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ViTPose/mmpose/models/detectors/mesh.py

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# Copyright (c) OpenMMLab. All rights reserved.
import cv2
import mmcv
import numpy as np
import torch
from mmpose.core.visualization.image import imshow_mesh_3d
from mmpose.models.misc.discriminator import SMPLDiscriminator
from .. import builder
from ..builder import POSENETS
from .base import BasePose
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
@POSENETS.register_module()
class ParametricMesh(BasePose):
"""Model-based 3D human mesh detector. Take a single color image as input
and output 3D joints, SMPL parameters and camera parameters.
Args:
backbone (dict): Backbone modules to extract feature.
mesh_head (dict): Mesh head to process feature.
smpl (dict): Config for SMPL model.
disc (dict): Discriminator for SMPL parameters. Default: None.
loss_gan (dict): Config for adversarial loss. Default: None.
loss_mesh (dict): Config for mesh loss. Default: None.
train_cfg (dict): Config for training. Default: None.
test_cfg (dict): Config for testing. Default: None.
pretrained (str): Path to the pretrained models.
"""
def __init__(self,
backbone,
mesh_head,
smpl,
disc=None,
loss_gan=None,
loss_mesh=None,
train_cfg=None,
test_cfg=None,
pretrained=None):
super().__init__()
self.backbone = builder.build_backbone(backbone)
self.mesh_head = builder.build_head(mesh_head)
self.generator = torch.nn.Sequential(self.backbone, self.mesh_head)
self.smpl = builder.build_mesh_model(smpl)
self.with_gan = disc is not None and loss_gan is not None
if self.with_gan:
self.discriminator = SMPLDiscriminator(**disc)
self.loss_gan = builder.build_loss(loss_gan)
self.disc_step_count = 0
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.loss_mesh = builder.build_loss(loss_mesh)
self.init_weights(pretrained=pretrained)
def init_weights(self, pretrained=None):
"""Weight initialization for model."""
self.backbone.init_weights(pretrained)
self.mesh_head.init_weights()
if self.with_gan:
self.discriminator.init_weights()
def train_step(self, data_batch, optimizer, **kwargs):
"""Train step function.
In this function, the detector will finish the train step following
the pipeline:
1. get fake and real SMPL parameters
2. optimize discriminator (if have)
3. optimize generator
If `self.train_cfg.disc_step > 1`, the train step will contain multiple
iterations for optimizing discriminator with different input data and
only one iteration for optimizing generator after `disc_step`
iterations for discriminator.
Args:
data_batch (torch.Tensor): Batch of data as input.
optimizer (dict[torch.optim.Optimizer]): Dict with optimizers for
generator and discriminator (if have).
Returns:
outputs (dict): Dict with loss, information for logger,
the number of samples.
"""
img = data_batch['img']
pred_smpl = self.generator(img)
pred_pose, pred_beta, pred_camera = pred_smpl
# optimize discriminator (if have)
if self.train_cfg['disc_step'] > 0 and self.with_gan:
set_requires_grad(self.discriminator, True)
fake_data = (pred_camera.detach(), pred_pose.detach(),
pred_beta.detach())
mosh_theta = data_batch['mosh_theta']
real_data = (mosh_theta[:, :3], mosh_theta[:,
3:75], mosh_theta[:,
75:])
fake_score = self.discriminator(fake_data)
real_score = self.discriminator(real_data)
disc_losses = {}
disc_losses['real_loss'] = self.loss_gan(
real_score, target_is_real=True, is_disc=True)
disc_losses['fake_loss'] = self.loss_gan(
fake_score, target_is_real=False, is_disc=True)
loss_disc, log_vars_d = self._parse_losses(disc_losses)
optimizer['discriminator'].zero_grad()
loss_disc.backward()
optimizer['discriminator'].step()
self.disc_step_count = \
(self.disc_step_count + 1) % self.train_cfg['disc_step']
