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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import argparse
import os
import pathlib
import numpy as np
import torch
import imageio
import util
import nvdiffrast.torch as dr
# Transform vertex positions to clip space
def transform_pos(mtx, pos):
t_mtx = torch.from_numpy(mtx).cuda() if isinstance(mtx, np.ndarray) else mtx
# (x,y,z) -> (x,y,z,1)
posw = torch.cat([pos, torch.ones([pos.shape[0], 1]).cuda()], axis=1)
return torch.matmul(posw, t_mtx.t())[None, ...]
def render(glctx, mtx, pos, pos_idx, vtx_col, col_idx, resolution: int):
pos_clip = transform_pos(mtx, pos)
rast_out, _ = dr.rasterize(glctx, pos_clip, pos_idx, resolution=[resolution, resolution])
color, _ = dr.interpolate(vtx_col[None, ...], rast_out, col_idx)
color = dr.antialias(color, rast_out, pos_clip, pos_idx)
return color
def make_grid(arr, ncols=2):
n, height, width, nc = arr.shape
nrows = n//ncols
assert n == nrows*ncols
return arr.reshape(nrows, ncols, height, width, nc).swapaxes(1,2).reshape(height*nrows, width*ncols, nc)
def fit_cube(max_iter = 5000,
resolution = 4,
discontinuous = False,
repeats = 1,
log_interval = 10,
display_interval = None,
display_res = 512,
out_dir = None,
log_fn = None,
mp4save_interval = None,
mp4save_fn = None):
log_file = None
writer = None
if out_dir:
os.makedirs(out_dir, exist_ok=True)
if log_fn:
log_file = open(f'{out_dir}/{log_fn}', 'wt')
if mp4save_interval != 0:
writer = imageio.get_writer(f'{out_dir}/{mp4save_fn}', mode='I', fps=30, codec='libx264', bitrate='16M')
else:
mp4save_interval = None
datadir = f'{pathlib.Path(__file__).absolute().parents[1]}/data'
fn = 'cube_%s.npz' % ('d' if discontinuous else 'c')
with np.load(f'{datadir}/{fn}') as f:
pos_idx, vtxp, col_idx, vtxc = f.values()
print("Mesh has %d triangles and %d vertices." % (pos_idx.shape[0], vtxp.shape[0]))
# Create position/triangle index tensors
pos_idx = torch.from_numpy(pos_idx.astype(np.int32)).cuda()
col_idx = torch.from_numpy(col_idx.astype(np.int32)).cuda()
vtx_pos = torch.from_numpy(vtxp.astype(np.float32)).cuda()
vtx_col = torch.from_numpy(vtxc.astype(np.float32)).cuda()
glctx = dr.RasterizeGLContext()
# Repeats.
for rep in range(repeats):
ang = 0.0
gl_avg = []
vtx_pos_rand = np.random.uniform(-0.5, 0.5, size=vtxp.shape) + vtxp
vtx_col_rand = np.random.uniform(0.0, 1.0, size=vtxc.shape)
vtx_pos_opt = torch.tensor(vtx_pos_rand, dtype=torch.float32, device='cuda', requires_grad=True)
vtx_col_opt = torch.tensor(vtx_col_rand, dtype=torch.float32, device='cuda', requires_grad=True)
# Adam optimizer for vertex position and color with a learning rate ramp.
optimizer = torch.optim.Adam([vtx_pos_opt, vtx_col_opt], lr=1e-2)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: max(0.01, 10**(-x*0.0005)))
for it in range(max_iter + 1):
# Random rotation/translation matrix for optimization.
r_rot = util.random_rotation_translation(0.25)
# Smooth rotation for display.
a_rot = np.matmul(util.rotate_x(-0.4), util.rotate_y(ang))
# Modelview and modelview + projection matrices.
proj = util.projection(x=0.4)
r_mv = np.matmul(util.translate(0, 0, -3.5), r_rot)
r_mvp = np.matmul(proj, r_mv).astype(np.float32)
a_mv = np.matmul(util.translate(0, 0, -3.5), a_rot)
a_mvp = np.matmul(proj, a_mv).astype(np.float32)
# Compute geometric error for logging.
with torch.no_grad():
geom_loss = torch.mean(torch.sum((torch.abs(vtx_pos_opt) - .5)**2, dim=1)**0.5)
gl_avg.append(float(geom_loss))
# Print/save log.
if log_interval and (it % log_interval == 0):
gl_val = np.mean(np.asarray(gl_avg))
gl_avg = []
s = ("rep=%d," % rep) if repeats > 1 else ""
s += "iter=%d,err=%f" % (it, gl_val)
print(s)
if log_file:
log_file.write(s + "\n")
color = render(glctx, r_mvp, vtx_pos, pos_idx, vtx_col, col_idx, resolution)
color_opt = render(glctx, r_mvp, vtx_pos_opt, pos_idx, vtx_col_opt, col_idx, resolution)
# Compute loss and train.
loss = torch.mean((color - color_opt)**2) # L2 pixel loss.
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
# Show/save image.
display_image = display_interval and (it % display_interval == 0)
save_mp4 = mp4save_interval and (it % mp4save_interval == 0)
if display_image or save_mp4:
ang = ang + 0.01
img_b = color[0].cpu().numpy()
img_o = color_opt[0].detach().cpu().numpy()
img_d = render(glctx, a_mvp, vtx_pos_opt, pos_idx, vtx_col_opt, col_idx, display_res)[0]
img_r = render(glctx, a_mvp, vtx_pos, pos_idx, vtx_col, col_idx, display_res)[0]
scl = display_res // img_o.shape[0]
img_b = np.repeat(np.repeat(img_b, scl, axis=0), scl, axis=1)
img_o = np.repeat(np.repeat(img_o, scl, axis=0), scl, axis=1)
result_image = make_grid(np.stack([img_o, img_b, img_d.detach().cpu().numpy(), img_r.cpu().numpy()]))
if display_image:
util.display_image(result_image, size=display_res, title='%d / %d' % (it, max_iter))
if save_mp4:
writer.append_data(np.clip(np.rint(result_image*255.0), 0, 255).astype(np.uint8))
# Done.
if writer is not None:
writer.close()
if log_file:
log_file.close()
#----------------------------------------------------------------------------
def main():
parser = argparse.ArgumentParser(description='Cube fit example')
parser.add_argument('--outdir', help='Specify output directory', default='')
parser.add_argument('--discontinuous', action='store_true', default=False)
parser.add_argument('--resolution', type=int, default=0, required=True)
parser.add_argument('--display-interval', type=int, default=0)
parser.add_argument('--mp4save-interval', type=int, default=100)
parser.add_argument('--max-iter', type=int, default=1000)
args = parser.parse_args()
# Set up logging.
if args.outdir:
ds = 'd' if args.discontinuous else 'c'
out_dir = f'{args.outdir}/cube_{ds}_{args.resolution}'
print (f'Saving results under {out_dir}')
else:
out_dir = None
print ('No output directory specified, not saving log or images')
# Run.
fit_cube(
max_iter=args.max_iter,
resolution=args.resolution,
discontinuous=args.discontinuous,
log_interval=10,
display_interval=args.display_interval,
out_dir=out_dir,
log_fn='log.txt',
mp4save_interval=args.mp4save_interval,
mp4save_fn='progress.mp4'
)
# Done.
print("Done.")
#----------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------------------------
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