def train_step(self, input_batch):
self.model.train()
# Get data from the batch
images = input_batch['img'] # input image
gt_keypoints_2d = input_batch['keypoints'] # 2D keypoints
gt_pose = input_batch['pose'] # SMPL pose parameters
gt_betas = input_batch['betas'] # SMPL beta parameters
gt_joints = input_batch['pose_3d'] # 3D pose
has_smpl = input_batch['has_smpl'].to(
torch.bool
) # flag that indicates whether SMPL parameters are valid
has_pose_3d = input_batch['has_pose_3d'].to(
torch.bool) # flag that indicates whether 3D pose is valid
is_flipped = input_batch[
'is_flipped'] # flag that indicates whether image was flipped during data augmentation
rot_angle = input_batch[
'rot_angle'] # rotation angle used for data augmentation
dataset_name = input_batch[
'dataset_name'] # name of the dataset the image comes from
indices = input_batch[
'sample_index'] # index of example inside its dataset
batch_size = images.shape[0]
# Get GT vertices and model joints
# Note that gt_model_joints is different from gt_joints as it comes from SMPL
gt_out = self.smpl(betas=gt_betas,
body_pose=gt_pose[:, 3:],
global_orient=gt_pose[:, :3])
gt_model_joints = gt_out.joints
gt_vertices = gt_out.vertices
# Get current best fits from the dictionary
opt_pose, opt_betas = self.fits_dict[(dataset_name, indices.cpu(),
rot_angle.cpu(),
is_flipped.cpu())]
opt_pose = opt_pose.to(self.device)
opt_betas = opt_betas.to(self.device)
# Replace extreme betas with zero betas
opt_betas[(opt_betas.abs() > 3).any(dim=-1)] = 0.
# Replace the optimized parameters with the ground truth parameters, if available
opt_pose[has_smpl, :] = gt_pose[has_smpl, :]
opt_betas[has_smpl, :] = gt_betas[has_smpl, :]
opt_output = self.smpl(betas=opt_betas,
body_pose=opt_pose[:, 3:],
global_orient=opt_pose[:, :3])
opt_vertices = opt_output.vertices
opt_joints = opt_output.joints
input_batch['verts'] = opt_vertices
# De-normalize 2D keypoints from [-1,1] to pixel space
gt_keypoints_2d_orig = gt_keypoints_2d.clone()
gt_keypoints_2d_orig[:, :, :-1] = 0.5 * self.options.img_res * (
gt_keypoints_2d_orig[:, :, :-1] + 1)
# Estimate camera translation given the model joints and 2D keypoints
# by minimizing a weighted least squares loss
gt_cam_t = estimate_translation(gt_model_joints,
gt_keypoints_2d_orig,
focal_length=self.focal_length,
img_size=self.options.img_res)
opt_cam_t = estimate_translation(opt_joints,
gt_keypoints_2d_orig,
focal_length=self.focal_length,
img_size=self.options.img_res)
# get fitted smpl parameters as pseudo ground truth
valid_fit = self.fits_dict.get_vaild_state(
dataset_name, indices.cpu()).to(torch.bool).to(self.device)
try:
valid_fit = valid_fit | has_smpl
except RuntimeError:
valid_fit = (valid_fit.byte() | has_smpl.byte()).to(torch.bool)
# Render Dense Correspondences
if self.options.regressor == 'pymaf_net' and cfg.MODEL.PyMAF.AUX_SUPV_ON:
gt_cam_t_nr = opt_cam_t.detach().clone()
gt_camera = torch.zeros(gt_cam_t_nr.shape).to(gt_cam_t_nr.device)
gt_camera[:, 1:] = gt_cam_t_nr[:, :2]
gt_camera[:, 0] = (2. * self.focal_length /
self.options.img_res) / gt_cam_t_nr[:, 2]
iuv_image_gt = torch.zeros(
(batch_size, 3, cfg.MODEL.PyMAF.DP_HEATMAP_SIZE,
cfg.MODEL.PyMAF.DP_HEATMAP_SIZE)).to(self.device)
if torch.sum(valid_fit.float()) > 0:
iuv_image_gt[valid_fit] = self.iuv_maker.verts2iuvimg(
opt_vertices[valid_fit],
cam=gt_camera[valid_fit]) # [B, 3, 56, 56]
input_batch['iuv_image_gt'] = iuv_image_gt
uvia_list = iuv_img2map(iuv_image_gt)
# Feed images in the network to predict camera and SMPL parameters
if self.options.regressor == 'hmr':
pred_rotmat, pred_betas, pred_camera = self.model(images)
# torch.Size([32, 24, 3, 3]) torch.Size([32, 10]) torch.Size([32, 3])
elif self.options.regressor == 'pymaf_net':
preds_dict, _ = self.model(images)
output = preds_dict
loss_dict = {}
if self.options.regressor == 'pymaf_net' and cfg.MODEL.PyMAF.AUX_SUPV_ON:
dp_out = preds_dict['dp_out']
for i in range(len(dp_out)):
r_i = i - len(dp_out)
u_pred, v_pred, index_pred, ann_pred = dp_out[r_i][
'predict_u'], dp_out[r_i]['predict_v'], dp_out[r_i][
'predict_uv_index'], dp_out[r_i]['predict_ann_index']
if index_pred.shape[-1] == iuv_image_gt.shape[-1]:
uvia_list_i = uvia_list
else:
iuv_image_gt_i = F.interpolate(iuv_image_gt,
u_pred.shape[-1],
mode='nearest')
uvia_list_i = iuv_img2map(iuv_image_gt_i)
loss_U, loss_V, loss_IndexUV, loss_segAnn = self.body_uv_losses(
u_pred, v_pred, index_pred, ann_pred, uvia_list_i,
valid_fit)
loss_dict[f'loss_U{r_i}'] = loss_U
loss_dict[f'loss_V{r_i}'] = loss_V
loss_dict[f'loss_IndexUV{r_i}'] = loss_IndexUV
loss_dict[f'loss_segAnn{r_i}'] = loss_segAnn
