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Yiwen Chen 2024-06-17 00:07:35 +08:00
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# Default ignored files
/shelf/
/workspace.xml
# Editor-based HTTP Client requests
/httpRequests/
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml

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<component name="InspectionProjectProfileManager">
<settings>
<option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" />
</settings>
</component>

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
<component name="SshConsoleOptionsProvider">
<option name="myEncoding" value="UTF-8" />
<option name="myConnectionType" value="NONE" />
<option name="myConnectionId" value="" />
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.idea/modules.xml Normal file
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
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<mapping directory="$PROJECT_DIR$" vcs="Git" />
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S-Lab License 1.0
Copyright 2023 S-Lab
Redistribution and use for non-commercial purpose in source and
binary forms, with or without modification, are permitted provided
that the following conditions are met:
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please contact the contributor(s) of the work.

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GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
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The hypothetical commands `show w' and `show c' should show the appropriate
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You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
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<https://www.gnu.org/licenses/why-not-lgpl.html>.

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MeshAnything/miche/encode.py Normal file
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# -*- coding: utf-8 -*-
import argparse
from omegaconf import OmegaConf
import numpy as np
import torch
from .michelangelo.utils.misc import instantiate_from_config
def load_surface(fp):
with np.load(fp) as input_pc:
surface = input_pc['points']
normal = input_pc['normals']
rng = np.random.default_rng()
ind = rng.choice(surface.shape[0], 4096, replace=False)
surface = torch.FloatTensor(surface[ind])
normal = torch.FloatTensor(normal[ind])
surface = torch.cat([surface, normal], dim=-1).unsqueeze(0).cuda()
return surface
def reconstruction(args, model, bounds=(-1.25, -1.25, -1.25, 1.25, 1.25, 1.25), octree_depth=7, num_chunks=10000):
surface = load_surface(args.pointcloud_path)
# old_surface = surface.clone()
# surface[0,:,0]*=-1
# surface[0,:,1]*=-1
surface[0,:,2]*=-1
# encoding
shape_embed, shape_latents = model.model.encode_shape_embed(surface, return_latents=True)
shape_zq, posterior = model.model.shape_model.encode_kl_embed(shape_latents)
# decoding
latents = model.model.shape_model.decode(shape_zq)
# geometric_func = partial(model.model.shape_model.query_geometry, latents=latents)
return 0
def load_model(ckpt_path="MeshAnything/miche/shapevae-256.ckpt"):
model_config = OmegaConf.load("MeshAnything/miche/shapevae-256.yaml")
# print(model_config)
if hasattr(model_config, "model"):
model_config = model_config.model
model = instantiate_from_config(model_config, ckpt_path=ckpt_path)
model = model.cuda()
model = model.eval()
return model
if __name__ == "__main__":
'''
1. Reconstruct point cloud
2. Image-conditioned generation
3. Text-conditioned generation
'''
parser = argparse.ArgumentParser()
parser.add_argument("--config_path", type=str, required=True)
parser.add_argument("--ckpt_path", type=str, required=True)
parser.add_argument("--pointcloud_path", type=str, default='./example_data/surface.npz', help='Path to the input point cloud')
parser.add_argument("--image_path", type=str, help='Path to the input image')
parser.add_argument("--text", type=str, help='Input text within a format: A 3D model of motorcar; Porsche 911.')
parser.add_argument("--output_dir", type=str, default='./output')
parser.add_argument("-s", "--seed", type=int, default=0)
args = parser.parse_args()
print(f'-----------------------------------------------------------------------------')
print(f'>>> Output directory: {args.output_dir}')
print(f'-----------------------------------------------------------------------------')
reconstruction(args, load_model(args))

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MeshAnything/miche/michelangelo/__init__.py Normal file
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# -*- coding: utf-8 -*-

1
MeshAnything/miche/michelangelo/data/__init__.py Normal file
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# -*- coding: utf-8 -*-

69
MeshAnything/miche/michelangelo/data/templates.json Normal file
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{
"shape": [
"a point cloud model of {}.",
"There is a {} in the scene.",
"There is the {} in the scene.",
"a photo of a {} in the scene.",
"a photo of the {} in the scene.",
"a photo of one {} in the scene.",
"itap of a {}.",
"itap of my {}.",
"itap of the {}.",
"a photo of a {}.",
"a photo of my {}.",
"a photo of the {}.",
"a photo of one {}.",
"a photo of many {}.",
"a good photo of a {}.",
"a good photo of the {}.",
"a bad photo of a {}.",
"a bad photo of the {}.",
"a photo of a nice {}.",
"a photo of the nice {}.",
"a photo of a cool {}.",
"a photo of the cool {}.",
"a photo of a weird {}.",
"a photo of the weird {}.",
"a photo of a small {}.",
"a photo of the small {}.",
"a photo of a large {}.",
"a photo of the large {}.",
"a photo of a clean {}.",
"a photo of the clean {}.",
"a photo of a dirty {}.",
"a photo of the dirty {}.",
"a bright photo of a {}.",
"a bright photo of the {}.",
"a dark photo of a {}.",
"a dark photo of the {}.",
"a photo of a hard to see {}.",
"a photo of the hard to see {}.",
"a low resolution photo of a {}.",
"a low resolution photo of the {}.",
"a cropped photo of a {}.",
"a cropped photo of the {}.",
"a close-up photo of a {}.",
"a close-up photo of the {}.",
"a jpeg corrupted photo of a {}.",
"a jpeg corrupted photo of the {}.",
"a blurry photo of a {}.",
"a blurry photo of the {}.",
"a pixelated photo of a {}.",
"a pixelated photo of the {}.",
"a black and white photo of the {}.",
"a black and white photo of a {}",
"a plastic {}.",
"the plastic {}.",
"a toy {}.",
"the toy {}.",
"a plushie {}.",
"the plushie {}.",
"a cartoon {}.",
"the cartoon {}.",
"an embroidered {}.",
"the embroidered {}.",
"a painting of the {}.",
"a painting of a {}."
]
}

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MeshAnything/miche/michelangelo/data/transforms.py Normal file
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# -*- coding: utf-8 -*-
import os
import time
import numpy as np
import warnings
import random
from omegaconf.listconfig import ListConfig
from webdataset import pipelinefilter
import torch
import torchvision.transforms.functional as TVF
from torchvision.transforms import InterpolationMode
from torchvision.transforms.transforms import _interpolation_modes_from_int
from typing import Sequence
from MeshAnything.miche.michelangelo.utils import instantiate_from_config
def _uid_buffer_pick(buf_dict, rng):
uid_keys = list(buf_dict.keys())
selected_uid = rng.choice(uid_keys)
buf = buf_dict[selected_uid]
k = rng.randint(0, len(buf) - 1)
sample = buf[k]
buf[k] = buf[-1]
buf.pop()
if len(buf) == 0:
del buf_dict[selected_uid]
return sample
def _add_to_buf_dict(buf_dict, sample):
key = sample["__key__"]
uid, uid_sample_id = key.split("_")
if uid not in buf_dict:
buf_dict[uid] = []
buf_dict[uid].append(sample)
return buf_dict
def _uid_shuffle(data, bufsize=1000, initial=100, rng=None, handler=None):
"""Shuffle the data in the stream.
This uses a buffer of size `bufsize`. Shuffling at
startup is less random; this is traded off against
yielding samples quickly.
data: iterator
bufsize: buffer size for shuffling
returns: iterator
rng: either random module or random.Random instance
"""
if rng is None:
rng = random.Random(int((os.getpid() + time.time()) * 1e9))
initial = min(initial, bufsize)
buf_dict = dict()
current_samples = 0
for sample in data:
_add_to_buf_dict(buf_dict, sample)
current_samples += 1
if current_samples < bufsize:
try:
_add_to_buf_dict(buf_dict, next(data)) # skipcq: PYL-R1708
current_samples += 1
except StopIteration:
pass
if current_samples >= initial:
current_samples -= 1
yield _uid_buffer_pick(buf_dict, rng)
while current_samples > 0:
current_samples -= 1
yield _uid_buffer_pick(buf_dict, rng)
uid_shuffle = pipelinefilter(_uid_shuffle)
class RandomSample(object):
def __init__(self,
num_volume_samples: int = 1024,
num_near_samples: int = 1024):
super().__init__()
self.num_volume_samples = num_volume_samples
self.num_near_samples = num_near_samples
def __call__(self, sample):
rng = np.random.default_rng()
# 1. sample surface input
total_surface = sample["surface"]
ind = rng.choice(total_surface.shape[0], replace=False)
surface = total_surface[ind]
# 2. sample volume/near geometric points
vol_points = sample["vol_points"]
vol_label = sample["vol_label"]
near_points = sample["near_points"]
near_label = sample["near_label"]
ind = rng.choice(vol_points.shape[0], self.num_volume_samples, replace=False)
vol_points = vol_points[ind]
vol_label = vol_label[ind]
vol_points_labels = np.concatenate([vol_points, vol_label[:, np.newaxis]], axis=1)
ind = rng.choice(near_points.shape[0], self.num_near_samples, replace=False)
near_points = near_points[ind]
near_label = near_label[ind]
near_points_labels = np.concatenate([near_points, near_label[:, np.newaxis]], axis=1)
# concat sampled volume and near points
geo_points = np.concatenate([vol_points_labels, near_points_labels], axis=0)
sample = {
"surface": surface,
"geo_points": geo_points
}
return sample
class SplitRandomSample(object):
def __init__(self,
use_surface_sample: bool = False,
num_surface_samples: int = 4096,
num_volume_samples: int = 1024,
num_near_samples: int = 1024):
super().__init__()
self.use_surface_sample = use_surface_sample
self.num_surface_samples = num_surface_samples
self.num_volume_samples = num_volume_samples
self.num_near_samples = num_near_samples
def __call__(self, sample):
rng = np.random.default_rng()
# 1. sample surface input
surface = sample["surface"]
if self.use_surface_sample:
replace = surface.shape[0] < self.num_surface_samples
ind = rng.choice(surface.shape[0], self.num_surface_samples, replace=replace)
surface = surface[ind]
# 2. sample volume/near geometric points
vol_points = sample["vol_points"]
vol_label = sample["vol_label"]
near_points = sample["near_points"]
near_label = sample["near_label"]
ind = rng.choice(vol_points.shape[0], self.num_volume_samples, replace=False)
vol_points = vol_points[ind]
vol_label = vol_label[ind]
vol_points_labels = np.concatenate([vol_points, vol_label[:, np.newaxis]], axis=1)
ind = rng.choice(near_points.shape[0], self.num_near_samples, replace=False)
near_points = near_points[ind]
near_label = near_label[ind]
near_points_labels = np.concatenate([near_points, near_label[:, np.newaxis]], axis=1)
# concat sampled volume and near points
geo_points = np.concatenate([vol_points_labels, near_points_labels], axis=0)
sample = {
"surface": surface,
"geo_points": geo_points
}
return sample
class FeatureSelection(object):
VALID_SURFACE_FEATURE_DIMS = {
"none": [0, 1, 2], # xyz
"watertight_normal": [0, 1, 2, 3, 4, 5], # xyz, normal
"normal": [0, 1, 2, 6, 7, 8]
}
def __init__(self, surface_feature_type: str):
self.surface_feature_type = surface_feature_type
self.surface_dims = self.VALID_SURFACE_FEATURE_DIMS[surface_feature_type]
def __call__(self, sample):
sample["surface"] = sample["surface"][:, self.surface_dims]
return sample
class AxisScaleTransform(object):
def __init__(self, interval=(0.75, 1.25), jitter=True, jitter_scale=0.005):
assert isinstance(interval, (tuple, list, ListConfig))
self.interval = interval
self.min_val = interval[0]
self.max_val = interval[1]
self.inter_size = interval[1] - interval[0]
self.jitter = jitter
self.jitter_scale = jitter_scale
def __call__(self, sample):
surface = sample["surface"][..., 0:3]
geo_points = sample["geo_points"][..., 0:3]
scaling = torch.rand(1, 3) * self.inter_size + self.min_val
# print(scaling)
surface = surface * scaling
geo_points = geo_points * scaling
scale = (1 / torch.abs(surface).max().item()) * 0.999999
surface *= scale
geo_points *= scale
if self.jitter:
surface += self.jitter_scale * torch.randn_like(surface)
surface.clamp_(min=-1.015, max=1.015)
sample["surface"][..., 0:3] = surface
sample["geo_points"][..., 0:3] = geo_points
return sample
class ToTensor(object):
def __init__(self, tensor_keys=("surface", "geo_points", "tex_points")):
self.tensor_keys = tensor_keys
def __call__(self, sample):
for key in self.tensor_keys:
if key not in sample:
continue
sample[key] = torch.tensor(sample[key], dtype=torch.float32)
return sample
class AxisScale(object):
def __init__(self, interval=(0.75, 1.25), jitter=True, jitter_scale=0.005):
assert isinstance(interval, (tuple, list, ListConfig))
self.interval = interval
self.jitter = jitter
self.jitter_scale = jitter_scale
def __call__(self, surface, *args):
scaling = torch.rand(1, 3) * 0.5 + 0.75
# print(scaling)
surface = surface * scaling
scale = (1 / torch.abs(surface).max().item()) * 0.999999
surface *= scale
args_outputs = []
for _arg in args:
_arg = _arg * scaling * scale
args_outputs.append(_arg)
if self.jitter:
surface += self.jitter_scale * torch.randn_like(surface)
surface.clamp_(min=-1, max=1)
if len(args) == 0:
return surface
else:
return surface, *args_outputs
class RandomResize(torch.nn.Module):
"""Apply randomly Resize with a given probability."""
def __init__(
self,
size,
resize_radio=(0.5, 1),
allow_resize_interpolations=(InterpolationMode.BICUBIC, InterpolationMode.BILINEAR, InterpolationMode.BILINEAR),
interpolation=InterpolationMode.BICUBIC,
max_size=None,
antialias=None,
):
super().__init__()
if not isinstance(size, (int, Sequence)):
raise TypeError(f"Size should be int or sequence. Got {type(size)}")
if isinstance(size, Sequence) and len(size) not in (1, 2):
raise ValueError("If size is a sequence, it should have 1 or 2 values")
self.size = size
self.max_size = max_size
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
"Please use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.interpolation = interpolation
self.antialias = antialias
self.resize_radio = resize_radio
self.allow_resize_interpolations = allow_resize_interpolations
def random_resize_params(self):
radio = torch.rand(1) * (self.resize_radio[1] - self.resize_radio[0]) + self.resize_radio[0]
if isinstance(self.size, int):
size = int(self.size * radio)
elif isinstance(self.size, Sequence):
size = list(self.size)
size = (int(size[0] * radio), int(size[1] * radio))
else:
raise RuntimeError()
interpolation = self.allow_resize_interpolations[
torch.randint(low=0, high=len(self.allow_resize_interpolations), size=(1,))
]
return size, interpolation
def forward(self, img):
size, interpolation = self.random_resize_params()
img = TVF.resize(img, size, interpolation, self.max_size, self.antialias)
img = TVF.resize(img, self.size, self.interpolation, self.max_size, self.antialias)
return img
def __repr__(self) -> str:
detail = f"(size={self.size}, interpolation={self.interpolation.value},"
detail += f"max_size={self.max_size}, antialias={self.antialias}), resize_radio={self.resize_radio}"
return f"{self.__class__.__name__}{detail}"
class Compose(object):
"""Composes several transforms together. This transform does not support torchscript.
Please, see the note below.
Args:
transforms (list of ``Transform`` objects): list of transforms to compose.
Example:
>>> transforms.Compose([
>>> transforms.CenterCrop(10),
>>> transforms.ToTensor(),
>>> ])
.. note::
In order to script the transformations, please use ``torch.nn.Sequential`` as below.
>>> transforms = torch.nn.Sequential(
>>> transforms.CenterCrop(10),
>>> transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
>>> )
>>> scripted_transforms = torch.jit.script(transforms)
Make sure to use only scriptable transformations, i.e. that work with ``torch.Tensor``, does not require
`lambda` functions or ``PIL.Image``.
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, *args):
for t in self.transforms:
args = t(*args)
return args
def __repr__(self):
format_string = self.__class__.__name__ + '('
for t in self.transforms:
format_string += '\n'
format_string += ' {0}'.format(t)
format_string += '\n)'
return format_string
def identity(*args, **kwargs):
if len(args) == 1:
return args[0]
else:
return args
def build_transforms(cfg):
if cfg is None:
return identity
transforms = []
for transform_name, cfg_instance in cfg.items():
transform_instance = instantiate_from_config(cfg_instance)
transforms.append(transform_instance)
print(f"Build transform: {transform_instance}")
transforms = Compose(transforms)
return transforms

