# from multiprocessing.spawn import prepare
# from turtle import forward
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import nvdiffrast.torch as dr
import numpy as np
import matplotlib.pyplot as plt
import os
import os.path as osp
from video3d.render.regularizer import get_edge_length, normal_consistency
from . import networks
from .renderer import *
from .utils import misc, meters, flow_viz, arap, custom_loss
from .dataloaders import get_sequence_loader, get_image_loader
from .cub_dataloaders import get_cub_loader
from .utils.skinning_v4 import estimate_bones, skinning
import lpips
from einops import rearrange
from .geometry.dmtet import DMTetGeometry
from .geometry.dlmesh import DLMesh
from .render import renderutils as ru
from .render import material
from .render import mlptexture
from .render import util
from .render import mesh
from .render import light
from .render import render
EPS = 1e-7
def get_optimizer(model, lr=0.0001, betas=(0.9, 0.999), weight_decay=0):
return torch.optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()),
lr=lr, betas=betas, weight_decay=weight_decay)
def set_requires_grad(model, requires_grad):
if model is not None:
for param in model.parameters():
param.requires_grad = requires_grad
def forward_to_matrix(vec_forward, up=[0,1,0]):
up = torch.FloatTensor(up).to(vec_forward.device)
# vec_forward = nn.functional.normalize(vec_forward, p=2, dim=-1) # x right, y up, z forward
vec_right = up.expand_as(vec_forward).cross(vec_forward, dim=-1)
vec_right = nn.functional.normalize(vec_right, p=2, dim=-1)
vec_up = vec_forward.cross(vec_right, dim=-1)
vec_up = nn.functional.normalize(vec_up, p=2, dim=-1)
rot_mat = torch.stack([vec_right, vec_up, vec_forward], -2)
return rot_mat
def sample_pose_hypothesis_from_quad_prediction(poses_raw, total_iter, batch_size, num_frames, pose_xflip_recon=False, input_image_xflip_flag=None, rot_temp_scalar=1., num_hypos=4, naive_probs_iter=2000, best_pose_start_iter=6000, random_sample=True):
rots_pred = poses_raw[..., :num_hypos*4].view(-1, num_hypos, 4)
rots_logits = rots_pred[..., 0] # Nx4
temp = 1 / np.clip(total_iter / 1000 / rot_temp_scalar, 1., 100.)
rots_probs = torch.nn.functional.softmax(-rots_logits / temp, dim=1) # N x K
# naive_probs = torch.FloatTensor([10] + [1] * (num_hypos - 1)).to(rots_logits.device)
naive_probs = torch.ones(num_hypos).to(rots_logits.device)
naive_probs = naive_probs / naive_probs.sum()
naive_probs_weight = np.clip(1 - (total_iter - naive_probs_iter) / 2000, 0, 1)
rots_probs = naive_probs.view(1, num_hypos) * naive_probs_weight + rots_probs * (1 - naive_probs_weight)
rots_pred = rots_pred[..., 1:4]
trans_pred = poses_raw[..., -3:]
best_rot_idx = torch.argmax(rots_probs, dim=1) # N
if random_sample:
# rand_rot_idx = torch.randint(0, 4, (batch_size * num_frames,), device=poses_raw.device) # N
rand_rot_idx = torch.randperm(batch_size * num_frames, device=poses_raw.device) % num_hypos # N
# rand_rot_idx = torch.randperm(batch_size, device=poses_raw.device)[:,None].repeat(1, num_frames).view(-1) % 4 # N
best_flag = (torch.randperm(batch_size * num_frames, device=poses_raw.device) / (batch_size * num_frames) < np.clip((total_iter - best_pose_start_iter)/2000, 0, 0.8)).long()
rand_flag = 1 - best_flag
# best_flag = torch.zeros_like(best_rot_idx)
rot_idx = best_rot_idx * best_flag + rand_rot_idx * (1 - best_flag)
else:
rand_flag = torch.zeros_like(best_rot_idx)
rot_idx = best_rot_idx
rot_pred = torch.gather(rots_pred, 1, rot_idx[:, None, None].expand(-1, 1, 3))[:, 0] # Nx3
pose_raw = torch.cat([rot_pred, trans_pred], -1)
rot_prob = torch.gather(rots_probs, 1, rot_idx[:, None].expand(-1, 1))[:, 0] # N
rot_logit = torch.gather(rots_logits, 1, rot_idx[:, None].expand(-1, 1))[:, 0] # N
if pose_xflip_recon:
raise NotImplementedError
rot_mat = forward_to_matrix(pose_raw[:, :3], up=[0, 1, 0])
pose = torch.cat([rot_mat.view(batch_size * num_frames, -1), pose_raw[:, 3:]], -1)
return pose_raw, pose, rot_idx, rot_prob, rot_logit, rots_probs, rand_flag
class PriorPredictor(nn.Module):
def __init__(self, cfgs):
super().__init__()
dmtet_grid = cfgs.get('dmtet_grid', 64)
grid_scale = cfgs.get('grid_scale', 5)
prior_sdf_mode = cfgs.get('prior_sdf_mode', 'mlp')
num_layers_shape = cfgs.get('num_layers_shape', 5)
hidden_size = cfgs.get('hidden_size', 64)
embedder_freq_shape = cfgs.get('embedder_freq_shape', 8)
embed_concat_pts = cfgs.get('embed_concat_pts', True)
init_sdf = cfgs.get('init_sdf', None)
jitter_grid = cfgs.get('jitter_grid', 0.)
perturb_sdf_iter = cfgs.get('perturb_sdf_iter', 10000)
sym_prior_shape = cfgs.get('sym_prior_shape', False)
self.netShape = DMTetGeometry(dmtet_grid, grid_scale, prior_sdf_mode, num_layers=num_layers_shape, hidden_size=hidden_size, embedder_freq=embedder_freq_shape, embed_concat_pts=embed_concat_pts, init_sdf=init_sdf, jitter_grid=jitter_grid, perturb_sdf_iter=perturb_sdf_iter, sym_prior_shape=sym_prior_shape)
mlp_hidden_size = cfgs.get('hidden_size', 64)
tet_bbox = self.netShape.getAABB()
self.render_dino_mode = cfgs.get('render_dino_mode', None)
num_layers_dino = cfgs.get("num_layers_dino", 5)
dino_feature_recon_dim = cfgs.get('dino_feature_recon_dim', 64)
sym_dino = cfgs.get("sym_dino", False)
dino_min = torch.zeros(dino_feature_recon_dim) + cfgs.get('dino_min', 0.)
dino_max = torch.zeros(dino_feature_recon_dim) + cfgs.get('dino_max', 1.)
min_max = torch.stack((dino_min, dino_max), dim=0)
if self.render_dino_mode is None:
pass
elif self.render_dino_mode == 'feature_mlpnv':
self.netDINO = mlptexture.MLPTexture3D(tet_bbox, channels=dino_feature_recon_dim, internal_dims=mlp_hidden_size, hidden=num_layers_dino-1, feat_dim=0, min_max=min_max, bsdf=None, perturb_normal=False, symmetrize=sym_dino)
elif self.render_dino_mode == 'feature_mlp':
embedder_scaler = 2 * np.pi / grid_scale * 0.9 # originally (-0.5*s, 0.5*s) rescale to (-pi, pi) * 0.9
embed_concat_pts = cfgs.get('embed_concat_pts', True)
self.netDINO = networks.MLPTextureSimple(
3, # x, y, z coordinates
dino_feature_recon_dim,
num_layers_dino,
nf=mlp_hidden_size,
dropout=0,
activation="sigmoid",
min_max=min_max,
n_harmonic_functions=cfgs.get('embedder_freq_dino', 8),
omega0=embedder_scaler,
extra_dim=0,
embed_concat_pts=embed_concat_pts,
perturb_normal=False,
symmetrize=sym_dino
)
elif self.render_dino_mode == 'cluster':
num_layers_dino = cfgs.get("num_layers_dino", 5)
dino_cluster_dim = cfgs.get('dino_cluster_dim', 64)
self.netDINO = mlptexture.MLPTexture3D(tet_bbox, channels=dino_cluster_dim, internal_dims=mlp_hidden_size, hidden=num_layers_dino-1, feat_dim=0, min_max=None, bsdf=None, perturb_normal=False, symmetrize=sym_dino)
else:
raise NotImplementedError
def forward(self, perturb_sdf=False, total_iter=None, is_training=True):
prior_shape = self.netShape.getMesh(perturb_sdf=perturb_sdf, total_iter=total_iter, jitter_grid=is_training)
return prior_shape, self.netDINO
class InstancePredictor(nn.Module):
def __init__(self, cfgs, tet_bbox=None):
super().__init__()
self.cfgs = cfgs
self.grid_scale = cfgs.get('grid_scale', 5)
self.enable_encoder = cfgs.get('enable_encoder', False)
if self.enable_encoder:
encoder_latent_dim = cfgs.get('latent_dim', 256)
encoder_pretrained = cfgs.get('encoder_pretrained', False)
encoder_frozen = cfgs.get('encoder_frozen', False)
encoder_arch = cfgs.get('encoder_arch', 'simple')
in_image_size = cfgs.get('in_image_size', 256)
self.dino_feature_input = cfgs.get('dino_feature_input', False)
dino_feature_dim = cfgs.get('dino_feature_dim', 64)
if encoder_arch == 'simple':
if self.dino_feature_input:
self.netEncoder = networks.EncoderWithDINO(cin_rgb=3, cin_dino=dino_feature_dim, cout=encoder_latent_dim, in_size=in_image_size, zdim=None, nf=64, activation=None)
else:
self.netEncoder = networks.Encoder(cin=3, cout=encoder_latent_dim, in_size=in_image_size, zdim=None, nf=64, activation=None)
elif encoder_arch == 'vgg':
self.netEncoder = networks.VGGEncoder(cout=encoder_latent_dim, pretrained=encoder_pretrained)
elif encoder_arch == 'resnet':
self.netEncoder = networks.ResnetEncoder(cout=encoder_latent_dim, pretrained=encoder_pretrained)
elif encoder_arch == 'vit':
which_vit = cfgs.get('which_vit', 'dino_vits8')
vit_final_layer_type = cfgs.get('vit_final_layer_type', 'conv')
self.netEncoder = networks.ViTEncoder(cout=encoder_latent_dim, which_vit=which_vit, pretrained=encoder_pretrained, frozen=encoder_frozen, in_size=in_image_size, final_layer_type=vit_final_layer_type)
else:
raise NotImplementedError
else:
encoder_latent_dim = 0
mlp_hidden_size = cfgs.get('hidden_size', 64)
bsdf = cfgs.get("bsdf", 'diffuse')
num_layers_tex = cfgs.get("num_layers_tex", 5)
feat_dim = cfgs.get("latent_dim", 64) if self.enable_encoder else 0
perturb_normal = cfgs.get("perturb_normal", False)
sym_texture = cfgs.get("sym_texture", False)
kd_min = torch.FloatTensor(cfgs.get('kd_min', [0., 0., 0., 0.]))
kd_max = torch.FloatTensor(cfgs.get('kd_max', [1., 1., 1., 1.]))
ks_min = torch.FloatTensor(cfgs.get('ks_min', [0., 0., 0.]))
ks_max = torch.FloatTensor(cfgs.get('ks_max', [0., 0., 0.]))
nrm_min = torch.FloatTensor(cfgs.get('nrm_min', [-1., -1., 0.]))
