat((real_past_det_locs, gt_past_points[:2]), dim=1),
3, colors, crop_box, (400, 500))
I_noww_det = draw_image_by_points(
image_noww,
torch.cat((real_noww_det_locs, gt_noww_points[:2]), dim=1),
3, colors, crop_box, (400, 500))
I_next_det = draw_image_by_points(
image_next,
torch.cat((real_next_det_locs, gt_next_points[:2]), dim=1),
3, colors, crop_box, (400, 500))
I_past = draw_image_by_points(
image_past,
torch.cat((real_past_locs, gt_past_points[:2]), dim=1), 3,
colors, crop_box, (400, 500))
I_next = draw_image_by_points(
image_next,
torch.cat((real_next_locs, gt_next_points[:2]), dim=1), 3,
colors, crop_box, (400, 500))
###
I_past.save(str(save_xdir / '{:05d}-v1-a-pastt.png'.format(i)))
I_noww_det.save(
str(save_xdir / '{:05d}-v1-b-curre.png'.format(i)))
I_next.save(str(save_xdir / '{:05d}-v1-c-nextt.png'.format(i)))
I_past_det.save(
str(save_xdir / '{:05d}-v1-det-a-past.png'.format(i)))
I_noww_det.save(
str(save_xdir / '{:05d}-v1-det-b-curr.png'.format(i)))
I_next_det.save(
str(save_xdir / '{:05d}-v1-det-c-next.png'.format(i)))
logger.log('TYPE-ERROR : {:}, landmark-index : {:}'.format(
i, spec_index))
else:
misses += 1
string = 'Handle {:05d}/{:05d} :: {:05d}'.format(
iidx, len(selected_list), i)
string += ', error-1 : {:} ({:.2f}%), error-2 : {:} ({:.2f}%)'.format(
type_error_1, type_error_1 * 100.0 / (iidx + 1), type_error_2,
type_error_2 * 100.0 / (iidx + 1))
string += ', error : {:} ({:.2f}%), miss : {:}'.format(
type_error, type_error * 100.0 / (iidx + 1), misses)
string += ', final-error : {:05d} / {:05d} = {:.2f}%'.format(
type_error_pts, total_pts, type_error_pts * 100.0 / total_pts)
logger.log(string)
def __process_affine(self,
frames,
target,
theta,
nopoints,
skipopt,
aux_info=None):
frames, target, theta = [frame.clone() for frame in frames
], target.copy(), theta.clone()
(C, H, W), (height, width) = frames[0].size(), self.shape
if nopoints: # do not have label
norm_trans_points = torch.zeros((3, self.NUM_PTS))
heatmaps = torch.zeros(
(self.NUM_PTS + 1, height // self.downsample,
width // self.downsample))
mask = torch.ones((self.NUM_PTS + 1, 1, 1), dtype=torch.uint8)
else:
norm_trans_points = apply_affine2point(target.get_points(), theta,
(H, W))
norm_trans_points = apply_boundary(norm_trans_points)
real_trans_points = norm_trans_points.clone()
real_trans_points[:2, :] = denormalize_points(
self.shape, real_trans_points[:2, :])
heatmaps, mask = generate_label_map(real_trans_points.numpy(),
height // self.downsample,
width // self.downsample,
self.sigma, self.downsample,
nopoints,
self.heatmap_type) # H*W*C
heatmaps = torch.from_numpy(heatmaps.transpose(
(2, 0, 1))).type(torch.FloatTensor)
mask = torch.from_numpy(mask.transpose(
(2, 0, 1))).type(torch.ByteTensor)
affineFrames = [
affine2image(frame, theta, self.shape) for frame in frames
]
if not skipopt:
Gframes = [self.tensor2img(frame) for frame in affineFrames]
forward_flow, backward_flow = [], []
for idx in range(len(Gframes)):
if idx > 0:
forward_flow.append(
self.optflow.calc(Gframes[idx - 1], Gframes[idx],
None))
if idx + 1 < len(Gframes):
#backward_flow.append( self.optflow.calc(Gframes[idx], Gframes[idx+1], None) ) ## HDXY
backward_flow.append(
self.optflow.calc(Gframes[idx + 1], Gframes[idx],
None))
forward_flow = torch.stack(
[torch.from_numpy(x) for x in forward_flow])
backward_flow = torch.stack(
[torch.from_numpy(x) for x in backward_flow])
else:
forward_flow, backward_flow = torch.zeros(
(len(affineFrames) - 1, height, width, 2)), torch.zeros(
(len(affineFrames) - 1, height, width, 2))
# affineFrames #frames x #channel x #height x #width
# forward_flow (#frames-1) x #height x #width x 2
# backward_flow (#frames-1) x #height x #width x 2
return torch.stack(
affineFrames