len_loop = len(preds_dict['smpl_out']
) if self.options.regressor == 'pymaf_net' else 1
for l_i in range(len_loop):
if self.options.regressor == 'pymaf_net':
if l_i == 0:
# initial parameters (mean poses)
continue
pred_rotmat = preds_dict['smpl_out'][l_i]['rotmat']
pred_betas = preds_dict['smpl_out'][l_i]['theta'][:, 3:13]
pred_camera = preds_dict['smpl_out'][l_i]['theta'][:, :3]
pred_output = self.smpl(betas=pred_betas,
body_pose=pred_rotmat[:, 1:],
global_orient=pred_rotmat[:,
0].unsqueeze(1),
pose2rot=False)
pred_vertices = pred_output.vertices
pred_joints = pred_output.joints
# Convert Weak Perspective Camera [s, tx, ty] to camera translation [tx, ty, tz] in 3D given the bounding box size
# This camera translation can be used in a full perspective projection
pred_cam_t = torch.stack([
pred_camera[:, 1], pred_camera[:, 2], 2 * self.focal_length /
(self.options.img_res * pred_camera[:, 0] + 1e-9)
],
dim=-1)
camera_center = torch.zeros(batch_size, 2, device=self.device)
pred_keypoints_2d = perspective_projection(
pred_joints,
rotation=torch.eye(3, device=self.device).unsqueeze(0).expand(
batch_size, -1, -1),
translation=pred_cam_t,
focal_length=self.focal_length,
camera_center=camera_center)
# Normalize keypoints to [-1,1]
pred_keypoints_2d = pred_keypoints_2d / (self.options.img_res / 2.)
# Compute loss on SMPL parameters
loss_regr_pose, loss_regr_betas = self.smpl_losses(
pred_rotmat, pred_betas, opt_pose, opt_betas, valid_fit)
loss_regr_pose *= cfg.LOSS.POSE_W
loss_regr_betas *= cfg.LOSS.SHAPE_W
loss_dict['loss_regr_pose_{}'.format(l_i)] = loss_regr_pose
loss_dict['loss_regr_betas_{}'.format(l_i)] = loss_regr_betas
# Compute 2D reprojection loss for the keypoints
if cfg.LOSS.KP_2D_W > 0:
loss_keypoints = self.keypoint_loss(
pred_keypoints_2d, gt_keypoints_2d,
self.options.openpose_train_weight,
self.options.gt_train_weight) * cfg.LOSS.KP_2D_W
loss_dict['loss_keypoints_{}'.format(l_i)] = loss_keypoints
# Compute 3D keypoint loss
loss_keypoints_3d = self.keypoint_3d_loss(
pred_joints, gt_joints, has_pose_3d) * cfg.LOSS.KP_3D_W
loss_dict['loss_keypoints_3d_{}'.format(l_i)] = loss_keypoints_3d
# Per-vertex loss for the shape
if cfg.LOSS.VERT_W > 0:
loss_shape = self.shape_loss(pred_vertices, opt_vertices,
valid_fit) * cfg.LOSS.VERT_W
loss_dict['loss_shape_{}'.format(l_i)] = loss_shape
# Camera
# force the network to predict positive depth values
loss_cam = ((torch.exp(-pred_camera[:, 0] * 10))**2).mean()
loss_dict['loss_cam_{}'.format(l_i)] = loss_cam
for key in loss_dict:
if len(loss_dict[key].shape) > 0:
loss_dict[key] = loss_dict[key][0]
# Compute total loss
loss = torch.stack(list(loss_dict.values())).sum()
# Do backprop
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# Pack output arguments for tensorboard logging
output.update({
'pred_vertices': pred_vertices.detach(),
'opt_vertices': opt_vertices,
'pred_cam_t': pred_cam_t.detach(),
'opt_cam_t': opt_cam_t
})
loss_dict['loss'] = loss.detach().item()
if self.step_count % 100 == 0:
if self.options.multiprocessing_distributed:
for loss_name, val in loss_dict.items():
val = val / self.options.world_size
if not torch.is_tensor(val):
val = torch.Tensor([val]).to(self.device)
dist.all_reduce(val)
loss_dict[loss_name] = val
if self.options.rank == 0:
for loss_name, val in loss_dict.items():
self.summary_writer.add_scalar(
'losses/{}'.format(loss_name), val, self.step_count)
return {'preds': output, 'losses': loss_dict}
def train_step(self, input_batch):
self.model.train()
# get data from batch
has_smpl = input_batch['has_smpl'].bool()
has_pose_3d = input_batch['has_pose_3d'].bool()
gt_pose1 = input_batch['pose'] # SMPL pose parameters
gt_betas1 = input_batch['betas'] # SMPL beta parameters
dataset_name = input_batch['dataset_name']
indices = input_batch[
'sample_index'] # index of example inside its dataset
is_flipped = input_batch[
'is_flipped'] # flag that indicates whether image was flipped during data augmentation
rot_angle = input_batch[
'rot_angle'] # rotation angle used for data augmentation
#print(rot_angle)
# Get GT vertices and model joints
# Note that gt_model_joints is different from gt_joints as it comes from SMPL
gt_betas = torch.cat((gt_betas1, gt_betas1, gt_betas1, gt_betas1), 0)
gt_pose = torch.cat((gt_pose1, gt_pose1, gt_pose1, gt_pose1), 0)
gt_out = self.smpl(betas=gt_betas,
body_pose=gt_pose[:, 3:],
global_orient=gt_pose[:, :3])
gt_model_joints = gt_out.joints
gt_vertices = gt_out.vertices
# Get current best fits from the dictionary
opt_pose1, opt_betas1 = self.fits_dict[(dataset_name, indices.cpu(),
rot_angle.cpu(),
is_flipped.cpu())]