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# -*- coding: utf-8 -*-
import torch
import numpy as np
def worker_init_fn(_):
worker_info = torch.utils.data.get_worker_info()
worker_id = worker_info.id
# dataset = worker_info.dataset
# split_size = dataset.num_records // worker_info.num_workers
# # reset num_records to the true number to retain reliable length information
# dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size]
# current_id = np.random.choice(len(np.random.get_state()[1]), 1)
# return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
return np.random.seed(np.random.get_state()[1][0] + worker_id)
def collation_fn(samples, combine_tensors=True, combine_scalars=True):
"""
Args:
samples (list[dict]):
combine_tensors:
combine_scalars:
Returns:
"""
result = {}
keys = samples[0].keys()
for key in keys:
result[key] = []
for sample in samples:
for key in keys:
val = sample[key]
result[key].append(val)
for key in keys:
val_list = result[key]
if isinstance(val_list[0], (int, float)):
if combine_scalars:
result[key] = np.array(result[key])
elif isinstance(val_list[0], torch.Tensor):
if combine_tensors:
result[key] = torch.stack(val_list)
elif isinstance(val_list[0], np.ndarray):
if combine_tensors:
result[key] = np.stack(val_list)
return result

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MeshAnything/miche/michelangelo/graphics/__init__.py Normal file
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# -*- coding: utf-8 -*-

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MeshAnything/miche/michelangelo/graphics/primitives/__init__.py Normal file
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# -*- coding: utf-8 -*-
from .volume import generate_dense_grid_points
from .mesh import (
MeshOutput,
save_obj,
savemeshtes2
)

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MeshAnything/miche/michelangelo/graphics/primitives/mesh.py Normal file
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# -*- coding: utf-8 -*-
import os
import cv2
import numpy as np
import PIL.Image
from typing import Optional
import trimesh
def save_obj(pointnp_px3, facenp_fx3, fname):
fid = open(fname, "w")
write_str = ""
for pidx, p in enumerate(pointnp_px3):
pp = p
write_str += "v %f %f %f\n" % (pp[0], pp[1], pp[2])
for i, f in enumerate(facenp_fx3):
f1 = f + 1
write_str += "f %d %d %d\n" % (f1[0], f1[1], f1[2])
fid.write(write_str)
fid.close()
return
def savemeshtes2(pointnp_px3, tcoords_px2, facenp_fx3, facetex_fx3, tex_map, fname):
fol, na = os.path.split(fname)
na, _ = os.path.splitext(na)
matname = "%s/%s.mtl" % (fol, na)
fid = open(matname, "w")
fid.write("newmtl material_0\n")
fid.write("Kd 1 1 1\n")
fid.write("Ka 0 0 0\n")
fid.write("Ks 0.4 0.4 0.4\n")
fid.write("Ns 10\n")
fid.write("illum 2\n")
fid.write("map_Kd %s.png\n" % na)
fid.close()
####
fid = open(fname, "w")
fid.write("mtllib %s.mtl\n" % na)
for pidx, p in enumerate(pointnp_px3):
pp = p
fid.write("v %f %f %f\n" % (pp[0], pp[1], pp[2]))
for pidx, p in enumerate(tcoords_px2):
pp = p
fid.write("vt %f %f\n" % (pp[0], pp[1]))
fid.write("usemtl material_0\n")
for i, f in enumerate(facenp_fx3):
f1 = f + 1
f2 = facetex_fx3[i] + 1
fid.write("f %d/%d %d/%d %d/%d\n" % (f1[0], f2[0], f1[1], f2[1], f1[2], f2[2]))
fid.close()
PIL.Image.fromarray(np.ascontiguousarray(tex_map), "RGB").save(
os.path.join(fol, "%s.png" % na))
return
class MeshOutput(object):
def __init__(self,
mesh_v: np.ndarray,
mesh_f: np.ndarray,
vertex_colors: Optional[np.ndarray] = None,
uvs: Optional[np.ndarray] = None,
mesh_tex_idx: Optional[np.ndarray] = None,
tex_map: Optional[np.ndarray] = None):
self.mesh_v = mesh_v
self.mesh_f = mesh_f
self.vertex_colors = vertex_colors
self.uvs = uvs
self.mesh_tex_idx = mesh_tex_idx
self.tex_map = tex_map
def contain_uv_texture(self):
return (self.uvs is not None) and (self.mesh_tex_idx is not None) and (self.tex_map is not None)
def contain_vertex_colors(self):
return self.vertex_colors is not None
def export(self, fname):
if self.contain_uv_texture():
savemeshtes2(
self.mesh_v,
self.uvs,
self.mesh_f,
self.mesh_tex_idx,
self.tex_map,
fname
)
elif self.contain_vertex_colors():
mesh_obj = trimesh.Trimesh(vertices=self.mesh_v, faces=self.mesh_f, vertex_colors=self.vertex_colors)
mesh_obj.export(fname)
else:
save_obj(
self.mesh_v,
self.mesh_f,
fname
)

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MeshAnything/miche/michelangelo/graphics/primitives/volume.py Normal file
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# -*- coding: utf-8 -*-
import numpy as np
def generate_dense_grid_points(bbox_min: np.ndarray,
bbox_max: np.ndarray,
octree_depth: int,
indexing: str = "ij"):
length = bbox_max - bbox_min
num_cells = np.exp2(octree_depth)
x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32)
y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32)
z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32)
[xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing)
xyz = np.stack((xs, ys, zs), axis=-1)
xyz = xyz.reshape(-1, 3)
grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1]
return xyz, grid_size, length

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MeshAnything/miche/michelangelo/models/__init__.py Normal file
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# -*- coding: utf-8 -*-

1
MeshAnything/miche/michelangelo/models/asl_diffusion/__init__.py Normal file
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# -*- coding: utf-8 -*-

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MeshAnything/miche/michelangelo/models/asl_diffusion/asl_diffuser_pl_module.py Normal file
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# -*- coding: utf-8 -*-
from omegaconf import DictConfig
from typing import List, Tuple, Dict, Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import lr_scheduler
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_only
from einops import rearrange
from diffusers.schedulers import (
DDPMScheduler,
DDIMScheduler,
KarrasVeScheduler,
DPMSolverMultistepScheduler
)
from MeshAnything.miche.michelangelo.utils import instantiate_from_config
# from MeshAnything.miche.michelangelo.models.tsal.tsal_base import ShapeAsLatentPLModule
from MeshAnything.miche.michelangelo.models.tsal.tsal_base import AlignedShapeAsLatentPLModule
from MeshAnything.miche.michelangelo.models.asl_diffusion.inference_utils import ddim_sample
SchedulerType = Union[DDIMScheduler, KarrasVeScheduler, DPMSolverMultistepScheduler]
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
class ASLDiffuser(pl.LightningModule):
first_stage_model: Optional[AlignedShapeAsLatentPLModule]
# cond_stage_model: Optional[Union[nn.Module, pl.LightningModule]]
model: nn.Module
def __init__(self, *,
first_stage_config,
denoiser_cfg,
scheduler_cfg,
optimizer_cfg,
loss_cfg,
first_stage_key: str = "surface",
cond_stage_key: str = "image",
cond_stage_trainable: bool = True,
scale_by_std: bool = False,
z_scale_factor: float = 1.0,
ckpt_path: Optional[str] = None,
ignore_keys: Union[Tuple[str], List[str]] = ()):
super().__init__()
self.first_stage_key = first_stage_key
self.cond_stage_key = cond_stage_key
self.cond_stage_trainable = cond_stage_trainable
# 1. initialize first stage.
# Note: the condition model contained in the first stage model.
self.first_stage_config = first_stage_config
self.first_stage_model = None
# self.instantiate_first_stage(first_stage_config)
# 2. initialize conditional stage
# self.instantiate_cond_stage(cond_stage_config)
self.cond_stage_model = {
"image": self.encode_image,
"image_unconditional_embedding": self.empty_img_cond,
"text": self.encode_text,
"text_unconditional_embedding": self.empty_text_cond,
"surface": self.encode_surface,
"surface_unconditional_embedding": self.empty_surface_cond,
}
# 3. diffusion model
self.model = instantiate_from_config(
denoiser_cfg, device=None, dtype=None
)
self.optimizer_cfg = optimizer_cfg
# 4. scheduling strategy
self.scheduler_cfg = scheduler_cfg
self.noise_scheduler: DDPMScheduler = instantiate_from_config(scheduler_cfg.noise)
self.denoise_scheduler: SchedulerType = instantiate_from_config(scheduler_cfg.denoise)
# 5. loss configures
self.loss_cfg = loss_cfg
self.scale_by_std = scale_by_std
if scale_by_std:
self.register_buffer("z_scale_factor", torch.tensor(z_scale_factor))
else:
self.z_scale_factor = z_scale_factor
self.ckpt_path = ckpt_path
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
def instantiate_first_stage(self, config):
model = instantiate_from_config(config)
self.first_stage_model = model.eval()
self.first_stage_model.train = disabled_train
for param in self.first_stage_model.parameters():
param.requires_grad = False
self.first_stage_model = self.first_stage_model.to(self.device)
# def instantiate_cond_stage(self, config):
# if not self.cond_stage_trainable:
# if config == "__is_first_stage__":
# print("Using first stage also as cond stage.")
# self.cond_stage_model = self.first_stage_model
# elif config == "__is_unconditional__":
# print(f"Training {self.__class__.__name__} as an unconditional model.")
# self.cond_stage_model = None
# # self.be_unconditional = True
# else:
# model = instantiate_from_config(config)
# self.cond_stage_model = model.eval()
# self.cond_stage_model.train = disabled_train
# for param in self.cond_stage_model.parameters():
# param.requires_grad = False
# else:
# assert config != "__is_first_stage__"
# assert config != "__is_unconditional__"
# model = instantiate_from_config(config)
# self.cond_stage_model = model
def init_from_ckpt(self, path, ignore_keys=()):
state_dict = torch.load(path, map_location="cpu")["state_dict"]
keys = list(state_dict.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del state_dict[k]
missing, unexpected = self.load_state_dict(state_dict, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
print(f"Unexpected Keys: {unexpected}")
@property
def zero_rank(self):
if self._trainer:
zero_rank = self.trainer.local_rank == 0
else:
zero_rank = True
return zero_rank
def configure_optimizers(self) -> Tuple[List, List]:
lr = self.learning_rate
trainable_parameters = list(self.model.parameters())
# if the conditional encoder is trainable
# if self.cond_stage_trainable:
# conditioner_params = [p for p in self.cond_stage_model.parameters() if p.requires_grad]
# trainable_parameters += conditioner_params
# print(f"number of trainable conditional parameters: {len(conditioner_params)}.")
if self.optimizer_cfg is None:
optimizers = [torch.optim.AdamW(trainable_parameters, lr=lr, betas=(0.9, 0.99), weight_decay=1e-3)]
schedulers = []
else:
optimizer = instantiate_from_config(self.optimizer_cfg.optimizer, params=trainable_parameters)
scheduler_func = instantiate_from_config(
self.optimizer_cfg.scheduler,
max_decay_steps=self.trainer.max_steps,
lr_max=lr
)
scheduler = {
"scheduler": lr_scheduler.LambdaLR(optimizer, lr_lambda=scheduler_func.schedule),
"interval": "step",
"frequency": 1
}
optimizers = [optimizer]
schedulers = [scheduler]
return optimizers, schedulers
@torch.no_grad()
def encode_text(self, text):
b = text.shape[0]
text_tokens = rearrange(text, "b t l -> (b t) l")
text_embed = self.first_stage_model.model.encode_text_embed(text_tokens)
text_embed = rearrange(text_embed, "(b t) d -> b t d", b=b)
text_embed = text_embed.mean(dim=1)
text_embed = text_embed / text_embed.norm(dim=-1, keepdim=True)
return text_embed
@torch.no_grad()
def encode_image(self, img):
return self.first_stage_model.model.encode_image_embed(img)
@torch.no_grad()
def encode_surface(self, surface):
return self.first_stage_model.model.encode_shape_embed(surface, return_latents=False)
@torch.no_grad()
def empty_text_cond(self, cond):
return torch.zeros_like(cond, device=cond.device)
@torch.no_grad()
def empty_img_cond(self, cond):
return torch.zeros_like(cond, device=cond.device)
@torch.no_grad()
def empty_surface_cond(self, cond):
return torch.zeros_like(cond, device=cond.device)
@torch.no_grad()
def encode_first_stage(self, surface: torch.FloatTensor, sample_posterior=True):
z_q = self.first_stage_model.encode(surface, sample_posterior)
z_q = self.z_scale_factor * z_q
return z_q
@torch.no_grad()
def decode_first_stage(self, z_q: torch.FloatTensor, **kwargs):
z_q = 1. / self.z_scale_factor * z_q
latents = self.first_stage_model.decode(z_q, **kwargs)
return latents
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx):
# only for very first batch
if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 \
and batch_idx == 0 and self.ckpt_path is None:
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
z_q = self.encode_first_stage(batch[self.first_stage_key])
z = z_q.detach()
del self.z_scale_factor
self.register_buffer("z_scale_factor", 1. / z.flatten().std())
print(f"setting self.z_scale_factor to {self.z_scale_factor}")
print("### USING STD-RESCALING ###")
def compute_loss(self, model_outputs, split):
"""
Args:
model_outputs (dict):
- x_0:
- noise:
- noise_prior:
- noise_pred:
- noise_pred_prior:
split (str):
Returns:
"""
pred = model_outputs["pred"]
if self.noise_scheduler.prediction_type == "epsilon":
target = model_outputs["noise"]
elif self.noise_scheduler.prediction_type == "sample":
target = model_outputs["x_0"]
else:
raise NotImplementedError(f"Prediction Type: {self.noise_scheduler.prediction_type} not yet supported.")
if self.loss_cfg.loss_type == "l1":
simple = F.l1_loss(pred, target, reduction="mean")
elif self.loss_cfg.loss_type in ["mse", "l2"]:
simple = F.mse_loss(pred, target, reduction="mean")
else:
raise NotImplementedError(f"Loss Type: {self.loss_cfg.loss_type} not yet supported.")
total_loss = simple
loss_dict = {
f"{split}/total_loss": total_loss.clone().detach(),
f"{split}/simple": simple.detach(),
}
return total_loss, loss_dict
def forward(self, batch):
"""
Args:
batch:
Returns:
"""
if self.first_stage_model is None:
self.instantiate_first_stage(self.first_stage_config)
latents = self.encode_first_stage(batch[self.first_stage_key])
# conditions = self.cond_stage_model.encode(batch[self.cond_stage_key])
conditions = self.cond_stage_model[self.cond_stage_key](batch[self.cond_stage_key]).unsqueeze(1)
mask = torch.rand((len(conditions), 1, 1), device=conditions.device, dtype=conditions.dtype) >= 0.1
conditions = conditions * mask.to(conditions)
# Sample noise that we"ll add to the latents
# [batch_size, n_token, latent_dim]
noise = torch.randn_like(latents)
bs = latents.shape[0]
# Sample a random timestep for each motion
timesteps = torch.randint(
0,
self.noise_scheduler.config.num_train_timesteps,
(bs,),
device=latents.device,
)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
noisy_z = self.noise_scheduler.add_noise(latents, noise, timesteps)
# diffusion model forward
noise_pred = self.model(noisy_z, timesteps, conditions)
diffusion_outputs = {
"x_0": noisy_z,
"noise": noise,
"pred": noise_pred
}
return diffusion_outputs
def training_step(self, batch: Dict[str, Union[torch.FloatTensor, List[str]]],
batch_idx: int, optimizer_idx: int = 0) -> torch.FloatTensor:
"""
Args:
batch (dict): the batch sample, and it contains:
- surface (torch.FloatTensor):
- image (torch.FloatTensor): if provide, [bs, 3, h, w], item range [0, 1]
- depth (torch.FloatTensor): if provide, [bs, 1, h, w], item range [-1, 1]
- normal (torch.FloatTensor): if provide, [bs, 3, h, w], item range [-1, 1]
- text (list of str):
batch_idx (int):
optimizer_idx (int):
Returns:
loss (torch.FloatTensor):
"""
diffusion_outputs = self(batch)
loss, loss_dict = self.compute_loss(diffusion_outputs, "train")
self.log_dict(loss_dict, prog_bar=True, logger=True, sync_dist=False, rank_zero_only=True)
return loss
def validation_step(self, batch: Dict[str, torch.FloatTensor],
batch_idx: int, optimizer_idx: int = 0) -> torch.FloatTensor:
"""
Args:
batch (dict): the batch sample, and it contains:
- surface_pc (torch.FloatTensor): [n_pts, 4]
- surface_feats (torch.FloatTensor): [n_pts, c]
- text (list of str):
batch_idx (int):
optimizer_idx (int):
Returns:
loss (torch.FloatTensor):
"""
diffusion_outputs = self(batch)
loss, loss_dict = self.compute_loss(diffusion_outputs, "val")
self.log_dict(loss_dict, prog_bar=True, logger=True, sync_dist=False, rank_zero_only=True)
return loss
@torch.no_grad()
def sample(self,
batch: Dict[str, Union[torch.FloatTensor, List[str]]],
sample_times: int = 1,
steps: Optional[int] = None,
guidance_scale: Optional[float] = None,
eta: float = 0.0,
return_intermediates: bool = False, **kwargs):
if self.first_stage_model is None:
self.instantiate_first_stage(self.first_stage_config)
if steps is None:
steps = self.scheduler_cfg.num_inference_steps
if guidance_scale is None:
guidance_scale = self.scheduler_cfg.guidance_scale
do_classifier_free_guidance = guidance_scale > 0
# conditional encode
xc = batch[self.cond_stage_key]
# cond = self.cond_stage_model[self.cond_stage_key](xc)
cond = self.cond_stage_model[self.cond_stage_key](xc).unsqueeze(1)
if do_classifier_free_guidance:
"""
Note: There are two kinds of uncond for text.
1: using "" as uncond text; (in SAL diffusion)
2: zeros_like(cond) as uncond text; (in MDM)
"""
# un_cond = self.cond_stage_model.unconditional_embedding(batch_size=len(xc))
un_cond = self.cond_stage_model[f"{self.cond_stage_key}_unconditional_embedding"](cond)
# un_cond = torch.zeros_like(cond, device=cond.device)
cond = torch.cat([un_cond, cond], dim=0)
outputs = []
latents = None
if not return_intermediates:
for _ in range(sample_times):
sample_loop = ddim_sample(
self.denoise_scheduler,
self.model,
shape=self.first_stage_model.latent_shape,
cond=cond,
steps=steps,
guidance_scale=guidance_scale,
do_classifier_free_guidance=do_classifier_free_guidance,
device=self.device,
eta=eta,
disable_prog=not self.zero_rank
)
for sample, t in sample_loop:
latents = sample
outputs.append(self.decode_first_stage(latents, **kwargs))
else:
sample_loop = ddim_sample(
self.denoise_scheduler,
self.model,
shape=self.first_stage_model.latent_shape,
cond=cond,
steps=steps,
guidance_scale=guidance_scale,
do_classifier_free_guidance=do_classifier_free_guidance,
device=self.device,
eta=eta,
disable_prog=not self.zero_rank
)
iter_size = steps // sample_times
i = 0
for sample, t in sample_loop:
latents = sample
if i % iter_size == 0 or i == steps - 1:
outputs.append(self.decode_first_stage(latents, **kwargs))
i += 1
return outputs