), forward_flow, backward_flow, heatmaps, mask, norm_trans_points, theta
def __process_affine(self,
frames,
target,
theta,
nopoints,
skip_opt,
aux_info=None):
frames, target, theta = [frame.clone() for frame in frames
], target.copy(), theta.clone()
(C, H, W), (height, width) = frames[0].size(), self.shape
if nopoints: # do not have label
norm_trans_points = torch.zeros((3, self.NUM_PTS))
heatmaps = torch.zeros(
(self.NUM_PTS + 1, height // self.downsample,
width // self.downsample))
mask = torch.ones((self.NUM_PTS + 1, 1, 1), dtype=torch.uint8)
transpose_theta = identity2affine(False)
else:
norm_trans_points = apply_affine2point(target.get_points(), theta,
(H, W))
norm_trans_points = apply_boundary(norm_trans_points)
real_trans_points = norm_trans_points.clone()
real_trans_points[:2, :] = denormalize_points(
self.shape, real_trans_points[:2, :])
heatmaps, mask = generate_label_map(real_trans_points.numpy(),
height // self.downsample,
width // self.downsample,
self.sigma, self.downsample,
nopoints,
self.heatmap_type) # H*W*C
heatmaps = torch.from_numpy(heatmaps.transpose(
(2, 0, 1))).type(torch.FloatTensor)
mask = torch.from_numpy(mask.transpose(
(2, 0, 1))).type(torch.ByteTensor)
if torch.sum(norm_trans_points[2, :] ==
1) < 3 or self.mean_face is None:
warnings.warn(
'In GeneralDatasetV2 after transformation, no visiable point, using identity instead. Aux: {:}'
.format(aux_info))
transpose_theta = identity2affine(False)
else:
transpose_theta = solve2theta(norm_trans_points,
self.mean_face.clone())
affineFrames = [
affine2image(frame, theta, self.shape) for frame in frames
]
if not skip_opt:
Gframes = [self.tensor2img(frame) for frame in affineFrames]
forward_flow, backward_flow = [], []
for idx in range(len(Gframes)):
if idx > 0:
forward_flow.append(
self.optflow.calc(Gframes[idx - 1], Gframes[idx],
None))
if idx + 1 < len(Gframes):
#backward_flow.append( self.optflow.calc(Gframes[idx], Gframes[idx+1], None) )
backward_flow.append(
self.optflow.calc(Gframes[idx + 1], Gframes[idx],
None))
forward_flow = torch.stack(
[torch.from_numpy(x) for x in forward_flow])
backward_flow = torch.stack(
[torch.from_numpy(x) for x in backward_flow])
else:
forward_flow, backward_flow = torch.zeros(
(len(affineFrames) - 1, height, width, 2)), torch.zeros(
(len(affineFrames) - 1, height, width, 2))
# affineFrames #frames x #channel x #height x #width
# forward_flow (#frames-1) x #height x #width x 2
# backward_flow (#frames-1) x #height x #width x 2
return torch.stack(
affineFrames
), forward_flow, backward_flow, heatmaps, mask, norm_trans_points, theta, transpose_theta
def main(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.set_num_threads(args.workers)
print('Training Base Detector : prepare_seed : {:}'.format(args.rand_seed))
prepare_seed(args.rand_seed)
temporal_main, eval_all = procedures['{:}-train'.format(
args.procedure)], procedures['{:}-test'.format(args.procedure)]
logger = prepare_logger(args)
# General Data Argumentation
normalize, train_transform, eval_transform, robust_transform = prepare_data_augmentation(
transforms, args)
recover = transforms.ToPILImage(normalize)
args.tensor2imageF = recover
assert (args.scale_min +
args.scale_max) / 2 == 1, 'The scale is not ok : {:} ~ {:}'.format(
args.scale_min, args.scale_max)
# Model Configure Load
model_config = load_configure(args.model_config, logger)
sbr_config = load_configure(args.sbr_config, logger)
shape = (args.height, args.width)
logger.log('--> {:}\n--> Sigma : {:}, Shape : {:}'.format(
model_config, args.sigma, shape))
logger.log('--> SBR Configuration : {:}\n'.format(sbr_config))
# Training Dataset
train_data = VDataset(eval_transform, args.sigma, model_config.downsample, args.heatmap_type, shape, args.use_gray, args.mean_point, \