opt_pose = torch.cat(
(opt_pose1.to(self.device), opt_pose1.to(self.device),
opt_pose1.to(self.device), opt_pose1.to(self.device)), 0)
#print(opt_pose.device)
#opt_betas = opt_betas.to(self.device)
opt_betas = torch.cat(
(opt_betas1.to(self.device), opt_betas1.to(self.device),
opt_betas1.to(self.device), opt_betas1.to(self.device)), 0)
opt_output = self.smpl(betas=opt_betas,
body_pose=opt_pose[:, 3:],
global_orient=opt_pose[:, :3])
opt_vertices = opt_output.vertices
opt_joints = opt_output.joints
# images
images = torch.cat((input_batch['img_0'], input_batch['img_1'],
input_batch['img_2'], input_batch['img_3']), 0)
batch_size = input_batch['img_0'].shape[0]
#input()
# Output of CNN
pred_rotmat, pred_betas, pred_camera = self.model(images)
pred_output = self.smpl(betas=pred_betas,
body_pose=pred_rotmat[:, 1:],
global_orient=pred_rotmat[:, 0].unsqueeze(1),
pose2rot=False)
pred_vertices = pred_output.vertices
pred_joints = pred_output.joints
pred_cam_t = torch.stack([
pred_camera[:, 1], pred_camera[:, 2], 2 * self.focal_length /
(self.options.img_res * pred_camera[:, 0] + 1e-9)
],
dim=-1)
camera_center = torch.zeros(batch_size * 4, 2, device=self.device)
pred_keypoints_2d = perspective_projection(
pred_joints,
rotation=torch.eye(3, device=self.device).unsqueeze(0).expand(
batch_size * 4, -1, -1),
translation=pred_cam_t,
focal_length=self.focal_length,
camera_center=camera_center)
pred_keypoints_2d = pred_keypoints_2d / (self.options.img_res / 2.)
# 2d joint points
gt_keypoints_2d = torch.cat(
(input_batch['keypoints_0'], input_batch['keypoints_1'],
input_batch['keypoints_2'], input_batch['keypoints_3']), 0)
gt_keypoints_2d_orig = gt_keypoints_2d.clone()
gt_keypoints_2d_orig[:, :, :-1] = 0.5 * self.options.img_res * (
gt_keypoints_2d_orig[:, :, :-1] + 1)
gt_cam_t = estimate_translation(gt_model_joints,
gt_keypoints_2d_orig,
focal_length=self.focal_length,
img_size=self.options.img_res)
opt_cam_t = estimate_translation(opt_joints,
gt_keypoints_2d_orig,
focal_length=self.focal_length,
img_size=self.options.img_res)
#input()
opt_joint_loss = self.smplify.get_fitting_loss(
opt_pose, opt_betas, opt_cam_t, 0.5 * self.options.img_res *
torch.ones(batch_size * 4, 2, device=self.device),
gt_keypoints_2d_orig).mean(dim=-1)
if self.options.run_smplify:
pred_rotmat_hom = torch.cat([
pred_rotmat.detach().view(-1, 3, 3).detach(),
torch.tensor(
[0, 0, 1], dtype=torch.float32, device=self.device).view(
1, 3, 1).expand(batch_size * 4 * 24, -1, -1)
],
dim=-1)
pred_pose = rotation_matrix_to_angle_axis(
pred_rotmat_hom).contiguous().view(batch_size * 4, -1)
pred_pose[torch.isnan(pred_pose)] = 0.0
#pred_pose_detach = pred_pose.detach()
#pred_betas_detach = pred_betas.detach()
#pred_cam_t_detach = pred_cam_t.detach()
new_opt_vertices, new_opt_joints,\
new_opt_pose, new_opt_betas,\
new_opt_cam_t, new_opt_joint_loss = self.smplify(
pred_pose.detach(), pred_betas.detach(),
pred_cam_t.detach(),
0.5 * self.options.img_res * torch.ones(batch_size*4, 2, device=self.device),
gt_keypoints_2d_orig)
new_opt_joint_loss = new_opt_joint_loss.mean(dim=-1)
# Will update the dictionary for the examples where the new loss is less than the current one
update = (new_opt_joint_loss < opt_joint_loss)
update1 = torch.cat((update, update, update, update), 0)
opt_joint_loss[update] = new_opt_joint_loss[update]
#print(opt_joints.size(),new_opt_joints.size())
#input()
opt_joints[update1, :] = new_opt_joints[update1, :]
#print(opt_pose.size(),new_opt_pose.size())
opt_betas[update1, :] = new_opt_betas[update1, :]
opt_pose[update1, :] = new_opt_pose[update1, :]
#print(i, opt_pose_mv[i])
opt_vertices[update1, :] = new_opt_vertices[update1, :]
opt_cam_t[update1, :] = new_opt_cam_t[update1, :]
# now we comput the loss on the four images
# Replace the optimized parameters with the ground truth parameters, if available
#for i in range(4):
#print('Here!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1')
has_smpl1 = torch.cat((has_smpl, has_smpl, has_smpl, has_smpl), 0)
opt_vertices[has_smpl1, :, :] = gt_vertices[has_smpl1, :, :]
opt_pose[has_smpl1, :] = gt_pose[has_smpl1, :]
opt_cam_t[has_smpl1, :] = gt_cam_t[has_smpl1, :]
opt_joints[has_smpl1, :, :] = gt_model_joints[has_smpl1, :, :]
opt_betas[has_smpl1, :] = gt_betas[has_smpl1, :]
#print(opt_cam_t[0:batch_size],opt_cam_t[batch_size:2*batch_size],opt_cam_t[2*batch_size:3*batch_size],opt_cam_t[3*batch_size:4*batch_size])
# Assert whether a fit is valid by comparing the joint loss with the threshold
valid_fit1 = (opt_joint_loss < self.options.smplify_threshold).to(
self.device)