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# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from typing import Optional
from diffusers.models.embeddings import Timesteps
import math
from MeshAnything.miche.michelangelo.models.modules.transformer_blocks import MLP
from MeshAnything.miche.michelangelo.models.modules.diffusion_transformer import UNetDiffusionTransformer
class ConditionalASLUDTDenoiser(nn.Module):
def __init__(self, *,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
input_channels: int,
output_channels: int,
n_ctx: int,
width: int,
layers: int,
heads: int,
context_dim: int,
context_ln: bool = True,
skip_ln: bool = False,
init_scale: float = 0.25,
flip_sin_to_cos: bool = False,
use_checkpoint: bool = False):
super().__init__()
self.use_checkpoint = use_checkpoint
init_scale = init_scale * math.sqrt(1.0 / width)
self.backbone = UNetDiffusionTransformer(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
layers=layers,
heads=heads,
skip_ln=skip_ln,
init_scale=init_scale,
use_checkpoint=use_checkpoint
)
self.ln_post = nn.LayerNorm(width, device=device, dtype=dtype)
self.input_proj = nn.Linear(input_channels, width, device=device, dtype=dtype)
self.output_proj = nn.Linear(width, output_channels, device=device, dtype=dtype)
# timestep embedding
self.time_embed = Timesteps(width, flip_sin_to_cos=flip_sin_to_cos, downscale_freq_shift=0)
self.time_proj = MLP(
device=device, dtype=dtype, width=width, init_scale=init_scale
)
self.context_embed = nn.Sequential(
nn.LayerNorm(context_dim, device=device, dtype=dtype),
nn.Linear(context_dim, width, device=device, dtype=dtype),
)
if context_ln:
self.context_embed = nn.Sequential(
nn.LayerNorm(context_dim, device=device, dtype=dtype),
nn.Linear(context_dim, width, device=device, dtype=dtype),
)
else:
self.context_embed = nn.Linear(context_dim, width, device=device, dtype=dtype)
def forward(self,
model_input: torch.FloatTensor,
timestep: torch.LongTensor,
context: torch.FloatTensor):
r"""
Args:
model_input (torch.FloatTensor): [bs, n_data, c]
timestep (torch.LongTensor): [bs,]
context (torch.FloatTensor): [bs, context_tokens, c]
Returns:
sample (torch.FloatTensor): [bs, n_data, c]
"""
_, n_data, _ = model_input.shape
# 1. time
t_emb = self.time_proj(self.time_embed(timestep)).unsqueeze(dim=1)
# 2. conditions projector
context = self.context_embed(context)
# 3. denoiser
x = self.input_proj(model_input)
x = torch.cat([t_emb, context, x], dim=1)
x = self.backbone(x)
x = self.ln_post(x)
x = x[:, -n_data:]
sample = self.output_proj(x)
return sample

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# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
class BaseDenoiser(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, t, context):
raise NotImplementedError

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# -*- coding: utf-8 -*-
from omegaconf import DictConfig
from typing import List, Tuple, Dict, Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import lr_scheduler
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_only
from diffusers.schedulers import (
DDPMScheduler,
DDIMScheduler,
KarrasVeScheduler,
DPMSolverMultistepScheduler
)
from MeshAnything.miche.michelangelo.utils import instantiate_from_config
from MeshAnything.miche.michelangelo.models.tsal.tsal_base import AlignedShapeAsLatentPLModule
from MeshAnything.miche.michelangelo.models.asl_diffusion.inference_utils import ddim_sample
SchedulerType = Union[DDIMScheduler, KarrasVeScheduler, DPMSolverMultistepScheduler]
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
class ClipASLDiffuser(pl.LightningModule):
first_stage_model: Optional[AlignedShapeAsLatentPLModule]
cond_stage_model: Optional[Union[nn.Module, pl.LightningModule]]
model: nn.Module
def __init__(self, *,
first_stage_config,
cond_stage_config,
denoiser_cfg,
scheduler_cfg,
optimizer_cfg,
loss_cfg,
first_stage_key: str = "surface",
cond_stage_key: str = "image",
scale_by_std: bool = False,
z_scale_factor: float = 1.0,
ckpt_path: Optional[str] = None,
ignore_keys: Union[Tuple[str], List[str]] = ()):
super().__init__()
self.first_stage_key = first_stage_key
self.cond_stage_key = cond_stage_key
# 1. lazy initialize first stage
self.instantiate_first_stage(first_stage_config)
# 2. initialize conditional stage
self.instantiate_cond_stage(cond_stage_config)
# 3. diffusion model
self.model = instantiate_from_config(
denoiser_cfg, device=None, dtype=None
)
self.optimizer_cfg = optimizer_cfg
# 4. scheduling strategy
self.scheduler_cfg = scheduler_cfg
self.noise_scheduler: DDPMScheduler = instantiate_from_config(scheduler_cfg.noise)
self.denoise_scheduler: SchedulerType = instantiate_from_config(scheduler_cfg.denoise)
# 5. loss configures
self.loss_cfg = loss_cfg
self.scale_by_std = scale_by_std
if scale_by_std:
self.register_buffer("z_scale_factor", torch.tensor(z_scale_factor))
else:
self.z_scale_factor = z_scale_factor
self.ckpt_path = ckpt_path
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
def instantiate_non_trainable_model(self, config):
model = instantiate_from_config(config)
model = model.eval()
model.train = disabled_train
for param in model.parameters():
param.requires_grad = False
return model
def instantiate_first_stage(self, first_stage_config):
self.first_stage_model = self.instantiate_non_trainable_model(first_stage_config)
self.first_stage_model.set_shape_model_only()
def instantiate_cond_stage(self, cond_stage_config):
self.cond_stage_model = self.instantiate_non_trainable_model(cond_stage_config)
def init_from_ckpt(self, path, ignore_keys=()):
state_dict = torch.load(path, map_location="cpu")["state_dict"]
keys = list(state_dict.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del state_dict[k]
missing, unexpected = self.load_state_dict(state_dict, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
print(f"Unexpected Keys: {unexpected}")
@property
def zero_rank(self):
if self._trainer:
zero_rank = self.trainer.local_rank == 0
else:
zero_rank = True
return zero_rank
def configure_optimizers(self) -> Tuple[List, List]:
lr = self.learning_rate
trainable_parameters = list(self.model.parameters())
if self.optimizer_cfg is None:
optimizers = [torch.optim.AdamW(trainable_parameters, lr=lr, betas=(0.9, 0.99), weight_decay=1e-3)]
schedulers = []
else:
optimizer = instantiate_from_config(self.optimizer_cfg.optimizer, params=trainable_parameters)
scheduler_func = instantiate_from_config(
self.optimizer_cfg.scheduler,
max_decay_steps=self.trainer.max_steps,
lr_max=lr
)
scheduler = {
"scheduler": lr_scheduler.LambdaLR(optimizer, lr_lambda=scheduler_func.schedule),
"interval": "step",
"frequency": 1
}
optimizers = [optimizer]
schedulers = [scheduler]
return optimizers, schedulers
@torch.no_grad()
def encode_first_stage(self, surface: torch.FloatTensor, sample_posterior=True):
z_q = self.first_stage_model.encode(surface, sample_posterior)
z_q = self.z_scale_factor * z_q
return z_q
@torch.no_grad()
def decode_first_stage(self, z_q: torch.FloatTensor, **kwargs):
z_q = 1. / self.z_scale_factor * z_q
latents = self.first_stage_model.decode(z_q, **kwargs)
return latents
@rank_zero_only
@torch.no_grad()
def on_train_batch_start(self, batch, batch_idx):
# only for very first batch
if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 \
and batch_idx == 0 and self.ckpt_path is None:
# set rescale weight to 1./std of encodings
print("### USING STD-RESCALING ###")
z_q = self.encode_first_stage(batch[self.first_stage_key])
z = z_q.detach()
del self.z_scale_factor
self.register_buffer("z_scale_factor", 1. / z.flatten().std())
print(f"setting self.z_scale_factor to {self.z_scale_factor}")
print("### USING STD-RESCALING ###")
def compute_loss(self, model_outputs, split):
"""
Args:
model_outputs (dict):
- x_0:
- noise:
- noise_prior:
- noise_pred:
- noise_pred_prior:
split (str):
Returns:
"""
pred = model_outputs["pred"]
if self.noise_scheduler.prediction_type == "epsilon":
target = model_outputs["noise"]
elif self.noise_scheduler.prediction_type == "sample":
target = model_outputs["x_0"]
else:
raise NotImplementedError(f"Prediction Type: {self.noise_scheduler.prediction_type} not yet supported.")
if self.loss_cfg.loss_type == "l1":
simple = F.l1_loss(pred, target, reduction="mean")
elif self.loss_cfg.loss_type in ["mse", "l2"]:
simple = F.mse_loss(pred, target, reduction="mean")
else:
raise NotImplementedError(f"Loss Type: {self.loss_cfg.loss_type} not yet supported.")
total_loss = simple
loss_dict = {
f"{split}/total_loss": total_loss.clone().detach(),
f"{split}/simple": simple.detach(),
}
return total_loss, loss_dict
def forward(self, batch):
"""
Args:
batch:
Returns:
"""
latents = self.encode_first_stage(batch[self.first_stage_key])
conditions = self.cond_stage_model.encode(batch[self.cond_stage_key])
# Sample noise that we"ll add to the latents
# [batch_size, n_token, latent_dim]
noise = torch.randn_like(latents)
bs = latents.shape[0]
# Sample a random timestep for each motion
timesteps = torch.randint(
0,
self.noise_scheduler.config.num_train_timesteps,
(bs,),
device=latents.device,
)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
noisy_z = self.noise_scheduler.add_noise(latents, noise, timesteps)
# diffusion model forward
noise_pred = self.model(noisy_z, timesteps, conditions)
diffusion_outputs = {
"x_0": noisy_z,
"noise": noise,
"pred": noise_pred
}
return diffusion_outputs
def training_step(self, batch: Dict[str, Union[torch.FloatTensor, List[str]]],
batch_idx: int, optimizer_idx: int = 0) -> torch.FloatTensor:
"""
Args:
batch (dict): the batch sample, and it contains:
- surface (torch.FloatTensor):
- image (torch.FloatTensor): if provide, [bs, 3, h, w], item range [0, 1]
- depth (torch.FloatTensor): if provide, [bs, 1, h, w], item range [-1, 1]
- normal (torch.FloatTensor): if provide, [bs, 3, h, w], item range [-1, 1]
- text (list of str):
batch_idx (int):
optimizer_idx (int):
Returns:
loss (torch.FloatTensor):
"""
diffusion_outputs = self(batch)
loss, loss_dict = self.compute_loss(diffusion_outputs, "train")
self.log_dict(loss_dict, prog_bar=True, logger=True, sync_dist=False, rank_zero_only=True)
return loss
def validation_step(self, batch: Dict[str, torch.FloatTensor],
batch_idx: int, optimizer_idx: int = 0) -> torch.FloatTensor:
"""
Args:
batch (dict): the batch sample, and it contains:
- surface_pc (torch.FloatTensor): [n_pts, 4]
- surface_feats (torch.FloatTensor): [n_pts, c]
- text (list of str):
batch_idx (int):
optimizer_idx (int):
Returns:
loss (torch.FloatTensor):
"""
diffusion_outputs = self(batch)
loss, loss_dict = self.compute_loss(diffusion_outputs, "val")
self.log_dict(loss_dict, prog_bar=True, logger=True, sync_dist=False, rank_zero_only=True)
return loss
@torch.no_grad()
def sample(self,
batch: Dict[str, Union[torch.FloatTensor, List[str]]],
sample_times: int = 1,
steps: Optional[int] = None,
guidance_scale: Optional[float] = None,
eta: float = 0.0,
return_intermediates: bool = False, **kwargs):
if steps is None:
steps = self.scheduler_cfg.num_inference_steps
if guidance_scale is None:
guidance_scale = self.scheduler_cfg.guidance_scale
do_classifier_free_guidance = guidance_scale > 0
# conditional encode
xc = batch[self.cond_stage_key]
# print(self.first_stage_model.device, self.cond_stage_model.device, self.device)
cond = self.cond_stage_model(xc)
if do_classifier_free_guidance:
un_cond = self.cond_stage_model.unconditional_embedding(batch_size=len(xc))
cond = torch.cat([un_cond, cond], dim=0)
outputs = []
latents = None
if not return_intermediates:
for _ in range(sample_times):
sample_loop = ddim_sample(
self.denoise_scheduler,
self.model,
shape=self.first_stage_model.latent_shape,
cond=cond,
steps=steps,
guidance_scale=guidance_scale,
do_classifier_free_guidance=do_classifier_free_guidance,
device=self.device,
eta=eta,
disable_prog=not self.zero_rank
)
for sample, t in sample_loop:
latents = sample
outputs.append(self.decode_first_stage(latents, **kwargs))
else:
sample_loop = ddim_sample(
self.denoise_scheduler,
self.model,
shape=self.first_stage_model.latent_shape,
cond=cond,
steps=steps,
guidance_scale=guidance_scale,
do_classifier_free_guidance=do_classifier_free_guidance,
device=self.device,
eta=eta,
disable_prog=not self.zero_rank
)
iter_size = steps // sample_times
i = 0
for sample, t in sample_loop:
latents = sample
if i % iter_size == 0 or i == steps - 1:
outputs.append(self.decode_first_stage(latents, **kwargs))
i += 1
return outputs

