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clip_actor.py
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clip_actor.py
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import os
import clip
from tqdm import tqdm
import kaolin.ops.mesh
import kaolin as kal
import torch
import numpy as np
import random
import copy
import torchvision
import argparse
from pathlib import Path
import torch.nn.functional as F
import warnings
warnings.simplefilter("ignore", UserWarning)
from torchvision import transforms
from smplx import SMPLX, SMPLH, SMPL
from pytorch3d.structures.meshes import Meshes
from pytorch3d.loss import mesh_laplacian_smoothing
import time
import wandb
import motion_retrieval.retrieval_ as rtr
from motion_retrieval.sent2vec import Sent2Vec
import cfg as cfg
from models import NeuralStyleField
from render import Renderer
from mesh import HumanMesh
from utils import device, clip_model, create_video
def parse_args():
parser = argparse.ArgumentParser()
# body mesh
parser.add_argument('--body_model', type=str, default='smplx', choices=['smpl', 'smplx', 'smplh'])
parser.add_argument('--symmetry', type=eval, default=True, choices=[True, False])
parser.add_argument('--standardize', type=eval, default=True, choices=[True, False])
parser.add_argument('--mesh_subdivide', type=eval, default=True, choices=[True, False])
# model
parser.add_argument('--sigma', type=float, default=8.0)
parser.add_argument('--depth', type=int, default=4)
parser.add_argument('--width', type=int, default=256)
parser.add_argument('--colordepth', type=int, default=2)
parser.add_argument('--normdepth', type=int, default=2)
parser.add_argument('--normwidth', type=int, default=256)
parser.add_argument('--decay', type=float, default=0) # weight decay
parser.add_argument('--lr_decay', type=float, default=0.9)
parser.add_argument('--lr_plateau', action='store_true')
parser.add_argument('--no_pe', dest='pe', default=False, action='store_false')
parser.add_argument('--decay_step', type=int, default=100)
parser.add_argument('--clamp', type=str, default="tanh")
parser.add_argument('--normclamp', type=str, default="tanh")
parser.add_argument('--normratio', type=float, default=0.1)
parser.add_argument('--exclude', type=int, default=0)
parser.add_argument('--input_verts', type=str, default='canonical', choices=['posed', 'canonical'])
# training
parser.add_argument('--prompt', nargs="+", default="a 3D rendering of the walking Steve Jobs in unreal engine")
parser.add_argument('--anchor_mesh', type=str, default='top3', choices=['center', 'top1', 'top3'])
parser.add_argument('--topk', type=int, default=3, choices=[1, 3, 5])
parser.add_argument('--score_views', type=str, default='front', choices=['front', 'uniform'])
parser.add_argument('--weighted_clip_mean', type=eval, default=True, choices=[True, False])
parser.add_argument('--weight_th', type=float, default=0.05)
parser.add_argument('--max_grad_norm', type=float, default=0.0)
parser.add_argument('--n_iter', type=int, default=1500) # can be increased
parser.add_argument('--seed', type=int, default=2022)
parser.add_argument('--learning_rate', type=float, default=0.0005)
parser.add_argument('--w_laplacian', type=float, default=250.0)
parser.add_argument('--normweight', type=float, default=1.0)
parser.add_argument('--clipavg', type=str, default='view')
parser.add_argument('--geoloss', default=True, action="store_true")
parser.add_argument('--mincrop', type=float, default=1)
parser.add_argument('--maxcrop', type=float, default=1)
parser.add_argument('--normmincrop', type=float, default=0.1)
parser.add_argument('--normmaxcrop', type=float, default=0.4)
parser.add_argument('--cropforward', action='store_true')
parser.add_argument('--use_2d_aug', type=eval, default=True, choices=[True, False])
parser.add_argument('--use_3d_aug', type=eval, default=True, choices=[True, False])
# render
parser.add_argument('--limited_elev', type=eval, default=True, choices=[True, False])
parser.add_argument('--rand_cam_distance', type=eval, default=False, choices=[True, False])
parser.add_argument('--elev_div', type=int, default=6)
parser.add_argument('--n_azim', type=int, default=8)
parser.add_argument('--n_elev', type=int, default=1)
parser.add_argument('--n_augs', type=int, default=3)
parser.add_argument('--n_normaugs', type=int, default=4)
parser.add_argument('--frontview_std', type=float, default=4.0)
parser.add_argument('--frontview_center', nargs=2, type=float, default=[0., 0.])