nrm_max = torch.FloatTensor(cfgs.get('nrm_max', [1., 1., 1.]))
mlp_min = torch.cat((kd_min[0:3], ks_min, nrm_min), dim=0)
mlp_max = torch.cat((kd_max[0:3], ks_max, nrm_max), dim=0)
min_max = torch.stack((mlp_min, mlp_max), dim=0)
out_chn = 9
# TODO: if the tet verts are deforming, we need to recompute tet_bbox
texture_mode = cfgs.get("texture_mode", 'mlp')
if texture_mode == 'mlpnv':
self.netTexture = mlptexture.MLPTexture3D(tet_bbox, channels=out_chn, internal_dims=mlp_hidden_size, hidden=num_layers_tex-1, feat_dim=feat_dim, min_max=min_max, bsdf=bsdf, perturb_normal=perturb_normal, symmetrize=sym_texture)
elif texture_mode == 'mlp':
embedder_scaler = 2 * np.pi / self.grid_scale * 0.9 # originally (-0.5*s, 0.5*s) rescale to (-pi, pi) * 0.9
embed_concat_pts = cfgs.get('embed_concat_pts', True)
self.netTexture = networks.MLPTextureSimple(
3, # x, y, z coordinates
out_chn,
num_layers_tex,
nf=mlp_hidden_size,
dropout=0,
activation="sigmoid",
min_max=min_max,
n_harmonic_functions=cfgs.get('embedder_freq_tex', 10),
omega0=embedder_scaler,
extra_dim=feat_dim,
embed_concat_pts=embed_concat_pts,
perturb_normal=perturb_normal,
symmetrize=sym_texture
)
self.rot_rep = cfgs.get('rot_rep', 'euler_angle')
self.enable_pose = cfgs.get('enable_pose', False)
if self.enable_pose:
cam_pos_z_offset = cfgs.get('cam_pos_z_offset', 10.)
fov = cfgs.get('crop_fov_approx', 25)
half_range = np.tan(fov /2 /180 * np.pi) * cam_pos_z_offset # 2.22
self.max_trans_xy_range = half_range * cfgs.get('max_trans_xy_range_ratio', 1.)
self.max_trans_z_range = half_range * cfgs.get('max_trans_z_range_ratio', 1.)
self.lookat_init = cfgs.get('lookat_init', None)
self.lookat_zeroy = cfgs.get('lookat_zeroy', False)
self.rot_temp_scalar = cfgs.get('rot_temp_scalar', 1.)
self.naive_probs_iter = cfgs.get('naive_probs_iter', 2000)
self.best_pose_start_iter = cfgs.get('best_pose_start_iter', 6000)
if self.rot_rep == 'euler_angle':
pose_cout = 6
elif self.rot_rep == 'quaternion':
pose_cout = 7
elif self.rot_rep == 'lookat':
pose_cout = 6
elif self.rot_rep == 'quadlookat':
self.num_pose_hypos = 4
pose_cout = (3 + 1) * self.num_pose_hypos + 3 # 4 forward vectors for 4 quadrants, 4 quadrant classification logits, 3 for translation
self.orthant_signs = torch.FloatTensor([[1,1,1], [-1,1,1], [-1,1,-1], [1,1,-1]])
elif self.rot_rep == 'octlookat':
self.num_pose_hypos = 8
pose_cout = (3 + 1) * self.num_pose_hypos + 3 # 4 forward vectors for 8 octants, 8 octant classification logits, 3 for translation
self.orthant_signs = torch.stack(torch.meshgrid([torch.arange(1, -2, -2)] *3), -1).view(-1, 3) # 8x3
else:
raise NotImplementedError
self.pose_arch = cfgs.get('pose_arch', 'mlp')
if self.pose_arch == 'mlp':
num_layers_pose = cfgs.get('num_layers_pose', 5)
self.netPose = networks.MLP(
encoder_latent_dim,
pose_cout,
num_layers_pose,
nf=mlp_hidden_size,
dropout=0,
activation=None
)
elif self.pose_arch == 'encoder':
if self.dino_feature_input:
dino_feature_dim = cfgs.get('dino_feature_dim', 64)
self.netPose = networks.EncoderWithDINO(cin_rgb=3, cin_dino=dino_feature_dim, cout=pose_cout, in_size=in_image_size, zdim=None, nf=64, activation=None)
else:
self.netPose = networks.Encoder(cin=3, cout=pose_cout, in_size=in_image_size, zdim=None, nf=64, activation=None)
elif self.pose_arch in ['encoder_dino_patch_out', 'encoder_dino_patch_key']:
if which_vit == 'dino_vits8':
dino_feat_dim = 384
elif which_vit == 'dinov2_vits14':
dino_feat_dim = 384
elif which_vit == 'dino_vitb8':
dino_feat_dim = 768
self.netPose = networks.Encoder32(cin=dino_feat_dim, cout=pose_cout, nf=256, activation=None)
elif self.pose_arch == 'vit':
encoder_pretrained = cfgs.get('encoder_pretrained', False)
encoder_frozen = cfgs.get('encoder_frozen', False)
which_vit = cfgs.get('which_vit', 'dino_vits8')
vit_final_layer_type = cfgs.get('vit_final_layer_type', 'conv')
self.netPose = networks.ViTEncoder(cout=encoder_latent_dim, which_vit=which_vit, pretrained=encoder_pretrained, frozen=encoder_frozen, in_size=in_image_size, final_layer_type=vit_final_layer_type)
else:
raise NotImplementedError
self.enable_deform = cfgs.get('enable_deform', False)
if self.enable_deform:
embedder_scaler = 2 * np.pi / self.grid_scale * 0.9 # originally (-0.5*s, 0.5*s) rescale to (-pi, pi) * 0.9
embed_concat_pts = cfgs.get('embed_concat_pts', True)
num_layers_deform = cfgs.get('num_layers_deform', 5)
self.deform_epochs = np.arange(*cfgs.get('deform_epochs', [0, 0]))
sym_deform = cfgs.get("sym_deform", False)
self.netDeform = networks.MLPWithPositionalEncoding(
3, # x, y, z coordinates
3, # dx, dy, dz deformation
num_layers_deform,
nf=mlp_hidden_size,
dropout=0,
activation=None,
n_harmonic_functions=cfgs.get('embedder_freq_deform', 10),
omega0=embedder_scaler,
extra_dim=encoder_latent_dim,
embed_concat_pts=embed_concat_pts,
symmetrize=sym_deform
)
self.enable_articulation = cfgs.get('enable_articulation', False)
if self.enable_articulation:
self.num_body_bones = cfgs.get('num_body_bones', 4)
self.articulation_multiplier = cfgs.get('articulation_multiplier', 1)
self.static_root_bones = cfgs.get('static_root_bones', False)
self.skinning_temperature = cfgs.get('skinning_temperature', 1)
self.articulation_epochs = np.arange(*cfgs.get('articulation_epochs', [0, 0]))
self.num_legs = cfgs.get('num_legs', 0)
self.num_leg_bones = cfgs.get('num_leg_bones', 0)
self.body_bones_type = cfgs.get('body_bones_type', 'z_minmax')
self.perturb_articulation_epochs = np.arange(*cfgs.get('perturb_articulation_epochs', [0, 0]))
self.num_bones = self.num_body_bones + self.num_legs * self.num_leg_bones
self.constrain_legs = cfgs.get('constrain_legs', False)
self.attach_legs_to_body_epochs = np.arange(*cfgs.get('attach_legs_to_body_epochs', [0, 0]))
self.max_arti_angle = cfgs.get('max_arti_angle', 60)
num_layers_arti = cfgs.get('num_layers_arti', 5)
which_vit = cfgs.get('which_vit', 'dino_vits8')
if which_vit == 'dino_vits8':
dino_feat_dim = 384
elif which_vit == 'dino_vitb8':
dino_feat_dim = 768
self.articulation_arch = cfgs.get('articulation_arch', 'mlp')
self.articulation_feature_mode = cfgs.get('articulation_feature_mode', 'sample')
embedder_freq_arti = cfgs.get('embedder_freq_arti', 8)
if self.articulation_feature_mode == 'global':
feat_dim = encoder_latent_dim
elif self.articulation_feature_mode == 'sample':
feat_dim = dino_feat_dim
elif self.articulation_feature_mode == 'sample+global':
feat_dim = encoder_latent_dim + dino_feat_dim
if self.articulation_feature_mode == 'attention':
arti_feat_attn_zdim = cfgs.get('arti_feat_attn_zdim', 128)
pos_dim = 1 + 2 + 3*2
self.netFeatureAttn = networks.FeatureAttention(which_vit, pos_dim, embedder_freq_arti, arti_feat_attn_zdim, img_size=in_image_size)
embedder_scaler = np.pi * 0.9 # originally (-1, 1) rescale to (-pi, pi) * 0.9
self.netArticulation = networks.ArticulationNetwork(self.articulation_arch, feat_dim, 1+2+3*2, num_layers_arti, mlp_hidden_size, n_harmonic_functions=embedder_freq_arti, omega0=embedder_scaler)
self.kinematic_tree_epoch = -1
self.enable_lighting = cfgs.get('enable_lighting', False)
if self.enable_lighting:
num_layers_light = cfgs.get('num_layers_light', 5)
amb_diff_min = torch.FloatTensor(cfgs.get('amb_diff_min', [0., 0.]))
amb_diff_max = torch.FloatTensor(cfgs.get('amb_diff_max', [1., 1.]))
intensity_min_max = torch.stack((amb_diff_min, amb_diff_max), dim=0)
self.netLight = light.DirectionalLight(encoder_latent_dim, num_layers_light, mlp_hidden_size, intensity_min_max=intensity_min_max)
self.cam_pos_z_offset = cfgs.get('cam_pos_z_offset', 10.)
self.crop_fov_approx = cfgs.get("crop_fov_approx", 25)
def forward_encoder(self, images, dino_features=None):
images_in = images.view(-1, *images.shape[2:]) * 2 - 1 # rescale to (-1, 1)
patch_out = patch_key = None
if self.dino_feature_input and self.cfgs.get('encoder_arch', 'simple') != 'vit':
dino_features_in = dino_features.view(-1, *dino_features.shape[2:]) * 2 - 1 # rescale to (-1, 1)
feat_out = self.netEncoder(images_in, dino_features_in) # Shape: (B, latent_dim)
elif self.cfgs.get('encoder_arch', 'simple') == 'vit':
feat_out, feat_key, patch_out, patch_key = self.netEncoder(images_in, return_patches=True)
else:
feat_out = self.netEncoder(images_in) # Shape: (B, latent_dim)
return feat_out, feat_key, patch_out, patch_key
def forward_pose(self, images, feat, patch_out, patch_key, dino_features):
if self.pose_arch == 'mlp':
pose = self.netPose(feat)
elif self.pose_arch == 'encoder':
images_in = images.view(-1, *images.shape[2:]) * 2 - 1 # rescale to (-1, 1)
if self.dino_feature_input:
dino_features_in = dino_features.view(-1, *dino_features.shape[2:]) * 2 - 1 # rescale to (-1, 1)
pose = self.netPose(images_in, dino_features_in) # Shape: (B, latent_dim)
else:
pose = self.netPose(images_in) # Shape: (B, latent_dim)
elif self.pose_arch == 'vit':
images_in = images.view(-1, *images.shape[2:]) * 2 - 1 # rescale to (-1, 1)
pose = self.netPose(images_in)
elif self.pose_arch == 'encoder_dino_patch_out':
pose = self.netPose(patch_out) # Shape: (B, latent_dim)
elif self.pose_arch == 'encoder_dino_patch_key':
pose = self.netPose(patch_key) # Shape: (B, latent_dim)
else:
raise NotImplementedError
trans_pred = pose[...,-3:].tanh() * torch.FloatTensor([self.max_trans_xy_range, self.max_trans_xy_range, self.max_trans_z_range]).to(pose.device)
if self.rot_rep == 'euler_angle':
multiplier = 1.