# Add the examples with GT parameters to the list of valid fits
valid_fit = torch.cat(
(valid_fit1, valid_fit1, valid_fit1, valid_fit1), 0) | has_smpl1
#gt_keypoints_2d = torch.cat((input_batch['keypoints_0'],input_batch['keypoints_1'],input_batch['keypoints_2'],input_batch['keypoints_3']),0)
loss_keypoints = self.keypoint_loss(pred_keypoints_2d, gt_keypoints_2d,
0, 1)
#gt_joints = torch.cat((input_batch['pose_3d_0'],input_batch['pose_3d_1'],input_batch['pose_3d_2'],input_batch['pose_3d_3']),0)
#loss_keypoints_3d = self.keypoint_3d_loss(pred_joints, gt_joints, torch.cat((has_pose_3d,has_pose_3d,has_pose_3d,has_pose_3d),0))
loss_regr_pose, loss_regr_betas = self.smpl_losses(
pred_rotmat, pred_betas, opt_pose, opt_betas, valid_fit)
loss_shape = self.shape_loss(pred_vertices, opt_vertices, valid_fit)
#print(loss_shape_sum,loss_keypoints_sum,loss_keypoints_3d_sum,loss_regr_pose_sum,loss_regr_betas_sum)
#input()
loss_all = 0 * loss_shape +\
5. * loss_keypoints +\
0. * loss_keypoints_3d +\
loss_regr_pose + 0.001* loss_regr_betas +\
((torch.exp(-pred_camera[:,0]*10)) ** 2 ).mean()
loss_all *= 60
#print(loss_all)
# Do backprop
self.optimizer.zero_grad()
loss_all.backward()
self.optimizer.step()
output = {
'pred_vertices': pred_vertices,
'opt_vertices': opt_vertices,
'pred_cam_t': pred_cam_t,
'opt_cam_t': opt_cam_t
}
losses = {
'loss': loss_all.detach().item(),
'loss_keypoints': loss_keypoints.detach().item(),
'loss_keypoints_3d': loss_keypoints_3d.detach().item(),
'loss_regr_pose': loss_regr_pose.detach().item(),
'loss_regr_betas': loss_regr_betas.detach().item(),
'loss_shape': loss_shape.detach().item()
}
return output, losses
def train_step(self, input_batch):
self.model.train()
images_hr = input_batch['img_hr']
images_lr_list = input_batch['img_lr']
images_list = [images_hr] + images_lr_list
scale_names = ['224', '224_128', '128_64', '64_40', '40_24']
scale_names = scale_names[:len(images_list)]
feat_names = ['layer4']
# Get data from the batch
gt_keypoints_2d = input_batch['keypoints'] # 2D keypoints
gt_pose = input_batch['pose'] # SMPL pose parameters
gt_betas = input_batch['betas'] # SMPL beta parameters
gt_joints = input_batch['pose_3d'] # 3D pose
has_smpl = input_batch['has_smpl'].byte(
) # flag that indicates whether SMPL parameters are valid
has_pose_3d = input_batch['has_pose_3d'].byte(
) # flag that indicates whether 3D pose is valid
dataset_name = input_batch[
'dataset_name'] # name of the dataset the image comes from
indices = input_batch['sample_index'].numpy(
) # index of example inside mixed dataset
batch_size = images_hr.shape[0]
# Get GT vertices and model joints
# Note that gt_model_joints is different from gt_joints as it comes from SMPL
gt_out = self.smpl(betas=gt_betas,
body_pose=gt_pose[:, 3:],
global_orient=gt_pose[:, :3])
gt_model_joints = gt_out.joints
gt_vertices = gt_out.vertices
# De-normalize 2D keypoints from [-1,1] to pixel space
gt_keypoints_2d_orig = gt_keypoints_2d.clone()
gt_keypoints_2d_orig[:, :, :-1] = 0.5 * self.options.img_res * (
gt_keypoints_2d_orig[:, :, :-1] + 1)
# Estimate camera translation given the model joints and 2D keypoints
# by minimizing a weighted least squares loss
gt_cam_t = estimate_translation(gt_model_joints,
gt_keypoints_2d_orig,
focal_length=self.focal_length,
img_size=self.options.img_res)
loss_shape = 0
loss_keypoints = 0
loss_keypoints_3d = 0
loss_regr_pose = 0
loss_regr_betas = 0
loss_regr_cam_t = 0
smpl_outputs = []
for i, (images, scale_name) in enumerate(
zip(images_list, scale_names[:len(images_list)])):
images = images.to(self.device)
# Feed images in the network to predict camera and SMPL parameters
pred_rotmat, pred_betas, pred_camera, feat_list = self.model(
images, scale=i)
pred_output = self.smpl(betas=pred_betas,
body_pose=pred_rotmat[:, 1:],
global_orient=pred_rotmat[:,
0].unsqueeze(1),
pose2rot=False)
pred_vertices = pred_output.vertices
pred_joints = pred_output.joints
# Convert Weak Perspective Camera [s, tx, ty] to camera translation [tx, ty, tz] in 3D given the bounding box size
# This camera translation can be used in a full perspective projection
pred_cam_t = torch.stack([
pred_camera[:, 1], pred_camera[:, 2], 2 * self.focal_length /
(self.options.img_res * pred_camera[:, 0] + 1e-9)
],
dim=-1)
camera_center = torch.zeros(batch_size, 2, device=self.device)
pred_keypoints_2d = perspective_projection(
pred_joints,
rotation=torch.eye(3, device=self.device).unsqueeze(0).expand(
batch_size, -1, -1),
translation=pred_cam_t,
focal_length=self.focal_length,
camera_center=camera_center)
# Normalize keypoints to [-1,1]
pred_keypoints_2d = pred_keypoints_2d / (self.options.img_res / 2.)
