def run():
# load settings
parser = argparse.ArgumentParser()
parser.add_argument('-im', '--filename_mesh', type=str)
parser.add_argument('-is', '--filename_style', type=str)
parser.add_argument('-o', '--filename_output', type=str)
parser.add_argument('-ls', '--lambda_style', type=float, default=1.)
parser.add_argument('-lc', '--lambda_content', type=float, default=2e9)
parser.add_argument('-ltv', '--lambda_tv', type=float, default=1e7)
parser.add_argument('-emax', '--elevation_max', type=float, default=40.)
parser.add_argument('-emin', '--elevation_min', type=float, default=20.)
parser.add_argument('-lrv', '--lr_vertices', type=float, default=0.01)
parser.add_argument('-lrt', '--lr_textures', type=float, default=1.0)
parser.add_argument('-cd', '--camera_distance', type=float, default=2.732)
parser.add_argument('-cdn',
'--camera_distance_noise',
type=float,
default=0.1)
parser.add_argument('-ts', '--texture_size', type=int, default=4)
parser.add_argument('-lr', '--adam_lr', type=float, default=0.05)
parser.add_argument('-ab1', '--adam_beta1', type=float, default=0.9)
parser.add_argument('-ab2', '--adam_beta2', type=float, default=0.999)
parser.add_argument('-bs', '--batch_size', type=int, default=4)
parser.add_argument('-im_s', '--image_size', type=int, default=400)
parser.add_argument('-ni', '--num_iteration', type=int, default=1000)
parser.add_argument('-g', '--gpu', type=int, default=0)
args = parser.parse_args()
# create output directory
directory_output = os.path.dirname(args.filename_output)
if not os.path.exists(directory_output):
os.makedirs(directory_output)
# setup chainer
#os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
#os.environ["CUDA_VISIBLE_DEVICES"]=str(args.gpu)
chainer.cuda.get_device_from_id(args.gpu).use()
cp.random.seed(0)
np.random.seed(0)
# setup scene
model = style_transfer_3d.StyleTransferModel(
filename_mesh=args.filename_mesh,
filename_style=args.filename_style,
lambda_style=args.lambda_style,
lambda_content=args.lambda_content,
lambda_tv=args.lambda_tv,
elevation_max=args.elevation_max,
elevation_min=args.elevation_min,
lr_vertices=args.lr_vertices,
lr_textures=args.lr_textures,
camera_distance=args.camera_distance,
camera_distance_noise=args.camera_distance_noise,
texture_size=args.texture_size,
image_size=args.image_size)
model.to_gpu()
optimizer = neural_renderer.Adam(alpha=args.adam_lr, beta1=args.adam_beta1)
optimizer.setup(model)
# optimization
#import pdb
#pdb.set_trace()
loop = tqdm.tqdm(range(args.num_iteration))
for _ in loop:
optimizer.target.cleargrads()
loss = model(args.batch_size)
loss.backward()
optimizer.update()
loop.set_description('Optimizing. Loss %.4f' % loss.data)
# save obj
##pdb.set_trace()
#model.textures_1 = chainer.functions.concat((model.mesh.textures, model.mesh.textures.transpose((0, 1, 4, 3, 2, 5))), axis=1)
##model.textures_1 = chainer.functions.concat((model.mesh.textures, model.mesh.textures), axis=1)
#obj_fn = args.filename_output.split('/')[-1].split('.')[0]
#output_directory = os.path.split(args.filename_output)[0]#'/'.join(args.filename_output.split('/')[-3:-1])
##neural_renderer.save_obj('%s/%s.obj'% (output_directory,obj_fn), model.mesh.vertices, model.mesh.faces, chainer.functions.tanh(model.textures_1).array)
#neural_renderer.save_obj('%s/%s.obj'% (output_directory,obj_fn), model.mesh.vertices[0], model.mesh.faces[0], chainer.functions.tanh(model.textures_1[0]).array)
#
vertices, faces, textures = model.mesh.get_batch(args.batch_size)
## fill back
textures_1 = chainer.functions.concat(
(textures, textures.transpose((0, 1, 4, 3, 2, 5))), axis=1)
faces_1 = chainer.functions.concat((faces, faces[:, :, ::-1]), axis=1).data
#
obj_fn = args.filename_output.split('/')[-1].split('.')[0]
output_directory = os.path.split(args.filename_output)[
0] #'/'.join(args.filename_output.split('/')[-3:-1])
#neural_renderer.save_obj('%s/%s.obj'% (output_directory,obj_fn), model.mesh.vertices.array, model.mesh.faces, model.mesh.textures.array)
neural_renderer.save_obj('%s/%s.obj' % (output_directory, obj_fn),