if self.gradually_expand_yaw:
# multiplier += (min(iteration, 20000) // 500) * 0.25
multiplier *= 1.2 ** (min(iteration, 20000) // 500) # 1.125^40 = 111.200
rot_pred = torch.cat([pose[...,:1], pose[...,1:2]*multiplier, pose[...,2:3]], -1).tanh()
rot_pred = rot_pred * torch.FloatTensor([self.max_rot_x_range, self.max_rot_y_range, self.max_rot_z_range]).to(pose.device) /180 * np.pi
elif self.rot_rep == 'quaternion':
quat_init = torch.FloatTensor([0.01,0,0,0]).to(pose.device)
rot_pred = pose[...,:4] + quat_init
rot_pred = nn.functional.normalize(rot_pred, p=2, dim=-1)
# rot_pred = torch.cat([rot_pred[...,:1].abs(), rot_pred[...,1:]], -1) # make real part non-negative
rot_pred = rot_pred * rot_pred[...,:1].sign() # make real part non-negative
elif self.rot_rep == 'lookat':
vec_forward_raw = pose[...,:3]
if self.lookat_init is not None:
vec_forward_raw = vec_forward_raw + torch.FloatTensor(self.lookat_init).to(pose.device)
if self.lookat_zeroy:
vec_forward_raw = vec_forward_raw * torch.FloatTensor([1,0,1]).to(pose.device)
vec_forward_raw = nn.functional.normalize(vec_forward_raw, p=2, dim=-1) # x right, y up, z forward
rot_pred = vec_forward_raw
elif self.rot_rep in ['quadlookat', 'octlookat']:
rots_pred = pose[..., :self.num_pose_hypos*4].view(-1, self.num_pose_hypos, 4) # (B, T, K, 4)
rots_logits = rots_pred[..., :1]
vec_forward_raw = rots_pred[..., 1:4]
xs, ys, zs = vec_forward_raw.unbind(-1)
margin = 0.
xs = nn.functional.softplus(xs, beta=np.log(2)/(0.5+margin)) - margin # initialize to 0.5
if self.rot_rep == 'octlookat':
ys = nn.functional.softplus(ys, beta=np.log(2)/(0.5+margin)) - margin # initialize to 0.5
if self.lookat_zeroy:
ys = ys * 0
zs = nn.functional.softplus(zs, beta=2*np.log(2)) # initialize to 0.5
vec_forward_raw = torch.stack([xs, ys, zs], -1)
vec_forward_raw = vec_forward_raw * self.orthant_signs.to(pose.device)
vec_forward_raw = nn.functional.normalize(vec_forward_raw, p=2, dim=-1) # x right, y up, z forward
rot_pred = torch.cat([rots_logits, vec_forward_raw], -1).view(-1, self.num_pose_hypos*4)
else:
raise NotImplementedError
pose = torch.cat([rot_pred, trans_pred], -1)
return pose
def forward_deformation(self, shape, feat=None):
original_verts = shape.v_pos
num_verts = original_verts.shape[1]
if feat is not None:
deform_feat = feat[:, None, :].repeat(1, num_verts, 1) # Shape: (B, num_verts, latent_dim)
original_verts = original_verts.repeat(len(feat),1,1)
deformation = self.netDeform(original_verts, deform_feat) * 0.1 # Shape: (B, num_verts, 3)
shape = shape.deform(deformation)
return shape, deformation
def forward_articulation(self, shape, feat, patch_feat, mvp, w2c, batch_size, num_frames, epoch):
"""
Forward propagation of articulation. For each bone, the network takes: 1) the 3D location of the bone; 2) the feature of the patch which
the bone is projected to; and 3) an encoding of the bone's index to predict the bone's rotation (represented by an Euler angle).
Args:
shape: a Mesh object, whose v_pos has batch size BxF or 1.
feat: the feature of the patches. Shape: (BxF, feat_dim, num_patches_per_axis, num_patches_per_axis)
mvp: the model-view-projection matrix. Shape: (BxF, 4, 4)
Returns:
shape: a Mesh object, whose v_pos has batch size BxF (collapsed).
articulation_angles: the predicted bone rotations. Shape: (B, F, num_bones, 3)
aux: a dictionary containing auxiliary information.
"""
verts = shape.v_pos
if len(verts) == 1:
verts = verts[None]
else:
verts = verts.view(batch_size, num_frames, *verts.shape[1:])
if self.kinematic_tree_epoch != epoch:
# if (epoch == self.articulation_epochs[0]) and (self.kinematic_tree_epoch != epoch):
# if (epoch in [self.articulation_epochs[0], self.articulation_epochs[0]+2, self.articulation_epochs[0]+4]) and (self.kinematic_tree_epoch != epoch):
attach_legs_to_body = epoch in self.attach_legs_to_body_epochs
bones, self.kinematic_tree, self.bone_aux = estimate_bones(verts.detach(), self.num_body_bones, n_legs=self.num_legs, n_leg_bones=self.num_leg_bones, body_bones_type=self.body_bones_type, compute_kinematic_chain=True, attach_legs_to_body=attach_legs_to_body)
self.kinematic_tree_epoch = epoch
else:
bones = estimate_bones(verts.detach(), self.num_body_bones, n_legs=self.num_legs, n_leg_bones=self.num_leg_bones, body_bones_type=self.body_bones_type, compute_kinematic_chain=False, aux=self.bone_aux)
bones_pos = bones # Shape: (B, F, K, 2, 3)
if batch_size > bones_pos.shape[0] or num_frames > bones_pos.shape[1]:
assert bones_pos.shape[0] == 1 and bones_pos.shape[1] == 1, "If there is a mismatch, then there must be only one canonical mesh."
bones_pos = bones_pos.repeat(batch_size, num_frames, 1, 1, 1)
num_bones = bones_pos.shape[2]
bones_pos = bones_pos.view(batch_size*num_frames, num_bones, 2, 3) # NxKx2x3
bones_mid_pos = bones_pos.mean(2) # NxKx3
bones_idx = torch.arange(num_bones).to(bones_pos.device)
bones_mid_pos_world4 = torch.cat([bones_mid_pos, torch.ones_like(bones_mid_pos[..., :1])], -1) # NxKx4
bones_mid_pos_clip4 = bones_mid_pos_world4 @ mvp.transpose(-1, -2)
bones_mid_pos_uv = bones_mid_pos_clip4[..., :2] / bones_mid_pos_clip4[..., 3:4]
bones_mid_pos_uv = bones_mid_pos_uv.detach()
bones_pos_world4 = torch.cat([bones_pos, torch.ones_like(bones_pos[..., :1])], -1) # NxKx2x4
bones_pos_cam4 = bones_pos_world4 @ w2c[:,None].transpose(-1, -2)
bones_pos_cam3 = bones_pos_cam4[..., :3] / bones_pos_cam4[..., 3:4]
bones_pos_cam3 = bones_pos_cam3 + torch.FloatTensor([0, 0, self.cam_pos_z_offset]).to(bones_pos_cam3.device).view(1, 1, 1, 3)
bones_pos_in = bones_pos_cam3.view(batch_size*num_frames, num_bones, 2*3) / self.grid_scale * 2 # (-1, 1), NxKx(2*3)
bones_idx_in = ((bones_idx[None, :, None] + 0.5) / num_bones * 2 - 1).repeat(batch_size * num_frames, 1, 1) # (-1, 1)
bones_pos_in = torch.cat([bones_mid_pos_uv, bones_pos_in, bones_idx_in], -1).detach()
if self.articulation_feature_mode == 'global':
bones_patch_features = feat[:, None].repeat(1, num_bones, 1) # (BxF, K, feat_dim)
elif self.articulation_feature_mode == 'sample':
bones_patch_features = F.grid_sample(patch_feat, bones_mid_pos_uv.view(batch_size * num_frames, 1, -1, 2), mode='bilinear').squeeze(dim=-2).permute(0, 2, 1) # (BxF, K, feat_dim)
elif self.articulation_feature_mode == 'sample+global':
bones_patch_features = F.grid_sample(patch_feat, bones_mid_pos_uv.view(batch_size * num_frames, 1, -1, 2), mode='bilinear').squeeze(dim=-2).permute(0, 2, 1) # (BxF, K, feat_dim)
bones_patch_features = torch.cat([feat[:, None].repeat(1, num_bones, 1), bones_patch_features], -1)
elif self.articulation_feature_mode == 'attention':
bones_patch_features = self.netFeatureAttn(bones_pos_in, patch_feat)
else:
raise NotImplementedError
articulation_angles = self.netArticulation(bones_patch_features, bones_pos_in).view(batch_size, num_frames, num_bones, 3) * self.articulation_multiplier
if self.static_root_bones:
root_bones = [self.num_body_bones // 2 - 1, self.num_body_bones - 1]
tmp_mask = torch.ones_like(articulation_angles)
tmp_mask[:, :, root_bones] = 0
articulation_angles = articulation_angles * tmp_mask
articulation_angles = articulation_angles.tanh()
if self.constrain_legs:
leg_bones_posx = [self.num_body_bones + i for i in range(self.num_leg_bones * self.num_legs // 2)]
leg_bones_negx = [self.num_body_bones + self.num_leg_bones * self.num_legs // 2 + i for i in range(self.num_leg_bones * self.num_legs // 2)]
tmp_mask = torch.zeros_like(articulation_angles)
tmp_mask[:, :, leg_bones_posx + leg_bones_negx, 2] = 1
articulation_angles = tmp_mask * (articulation_angles * 0.3) + (1 - tmp_mask) * articulation_angles # no twist
tmp_mask = torch.zeros_like(articulation_angles)
tmp_mask[:, :, leg_bones_posx + leg_bones_negx, 1] = 1
articulation_angles = tmp_mask * (articulation_angles * 0.3) + (1 - tmp_mask) * articulation_angles # (-0.4, 0.4), limit side bending
if epoch in self.perturb_articulation_epochs:
articulation_angles = articulation_angles + torch.randn_like(articulation_angles) * 0.1
articulation_angles = articulation_angles * self.max_arti_angle / 180 * np.pi
verts_articulated, aux = skinning(verts, bones, self.kinematic_tree, articulation_angles,
output_posed_bones=True, temperature=self.skinning_temperature)
verts_articulated = verts_articulated.view(batch_size*num_frames, *verts_articulated.shape[2:])
v_tex = shape.v_tex
if len(v_tex) != len(verts_articulated):
v_tex = v_tex.repeat(len(verts_articulated), 1, 1)
shape = mesh.make_mesh(
verts_articulated,
shape.t_pos_idx,
v_tex,
shape.t_tex_idx,
shape.material)
return shape, articulation_angles, aux
def get_camera_extrinsics_from_pose(self, pose, znear=0.1, zfar=1000.):
N = len(pose)
cam_pos_offset = torch.FloatTensor([0, 0, -self.cam_pos_z_offset]).to(pose.device)
pose_R = pose[:, :9].view(N, 3, 3).transpose(2, 1)
pose_T = pose[:, -3:] + cam_pos_offset[None, None, :]
pose_T = pose_T.view(N, 3, 1)
pose_RT = torch.cat([pose_R, pose_T], axis=2) # Nx3x4
w2c = torch.cat([pose_RT, torch.FloatTensor([0, 0, 0, 1]).repeat(N, 1, 1).to(pose.device)], axis=1) # Nx4x4
