Source code for mmhuman3d.models.architectures.hybrik
# isort: skip_file
from abc import ABCMeta
import torch
from mmhuman3d.data.datasets.pipelines.hybrik_transforms import heatmap2coord
from ..builder import (
ARCHITECTURES,
build_backbone,
build_body_model,
build_head,
build_loss,
build_neck,
)
from .base_architecture import BaseArchitecture
def set_requires_grad(nets, requires_grad=False):
"""Set requies_grad for all the networks.
Args:
nets (nn.Module | list[nn.Module]): A list of networks or a single
network.
requires_grad (bool): Whether the networks require gradients or not
"""
if not isinstance(nets, list):
nets = [nets]
for net in nets:
if net is not None:
for param in net.parameters():
param.requires_grad = requires_grad
[docs]@ARCHITECTURES.register_module()
class HybrIK_trainer(BaseArchitecture, metaclass=ABCMeta):
"""Hybrik_trainer Architecture.
Args:
backbone (dict | None, optional): Backbone config dict. Default: None.
neck (dict | None, optional): Neck config dict. Default: None
head (dict | None, optional): Regressor config dict. Default: None.
body_model (dict | None, optional): SMPL config dict. Default: None.
loss_beta (dict | None, optional): Losses config dict for
beta (shape parameters) estimation. Default: None
loss_theta (dict | None, optional): Losses config dict for
theta (pose parameters) estimation. Default: None
loss_twist (dict | None, optional): Losses config dict
for twist angles estimation. Default: None
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None
"""
def __init__(self,
backbone=None,
neck=None,
head=None,
body_model=None,
loss_beta=None,
loss_theta=None,
loss_twist=None,
loss_uvd=None,
init_cfg=None):
super(HybrIK_trainer, self).__init__(init_cfg)
self.backbone = build_backbone(backbone)
self.neck = build_neck(neck)
self.head = build_head(head)
self.smpl = build_body_model(body_model)
self.loss_beta = build_loss(loss_beta)
self.loss_theta = build_loss(loss_theta)
self.loss_twist = build_loss(loss_twist)
self.loss_uvd = build_loss(loss_uvd)
self.head._initialize()
[docs] def forward_train(self, img, img_metas, **kwargs):
"""Train step function.
In this function, train step is carried out
with following the pipeline:
1. extract features with the backbone
2. feed the extracted features into the head to
predicte beta, theta, twist angle, and heatmap (uvd map)
3. compute regression losses of the predictions
and optimize backbone and head
Args:
img (torch.Tensor): Batch of data as input.
kwargs (dict): Dict with ground-truth
Returns:
output (dict): Dict with loss, information for logger,
the number of samples.
"""
labels = {}
labels['trans_inv'] = kwargs['trans_inv']
labels['intrinsic_param'] = kwargs['intrinsic_param']
labels['joint_root'] = kwargs['joint_root']
labels['depth_factor'] = kwargs['depth_factor']
labels['target_uvd_29'] = kwargs['target_uvd_29']
labels['target_xyz_24'] = kwargs['target_xyz_24']
labels['target_weight_24'] = kwargs['target_weight_24']
labels['target_weight_29'] = kwargs['target_weight_29']
labels['target_xyz_17'] = kwargs['target_xyz_17']
labels['target_weight_17'] = kwargs['target_weight_17']
labels['target_theta'] = kwargs['target_theta']
labels['target_beta'] = kwargs['target_beta']
labels['target_smpl_weight'] = kwargs['target_smpl_weight']
labels['target_theta_weight'] = kwargs['target_theta_weight']
labels['target_twist'] = kwargs['target_twist']
labels['target_twist_weight'] = kwargs['target_twist_weight']
# flip_output = kwargs.pop('is_flipped', None)
for k, _ in labels.items():
labels[k] = labels[k].cuda()
trans_inv = labels.pop('trans_inv')
intrinsic_param = labels.pop('intrinsic_param')
joint_root = labels.pop('joint_root')
depth_factor = labels.pop('depth_factor')
if self.backbone is not None:
img = img.cuda().requires_grad_()
features = self.backbone(img)
features = features[0]
else:
features = img['features']
if self.neck is not None:
features = self.neck(features)
predictions = self.head(features, trans_inv, intrinsic_param,
joint_root, depth_factor, self.smpl)
losses = self.compute_losses(predictions, labels)
loss, log_vars = self._parse_losses(losses)
output = {
'loss': loss,
}
return output
[docs] def compute_losses(self, predictions, targets):
"""Compute regression losses for beta, theta, twist and uvd."""
smpl_weight = targets['target_smpl_weight']
losses = {}
if self.loss_beta is not None:
losses['loss_beta'] = self.loss_beta(
predictions['pred_shape'] * smpl_weight,
targets['target_beta'] * smpl_weight)
if self.loss_theta is not None:
losses['loss_theta'] = self.loss_theta(
predictions['pred_theta_mats'] * smpl_weight *
targets['target_theta_weight'], targets['target_theta'] *
smpl_weight * targets['target_theta_weight'])
if self.loss_twist is not None:
losses['loss_twist'] = self.loss_twist(
predictions['pred_phi'] * targets['target_twist_weight'],
targets['target_twist'] * targets['target_twist_weight'])
if self.loss_uvd is not None:
pred_uvd = predictions['pred_uvd_jts']
target_uvd = targets['target_uvd_29'][:, :pred_uvd.shape[1]]
target_uvd_weight = targets['target_weight_29'][:, :pred_uvd.
shape[1]]
losses['loss_uvd'] = self.loss_uvd(
64 * predictions['pred_uvd_jts'],
64 * target_uvd,
target_uvd_weight,
avg_factor=target_uvd_weight.sum())
return losses
[docs] def forward_test(self, img, img_metas, **kwargs):
"""Test step function.