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# -*- coding: utf-8 -*-
import torch
from tqdm import tqdm
from typing import Tuple, List, Union, Optional
from diffusers.schedulers import DDIMScheduler
__all__ = ["ddim_sample"]
def ddim_sample(ddim_scheduler: DDIMScheduler,
diffusion_model: torch.nn.Module,
shape: Union[List[int], Tuple[int]],
cond: torch.FloatTensor,
steps: int,
eta: float = 0.0,
guidance_scale: float = 3.0,
do_classifier_free_guidance: bool = True,
generator: Optional[torch.Generator] = None,
device: torch.device = "cuda:0",
disable_prog: bool = True):
assert steps > 0, f"{steps} must > 0."
# init latents
bsz = cond.shape[0]
if do_classifier_free_guidance:
bsz = bsz // 2
latents = torch.randn(
(bsz, *shape),
generator=generator,
device=cond.device,
dtype=cond.dtype,
)
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * ddim_scheduler.init_noise_sigma
# set timesteps
ddim_scheduler.set_timesteps(steps)
timesteps = ddim_scheduler.timesteps.to(device)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, and between [0, 1]
extra_step_kwargs = {
"eta": eta,
"generator": generator
}
# reverse
for i, t in enumerate(tqdm(timesteps, disable=disable_prog, desc="DDIM Sampling:", leave=False)):
# expand the latents if we are doing classifier free guidance
latent_model_input = (
torch.cat([latents] * 2)
if do_classifier_free_guidance
else latents
)
# latent_model_input = scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
timestep_tensor = torch.tensor([t], dtype=torch.long, device=device)
timestep_tensor = timestep_tensor.expand(latent_model_input.shape[0])
noise_pred = diffusion_model.forward(latent_model_input, timestep_tensor, cond)
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (
noise_pred_text - noise_pred_uncond
)
# text_embeddings_for_guidance = encoder_hidden_states.chunk(
# 2)[1] if do_classifier_free_guidance else encoder_hidden_states
# compute the previous noisy sample x_t -> x_t-1
latents = ddim_scheduler.step(
noise_pred, t, latents, **extra_step_kwargs
).prev_sample
yield latents, t
def karra_sample():
pass

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# -*- coding: utf-8 -*-
from .clip import CLIPEncoder

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# -*- coding: utf-8 -*-
import torch
import numpy as np
from PIL import Image
from dataclasses import dataclass
from torchvision.transforms import Normalize
from transformers import CLIPModel, CLIPTokenizer
from transformers.utils import ModelOutput
from typing import Iterable, Optional, Union, List
ImageType = Union[np.ndarray, torch.Tensor, Image.Image]
@dataclass
class CLIPEmbedOutput(ModelOutput):
last_hidden_state: torch.FloatTensor = None
pooler_output: torch.FloatTensor = None
embeds: torch.FloatTensor = None
class CLIPEncoder(torch.nn.Module):
def __init__(self, model_path="openai/clip-vit-base-patch32"):
super().__init__()
# Load the CLIP model and processor
self.model: CLIPModel = CLIPModel.from_pretrained(model_path)
self.tokenizer = CLIPTokenizer.from_pretrained(model_path)
self.image_preprocess = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.model.training = False
for p in self.model.parameters():
p.requires_grad = False
@torch.no_grad()
def encode_image(self, images: Iterable[Optional[ImageType]]):
pixel_values = self.image_preprocess(images)
vision_outputs = self.model.vision_model(pixel_values=pixel_values)
pooler_output = vision_outputs[1] # pooled_output
image_features = self.model.visual_projection(pooler_output)
visual_embeds = CLIPEmbedOutput(
last_hidden_state=vision_outputs.last_hidden_state,
pooler_output=pooler_output,
embeds=image_features
)
return visual_embeds
@torch.no_grad()
def encode_text(self, texts: List[str]):
text_inputs = self.tokenizer(texts, padding=True, return_tensors="pt")
text_outputs = self.model.text_model(input_ids=text_inputs)
pooler_output = text_outputs[1] # pooled_output
text_features = self.model.text_projection(pooler_output)
text_embeds = CLIPEmbedOutput(
last_hidden_state=text_outputs.last_hidden_state,
pooler_output=pooler_output,
embeds=text_features
)
return text_embeds
def forward(self,
images: Iterable[Optional[ImageType]],
texts: List[str]):
visual_embeds = self.encode_image(images)
text_embeds = self.encode_text(texts)
return visual_embeds, text_embeds