parser.add_argument('--background', nargs=3, type=float, default=[1.0, 1.0, 1.0])
# logging
parser.add_argument('--wandb_logging', type=eval, default=False, choices=[True, False])
parser.add_argument('--exp_name', type=str, default='debug', required=True)
parser.add_argument('--save_render', default=True, action="store_true")
args = parser.parse_args()
return args
def seed_all(args):
# Constrain all sources of randomness
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
def retrieve_raw_label(encoded_text, encoded_raw_label):
sim_raw = torch.cosine_similarity(encoded_text, encoded_raw_label, dim=1)
idx = torch.topk(sim_raw, k=3)[1].cpu().numpy()
return idx
def get_body_meshes(body_pose, right_hand_pose, left_hand_pose, args, device):
smpl_vertices, smpl_faces = None, None
batch_size = body_pose.shape[0] # L
# Rectify the randomly oriented SMPL mesh (head towards +y direction in world coordinate)
global_orient = torch.zeros((batch_size, 3), device=device)
global_orient[:, 1] = np.pi / 2 # Rectify
betas = torch.zeros((batch_size, 16), device=device) # Shape parameter
# Get body mesh: vertices and faces
if args.body_model == 'smplx':
print("=> Loading SMPL-X model ...")
smplx = SMPLX(model_path=cfg.smplx_path, ext='pkl', use_pca=False, num_betas=16, batch_size=batch_size).to(device)
smplx_mesh = smplx(betas=betas, global_orient=global_orient, body_pose=body_pose, left_hand_pose=left_hand_pose, right_hand_pose=right_hand_pose)
smpl_vertices = smplx_mesh.vertices.detach()
smpl_vertices -= smplx_mesh.joints.detach()[:, 0, :][:, None, :]
smpl_faces = torch.tensor((smplx.faces.astype(np.int64)), device=device).unsqueeze(0) # [1, 20908, 3]
elif args.body_model == 'smpl':
print("=> Loading SMPL model ...")
smpl = SMPL(model_path=cfg.smpl_path, num_betas=16, batch_size=batch_size).to(device)
body_pose = torch.cat([body_pose, torch.zeros((batch_size, 2 * 3), device=device)], dim=1)
smpl_mesh = smpl(betas=betas, global_orient=global_orient, body_pose=body_pose)
smpl_vertices = smpl_mesh.vertices.detach() # [L, 6890, 3]
smpl_vertices -= smpl_mesh.joints.detach()[:, 0, :][:, None, :]
smpl_faces = smpl.faces_tensor.unsqueeze(0) # [1, 13776, 3]
return smpl_vertices, smpl_faces
def get_body_mesh(args, device):
########### Action 2 SMPLH param ###########
'''
input: args.prompt[0] (dtype = str)
output: SMPL-H parameters
'''
smpl_vertices, smpl_faces = None, None
# Define pose and shape parameters
betas = torch.zeros((1, 16), device=device) # Shape parameter
global_orient = torch.zeros((1, 3), device=device)
global_orient[:, 1] = np.pi / 2 # Rectify
body_pose = torch.zeros((1, 21 * 3), device=device) # Pose parameter (global_orient + body_pose): [1 x 3 + 22 x 3]
left_hand_pose = torch.zeros((45,), device=device) # Left hand parameter (15 x 3)