if self.disc_step_count != 0:
outputs = dict(
loss=loss_disc,
log_vars=log_vars_d,
num_samples=len(next(iter(data_batch.values()))))
return outputs
# optimize generator
pred_out = self.smpl(
betas=pred_beta,
body_pose=pred_pose[:, 1:],
global_orient=pred_pose[:, :1])
pred_vertices, pred_joints_3d = pred_out['vertices'], pred_out[
'joints']
gt_beta = data_batch['beta']
gt_pose = data_batch['pose']
gt_vertices = self.smpl(
betas=gt_beta,
body_pose=gt_pose[:, 3:],
global_orient=gt_pose[:, :3])['vertices']
pred = dict(
pose=pred_pose,
beta=pred_beta,
camera=pred_camera,
vertices=pred_vertices,
joints_3d=pred_joints_3d)
target = {
key: data_batch[key]
for key in [
'pose', 'beta', 'has_smpl', 'joints_3d', 'joints_2d',
'joints_3d_visible', 'joints_2d_visible'
]
}
target['vertices'] = gt_vertices
losses = self.loss_mesh(pred, target)
if self.with_gan:
set_requires_grad(self.discriminator, False)
pred_theta = (pred_camera, pred_pose, pred_beta)
pred_score = self.discriminator(pred_theta)
loss_adv = self.loss_gan(
pred_score, target_is_real=True, is_disc=False)
losses['adv_loss'] = loss_adv
loss, log_vars = self._parse_losses(losses)
optimizer['generator'].zero_grad()
loss.backward()
optimizer['generator'].step()
outputs = dict(
loss=loss,
log_vars=log_vars,
num_samples=len(next(iter(data_batch.values()))))
return outputs
def forward_train(self, *args, **kwargs):
"""Forward function for training.
For ParametricMesh, we do not use this interface.
"""
raise NotImplementedError('This interface should not be used in '
'current training schedule. Please use '
'`train_step` for training.')
def val_step(self, data_batch, **kwargs):
"""Forward function for evaluation.
Args:
data_batch (dict): Contain data for forward.
Returns:
dict: Contain the results from model.
"""
output = self.forward_test(**data_batch, **kwargs)
return output
def forward_dummy(self, img):
"""Used for computing network FLOPs.
See ``tools/get_flops.py``.
Args:
img (torch.Tensor): Input image.
Returns:
Tensor: Outputs.
"""
output = self.generator(img)
return output
def forward_test(self,
img,
img_metas,
return_vertices=False,
return_faces=False,
**kwargs):
"""Defines the computation performed at every call when testing."""
pred_smpl = self.generator(img)
pred_pose, pred_beta, pred_camera = pred_smpl
pred_out = self.smpl(
betas=pred_beta,
body_pose=pred_pose[:, 1:],
global_orient=pred_pose[:, :1])
pred_vertices, pred_joints_3d = pred_out['vertices'], pred_out[
'joints']
all_preds = {}
all_preds['keypoints_3d'] = pred_joints_3d.detach().cpu().numpy()
all_preds['smpl_pose'] = pred_pose.detach().cpu().numpy()
all_preds['smpl_beta'] = pred_beta.detach().cpu().numpy()
all_preds['camera'] = pred_camera.detach().cpu().numpy()
if return_vertices:
all_preds['vertices'] = pred_vertices.detach().cpu().numpy()
if return_faces:
all_preds['faces'] = self.smpl.get_faces()
all_boxes = []
image_path = []
for img_meta in img_metas:
box = np.zeros(6, dtype=np.float32)
c = img_meta['center']
s = img_meta['scale']
if 'bbox_score' in img_metas:
score = np.array(img_metas['bbox_score']).reshape(-1)
else:
score = 1.0
box[0:2] = c
box[2:4] = s
box[4] = np.prod(s * 200.0, axis=0)
box[5] = score
all_boxes.append(box)
image_path.append(img_meta['image_file'])
all_preds['bboxes'] = np.stack(all_boxes, axis=0)
all_preds['image_path'] = image_path
return all_preds
def get_3d_joints_from_mesh(self, vertices):
"""Get 3D joints from 3D mesh using predefined joints regressor."""
return torch.matmul(
self.joints_regressor.to(vertices.device), vertices)
def forward(self, img, img_metas=None, return_loss=False, **kwargs):
"""Forward function.
Calls either forward_train or forward_test depending on whether
return_loss=True.