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# -*- coding: utf-8 -*-
import os
import torch
import torch.nn as nn
from torchvision import transforms
from transformers import CLIPModel, CLIPTokenizer
from collections import OrderedDict
from MeshAnything.miche.michelangelo.data.transforms import RandomResize
class AbstractEncoder(nn.Module):
embedding_dim: int
def __init__(self):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
class ClassEmbedder(nn.Module):
def __init__(self, embed_dim, n_classes=1000, key="class"):
super().__init__()
self.key = key
self.embedding = nn.Embedding(n_classes, embed_dim)
def forward(self, batch, key=None):
if key is None:
key = self.key
# this is for use in crossattn
c = batch[key][:, None]
c = self.embedding(c)
return c
class FrozenCLIPTextEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
def __init__(
self,
version="openai/clip-vit-large-patch14",
tokenizer_version=None,
device="cuda",
max_length=77,
zero_embedding_radio: float = 0.1,
):
super().__init__()
self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_version or version)
self.device = device
self.max_length = max_length
self.zero_embedding_radio = zero_embedding_radio
self.clip_dict = OrderedDict()
self.clip_name = os.path.split(version)[-1]
transformer = CLIPModel.from_pretrained(version).text_model
for param in transformer.parameters():
param.requires_grad = False
self.clip_dict[self.clip_name] = transformer
self._move_flag = False
@property
def clip(self):
return self.clip_dict[self.clip_name]
def move(self):
if self._move_flag:
return
self.clip_dict[self.clip_name] = self.clip_dict[self.clip_name].to(self.device)
self._move_flag = True
def unconditional_embedding(self, batch_size):
empty_text = [""] * batch_size
empty_z = self.forward(empty_text)
return empty_z
def forward(self, text):
self.move()
batch_encoding = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
return_length=True,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="pt",
)
tokens = batch_encoding["input_ids"].to(self.device)
outputs = self.clip(input_ids=tokens)
z = outputs.last_hidden_state
return z
def encode(self, text):
batch_size = len(text)
batch_mask = torch.rand((batch_size,))
for i in range(batch_size):
if batch_mask[i] < self.zero_embedding_radio:
text[i] = ""
return self(text)
class FrozenAlignedCLIPTextEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
def __init__(
self,
version="openai/clip-vit-large-patch14",
tokenizer_version=None,
device="cuda",
max_length=77,
zero_embedding_radio: float = 0.1,
):
super().__init__()
self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_version or version)
self.device = device
self.max_length = max_length
self.zero_embedding_radio = zero_embedding_radio
self.clip_dict = OrderedDict()
self.clip_name = os.path.split(version)[-1]
transformer = CLIPModel.from_pretrained(version).text_model
for param in transformer.parameters():
param.requires_grad = False
self.clip_dict[self.clip_name] = transformer
self._move_flag = False
@property
def clip(self):
return self.clip_dict[self.clip_name]
def move(self):
if self._move_flag:
return
self.clip_dict[self.clip_name] = self.clip_dict[self.clip_name].to(self.device)
self._move_flag = True
def unconditional_embedding(self, batch_size):
empty_text = [""] * batch_size
empty_z = self.forward(empty_text)
return empty_z
def forward(self, text):
self.move()
batch_encoding = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
return_length=True,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="pt",
)
tokens = batch_encoding["input_ids"].to(self.device)
outputs = self.clip(input_ids=tokens)
z = outputs.last_hidden_state
return z
def encode(self, text):
batch_size = len(text)
batch_mask = torch.rand((batch_size,))
for i in range(batch_size):
if batch_mask[i] < self.zero_embedding_radio:
text[i] = ""
return self(text)
class FrozenCLIPImageEmbedder(AbstractEncoder):
"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
def __init__(
self,
version="openai/clip-vit-large-patch14",
device="cuda",
zero_embedding_radio=0.1,
normalize_embedding=True,
num_projection_vector=0,
linear_mapping_bias=True,
reverse_visual_projection=False,
):
super().__init__()
self.device = device
self.clip_dict = OrderedDict()
self.clip_name = os.path.split(version)[-1]
clip_model = CLIPModel.from_pretrained(version)
clip_model.text_model = None
clip_model.text_projection = None
clip_model = clip_model.eval()
for param in self.parameters():
param.requires_grad = False
self.clip_dict[self.clip_name] = clip_model
self.transform = transforms.Compose(
[
transforms.Resize(224, transforms.InterpolationMode.BICUBIC, antialias=True),
transforms.CenterCrop(224), # crop a (224, 224) square
transforms.Normalize(
mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711],
),
]
)
self.zero_embedding_radio = zero_embedding_radio
self.num_projection_vector = num_projection_vector
self.reverse_visual_projection = reverse_visual_projection
self.normalize_embedding = normalize_embedding
embedding_dim = (
clip_model.visual_projection.in_features
if reverse_visual_projection
else clip_model.visual_projection.out_features
)
self.embedding_dim = embedding_dim
if self.num_projection_vector > 0:
self.projection = nn.Linear(
embedding_dim,
clip_model.visual_projection.out_features * num_projection_vector,
bias=linear_mapping_bias,
)
nn.init.normal_(self.projection.weight, std=embedding_dim ** -0.5)
self._move_flag = False
@property
def clip(self):
return self.clip_dict[self.clip_name]
def unconditional_embedding(self, batch_size):
zero = torch.zeros(
batch_size,
1,
self.embedding_dim,
device=self.device,
dtype=self.clip.visual_projection.weight.dtype,
)
if self.num_projection_vector > 0:
zero = self.projection(zero).view(batch_size, self.num_projection_vector, -1)
return zero
def forward(self, image, value_range=(-1, 1), zero_embedding_radio=0):
if value_range is not None:
low, high = value_range
image = (image - low) / (high - low)
image = image.to(self.device, dtype=self.clip.visual_projection.weight.dtype)
if self.reverse_visual_projection:
z = self.clip.vision_model(self.transform(image))[1]
else:
z = self.clip.get_image_features(self.transform(image))
if self.normalize_embedding:
z = z / z.norm(dim=-1, keepdim=True)
if z.ndim == 2:
z = z.unsqueeze(dim=-2)
if zero_embedding_radio > 0:
mask = torch.rand((len(image), 1, 1), device=z.device, dtype=z.dtype) < zero_embedding_radio
z = z * mask.to(z)
if self.num_projection_vector > 0:
z = self.projection(z).view(len(image), self.num_projection_vector, -1)
return z
def move(self):
if self._move_flag:
return
self.clip_dict[self.clip_name] = self.clip_dict[self.clip_name].to(self.device)
self._move_flag = True
def encode(self, image):
self.move()
return self(image, zero_embedding_radio=self.zero_embedding_radio)
class FrozenCLIPImageGridEmbedder(AbstractEncoder):
def __init__(
self,
version="openai/clip-vit-large-patch14",
device="cuda",
zero_embedding_radio=0.1,
):
super().__init__()
self.device = device
self.clip_dict = OrderedDict()
self.clip_name = os.path.split(version)[-1]
clip_model: CLIPModel = CLIPModel.from_pretrained(version)
clip_model.text_model = None
clip_model.text_projection = None
clip_model = clip_model.eval()
for param in self.parameters():
param.requires_grad = False
self.clip_dict[self.clip_name] = clip_model
self.transform = transforms.Compose(
[
transforms.Resize(224, transforms.InterpolationMode.BILINEAR, antialias=True),
transforms.CenterCrop(224), # crop a (224, 224) square
transforms.Normalize(
mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711],
),
]
)
self.zero_embedding_radio = zero_embedding_radio
self.embedding_dim = clip_model.vision_embed_dim
self._move_flag = False
@property
def clip(self):
return self.clip_dict[self.clip_name]
def move(self):
if self._move_flag:
return
self.clip_dict[self.clip_name] = self.clip_dict[self.clip_name].to(self.device)
self._move_flag = True
def unconditional_embedding(self, batch_size):
zero = torch.zeros(
batch_size,
self.clip.vision_model.embeddings.num_positions,
self.embedding_dim,
device=self.device,
dtype=self.clip.visual_projection.weight.dtype,
)
return zero
def forward(self, image, value_range=(-1, 1), zero_embedding_radio=0):
self.move()
if value_range is not None:
low, high = value_range
image = (image - low) / (high - low)
image = image.to(self.device, dtype=self.clip.visual_projection.weight.dtype)
z = self.clip.vision_model(self.transform(image)).last_hidden_state
if zero_embedding_radio > 0:
mask = torch.rand((len(image), 1, 1), device=z.device, dtype=z.dtype) >= zero_embedding_radio
z = z * mask.to(z)
return z
def encode(self, image):
return self(image, zero_embedding_radio=self.zero_embedding_radio)
class MoECLIPImageEncoder(nn.Module):
def __init__(
self,
versions,
hidden_state_dim,
num_projection_vector=8,
zero_embedding_radio=0.1,
device="cuda",
precision="fp16",
normalize=False,
clip_max=0,
transform_type="base",
argument_p=0.2,
):
super().__init__()
self.device = torch.device(device)
self.hidden_state_dim = hidden_state_dim
self.zero_embedding_radio = zero_embedding_radio
self.num_projection_vector = num_projection_vector
self.dtype = dict(fp16=torch.float16, fp32=torch.float32, bf16=torch.bfloat16)[precision]
self.normalize = normalize
self.clip_max = clip_max
if transform_type == "base":
self.transform = transforms.Compose(
[
transforms.Resize(224, transforms.InterpolationMode.BICUBIC, antialias=True),
transforms.CenterCrop(224), # crop a (224, 224) square
transforms.Normalize(
mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711],
),
]
)
elif transform_type == "crop_blur_resize":
self.transform = transforms.Compose(
[
transforms.Resize(224, transforms.InterpolationMode.BICUBIC, antialias=True),
transforms.CenterCrop(224), # crop a (224, 224) square
transforms.RandomApply(
transforms=[
transforms.RandomResizedCrop(
size=224,
scale=(0.8, 1.0),
ratio=(0.99, 1.01),
interpolation=transforms.InterpolationMode.BICUBIC,
),
],
p=argument_p,
),
transforms.RandomApply(
transforms=[
transforms.GaussianBlur(kernel_size=9, sigma=(0.1, 5)),
],
p=argument_p,
),
transforms.RandomApply(
transforms=[
RandomResize(size=224, resize_radio=(0.2, 1)),
],
p=argument_p,
),
transforms.Normalize(
mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711],
),
]
)
else:
raise ValueError(f"invalid {transform_type=}")
if isinstance(versions, str):
versions = (versions,)
# 如果直接把clips定位为当前类的子module，1. 会在保存ckp时存无用的多个权重。 2. pl会调用to，导致layer_norm的权重也被转换成fp16
clips = OrderedDict()
for v in versions:
# 因为clips不是子module，直接指定device="cuda"会错误地导致clip模型权重都被放到cuda:0上。
clips[v], _ = clip.load(name=v, device="cpu", jit=False, download_root=None)
delattr(clips[v], "transformer")
clips[v].eval()
clips[v].requires_grad_(False)
self.clips_hidden_dim = sum(clips[v].ln_final.weight.size(0) for v in clips)
if self.num_projection_vector == 0:
self.projection = nn.Identity()
else:
self.projection = nn.Linear(self.clips_hidden_dim, hidden_state_dim * self.num_projection_vector, bias=True)
self.projection.to(dtype=self.dtype)
nn.init.normal_(self.projection.weight, std=self.clips_hidden_dim ** -0.5)
self.clips = clips
self._move_flag = False
def move(self):
if self._move_flag:
return
def convert_weights(model: nn.Module):
"""Convert applicable model parameters to fp16"""
def _convert_weights_to_fp16(l):
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
l.weight.data = l.weight.data.type(self.dtype)
if l.bias is not None:
l.bias.data = l.bias.data.type(self.dtype)
if isinstance(l, nn.MultiheadAttention):
for attr in [
*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]],
"in_proj_bias",
"bias_k",
"bias_v",
]:
tensor = getattr(l, attr)
if tensor is not None:
tensor.data = tensor.data.type(self.dtype)
for name in ["text_projection", "proj"]:
if hasattr(l, name):
attr = getattr(l, name)
if attr is not None:
attr.data = attr.data.type(self.dtype)
model.apply(_convert_weights_to_fp16)
for k in self.clips:
self.clips[k].to(self.device)
convert_weights(self.clips[k]) # fp32 -> self.dtype
self._move_flag = True
def unconditional_embedding(self, batch_size=None):
zero = torch.zeros(
batch_size,
self.clips_hidden_dim,
device=self.device,
dtype=self.dtype,
)
if self.num_projection_vector > 0:
zero = self.projection(zero).view(batch_size, self.num_projection_vector, -1)
return zero
def convert_embedding(self, z):
if self.num_projection_vector > 0:
z = self.projection(z.type(self.projection.weight.dtype)).view(len(z), self.num_projection_vector, -1)
return z
def forward(self, image, value_range=(-1, 1), zero_embedding_radio=0):
if value_range is not None:
low, high = value_range
image = (image - low) / (high - low)
image = self.transform(image)
with torch.no_grad():
embs = []
for v in self.clips:
x = self.clips[v].encode_image(image)
if self.normalize:
x = x / x.norm(p=2, dim=-1, keepdim=True) * (x.size(-1) ** 0.5)
# clip_max only works with normalization
if self.clip_max > 0:
x = x.clamp(-self.clip_max, self.clip_max)
embs.append(x)
z = torch.cat(embs, dim=-1)
if self.normalize:
z /= z.size(-1) ** 0.5
if zero_embedding_radio > 0:
mask = torch.rand((len(image), 1, 1), device=z.device, dtype=z.dtype) >= zero_embedding_radio
z = z + mask.to(z)
if self.num_projection_vector > 0:
z = self.projection(z).view(len(image), self.num_projection_vector, -1)
return z
def encode(self, image):
self.move()
return self(image, zero_embedding_radio=self.zero_embedding_radio)

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MeshAnything/miche/michelangelo/models/modules/__init__.py Normal file
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# -*- coding: utf-8 -*-
from .checkpoint import checkpoint

69
MeshAnything/miche/michelangelo/models/modules/checkpoint.py Normal file
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# -*- coding: utf-8 -*-
"""
Adapted from: https://github.com/openai/guided-diffusion/blob/22e0df8183507e13a7813f8d38d51b072ca1e67c/guided_diffusion/nn.py#L124
"""
import torch
from typing import Callable, Iterable, Sequence, Union
def checkpoint(
func: Callable[..., Union[torch.Tensor, Sequence[torch.Tensor]]],
inputs: Sequence[torch.Tensor],
params: Iterable[torch.Tensor],
flag: bool,
use_deepspeed: bool = False
):
"""
Evaluate a function without caching intermediate activations, allowing for
reduced memory at the expense of extra compute in the backward pass.
:param func: the function to evaluate.
:param inputs: the argument sequence to pass to `func`.
:param params: a sequence of parameters `func` depends on but does not
explicitly take as arguments.
:param flag: if False, disable gradient checkpointing.
:param use_deepspeed: if True, use deepspeed
"""
if flag:
if use_deepspeed:
import deepspeed
return deepspeed.checkpointing.checkpoint(func, *inputs)
args = tuple(inputs) + tuple(params)
return CheckpointFunction.apply(func, len(inputs), *args)
else:
return func(*inputs)
class CheckpointFunction(torch.autograd.Function):
@staticmethod
@torch.cuda.amp.custom_fwd
def forward(ctx, run_function, length, *args):
ctx.run_function = run_function
ctx.input_tensors = list(args[:length])
ctx.input_params = list(args[length:])
with torch.no_grad():
output_tensors = ctx.run_function(*ctx.input_tensors)
return output_tensors
@staticmethod
@torch.cuda.amp.custom_bwd
def backward(ctx, *output_grads):
ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
with torch.enable_grad():
# Fixes a bug where the first op in run_function modifies the
# Tensor storage in place, which is not allowed for detach()'d
# Tensors.
shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
output_tensors = ctx.run_function(*shallow_copies)
input_grads = torch.autograd.grad(
output_tensors,
ctx.input_tensors + ctx.input_params,
output_grads,
allow_unused=True,
)
del ctx.input_tensors
del ctx.input_params
del output_tensors
return (None, None) + input_grads

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MeshAnything/miche/michelangelo/models/modules/diffusion_transformer.py Normal file
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# -*- coding: utf-8 -*-
import math
import torch
import torch.nn as nn
from typing import Optional
from MeshAnything.miche.michelangelo.models.modules.checkpoint import checkpoint
from MeshAnything.miche.michelangelo.models.modules.transformer_blocks import (
init_linear,
MLP,
MultiheadCrossAttention,
MultiheadAttention,
ResidualAttentionBlock
)
class AdaLayerNorm(nn.Module):
def __init__(self,
device: torch.device,
dtype: torch.dtype,
width: int):
super().__init__()
self.silu = nn.SiLU(inplace=True)
self.linear = nn.Linear(width, width * 2, device=device, dtype=dtype)
self.layernorm = nn.LayerNorm(width, elementwise_affine=False, device=device, dtype=dtype)
def forward(self, x, timestep):
emb = self.linear(timestep)
scale, shift = torch.chunk(emb, 2, dim=2)
x = self.layernorm(x) * (1 + scale) + shift
return x
class DitBlock(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
n_ctx: int,
width: int,
heads: int,
context_dim: int,
qkv_bias: bool = False,
init_scale: float = 1.0,
use_checkpoint: bool = False
):
super().__init__()
self.use_checkpoint = use_checkpoint
self.attn = MultiheadAttention(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias
)
self.ln_1 = AdaLayerNorm(device, dtype, width)
if context_dim is not None:
self.ln_2 = AdaLayerNorm(device, dtype, width)
self.cross_attn = MultiheadCrossAttention(
device=device,
dtype=dtype,
width=width,
heads=heads,
data_width=context_dim,
init_scale=init_scale,
qkv_bias=qkv_bias
)
self.mlp = MLP(device=device, dtype=dtype, width=width, init_scale=init_scale)
self.ln_3 = AdaLayerNorm(device, dtype, width)
def forward(self, x: torch.Tensor, t: torch.Tensor, context: Optional[torch.Tensor] = None):
return checkpoint(self._forward, (x, t, context), self.parameters(), self.use_checkpoint)
def _forward(self, x: torch.Tensor, t: torch.Tensor, context: Optional[torch.Tensor] = None):
x = x + self.attn(self.ln_1(x, t))
if context is not None:
x = x + self.cross_attn(self.ln_2(x, t), context)
x = x + self.mlp(self.ln_3(x, t))
return x
class DiT(nn.Module):
def __init__(
self,
*,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
n_ctx: int,
width: int,
layers: int,
heads: int,
context_dim: int,
init_scale: float = 0.25,
qkv_bias: bool = False,
use_checkpoint: bool = False
):
super().__init__()
self.n_ctx = n_ctx
self.width = width
self.layers = layers
self.resblocks = nn.ModuleList(
[
DitBlock(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
heads=heads,
context_dim=context_dim,
qkv_bias=qkv_bias,
init_scale=init_scale,
use_checkpoint=use_checkpoint
)
for _ in range(layers)
]
)
def forward(self, x: torch.Tensor, t: torch.Tensor, context: Optional[torch.Tensor] = None):
for block in self.resblocks:
x = block(x, t, context)
return x
class UNetDiffusionTransformer(nn.Module):
def __init__(
self,
*,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
n_ctx: int,
width: int,
layers: int,
heads: int,
init_scale: float = 0.25,
qkv_bias: bool = False,
skip_ln: bool = False,
use_checkpoint: bool = False
):
super().__init__()
self.n_ctx = n_ctx
self.width = width
self.layers = layers
self.encoder = nn.ModuleList()
for _ in range(layers):
resblock = ResidualAttentionBlock(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
use_checkpoint=use_checkpoint
)
self.encoder.append(resblock)
self.middle_block = ResidualAttentionBlock(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
use_checkpoint=use_checkpoint
)
self.decoder = nn.ModuleList()
for _ in range(layers):
resblock = ResidualAttentionBlock(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
use_checkpoint=use_checkpoint
)
linear = nn.Linear(width * 2, width, device=device, dtype=dtype)
init_linear(linear, init_scale)
layer_norm = nn.LayerNorm(width, device=device, dtype=dtype) if skip_ln else None
self.decoder.append(nn.ModuleList([resblock, linear, layer_norm]))
def forward(self, x: torch.Tensor):
enc_outputs = []
for block in self.encoder:
x = block(x)
enc_outputs.append(x)
x = self.middle_block(x)
for i, (resblock, linear, layer_norm) in enumerate(self.decoder):
x = torch.cat([enc_outputs.pop(), x], dim=-1)
x = linear(x)
if layer_norm is not None:
x = layer_norm(x)
x = resblock(x)
return x