args.data_indicator, sbr_config, transforms.ToPILImage(normalize, 'cv2gray'))
train_data.load_list(args.train_lists, args.num_pts, args.boxindicator,
args.normalizeL, True)
# Evaluation Dataloader
assert len(
args.eval_ilists) == 1, 'invalid length of eval_ilists : {:}'.format(
len(eval_ilists))
eval_data = IDataset(eval_transform, args.sigma, model_config.downsample,
args.heatmap_type, shape, args.use_gray,
args.mean_point, args.data_indicator)
eval_data.load_list(args.eval_ilists[0], args.num_pts, args.boxindicator,
args.normalizeL, True)
if args.x68to49:
assert args.num_pts == 68, 'args.num_pts is not 68 vs. {:}'.format(
args.num_pts)
if train_data is not None: train_data = convert68to49(train_data)
eval_data = convert68to49(eval_data)
args.num_pts = 49
# define the temporal model (accelerated SBR)
net = obtain_pro_temporal(model_config, sbr_config, args.num_pts,
args.sigma, args.use_gray)
assert model_config.downsample == net.downsample, 'downsample is not correct : {:} vs {:}'.format(
model_config.downsample, net.downsample)
logger.log("=> network :\n {}".format(net))
logger.log('Training-data : {:}'.format(train_data))
logger.log('Evaluate-data : {:}'.format(eval_data))
logger.log('arguments : {:}'.format(args))
opt_config = load_configure(args.opt_config, logger)
optimizer, scheduler, criterion = obtain_optimizer(net.parameters(),
opt_config, logger)
logger.log('criterion : {:}'.format(criterion))
net, criterion = net.cuda(), criterion.cuda()
net = torch.nn.DataParallel(net)
last_info = logger.last_info()
try:
last_checkpoint = load_checkpoint(args.init_model)
checkpoint = remove_module_dict(last_checkpoint['state_dict'], False)
net.module.detector.load_state_dict(checkpoint)
except:
last_checkpoint = load_checkpoint(args.init_model)
net.load_state_dict(last_checkpoint['state_dict'])
detector = torch.nn.DataParallel(net.module.detector)
logger.log("=> initialize the detector : {:}".format(args.init_model))
net.eval()
detector.eval()
logger.log('SBR Config : {:}'.format(sbr_config))
save_xdir = logger.path('meta')
type_error = 0
random.seed(111)
index_list = list(range(len(train_data)))
random.shuffle(index_list)
#selected_list = index_list[: min(200, len(index_list))]
selected_list = [
7260, 11506, 39952, 75196, 51614, 41061, 37747, 41355, 47875
]
for iidx, i in enumerate(selected_list):
frames, Fflows, Bflows, targets, masks, normpoints, transthetas, meanthetas, image_index, nopoints, shapes, is_images = train_data[
i]
frames, Fflows, Bflows, is_images = frames.unsqueeze(
0), Fflows.unsqueeze(0), Bflows.unsqueeze(0), is_images.unsqueeze(
0)
# batch_heatmaps is a list for stage-predictions, each element should be [Batch, Sequence, PTS, H/Down, W/Down]
if args.procedure == 'heatmap':
batch_heatmaps, batch_locs, batch_scos, batch_past2now, batch_future2now, batch_FBcheck = net(
frames, Fflows, Bflows, is_images)
else:
batch_locs, batch_past2now, batch_future2now, batch_FBcheck = net(
frames, Fflows, Bflows, is_images)
(batch_size, frame_length, C, H,
W), num_pts, annotate_index = frames.size(
), args.num_pts, train_data.video_L
batch_locs = batch_locs.cpu()[:, :, :num_pts]
video_mask = masks.unsqueeze(0)[:, :num_pts]
batch_past2now = batch_past2now.cpu()[:, :, :num_pts]
batch_future2now = batch_future2now.cpu()[:, :, :num_pts]
batch_FBcheck = batch_FBcheck[:, :num_pts].cpu()
FB_check_oks = FB_communication(criterion, batch_locs, batch_past2now,
batch_future2now, batch_FBcheck,
video_mask, sbr_config)
# locations
norm_past_det_locs = torch.cat(
(batch_locs[0, annotate_index - 1, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_noww_det_locs = torch.cat(
(batch_locs[0, annotate_index, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_next_det_locs = torch.cat(