Note:
- batch_size: N
- num_img_channel: C (Default: 3)
- img height: imgH
- img width: imgW
Args:
img (torch.Tensor[N x C x imgH x imgW]): Input images.
img_metas (list(dict)): Information about data augmentation
By default this includes:
- "image_file: path to the image file
- "center": center of the bbox
- "scale": scale of the bbox
- "rotation": rotation of the bbox
- "bbox_score": score of bbox
return_loss (bool): Option to `return loss`. `return loss=True`
for training, `return loss=False` for validation & test.
Returns:
Return predicted 3D joints, SMPL parameters, boxes and image paths.
"""
if return_loss:
return self.forward_train(img, img_metas, **kwargs)
return self.forward_test(img, img_metas, **kwargs)
def show_result(self,
result,
img,
show=False,
out_file=None,
win_name='',
wait_time=0,
bbox_color='green',
mesh_color=(76, 76, 204),
**kwargs):
"""Visualize 3D mesh estimation results.
Args:
result (list[dict]): The mesh estimation results containing:
- "bbox" (ndarray[4]): instance bounding bbox
- "center" (ndarray[2]): bbox center
- "scale" (ndarray[2]): bbox scale
- "keypoints_3d" (ndarray[K,3]): predicted 3D keypoints
- "camera" (ndarray[3]): camera parameters
- "vertices" (ndarray[V, 3]): predicted 3D vertices
- "faces" (ndarray[F, 3]): mesh faces
img (str or Tensor): Optional. The image to visualize 2D inputs on.
win_name (str): The window name.
show (bool): Whether to show the image. Default: False.
wait_time (int): Value of waitKey param. Default: 0.
out_file (str or None): The filename to write the image.
Default: None.
bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
mesh_color (str or tuple or :obj:`Color`): Color of mesh surface.
Returns:
ndarray: Visualized img, only if not `show` or `out_file`.
"""
if img is not None:
img = mmcv.imread(img)
focal_length = self.loss_mesh.focal_length
H, W, C = img.shape
img_center = np.array([[0.5 * W], [0.5 * H]])
# show bounding boxes
bboxes = [res['bbox'] for res in result]
bboxes = np.vstack(bboxes)
mmcv.imshow_bboxes(
img, bboxes, colors=bbox_color, top_k=-1, thickness=2, show=False)
vertex_list = []
face_list = []
for res in result:
vertices = res['vertices']
faces = res['faces']
camera = res['camera']
camera_center = res['center']
scale = res['scale']
# predicted vertices are in root-relative space,
# we need to translate them to camera space.
translation = np.array([
camera[1], camera[2],
2 * focal_length / (scale[0] * 200.0 * camera[0] + 1e-9)
])
mean_depth = vertices[:, -1].mean() + translation[-1]
translation[:2] += (camera_center -
img_center[:, 0]) / focal_length * mean_depth
vertices += translation[None, :]
vertex_list.append(vertices)
face_list.append(faces)
# render from front view
img_vis = imshow_mesh_3d(
img,
vertex_list,
face_list,
img_center, [focal_length, focal_length],
colors=mesh_color)
# render from side view
# rotate mesh vertices
R = cv2.Rodrigues(np.array([0, np.radians(90.), 0]))[0]
rot_vertex_list = [np.dot(vert, R) for vert in vertex_list]
# get the 3D bbox containing all meshes
rot_vertices = np.concatenate(rot_vertex_list, axis=0)
min_corner = rot_vertices.min(0)
max_corner = rot_vertices.max(0)
center_3d = 0.5 * (min_corner + max_corner)
ratio = 0.8
bbox3d_size = max_corner - min_corner
# set appropriate translation to make all meshes appear in the image
z_x = bbox3d_size[0] * focal_length / (ratio * W) - min_corner[2]
z_y = bbox3d_size[1] * focal_length / (ratio * H) - min_corner[2]
z = max(z_x, z_y)
translation = -center_3d
translation[2] = z
translation = translation[None, :]
rot_vertex_list = [
rot_vert + translation for rot_vert in rot_vertex_list
]
# render from side view
img_side = imshow_mesh_3d(
np.ones_like(img) * 255, rot_vertex_list, face_list, img_center,
[focal_length, focal_length])
# merger images from front view and side view
img_vis = np.concatenate([img_vis, img_side], axis=1)
if show:
mmcv.visualization.imshow(img_vis, win_name, wait_time)
if out_file is not None:
mmcv.imwrite(img_vis, out_file)
return img_vis