# We assume the images are perfect square.
proj = util.perspective(self.crop_fov_approx / 180 * np.pi, 1, znear, zfar)[None].to(pose.device)
mvp = torch.matmul(proj, w2c)
campos = -torch.matmul(pose_R.transpose(2, 1), pose_T).view(N, 3)
return mvp, w2c, campos
def forward(self, images=None, prior_shape=None, epoch=None, dino_features=None, dino_clusters=None, total_iter=None, is_training=True):
batch_size, num_frames = images.shape[:2]
if self.enable_encoder:
feat_out, feat_key, patch_out, patch_key = self.forward_encoder(images, dino_features)
else:
feat_out = feat_key = patch_out = patch_key = None
shape = prior_shape
texture = self.netTexture
multi_hypothesis_aux = {}
if self.enable_pose:
poses_raw = self.forward_pose(images, feat_out, patch_out, patch_key, dino_features)
pose_raw, pose, rot_idx, rot_prob, rot_logit, rots_probs, rand_pose_flag = sample_pose_hypothesis_from_quad_prediction(poses_raw, total_iter, batch_size, num_frames, rot_temp_scalar=self.rot_temp_scalar, num_hypos=self.num_pose_hypos, naive_probs_iter=self.naive_probs_iter, best_pose_start_iter=self.best_pose_start_iter, random_sample=is_training)
multi_hypothesis_aux['rot_idx'] = rot_idx
multi_hypothesis_aux['rot_prob'] = rot_prob
multi_hypothesis_aux['rot_logit'] = rot_logit
multi_hypothesis_aux['rots_probs'] = rots_probs
multi_hypothesis_aux['rand_pose_flag'] = rand_pose_flag
else:
raise NotImplementedError
mvp, w2c, campos = self.get_camera_extrinsics_from_pose(pose)
deformation = None
if self.enable_deform and epoch in self.deform_epochs:
shape, deformation = self.forward_deformation(shape, feat_key)
arti_params, articulation_aux = None, {}
if self.enable_articulation and epoch in self.articulation_epochs:
shape, arti_params, articulation_aux = self.forward_articulation(shape, feat_key, patch_key, mvp, w2c, batch_size, num_frames, epoch)
if self.enable_lighting:
light = self.netLight
else:
light = None
aux = articulation_aux
aux.update(multi_hypothesis_aux)
return shape, pose_raw, pose, mvp, w2c, campos, texture, feat_out, deformation, arti_params, light, aux
class Unsup3D:
def __init__(self, cfgs):
self.cfgs = cfgs
self.device = cfgs.get('device', 'cpu')
self.in_image_size = cfgs.get('in_image_size', 128)
self.out_image_size = cfgs.get('out_image_size', 128)
self.num_epochs = cfgs.get('num_epochs', 10)
self.lr = cfgs.get('lr', 1e-4)
self.use_scheduler = cfgs.get('use_scheduler', False)
if self.use_scheduler:
scheduler_milestone = cfgs.get('scheduler_milestone', [1,2,3,4,5])
scheduler_gamma = cfgs.get('scheduler_gamma', 0.5)
self.make_scheduler = lambda optim: torch.optim.lr_scheduler.MultiStepLR(optim, milestones=scheduler_milestone, gamma=scheduler_gamma)
self.cam_pos_z_offset = cfgs.get('cam_pos_z_offset', 10.)
self.full_size_h = cfgs.get('full_size_h', 1080)
self.full_size_w = cfgs.get('full_size_w', 1920)
# self.fov_w = cfgs.get('fov_w', 60)
# self.fov_h = np.arctan(np.tan(self.fov_w /2 /180*np.pi) / self.full_size_w * self.full_size_h) *2 /np.pi*180 # 36
self.crop_fov_approx = cfgs.get("crop_fov_approx", 25)
self.mesh_regularization_mode = cfgs.get('mesh_regularization_mode', 'seq')
self.enable_prior = cfgs.get('enable_prior', False)
if self.enable_prior:
self.netPrior = PriorPredictor(self.cfgs)
self.prior_lr = cfgs.get('prior_lr', self.lr)
self.prior_weight_decay = cfgs.get('prior_weight_decay', 0.)
self.prior_only_epochs = cfgs.get('prior_only_epochs', 0)
self.netInstance = InstancePredictor(self.cfgs, tet_bbox=self.netPrior.netShape.getAABB())
self.perturb_sdf = cfgs.get('perturb_sdf', False)
self.blur_mask = cfgs.get('blur_mask', False)
self.blur_mask_iter = cfgs.get('blur_mask_iter', 1)
self.seqshape_epochs = np.arange(*cfgs.get('seqshape_epochs', [0, self.num_epochs]))
self.avg_texture_epochs = np.arange(*cfgs.get('avg_texture_epochs', [0, 0]))
self.swap_texture_epochs = np.arange(*cfgs.get('swap_texture_epochs', [0, 0]))
self.swap_priorshape_epochs = np.arange(*cfgs.get('swap_priorshape_epochs', [0, 0]))
self.avg_seqshape_epochs = np.arange(*cfgs.get('avg_seqshape_epochs', [0, 0]))
self.swap_seqshape_epochs = np.arange(*cfgs.get('swap_seqshape_epochs', [0, 0]))
self.pose_epochs = np.arange(*cfgs.get('pose_epochs', [0, 0]))
self.pose_iters = cfgs.get('pose_iters', 0)
self.deform_type = cfgs.get('deform_type', None)
self.mesh_reg_decay_epoch = cfgs.get('mesh_reg_decay_epoch', 0)
self.sdf_reg_decay_start_iter = cfgs.get('sdf_reg_decay_start_iter', 0)
self.mesh_reg_decay_rate = cfgs.get('mesh_reg_decay_rate', 1)
self.texture_epochs = np.arange(*cfgs.get('texture_epochs', [0, self.num_epochs]))
self.zflip_epochs = np.arange(*cfgs.get('zflip_epochs', [0, self.num_epochs]))
self.lookat_zflip_loss_epochs = np.arange(*cfgs.get('lookat_zflip_loss_epochs', [0, self.num_epochs]))
self.lookat_zflip_no_other_losses = cfgs.get('lookat_zflip_no_other_losses', False)
self.flow_loss_epochs = np.arange(*cfgs.get('flow_loss_epochs', [0, self.num_epochs]))
self.sdf_inflate_reg_loss_epochs = np.arange(*cfgs.get('sdf_inflate_reg_loss_epochs', [0, self.num_epochs]))
self.arti_reg_loss_epochs = np.arange(*cfgs.get('arti_reg_loss_epochs', [0, self.num_epochs]))
self.background_mode = cfgs.get('background_mode', 'background')
self.shape_prior_type = cfgs.get('shape_prior_type', 'deform')
self.backward_prior = cfgs.get('backward_prior', True)
self.resume_prior_optim = cfgs.get('resume_prior_optim', True)
self.dmtet_grid_smaller_epoch = cfgs.get('dmtet_grid_smaller_epoch', 0)
self.dmtet_grid_smaller = cfgs.get('dmtet_grid_smaller', 128)
self.dmtet_grid = cfgs.get('dmtet_grid', 256)
self.pose_xflip_recon_epochs = np.arange(*cfgs.get('pose_xflip_recon_epochs', [0, 0]))
self.rot_rand_quad_epochs = np.arange(*cfgs.get('rot_rand_quad_epochs', [0, 0]))
self.rot_all_quad_epochs = np.arange(*cfgs.get('rot_all_quad_epochs', [0, 0]))
## perceptual loss
if cfgs.get('perceptual_loss_weight', 0.) > 0:
self.perceptual_loss_use_lin = cfgs.get('perceptual_loss_use_lin', True)
self.perceptual_loss = lpips.LPIPS(net='vgg', lpips=self.perceptual_loss_use_lin)
self.glctx = dr.RasterizeGLContext()
self.render_flow = self.cfgs.get('flow_loss_weight', 0.) > 0.
self.extra_renders = cfgs.get('extra_renders', [])
self.renderer_spp = cfgs.get('renderer_spp', 1)
self.dino_feature_recon_dim = cfgs.get('dino_feature_recon_dim', 64)
self.total_loss = 0.