right_hand_pose = torch.zeros((45,), device=device) # Right hand parameter (15 x 3)
# Rectify the randomly oriented SMPL mesh (head towards +y direction in world coordinate)
global_orient = torch.zeros((1, 3), device=device)
global_orient[:, 1] = np.pi / 2 # Rectify
# Get body mesh: vertices and faces
if args.body_model == 'smplx':
smplx = SMPLX(model_path=cfg.smplx_path, ext='pkl', use_pca=False, num_betas=16).to(device)
smplx_mesh = smplx(betas=betas, global_orient=global_orient, body_pose=body_pose, left_hand_pose=left_hand_pose, right_hand_pose=right_hand_pose)
smpl_vertices = smplx_mesh.vertices.detach()
smpl_vertices -= smplx_mesh.joints.detach()[:, 0, :]
smpl_faces = torch.tensor((smplx.faces.astype(np.int64)), device=device).unsqueeze(0) # [1, 20908, 3]
elif args.body_model == 'smpl':
smpl = SMPL(model_path=cfg.smpl_path, num_betas=16).to(device)
body_pose = torch.cat([body_pose, torch.zeros((1, 2 * 3), device=device)], dim=1)
smpl_mesh = smpl(betas=betas, global_orient=global_orient, body_pose=body_pose)
smpl_vertices = smpl_mesh.vertices.detach() # [L, 6890, 3]
smpl_vertices -= smpl_mesh.joints.detach()[:, 0, :]
smpl_faces = torch.tensor((smpl.faces.astype(np.int64)), device=device).unsqueeze(0) # [1, 13776, 3]
return smpl_vertices, smpl_faces
def get_augmentation(args):
clip_normalizer = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
# CLIP Transform
clip_transform = transforms.Compose([
transforms.Resize((224, 224)),
clip_normalizer
])
# Augmentation settings
full_transform = transforms.Compose([
transforms.RandomResizedCrop(224, scale=(1, 1)),
transforms.RandomPerspective(fill=1, p=0.8, distortion_scale=0.5),
clip_normalizer
])
# Augmentations for normal network
if args.cropforward:
curcrop = args.normmincrop
else:
curcrop = args.normmaxcrop
# Local crop augmentations
local_transform = transforms.Compose([
transforms.RandomResizedCrop(224, scale=(curcrop, curcrop)),
transforms.RandomPerspective(fill=1, p=0.8, distortion_scale=0.5),
clip_normalizer
])
if args.weighted_clip_mean:
# Displacement-only augmentations
dispaug_transform = transforms.Compose([
transforms.RandomPerspective(fill=1, p=0.8, distortion_scale=0.5),
clip_normalizer
])
else:
# Displacement-only augmentations
dispaug_transform = transforms.Compose([
transforms.RandomResizedCrop(224, scale=(args.normmincrop, args.normmincrop)),
transforms.RandomPerspective(fill=1, p=0.8, distortion_scale=0.5),
clip_normalizer
])
return clip_transform, full_transform, local_transform, dispaug_transform
def get_model(args):
# MLP Settings
input_dim = 3
mlp = NeuralStyleField(args.sigma, args.depth, args.width, 'gaussian', args.colordepth, args.normdepth,
args.normratio, args.clamp, args.normclamp, niter=args.n_iter,
progressive_encoding=args.pe, input_dim=input_dim, exclude=args.exclude).to(device)
mlp.reset_weights()
