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Repository: yifita/deep_cage
Branch: master
Commit: da4c07dcc5c1
Files: 68
Total size: 135.1 MB
Directory structure:
gitextract_vwy_tgog/
├── .gitignore
├── .gitmodules
├── LICENSE
├── cage_deformer_3d.py
├── common.py
├── data/
│ ├── cage_rpose.obj
│ ├── cage_tpose.ply
│ ├── elaborated_chairs/
│ │ ├── Chaise_longue_noir_House_Doctor.ply
│ │ ├── throne_no_base.obj
│ │ └── throne_no_base.obj.mtl
│ ├── fancy_humanoid/
│ │ ├── Skeleton/
│ │ │ ├── Skeleton.mtl
│ │ │ ├── Skeleton.obj
│ │ │ ├── Skeleton.picked
│ │ │ ├── Skeleton_normalized.obj
│ │ │ ├── Skeleton_normalized.picked
│ │ │ ├── skeleton_tpose.obj
│ │ │ └── skeleton_tpose.picked
│ │ ├── robot.obj
│ │ ├── robot.picked
│ │ ├── robot_tpose.obj
│ │ ├── robot_tpose.picked
│ │ └── treebeard-the-Ent-obj/
│ │ ├── Treebeard-the-Ent-obj.mtl
│ │ ├── Treebeard-the-Ent-obj.obj
│ │ ├── Treebeard-the-Ent-obj.picked
│ │ ├── bump.tga
│ │ ├── diffent.tga
│ │ ├── norment.tga
│ │ └── opa.tga
│ ├── processed_shapenetseg/
│ │ ├── .gitattributes
│ │ ├── synsetoffset2category.txt
│ │ ├── test_Chair_-1_2500_pairs.txt
│ │ ├── test_Chair_100_2500.pkl
│ │ ├── test_Table_100_2500.pkl
│ │ ├── test_Table_100_2500_pairs.txt
│ │ ├── train_Chair_-1_2500.pkl
│ │ └── train_Table_-1_2500.pkl
│ ├── shapenet_target/
│ │ ├── 10a1783f635b3fc181dff5c2e57ad46e/
│ │ │ └── model.obj
│ │ ├── 1aa07508b731af79814e2be0234da26c/
│ │ │ └── model.obj
│ │ ├── 1f8e18d42ddded6a4b3c42e318f3affc/
│ │ │ └── model.obj
│ │ ├── 200324d0bafb1c2e19fb4103277a6b93/
│ │ │ └── model.obj
│ │ ├── 36843ea8984df5a63719086e0b4ab8be/
│ │ │ └── model.obj
│ │ ├── 50afe00f341993ae7d63360731b4227a/
│ │ │ └── model.obj
│ │ ├── 6621723f7af35f2dcd344c2b2cefcda6/
│ │ │ └── model.obj
│ │ ├── 81276e5b6c8871634af957103f4767ac/
│ │ │ └── model.obj
│ │ ├── 8979c1aaa6675009bf80985a99195eb8/
│ │ │ └── model.obj
│ │ ├── 92373022868b812fe9aa238b4bc8322e/
│ │ │ └── model.obj
│ │ ├── a09091780fcf3af2e9777a9dc292bbd2/
│ │ │ └── model.obj
│ │ ├── d4edd167061dac5f52a3901fa1436b1a/
│ │ │ └── model.obj
│ │ ├── e6408c4be8e6502837a346dba83c013b/
│ │ │ └── model.obj
│ │ ├── eaf231f17fccb96d81dff5c2e57ad46e/
│ │ │ └── model.obj
│ │ └── fe6b3c001a86d844d5767a0de8dd037e/
│ │ └── model.obj
│ ├── sphere_V42_F80.off
│ ├── surreal_template.picked
│ ├── surreal_template.ply
│ ├── surreal_template_tpose.picked
│ ├── surreal_template_tpose.ply
│ ├── surreal_template_v77.ply
│ └── synsetoffset2category.txt
├── datasets.py
├── deformer_3d.py
├── losses.py
├── network2.py
├── networks.py
├── optimize_cage.py
├── option.py
├── pymesh/
│ └── pymesh2-0.2.1-cp37-cp37m-linux_x86_64.whl
├── readme.md
└── requirements.txt
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
__pycache__
.vscode
vanilla_data
test/test
trained_models
log
scripts/wlop/build
scripts/mnist_normals/build
scripts/
scripts/*/
test/*/
================================================
FILE: .gitmodules
================================================
[submodule "pytorch_points"]
path = pytorch_points
url = https://github.com/yifita/pytorch_points.git
branch = master
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2019 Yifan Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: cage_deformer_3d.py
================================================
from __future__ import print_function
from pprint import pprint
import traceback
import sys
import shutil
import openmesh as om
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import numpy as np
import pymesh
from pytorch_points.misc import logger
from pytorch_points.network.geo_operations import mean_value_coordinates_3D, edge_vertex_indices
from pytorch_points.utils.pc_utils import load, save_ply, save_pts, center_bounding_box
from pytorch_points.utils.geometry_utils import read_trimesh, write_trimesh, build_gemm, Mesh, get_edge_points, generatePolygon
from pytorch_points.utils.pytorch_utils import weights_init, check_values, save_network, load_network, save_grad, saved_variables, \
clamp_gradient_norm, linear_loss_weight, tolerating_collate, clamp_gradient, fix_network_parameters
import losses
import networks
from common import loadInitCage, build_dataset, crisscross_input, log_outputs, deform_with_MVC
def test(net=None, save_subdir="test"):
opt.phase = "test"
dataset = build_dataset(opt)
if opt.dim == 3:
init_cage_V, init_cage_Fs = loadInitCage([opt.template])
cage_V_t = init_cage_V.transpose(1,2).detach().cuda()
else:
init_cage_V = generatePolygon(0, 0, 1.5, 0, 0, 0, opt.cage_deg)
init_cage_V = torch.tensor([(x, y) for x, y in init_cage_V], dtype=torch.float).unsqueeze(0)
cage_V_t = init_cage_V.transpose(1,2).detach().cuda()
init_cage_Fs = [torch.arange(opt.cage_deg, dtype=torch.int64).view(1,1,-1).cuda()]
if net is None:
# network
net = networks.NetworkFull(opt, dim=opt.dim, bottleneck_size=opt.bottleneck_size,
template_vertices=cage_V_t, template_faces=init_cage_Fs[-1],
).cuda()
net.eval()
load_network(net, opt.ckpt)
else:
net.eval()
print(net)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, drop_last=False,
collate_fn=tolerating_collate,
num_workers=0,
worker_init_fn=lambda id: np.random.seed(np.random.get_state()[1][0] + id))
test_output_dir = os.path.join(opt.log_dir, save_subdir)
os.makedirs(test_output_dir, exist_ok=True)
with open(os.path.join(test_output_dir, "eval.txt"), "w") as f:
with torch.no_grad():
for i, data in enumerate(dataloader):
data = dataset.uncollate(data)
############# blending ############
# sample 4 different alpha
if opt.blend_style:
num_alpha = 4
blend_alpha = torch.linspace(0, 1, steps=num_alpha, dtype=torch.float32).cuda().reshape(num_alpha, 1)
data["source_shape"] = data["source_shape"].expand(num_alpha, -1, -1).contiguous()