(batch_locs[0, annotate_index + 1, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_next_locs = torch.cat(
(batch_past2now[0, annotate_index, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
norm_past_locs = torch.cat(
(batch_future2now[0, annotate_index - 1, :num_pts].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
transtheta = transthetas[:2, :]
norm_past_det_locs = torch.mm(transtheta, norm_past_det_locs)
norm_noww_det_locs = torch.mm(transtheta, norm_noww_det_locs)
norm_next_det_locs = torch.mm(transtheta, norm_next_det_locs)
norm_next_locs = torch.mm(transtheta, norm_next_locs)
norm_past_locs = torch.mm(transtheta, norm_past_locs)
real_past_det_locs = denormalize_points(shapes.tolist(),
norm_past_det_locs)
real_noww_det_locs = denormalize_points(shapes.tolist(),
norm_noww_det_locs)
real_next_det_locs = denormalize_points(shapes.tolist(),
norm_next_det_locs)
real_next_locs = denormalize_points(shapes.tolist(), norm_next_locs)
real_past_locs = denormalize_points(shapes.tolist(), norm_past_locs)
gt_noww_points = train_data.labels[image_index.item()].get_points()
FB_check_oks = FB_check_oks[:num_pts].squeeze()
#import pdb; pdb.set_trace()
if FB_check_oks.sum().item() > 2:
point_index = FB_check_oks.nonzero().squeeze().tolist()
something_wrong = False
for pidx in point_index:
real_now_det_loc = real_noww_det_locs[:, pidx]
real_pst_det_loc = real_past_det_locs[:, pidx]
real_net_det_loc = real_next_det_locs[:, pidx]
real_nex_loc = real_next_locs[:, pidx]
real_pst_loc = real_next_locs[:, pidx]
grdt_now_loc = gt_noww_points[:2, pidx]
#if torch.abs(real_now_loc - grdt_now_loc).max() > 5:
# something_wrong = True
#if torch.abs(real_nex_loc - grdt_nex_loc).max() > 5:
# something_wrong = True
#if something_wrong == True:
if True:
[image_past, image_noww,
image_next] = train_data.datas[image_index.item()]
try:
crop_box = train_data.labels[
image_index.item()].get_box().tolist()
#crop_box = [crop_box[0]-20, crop_box[1]-20, crop_box[2]+20, crop_box[3]+20]
except:
crop_box = False
RED, GREEN, BLUE = (255, 0, 0), (0, 255, 0), (0, 0, 255)
colors = [
GREEN if _i in point_index else RED
for _i in range(num_pts)
]
if crop_box != False or True:
I_past_det = draw_image_by_points(image_past,
real_past_det_locs[:], 3,
colors, crop_box,
(400, 500))
I_noww_det = draw_image_by_points(image_noww,
real_noww_det_locs[:], 3,
colors, crop_box,
(400, 500))
I_next_det = draw_image_by_points(image_next,
real_next_det_locs[:], 3,
colors, crop_box,
(400, 500))
I_next = draw_image_by_points(image_next,
real_next_locs[:], 3, colors,
crop_box, (400, 500))
I_past = draw_image_by_points(image_past,
real_past_locs[:], 3, colors,
crop_box, (400, 500))
I_past.save(
str(save_xdir / '{:05d}-v1-a-pastt.png'.format(i)))
I_noww_det.save(
str(save_xdir / '{:05d}-v1-b-curre.png'.format(i)))
I_next.save(
str(save_xdir / '{:05d}-v1-c-nextt.png'.format(i)))
I_past_det.save(
str(save_xdir / '{:05d}-v1-det-a-past.png'.format(i)))
I_noww_det.save(
str(save_xdir / '{:05d}-v1-det-b-curr.png'.format(i)))
I_next_det.save(
str(save_xdir / '{:05d}-v1-det-c-next.png'.format(i)))
#[image_past, image_noww, image_next] = train_data.datas[image_index.item()]
#image_noww = draw_image_by_points(image_noww, real_noww_locs[:], 2, colors, False, False)
#image_next = draw_image_by_points(image_next, real_next_locs[:], 2, colors, False, False)
#image_past = draw_image_by_points(image_past, real_past_locs[:], 2, colors, False, False)
#image_noww.save( str(save_xdir / '{:05d}-v2-b-curre.png'.format(i)) )
#image_next.save( str(save_xdir / '{:05d}-v2-c-nextt.png'.format(i)) )
#image_past.save( str(save_xdir / '{:05d}-v2-a-pastt.png'.format(i)) )
#type_error += 1
logger.log(
'Handle {:05d}/{:05d} :: {:05d}, ok-points={:.3f}, wrong data={:}'.