optim = torch.optim.Adam(mlp.parameters(), args.learning_rate, weight_decay=args.decay)
activate_scheduler = args.lr_decay < 1 and args.decay_step > 0 and not args.lr_plateau
if activate_scheduler:
lr_scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=args.decay_step, gamma=args.lr_decay)
return mlp, optim, activate_scheduler, lr_scheduler
def report_process(args, dir, i, loss, rendered_images, exp_name, clip_score, pose_label):
full_loss = loss['global_loss'] + loss['local_loss'] + loss['geo_loss']
if i == args.n_iter:
print('[{}] Final CLIP score: {}'.format(exp_name, clip_score))
else:
print('[{}] iter: {} loss: {} CLIP score: {}'.format(exp_name, i, full_loss, clip_score))
# WandB support
if args.wandb_logging:
render_img_log = wandb.Image(rendered_images, caption='/'.join(args.prompt) + ' [' + pose_label + ']')
logs = dict(
# loss=full_loss,
# global_loss=loss['global_loss'],
# local_loss=loss['local_loss'],
# geo_loss=loss['geo_loss'],
# clip_score=clip_score,
rendered_images=render_img_log
)
wandb.log(logs, step=i)
else:
torchvision.utils.save_image(rendered_images, os.path.join(dir, 'iter_{}.jpg'.format(i)))
def export_medium_results(args, dir, mesh, mlp, network_input, vertices, n_iter):
with torch.no_grad():
pred_rgb, pred_normal = mlp(network_input)
pred_rgb = pred_rgb.detach().cpu()
pred_normal = pred_normal.detach().cpu()
base_color = torch.full(size=(mesh.vertices.shape[0], 3), fill_value=0.5)
final_color = torch.clamp(pred_rgb + base_color, 0, 1)
mesh.vertices = vertices.detach().cpu() + mesh.vertex_normals.detach().cpu() * pred_normal
save_rendered_results(args, dir, final_color, mesh, str(n_iter))
def export_final_results(args, dir, losses, mesh, pred_rgb, pred_normal, vertices, exp_name='YYYYMMDD'):
with torch.no_grad():
pred_rgb = pred_rgb.detach().cpu()
pred_normal = pred_normal.detach().cpu()
torch.save(pred_rgb, os.path.join(dir, f"colors_final.pt"))
torch.save(pred_normal, os.path.join(dir, f"normals_final.pt"))
base_color = torch.full(size=(mesh.vertices.shape[0], 3), fill_value=0.5)
final_color = torch.clamp(pred_rgb + base_color, 0, 1)
mesh.vertices = vertices.detach().cpu() + mesh.vertex_normals.detach().cpu() * pred_normal
mesh.export(os.path.join(dir, f"{exp_name}_final.obj"), color=final_color)
# Run renders
if args.save_render:
save_rendered_results(args, dir, final_color, mesh)
def export_motion_results(args, dir, mesh_t, pred_rgb, pred_normal, meshes, i=None):
pred_rgb = pred_rgb.detach().cpu()
pred_normal = pred_normal.detach().cpu()
base_color = torch.full(size=(mesh_t.vertices.shape[0], 3), fill_value=0.5)
final_color = torch.clamp(pred_rgb + base_color, 0, 1)
if i is None:
os.mkdir(os.path.join(dir, 'motion_res'))
else:
os.mkdir(os.path.join(dir, 'initial_motion_res'))
print("=> Saving the textured meshes...")