data["target_shape"] = data["target_shape"].expand(num_alpha, -1, -1).contiguous()
else:
blend_alpha = 1.0
data["alpha"] = blend_alpha
###################################
source_shape_t = data["source_shape"].transpose(1,2).contiguous().detach()
target_shape_t = data["target_shape"].transpose(1,2).contiguous().detach()
outputs = net(source_shape_t, target_shape_t, blend_alpha)
deformed = outputs["deformed"]
####################### evaluation ########################
s_filename = os.path.splitext(data["source_file"][0])[0]
t_filename = os.path.splitext(data["target_file"][0])[0]
log_str = "{}/{} {}-{} ".format(i, len(dataloader), s_filename, t_filename)
print(log_str)
f.write(log_str+"\n")
###################### outputs ############################
for b in range(deformed.shape[0]):
if "source_mesh" in data and data["source_mesh"] is not None:
if isinstance(data["source_mesh"][0], str):
source_mesh = om.read_polymesh(data["source_mesh"][0]).points().copy()
source_mesh = dataset.normalize(source_mesh, opt.isV2)
source_mesh = torch.from_numpy(source_mesh.astype(np.float32)).unsqueeze(0).cuda()
deformed = deform_with_MVC(outputs["cage"][b:b+1], outputs["new_cage"][b:b+1],
outputs["cage_face"], source_mesh)
else:
deformed = deform_with_MVC(outputs["cage"][b:b+1], outputs["new_cage"][b:b+1],
outputs["cage_face"], data["source_mesh"])
deformed[b] = center_bounding_box(deformed[b])[0]
if data["source_face"] is not None and data["source_mesh"] is not None:
source_mesh = data["source_mesh"][0].detach().cpu()
source_mesh = center_bounding_box(source_mesh)[0]
source_face = data["source_face"][0].detach().cpu()
tosave = pymesh.form_mesh(vertices=source_mesh, faces=source_face)
pymesh.save_mesh(os.path.join(opt.log_dir, save_subdir, "{}-{}-Sa.obj".format(s_filename, t_filename)),
tosave, use_float=True
)
tosave = pymesh.form_mesh(vertices=deformed[0].detach().cpu(), faces=source_face)
pymesh.save_mesh(os.path.join(opt.log_dir, save_subdir, "{}-{}-Sab-{}.obj".format(s_filename, t_filename, b)),
tosave, use_float=True,
)
elif data["source_face"] is None and isinstance(data["source_mesh"][0], str):
orig_file_path = data["source_mesh"][0]
mesh = om.read_polymesh(orig_file_path)
points_arr = mesh.points()
points_arr[:] = source_mesh[0].detach().cpu().numpy()
om.write_mesh(os.path.join(opt.log_dir, save_subdir, "{}-{}-Sa.obj".format(s_filename, t_filename)), mesh)
points_arr[:] = deformed[0].detach().cpu().numpy()
om.write_mesh(os.path.join(opt.log_dir, save_subdir, "{}-{}-Sab-{}.obj".format(s_filename, t_filename, b)), mesh)
else:
# save to "pts" for rendering
save_pts(os.path.join(opt.log_dir, save_subdir,"{}-{}-Sa.pts".format(s_filename,t_filename)), data["source_shape"][b].detach().cpu())
save_pts(os.path.join(opt.log_dir, save_subdir,"{}-{}-Sab-{}.pts".format(s_filename,t_filename, b)), deformed[0].detach().cpu())
if data["target_face"] is not None and data["target_mesh"] is not None:
data["target_mesh"][0] = center_bounding_box(data["target_mesh"][0])[0]
tosave = pymesh.form_mesh(vertices=data["target_mesh"][0].detach().cpu(), faces=data["target_face"][0].detach().cpu())
pymesh.save_mesh(os.path.join(opt.log_dir, save_subdir, "{}-{}-Sb.obj".format(s_filename, t_filename)),
tosave, use_float=True,
)
elif data["target_face"] is None and isinstance(data["target_mesh"][0], str):
orig_file_path = data["target_mesh"][0]
mesh = om.read_polymesh(orig_file_path)
points_arr = mesh.points()
points_arr[:] = dataset.normalize(points_arr.copy(), opt.isV2)
om.write_mesh(os.path.join(opt.log_dir, save_subdir, "{}-{}-Sb.obj".format(s_filename, t_filename)), mesh)
else:
save_pts(os.path.join(opt.log_dir, save_subdir,"{}-{}-Sb.pts".format(s_filename,t_filename)), data["target_shape"][0].detach().cpu())
outputs["cage"][b] = center_bounding_box(outputs["cage"][b])[0]
outputs["new_cage"][b] = center_bounding_box(outputs["new_cage"][b])[0]
pymesh.save_mesh_raw(
os.path.join(opt.log_dir, save_subdir, "{}-{}-cage1-{}.ply".format(s_filename, t_filename, b)),
outputs["cage"][b].detach().cpu(), outputs["cage_face"][0].detach().cpu(), binary=True)
pymesh.save_mesh_raw(
os.path.join(opt.log_dir, save_subdir, "{}-{}-cage2-{}.ply".format(s_filename, t_filename, b)),
outputs["new_cage"][b].detach().cpu(), outputs["cage_face"][0].detach().cpu(), binary=True)
dataset.render_result(test_output_dir)
def train():
dataset = build_dataset(opt)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=True, drop_last=True,
collate_fn=tolerating_collate,
num_workers=2, worker_init_fn=lambda id: np.random.seed(np.random.get_state()[1][0] + id))
if opt.dim == 3:
# cage (1,N,3)
init_cage_V, init_cage_Fs = loadInitCage([opt.template])
cage_V_t = init_cage_V.transpose(1,2).detach().cuda()
cage_edge_points_list = []
cage_edges_list = []
for F in init_cage_Fs:
mesh = Mesh(vertices=init_cage_V[0], faces=F[0])
build_gemm(mesh, F[0])
cage_edge_points = torch.from_numpy(get_edge_points(mesh)).cuda()
cage_edge_points_list.append(cage_edge_points)
cage_edges_list = [edge_vertex_indices(F[0])]
else:
init_cage_V = generatePolygon(0, 0, 1.5, 0, 0, 0, opt.cage_deg)
init_cage_V = torch.tensor([(x, y) for x, y in init_cage_V], dtype=torch.float).unsqueeze(0)
cage_V_t = init_cage_V.transpose(1,2).detach().cuda()
init_cage_Fs = [torch.arange(opt.cage_deg, dtype=torch.int64).view(1,1,-1).cuda()]
# network
net = networks.NetworkFull(opt, dim=opt.dim, bottleneck_size=opt.bottleneck_size,
template_vertices=cage_V_t, template_faces=init_cage_Fs[-1],
).cuda()
net.apply(weights_init)
if opt.ckpt:
load_network(net, opt.ckpt)
all_losses = losses.AllLosses(opt)
# optimizer
optimizer = torch.optim.Adam([
{"params": net.encoder.parameters()},