# Compute loss on SMPL parameters
loss_pose, loss_betas, loss_cam_t = self.smpl_losses(
pred_rotmat, pred_betas, pred_cam_t, gt_pose, gt_betas,
gt_cam_t, has_smpl)
loss_regr_pose = loss_regr_pose + (i + 1) * loss_pose
loss_regr_betas = loss_regr_betas + (i + 1) * loss_betas
loss_regr_cam_t = loss_regr_cam_t + (i + 1) * loss_cam_t
# Compute 2D reprojection loss for the keypoints
loss_keypoints = loss_keypoints + (i + 1) * self.keypoint_loss(
pred_keypoints_2d, gt_keypoints_2d, self.options.
openpose_train_weight, self.options.gt_train_weight)
# Compute 3D keypoint loss
loss_keypoints_3d = loss_keypoints_3d + (
i + 1) * self.keypoint_3d_loss(pred_joints, gt_joints,
has_pose_3d)
# Per-vertex loss for the shape
loss_shape = loss_shape + (i + 1) * self.shape_loss(
pred_vertices, gt_vertices, has_smpl)
# save pred_rotmat, pred_betas, pred_cam_t for later, from large images to smaller images
smpl_outputs.append(
[pred_rotmat, pred_betas, pred_cam_t, feat_list])
# update queue size
self.feat_queue.update_queue_size(batch_size)
# update the queue
self.feat_queue.update_all([feat.detach() for feat in feat_list],
[name for name in feat_names])
# update dataset name and index for each scale
self.feat_queue.update('dataset_names', np.array(dataset_name))
self.feat_queue.update('dataset_indices', indices)
# Compute total loss except the consistency loss
loss = self.options.shape_loss_weight * loss_shape +\
self.options.keypoint_loss_weight * loss_keypoints + \
self.options.keypoint_loss_weight * loss_keypoints_3d +\
self.options.pose_loss_weight * loss_regr_pose + \
self.options.beta_loss_weight * loss_regr_betas + \
self.options.cam_loss_weight * loss_regr_cam_t
loss = loss / len(images_list)
# compute the consistency loss
loss_consistency = 0
for i in range(len(smpl_outputs)):
gt_rotmat, gt_betas, gt_cam_t, gt_feat_list = smpl_outputs[i]
gt_rotmat = gt_rotmat.detach()
gt_betas = gt_betas.detach()
gt_cam_t = gt_cam_t.detach()
gt_feat_list = [feat.detach() for feat in gt_feat_list]
# sample negative index
indices_list = self.feat_queue.select_indices(
dataset_name, indices, self.options.sample_size)
neg_feat_list = self.feat_queue.batch_sample_all(indices_list,
names=feat_names)
for j in range(i + 1, len(smpl_outputs)):
# compute the consistency loss from high to low: 1:2, 1:3, 2:3 and weighted by 1/(j-i)
pred_rotmat, pred_betas, pred_cam_t, pred_feat_list = smpl_outputs[
j]
loss_consistency_total, loss_consistency_smpl, loss_consistency_feat = self.consistency_losses(
pred_rotmat, pred_betas, pred_cam_t, pred_feat_list,
gt_rotmat, gt_betas, gt_cam_t, gt_feat_list, neg_feat_list)
loss_consistency = loss_consistency + (
(j - i) / len(smpl_outputs)) * loss_consistency_total
loss_consistency = loss_consistency * self.consistency_loss_ramp * self.options.consistency_loss_weight
loss += loss_consistency
loss *= 60
# Do backprop
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# Pack output arguments
output = {
'pred_vertices': pred_vertices.detach(),
'pred_cam_t': pred_cam_t.detach()
}
losses = {
'lr': self.optimizer.param_groups[0]['lr'],
'loss_ramp': self.consistency_loss_ramp,
'loss': loss.detach().item(),
'loss_consistency': loss_consistency.detach().item(),
'loss_consistency_smpl': loss_consistency_smpl.detach().item(),
'loss_consistency_feat': loss_consistency_feat.detach().item(),
'loss_keypoints': loss_keypoints.detach().item(),
'loss_keypoints_3d': loss_keypoints_3d.detach().item(),
'loss_regr_pose': loss_regr_pose.detach().item(),
'loss_regr_betas': loss_regr_betas.detach().item(),
'loss_shape': loss_shape.detach().item()
}
return output, losses
def train_step(self, input_batch):