self.all_scores = torch.Tensor()
self.checkpoint_dir = cfgs.get('checkpoint_dir', 'results')
@staticmethod
def get_data_loaders(cfgs, dataset, in_image_size=256, out_image_size=256, batch_size=64, num_workers=4, run_train=False, run_test=False, train_data_dir=None, val_data_dir=None, test_data_dir=None):
train_loader = val_loader = test_loader = None
color_jitter_train = cfgs.get('color_jitter_train', None)
color_jitter_val = cfgs.get('color_jitter_val', None)
random_flip_train = cfgs.get('random_flip_train', False)
## video dataset
if dataset == 'video':
data_loader_mode = cfgs.get('data_loader_mode', 'n_frame')
skip_beginning = cfgs.get('skip_beginning', 4)
skip_end = cfgs.get('skip_end', 4)
num_sample_frames = cfgs.get('num_sample_frames', 2)
min_seq_len = cfgs.get('min_seq_len', 10)
max_seq_len = cfgs.get('max_seq_len', 10)
debug_seq = cfgs.get('debug_seq', False)
random_sample_train_frames = cfgs.get('random_sample_train_frames', False)
shuffle_train_seqs = cfgs.get('shuffle_train_seqs', False)
random_sample_val_frames = cfgs.get('random_sample_val_frames', False)
load_background = cfgs.get('background_mode', 'none') == 'background'
rgb_suffix = cfgs.get('rgb_suffix', '.png')
load_dino_feature = cfgs.get('load_dino_feature', False)
load_dino_cluster = cfgs.get('load_dino_cluster', False)
dino_feature_dim = cfgs.get('dino_feature_dim', 64)
get_loader = lambda **kwargs: get_sequence_loader(
mode=data_loader_mode,
batch_size=batch_size,
num_workers=num_workers,
in_image_size=in_image_size,
out_image_size=out_image_size,
debug_seq=debug_seq,
skip_beginning=skip_beginning,
skip_end=skip_end,
num_sample_frames=num_sample_frames,
min_seq_len=min_seq_len,
max_seq_len=max_seq_len,
load_background=load_background,
rgb_suffix=rgb_suffix,
load_dino_feature=load_dino_feature,
load_dino_cluster=load_dino_cluster,
dino_feature_dim=dino_feature_dim,
**kwargs)
if run_train:
assert osp.isdir(train_data_dir), f"Training data directory does not exist: {train_data_dir}"
print(f"Loading training data from {train_data_dir}")
train_loader = get_loader(data_dir=train_data_dir, is_validation=False, random_sample=random_sample_train_frames, shuffle=shuffle_train_seqs, dense_sample=True, color_jitter=color_jitter_train, random_flip=random_flip_train)
if val_data_dir is not None:
assert osp.isdir(val_data_dir), f"Validation data directory does not exist: {val_data_dir}"
print(f"Loading validation data from {val_data_dir}")
val_loader = get_loader(data_dir=val_data_dir, is_validation=True, random_sample=random_sample_val_frames, shuffle=False, dense_sample=False, color_jitter=color_jitter_val, random_flip=False)
if run_test:
assert osp.isdir(test_data_dir), f"Testing data directory does not exist: {test_data_dir}"
print(f"Loading testing data from {test_data_dir}")
test_loader = get_loader(data_dir=test_data_dir, is_validation=True, dense_sample=False, color_jitter=None, random_flip=False)
## CUB dataset
elif dataset == 'cub':
get_loader = lambda **kwargs: get_cub_loader(
batch_size=batch_size,
num_workers=num_workers,
image_size=in_image_size,
**kwargs)
if run_train:
assert osp.isdir(train_data_dir), f"Training data directory does not exist: {train_data_dir}"
print(f"Loading training data from {train_data_dir}")
train_loader = get_loader(data_dir=train_data_dir, split='train', is_validation=False)
val_loader = get_loader(data_dir=val_data_dir, split='val', is_validation=True)
if run_test:
assert osp.isdir(test_data_dir), f"Testing data directory does not exist: {test_data_dir}"
print(f"Loading testing data from {test_data_dir}")
test_loader = get_loader(data_dir=test_data_dir, split='test', is_validation=True)
## other datasets
else:
get_loader = lambda **kwargs: get_image_loader(
batch_size=batch_size,
num_workers=num_workers,
image_size=in_image_size,
**kwargs)
if run_train:
assert osp.isdir(train_data_dir), f"Training data directory does not exist: {train_data_dir}"
print(f"Loading training data from {train_data_dir}")
train_loader = get_loader(data_dir=train_data_dir, is_validation=False, color_jitter=color_jitter_train)
if val_data_dir is not None:
assert osp.isdir(val_data_dir), f"Validation data directory does not exist: {val_data_dir}"
print(f"Loading validation data from {val_data_dir}")
val_loader = get_loader(data_dir=val_data_dir, is_validation=True, color_jitter=color_jitter_val)
if run_test:
assert osp.isdir(test_data_dir), f"Testing data directory does not exist: {test_data_dir}"
print(f"Loading testing data from {test_data_dir}")
test_loader = get_loader(data_dir=test_data_dir, is_validation=True, color_jitter=None)
return train_loader, val_loader, test_loader
def load_model_state(self, cp):
self.netInstance.load_state_dict(cp["netInstance"])
if self.enable_prior:
self.netPrior.load_state_dict(cp["netPrior"])
def load_optimizer_state(self, cp):
self.optimizerInstance.load_state_dict(cp["optimizerInstance"])
if self.use_scheduler:
if 'schedulerInstance' in cp:
self.schedulerInstance.load_state_dict(cp["schedulerInstance"])
if self.enable_prior and self.resume_prior_optim:
self.optimizerPrior.load_state_dict(cp["optimizerPrior"])
if self.use_scheduler:
if 'schedulerPrior' in cp:
self.schedulerPrior.load_state_dict(cp["schedulerPrior"])
def get_model_state(self):
state = {"netInstance": self.netInstance.state_dict()}
if self.enable_prior:
state["netPrior"] = self.netPrior.state_dict()
return state
def get_optimizer_state(self):
state = {"optimizerInstance": self.optimizerInstance.state_dict()}
if self.use_scheduler:
state["schedulerInstance"] = self.schedulerInstance.state_dict()
if self.enable_prior:
state["optimizerPrior"] = self.optimizerPrior.state_dict()
if self.use_scheduler:
state["schedulerPrior"] = self.schedulerPrior.state_dict()
return state
def to(self, device):
self.device = device
self.netInstance.to(device)
if self.enable_prior:
self.netPrior.to(device)
if hasattr(self, 'perceptual_loss'):
self.perceptual_loss.to(device)
def set_train(self):
self.netInstance.train()
if self.enable_prior:
self.netPrior.train()
def set_eval(self):
self.netInstance.eval()
if self.enable_prior:
self.netPrior.eval()
def reset_optimizers(self):
print("Resetting optimizers...")
self.optimizerInstance = get_optimizer(self.netInstance, self.lr)
if self.use_scheduler:
self.schedulerInstance = self.make_scheduler(self.optimizerInstance)
if self.enable_prior:
self.optimizerPrior = get_optimizer(self.netPrior, lr=self.prior_lr, weight_decay=self.prior_weight_decay)
if self.use_scheduler:
self.schedulerPrior = self.make_scheduler(self.optimizerPrior)
def backward(self):
self.optimizerInstance.zero_grad()
if self.backward_prior:
self.optimizerPrior.zero_grad()
self.total_loss.backward()
self.optimizerInstance.step()
if self.backward_prior:
self.optimizerPrior.step()
self.total_loss = 0.
def scheduler_step(self):
if self.use_scheduler:
self.schedulerInstance.step()
if self.enable_prior:
self.schedulerPrior.step()
def zflip_pose(self, pose):
if self.rot_rep == 'lookat':
vec_forward = pose[:,:,6:9]
vec_forward = vec_forward * torch.FloatTensor([1,1,-1]).view(1,1,3).to(vec_forward.device)
up = torch.FloatTensor([0,1,0]).to(pose.device).view(1,1,3)
vec_right = up.expand_as(vec_forward).cross(vec_forward, dim=-1)
vec_right = nn.functional.normalize(vec_right, p=2, dim=-1)
vec_up = vec_forward.cross(vec_right, dim=-1)
vec_up = nn.functional.normalize(vec_up, p=2, dim=-1)
rot_mat = torch.stack([vec_right, vec_up, vec_forward], 2)
rot_pred = rot_mat.reshape(*pose.shape[:-1], -1)
pose_zflip = torch.cat([rot_pred, pose[:,:,9:]], -1)
else:
raise NotImplementedError
return pose_zflip
def render(self, shape, texture, mvp, w2c, campos, resolution, background='none', im_features=None, light=None, prior_shape=None, render_flow=True, dino_pred=None, render_mode='diffuse', two_sided_shading=True, num_frames=None, spp=1):
h, w = resolution
N = len(mvp)
if background in ['none', 'black']:
bg_image = torch.zeros((N, h, w, 3), device=mvp.device)
elif background == 'white':
bg_image = torch.ones((N, h, w, 3), device=mvp.device)
elif background == 'checkerboard':
bg_image = torch.FloatTensor(util.checkerboard((h, w), 8), device=self.device).repeat(N, 1, 1, 1) # NxHxWxC
else:
raise NotImplementedError
frame_rendered = render.render_mesh(
self.glctx,
shape,
mtx_in=mvp,
w2c=w2c,
view_pos=campos,
material=texture,
lgt=light,
resolution=resolution,
spp=spp,
msaa=True,
background=bg_image,
bsdf=render_mode,
feat=im_features,
prior_mesh=prior_shape,
two_sided_shading=two_sided_shading,
render_flow=render_flow,
dino_pred=dino_pred,
num_frames=num_frames)
shaded = frame_rendered['shaded'].permute(0, 3, 1, 2)
image_pred = shaded[:, :3, :, :]
mask_pred = shaded[:, 3, :, :]
albedo = frame_rendered['kd'].permute(0, 3, 1, 2)[:, :3, :, :]
if 'shading' in frame_rendered:
shading = frame_rendered['shading'].permute(0, 3, 1, 2)[:, :1, :, :]
else:
shading = None
if render_flow:
flow_pred = frame_rendered['flow']
flow_pred = flow_pred.permute(0, 3, 1, 2)[:, :2, :, :]
else:
flow_pred = None
if dino_pred is not None:
dino_feat_im_pred = frame_rendered['dino_feat_im_pred']
dino_feat_im_pred = dino_feat_im_pred.permute(0, 3, 1, 2)[:, :-1]
else:
dino_feat_im_pred = None
return image_pred, mask_pred, flow_pred, dino_feat_im_pred, albedo, shading
def compute_reconstruction_losses(self, image_pred, image_gt, mask_pred, mask_gt, mask_dt, mask_valid, flow_pred, flow_gt, dino_feat_im_gt, dino_feat_im_pred, background_mode='none', reduce=False):
losses = {}
batch_size, num_frames, _, h, w = image_pred.shape # BxFxCxHxW
# image_loss = (image_pred - image_gt) ** 2
image_loss = (image_pred - image_gt).abs()
## silhouette loss
mask_pred_valid = mask_pred * mask_valid
# mask_pred_valid = mask_pred
# losses["silhouette_loss"] = ((mask_pred - mask_gt) ** 2).mean()
# mask_loss_mask = (image_loss.mean(2).detach() > 0.05).float()
mask_loss = (mask_pred_valid - mask_gt) ** 2
# mask_loss = nn.functional.mse_loss(mask_pred, mask_gt)
# num_mask_pixels = mask_loss_mask.reshape(batch_size*num_frames, -1).sum(1).clamp(min=1)
# losses["silhouette_loss"] = (mask_loss.reshape(batch_size*num_frames, -1).sum(1) / num_mask_pixels).mean()
losses['silhouette_loss'] = mask_loss.view(batch_size, num_frames, -1).mean(2)
losses['silhouette_dt_loss'] = (mask_pred * mask_dt[:,:,1]).view(batch_size, num_frames, -1).mean(2)
losses['silhouette_inv_dt_loss'] = ((1-mask_pred) * mask_dt[:,:,0]).view(batch_size, num_frames, -1).mean(2)
mask_pred_binary = (mask_pred_valid > 0.).float().detach()
mask_both_binary = (mask_pred_binary * mask_gt).view(batch_size*num_frames, 1, *mask_pred.shape[2:])
mask_both_binary = (nn.functional.avg_pool2d(mask_both_binary, 3, stride=1, padding=1).view(batch_size, num_frames, *mask_pred.shape[2:]) > 0.99).float().detach() # erode by 1 pixel