{"params": net.nd_decoder.parameters()},
{"params": net.merger.parameters()}], lr=opt.lr)
if opt.full_net:
optimizer.add_param_group({'params': net.nc_decoder.parameters(), 'lr': 0.1*opt.lr})
if opt.optimize_template:
optimizer.add_param_group({'params': net.template_vertices, 'lr': opt.lr})
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, int(opt.nepochs*0.4), gamma=0.1, last_epoch=-1)
# train
net.train()
start_epoch = 0
t = 0
steps_C = 20
steps_D = 20
# train
os.makedirs(opt.log_dir, exist_ok=True)
shutil.copy2(__file__, opt.log_dir)
shutil.copy2(os.path.join(os.path.dirname(__file__), "networks.py"), opt.log_dir)
shutil.copy2(os.path.join(os.path.dirname(__file__), "losses.py"), opt.log_dir)
shutil.copy2(os.path.join(os.path.dirname(__file__), "datasets.py"), opt.log_dir)
shutil.copy2(os.path.join(os.path.dirname(__file__), "common.py"), opt.log_dir)
shutil.copy2(os.path.join(os.path.dirname(__file__), "option.py"), opt.log_dir)
print(net)
log_file = open(os.path.join(opt.log_dir, "training_log.txt"), "a")
log_file.write(str(net)+"\n")
log_interval = max(len(dataloader)//5, 50)
save_interval = max(opt.nepochs//10, 1)
running_avg_loss = -1
with torch.autograd.detect_anomaly():
if opt.epoch:
start_epoch = opt.epoch % opt.nepochs
t += start_epoch*len(dataloader)
for epoch in range(start_epoch, opt.nepochs):
for t_epoch, data in enumerate(dataloader):
warming_up = epoch < opt.warmup_epochs
progress = t_epoch/len(dataloader)+epoch
optimize_C = (t % (steps_C+steps_D)) > steps_D
############# get data ###########
data = dataset.uncollate(data)
data = crisscross_input(data)
if opt.dim == 3:
data["cage_edge_points"] = cage_edge_points_list[-1]
data["cage_edges"] = cage_edges_list[-1]
source_shape, target_shape = data["source_shape"], data["target_shape"]
############# blending ############
if opt.blend_style:
blend_alpha = torch.rand((source_shape.shape[0], 1), dtype=torch.float32).to(device=source_shape.device)
else:
blend_alpha = 1.0
data["alpha"] = blend_alpha
############# run network ###########
optimizer.zero_grad()
# optimizer_C.zero_grad()
# optimizer_D.zero_grad()
source_shape_t = source_shape.transpose(1,2)
target_shape_t = target_shape.transpose(1,2)
outputs = net(source_shape_t, target_shape_t, data["alpha"])
############# get losses ###########
current_loss = all_losses(data, outputs, progress)
loss_sum = torch.sum(torch.stack([v for v in current_loss.values()], dim=0))
if running_avg_loss < 0:
running_avg_loss = loss_sum
else:
running_avg_loss = running_avg_loss + (loss_sum.item() - running_avg_loss)/(t+1)
if (t % log_interval == 0) or (loss_sum > 5*running_avg_loss):
log_str = "warming up {} e {:03d} t {:05d}: {}".format(warming_up, epoch, t,
", ".join(["{} {:.3g}".format(k, v.mean().item()) for k, v in current_loss.items()]))
print(log_str)
log_file.write(log_str+"\n")
log_outputs(opt, t, outputs, data)
if loss_sum > 100*running_avg_loss:
logger.info("loss ({}) > 5*running_average_loss ({}). Skip without update.".format(loss_sum, 5*running_avg_loss))
torch.cuda.empty_cache()
continue
loss_sum.backward()
if epoch < opt.warmup_epochs:
try:
net.nc_decoder.zero_grad()
net.encoder.zero_grad()
except AttributeError:
net.template_vertices.grad.zero_()
if opt.alternate_cd:
optimize_C = (epoch > opt.warmup_epochs) and (epoch % (opt.c_epoch+opt.d_epoch)) > opt.d_epoch
if optimize_C:
net.nd_decoder.zero_grad()
else:
try:
net.encoder.zero_grad()
net.nc_decoder.zero_grad()
except AttributeError:
net.template_vertices.grad.zero_()
clamp_gradient(net, 0.1)
optimizer.step()
if (t + 1) % 500 == 0:
save_network(net, opt.log_dir, network_label="net", epoch_label="latest")
t += 1
if (epoch + 1) % save_interval == 0:
save_network(net, opt.log_dir, network_label="net", epoch_label=epoch)
scheduler.step()
if opt.eval:
try:
test(net=net, save_subdir="epoch_{}".format(epoch))
except Exception as e:
traceback.print_exc(file=sys.stdout)
logger.warn("Failed to run test", str(e))
log_file.close()
save_network(net, opt.log_dir, network_label="net", epoch_label="final")
test(net=net)
if __name__ == "__main__":
from option import BaseOptions
import datetime
import os
parser = BaseOptions()
opt = parser.parse()
# reproducability
torch.manual_seed(24)
torch.backends.cudnn.deterministic = True # type: ignore
torch.backends.cudnn.benchmark = False # type: ignore
np.random.seed(24)
if opt.phase == "train":
if opt.ckpt is not None:
opt.log_dir = os.path.dirname(opt.ckpt)
else:
opt.log_dir = os.path.join(opt.log_dir, "-".join(filter(None, [os.path.basename(__file__)[:-3],
datetime.datetime.now().strftime("%Y-%m-%d_%H:%M:%S"),
opt.name])))
else:
opt.log_dir = os.path.dirname(opt.ckpt)
if opt.phase == "test":
test(save_subdir=opt.subdir)
else:
os.makedirs(opt.log_dir, exist_ok=True)
log_file = open(os.path.join(opt.log_dir, "training_log.txt"), "a")
parser.print_options(opt, log_file)
log_file.close()
train()
================================================
FILE: common.py
================================================
import numpy as np
import torch
import os
import shlex
import subprocess
import pymesh
from multiprocessing.pool import ThreadPool
from pytorch_points.utils.geometry_utils import read_trimesh, write_trimesh
from pytorch_points.utils.pc_utils import save_ply, save_ply_property
from pytorch_points.network.geo_operations import mean_value_coordinates_3D, green_coordinates_3D, compute_face_normals_and_areas
from glob import glob
def is_type(file, file_ext):
if isinstance(file_ext, str):
file_ext = [file_ext]
tmp = [os.path.splitext(file)[-1].lower()[1:] == ext for ext in file_ext]
return any(tmp)
def find_files(source, file_ext=["txt",]):
# If file_ext is a list
if source is None:
return []