format(iidx, len(selected_list), i,
FB_check_oks.float().mean().item(), type_error))
save_xx_dir = save_xdir.parent / 'image-data'
save_xx_dir.mkdir(parents=True, exist_ok=True)
selected_list = [100, 115, 200, 300, 400] + list(range(200, 220))
for iidx, i in enumerate(selected_list):
inputs, targets, masks, normpoints, transthetas, meanthetas, image_index, nopoints, shapes = eval_data[
i]
inputs = inputs.unsqueeze(0)
(batch_size, C, H, W), num_pts = inputs.size(), args.num_pts
_, _, batch_locs, batch_scos = detector(inputs) # inputs
batch_locs, batch_scos = batch_locs.cpu(), batch_scos.cpu()
norm_locs = normalize_points((H, W),
batch_locs[0, :num_pts].transpose(1, 0))
norm_det_locs = torch.cat((norm_locs, torch.ones(1, num_pts)), dim=0)
norm_det_locs = torch.mm(transthetas[:2, :], norm_det_locs)
real_det_locs = denormalize_points(shapes.tolist(), norm_det_locs)
gt_now_points = eval_data.labels[image_index.item()].get_points()
image_now = eval_data.datas[image_index.item()]
crop_box = eval_data.labels[image_index.item()].get_box().tolist()
RED, GREEN, BLUE = (255, 0, 0), (0, 255, 0), (0, 0, 255)
Gcolors = [GREEN for _ in range(num_pts)]
points = torch.cat((real_det_locs, gt_now_points[:2]), dim=1)
colors = [GREEN
for _ in range(num_pts)] + [BLUE for _ in range(num_pts)]
image = draw_image_by_points(image_now, real_det_locs, 3, Gcolors,
crop_box, (400, 500))
image.save(str(save_xx_dir / '{:05d}-crop.png'.format(i)))
image = draw_image_by_points(image_now, points, 3, colors, False,
False)
#image = draw_image_by_points(image_now, real_det_locs, 3, colors , False, False)
image.save(str(save_xx_dir / '{:05d}-orig.png'.format(i)))
logger.log('Finish drawing : {:}'.format(save_xdir))
logger.log('Finish drawing : {:}'.format(save_xx_dir))
logger.close()
def main(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.set_num_threads(args.workers)
print('Training Base Detector : prepare_seed : {:}'.format(args.rand_seed))
prepare_seed(args.rand_seed)
logger = prepare_logger(args)
checkpoint = load_checkpoint(args.init_model)
xargs = checkpoint['args']
logger.log('Previous args : {:}'.format(xargs))
# General Data Augmentation
if xargs.use_gray == False:
mean_fill = tuple([int(x * 255) for x in [0.485, 0.456, 0.406]])
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
else:
mean_fill = (0.5, )
normalize = transforms.Normalize(mean=[mean_fill[0]], std=[0.5])
eval_transform = transforms.Compose2V([transforms.ToTensor(), normalize, \
transforms.PreCrop(xargs.pre_crop_expand), \
transforms.CenterCrop(xargs.crop_max)])
# Model Configure Load
model_config = load_configure(xargs.model_config, logger)
shape = (xargs.height, xargs.width)
logger.log('--> {:}\n--> Sigma : {:}, Shape : {:}'.format(
model_config, xargs.sigma, shape))
# Evaluation Dataloader
eval_loaders = []
if args.eval_ilists is not None:
for eval_ilist in args.eval_ilists:
eval_idata = EvalDataset(eval_transform, xargs.sigma,
model_config.downsample,
xargs.heatmap_type, shape, xargs.use_gray,
xargs.data_indicator)
eval_idata.load_list(eval_ilist, args.num_pts, xargs.boxindicator,
xargs.normalizeL, True)
eval_iloader = torch.utils.data.DataLoader(
eval_idata,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
eval_loaders.append((eval_iloader, False))