vertices[0], faces[0], textures[0].array)
#neural_renderer.save_obj('%s/%s.obj'% (output_directory,obj_fn), model.mesh.vertices[0], model.mesh.faces[0], chainer.functions.tanh(model.textures_1[0]).array)
# draw object
model.renderer.background_color = (1, 1, 1)
#model.renderer.background_color = (0, 0, 0)
loop = tqdm.tqdm(range(0, 360, 4))
for num, azimuth in enumerate(loop):
loop.set_description('Drawing')
model.renderer.eye = neural_renderer.get_points_from_angles(
2.732, 30, azimuth)
images = model.renderer.render(*model.mesh.get_batch(1))
image = images.data.get()[0].transpose((1, 2, 0))
scipy.misc.toimage(image, cmin=0, cmax=1).save('%s/_tmp_%04d.png' %
(directory_output, num))
make_gif(directory_output, args.filename_output)
def run():
# load settings
parser = argparse.ArgumentParser()
parser.add_argument('-im', '--filename_mesh', type=str)
parser.add_argument('-is', '--filename_style', type=str)
parser.add_argument('-o', '--filename_output', type=str)
parser.add_argument('-ls', '--lambda_style', type=float, default=1)
parser.add_argument('-lc', '--lambda_content', type=float, default=1e5)
parser.add_argument('-ltv', '--lambda_tv', type=float, default=1e2)
parser.add_argument('-emax', '--elevation_max', type=float, default=40.)
parser.add_argument('-emin', '--elevation_min', type=float, default=20.)
parser.add_argument('-lrv', '--lr_vertices', type=float, default=0.1)
parser.add_argument('-lrt', '--lr_textures', type=float, default=0.001)
parser.add_argument('-cd', '--camera_distance', type=float, default=2.732)
parser.add_argument('-cdn',
'--camera_distance_noise',
type=float,
default=0.1)
parser.add_argument('-ts', '--texture_size', type=int, default=4)
parser.add_argument('-lr', '--adam_lr', type=float, default=0.05)
parser.add_argument('-ab1', '--adam_beta1', type=float, default=0.5)
parser.add_argument('-ab2', '--adam_beta2', type=float, default=0.999)
parser.add_argument('-bs', '--batch_size', type=int, default=4)
parser.add_argument('-ni', '--num_iteration', type=int, default=100)
parser.add_argument('-g', '--gpu', type=int, default=0)
args = parser.parse_args()
# create output directory
directory_output = os.path.dirname(args.filename_output)
if not os.path.exists(directory_output):
os.makedirs(directory_output)
np.random.seed(0)
# setup scene
model = style_transfer_3d.StyleTransferModel(
filename_mesh=args.filename_mesh,
filename_style=args.filename_style,
lambda_style=args.lambda_style,
lambda_content=args.lambda_content,
lambda_tv=args.lambda_tv,
elevation_max=args.elevation_max,
elevation_min=args.elevation_min,
lr_vertices=args.lr_vertices,
lr_textures=args.lr_textures,
camera_distance=args.camera_distance,
camera_distance_noise=args.camera_distance_noise,
texture_size=args.texture_size,
)
optimizer = torch.optim.Adam([{
'params': model.vertices,
'lr': args.lr_vertices
}, {
'params': model.textures,
'lr': args.lr_textures
}],
betas=(args.adam_beta1, args.adam_beta2))
# optimization
loop = tqdm.tqdm(range(args.num_iteration))
for _ in loop:
optimizer.zero_grad()
loss = model(args.batch_size)
loss.backward()
optimizer.step()
loop.set_description('Optimizing. Loss %.4f' % loss.data)
# draw object
#model.renderer.background_color = (1, 1, 1)
loop = tqdm.tqdm(range(0, 360, 4))
output_images = []
for num, azimuth in enumerate(loop):
loop.set_description('Drawing')
model.renderer.eye = neural_renderer.get_points_from_angles(
2.732, 30, azimuth)
images, _, _ = model.renderer.render(
model.vertices.to(device), model.faces.to(device),
torch.tanh(model.textures.to(device)))
image = images.detach().cpu().numpy()[0].transpose((1, 2, 0))
imsave('/tmp/_tmp_%04d.png' % num, image)
make_gif(args.filename_output)
def run():
# load settings
parser = argparse.ArgumentParser()
parser.add_argument('-im', '--filename_mesh', type=str)
parser.add_argument('-is', '--filename_style', type=str)
parser.add_argument('-o', '--filename_output', type=str)
parser.add_argument('-ls', '--lambda_style', type=float, default=1.)