# Seamlessy pc_utils.load single file, list of files and files from directories.
source_fns = []
if isinstance(source, str):
if os.path.isdir(source) or source[-1] == '*':
if isinstance(file_ext, list):
for fmt in file_ext:
source_fns += find_files(source, fmt)
else:
source_fns = sorted(glob("{}/**/*.{}".format(source, file_ext),recursive=True))
elif os.path.isfile(source):
source_fns = [source]
assert (all([is_type(f, file_ext) for f in source_fns])), "Given files contain files with unsupported format"
elif len(source) and isinstance(source[0], str):
for s in source:
source_fns.extend(find_files(s, file_ext=file_ext))
return source_fns
def loadInitCage(templates):
init_cage_Fs = []
for i, template in enumerate(templates):
init_cage_V, init_cage_F = read_trimesh(template)
init_cage_V = torch.from_numpy(init_cage_V[:,:3].astype(np.float32)).unsqueeze(0).cuda()*2.0
init_cage_F = torch.from_numpy(init_cage_F[:,:3].astype(np.int64)).unsqueeze(0).cuda()
init_cage_Fs.append(init_cage_F)
return init_cage_V, init_cage_Fs
def renderMeshes(shape_dir, forward=(0.5,0.5,0), pos=(-4,-4,0), up=(0,0,1), color=None, suffix="", img_size=(480, 480), other_method=False, otherStr=""):
"""render shapes inside a directory with thea"""
# mycolor = "e0f2d79b"
mycolor = "c2d2e9"
try:
len(img_size)
except Exception as e:
img_size = [img_size]*2
finally:
assert(len(img_size)==2)
thea_render_bin = "RenderShape"
output_dir = os.path.join(shape_dir, "renders")
os.makedirs(output_dir, exist_ok=True)
files = find_files(shape_dir, ["ply", "obj", "pts"])
view_opt = ",".join([str(_) for _ in forward])+","+",".join([str(_) for _ in pos])+","+",".join([str(_) for _ in up])
cage_view_opt = ",".join([str(_) for _ in forward])+","+",".join([str(_*1.8) for _ in pos])+","+",".join([str(_) for _ in up])
pool = ThreadPool(processes=4)
results =[]
for input_file in files:
myotherStr = otherStr
output_file = os.path.join(output_dir, os.path.splitext(os.path.basename(input_file))[0]+".png")
# ./MeshSample -n2048 -l LABEL INPUT OUTPUT
if "Sa" in input_file and ("Sab" not in input_file):
fname = os.path.basename(input_file).split('-')[0]
mycolor = color or "f7d6bf"
elif "Sab" in input_file:
fname = os.path.basename(input_file).split('-')[0]
mycolor = color or "c2d2e9"
if not other_method:
myotherStr = myotherStr + " -b f6f7e4 "
elif "Sb" in input_file:
fname = os.path.basename(input_file).split('-')[1]
mycolor = color or "b0cac7"
elif "cage1" in input_file:
fname = os.path.splitext(os.path.basename(input_file))[0]
overlay_file = input_file.replace(fname[fname.find("cage1"):], "Sa")
overlay_file = glob(os.path.splitext(overlay_file)[0]+".*")
mycolor = color or "c2d2e9"
if len(overlay_file) == 1:
overlay_file = overlay_file[0]
results.append(pool.apply_async(call_proc, (thea_render_bin + " {} -0 -c {} -v {} -j 666660 -o {} -i 0 {} {} {} {}".format(
myotherStr, mycolor, cage_view_opt, input_file, overlay_file, output_file, img_size[0], img_size[1]),)))
continue
elif "cage2" in input_file:
overlay_file = input_file.replace("cage2", "Sab")
overlay_file = glob(os.path.splitext(overlay_file)[0]+".*")
mycolor = color or "c2d2e9"
if len(overlay_file) == 1:
overlay_file = overlay_file[0]
results.append(pool.apply_async(call_proc, (thea_render_bin + " {} -0 -c {} -v {} -j 666660 -o {} -i 0 {} {} {} {}".format(
myotherStr, mycolor, cage_view_opt, input_file, overlay_file, output_file, img_size[0], img_size[1]),)))
continue
if input_file[-4:] == ".pts":
oname, oext = os.path.splitext(output_file)
results.append(pool.apply_async(call_proc, (thea_render_bin + " {} -0 -p 4 -c {} -v {} {} {} {} {}".format(
myotherStr, mycolor, view_opt, input_file, oname+"_pts"+oext, img_size[0], img_size[1]),)))
else:
results.append(pool.apply_async(call_proc, (thea_render_bin + " {} -0 -c {} -v {} {} {} {} {}".format(
myotherStr, mycolor, view_opt, input_file, output_file, img_size[0], img_size[1]),)))
# Close the pool and wait for each running task to complete
pool.close()
pool.join()
for result in results:
out, err = result.get()
if len(err) > 0:
print("err: {}".format(err))
def call_proc(cmd):
""" This runs in a separate thread. """
#subprocess.call(shlex.split(cmd)) # This will block until cmd finishes
p = subprocess.Popen(shlex.split(cmd), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
out, err = p.communicate()
return (out, err)
def deform_with_MVC(cage, cage_deformed, cage_face, query, verbose=False):
"""
cage (B,C,3)
cage_deformed (B,C,3)
cage_face (B,F,3) int64
query (B,Q,3)
"""
weights, weights_unnormed = mean_value_coordinates_3D(query, cage, cage_face, verbose=True)
# weights = weights.detach()
deformed = torch.sum(weights.unsqueeze(-1)*cage_deformed.unsqueeze(1), dim=2)
if verbose:
return deformed, weights, weights_unnormed
return deformed
# def deform_with_GC(cage, cage_deformed, cage_face, query, verbose=False):
# cage_FN, _ = compute_face_normals_and_areas(cage, cage_face)
# coords_V, coords_F, is_exterior = green_coordinates_3D(query, cage, cage_face, face_normals=cage_FN, verbose=True)
# # (B,P,N)*(B,N,3)->(B,N,3)
# cage_deformed_FN, _ = compute_face_normals_and_areas(cage_deformed, cage_face)
# deformed = torch.sum(coords_V.unsqueeze(-1)*cage_deformed.unsqueeze(1), dim=-2) + torch.sum(coords_F.unsqueeze(-1)*cage_deformed_FN.unsqueeze(1), dim=-2)
# # if verbose:
# # return deformed, weights, is_exterior
# return deformed
def load_shapenet_cat():
"""return two dictionaries: name-to-id and id-to-name"""
namecat2numbercat = {}
numbercat2namecat = {}
with open(os.path.join("data","synsetoffset2category.txt"), 'r') as f:
for line in f:
ls = line.strip().split()
namecat2numbercat[ls[0]] = ls[1]
numbercat2namecat[ls[1]] = ls[1]
return (namecat2numbercat, numbercat2namecat)
def build_dataset(opt):
import datasets
if opt.target_model is not None and opt.source_model is not None:
normalization_fn = None
if opt.dataset == "COSEG":
normalization_fn = datasets.CoSegDataset.normalize
render_fn = datasets.CoSegDataset.render_result
elif opt.dataset == "FAUST":
normalization_fn = datasets.FaustDataset.normalize
render_fn = datasets.FaustDataset.render_result
elif opt.dataset == "SHAPENET":
normalization_fn = datasets.ShapeNetSeg.normalize
render_fn = datasets.ShapeNetSeg.render_result
elif opt.dataset == "SHAPENETV2":