self.model.train()
# Get data from the batch
images = input_batch['img'] # input image
gt_keypoints_2d = input_batch['keypoints'] # 2D keypoints
gt_pose = input_batch['pose'] # SMPL pose parameters
gt_betas = input_batch['betas'] # SMPL beta parameters
gt_joints = input_batch['pose_3d'] # 3D pose
has_smpl = input_batch['has_smpl'].byte(
) # flag that indicates whether SMPL parameters are valid
has_pose_3d = input_batch['has_pose_3d'].byte(
) # flag that indicates whether 3D pose is valid
is_flipped = input_batch[
'is_flipped'] # flag that indicates whether image was flipped during data augmentation
rot_angle = input_batch[
'rot_angle'] # rotation angle used for data augmentation
dataset_name = input_batch[
'dataset_name'] # name of the dataset the image comes from
indices = input_batch[
'sample_index'] # index of example inside its dataset
batch_size = images.shape[0]
# Get GT vertices and model joints
# Note that gt_model_joints is different from gt_joints as it comes from SMPL
gt_out = self.smpl(betas=gt_betas,
body_pose=gt_pose[:, 3:],
global_orient=gt_pose[:, :3])
gt_model_joints = gt_out.joints
gt_vertices = gt_out.vertices
# Get current best fits from the dictionary
opt_pose, opt_betas = self.fits_dict[(dataset_name, indices.cpu(),
rot_angle.cpu(),
is_flipped.cpu())]
opt_pose = opt_pose.to(self.device)
opt_betas = opt_betas.to(self.device)
opt_output = self.smpl(betas=opt_betas,
body_pose=opt_pose[:, 3:],
global_orient=opt_pose[:, :3])
opt_vertices = opt_output.vertices
if opt_vertices.shape != (self.options.batch_size, 6890, 3):
opt_vertices = torch.zeros_like(opt_vertices, device=self.device)
opt_joints = opt_output.joints
# De-normalize 2D keypoints from [-1,1] to pixel space
gt_keypoints_2d_orig = gt_keypoints_2d.clone()
gt_keypoints_2d_orig[:, :, :-1] = 0.5 * self.options.img_res * (
gt_keypoints_2d_orig[:, :, :-1] + 1)
# Estimate camera translation given the model joints and 2D keypoints
# by minimizing a weighted least squares loss
gt_cam_t = estimate_translation(gt_model_joints,
gt_keypoints_2d_orig,
focal_length=self.focal_length,
img_size=self.options.img_res)
opt_cam_t = estimate_translation(opt_joints,
gt_keypoints_2d_orig,
focal_length=self.focal_length,
img_size=self.options.img_res)
opt_joint_loss = self.smplify.get_fitting_loss(
opt_pose, opt_betas, opt_cam_t, 0.5 * self.options.img_res *
torch.ones(batch_size, 2, device=self.device),
gt_keypoints_2d_orig).mean(dim=-1)
# Feed images in the network to predict camera and SMPL parameters
pred_rotmat, pred_betas, pred_camera = self.model(images)
pred_output = self.smpl(betas=pred_betas,
body_pose=pred_rotmat[:, 1:],
global_orient=pred_rotmat[:, 0].unsqueeze(1),
pose2rot=False)
pred_vertices = pred_output.vertices
if pred_vertices.shape != (self.options.batch_size, 6890, 3):
pred_vertices = torch.zeros_like(pred_vertices, device=self.device)
pred_joints = pred_output.joints
# Convert Weak Perspective Camera [s, tx, ty] to camera translation [tx, ty, tz] in 3D given the bounding box size
# This camera translation can be used in a full perspective projection
pred_cam_t = torch.stack([
pred_camera[:, 1], pred_camera[:, 2], 2 * self.focal_length /
(self.options.img_res * pred_camera[:, 0] + 1e-9)
],
dim=-1)
camera_center = torch.zeros(batch_size, 2, device=self.device)
pred_keypoints_2d = perspective_projection(
pred_joints,
rotation=torch.eye(3, device=self.device).unsqueeze(0).expand(
batch_size, -1, -1),
translation=pred_cam_t,
focal_length=self.focal_length,
camera_center=camera_center)
# Normalize keypoints to [-1,1]
pred_keypoints_2d = pred_keypoints_2d / (self.options.img_res / 2.)