save_path = os.path.join(dir, 'motion_res')
for idx, mesh in enumerate(tqdm(meshes)):
mesh.vertices = mesh.vertices.detach().cpu() + mesh.vertex_normals.detach().cpu() * pred_normal
save_rendered_results(args, save_path, final_color, mesh, str(idx))
create_video(os.path.join(save_path + '/%04d.png'), dir + '/motion.mp4')
def save_rendered_results(args, dir, final_color, mesh, n_iter='final', clip_sim_render=None, encoded_text=None):
kal_render = Renderer(
camera=kal.render.camera.generate_perspective_projection(np.pi / 4, 1280 / 720).to(device),
dim=(1280, 720))
## If you want to render de-colorized mesh
# default_color = torch.full(size=(mesh.vertices.shape[0], 3), fill_value=0.5, device=device)
# mesh.face_attributes = kaolin.ops.mesh.index_vertices_by_faces(default_color.unsqueeze(0),
# mesh.faces.to(device))
# # MeshNormalizer(mesh)()
# img, mask = kal_render.render_single_view(mesh, args.frontview_center[1], args.frontview_center[0],
# radius=2.5,
# background=torch.tensor([1, 1, 1]).to(device).float(),
# return_mask=True)
# img = img[0].cpu()
# mask = mask[0].cpu()
# Manually add alpha channel using background color
# alpha = torch.ones(img.shape[1], img.shape[2])
# alpha[torch.where(mask == 0)] = 0
# img = torch.cat((img, alpha.unsqueeze(0)), dim=0)
# img = transforms.ToPILImage()(img)
# img.save(os.path.join(dir, "normal_{}.png".format(n_iter)))
# Vertex colorings
mesh.face_attributes = kaolin.ops.mesh.index_vertices_by_faces(final_color.unsqueeze(0).to(device),
mesh.faces.to(device))
img, mask = kal_render.render_single_view(mesh, args.frontview_center[1], args.frontview_center[0],
radius=2.5,
background=torch.tensor([1, 1, 1]).to(device).float(),
return_mask=True)
img = img[0].cpu()
mask = mask[0].cpu()
# Manually add alpha channel using background color
alpha = torch.ones(img.shape[1], img.shape[2])
alpha[torch.where(mask == 0)] = 0
img = torch.cat((img, alpha.unsqueeze(0)), dim=0)
img = transforms.ToPILImage()(img)
save_path = os.path.join(dir, "{0:0>4}.png".format(n_iter))
img.save(save_path)
def update_mesh(pred_rgb, pred_normal, prior_color, sampled_mesh):
sampled_mesh.face_attributes = prior_color + kaolin.ops.mesh.index_vertices_by_faces(pred_rgb.unsqueeze(0),
sampled_mesh.faces)
sampled_mesh.vertices = sampled_mesh.vertices + sampled_mesh.vertex_normals * pred_normal
def update_mesh_sequences(pred_rgb, pred_normal, prior_color, meshes):
L = len(meshes)
for idx in range(L):
sampled_mesh = meshes[idx]
sampled_mesh.face_attributes = prior_color + kaolin.ops.mesh.index_vertices_by_faces(pred_rgb.unsqueeze(0),
sampled_mesh.faces)
sampled_mesh.vertices = sampled_mesh.vertices + sampled_mesh.vertex_normals * pred_normal
# training
def main(args):
exp_name = time.strftime('%Y%m%d', time.localtime()) + '_' + args.exp_name
output_dir = cfg.log_path + exp_name
Path(output_dir).mkdir(parents=True, exist_ok=True)
seed_all(args)
# Load predefined npy files
babel_raw_label = np.load(cfg.raw_label_path)
en_raw_label = np.load(cfg.encoded_raw_label_path)
en_raw_label = torch.tensor(en_raw_label).to(device)
# Initialize wandb logging
if args.wandb_logging:
wandb.login()
wandb.init(project="project_name", entity="your_wandb_user_name",
name=time.strftime('%Y%m%d', time.localtime()) + '_' + args.exp_name)
wandb.config.update(args)
# Define the renderer
render = Renderer()
# Get text prompt and tokenize it (and save it)
prompt = "a 3D rendering of " + args.prompt[0] + " in unreal engine"
prompt_token = clip.tokenize([prompt]).to(device)
# Normalized CLIP text embedding
encoded_text_unnorm = clip_model.encode_text(prompt_token)
encoded_text = F.normalize(encoded_text_unnorm)
# Retrieve pose from BABEL
sent2vec = Sent2Vec()
idx = retrieve_raw_label(encoded_text_unnorm, en_raw_label)
pose_label = sent2vec.compute_distance(args.prompt[0], babel_raw_label, idx)
body_pose, right_hand_pose, left_hand_pose = rtr.retrieval_motion(pose_label)
L = body_pose.shape[0]
print("Retrieved pose label: " + pose_label)
del babel_raw_label, en_raw_label
# define image transforms for CLIP loss
if args.use_2d_aug:
clip_transform, full_transform, local_transform, dispaug_transform = get_augmentation(args)
else:
clip_transform, _, _, _ = get_augmentation(args)
background = torch.tensor(args.background, device=device)
# get body mesh and convert to custom HumanMesh class
temp_vertices, temp_faces = get_body_mesh(args, device)
smpl_vertices, smpl_faces = get_body_meshes(body_pose, right_hand_pose, left_hand_pose, args, device)
print("=> Loading template mesh ...")