normalization_fn = datasets.ShapeNetV2.normalize
render_fn = datasets.ShapeNetV2.render_result
dataset = datasets.FileListDataset(opt, normalization_fn)
dataset.render_result = render_fn
elif opt.dataset == "COSEG":
dataset = datasets.CoSegDataset(root_dir=opt.data_dir, cat=opt.data_cat, phase=opt.phase, max=(opt.data_max if opt.phase=="test" else -1))
data_dict = dataset[0]
opt.sym_plane = dataset.sym_plane
elif opt.dataset == "FAUST":
dataset = datasets.FaustDataset(root_dir=opt.data_dir, phase=opt.phase, npoints=opt.num_point,
template=opt.template,
source=opt.source_model,
max=opt.data_max, regular_sampling=opt.regular_sampling,
normalization=False)
opt.sym_plane = None
elif opt.dataset == "SHAPENET":
dataset = datasets.ShapeNetSeg(root_dir=opt.data_dir,
phase=opt.phase,
shuffle=(opt.phase=="train"),
knn=False,
num_neighbors=40,
normalization="BoundingBox",
class_choice=opt.data_cat,
data_augmentation_Z_rotation=False,
data_augmentation_Z_rotation_range=40,
anisotropic_scaling=(opt.phase=="train"),
npoints=opt.num_point,
random_translation=False,
use_fixed_pairs=(opt.phase=="test"),
num_samples=(opt.data_max if opt.phase=="test" else -1),
isV2=opt.isV2,
use_preprocessed=opt.use_preprocessed)
opt.sym_plane = dataset.sym_plane
elif opt.dataset == "SHAPENETV2":
dataset = datasets.ShapeNetV2(phase=opt.phase,
shuffle=(opt.phase=="train"),
knn=False,
num_neighbors=40,
normalization="BoundingBox",
class_choice=opt.data_cat,
data_augmentation_Z_rotation=False,
data_augmentation_Z_rotation_range=40,
anisotropic_scaling=(opt.phase=="train"),
npoints=opt.num_point,
random_translation=False,
use_fixed_pairs=(opt.phase=="test"),
num_samples=(opt.data_max if opt.phase=="test" else -1),
use_preprocessed=opt.use_preprocessed)
opt.sym_plane = dataset.sym_plane
elif opt.dataset == "SURREAL":
dataset = datasets.PairedSurreal(root_dir=opt.data_dir,
template=opt.template,
source=opt.source_model,
max=(opt.data_max if opt.phase=="test" else -1),
regular_sampling=opt.regular_sampling,
phase=opt.phase,
data_augmentation_Z_rotation=False,
data_augmentation_Z_rotation_range=40,
npoints=opt.num_point,
)
opt.sym_plane = dataset.sym_plane
elif opt.dataset == "MNIST_MIXED":
dataset = datasets.CrossCategoryPairsDataset(opt.data_dir, phase=opt.phase, num_point=opt.num_point, training_size=0.9,
source_digits=opt.source_digit, target_digits=opt.target_digit, max=(100 if opt.phase=="test" else -1))
opt.sym_plane = None
elif opt.dataset == "MNIST_SINGLE":
dataset = datasets.SameCategoryPairsDataset(opt.data_dir, phase=opt.phase, num_point=opt.num_point, training_size=0.9,
categories=opt.source_digit, max=(100 if opt.phase=="test" else -1))
opt.sym_plane = None
else:
raise ValueError("Unsupported dataset")
opt.mesh_data = dataset.mesh_data
return dataset
def log_outputs(opt, step, all_outputs, all_inputs):
# Source
color = all_inputs["source_shape"][:,:,1].cpu().numpy()
save_ply_property(os.path.join(opt.log_dir,"step-{:06d}-Sa.ply".format(step)), all_inputs["source_shape"][0].detach().cpu().numpy(), color[0], cmap_name="rainbow")
# Target
save_ply_property(os.path.join(opt.log_dir,"step-{:06d}-Sb.ply".format(step)), all_inputs["target_shape"][0].detach().cpu().numpy(), color[0], cmap_name="rainbow")
for batch in range(0, all_outputs["cage"].shape[0], opt.batch_size):
if batch // opt.batch_size == 0:
tag = "StoT"
elif batch // opt.batch_size == 2:
tag = "StoS"
elif batch // opt.batch_size == 1:
tag = "TtoS"
elif batch // opt.batch_size == 3:
tag = "TtoT"
# deformed and cage
save_ply_property(os.path.join(opt.log_dir,"step-{:06d}-{}-Sab.ply".format(step, tag)),
all_outputs["deformed"][batch].detach().cpu().numpy(), color[batch], cmap_name="rainbow")
write_trimesh(os.path.join(opt.log_dir, "step-{:06d}-{}-cage1.ply".format(step, tag)),
all_outputs["cage"][batch].detach().cpu(), all_outputs["cage_face"][0].detach().cpu(), binary=True)
write_trimesh(os.path.join(opt.log_dir, "step-{:06d}-{}-cage2.ply".format(step, tag)),
all_outputs["new_cage"][batch].detach().cpu(), all_outputs["cage_face"][0].detach().cpu(), binary=True)
# if using network2
if "cage_surface" in all_outputs:
save_ply(os.path.join(opt.log_dir,"step-{:06d}-{}-cage_surface1.ply".format(step, tag)), all_outputs["cage_surface"][batch].detach().cpu().numpy())
save_ply(os.path.join(opt.log_dir,"step-{:06d}-{}-cage_surface2.ply".format(step, tag)), all_outputs["new_cage_surface"][batch].detach().cpu().numpy())
def remesh(path1):
"""
This function takes a path to the orginal shapenet model and subsample it nicely
"""
obj1 = pymesh.load_mesh(path1)
obj1, info = pymesh.remove_isolated_vertices(obj1)
print("Removed {} isolated vertices".format(info["num_vertex_removed"]))
obj1, info = pymesh.remove_duplicated_vertices(obj1)
print("Merged {} duplicated vertices".format(info["num_vertex_merged"]))
obj1, _ = pymesh.remove_degenerated_triangles(obj1)
if len(obj1.vertices)<5000:
while len(obj1.vertices)<5000:
obj1 = pymesh.subdivide(obj1)
obj1 = pymesh.form_mesh(obj1.vertices, obj1.faces)
return obj1
def read_trimesh(path, normal=False, clean=True):
mesh = pymesh.load_mesh(path)
if clean:
mesh, info = pymesh.remove_isolated_vertices(mesh)
print("Removed {} isolated vertices".format(info["num_vertex_removed"]))
mesh, info = pymesh.remove_duplicated_vertices(mesh)
print("Merged {} duplicated vertices".format(info["num_vertex_merged"]))
mesh, info = pymesh.remove_degenerated_triangles(mesh)
mesh = pymesh.form_mesh(mesh.vertices, mesh.faces)
vertices = mesh.vertices
if normal:
mesh.add_attribute("vertex_normal")
vertex_normals = mesh.get_attribute("vertex_normal").reshape(-1, 3)
vertices = np.concatenate([vertices, vertex_normals], axis=-1)
return vertices, mesh.faces
def crisscross_input(data):
_source_shape, _source_normals, _source_face, _source_filename, \
_target_shape, _target_normals, _target_face, _target_filename = \
data["source_shape"], data["source_normals"], data["source_face"], data["source_file"], \
data["target_shape"], data["target_normals"], data["target_face"], data["target_file"] \
data["source_shape"] = torch.cat([_source_shape, _target_shape, _source_shape], dim=0).contiguous()
data["target_shape"] = torch.cat([_target_shape, _source_shape, _source_shape], dim=0).contiguous()