if self.options.run_smplify:
# Convert predicted rotation matrices to axis-angle
pred_rotmat_hom = torch.cat([
pred_rotmat.detach().view(-1, 3, 3).detach(),
torch.tensor(
[0, 0, 1], dtype=torch.float32, device=self.device).view(
1, 3, 1).expand(batch_size * 24, -1, -1)
],
dim=-1)
pred_pose = rotation_matrix_to_angle_axis(
pred_rotmat_hom).contiguous().view(batch_size, -1)
# tgm.rotation_matrix_to_angle_axis returns NaN for 0 rotation, so manually hack it
pred_pose[torch.isnan(pred_pose)] = 0.0
# Run SMPLify optimization starting from the network prediction
new_opt_vertices, new_opt_joints,\
new_opt_pose, new_opt_betas,\
new_opt_cam_t, new_opt_joint_loss = self.smplify(
pred_pose.detach(), pred_betas.detach(),
pred_cam_t.detach(),
0.5 * self.options.img_res * torch.ones(batch_size, 2, device=self.device),
gt_keypoints_2d_orig)
new_opt_joint_loss = new_opt_joint_loss.mean(dim=-1)
# Will update the dictionary for the examples where the new loss is less than the current one
update = (new_opt_joint_loss < opt_joint_loss)
opt_joint_loss[update] = new_opt_joint_loss[update]
opt_vertices[update, :] = new_opt_vertices[update, :]
opt_joints[update, :] = new_opt_joints[update, :]
opt_pose[update, :] = new_opt_pose[update, :]
opt_betas[update, :] = new_opt_betas[update, :]
opt_cam_t[update, :] = new_opt_cam_t[update, :]
self.fits_dict[(dataset_name, indices.cpu(), rot_angle.cpu(),
is_flipped.cpu(),
update.cpu())] = (opt_pose.cpu(), opt_betas.cpu())
else:
update = torch.zeros(batch_size, device=self.device).byte()
# Replace extreme betas with zero betas
opt_betas[(opt_betas.abs() > 3).any(dim=-1)] = 0.
# Replace the optimized parameters with the ground truth parameters, if available
opt_vertices[has_smpl, :, :] = gt_vertices[has_smpl, :, :]
opt_cam_t[has_smpl, :] = gt_cam_t[has_smpl, :]
opt_joints[has_smpl, :, :] = gt_model_joints[has_smpl, :, :]
opt_pose[has_smpl, :] = gt_pose[has_smpl, :]
opt_betas[has_smpl, :] = gt_betas[has_smpl, :]
# Assert whether a fit is valid by comparing the joint loss with the threshold
valid_fit = (opt_joint_loss < self.options.smplify_threshold).to(
self.device)
# Add the examples with GT parameters to the list of valid fits
# print(valid_fit.dtype)
valid_fit = valid_fit.to(torch.uint8)
valid_fit = valid_fit | has_smpl
opt_keypoints_2d = perspective_projection(
opt_joints,
rotation=torch.eye(3, device=self.device).unsqueeze(0).expand(
batch_size, -1, -1),
translation=opt_cam_t,
focal_length=self.focal_length,
camera_center=camera_center)
opt_keypoints_2d = opt_keypoints_2d / (self.options.img_res / 2.)
# Compute loss on SMPL parameters
loss_regr_pose, loss_regr_betas = self.smpl_losses(
pred_rotmat, pred_betas, opt_pose, opt_betas, valid_fit)
# Compute 2D reprojection loss for the keypoints
loss_keypoints = self.keypoint_loss(pred_keypoints_2d, gt_keypoints_2d,
self.options.openpose_train_weight,
self.options.gt_train_weight)
# Compute 3D keypoint loss
loss_keypoints_3d = self.keypoint_3d_loss(pred_joints, gt_joints,
has_pose_3d)
# Per-vertex loss for the shape
loss_shape = self.shape_loss(pred_vertices, opt_vertices, valid_fit)
# Compute total loss
# The last component is a loss that forces the network to predict positive depth values
loss = self.options.shape_loss_weight * loss_shape +\
self.options.keypoint_loss_weight * loss_keypoints +\
self.options.keypoint_loss_weight * loss_keypoints_3d +\
loss_regr_pose + self.options.beta_loss_weight * loss_regr_betas +\
((torch.exp(-pred_camera[:,0]*10)) ** 2 ).mean()
loss *= 60
# Do backprop
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# Pack output arguments for tensorboard logging
output = {
'pred_vertices': pred_vertices.detach(),
'opt_vertices': opt_vertices,
'pred_cam_t': pred_cam_t.detach(),
'opt_cam_t': opt_cam_t
}
losses = {
'loss': loss.detach().item(),
'loss_keypoints': loss_keypoints.detach().item(),
'loss_keypoints_3d': loss_keypoints_3d.detach().item(),
'loss_regr_pose': loss_regr_pose.detach().item(),
'loss_regr_betas': loss_regr_betas.detach().item(),
'loss_shape': loss_shape.detach().item()
}
return output, losses
def train_step(self, input_batch):
# Learning rate decay
if self.decay_steps_ind < len(cfg.SOLVER.STEPS) and input_batch[
'step_count'] == cfg.SOLVER.STEPS[self.decay_steps_ind]:
lr = self.optimizer.param_groups[0]['lr']
lr_new = lr * cfg.SOLVER.GAMMA
print('Decay the learning on step {} from {} to {}'.format(
input_batch['step_count'], lr, lr_new))
for param_group in self.optimizer.param_groups:
param_group['lr'] = lr_new
lr = self.optimizer.param_groups[0]['lr']
assert lr == lr_new
self.decay_steps_ind += 1
self.model.train()
# Get data from the batch
images = input_batch['img'] # input image
gt_keypoints_2d = input_batch['keypoints'] # 2D keypoints
gt_pose = input_batch['pose'] # SMPL pose parameters
gt_betas = input_batch['betas'] # SMPL beta parameters
gt_joints = input_batch['pose_3d'] # 3D pose
has_smpl = input_batch['has_smpl'].byte(
) # flag that indicates whether SMPL parameters are valid
has_pose_3d = input_batch['has_pose_3d'].byte(
) # flag that indicates whether 3D pose is valid
is_flipped = input_batch[
'is_flipped'] # flag that indicates whether image was flipped during data augmentation
rot_angle = input_batch[
'rot_angle'] # rotation angle used for data augmentation
dataset_name = input_batch[
'dataset_name'] # name of the dataset the image comes from
indices = input_batch[
'sample_index'] # index of example inside its dataset
batch_size = images.shape[0]
# Get GT vertices and model joints
# Note that gt_model_joints is different from gt_joints as it comes from SMPL
gt_out = self.smpl(betas=gt_betas,
body_pose=gt_pose[:, 3:],
global_orient=gt_pose[:, :3])
gt_model_joints = gt_out.joints
gt_vertices = gt_out.vertices
# Get current pseudo labels (final fits of SPIN) from the dictionary
opt_pose, opt_betas = self.fits_dict[(dataset_name, indices.cpu(),
rot_angle.cpu(),
is_flipped.cpu())]
opt_pose = opt_pose.to(self.device)
opt_betas = opt_betas.to(self.device)
# Replace extreme betas with zero betas
opt_betas[(opt_betas.abs() > 3).any(dim=-1)] = 0.