if args.eval_vlists is not None:
for eval_vlist in args.eval_vlists:
eval_vdata = EvalDataset(eval_transform, xargs.sigma,
model_config.downsample,
xargs.heatmap_type, shape, xargs.use_gray,
xargs.data_indicator)
eval_vdata.load_list(eval_vlist, args.num_pts, xargs.boxindicator,
xargs.normalizeL, True)
eval_vloader = torch.utils.data.DataLoader(
eval_vdata,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
eval_loaders.append((eval_vloader, True))
# define the detector
detector = obtain_pro_model(model_config, xargs.num_pts, xargs.sigma,
xargs.use_gray)
assert model_config.downsample == detector.downsample, 'downsample is not correct : {:} vs {:}'.format(
model_config.downsample, detector.downsample)
logger.log("=> detector :\n {:}".format(detector))
logger.log("=> Net-Parameters : {:} MB".format(
count_parameters_in_MB(detector)))
logger.log('=> Eval-Transform : {:}'.format(eval_transform))
detector = detector.cuda()
net = torch.nn.DataParallel(detector)
net.eval()
net.load_state_dict(checkpoint['detector'])
cpu = torch.device('cpu')
assert len(args.use_stable) == 2
for iLOADER, (loader, is_video) in enumerate(eval_loaders):
logger.log(
'{:} The [{:2d}/{:2d}]-th test set [{:}] = {:} with {:} batches.'.
format(time_string(), iLOADER, len(eval_loaders),
'video' if is_video else 'image', loader.dataset,
len(loader)))
with torch.no_grad():
all_points, all_results, all_image_ps = [], [], []
for i, (inputs, targets, masks, normpoints, transthetas,
image_index, nopoints, shapes) in enumerate(loader):
image_index = image_index.squeeze(1).tolist()
(batch_size, C, H, W), num_pts = inputs.size(), xargs.num_pts
# batch_heatmaps is a list for stage-predictions, each element should be [Batch, C, H, W]
if xargs.procedure == 'heatmap':
batch_features, batch_heatmaps, batch_locs, batch_scos = net(
inputs)
batch_locs = batch_locs[:, :-1, :]
else:
batch_locs = net(inputs)
batch_locs = batch_locs.detach().to(cpu)
# evaluate the training data
for ibatch, (imgidx,
nopoint) in enumerate(zip(image_index, nopoints)):
if xargs.procedure == 'heatmap':
norm_locs = normalize_points(
(H, W), batch_locs[ibatch].transpose(1, 0))
norm_locs = torch.cat(
(norm_locs, torch.ones(1, num_pts)), dim=0)
else:
norm_locs = torch.cat((batch_locs[ibatch].permute(
1, 0), torch.ones(1, num_pts)),
dim=0)
transtheta = transthetas[ibatch][:2, :]
norm_locs = torch.mm(transtheta, norm_locs)
real_locs = denormalize_points(shapes[ibatch].tolist(),
norm_locs)
#real_locs = torch.cat((real_locs, batch_scos[ibatch].permute(1,0)), dim=0)
real_locs = torch.cat((real_locs, torch.ones(1, num_pts)),
dim=0)
xpoints = loader.dataset.labels[imgidx].get_points().numpy(
)
image_path = loader.dataset.datas[imgidx]
# put into the list
all_points.append(torch.from_numpy(xpoints))
all_results.append(real_locs)
all_image_ps.append(image_path)
total = len(all_points)
logger.log(
'{:} The [{:2d}/{:2d}]-th test set finishes evaluation : {:} frames/images'
.format(time_string(), iLOADER, len(eval_loaders), total))
"""
if args.use_stable[0] > 0:
save_dir = Path( osp.join(args.save_path, '{:}-X-{:03d}'.format(args.model_name, iLOADER)) )
save_dir.mkdir(parents=True, exist_ok=True)
wrap_parallel = WrapParallel(save_dir, all_image_ps, all_results, all_points, 180, (255, 0, 0))