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import torch
import numpy as np
from typing import Union, List
class AbstractDistribution(object):
def sample(self):
raise NotImplementedError()
def mode(self):
raise NotImplementedError()
class DiracDistribution(AbstractDistribution):
def __init__(self, value):
self.value = value
def sample(self):
return self.value
def mode(self):
return self.value
class DiagonalGaussianDistribution(object):
def __init__(self, parameters: Union[torch.Tensor, List[torch.Tensor]], deterministic=False, feat_dim=1):
self.feat_dim = feat_dim
self.parameters = parameters
if isinstance(parameters, list):
self.mean = parameters[0]
self.logvar = parameters[1]
else:
self.mean, self.logvar = torch.chunk(parameters, 2, dim=feat_dim)
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
self.deterministic = deterministic
self.std = torch.exp(0.5 * self.logvar)
self.var = torch.exp(self.logvar)
if self.deterministic:
self.var = self.std = torch.zeros_like(self.mean)
def sample(self):
x = self.mean + self.std * torch.randn_like(self.mean)
return x
def kl(self, other=None, dims=(1, 2, 3)):
if self.deterministic:
return torch.Tensor([0.])
else:
if other is None:
return 0.5 * torch.mean(torch.pow(self.mean, 2)
+ self.var - 1.0 - self.logvar,
dim=dims)
else:
return 0.5 * torch.mean(
torch.pow(self.mean - other.mean, 2) / other.var
+ self.var / other.var - 1.0 - self.logvar + other.logvar,
dim=dims)
def nll(self, sample, dims=(1, 2, 3)):
if self.deterministic:
return torch.Tensor([0.])
logtwopi = np.log(2.0 * np.pi)
return 0.5 * torch.sum(
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
dim=dims)
def mode(self):
return self.mean
def normal_kl(mean1, logvar1, mean2, logvar2):
"""
source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
Compute the KL divergence between two gaussians.
Shapes are automatically broadcasted, so batches can be compared to
scalars, among other use cases.
"""
tensor = None
for obj in (mean1, logvar1, mean2, logvar2):
if isinstance(obj, torch.Tensor):
tensor = obj
break
assert tensor is not None, "at least one argument must be a Tensor"
# Force variances to be Tensors. Broadcasting helps convert scalars to
# Tensors, but it does not work for torch.exp().
logvar1, logvar2 = [
x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
for x in (logvar1, logvar2)
]
return 0.5 * (
-1.0
+ logvar2
- logvar1
+ torch.exp(logvar1 - logvar2)
+ ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
)

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MeshAnything/miche/michelangelo/models/modules/embedder.py Normal file
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# -*- coding: utf-8 -*-
import numpy as np
import torch
import torch.nn as nn
import math
VALID_EMBED_TYPES = ["identity", "fourier", "hashgrid", "sphere_harmonic", "triplane_fourier"]
class FourierEmbedder(nn.Module):
"""The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts
each feature dimension of `x[..., i]` into:
[
sin(x[..., i]),
sin(f_1*x[..., i]),
sin(f_2*x[..., i]),
...
sin(f_N * x[..., i]),
cos(x[..., i]),
cos(f_1*x[..., i]),
cos(f_2*x[..., i]),
...
cos(f_N * x[..., i]),
x[..., i] # only present if include_input is True.
], here f_i is the frequency.
Denote the space is [0 / num_freqs, 1 / num_freqs, 2 / num_freqs, 3 / num_freqs, ..., (num_freqs - 1) / num_freqs].
If logspace is True, then the frequency f_i is [2^(0 / num_freqs), ..., 2^(i / num_freqs), ...];
Otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)].
Args:
num_freqs (int): the number of frequencies, default is 6;
logspace (bool): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...],
otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)];
input_dim (int): the input dimension, default is 3;
include_input (bool): include the input tensor or not, default is True.
Attributes:
frequencies (torch.Tensor): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...],
otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1);
out_dim (int): the embedding size, if include_input is True, it is input_dim * (num_freqs * 2 + 1),
otherwise, it is input_dim * num_freqs * 2.
"""
def __init__(self,
num_freqs: int = 6,
logspace: bool = True,
input_dim: int = 3,
include_input: bool = True,
include_pi: bool = True) -> None:
"""The initialization"""
super().__init__()
if logspace:
frequencies = 2.0 ** torch.arange(
num_freqs,
dtype=torch.float32
)
else:
frequencies = torch.linspace(
1.0,
2.0 ** (num_freqs - 1),
num_freqs,
dtype=torch.float32
)
if include_pi:
frequencies *= torch.pi
self.register_buffer("frequencies", frequencies, persistent=False)
self.include_input = include_input
self.num_freqs = num_freqs
self.out_dim = self.get_dims(input_dim)
def get_dims(self, input_dim):
temp = 1 if self.include_input or self.num_freqs == 0 else 0
out_dim = input_dim * (self.num_freqs * 2 + temp)
return out_dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
""" Forward process.
Args:
x: tensor of shape [..., dim]
Returns:
embedding: an embedding of `x` of shape [..., dim * (num_freqs * 2 + temp)]
where temp is 1 if include_input is True and 0 otherwise.
"""
if self.num_freqs > 0:
embed = (x[..., None].contiguous() * self.frequencies).view(*x.shape[:-1], -1)
if self.include_input:
return torch.cat((x, embed.sin(), embed.cos()), dim=-1)
else:
return torch.cat((embed.sin(), embed.cos()), dim=-1)
else:
return x
class LearnedFourierEmbedder(nn.Module):
""" following @crowsonkb "s lead with learned sinusoidal pos emb """
""" https://github.com/crowsonkb/v-diffusion-jax/blob/master/diffusion/models/danbooru_128.py#L8 """
def __init__(self, in_channels, dim):
super().__init__()
assert (dim % 2) == 0
half_dim = dim // 2
per_channel_dim = half_dim // in_channels
self.weights = nn.Parameter(torch.randn(per_channel_dim))
def forward(self, x):
"""
Args:
x (torch.FloatTensor): [..., c]
Returns:
x (torch.FloatTensor): [..., d]
"""
# [b, t, c, 1] * [1, d] = [b, t, c, d] -> [b, t, c * d]
freqs = (x[..., None] * self.weights[None] * 2 * np.pi).view(*x.shape[:-1], -1)
fouriered = torch.cat((x, freqs.sin(), freqs.cos()), dim=-1)
return fouriered
class TriplaneLearnedFourierEmbedder(nn.Module):
def __init__(self, in_channels, dim):
super().__init__()
self.yz_plane_embedder = LearnedFourierEmbedder(in_channels, dim)
self.xz_plane_embedder = LearnedFourierEmbedder(in_channels, dim)
self.xy_plane_embedder = LearnedFourierEmbedder(in_channels, dim)
self.out_dim = in_channels + dim
def forward(self, x):
yz_embed = self.yz_plane_embedder(x)
xz_embed = self.xz_plane_embedder(x)
xy_embed = self.xy_plane_embedder(x)
embed = yz_embed + xz_embed + xy_embed
return embed
def sequential_pos_embed(num_len, embed_dim):
assert embed_dim % 2 == 0
pos = torch.arange(num_len, dtype=torch.float32)
omega = torch.arange(embed_dim // 2, dtype=torch.float32)
omega /= embed_dim / 2.
omega = 1. / 10000 ** omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = torch.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = torch.sin(out) # (M, D/2)
emb_cos = torch.cos(out) # (M, D/2)
embeddings = torch.cat([emb_sin, emb_cos], dim=1) # (M, D)
return embeddings
def timestep_embedding(timesteps, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of positional embeddings.
"""
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=timesteps.device)
args = timesteps[:, None].to(timesteps.dtype) * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def get_embedder(embed_type="fourier", num_freqs=-1, input_dim=3, degree=4,
num_levels=16, level_dim=2, per_level_scale=2, base_resolution=16,
log2_hashmap_size=19, desired_resolution=None):
if embed_type == "identity" or (embed_type == "fourier" and num_freqs == -1):
return nn.Identity(), input_dim
elif embed_type == "fourier":
embedder_obj = FourierEmbedder(num_freqs=num_freqs, input_dim=input_dim,
logspace=True, include_input=True)
return embedder_obj, embedder_obj.out_dim
elif embed_type == "hashgrid":
raise NotImplementedError
elif embed_type == "sphere_harmonic":
raise NotImplementedError
else:
raise ValueError(f"{embed_type} is not valid. Currently only supprts {VALID_EMBED_TYPES}")

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# -*- coding: utf-8 -*-
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional
from MeshAnything.miche.michelangelo.models.modules.checkpoint import checkpoint
def init_linear(l, stddev):
nn.init.normal_(l.weight, std=stddev)
if l.bias is not None:
nn.init.constant_(l.bias, 0.0)
class MultiheadAttention(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
n_ctx: int,
width: int,
heads: int,
init_scale: float,
qkv_bias: bool,
flash: bool = False
):
super().__init__()
self.n_ctx = n_ctx
self.width = width
self.heads = heads
self.c_qkv = nn.Linear(width, width * 3, bias=qkv_bias, device=device, dtype=dtype)
self.c_proj = nn.Linear(width, width, device=device, dtype=dtype)
self.attention = QKVMultiheadAttention(device=device, dtype=dtype, heads=heads, n_ctx=n_ctx, flash=flash)
init_linear(self.c_qkv, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x):
x = self.c_qkv(x)
x = checkpoint(self.attention, (x,), (), True)
x = self.c_proj(x)
return x
class QKVMultiheadAttention(nn.Module):
def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int, n_ctx: int, flash: bool = False):
super().__init__()
self.device = device
self.dtype = dtype
self.heads = heads
self.n_ctx = n_ctx
self.flash = flash
def forward(self, qkv):
bs, n_ctx, width = qkv.shape
attn_ch = width // self.heads // 3
scale = 1 / math.sqrt(math.sqrt(attn_ch))
qkv = qkv.view(bs, n_ctx, self.heads, -1)
q, k, v = torch.split(qkv, attn_ch, dim=-1)
if self.flash:
out = F.scaled_dot_product_attention(q, k, v)
else:
weight = torch.einsum(
"bthc,bshc->bhts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
wdtype = weight.dtype
weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
return out
class ResidualAttentionBlock(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
n_ctx: int,
width: int,
heads: int,
init_scale: float = 1.0,
qkv_bias: bool = True,
flash: bool = False,
use_checkpoint: bool = False
):
super().__init__()
self.use_checkpoint = use_checkpoint
self.attn = MultiheadAttention(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash
)
self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype)
self.mlp = MLP(device=device, dtype=dtype, width=width, init_scale=init_scale)
self.ln_2 = nn.LayerNorm(width, device=device, dtype=dtype)
def _forward(self, x: torch.Tensor):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
def forward(self, x: torch.Tensor):
return checkpoint(self._forward, (x,), self.parameters(), self.use_checkpoint)
class MultiheadCrossAttention(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
width: int,
heads: int,
init_scale: float,
qkv_bias: bool = True,
flash: bool = False,
n_data: Optional[int] = None,
data_width: Optional[int] = None,
):
super().__init__()
self.n_data = n_data
self.width = width
self.heads = heads
self.data_width = width if data_width is None else data_width
self.c_q = nn.Linear(width, width, bias=qkv_bias, device=device, dtype=dtype)
self.c_kv = nn.Linear(self.data_width, width * 2, bias=qkv_bias, device=device, dtype=dtype)
self.c_proj = nn.Linear(width, width, device=device, dtype=dtype)
self.attention = QKVMultiheadCrossAttention(
device=device, dtype=dtype, heads=heads, n_data=n_data, flash=flash
)
init_linear(self.c_q, init_scale)
init_linear(self.c_kv, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x, data):
x = self.c_q(x)
data = self.c_kv(data)
x = checkpoint(self.attention, (x, data), (), True)
x = self.c_proj(x)
return x
class QKVMultiheadCrossAttention(nn.Module):
def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int,
flash: bool = False, n_data: Optional[int] = None):
super().__init__()
self.device = device
self.dtype = dtype
self.heads = heads
self.n_data = n_data
self.flash = flash
def forward(self, q, kv):
_, n_ctx, _ = q.shape
bs, n_data, width = kv.shape
attn_ch = width // self.heads // 2
scale = 1 / math.sqrt(math.sqrt(attn_ch))
q = q.view(bs, n_ctx, self.heads, -1)
kv = kv.view(bs, n_data, self.heads, -1)
k, v = torch.split(kv, attn_ch, dim=-1)
if self.flash:
out = F.scaled_dot_product_attention(q, k, v)
else:
weight = torch.einsum(
"bthc,bshc->bhts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
wdtype = weight.dtype
weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
return out
class ResidualCrossAttentionBlock(nn.Module):
def __init__(
self,
*,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
n_data: Optional[int] = None,
width: int,
heads: int,
data_width: Optional[int] = None,
init_scale: float = 0.25,
qkv_bias: bool = True,
flash: bool = False
):
super().__init__()
if data_width is None:
data_width = width
self.attn = MultiheadCrossAttention(
device=device,
dtype=dtype,
n_data=n_data,
width=width,
heads=heads,
data_width=data_width,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
)
self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype)
self.ln_2 = nn.LayerNorm(data_width, device=device, dtype=dtype)
self.mlp = MLP(device=device, dtype=dtype, width=width, init_scale=init_scale)
self.ln_3 = nn.LayerNorm(width, device=device, dtype=dtype)
def forward(self, x: torch.Tensor, data: torch.Tensor):
x = x + self.attn(self.ln_1(x), self.ln_2(data))
x = x + self.mlp(self.ln_3(x))
return x
class MLP(nn.Module):
def __init__(self, *,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
width: int,
init_scale: float):
super().__init__()
self.width = width
self.c_fc = nn.Linear(width, width * 4, device=device, dtype=dtype)
self.c_proj = nn.Linear(width * 4, width, device=device, dtype=dtype)
self.gelu = nn.GELU()
init_linear(self.c_fc, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x):
return self.c_proj(self.gelu(self.c_fc(x)))
class Transformer(nn.Module):
def __init__(
self,
*,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
n_ctx: int,
width: int,
layers: int,
heads: int,
init_scale: float = 0.25,
qkv_bias: bool = True,
flash: bool = False,
use_checkpoint: bool = False
):
super().__init__()
self.n_ctx = n_ctx
self.width = width
self.layers = layers
self.resblocks = nn.ModuleList(
[
ResidualAttentionBlock(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
use_checkpoint=use_checkpoint
)
for _ in range(layers)
]
)
def forward(self, x: torch.Tensor):
for block in self.resblocks:
x = block(x)
return x