data["source_normals"] = torch.cat([_source_normals, _target_normals, _source_normals], dim=0).contiguous()
data["target_normals"] = torch.cat([_target_normals, _source_normals, _source_normals], dim=0).contiguous()
data["source_file"] = [_source_filename] + [_target_filename] + [_source_filename]
data["target_file"] = [_target_filename] + [_source_filename] + [_source_filename]
if _source_face is not None and _target_face is not None:
data["source_face"] = torch.cat([_source_face, _target_face, _source_face], dim=0).contiguous()
data["target_face"] = torch.cat([_target_face, _source_face, _source_face], dim=0).contiguous()
if "source_label" in data and data["source_label"] is not None and "source_label" in data and data["target_label"] is not None:
_source_label = data["source_label"]
_target_label = data["target_label"]
data["source_label"] = torch.cat([_source_label, _target_label, _source_label], dim=0).contiguous()
data["target_label"] = torch.cat([_target_label, _source_label, _source_label], dim=0).contiguous()
return data
================================================
FILE: data/cage_rpose.obj
================================================
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FILE: data/elaborated_chairs/throne_no_base.obj
================================================
####
#
# OBJ File Generated by Meshlab
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# Object throne_no_base.obj
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# Vertices: 38939
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gitextract_vwy_tgog/ ├── .gitignore ├── .gitmodules ├── LICENSE ├── cage_deformer_3d.py ├── common.py ├── data/ │ ├── cage_rpose.obj │ ├── cage_tpose.ply │ ├── elaborated_chairs/ │ │ ├── Chaise_longue_noir_House_Doctor.ply │ │ ├── throne_no_base.obj │ │ └── throne_no_base.obj.mtl │ ├── fancy_humanoid/ │ │ ├── Skeleton/ │ │ │ ├── Skeleton.mtl │ │ │ ├── Skeleton.obj │ │ │ ├── Skeleton.picked │ │ │ ├── Skeleton_normalized.obj │ │ │ ├── Skeleton_normalized.picked │ │ │ ├── skeleton_tpose.obj │ │ │ └── skeleton_tpose.picked │ │ ├── robot.obj │ │ ├── robot.picked │ │ ├── robot_tpose.obj │ │ ├── robot_tpose.picked │ │ └── treebeard-the-Ent-obj/ │ │ ├── Treebeard-the-Ent-obj.mtl │ │ ├── Treebeard-the-Ent-obj.obj │ │ ├── Treebeard-the-Ent-obj.picked │ │ ├── bump.tga │ │ ├── diffent.tga │ │ ├── norment.tga │ │ └── opa.tga │ ├── processed_shapenetseg/ │ │ ├── .gitattributes │ │ ├── synsetoffset2category.txt │ │ ├── test_Chair_-1_2500_pairs.txt │ │ ├── test_Chair_100_2500.pkl │ │ ├── test_Table_100_2500.pkl │ │ ├── test_Table_100_2500_pairs.txt │ │ ├── train_Chair_-1_2500.pkl │ │ └── train_Table_-1_2500.pkl │ ├── shapenet_target/ │ │ ├── 10a1783f635b3fc181dff5c2e57ad46e/ │ │ │ └── model.obj │ │ ├── 1aa07508b731af79814e2be0234da26c/ │ │ │ └── model.obj │ │ ├── 1f8e18d42ddded6a4b3c42e318f3affc/ │ │ │ └── model.obj │ │ ├── 200324d0bafb1c2e19fb4103277a6b93/ │ │ │ └── model.obj │ │ ├── 36843ea8984df5a63719086e0b4ab8be/ │ │ │ └── model.obj │ │ ├── 50afe00f341993ae7d63360731b4227a/ │ │ │ └── model.obj │ │ ├── 6621723f7af35f2dcd344c2b2cefcda6/ │ │ │ └── model.obj │ │ ├── 81276e5b6c8871634af957103f4767ac/ │ │ │ └── model.obj │ │ ├── 8979c1aaa6675009bf80985a99195eb8/ │ │ │ └── model.obj │ │ ├── 92373022868b812fe9aa238b4bc8322e/ │ │ │ └── model.obj │ │ ├── a09091780fcf3af2e9777a9dc292bbd2/ │ │ │ └── model.obj │ │ ├── d4edd167061dac5f52a3901fa1436b1a/ │ │ │ └── model.obj │ │ ├── e6408c4be8e6502837a346dba83c013b/ │ │ │ └── model.obj │ │ ├── eaf231f17fccb96d81dff5c2e57ad46e/ │ │ │ └── model.obj │ │ └── fe6b3c001a86d844d5767a0de8dd037e/ │ │ └── model.obj │ ├── sphere_V42_F80.off │ ├── surreal_template.picked │ ├── surreal_template.ply │ ├── surreal_template_tpose.picked │ ├── surreal_template_tpose.ply │ ├── surreal_template_v77.ply │ └── synsetoffset2category.txt ├── datasets.py ├── deformer_3d.py ├── losses.py ├── network2.py ├── networks.py ├── optimize_cage.py ├── option.py ├── pymesh/ │ └── pymesh2-0.2.1-cp37-cp37m-linux_x86_64.whl ├── readme.md └── requirements.txt
SYMBOL INDEX (161 symbols across 9 files)
FILE: cage_deformer_3d.py
function test (line 24) | def test(net=None, save_subdir="test"):
function train (line 155) | def train():
FILE: common.py
function is_type (line 13) | def is_type(file, file_ext):
function find_files (line 19) | def find_files(source, file_ext=["txt",]):
function loadInitCage (line 40) | def loadInitCage(templates):
function renderMeshes (line 51) | def renderMeshes(shape_dir, forward=(0.5,0.5,0), pos=(-4,-4,0), up=(0,0,...
function call_proc (line 122) | def call_proc(cmd):
function deform_with_MVC (line 129) | def deform_with_MVC(cage, cage_deformed, cage_face, query, verbose=False):
function load_shapenet_cat (line 153) | def load_shapenet_cat():
function build_dataset (line 164) | def build_dataset(opt):
function log_outputs (line 254) | def log_outputs(opt, step, all_outputs, all_inputs):
function remesh (line 283) | def remesh(path1):
function read_trimesh (line 299) | def read_trimesh(path, normal=False, clean=True):
function crisscross_input (line 316) | def crisscross_input(data):
FILE: datasets.py
function _numpy_chamfer (line 27) | def _numpy_chamfer(P1, P2):
class FileListDataset (line 49) | class FileListDataset(torch.utils.data.Dataset):
method __init__ (line 50) | def __init__(self, opt, normalization_function=lambda x: pc_utils.norm...
method __len__ (line 64) | def __len__(self):
method __getitem__ (line 67) | def __getitem__(self,idx):
method uncollate (line 125) | def uncollate(batch_data):
class CoSegDataset (line 152) | class CoSegDataset(torch.utils.data.Dataset):
method __init__ (line 153) | def __init__(self, root_dir="/home/mnt/points/data/Coseg_Wang/Coseg_Wa...
method normalize (line 193) | def normalize(shape):
method __getitem__ (line 197) | def __getitem__(self, idx):
method uncollate (line 287) | def uncollate(batch_data):
method __len__ (line 303) | def __len__(self):
method render_result (line 310) | def render_result(shape_dir, **kwargs):
function _unwrap_self (line 317) | def _unwrap_self(arg, **kwarg):
class ShapeNetSeg (line 320) | class ShapeNetSeg(torch.utils.data.Dataset):
method __init__ (line 321) | def __init__(self, root_dir="/home/mnt/points/data/ShapeNet/PartSeg_v0...