wrap_loader = torch.utils.data.DataLoader(wrap_parallel, batch_size=args.workers, shuffle=False, num_workers=args.workers, pin_memory=True)
for iL, INDEXES in enumerate(wrap_loader): _ = INDEXES
cmd = 'ffmpeg -y -i {:}/%06d.png -framerate 30 {:}.avi'.format(save_dir, save_dir)
logger.log('{:} possible >>>>> : {:}'.format(time_string(), cmd))
os.system( cmd )
if args.use_stable[1] > 0:
save_dir = Path( osp.join(args.save_path, '{:}-Y-{:03d}'.format(args.model_name, iLOADER)) )
save_dir.mkdir(parents=True, exist_ok=True)
Xpredictions, Xgts = torch.stack(all_results), torch.stack(all_points)
new_preds = fc_solve(Xgts, Xpredictions, is_cuda=True)
wrap_parallel = WrapParallel(save_dir, all_image_ps, new_preds, all_points, 180, (0, 0, 255))
wrap_loader = torch.utils.data.DataLoader(wrap_parallel, batch_size=args.workers, shuffle=False, num_workers=args.workers, pin_memory=True)
for iL, INDEXES in enumerate(wrap_loader): _ = INDEXES
cmd = 'ffmpeg -y -i {:}/%06d.png -framerate 30 {:}.avi'.format(save_dir, save_dir)
logger.log('{:} possible >>>>> : {:}'.format(time_string(), cmd))
os.system( cmd )
"""
Xpredictions, Xgts = torch.stack(all_results), torch.stack(all_points)
save_path = Path(
osp.join(args.save_path,
'{:}-result-{:03d}.pth'.format(args.model_name, iLOADER)))
torch.save(
{
'paths': all_image_ps,
'ground-truths': Xgts,
'predictions': all_results
}, save_path)
logger.log('{:} save into {:}'.format(time_string(), save_path))
if False:
new_preds = fc_solve_v2(Xgts, Xpredictions, is_cuda=True)
# create the dir
save_dir = Path(
osp.join(args.save_path,
'{:}-T-{:03d}'.format(args.model_name, iLOADER)))
save_dir.mkdir(parents=True, exist_ok=True)
wrap_parallel = WrapParallelV2(save_dir, all_image_ps, Xgts,
all_results, new_preds, all_points,
180, [args.model_name, 'SRT'])
wrap_parallel[0]
wrap_loader = torch.utils.data.DataLoader(wrap_parallel,
batch_size=args.workers,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
for iL, INDEXES in enumerate(wrap_loader):
_ = INDEXES
cmd = 'ffmpeg -y -i {:}/%06d.png -vb 5000k {:}.avi'.format(
save_dir, save_dir)
logger.log('{:} possible >>>>> : {:}'.format(time_string(), cmd))
os.system(cmd)
logger.close()
return
def basic_main_regression(args, loader, net, criterion, optimizer, epoch_str,
logger, opt_config, mode):
assert mode == 'train' or mode == 'test', 'invalid mode : {:}'.format(mode)
args = copy.deepcopy(args)
batch_time, pre_data_time, data_time, forward_time, eval_time = AverageMeter(
), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()
visible_points, LOSSES, LOSSES_LOCS, LOSSES_SCOS = AverageMeter(
), AverageMeter(), AverageMeter(), AverageMeter()
eval_meta = Eval_Meta()
cpu = torch.device('cpu')
if args.debug:
save_dir = Path(
args.save_path) / 'DEBUG' / ('{:}-'.format(mode) + epoch_str)
else:
save_dir = None
# switch to train mode
if mode == 'train':
logger.log(
'basic-main-Regression : training mode :: {:}'.format(criterion))
print_freq = args.print_freq
net.train()
criterion.train()
else:
logger.log(
'basic-main-Regression : evaluation mode :: {:}'.format(criterion))
print_freq = args.print_freq_eval
net.eval()
criterion.eval()
end = time.time()
for i, (inputs, targets, masks, normpoints, transthetas, meanthetas,
image_index, nopoints, shapes) in enumerate(loader):