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# -*- coding: utf-8 -*-
import math
import torch
import torch.nn as nn
from typing import Optional
import warnings
from MeshAnything.miche.michelangelo.models.modules.checkpoint import checkpoint
def _trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
def norm_cdf(x):
# Computes standard normal cumulative distribution function
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (mean < a - 2 * std) or (mean > b + 2 * std):
warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
"The distribution of values may be incorrect.",
stacklevel=2)
# Values are generated by using a truncated uniform distribution and
# then using the inverse CDF for the normal distribution.
# Get upper and lower cdf values
l = norm_cdf((a - mean) / std)
u = norm_cdf((b - mean) / std)
# Uniformly fill tensor with values from [l, u], then translate to
# [2l-1, 2u-1].
tensor.uniform_(2 * l - 1, 2 * u - 1)
# Use inverse cdf transform for normal distribution to get truncated
# standard normal
tensor.erfinv_()
# Transform to proper mean, std
tensor.mul_(std * math.sqrt(2.))
tensor.add_(mean)
# Clamp to ensure it's in the proper range
tensor.clamp_(min=a, max=b)
return tensor
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
# type: (Tensor | nn.Parameter, float, float, float, float) -> Tensor
r"""Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.
NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are
applied while sampling the normal with mean/std applied, therefore a, b args
should be adjusted to match the range of mean, std args.
Args:
tensor: an n-dimensional `torch.Tensor`
mean: the mean of the normal distribution
std: the standard deviation of the normal distribution
a: the minimum cutoff value
b: the maximum cutoff value
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.trunc_normal_(w)
"""
with torch.no_grad():
return _trunc_normal_(tensor, mean, std, a, b)
def init_weights(m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
class MultiheadAttention(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
n_ctx: int,
width: int,
heads: int,
qkv_bias: bool
):
super().__init__()
self.n_ctx = n_ctx
self.width = width
self.heads = heads
self.c_qkv = nn.Linear(width, width * 3, bias=qkv_bias, device=device, dtype=dtype)
self.c_proj = nn.Linear(width, width, device=device, dtype=dtype)
self.attention = QKVMultiheadAttention(device=device, dtype=dtype, heads=heads, n_ctx=n_ctx)
def forward(self, x):
x = self.c_qkv(x)
x = checkpoint(self.attention, (x,), (), True)
x = self.c_proj(x)
return x
class QKVMultiheadAttention(nn.Module):
def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int, n_ctx: int):
super().__init__()
self.device = device
self.dtype = dtype
self.heads = heads
self.n_ctx = n_ctx
def forward(self, qkv):
bs, n_ctx, width = qkv.shape
attn_ch = width // self.heads // 3
scale = 1 / math.sqrt(attn_ch)
qkv = qkv.view(bs, n_ctx, self.heads, -1)
q, k, v = torch.split(qkv, attn_ch, dim=-1)
weight = torch.einsum("bthc,bshc->bhts", q, k) * scale
wdtype = weight.dtype
weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
return torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
class ResidualAttentionBlock(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
n_ctx: int,
width: int,
heads: int,
qkv_bias: bool = True,
use_checkpoint: bool = False
):
super().__init__()
self.use_checkpoint = use_checkpoint
self.attn = MultiheadAttention(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
heads=heads,
qkv_bias=qkv_bias
)
self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype)
self.mlp = MLP(device=device, dtype=dtype, width=width)
self.ln_2 = nn.LayerNorm(width, device=device, dtype=dtype)
def _forward(self, x: torch.Tensor):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
def forward(self, x: torch.Tensor):
return checkpoint(self._forward, (x,), self.parameters(), self.use_checkpoint)
class MultiheadCrossAttention(nn.Module):
def __init__(
self,
*,
device: torch.device,
dtype: torch.dtype,
width: int,
heads: int,
qkv_bias: bool = True,
n_data: Optional[int] = None,
data_width: Optional[int] = None,
):
super().__init__()
self.n_data = n_data
self.width = width
self.heads = heads
self.data_width = width if data_width is None else data_width
self.c_q = nn.Linear(width, width, bias=qkv_bias, device=device, dtype=dtype)
self.c_kv = nn.Linear(self.data_width, width * 2, bias=qkv_bias, device=device, dtype=dtype)
self.c_proj = nn.Linear(width, width, device=device, dtype=dtype)
self.attention = QKVMultiheadCrossAttention(
device=device, dtype=dtype, heads=heads, n_data=n_data
)
def forward(self, x, data):
x = self.c_q(x)
data = self.c_kv(data)
x = checkpoint(self.attention, (x, data), (), True)
x = self.c_proj(x)
return x
class QKVMultiheadCrossAttention(nn.Module):
def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int, n_data: Optional[int] = None):
super().__init__()
self.device = device
self.dtype = dtype
self.heads = heads
self.n_data = n_data
def forward(self, q, kv):
_, n_ctx, _ = q.shape
bs, n_data, width = kv.shape
attn_ch = width // self.heads // 2
scale = 1 / math.sqrt(attn_ch)
q = q.view(bs, n_ctx, self.heads, -1)
kv = kv.view(bs, n_data, self.heads, -1)
k, v = torch.split(kv, attn_ch, dim=-1)
weight = torch.einsum("bthc,bshc->bhts", q, k) * scale
wdtype = weight.dtype
weight = torch.softmax(weight.float(), dim=-1).type(wdtype)
return torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1)
class ResidualCrossAttentionBlock(nn.Module):
def __init__(
self,
*,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
n_data: Optional[int] = None,
width: int,
heads: int,
data_width: Optional[int] = None,
qkv_bias: bool = True
):
super().__init__()
if data_width is None:
data_width = width
self.attn = MultiheadCrossAttention(
device=device,
dtype=dtype,
n_data=n_data,
width=width,
heads=heads,
data_width=data_width,
qkv_bias=qkv_bias
)
self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype)
self.ln_2 = nn.LayerNorm(data_width, device=device, dtype=dtype)
self.mlp = MLP(device=device, dtype=dtype, width=width)
self.ln_3 = nn.LayerNorm(width, device=device, dtype=dtype)
def forward(self, x: torch.Tensor, data: torch.Tensor):
x = x + self.attn(self.ln_1(x), self.ln_2(data))
x = x + self.mlp(self.ln_3(x))
return x
class MLP(nn.Module):
def __init__(self, *,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
width: int):
super().__init__()
self.width = width
self.c_fc = nn.Linear(width, width * 4, device=device, dtype=dtype)
self.c_proj = nn.Linear(width * 4, width, device=device, dtype=dtype)
self.gelu = nn.GELU()
def forward(self, x):
return self.c_proj(self.gelu(self.c_fc(x)))
class Transformer(nn.Module):
def __init__(
self,
*,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
n_ctx: int,
width: int,
layers: int,
heads: int,
qkv_bias: bool = True,
use_checkpoint: bool = False
):
super().__init__()
self.n_ctx = n_ctx
self.width = width
self.layers = layers
self.resblocks = nn.ModuleList(
[
ResidualAttentionBlock(
device=device,
dtype=dtype,
n_ctx=n_ctx,
width=width,
heads=heads,
qkv_bias=qkv_bias,
use_checkpoint=use_checkpoint
)
for _ in range(layers)
]
)
self.apply(init_weights)
def forward(self, x: torch.Tensor):
for block in self.resblocks:
x = block(x)
return x

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# -*- coding: utf-8 -*-

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# -*- coding: utf-8 -*-
from typing import List, Tuple, Dict, Optional
from omegaconf import DictConfig
import torch
import torch.nn.functional as F
from torch import nn
from torch.optim import lr_scheduler
from typing import Union
from functools import partial
from MeshAnything.miche.michelangelo.utils import instantiate_from_config
from .tsal_base import (
AlignedShapeAsLatentModule,
ShapeAsLatentModule,
Latent2MeshOutput,
AlignedMeshOutput
)
from MeshAnything.miche.michelangelo.models.tsal.inference_utils import extract_geometry
import trimesh
class AlignedShapeAsLatentPLModule(nn.Module):
def __init__(self, *,
shape_module_cfg,
aligned_module_cfg,
loss_cfg,
optimizer_cfg: Optional[DictConfig] = None,
ckpt_path: Optional[str] = None,
ignore_keys: Union[Tuple[str], List[str]] = ()):
super().__init__()
shape_model: ShapeAsLatentModule = instantiate_from_config(
shape_module_cfg, device=None, dtype=None
)
self.model: AlignedShapeAsLatentModule = instantiate_from_config(
aligned_module_cfg, shape_model=shape_model
)
self.loss = instantiate_from_config(loss_cfg)
self.optimizer_cfg = optimizer_cfg
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
def set_shape_model_only(self):
self.model.set_shape_model_only()
@property
def latent_shape(self):
return self.model.shape_model.latent_shape
@property
def zero_rank(self):
if self._trainer:
zero_rank = self.trainer.local_rank == 0
else:
zero_rank = True
return zero_rank
def init_from_ckpt(self, path, ignore_keys=()):
state_dict = torch.load(path, map_location="cpu")["state_dict"]
keys = list(state_dict.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del state_dict[k]
missing, unexpected = self.load_state_dict(state_dict, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
print(f"Unexpected Keys: {unexpected}")
def configure_optimizers(self) -> Tuple[List, List]:
lr = self.learning_rate
trainable_parameters = list(self.model.parameters())
if self.optimizer_cfg is None:
optimizers = [torch.optim.AdamW(trainable_parameters, lr=lr, betas=(0.9, 0.99), weight_decay=1e-3)]
schedulers = []
else:
optimizer = instantiate_from_config(self.optimizer_cfg.optimizer, params=trainable_parameters)
scheduler_func = instantiate_from_config(
self.optimizer_cfg.scheduler,
max_decay_steps=self.trainer.max_steps,
lr_max=lr
)
scheduler = {
"scheduler": lr_scheduler.LambdaLR(optimizer, lr_lambda=scheduler_func.schedule),
"interval": "step",
"frequency": 1
}
optimizers = [optimizer]
schedulers = [scheduler]
return optimizers, schedulers
def forward(self,
surface: torch.FloatTensor,
image: torch.FloatTensor,
text: torch.FloatTensor,
volume_queries: torch.FloatTensor):
"""
Args:
surface (torch.FloatTensor):
image (torch.FloatTensor):
text (torch.FloatTensor):
volume_queries (torch.FloatTensor):
Returns:
"""
embed_outputs, shape_z = self.model(surface, image, text)
shape_zq, posterior = self.model.shape_model.encode_kl_embed(shape_z)
latents = self.model.shape_model.decode(shape_zq)
logits = self.model.shape_model.query_geometry(volume_queries, latents)
return embed_outputs, logits, posterior
def encode(self, surface: torch.FloatTensor, sample_posterior=True):
pc = surface[..., 0:3]
feats = surface[..., 3:6]
shape_embed, shape_zq, posterior = self.model.shape_model.encode(
pc=pc, feats=feats, sample_posterior=sample_posterior
)
return shape_zq
def encode_latents(self, surface: torch.FloatTensor):
pc = surface[..., 0:3]
feats = surface[..., 3:6]
shape_embed, shape_latents = self.model.shape_model.encode_latents(
pc=pc, feats=feats
)
shape_embed = shape_embed.unsqueeze(1)
assert shape_embed.shape[1] == 1 and shape_latents.shape[1] == 256
cat_latents = torch.cat([shape_embed, shape_latents], dim=1)
return cat_latents
def recon(self, surface):
cat_latents = self.encode_latents(surface)
shape_latents = cat_latents[:, 1:]
shape_zq, posterior = self.model.shape_model.encode_kl_embed(shape_latents)
# decoding
latents = self.model.shape_model.decode(shape_zq)
geometric_func = partial(self.model.shape_model.query_geometry, latents=latents)
# reconstruction
mesh_v_f, has_surface = extract_geometry(
geometric_func=geometric_func,
device=surface.device,
batch_size=surface.shape[0],
bounds=(-1.25, -1.25, -1.25, 1.25, 1.25, 1.25),
octree_depth=7,
num_chunks=10000,
)
recon_mesh = trimesh.Trimesh(mesh_v_f[0][0], mesh_v_f[0][1])
return recon_mesh
def to_shape_latents(self, latents):
shape_zq, posterior = self.model.shape_model.encode_kl_embed(latents, sample_posterior = False)
return self.model.shape_model.decode(shape_zq)
def decode(self,
z_q,
bounds: Union[Tuple[float], List[float], float] = 1.1,
octree_depth: int = 7,
num_chunks: int = 10000) -> List[Latent2MeshOutput]:
latents = self.model.shape_model.decode(z_q) # latents: [bs, num_latents, dim]
outputs = self.latent2mesh(latents, bounds=bounds, octree_depth=octree_depth, num_chunks=num_chunks)
return outputs
def training_step(self, batch: Dict[str, torch.FloatTensor],
batch_idx: int, optimizer_idx: int = 0) -> torch.FloatTensor:
"""
Args:
batch (dict): the batch sample, and it contains:
- surface (torch.FloatTensor): [bs, n_surface, (3 + input_dim)]
- image (torch.FloatTensor): [bs, 3, 224, 224]
- text (torch.FloatTensor): [bs, num_templates, 77]
- geo_points (torch.FloatTensor): [bs, n_pts, (3 + 1)]
batch_idx (int):
optimizer_idx (int):
Returns:
loss (torch.FloatTensor):
"""
surface = batch["surface"]
image = batch["image"]
text = batch["text"]
volume_queries = batch["geo_points"][..., 0:3]
shape_labels = batch["geo_points"][..., -1]
embed_outputs, shape_logits, posteriors = self(surface, image, text, volume_queries)
aeloss, log_dict_ae = self.loss(
**embed_outputs,
posteriors=posteriors,
shape_logits=shape_logits,
shape_labels=shape_labels,
split="train"
)
self.log_dict(log_dict_ae, prog_bar=True, logger=True, batch_size=shape_logits.shape[0],
sync_dist=False, rank_zero_only=True)
return aeloss
def validation_step(self, batch: Dict[str, torch.FloatTensor], batch_idx: int) -> torch.FloatTensor:
surface = batch["surface"]
image = batch["image"]
text = batch["text"]
volume_queries = batch["geo_points"][..., 0:3]
shape_labels = batch["geo_points"][..., -1]
embed_outputs, shape_logits, posteriors = self(surface, image, text, volume_queries)
aeloss, log_dict_ae = self.loss(
**embed_outputs,
posteriors=posteriors,
shape_logits=shape_logits,
shape_labels=shape_labels,
split="val"
)
self.log_dict(log_dict_ae, prog_bar=True, logger=True, batch_size=shape_logits.shape[0],
sync_dist=False, rank_zero_only=True)
return aeloss
def visual_alignment(self,
surface: torch.FloatTensor,
image: torch.FloatTensor,
text: torch.FloatTensor,
description: Optional[List[str]] = None,
bounds: Union[Tuple[float], List[float]] = (-1.25, -1.25, -1.25, 1.25, 1.25, 1.25),
octree_depth: int = 7,
num_chunks: int = 10000) -> List[AlignedMeshOutput]:
"""
Args:
surface:
image:
text:
description:
bounds:
octree_depth:
num_chunks:
Returns:
mesh_outputs (List[AlignedMeshOutput]): the mesh outputs list.
"""
outputs = []
device = surface.device
bs = surface.shape[0]
embed_outputs, shape_z = self.model(surface, image, text)
# calculate the similarity
image_embed = embed_outputs["image_embed"]
text_embed = embed_outputs["text_embed"]
shape_embed = embed_outputs["shape_embed"]
# normalized features
shape_embed = F.normalize(shape_embed, dim=-1, p=2)
text_embed = F.normalize(text_embed, dim=-1, p=2)
image_embed = F.normalize(image_embed, dim=-1, p=2)
# B x B
shape_text_similarity = (100.0 * shape_embed @ text_embed.T).softmax(dim=-1)
# B x B
shape_image_similarity = (100.0 * shape_embed @ image_embed.T).softmax(dim=-1)
# shape reconstruction
shape_zq, posterior = self.model.shape_model.encode_kl_embed(shape_z)
latents = self.model.shape_model.decode(shape_zq)
geometric_func = partial(self.model.shape_model.query_geometry, latents=latents)
# 2. decode geometry
mesh_v_f, has_surface = extract_geometry(
geometric_func=geometric_func,
device=device,
batch_size=bs,
bounds=bounds,
octree_depth=octree_depth,
num_chunks=num_chunks,
disable=not self.zero_rank
)
# 3. decode texture
for i, ((mesh_v, mesh_f), is_surface) in enumerate(zip(mesh_v_f, has_surface)):
if not is_surface:
outputs.append(None)
continue
out = AlignedMeshOutput()
out.mesh_v = mesh_v
out.mesh_f = mesh_f
out.surface = surface[i].cpu().numpy()
out.image = image[i].cpu().numpy()
if description is not None:
out.text = description[i]
out.shape_text_similarity = shape_text_similarity[i, i]
out.shape_image_similarity = shape_image_similarity[i, i]
outputs.append(out)
return outputs
def latent2mesh(self,
latents: torch.FloatTensor,
bounds: Union[Tuple[float], List[float], float] = 1.1,
octree_depth: int = 7,
num_chunks: int = 10000) -> List[Latent2MeshOutput]:
"""
Args:
latents: [bs, num_latents, dim]
bounds:
octree_depth:
num_chunks:
Returns:
mesh_outputs (List[MeshOutput]): the mesh outputs list.
"""
outputs = []
geometric_func = partial(self.model.shape_model.query_geometry, latents=latents)
# 2. decode geometry
device = latents.device
mesh_v_f, has_surface = extract_geometry(
geometric_func=geometric_func,
device=device,
batch_size=len(latents),
bounds=bounds,
octree_depth=octree_depth,
num_chunks=num_chunks,
disable=not self.zero_rank
)
# 3. decode texture
for i, ((mesh_v, mesh_f), is_surface) in enumerate(zip(mesh_v_f, has_surface)):
if not is_surface:
outputs.append(None)
continue
out = Latent2MeshOutput()
out.mesh_v = mesh_v
out.mesh_f = mesh_f
outputs.append(out)
return outputs