mesh_t = HumanMesh(v=temp_vertices, f=temp_faces, args=args)
meshes = []
anchor_rdr_all = torch.cuda.FloatTensor()
print("=> Loading mesh sequences and analyzing anchor views ...")
for idx in tqdm(range(L)):
mesh_i = HumanMesh(v=smpl_vertices[idx].unsqueeze(0), f=smpl_faces, args=args)
meshes.append(mesh_i)
for m_i in range(L):
rdr = render.render_single_view(meshes[m_i], background=background)
anchor_rdr_all = torch.cat((anchor_rdr_all, rdr), dim=0)
with torch.no_grad():
anchor_rdr = clip_transform(anchor_rdr_all)
encoded_anchor_rdr = F.normalize(clip_model.encode_image(anchor_rdr))
anchor_sim = torch.cosine_similarity(encoded_anchor_rdr, encoded_text)
_, anchor_idx = torch.topk(anchor_sim, k=args.topk)
del anchor_rdr_all, anchor_rdr, rdr
if args.input_verts == 'posed':
if args.anchor_mesh == 'center':
frame_idx = [int(round(L / 2))]
else:
frame_idx = [int(anchor_idx[0].item())]
mesh_input = copy.deepcopy(meshes[frame_idx[0]])
else:
if args.anchor_mesh == 'center':
frame_idx = [int(round(L / 2))]
elif args.anchor_mesh == 'top1':
frame_idx = [int(anchor_idx[0].item())]
else:
frame_idx = [int(k.item()) for k in anchor_idx]
mesh_input = copy.deepcopy(mesh_t)
prior_color = torch.full(size=(mesh_t.faces.shape[0], 3, 3), fill_value=0.5, device=device)
vertices = copy.deepcopy(mesh_input.vertices)
network_input = copy.deepcopy(vertices)
losses = []
# get model and optimizer
mlp, optim, activate_scheduler, lr_scheduler = get_model(args)
if args.symmetry:
network_input[:, 2] = torch.abs(network_input[:, 2])
if args.standardize:
# Each channel into z-score
network_input = (network_input - torch.mean(network_input, dim=0)) / torch.std(network_input, dim=0)
# Main training loop
for i in tqdm(range(args.n_iter)):
optim.zero_grad()
mesh_list = [copy.deepcopy(meshes[j]) for j in frame_idx]
pred_rgb, pred_normal = mlp(network_input)
update_mesh_sequences(pred_rgb, pred_normal, prior_color, mesh_list)
loss = 0.0
# Compute S_full (from paper, Eq.(2)) and update the entire network
rendered_images_all = torch.cuda.FloatTensor()
masks_all = torch.cuda.FloatTensor()
if args.use_3d_aug:
n_azim = args.n_azim
else:
n_azim = 1
for sampled_mesh in mesh_list:
rendered_images, masks, elev, azim = render.render_views(sampled_mesh, num_azim=n_azim,
num_elev=args.n_elev,
center_azim=args.frontview_center[0],
center_elev=args.frontview_center[1],
elev_div=args.elev_div,
std=args.frontview_std,
return_views=True,
background=background,
limited_elev=args.limited_elev,
rand_cam_distance=args.rand_cam_distance,
args=args)
rendered_images_all = torch.cat((rendered_images_all, rendered_images), dim=0)
masks_all = torch.cat((masks_all, masks), dim=0)
for _ in range(args.n_augs):
if args.use_2d_aug:
augmented_image = full_transform(rendered_images_all)
else:
augmented_image = rendered_images_all
encoded_renders = clip_model.encode_image(augmented_image)
if args.clipavg == "view":