## reconstruction loss
# image_loss_mask = (mask_pred*mask_gt).unsqueeze(2).expand_as(image_gt)
# image_loss = image_loss * image_loss_mask
# num_mask_pixels = image_loss_mask.reshape(batch_size*num_frames, -1).sum(1).clamp(min=1)
# losses["rgb_loss"] = (image_loss.reshape(batch_size*num_frames, -1).sum(1) / num_mask_pixels).mean()
if background_mode in ['background', 'input']:
pass
else:
image_loss = image_loss * mask_both_binary.unsqueeze(2)
losses['rgb_loss'] = image_loss.reshape(batch_size, num_frames, -1).mean(2)
if self.cfgs.get('perceptual_loss_weight', 0.) > 0:
if background_mode in ['background', 'input']:
perc_image_pred = image_pred
perc_image_gt = image_gt
else:
perc_image_pred = image_pred * mask_pred_binary.unsqueeze(2) + 0.5 * (1-mask_pred_binary.unsqueeze(2))
perc_image_gt = image_gt * mask_pred_binary.unsqueeze(2) + 0.5 * (1-mask_pred_binary.unsqueeze(2))
losses['perceptual_loss'] = self.perceptual_loss(perc_image_pred.view(-1, *image_pred.shape[2:]) *2-1, perc_image_gt.view(-1, *image_gt.shape[2:]) *2-1).view(batch_size, num_frames)
## flow loss - between first and second frame
if flow_pred is not None:
flow_loss = (flow_pred - flow_gt).abs()
flow_loss_mask = mask_both_binary[:,:-1].unsqueeze(2).expand_as(flow_gt).detach()
## ignore frames where GT flow is too large (likely inaccurate)
large_flow = (flow_gt.abs() > 0.5).float() * flow_loss_mask
large_flow = (large_flow.view(batch_size, num_frames-1, -1).sum(2) > 0).float()
self.large_flow = large_flow
flow_loss = flow_loss * flow_loss_mask * (1 - large_flow[:,:,None,None,None])
num_mask_pixels = flow_loss_mask.reshape(batch_size, num_frames-1, -1).sum(2).clamp(min=1)
losses['flow_loss'] = (flow_loss.reshape(batch_size, num_frames-1, -1).sum(2) / num_mask_pixels)
# losses["flow_loss"] = flow_loss.mean()
if dino_feat_im_pred is not None:
dino_feat_loss = (dino_feat_im_pred - dino_feat_im_gt) ** 2
dino_feat_loss = dino_feat_loss * mask_both_binary.unsqueeze(2)
losses['dino_feat_im_loss'] = dino_feat_loss.reshape(batch_size, num_frames, -1).mean(2)
if reduce:
for k, v in losses.item():
losses[k] = v.mean()
return losses
def compute_pose_xflip_reg_loss(self, input_image, dino_feat_im, pose_raw, input_image_xflip_flag=None):
image_xflip = input_image.flip(4)
if dino_feat_im is not None:
dino_feat_im_xflip = dino_feat_im.flip(4)
else:
dino_feat_im_xflip = None
feat_xflip, _ = self.netInstance.forward_encoder(image_xflip, dino_feat_im_xflip)
batch_size, num_frames = input_image.shape[:2]
pose_xflip_raw = self.netInstance.forward_pose(image_xflip, feat_xflip, dino_feat_im_xflip)
if input_image_xflip_flag is not None:
pose_xflip_raw_xflip = pose_xflip_raw * torch.FloatTensor([-1,1,1,-1,1,1]).to(pose_raw.device) # forward x, trans x
pose_xflip_raw = pose_xflip_raw * (1 - input_image_xflip_flag.view(batch_size * num_frames, 1)) + pose_xflip_raw_xflip * input_image_xflip_flag.view(batch_size * num_frames, 1)
rot_rep = self.netInstance.rot_rep
if rot_rep == 'euler_angle' or rot_rep == 'soft_calss':
pose_xflip_xflip = pose_xflip * torch.FloatTensor([1,-1,-1,-1,1,1]).to(pose_xflip.device) # rot y+z, trans x
pose_xflip_reg_loss = ((pose_xflip_xflip - pose) ** 2.).mean()
elif rot_rep == 'quaternion':
rot_euler = pytorch3d.transforms.matrix_to_euler_angles(pytorch3d.transforms.quaternion_to_matrix(pose[...,:4]), convention='XYZ')
pose_euler = torch.cat([rot_euler, pose[...,4:]], -1)
rot_xflip_euler = pytorch3d.transforms.matrix_to_euler_angles(pytorch3d.transforms.quaternion_to_matrix(pose_xflip[...,:4]), convention='XYZ')
pose_xflip_euler = torch.cat([rot_xflip_euler, pose_xflip[...,4:]], -1)
pose_xflip_euler_xflip = pose_xflip_euler * torch.FloatTensor([1,-1,-1,-1,1,1]).to(pose_xflip.device) # rot y+z, trans x
pose_xflip_reg_loss = ((pose_xflip_euler_xflip - pose_euler) ** 2.).mean()
elif rot_rep == 'lookat':
pose_xflip_raw_xflip = pose_xflip_raw * torch.FloatTensor([-1,1,1,-1,1,1]).to(pose_raw.device) # forward x, trans x
pose_xflip_reg_loss = ((pose_xflip_raw_xflip - pose_raw)[...,0] ** 2.) # compute x only
# if epoch >= self.nolookat_zflip_loss_epochs and self.lookat_zflip_no_other_losses:
# pose_xflip_reg_loss = pose_xflip_reg_loss.mean(1) * is_pose_1_better
pose_xflip_reg_loss = pose_xflip_reg_loss.mean()
return pose_xflip_reg_loss, pose_xflip_raw
def compute_edge_length_reg_loss(self, mesh, prior_mesh):
prior_edge_lengths = get_edge_length(prior_mesh.v_pos, prior_mesh.t_pos_idx)
max_length = prior_edge_lengths.max().detach() *1.1
edge_lengths = get_edge_length(mesh.v_pos, mesh.t_pos_idx)
mesh_edge_length_loss = ((edge_lengths - max_length).clamp(min=0)**2).mean()
return mesh_edge_length_loss, edge_lengths
def compute_regularizers(self, mesh, prior_mesh, input_image, dino_feat_im, pose_raw, input_image_xflip_flag=None, arti_params=None, deformation=None):
losses = {}
aux = {}
if self.enable_prior:
losses.update(self.netPrior.netShape.get_sdf_reg_loss())
if self.cfgs.get('pose_xflip_reg_loss_weight', 0.) > 0:
losses["pose_xflip_reg_loss"], aux['pose_xflip_raw'] = self.compute_pose_xflip_reg_loss(input_image, dino_feat_im, pose_raw, input_image_xflip_flag)
b, f = input_image.shape[:2]
if b >= 2:
vec_forward = pose_raw[..., :3]
losses['pose_entropy_loss'] = (vec_forward[:b//2] * vec_forward[b//2:(b//2)*2]).sum(-1).mean()
else:
losses['pose_entropy_loss'] = 0.
losses['mesh_normal_consistency_loss'] = normal_consistency(mesh.v_pos, mesh.t_pos_idx)
losses['mesh_edge_length_loss'], aux['edge_lengths'] = self.compute_edge_length_reg_loss(mesh, prior_mesh)
if arti_params is not None:
losses['arti_reg_loss'] = (arti_params ** 2).mean()
if deformation is not None:
losses['deformation_reg_loss'] = (deformation ** 2).mean()
# losses['deformation_reg_loss'] = deformation.abs().mean()
return losses, aux
def forward(self, batch, epoch, iter, is_train=True, viz_logger=None, total_iter=None, save_results=False, save_dir=None, which_data='', logger_prefix='', is_training=True):
batch = [x.to(self.device) if x is not None else None for x in batch]
input_image, mask_gt, mask_dt, mask_valid, flow_gt, bbox, bg_image, dino_feat_im, dino_cluster_im, seq_idx, frame_idx = batch
batch_size, num_frames, _, h0, w0 = input_image.shape # BxFxCxHxW
h = w = self.out_image_size
def collapseF(x):
return None if x is None else x.view(batch_size * num_frames, *x.shape[2:])
def expandF(x):
return None if x is None else x.view(batch_size, num_frames, *x.shape[1:])
if flow_gt.dim() == 2: # dummy tensor for not loading flow
flow_gt = None
if dino_feat_im.dim() == 2: # dummy tensor for not loading dino features
dino_feat_im = None
dino_feat_im_gt = None
else:
dino_feat_im_gt = expandF(torch.nn.functional.interpolate(collapseF(dino_feat_im), size=[h, w], mode="bilinear"))[:, :, :self.dino_feature_recon_dim]
if dino_cluster_im.dim() == 2: # dummy tensor for not loading dino clusters
dino_cluster_im = None
dino_cluster_im_gt = None
else:
dino_cluster_im_gt = expandF(torch.nn.functional.interpolate(collapseF(dino_cluster_im), size=[h, w], mode="nearest"))
seq_idx = seq_idx.squeeze(1)
# seq_idx = seq_idx * 0 # single sequnce model
frame_id, crop_x0, crop_y0, crop_w, crop_h, full_w, full_h, sharpness = bbox.unbind(2) # BxFx7
bbox = torch.stack([crop_x0, crop_y0, crop_w, crop_h], 2)
mask_gt = (mask_gt[:, :, 0, :, :] > 0.9).float() # BxFxHxW
mask_dt = mask_dt / self.in_image_size
if which_data != 'video':
flow_gt = None
aux_viz = {}
## GT
image_gt = input_image
if self.out_image_size != self.in_image_size:
image_gt = expandF(torch.nn.functional.interpolate(collapseF(image_gt), size=[h, w], mode='bilinear'))
if flow_gt is not None:
flow_gt = torch.nn.functional.interpolate(flow_gt.view(batch_size*(num_frames-1), 2, h0, w0), size=[h, w], mode="bilinear").view(batch_size, num_frames-1, 2, h, w)
self.train_pose_only = False
if epoch in self.pose_epochs:
if (total_iter // self.pose_iters) % 2 == 0:
self.train_pose_only = True
## flip input and pose
if epoch in self.pose_xflip_recon_epochs:
input_image_xflip = input_image.flip(-1)
input_image_xflip_flag = torch.randint(0, 2, (batch_size, num_frames), device=input_image.device)
input_image = input_image * (1 - input_image_xflip_flag[:,:,None,None,None]) + input_image_xflip * input_image_xflip_flag[:,:,None,None,None]
else:
input_image_xflip_flag = None
## 1st pose hypothesis with original predictions
# ==============================================================================================
# Predict prior mesh.
# ==============================================================================================
if self.enable_prior:
if epoch < self.dmtet_grid_smaller_epoch:
if self.netPrior.netShape.grid_res != self.dmtet_grid_smaller:
self.netPrior.netShape.load_tets(self.dmtet_grid_smaller)
else:
if self.netPrior.netShape.grid_res != self.dmtet_grid:
self.netPrior.netShape.load_tets(self.dmtet_grid)
perturb_sdf = self.perturb_sdf if is_train else False
prior_shape, dino_pred = self.netPrior(perturb_sdf=perturb_sdf, total_iter=total_iter, is_training=is_training)
else:
prior_shape = None
raise NotImplementedError
shape, pose_raw, pose, mvp, w2c, campos, texture, im_features, deformation, arti_params, light, forward_aux = self.netInstance(input_image, prior_shape, epoch, dino_feat_im, dino_cluster_im, total_iter, is_training=is_training) # frame dim collapsed N=(B*F)
rot_logit = forward_aux['rot_logit']
rot_idx = forward_aux['rot_idx']
rot_prob = forward_aux['rot_prob']
aux_viz.update(forward_aux)
if self.train_pose_only:
safe_detach = lambda x: x.detach() if x is not None else None
prior_shape = safe_detach(prior_shape)
shape = safe_detach(shape)
im_features = safe_detach(im_features)
arti_params = safe_detach(arti_params)
deformation = safe_detach(deformation)
set_requires_grad(texture, False)
set_requires_grad(light, False)
set_requires_grad(dino_pred, False)
else:
set_requires_grad(texture, True)
set_requires_grad(light, True)
set_requires_grad(dino_pred, True)
render_flow = self.render_flow and num_frames > 1
image_pred, mask_pred, flow_pred, dino_feat_im_pred, albedo, shading = self.render(shape, texture, mvp, w2c, campos, (h, w), background=self.background_mode, im_features=im_features, light=light, prior_shape=prior_shape, render_flow=render_flow, dino_pred=dino_pred, num_frames=num_frames, spp=self.renderer_spp)
image_pred, mask_pred, flow_pred, dino_feat_im_pred = map(expandF, (image_pred, mask_pred, flow_pred, dino_feat_im_pred))
if flow_pred is not None:
flow_pred = flow_pred[:, :-1] # Bx(F-1)x2xHxW
if self.blur_mask:
sigma = max(0.5, 3 * (1 - total_iter / self.blur_mask_iter))
if sigma > 0.5:
mask_gt = util.blur_image(mask_gt, kernel_size=9, sigma=sigma, mode='gaussian')
# mask_pred = util.blur_image(mask_pred, kernel_size=7, mode='average')
losses = self.compute_reconstruction_losses(image_pred, image_gt, mask_pred, mask_gt, mask_dt, mask_valid, flow_pred, flow_gt, dino_feat_im_gt, dino_feat_im_pred, background_mode=self.background_mode, reduce=False)
## TODO: assume flow loss is not used
logit_loss_target = torch.zeros_like(expandF(rot_logit))
final_losses = {}
for name, loss in losses.items():
loss_weight_logit = self.cfgs.get(f"{name}_weight", 0.)