In this function, train step is carried out
with following the pipeline:
1. extract features with the backbone
2. feed the extracted features into the head to
predicte beta, theta, twist angle, and heatmap (uvd map)
3. store predictions for evaluation
Args:
img (torch.Tensor): Batch of data as input.
img_metas (dict): Dict with image metas i.e. path
kwargs (dict): Dict with ground-truth
Returns:
all_preds (dict): Dict with image_path, vertices, xyz_17, uvd_jts,
xyz_24 for predictions.
"""
labels = {}
labels['trans_inv'] = kwargs['trans_inv']
labels['intrinsic_param'] = kwargs['intrinsic_param']
labels['joint_root'] = kwargs['joint_root']
labels['depth_factor'] = kwargs['depth_factor']
labels['target_uvd_29'] = kwargs['target_uvd_29']
labels['target_xyz_24'] = kwargs['target_xyz_24']
labels['target_weight_24'] = kwargs['target_weight_24']
labels['target_weight_29'] = kwargs['target_weight_29']
labels['target_xyz_17'] = kwargs['target_xyz_17']
labels['target_weight_17'] = kwargs['target_weight_17']
labels['target_theta'] = kwargs['target_theta']
labels['target_beta'] = kwargs['target_beta']
labels['target_smpl_weight'] = kwargs['target_smpl_weight']
labels['target_theta_weight'] = kwargs['target_theta_weight']
labels['target_twist'] = kwargs['target_twist']
labels['target_twist_weight'] = kwargs['target_twist_weight']
bboxes = kwargs['bbox']
for k, _ in labels.items():
labels[k] = labels[k].cuda()
trans_inv = labels.pop('trans_inv')
intrinsic_param = labels.pop('intrinsic_param')
joint_root = labels.pop('joint_root')
depth_factor = labels.pop('depth_factor')
if len(depth_factor.shape) != 2:
depth_factor = torch.unsqueeze(depth_factor, dim=1)
if self.backbone is not None:
img = img.cuda().requires_grad_()
features = self.backbone(img)
features = features[0]
else:
features = img['features']
if self.neck is not None:
features = self.neck(features)
output = self.head(features, trans_inv, intrinsic_param, joint_root,
depth_factor, self.smpl)
pred_uvd_jts = output['pred_uvd_jts']
batch_num = pred_uvd_jts.shape[0]
pred_xyz_jts_24 = output['pred_xyz_jts_24'].reshape(batch_num, -1,
3)[:, :24, :]
pred_xyz_jts_24_struct = output['pred_xyz_jts_24_struct'].reshape(
batch_num, 24, 3)
pred_xyz_jts_17 = output['pred_xyz_jts_17'].reshape(batch_num, 17, 3)
pred_mesh = output['pred_vertices'].reshape(batch_num, -1, 3)
pred_xyz_jts_24 = pred_xyz_jts_24.cpu().data.numpy()
pred_xyz_jts_24_struct = pred_xyz_jts_24_struct.cpu().data.numpy()
pred_xyz_jts_17 = pred_xyz_jts_17.cpu().data.numpy()
pred_uvd_jts = pred_uvd_jts.cpu().data
pred_mesh = pred_mesh.cpu().data.numpy()
assert pred_xyz_jts_17.ndim in [2, 3]
pred_xyz_jts_17 = pred_xyz_jts_17.reshape(pred_xyz_jts_17.shape[0], 17,
3)
pred_uvd_jts = pred_uvd_jts.reshape(pred_uvd_jts.shape[0], -1, 3)
pred_xyz_jts_24 = pred_xyz_jts_24.reshape(pred_xyz_jts_24.shape[0], 24,
3)
pred_scores = output['maxvals'].cpu().data[:, :29]
hm_shape = [64, 64]
pose_coords_list = []
for i in range(pred_xyz_jts_17.shape[0]):
bbox = bboxes[i].tolist()
pose_coords, _ = heatmap2coord(
pred_uvd_jts[i],
pred_scores[i],
hm_shape,
bbox,
mean_bbox_scale=None)
pose_coords_list.append(pose_coords)
all_preds = {}
all_preds['vertices'] = pred_mesh
all_preds['xyz_17'] = pred_xyz_jts_17
all_preds['uvd_jts'] = pose_coords
all_preds['xyz_24'] = pred_xyz_jts_24_struct
image_path = []
for img_meta in img_metas:
image_path.append(img_meta['image_path'])
all_preds['image_path'] = image_path
all_preds['image_idx'] = kwargs['sample_idx']
return all_preds