# inputs : Batch, Channel, Height, Width
pre_data_time.update(time.time() - end)
# information
image_index = image_index.squeeze(1).tolist()
(batch_size, C, H, W), num_pts = inputs.size(), args.num_pts
visible_points.update(
float(masks.numpy()[:, :-1].sum()) / batch_size, batch_size)
normpoints = normpoints.permute(0, 2, 1)
target_points = normpoints[:, :, :2].contiguous().cuda(
non_blocking=True)
target_scores = normpoints[:, :,
2:].contiguous().cuda(non_blocking=True)
det_masks = (1 - nopoints).view(
batch_size, 1, 1) * masks[:, :num_pts].contiguous().view(
batch_size, num_pts, 1)
det_masks = det_masks.cuda(non_blocking=True)
nopoints = nopoints.squeeze(1).tolist()
# measure data loading time
data_time.update(time.time() - end)
batch_locs = net(inputs)
forward_time.update(time.time() - end)
loss = criterion(batch_locs, target_points, det_masks)
#loss_scos = criterion(batch_scos, target_scores, None)
#loss = loss_locs + loss_scos * opt_config.scos_weight
# measure accuracy and record loss
LOSSES.update(loss.item(), batch_size)
# compute gradient and do SGD step
if mode == 'train': # training mode
optimizer.zero_grad()
loss.backward()
optimizer.step()
eval_time.update(time.time() - end)
with torch.no_grad():
#batch_locs, batch_scos = batch_locs.detach().to(cpu), batch_scos.detach().to(cpu)
batch_locs = batch_locs.detach().to(cpu)
# evaluate the training data
for ibatch, (imgidx,
nopoint) in enumerate(zip(image_index, nopoints)):
norm_locs = torch.cat(
(batch_locs[ibatch].permute(1, 0), torch.ones(1, num_pts)),
dim=0)
transtheta = transthetas[ibatch][:2, :]
norm_locs = torch.mm(transtheta, norm_locs)
real_locs = denormalize_points(shapes[ibatch].tolist(),
norm_locs)
#real_locs = torch.cat((real_locs, batch_scos[ibatch].permute(1,0)), dim=0)
real_locs = torch.cat((real_locs, torch.ones(1, num_pts)),
dim=0)
image_path = loader.dataset.datas[imgidx]
normDistce = loader.dataset.NormDistances[imgidx]
if nopoint == 1: xpoints = None
else:
xpoints = loader.dataset.labels[imgidx].get_points().numpy(
)
eval_meta.append(real_locs.numpy(), xpoints, image_path,
normDistce)
#if save_dir:
# pro_debug_save(save_dir, Path(image_path).name, inputs[ibatch], targets[ibatch], normpoints[ibatch], meanthetas[ibatch], batch_heatmaps[-1][ibatch], args.tensor2imageF)
# measure elapsed time
batch_time.update(time.time() - end)
last_time = convert_secs2time(batch_time.avg * (len(loader) - i - 1),
True)
end = time.time()
if i % print_freq == 0 or i + 1 == len(loader):
logger.log(' -->>[{:}]: [{:}][{:03d}/{:03d}] '
'Time {batch_time.val:4.2f} ({batch_time.avg:4.2f}) '
'PRE {pre_data_time.val:4.2f} ({pre_data_time.avg:4.2f}) '
'Data {data_time.val:4.2f} ({data_time.avg:4.2f}) '
'Forward {forward_time.val:4.2f} ({forward_time.avg:4.2f}) '
'Loss {loss.val:7.4f} ({loss.avg:7.4f}) [locs={locs.avg:7.4f} scos={scos.avg:7.4f}]'.format(
mode, epoch_str, i, len(loader), batch_time=batch_time, pre_data_time=pre_data_time,
data_time=data_time, forward_time=forward_time, loss=LOSSES, locs=LOSSES_LOCS, scos=LOSSES_SCOS)
+ last_time \
+ ' I={:}'.format(list(inputs.size())) \
+ ' Vis={:2d} ({:.1f})'.format(int(visible_points.val), visible_points.avg))
nme, _, _ = eval_meta.compute_mse(loader.dataset.dataset_name, logger)
return LOSSES.avg, eval_meta, nme
