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run_material.py
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run_material.py
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import os
from lib.config import cfg, args
import torch
import torch.nn.functional as F
import numpy as np
import cv2
import imageio
if cfg.fix_random:
torch.manual_seed(0)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def run_network():
from lib.networks import make_network
from lib.datasets import make_data_loader
from lib.utils.net_utils import load_network
import tqdm
import torch
import time
network = make_network(cfg).cuda()
load_network(network, cfg.trained_model_dir, epoch=cfg.test.epoch)
network.eval()
data_loader = make_data_loader(cfg, is_train=False)
total_time = 0
for batch in tqdm.tqdm(data_loader):
for k in batch:
if k != 'meta':
batch[k] = batch[k].cuda()
with torch.no_grad():
torch.cuda.synchronize()
start = time.time()
network(batch)
torch.cuda.synchronize()
total_time += time.time() - start
print(total_time / len(data_loader))
def run_evaluate():
from lib.datasets import make_data_loader
from lib.evaluators import make_evaluator
import tqdm
import torch
from lib.networks import make_network
from lib.utils import net_utils
from lib.networks.renderer import make_renderer
cfg.perturb = 0
cfg.eval = True
network = make_network(cfg).cuda()
net_utils.load_network(network,
cfg.trained_model_dir,
resume=cfg.resume,
epoch=cfg.test.epoch)
network.eval()
data_loader = make_data_loader(cfg, is_train=False)
renderer = make_renderer(cfg, network)
evaluator = make_evaluator(cfg)
for batch in tqdm.tqdm(data_loader):
for k in batch:
if k != 'meta':
batch[k] = batch[k].cuda()
with torch.no_grad():
output = renderer.render(batch)
evaluator.evaluate(output, batch)
evaluator.summarize()
def run_visualize():
from lib.networks import make_network
from lib.datasets import make_data_loader
from lib.utils.net_utils import load_network
from lib.utils import net_utils
import tqdm
import torch
from lib.visualizers import make_visualizer
from lib.networks.renderer import make_renderer
cfg.perturb = 0
network = make_network(cfg).cuda()
load_network(network,
cfg.trained_model_dir,
resume=cfg.resume,
epoch=cfg.test.epoch,
strict=False)
network.eval()
if cfg.novel_light:
load_envmap(network, cfg.novel_light_path)
save_envmap(network, cfg)
data_loader = make_data_loader(cfg, is_train=False)
if cfg.vis_pose_sequence:
data_loader.dataset.set_beta(network.body_poses['betas'].detach().cpu().numpy())
renderer = make_renderer(cfg, network)
visualizer = make_visualizer(cfg)
for batch in tqdm.tqdm(data_loader):
for k in batch:
if k != 'meta':
batch[k] = batch[k].cuda()
with torch.no_grad():
output = renderer.render(batch)
visualizer.visualize(output, batch)
def load_envmap(network, path):
sg_path = os.path.join(path, 'sg_scaled_128.npy')
sg_np = np.load(sg_path)
sg_np = sg_np.astype(np.float32)
lgtSGs = network.tpose_human.color_sg_network.envmap_material_network.lgtSGs
load_sgs = torch.from_numpy(sg_np).to(lgtSGs.data.device)
lgtSGs.data = load_sgs
def save_envmap(network, cfg):
sg = network.tpose_human.color_sg_network.envmap_material_network.lgtSGs.data.clone().detach().cpu()
sg[:, 3:] = torch.abs(sg[:, 3:])
env_map = compute_envmap(sg, 256, 512).numpy()
result_dir = os.path.join(cfg.result_dir, 'envmap')
os.makedirs(result_dir, exist_ok=True)
cv2.imwrite('{}/envmap.png'.format(result_dir), (env_map[..., [2, 1, 0]] / 1.0 * 255))
np.save('{}/sg_128.npy'.format(result_dir), sg)
imageio.imwrite('{}/envmap.exr'.format(result_dir), env_map, flags=0x0001)
env_map = np.clip(np.power(env_map, 1./2.2), 0., 1.)
cv2.imwrite('{}/envmap_gamma.png'.format(result_dir), (env_map[..., [2, 1, 0]] / 1.0 * 255))
def compute_envmap(lgtSGs, H, W, upper_hemi=False):
# same convetion as blender
if upper_hemi:
phi, theta = torch.meshgrid([torch.linspace(0., np.pi/2., H),
torch.linspace(1.0 * np.pi, -1.0 * np.pi, W)])
else:
phi, theta = torch.meshgrid([torch.linspace(0., np.pi, H),
torch.linspace(1.0 * np.pi, -1.0 * np.pi, W)])
viewdirs = torch.stack([torch.cos(theta) * torch.sin(phi),
torch.sin(theta) * torch.sin(phi),
torch.cos(phi)], dim=-1) # [H, W, 3]
rgb = render_envmap_sg(lgtSGs, viewdirs)
envmap = rgb.reshape((H, W, 3))
return envmap
def render_envmap_sg(lgtSGs, viewdirs):
viewdirs = viewdirs.to(lgtSGs.device)
viewdirs = viewdirs.unsqueeze(-2) # [..., 1, 3]
# [M, 7] ---> [..., M, 7]
dots_sh = list(viewdirs.shape[:-2])
M = lgtSGs.shape[0]
lgtSGs = lgtSGs.view([1,] * len(dots_sh) + [M, 7]).expand(dots_sh + [M, 7])
lgtSGLobes = lgtSGs[..., :3] / (torch.norm(lgtSGs[..., :3], dim=-1, keepdim=True))
lgtSGLambdas = torch.abs(lgtSGs[..., 3:4])
lgtSGMus = torch.abs(lgtSGs[..., -3:])
# [..., M, 3]
rgb = lgtSGMus * torch.exp(lgtSGLambdas * \
(torch.sum(viewdirs * lgtSGLobes, dim=-1, keepdim=True) - 1.))
rgb = torch.sum(rgb, dim=-2) # [..., 3]
return rgb
if __name__ == '__main__':
globals()['run_' + args.type]()