# if (name in ['flow_loss'] and epoch not in self.flow_loss_epochs) or (name in ['rgb_loss', 'perceptual_loss'] and epoch not in self.texture_epochs):
# if name in ['flow_loss', 'rgb_loss', 'perceptual_loss']:
# loss_weight_logit = 0.
if name in ['sdf_bce_reg_loss', 'sdf_gradient_reg_loss', 'sdf_inflate_reg_loss']:
if total_iter >= self.sdf_reg_decay_start_iter:
decay_rate = max(0, 1 - (total_iter-self.sdf_reg_decay_start_iter) / 10000)
loss_weight_logit = max(loss_weight_logit * decay_rate, self.cfgs.get(f"{name}_min_weight", 0.))
if name in ['dino_feat_im_loss']:
loss_weight_logit = loss_weight_logit * self.cfgs.get("logit_loss_dino_feat_im_loss_multiplier", 1.)
if loss_weight_logit > 0:
logit_loss_target += loss * loss_weight_logit
if self.netInstance.rot_rep in ['quadlookat', 'octlookat']:
loss = loss * rot_prob.detach().view(batch_size, num_frames)[:, :loss.shape[1]] *self.netInstance.num_pose_hypos
if name == 'flow_loss' and num_frames > 1:
ri = rot_idx.view(batch_size, num_frames)
same_rot_idx = (ri[:, 1:] == ri[:, :-1]).float()
loss = loss * same_rot_idx
final_losses[name] = loss.mean()
final_losses['logit_loss'] = ((expandF(rot_logit) - logit_loss_target.detach())**2.).mean()
## regularizers
regularizers, aux = self.compute_regularizers(shape, prior_shape, input_image, dino_feat_im, pose_raw, input_image_xflip_flag, arti_params, deformation)
final_losses.update(regularizers)
aux_viz.update(aux)
total_loss = 0
for name, loss in final_losses.items():
loss_weight = self.cfgs.get(f"{name}_weight", 0.)
if loss_weight <= 0:
continue
if self.train_pose_only:
if name not in ['silhouette_loss', 'silhouette_dt_loss', 'silhouette_inv_dt_loss', 'flow_loss', 'pose_xflip_reg_loss', 'lookat_zflip_loss', 'dino_feat_im_loss']:
continue
if epoch not in self.flow_loss_epochs:
if name in ['flow_loss']:
continue
if epoch not in self.texture_epochs:
if name in ['rgb_loss', 'perceptual_loss']:
continue
if epoch not in self.lookat_zflip_loss_epochs:
if name in ['lookat_zflip_loss']:
continue
if name in ['mesh_laplacian_smoothing_loss', 'mesh_normal_consistency_loss']:
if total_iter < self.cfgs.get('mesh_reg_start_iter', 0):
continue
if epoch >= self.mesh_reg_decay_epoch:
decay_rate = self.mesh_reg_decay_rate ** (epoch - self.mesh_reg_decay_epoch)
loss_weight = max(loss_weight * decay_rate, self.cfgs.get(f"{name}_min_weight", 0.))
if epoch not in self.sdf_inflate_reg_loss_epochs:
if name in ['sdf_inflate_reg_loss']:
continue
if epoch not in self.arti_reg_loss_epochs:
if name in ['arti_reg_loss']:
continue
if name in ['sdf_bce_reg_loss', 'sdf_gradient_reg_loss', 'sdf_inflate_reg_loss']:
if total_iter >= self.sdf_reg_decay_start_iter:
decay_rate = max(0, 1 - (total_iter-self.sdf_reg_decay_start_iter) / 10000)
loss_weight = max(loss_weight * decay_rate, self.cfgs.get(f"{name}_min_weight", 0.))
total_loss += loss * loss_weight
self.total_loss += total_loss # reset to 0 in backward step
if torch.isnan(self.total_loss):
print("NaN in loss...")
import ipdb; ipdb.set_trace()
final_losses['logit_loss_target'] = logit_loss_target.mean()
metrics = {'loss': total_loss, **final_losses}
## log visuals
if viz_logger is not None:
b0 = max(min(batch_size, 16//num_frames), 1)
viz_logger.add_image(logger_prefix+'image/image_gt', misc.image_grid(image_gt.detach().cpu()[:b0,:].reshape(-1,*input_image.shape[2:]).clamp(0,1)), total_iter)
viz_logger.add_image(logger_prefix+'image/image_pred', misc.image_grid(image_pred.detach().cpu()[:b0,:].reshape(-1,*image_pred.shape[2:]).clamp(0,1)), total_iter)
# viz_logger.add_image(logger_prefix+'image/flow_loss_mask', misc.image_grid(flow_loss_mask[:b0,:,:1].reshape(-1,1,*flow_loss_mask.shape[3:]).repeat(1,3,1,1).clamp(0,1)), total_iter)
viz_logger.add_image(logger_prefix+'image/mask_gt', misc.image_grid(mask_gt.detach().cpu()[:b0,:].reshape(-1,*mask_gt.shape[2:]).unsqueeze(1).repeat(1,3,1,1).clamp(0,1)), total_iter)
viz_logger.add_image(logger_prefix+'image/mask_pred', misc.image_grid(mask_pred.detach().cpu()[:b0,:].reshape(-1,*mask_pred.shape[2:]).unsqueeze(1).repeat(1,3,1,1).clamp(0,1)), total_iter)
if self.render_flow and flow_gt is not None:
flow_gt = flow_gt.detach().cpu()
flow_gt_viz = torch.cat([flow_gt[:b0], torch.zeros_like(flow_gt[:b0,:,:1])], 2) + 0.5 # -0.5~1.5
flow_gt_viz = torch.nn.functional.pad(flow_gt_viz, pad=[0, 0, 0, 0, 0, 0, 0, 1])
## draw marker on large flow frames
large_flow_marker_mask = torch.zeros_like(flow_gt_viz)
large_flow_marker_mask[:,:,:,:8,:8] = 1.
large_flow = torch.cat([self.large_flow, self.large_flow[:,:1] *0.], 1).detach().cpu()[:b0]
large_flow_marker_mask = large_flow_marker_mask * large_flow[:,:,None,None,None]
red = torch.FloatTensor([1,0,0])[None,None,:,None,None]
flow_gt_viz = large_flow_marker_mask * red + (1-large_flow_marker_mask) * flow_gt_viz
viz_logger.add_image(logger_prefix+'image/flow_gt', misc.image_grid(flow_gt_viz.reshape(-1,*flow_gt_viz.shape[2:])), total_iter)
if self.render_flow and flow_pred is not None:
flow_pred = flow_pred.detach().cpu()
flow_pred_viz = torch.cat([flow_pred[:b0], torch.zeros_like(flow_pred[:b0,:,:1])], 2) + 0.5 # -0.5~1.5
flow_pred_viz = torch.nn.functional.pad(flow_pred_viz, pad=[0, 0, 0, 0, 0, 0, 0, 1])
viz_logger.add_image(logger_prefix+'image/flow_pred', misc.image_grid(flow_pred_viz.reshape(-1,*flow_pred_viz.shape[2:])), total_iter)
if light is not None:
param_names = ['dir_x', 'dir_y', 'dir_z', 'int_ambient', 'int_diffuse']
for name, param in zip(param_names, light.light_params.unbind(-1)):
viz_logger.add_histogram(logger_prefix+'light/'+name, param, total_iter)
viz_logger.add_image(
logger_prefix + f'image/albedo',
misc.image_grid(expandF(albedo)[:b0, ...].view(-1, *albedo.shape[1:])),
total_iter)
viz_logger.add_image(
logger_prefix + f'image/shading',
misc.image_grid(expandF(shading)[:b0, ...].view(-1, *shading.shape[1:]).repeat(1, 3, 1, 1) /2.),
total_iter)
viz_logger.add_histogram(logger_prefix+'sdf', self.netPrior.netShape.get_sdf(perturb_sdf=False), total_iter)
viz_logger.add_histogram(logger_prefix+'coordinates', shape.v_pos, total_iter)
if arti_params is not None:
viz_logger.add_histogram(logger_prefix+'arti_params', arti_params, total_iter)
viz_logger.add_histogram(logger_prefix+'edge_lengths', aux_viz['edge_lengths'], total_iter)
if deformation is not None:
viz_logger.add_histogram(logger_prefix+'deformation', deformation, total_iter)
rot_rep = self.netInstance.rot_rep
if rot_rep == 'euler_angle' or rot_rep == 'soft_calss':
for i, name in enumerate(['rot_x', 'rot_y', 'rot_z', 'trans_x', 'trans_y', 'trans_z']):
viz_logger.add_histogram(logger_prefix+'pose/'+name, pose[...,i], total_iter)
elif rot_rep == 'quaternion':
for i, name in enumerate(['qt_0', 'qt_1', 'qt_2', 'qt_3', 'trans_x', 'trans_y', 'trans_z']):
viz_logger.add_histogram(logger_prefix+'pose/'+name, pose[...,i], total_iter)
rot_euler = pytorch3d.transforms.matrix_to_euler_angles(pytorch3d.transforms.quaternion_to_matrix(pose.detach().cpu()[...,:4]), convention='XYZ')
for i, name in enumerate(['rot_x', 'rot_y', 'rot_z']):
viz_logger.add_histogram(logger_prefix+'pose/'+name, rot_euler[...,i], total_iter)
elif rot_rep in ['lookat', 'quadlookat', 'octlookat']:
for i, name in enumerate(['fwd_x', 'fwd_y', 'fwd_z']):
viz_logger.add_histogram(logger_prefix+'pose/'+name, pose_raw[...,i], total_iter)
for i, name in enumerate(['trans_x', 'trans_y', 'trans_z']):
viz_logger.add_histogram(logger_prefix+'pose/'+name, pose_raw[...,-3+i], total_iter)
if rot_rep in ['quadlookat', 'octlookat']:
for i, rp in enumerate(forward_aux['rots_probs'].unbind(-1)):
viz_logger.add_histogram(logger_prefix+'pose/rot_prob_%d'%i, rp, total_iter)
if 'pose_xflip_raw' in aux_viz:
pose_xflip_raw = aux_viz['pose_xflip_raw']
if rot_rep == 'euler_angle' or rot_rep == 'soft_calss':
for i, name in enumerate(['rot_x', 'rot_y', 'rot_z', 'trans_x', 'trans_y', 'trans_z']):
viz_logger.add_histogram(logger_prefix+'pose_xflip/'+name, pose_xflip[...,i], total_iter)
elif rot_rep == 'quaternion':
for i, name in enumerate(['qt_0', 'qt_1', 'qt_2', 'qt_3', 'trans_x', 'trans_y', 'trans_z']):
viz_logger.add_histogram(logger_prefix+'pose_xflip/'+name, pose_xflip[...,i], total_iter)
rot_euler = pytorch3d.transforms.matrix_to_euler_angles(pytorch3d.transforms.quaternion_to_matrix(pose_xflip.detach().cpu()[...,:4]), convention='XYZ')
for i, name in enumerate(['rot_x', 'rot_y', 'rot_z']):
viz_logger.add_histogram(logger_prefix+'pose_xflip/'+name, rot_euler[...,i], total_iter)
elif rot_rep in ['lookat', 'quadlookat', 'octlookat']:
for i, name in enumerate(['fwd_x', 'fwd_y', 'fwd_z']):