loss += 1 - torch.cosine_similarity(F.normalize(torch.mean(encoded_renders, dim=0, keepdim=True)),
encoded_text).squeeze()
else:
loss += 1 - torch.mean(torch.cosine_similarity(encoded_renders, encoded_text)).squeeze()
loss.backward(retain_graph=True)
if args.max_grad_norm > 0.0:
torch.nn.utils.clip_grad_norm_(mlp.parameters(), max_norm=args.max_grad_norm)
# Normal augment transform
if args.n_normaugs > 0:
# Compute S_local (from paper, Eq.(3)) and update the entire network
normloss = 0.0
for _ in range(args.n_normaugs):
if args.use_2d_aug:
augmented_image = local_transform(rendered_images_all)
else:
augmented_image = rendered_images_all
encoded_renders = clip_model.encode_image(augmented_image)
if args.clipavg == "view":
normloss += args.normweight * (1 - torch.cosine_similarity(
F.normalize(torch.mean(encoded_renders, dim=0, keepdim=True)), encoded_text))
else:
normloss += args.normweight * (
1 - torch.mean(torch.cosine_similarity(encoded_renders, encoded_text)))
normloss.backward(retain_graph=True)
if args.max_grad_norm > 0.0:
torch.nn.utils.clip_grad_norm_(mlp.parameters(), max_norm=args.max_grad_norm)
# Also run separate loss on the uncolored displacements
if args.geoloss:
# Compute S_displ (from paper, Eq.(4)) update only backbone and displacement branch
geoloss = 0.0
default_color = 0.5 * torch.ones((len(mesh_t.vertices), 3), device=device)
geo_renders_all = torch.cuda.FloatTensor()
geo_masks_all = torch.cuda.FloatTensor()
for sampled_mesh in mesh_list:
sampled_mesh.face_attributes = kaolin.ops.mesh.index_vertices_by_faces(default_color.unsqueeze(0),
sampled_mesh.faces)
geo_renders, geo_masks, elev, azim = render.render_views(sampled_mesh, num_azim=n_azim,
num_elev=args.n_elev,
center_azim=args.frontview_center[0],
center_elev=args.frontview_center[1],
elev_div=args.elev_div,
std=args.frontview_std,
return_views=True,
background=background,
limited_elev=args.limited_elev,
rand_cam_distance=args.rand_cam_distance,
args=args)
geo_renders_all = torch.cat((geo_renders_all, geo_renders), dim=0)
geo_masks_all = torch.cat((geo_masks_all, geo_masks), dim=0)
updated_mesh_p3d = Meshes(sampled_mesh.vertices.unsqueeze(0), sampled_mesh.faces.unsqueeze(0))
norm_laplacian_loss = args.w_laplacian * mesh_laplacian_smoothing(updated_mesh_p3d)
geoloss += norm_laplacian_loss
if args.n_normaugs > 0:
for _ in range(args.n_normaugs):
if args.weighted_clip_mean:
if args.use_2d_aug:
n_renders = n_azim * args.n_elev * args.topk
geo_crops_all = torch.cuda.FloatTensor()
geo_cropped_images = torch.cuda.FloatTensor()
geo_crop_emb_weights = torch.zeros((n_renders,), device=device)
geo_render_embeddings = torch.cuda.FloatTensor()
for crop_i in range(n_azim * args.n_elev * args.topk):
geo_crop = transforms.RandomResizedCrop(224)
geo_crop_i, geo_crop_j, geo_crop_h, geo_crop_w \
= geo_crop.get_params(geo_renders_all, scale=[args.normmincrop, args.normmincrop], ratio=[3. / 4., 4. / 3.])