parser.add_argument('-lc', '--lambda_content', type=float, default=2e9)
parser.add_argument('-ltv', '--lambda_tv', type=float, default=1e7)
parser.add_argument('-emax', '--elevation_max', type=float, default=40.)
parser.add_argument('-emin', '--elevation_min', type=float, default=20.)
parser.add_argument('-lrv', '--lr_vertices', type=float, default=0.01)
parser.add_argument('-lrt', '--lr_textures', type=float, default=1.0)
parser.add_argument('-cd', '--camera_distance', type=float, default=2.732)
parser.add_argument('-cdn',
'--camera_distance_noise',
type=float,
default=0.1)
parser.add_argument('-ts', '--texture_size', type=int, default=4)
parser.add_argument('-lr', '--adam_lr', type=float, default=0.05)
parser.add_argument('-ab1', '--adam_beta1', type=float, default=0.9)
parser.add_argument('-ab2', '--adam_beta2', type=float, default=0.999)
parser.add_argument('-bs', '--batch_size', type=int, default=4)
parser.add_argument('-ni', '--num_iteration', type=int, default=1000)
parser.add_argument('-g', '--gpu', type=int, default=0)
args = parser.parse_args()
# create output directory
directory_output = os.path.dirname(args.filename_output)
if not os.path.exists(directory_output):
os.makedirs(directory_output)
# setup chainer
chainer.cuda.get_device_from_id(args.gpu).use()
cp.random.seed(0)
np.random.seed(0)
# setup scene
model = style_transfer_3d.StyleTransferModel(
filename_mesh=args.filename_mesh,
filename_style=args.filename_style,
lambda_style=args.lambda_style,
lambda_content=args.lambda_content,
lambda_tv=args.lambda_tv,
elevation_max=args.elevation_max,
elevation_min=args.elevation_min,
lr_vertices=args.lr_vertices,
lr_textures=args.lr_textures,
camera_distance=args.camera_distance,
camera_distance_noise=args.camera_distance_noise,
texture_size=args.texture_size,
)
model.to_gpu()
optimizer = neural_renderer.Adam(alpha=args.adam_lr, beta1=args.adam_beta1)
optimizer.setup(model)
# optimization
loop = tqdm.tqdm(range(args.num_iteration))
for _ in loop:
optimizer.target.cleargrads()
loss = model(args.batch_size)
loss.backward()
optimizer.update()
loop.set_description('Optimizing. Loss %.4f' % loss.data)
# draw object
model.renderer.background_color = (1, 1, 1)
loop = tqdm.tqdm(range(0, 360, 4))
for num, azimuth in enumerate(loop):
loop.set_description('Drawing')
model.renderer.eye = neural_renderer.get_points_from_angles(
2.732, 30, azimuth)
images = model.renderer.render(*model.mesh.get_batch(1))
image = images.data.get()[0].transpose((1, 2, 0))
scipy.misc.toimage(image, cmin=0, cmax=1).save('%s/_tmp_%04d.png' %
(directory_output, num))
make_gif(directory_output, args.filename_output)
print(model.getLosses())