viz_logger.add_histogram(logger_prefix+'pose_xflip/'+name, pose_xflip_raw[...,i], total_iter)
for i, name in enumerate(['trans_x', 'trans_y', 'trans_z']):
viz_logger.add_histogram(logger_prefix+'pose_xflip/'+name, pose_xflip_raw[...,-3+i], total_iter)
if dino_feat_im_gt is not None:
dino_feat_im_gt_first3 = dino_feat_im_gt[:,:,:3]
viz_logger.add_image(logger_prefix+'image/dino_feat_im_gt', misc.image_grid(dino_feat_im_gt_first3.detach().cpu()[:b0,:].reshape(-1,*dino_feat_im_gt_first3.shape[2:]).clamp(0,1)), total_iter)
if dino_cluster_im_gt is not None:
viz_logger.add_image(logger_prefix+'image/dino_cluster_im_gt', misc.image_grid(dino_cluster_im_gt.detach().cpu()[:b0,:].reshape(-1,*dino_cluster_im_gt.shape[2:]).clamp(0,1)), total_iter)
if dino_feat_im_pred is not None:
dino_feat_im_pred_first3 = dino_feat_im_pred[:,:,:3]
viz_logger.add_image(logger_prefix+'image/dino_feat_im_pred', misc.image_grid(dino_feat_im_pred_first3.detach().cpu()[:b0,:].reshape(-1,*dino_feat_im_pred_first3.shape[2:]).clamp(0,1)), total_iter)
for which_shape, modes in self.extra_renders.items():
# This is wrong
# if which_shape == "prior":
# shape_to_render = prior_shape.extend(im_features.shape[0])
# needed_im_features = None
if which_shape == "instance":
shape_to_render = shape
needed_im_features = im_features
else:
raise NotImplementedError
for mode in modes:
rendered, _, _, _, _, _ = self.render(shape_to_render, texture, mvp, w2c, campos, (h, w), background=self.background_mode, im_features=needed_im_features, prior_shape=prior_shape, render_mode=mode, render_flow=False, dino_pred=None)
if 'kd' in mode:
rendered = util.rgb_to_srgb(rendered)
rendered = rendered.detach().cpu()
if 'posed_bones' in aux_viz:
rendered_bone_image = self.render_bones(mvp, aux_viz['posed_bones'], (h, w))
rendered_bone_image_mask = (rendered_bone_image < 1).any(1, keepdim=True).float()
# viz_logger.add_image(logger_prefix+'image/articulation_bones', misc.image_grid(self.render_bones(mvp, aux_viz['posed_bones'])), total_iter)
rendered = rendered_bone_image_mask*0.8 * rendered_bone_image + (1-rendered_bone_image_mask*0.8) * rendered
if rot_rep in ['quadlookat', 'octlookat']:
rand_pose_flag = forward_aux['rand_pose_flag'].detach().cpu()
rand_pose_marker_mask = torch.zeros_like(rendered)
rand_pose_marker_mask[:,:,:16,:16] = 1.
rand_pose_marker_mask = rand_pose_marker_mask * rand_pose_flag[:,None,None,None]
red = torch.FloatTensor([1,0,0])[None,:,None,None]
rendered = rand_pose_marker_mask * red + (1-rand_pose_marker_mask) * rendered
viz_logger.add_image(
logger_prefix + f'image/{which_shape}_{mode}',
misc.image_grid(expandF(rendered)[:b0, ...].view(-1, *rendered.shape[1:])),
total_iter)
viz_logger.add_video(
logger_prefix + f'animation/{which_shape}_{mode}',
self.render_rotation_frames(shape_to_render, texture, light, (h, w), background=self.background_mode, im_features=needed_im_features, prior_shape=prior_shape, num_frames=15, render_mode=mode, b=1).detach().cpu().unsqueeze(0),
total_iter,
fps=2)
viz_logger.add_video(
logger_prefix+'animation/prior_image_rotation',
self.render_rotation_frames(prior_shape, texture, light, (h, w), background=self.background_mode, im_features=im_features, num_frames=15, b=1).detach().cpu().unsqueeze(0).clamp(0,1),
total_iter,
fps=2)
viz_logger.add_video(
logger_prefix+'animation/prior_normal_rotation',
self.render_rotation_frames(prior_shape, texture, light, (h, w), background=self.background_mode, im_features=im_features, num_frames=15, render_mode='geo_normal', b=1).detach().cpu().unsqueeze(0),
total_iter,
fps=2)
if save_results:
b0 = self.cfgs.get('num_saved_from_each_batch', batch_size*num_frames)
fnames = [f'{total_iter:07d}_{fid:10d}' for fid in collapseF(frame_id.int())][:b0]
misc.save_images(save_dir, collapseF(image_gt)[:b0].clamp(0,1).detach().cpu().numpy(), suffix='image_gt', fnames=fnames)
misc.save_images(save_dir, collapseF(image_pred)[:b0].clamp(0,1).detach().cpu().numpy(), suffix='image_pred', fnames=fnames)
misc.save_images(save_dir, collapseF(mask_gt)[:b0].unsqueeze(1).repeat(1,3,1,1).clamp(0,1).detach().cpu().numpy(), suffix='mask_gt', fnames=fnames)
misc.save_images(save_dir, collapseF(mask_pred)[:b0].unsqueeze(1).repeat(1,3,1,1).clamp(0,1).detach().cpu().numpy(), suffix='mask_pred', fnames=fnames)
# tmp_shape = shape.first_n(b0).clone()
# tmp_shape.material = texture
# feat = im_features[:b0] if im_features is not None else None
# misc.save_obj(save_dir, tmp_shape, save_material=False, feat=feat, suffix="mesh", fnames=fnames) # Save the first mesh.
# if self.render_flow and flow_gt is not None:
# flow_gt_viz = torch.cat([flow_gt, torch.zeros_like(flow_gt[:,:,:1])], 2) + 0.5 # -0.5~1.5
# flow_gt_viz = flow_gt_viz.view(-1, *flow_gt_viz.shape[2:])
# misc.save_images(save_dir, flow_gt_viz[:b0].clamp(0,1).detach().cpu().numpy(), suffix='flow_gt', fnames=fnames)
# if flow_pred is not None:
# flow_pred_viz = torch.cat([flow_pred, torch.zeros_like(flow_pred[:,:,:1])], 2) + 0.5 # -0.5~1.5
# flow_pred_viz = flow_pred_viz.view(-1, *flow_pred_viz.shape[2:])
# misc.save_images(save_dir, flow_pred_viz[:b0].clamp(0,1).detach().cpu().numpy(), suffix='flow_pred', fnames=fnames)
misc.save_txt(save_dir, pose[:b0].detach().cpu().numpy(), suffix='pose', fnames=fnames)
return metrics
def save_scores(self, path):
header = 'mask_mse, \
mask_iou, \
image_mse, \
flow_mse'
mean = self.all_scores.mean(0)
std = self.all_scores.std(0)
header = header + '\nMean: ' + ',\t'.join(['%.8f'%x for x in mean])
header = header + '\nStd: ' + ',\t'.join(['%.8f'%x for x in std])
misc.save_scores(path, self.all_scores, header=header)
print(header)
def render_rotation_frames(self, mesh, texture, light, resolution, background='none', im_features=None, prior_shape=None, num_frames=36, render_mode='diffuse', b=None):
frames = []
if b is None:
b = len(mesh)
else:
mesh = mesh.first_n(b)
feat = im_features[:b] if im_features is not None else None
delta_angle = np.pi / num_frames * 2
delta_rot_matrix = torch.FloatTensor([
[np.cos(delta_angle), 0, np.sin(delta_angle), 0],
[0, 1, 0, 0],
[-np.sin(delta_angle), 0, np.cos(delta_angle), 0],
[0, 0, 0, 1],
]).to(self.device).repeat(b, 1, 1)
w2c = torch.FloatTensor(np.diag([1., 1., 1., 1]))
w2c[:3, 3] = torch.FloatTensor([0, 0, -self.cam_pos_z_offset *1.1])
w2c = w2c.repeat(b, 1, 1).to(self.device)
proj = util.perspective(self.crop_fov_approx / 180 * np.pi, 1, n=0.1, f=1000.0).repeat(b, 1, 1).to(self.device)
mvp = torch.bmm(proj, w2c)
campos = -w2c[:, :3, 3]
def rotate_pose(mvp, campos):
mvp = torch.matmul(mvp, delta_rot_matrix)
campos = torch.matmul(delta_rot_matrix[:,:3,:3].transpose(2,1), campos[:,:,None])[:,:,0]
return mvp, campos
for _ in range(num_frames):
image_pred, _, _, _, _, _ = self.render(mesh, texture, mvp, w2c, campos, resolution, background=background, im_features=feat, light=light, prior_shape=prior_shape, render_flow=False, dino_pred=None, render_mode=render_mode, two_sided_shading=False)
frames += [misc.image_grid(image_pred)]
mvp, campos = rotate_pose(mvp, campos)
return torch.stack(frames, dim=0) # Shape: (T, C, H, W)
def render_bones(self, mvp, bones_pred, size=(256, 256)):
bone_world4 = torch.concat([bones_pred, torch.ones_like(bones_pred[..., :1]).to(bones_pred.device)], dim=-1)
b, f, num_bones = bone_world4.shape[:3]
bones_clip4 = (bone_world4.view(b, f, num_bones*2, 1, 4) @ mvp.transpose(-1, -2).reshape(b, f, 1, 4, 4)).view(b, f, num_bones, 2, 4)
bones_uv = bones_clip4[..., :2] / bones_clip4[..., 3:4] # b, f, num_bones, 2, 2
dpi = 32
fx, fy = size[1] // dpi, size[0] // dpi
rendered = []
for b_idx in range(b):
for f_idx in range(f):
frame_bones_uv = bones_uv[b_idx, f_idx].cpu().numpy()
fig = plt.figure(figsize=(fx, fy), dpi=dpi, frameon=False)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
for bone in frame_bones_uv:
ax.plot(bone[:, 0], bone[:, 1], marker='o', linewidth=8, markersize=20)
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.invert_yaxis()
# Convert to image
fig.add_axes(ax)
fig.canvas.draw_idle()
image = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
w, h = fig.canvas.get_width_height()
image.resize(h, w, 3)
rendered += [image / 255.]
return torch.from_numpy(np.stack(rendered, 0).transpose(0, 3, 1, 2))
def render_deformation_frames(self, mesh, texture, batch_size, num_frames, resolution, background='none', im_features=None, render_mode='diffuse', b=None):
# frames = []
# if b is None:
# b = batch_size
# im_features = im_features[]
# mesh = mesh.first_n(num_frames * b)
# for i in range(b):
# tmp_mesh = mesh.get_m_to_n(i*num_frames:(i+1)*num_frames)
pass