geo_crop_params = torch.tensor([geo_crop_i, geo_crop_j, geo_crop_h, geo_crop_w],
device=device).unsqueeze(0)
geo_crops_all = torch.cat((geo_crops_all, geo_crop_params), dim=0)
geo_cropped_img = \
transforms.functional.resized_crop(geo_renders_all[crop_i], geo_crop_i, geo_crop_j,
geo_crop_h, geo_crop_w, (224, 224)).unsqueeze(0)
geo_cropped_images = torch.cat((geo_cropped_images, geo_cropped_img), dim=0)
# Compute cropped mask
geo_cropped_mask = geo_masks_all[crop_i, geo_crop_i:geo_crop_i + geo_crop_h,
geo_crop_j:geo_crop_j + geo_crop_w]
geo_crop_emb_weights[crop_i] = torch.sum(geo_cropped_mask) / (geo_crop_h * geo_crop_w)
if torch.max(geo_crop_emb_weights) > 0.0:
if args.weight_th > 0.0:
weight_thresh = torch.nn.Threshold(args.weight_th, 0)
geo_crop_emb_weights = weight_thresh(geo_crop_emb_weights) # gradient flow through geo_embedding_weight
augmented_image = dispaug_transform(geo_cropped_images)
encoded_renders_no_weight = clip_model.encode_image(augmented_image)
valid_render_embeddings = encoded_renders_no_weight[torch.nonzero(geo_crop_emb_weights)].squeeze(1)
geoloss += 1 - torch.cosine_similarity(
F.normalize(torch.mean(valid_render_embeddings, dim=0, keepdim=True)), encoded_text).squeeze()
else:
continue
else:
encoded_renders = clip_model.encode_image(geo_renders_all)
geoloss += 1 - torch.cosine_similarity(
F.normalize(torch.mean(encoded_renders, dim=0, keepdim=True)), encoded_text).squeeze()
else:
augmented_image = dispaug_transform(geo_renders_all)
encoded_renders = clip_model.encode_image(augmented_image)
geoloss += 1 - torch.cosine_similarity(
F.normalize(torch.mean(encoded_renders, dim=0, keepdim=True)), encoded_text).squeeze()
geoloss.backward(retain_graph=True)
if args.max_grad_norm > 0.0:
torch.nn.utils.clip_grad_norm_(mlp.parameters(), max_norm=args.max_grad_norm)
optim.step()
if activate_scheduler:
lr_scheduler.step()
with torch.no_grad():
losses = dict(
global_loss=loss.item(),
local_loss=normloss.item(),
geo_loss=geoloss.item()
)
with torch.no_grad():
mesh = copy.deepcopy(meshes[frame_idx[0]])
vis_vertices = copy.deepcopy(mesh.vertices)
pred_rgb, pred_normal = mlp(network_input)
update_mesh(pred_rgb, pred_normal, prior_color, mesh)
if i % 100 == 0:
if args.score_views == 'front':
score_views = clip_transform(render.render_front_views(mesh, num_views=1, background=background))
else:
score_views = clip_transform(render.render_uniform_views(mesh, background=background))
clip_score = torch.mean(torch.cosine_similarity(F.normalize(clip_model.encode_image(score_views)),
encoded_text)).squeeze().item()
report_process(args, output_dir, i, losses, rendered_images_all, exp_name, clip_score, pose_label)
with torch.no_grad():
if args.score_views == 'front':
score_views = clip_transform(render.render_front_views(mesh, num_views=1, background=background))
else:
score_views = clip_transform(render.render_uniform_views(mesh, background=background))
final_clip_score = torch.mean(
torch.cosine_similarity(F.normalize(clip_model.encode_image(score_views)), encoded_text)).squeeze().item()
export_final_results(args, output_dir, losses, mesh, pred_rgb, pred_normal, vis_vertices, exp_name)
report_process(args, output_dir, args.n_iter, losses, rendered_images_all, exp_name, final_clip_score, pose_label)
export_motion_results(args, output_dir, mesh_t, pred_rgb, pred_normal, meshes)
if __name__ == '__main__':
torch.cuda.empty_cache()
args = parse_args()
main(args)