method _uniformize_sizes (line 463) | def _uniformize_sizes(knn):
method compute_nearest_neighbors_graph (line 487) | def compute_nearest_neighbors_graph(self):
method _getitem (line 503) | def _getitem(self, index):
method preprocess (line 516) | def preprocess(self):
method generate_parts_by_cat (line 557) | def generate_parts_by_cat(self):
method normalize (line 569) | def normalize(x, isV2=False):
method getAnItem (line 582) | def getAnItem(self, index):
method __getitem__ (line 644) | def __getitem__(self, index):
method uncollate (line 670) | def uncollate(batch_data):
method __len__ (line 688) | def __len__(self):
method render_result (line 700) | def render_result(shape_dir, **kwargs):
class ShapeNetV2 (line 709) | class ShapeNetV2(ShapeNetSeg):
method __init__ (line 710) | def __init__(self, root_dir="/home/mnt/points/data/ShapeNet/ShapeNetCo...
method normalize (line 858) | def normalize(x):
method getAnItem (line 862) | def getAnItem(self, index):
method __getitem__ (line 903) | def __getitem__(self, index):
method render_result (line 928) | def render_result(shape_dir, **kwargs):
class PairedSurreal (line 935) | class PairedSurreal(torch.utils.data.Dataset):
method __init__ (line 936) | def __init__(self, root_dir, phase="train", npoints=6890, regular_samp...
method getPairIdx (line 995) | def getPairIdx(self, index):
method __getitem__ (line 1000) | def __getitem__(self, index):
method uncollate (line 1036) | def uncollate(batch_data):
method __len__ (line 1059) | def __len__(self):
method render_result (line 1063) | def render_result(shape_dir, **kwargs):
FILE: deformer_3d.py
function test (line 23) | def test(net=None, subdir="test"):
function test_all (line 108) | def test_all(net=None, subdir="test"):
function train (line 186) | def train():
FILE: losses.py
class AllLosses (line 15) | class AllLosses(torch.nn.Module):
method __init__ (line 16) | def __init__(self, opt):
method forward (line 51) | def forward(self, all_inputs, all_outputs, progress=1.0):
class FaceNormalLoss (line 193) | class FaceNormalLoss(torch.nn.Module):
method __init__ (line 194) | def __init__(self, n_faces=100):
method forward (line 199) | def forward(self, ref_mesh_V, mesh_V, mesh_F):
class GroundingLoss (line 208) | class GroundingLoss(torch.nn.Module):
method __init__ (line 209) | def __init__(self, up_dim=1):
method forward (line 213) | def forward(self, source, deformed):
class ExtPointToNearestFaceDistance (line 229) | class ExtPointToNearestFaceDistance(torch.nn.Module):
method __init__ (line 233) | def __init__(self, min_dist=0.1, reduction="mean"):
method forward (line 238) | def forward(self, mesh_V, mesh_F, points, exterior_flag, mesh_FN=None):
class MVCRegularizer (line 288) | class MVCRegularizer(torch.nn.Module):
method __init__ (line 293) | def __init__(self, alpha=1.0, beta=1.0, threshold=5.0):
method forward (line 299) | def forward(self, weights):
class LabeledChamferDistance (line 314) | class LabeledChamferDistance(torch.nn.Module):
method __init__ (line 323) | def __init__(self, beta=1.0, gamma=1, delta=0):
method forward (line 329) | def forward(self, xyz1, xyz2, label1=None, label2=None):
class SymmetryLoss (line 340) | class SymmetryLoss(torch.nn.Module):
method __init__ (line 350) | def __init__(self, sym_plane=("yz",), NCHW=True):
method get_mirror_multiplier (line 369) | def get_mirror_multiplier(self, dim_id):
method forward (line 377) | def forward(self, xyz):
class ConditionNumberLoss (line 385) | class ConditionNumberLoss(torch.nn.Module):
method __init__ (line 393) | def __init__(self, ball_size, metric, reduction="mean"):
method forward (line 400) | def forward(self, ref_points, points, *args, **kwargs):
class InsideLoss2D (line 432) | class InsideLoss2D(torch.nn.Module):
method __init__ (line 433) | def __init__(self, reduction="mean"):
method forward (line 437) | def forward(self, cage, shape, shape_normals, epsilon=0.01, interpolat...
class InterpolatedCDTriMesh (line 478) | class InterpolatedCDTriMesh(torch.nn.Module):
method __init__ (line 483) | def __init__(self, interpolate_n=4, beta=1.0, gamma=1, delta=0):
method forward (line 501) | def forward(self, cage_v, cage_f, shape, interpolate=True):
class InsideLoss3DTriMesh (line 520) | class InsideLoss3DTriMesh(torch.nn.Module):
method __init__ (line 529) | def __init__(self, reduction="mean", interpolate_n=4):
method forward (line 542) | def forward(self, cage_v, cage_f, shape, shape_vn, epsilon=0.01, inter...
class MeshDihedralAngleLoss (line 583) | class MeshDihedralAngleLoss(torch.nn.Module):
method __init__ (line 591) | def __init__(self, threshold=np.pi/6, edge_points=None, reduction="mea...
method forward (line 597) | def forward(self, vert1, vert2=None, edge_points=None):
class GTNormalLoss (line 621) | class GTNormalLoss(torch.nn.Module):
method __init__ (line 629) | def __init__(self, nn_size=10, NCHW=True):
method forward (line 635) | def forward(self, pred, gt_normals):
class MeshSmoothLoss (line 641) | class MeshSmoothLoss(torch.nn.Module):
method __init__ (line 649) | def __init__(self, metric, use_cot=False, use_norm=False):
method forward (line 657) | def forward(self, vert1, face=None):
class LocalFeatureLoss (line 662) | class LocalFeatureLoss(torch.nn.Module):
method __init__ (line 670) | def __init__(self, nn_size=10, metric=torch.nn.MSELoss("mean"), **kwar...
method forward (line 675) | def forward(self, xyz1, xyz2, **kwargs):
FILE: network2.py
class DeformationSharedMLP (line 19) | class DeformationSharedMLP(nn.Module):
method __init__ (line 22) | def __init__(self,dim=3, residual=True, normalization="none"):
method forward (line 31) | def forward(self, x):
class MLPDeformer (line 40) | class MLPDeformer(nn.Module):
method __init__ (line 41) | def __init__(self, dim, bottleneck_size, npoint, residual=True, normal...
method forward (line 52) | def forward(self, code, template):
class FixedSourceDeformer (line 64) | class FixedSourceDeformer(torch.nn.Module):
method __init__ (line 65) | def __init__(self, opt, dim, num_points, bottleneck_size,
method initialize_buffers (line 103) | def initialize_buffers(self, template_vertices=None, template_faces=No...
method forward (line 134) | def forward(self, target_shape, sample_idx=None, alpha=1.0, cage_only=...