# Replace the optimized parameters with the ground truth parameters, if available
opt_pose[has_smpl, :] = gt_pose[has_smpl, :]
opt_betas[has_smpl, :] = gt_betas[has_smpl, :]
opt_output = self.smpl(betas=opt_betas,
body_pose=opt_pose[:, 3:],
global_orient=opt_pose[:, :3])
opt_vertices = opt_output.vertices
opt_joints = opt_output.joints
# De-normalize 2D keypoints from [-1,1] to pixel space
gt_keypoints_2d_orig = gt_keypoints_2d.clone()
gt_keypoints_2d_orig[:, :, :-1] = 0.5 * self.options.img_res * (
gt_keypoints_2d_orig[:, :, :-1] + 1)
# Estimate camera translation given the model joints and 2D keypoints
# by minimizing a weighted least squares loss
gt_cam_t = estimate_translation(gt_model_joints,
gt_keypoints_2d_orig,
focal_length=self.focal_length,
img_size=self.options.img_res)
opt_cam_t = estimate_translation(opt_joints,
gt_keypoints_2d_orig,
focal_length=self.focal_length,
img_size=self.options.img_res)
if self.options.train_data in ['h36m_coco_itw']:
valid_fit = self.fits_dict.get_vaild_state(dataset_name,
indices.cpu()).to(
self.device)
valid_fit = valid_fit | has_smpl
else:
valid_fit = has_smpl
# Feed images in the network to predict camera and SMPL parameters
input_batch['opt_pose'] = opt_pose
input_batch['opt_betas'] = opt_betas
input_batch['valid_fit'] = valid_fit
input_batch['dp_dict'] = {
k: v.to(self.device) if isinstance(v, torch.Tensor) else v
for k, v in input_batch['dp_dict'].items()
}
has_iuv = torch.tensor([dn not in ['dp_coco'] for dn in dataset_name],
dtype=torch.uint8).to(self.device)
has_iuv = has_iuv & valid_fit
input_batch['has_iuv'] = has_iuv
has_dp = input_batch['has_dp']
target_smpl_kps = torch.zeros(
(batch_size, 24, 3)).to(opt_output.smpl_joints.device)
target_smpl_kps[:, :, :2] = perspective_projection(
opt_output.smpl_joints.detach().clone(),
rotation=torch.eye(3, device=self.device).unsqueeze(0).expand(
batch_size, -1, -1),
translation=opt_cam_t,
focal_length=self.focal_length,
camera_center=torch.zeros(batch_size, 2, device=self.device) +
(0.5 * self.options.img_res))
target_smpl_kps[:, :, :2] = target_smpl_kps[:, :, :2] / (
0.5 * self.options.img_res) - 1
target_smpl_kps[has_iuv == 1, :, 2] = 1
target_smpl_kps[has_dp == 1] = input_batch['smpl_2dkps'][has_dp == 1]
input_batch['target_smpl_kps'] = target_smpl_kps # [B, 24, 3]
input_batch['target_verts'] = opt_vertices.detach().clone(
) # [B, 6890, 3]
# camera translation for neural renderer
gt_cam_t_nr = opt_cam_t.detach().clone()
gt_camera = torch.zeros(gt_cam_t_nr.shape).to(gt_cam_t_nr.device)
gt_camera[:, 1:] = gt_cam_t_nr[:, :2]
gt_camera[:, 0] = (2. * self.focal_length /
self.options.img_res) / gt_cam_t_nr[:, 2]
input_batch['target_cam'] = gt_camera
# Do forward
danet_return_dict = self.model(input_batch)
loss_tatal = 0
losses_dict = {}
for loss_key in danet_return_dict['losses']:
loss_tatal += danet_return_dict['losses'][loss_key]
losses_dict['loss_{}'.format(loss_key)] = danet_return_dict[
'losses'][loss_key].detach().item()
# Do backprop
self.optimizer.zero_grad()
loss_tatal.backward()
self.optimizer.step()
if input_batch['pretrain_mode']:
pred_vertices = None
pred_cam_t = None
else:
pred_vertices = danet_return_dict['prediction']['vertices'].detach(
)
pred_cam_t = danet_return_dict['prediction']['cam_t'].detach()
# Pack output arguments for tensorboard logging
output = {
'pred_vertices': pred_vertices,
'opt_vertices': opt_vertices,
'pred_cam_t': pred_cam_t,
'opt_cam_t': opt_cam_t,
'visualization': danet_return_dict['visualization']
}
losses_dict.update({'loss_tatal': loss_tatal.detach().item()})
return output, losses_dict