FILE: networks.py
class STN (line 28) | class STN(nn.Module):
method __init__ (line 29) | def __init__(self, num_points = 2500, dim=3):
method forward (line 41) | def forward(self, x):
class PointNet2feat (line 61) | class PointNet2feat(nn.Module):
method __init__ (line 66) | def __init__(self, dim=3, num_points=2048, num_levels=3, bottleneck_si...
method _break_up_pc (line 101) | def _break_up_pc(self, pc):
method forward (line 110) | def forward(self, pointcloud: torch.cuda.FloatTensor, return_all=False):
class PointNetfeat3DCoded (line 135) | class PointNetfeat3DCoded(nn.Module):
method __init__ (line 136) | def __init__(self, npoint=2500, nlatent=1024):
method forward (line 155) | def forward(self, x):
class UnetCageGen (line 167) | class UnetCageGen(nn.Module):
method __init__ (line 179) | def __init__(self, bottleneck_size, dim=3, knn_k=3,
method interpolate_features (line 187) | def interpolate_features(self, query, points, feats, q_normals=None, p...
method forward (line 225) | def forward(self, template, l_xyz, l_features, return_aux=False):
class UnetDeformGen (line 249) | class UnetDeformGen(UnetCageGen):
method interpolate_features (line 260) | def interpolate_features(self, query_feats, feats, points):
method forward (line 292) | def forward(self, template, template_features, l_xyz, l_features, retu...
class PointNetfeat (line 317) | class PointNetfeat(nn.Module):
method __init__ (line 318) | def __init__(self, dim=3, num_points=2500, global_feat=True, trans=Fal...
method forward (line 331) | def forward(self, x):
class PointGenCon (line 352) | class PointGenCon(nn.Module):
method __init__ (line 353) | def __init__(self, bottleneck_size, out_dim, prim_dim, normalization=N...
method forward (line 373) | def forward(self, x, primative):
class MultiFoldPointGen (line 386) | class MultiFoldPointGen(nn.Module):
method __init__ (line 396) | def __init__(self, bottleneck_size, out_dim=3, prim_dim=3,
method forward (line 410) | def forward(self, code, primative):
class MLPDeformer (line 426) | class MLPDeformer(nn.Module):
method __init__ (line 427) | def __init__(self, dim, bottleneck_size, npoint, residual=True, normal...
method forward (line 437) | def forward(self, code, template):
class NetworkFull (line 450) | class NetworkFull(nn.Module):
method __init__ (line 451) | def __init__(self, opt, dim, bottleneck_size,
method set_up_template (line 500) | def set_up_template(self, template_vertices, template_faces):
method forward (line 512) | def forward(self, source_shape, target_shape, alpha=1.0):
FILE: optimize_cage.py
class MyOptions (line 30) | class MyOptions(DeformationOptions):
method initialize (line 31) | def initialize(self, parser):
method parse (line 40) | def parse(self):
function visualize_correspondence (line 48) | def visualize_correspondence(opt, source_shape, source_face, target_shap...
function optimize (line 75) | def optimize(opt):
function test_one (line 272) | def test_one(opt, cage_shape, new_source, new_source_face, new_target, n...
function test_all (line 305) | def test_all(opt, new_cage_shape):
FILE: option.py
class BaseOptions (line 8) | class BaseOptions():
method __init__ (line 14) | def __init__(self):
method initialize (line 18) | def initialize(self, parser):
method gather_options (line 97) | def gather_options(self):
method print_options (line 115) | def print_options(self, opt, output_file=None):
method parse (line 143) | def parse(self):
class DeformationOptions (line 185) | class DeformationOptions(BaseOptions):
method parse (line 189) | def parse(self):
Copy disabled (too large)
Download .json
Condensed preview — 68 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (48,714K chars).
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"path": "data/shapenet_target/92373022868b812fe9aa238b4bc8322e/model.obj",
"chars": 1558006,
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},
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"path": "data/shapenet_target/a09091780fcf3af2e9777a9dc292bbd2/model.obj",
"chars": 2278476,
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},
{
"path": "data/shapenet_target/d4edd167061dac5f52a3901fa1436b1a/model.obj",
"chars": 2033283,
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},
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"chars": 2258139,
"preview": "# Generated with PyMesh\nv -0.166292 0.419887 -0.174606\nv -0.207865 0.419887 -0.133033\nv -0.207865 0.419887 -0.174606\nv -"
},
{
"path": "data/shapenet_target/eaf231f17fccb96d81dff5c2e57ad46e/model.obj",
"chars": 1003638,
"preview": "# Generated with PyMesh\nv -0.150247 -0.338954 0.176673\nv -0.150247 -0.338954 0.180605\nv -0.150093 -0.338954 0.178639\nv -"
},
{
"path": "data/shapenet_target/fe6b3c001a86d844d5767a0de8dd037e/model.obj",
"chars": 531277,
"preview": "# Generated with PyMesh\nv -0.161544 0.406163 -0.189815\nv -0.161544 0.267078 0.181077\nv -0.161544 0.267078 -0.189815\nv -0"
},
{
"path": "data/sphere_V42_F80.off",
"chars": 2003,
"preview": "OFF\n42 80 0\n0 0.8506508 0.5257311 \n0 0.8506508 -0.5257311 \n0 -0.8506508 0.5257311 \n0 -0.8506508 -0.5257311 \n0.8506508 0."
},
{
"path": "data/surreal_template.picked",
"chars": 12209,
"preview": "122 \nPicked 10799 0.000106162 4.7481599999999996e-05 0.9998459999999999 ThumbR -0.345323 -0.37017 0.165608 6191\n"
},
{
"path": "data/surreal_template_tpose.picked",
"chars": 14983,
"preview": "122 \nPicked 10799 0.000106162 4.7481599999999996e-05 0.9998459999999999 ThumbR -0.8187130093574524 0.20360399782"
},
{
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"chars": 635563,
"preview": "ply\nformat ascii 1.0\ncomment VCGLIB generated\nelement vertex 6890\nproperty float x\nproperty float y\nproperty float z\npro"
},
{
"path": "data/synsetoffset2category.txt",
"chars": 269,
"preview": "Airplane\t02691156\nBag\t02773838\nCap\t02954340\nCar\t02958343\nChair\t03001627\nEarphone\t03261776\nGuitar\t03467517\nKnife\t03624134"
},
{
"path": "datasets.py",
"chars": 59990,
"preview": "import torch\nimport os\nfrom glob import glob\nimport itertools\nfrom collections import OrderedDict\nimport numpy as np\nimp"
},
{
"path": "deformer_3d.py",
"chars": 19337,
"preview": "from __future__ import print_function\nfrom pprint import pprint\nimport shutil\nimport datetime\nfrom glob import glob\nimpo"
},
{
"path": "losses.py",
"chars": 33716,
"preview": "from collections import defaultdict\nimport numpy as np\nimport torch\nfrom pytorch_points.network.operations import faiss_"
},
{
"path": "network2.py",
"chars": 10461,
"preview": "from __future__ import print_function\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom pytorch_points.misc imp"
},
{
"path": "networks.py",
"chars": 28476,
"preview": "from __future__ import print_function\nimport warnings\nfrom pprint import pprint\nimport numpy as np\nimport torch\nimport t"
},
{
"path": "optimize_cage.py",
"chars": 20963,
"preview": "\"\"\" Optimize the initial cage for a new source shape \"\"\"\nfrom __future__ import print_function\nfrom pprint import pprint"
},
{
"path": "option.py",
"chars": 12664,
"preview": "import argparse\nimport datetime\nimport os\nimport io\nfrom glob import glob\nimport itertools\n\nclass BaseOptions():\n \"\"\""
},
{
"path": "readme.md",
"chars": 6287,
"preview": "# Neural Cages for Detail-Preserving 3D Deformations\n[[project page][project-page]][[pdf][pdf]][[supplemental][supp-pdf]"
},
{
"path": "requirements.txt",
"chars": 22,
"preview": "pymesh2==0.2.1\njoblib\n"
}
]
// ... and 9 more files (download for full content)
About this extraction
This page contains the full source code of the yifita/deep_cage GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 68 files (135.1 MB), approximately 11.8M tokens, and a symbol index with 161 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.