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# -*- coding: utf-8 -*-
import torch
from torch import nn
from einops import rearrange
from transformers import CLIPModel
from MeshAnything.miche.michelangelo.models.tsal.tsal_base import AlignedShapeAsLatentModule
class CLIPAlignedShapeAsLatentModule(AlignedShapeAsLatentModule):
def __init__(self, *,
shape_model,
clip_model_version: str = "openai/clip-vit-large-patch14"):
super().__init__()
# self.clip_model: CLIPModel = CLIPModel.from_pretrained(clip_model_version)
# for params in self.clip_model.parameters():
# params.requires_grad = False
self.clip_model = None
self.shape_model = shape_model
self.shape_projection = nn.Parameter(torch.empty(self.shape_model.width, self.shape_model.width))
# nn.init.normal_(self.shape_projection, std=self.shape_model.width ** -0.5)
def set_shape_model_only(self):
self.clip_model = None
def encode_shape_embed(self, surface, return_latents: bool = False):
"""
Args:
surface (torch.FloatTensor): [bs, n, 3 + c]
return_latents (bool):
Returns:
x (torch.FloatTensor): [bs, projection_dim]
shape_latents (torch.FloatTensor): [bs, m, d]
"""
pc = surface[..., 0:3]
feats = surface[..., 3:]
shape_embed, shape_latents = self.shape_model.encode_latents(pc, feats)
x = shape_embed @ self.shape_projection
if return_latents:
return x, shape_latents
else:
return x
def encode_image_embed(self, image):
"""
Args:
image (torch.FloatTensor): [bs, 3, h, w]
Returns:
x (torch.FloatTensor): [bs, projection_dim]
"""
x = self.clip_model.get_image_features(image)
return x
def encode_text_embed(self, text):
x = self.clip_model.get_text_features(text)
return x
def forward(self, surface, image, text):
"""
Args:
surface (torch.FloatTensor):
image (torch.FloatTensor): [bs, 3, 224, 224]
text (torch.LongTensor): [bs, num_templates, 77]
Returns:
embed_outputs (dict): the embedding outputs, and it contains:
- image_embed (torch.FloatTensor):
- text_embed (torch.FloatTensor):
- shape_embed (torch.FloatTensor):
- logit_scale (float):
"""
# # text embedding
# text_embed_all = []
# for i in range(text.shape[0]):
# text_for_one_sample = text[i]
# text_embed = self.encode_text_embed(text_for_one_sample)
# text_embed = text_embed / text_embed.norm(dim=-1, keepdim=True)
# text_embed = text_embed.mean(dim=0)
# text_embed = text_embed / text_embed.norm(dim=-1, keepdim=True)
# text_embed_all.append(text_embed)
# text_embed_all = torch.stack(text_embed_all)
b = text.shape[0]
text_tokens = rearrange(text, "b t l -> (b t) l")
text_embed = self.encode_text_embed(text_tokens)
text_embed = rearrange(text_embed, "(b t) d -> b t d", b=b)
text_embed = text_embed.mean(dim=1)
text_embed = text_embed / text_embed.norm(dim=-1, keepdim=True)
# image embedding
image_embed = self.encode_image_embed(image)
# shape embedding
shape_embed, shape_latents = self.encode_shape_embed(surface, return_latents=True)
embed_outputs = {
"image_embed": image_embed,
"text_embed": text_embed,
"shape_embed": shape_embed,
# "logit_scale": self.clip_model.logit_scale.exp()
}
return embed_outputs, shape_latents

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# -*- coding: utf-8 -*-
import torch
from tqdm import tqdm
from einops import repeat
import numpy as np
from typing import Callable, Tuple, List, Union, Optional
from skimage import measure
from MeshAnything.miche.michelangelo.graphics.primitives import generate_dense_grid_points
@torch.no_grad()
def extract_geometry(geometric_func: Callable,
device: torch.device,
batch_size: int = 1,
bounds: Union[Tuple[float], List[float], float] = (-1.25, -1.25, -1.25, 1.25, 1.25, 1.25),
octree_depth: int = 7,
num_chunks: int = 10000,
disable: bool = True):
"""
Args:
geometric_func:
device:
bounds:
octree_depth:
batch_size:
num_chunks:
disable:
Returns:
"""
if isinstance(bounds, float):
bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
bbox_min = np.array(bounds[0:3])
bbox_max = np.array(bounds[3:6])
bbox_size = bbox_max - bbox_min
xyz_samples, grid_size, length = generate_dense_grid_points(
bbox_min=bbox_min,
bbox_max=bbox_max,
octree_depth=octree_depth,
indexing="ij"
)
xyz_samples = torch.FloatTensor(xyz_samples)
batch_logits = []
for start in tqdm(range(0, xyz_samples.shape[0], num_chunks),
desc="Implicit Function:", disable=disable, leave=False):
queries = xyz_samples[start: start + num_chunks, :].to(device)
batch_queries = repeat(queries, "p c -> b p c", b=batch_size)
logits = geometric_func(batch_queries)
batch_logits.append(logits.cpu())
grid_logits = torch.cat(batch_logits, dim=1).view((batch_size, grid_size[0], grid_size[1], grid_size[2])).numpy()
mesh_v_f = []
has_surface = np.zeros((batch_size,), dtype=np.bool_)
for i in range(batch_size):
try:
vertices, faces, normals, _ = measure.marching_cubes(grid_logits[i], 0, method="lewiner")
vertices = vertices / grid_size * bbox_size + bbox_min
# vertices[:, [0, 1]] = vertices[:, [1, 0]]
mesh_v_f.append((vertices.astype(np.float32), np.ascontiguousarray(faces)))
has_surface[i] = True
except ValueError:
mesh_v_f.append((None, None))
has_surface[i] = False
except RuntimeError:
mesh_v_f.append((None, None))
has_surface[i] = False
return mesh_v_f, has_surface

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# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Dict
from MeshAnything.miche.michelangelo.models.modules.distributions import DiagonalGaussianDistribution
from MeshAnything.miche.michelangelo.utils.eval import compute_psnr
from MeshAnything.miche.michelangelo.utils import misc
class KLNearFar(nn.Module):
def __init__(self,
near_weight: float = 0.1,
kl_weight: float = 1.0,
num_near_samples: Optional[int] = None):
super().__init__()
self.near_weight = near_weight
self.kl_weight = kl_weight
self.num_near_samples = num_near_samples
self.geo_criterion = nn.BCEWithLogitsLoss()
def forward(self,
posteriors: Optional[DiagonalGaussianDistribution],
logits: torch.FloatTensor,
labels: torch.FloatTensor,
split: Optional[str] = "train", **kwargs) -> Tuple[torch.FloatTensor, Dict[str, float]]:
"""
Args:
posteriors (DiagonalGaussianDistribution or torch.distributions.Normal):
logits (torch.FloatTensor): [B, 2*N], logits[:, 0:N] is the volume points; logits[:, N:2N] is the near points;
labels (torch.FloatTensor): [B, 2*N], labels[:, 0:N] is the volume points; labels[:, N:2N] is the near points;
split (str):
**kwargs:
Returns:
loss (torch.Tensor): (,)
log (dict):
"""
if self.num_near_samples is None:
num_vol = logits.shape[1] // 2
else:
num_vol = logits.shape[1] - self.num_near_samples
vol_logits = logits[:, 0:num_vol]
vol_labels = labels[:, 0:num_vol]
near_logits = logits[:, num_vol:]
near_labels = labels[:, num_vol:]
# occupancy loss
# vol_bce = self.geo_criterion(vol_logits, vol_labels)
# near_bce = self.geo_criterion(near_logits, near_labels)
vol_bce = self.geo_criterion(vol_logits.float(), vol_labels.float())
near_bce = self.geo_criterion(near_logits.float(), near_labels.float())
if posteriors is None:
kl_loss = torch.tensor(0.0, dtype=vol_logits.dtype, device=vol_logits.device)
else:
kl_loss = posteriors.kl(dims=(1, 2))
kl_loss = torch.mean(kl_loss)
loss = vol_bce + near_bce * self.near_weight + kl_loss * self.kl_weight
with torch.no_grad():
preds = logits >= 0
accuracy = (preds == labels).float()
accuracy = accuracy.mean()
pos_ratio = torch.mean(labels)
log = {
"{}/total_loss".format(split): loss.clone().detach(),
"{}/near".format(split): near_bce.detach(),
"{}/far".format(split): vol_bce.detach(),
"{}/kl".format(split): kl_loss.detach(),
"{}/accuracy".format(split): accuracy,
"{}/pos_ratio".format(split): pos_ratio
}
if posteriors is not None:
log[f"{split}/mean"] = posteriors.mean.mean().detach()
log[f"{split}/std_mean"] = posteriors.std.mean().detach()
log[f"{split}/std_max"] = posteriors.std.max().detach()
return loss, log
class KLNearFarColor(nn.Module):
def __init__(self,
near_weight: float = 0.1,
kl_weight: float = 1.0,
color_weight: float = 1.0,
color_criterion: str = "mse",
num_near_samples: Optional[int] = None):
super().__init__()
self.color_weight = color_weight
self.near_weight = near_weight
self.kl_weight = kl_weight
self.num_near_samples = num_near_samples
if color_criterion == "mse":
self.color_criterion = nn.MSELoss()
elif color_criterion == "l1":
self.color_criterion = nn.L1Loss()
else:
raise ValueError(f"{color_criterion} must be [`mse`, `l1`].")
self.geo_criterion = nn.BCEWithLogitsLoss()
def forward(self,
posteriors: Optional[DiagonalGaussianDistribution],
logits: torch.FloatTensor,
labels: torch.FloatTensor,
pred_colors: torch.FloatTensor,
gt_colors: torch.FloatTensor,
split: Optional[str] = "train", **kwargs) -> Tuple[torch.FloatTensor, Dict[str, float]]:
"""
Args:
posteriors (DiagonalGaussianDistribution or torch.distributions.Normal):
logits (torch.FloatTensor): [B, 2*N], logits[:, 0:N] is the volume points; logits[:, N:2N] is the near points;
labels (torch.FloatTensor): [B, 2*N], labels[:, 0:N] is the volume points; labels[:, N:2N] is the near points;
pred_colors (torch.FloatTensor): [B, M, 3]
gt_colors (torch.FloatTensor): [B, M, 3]
split (str):
**kwargs:
Returns:
loss (torch.Tensor): (,)
log (dict):
"""
if self.num_near_samples is None:
num_vol = logits.shape[1] // 2
else:
num_vol = logits.shape[1] - self.num_near_samples
vol_logits = logits[:, 0:num_vol]
vol_labels = labels[:, 0:num_vol]
near_logits = logits[:, num_vol:]
near_labels = labels[:, num_vol:]
# occupancy loss
# vol_bce = self.geo_criterion(vol_logits, vol_labels)
# near_bce = self.geo_criterion(near_logits, near_labels)
vol_bce = self.geo_criterion(vol_logits.float(), vol_labels.float())
near_bce = self.geo_criterion(near_logits.float(), near_labels.float())
# surface color loss
color = self.color_criterion(pred_colors, gt_colors)
if posteriors is None:
kl_loss = torch.tensor(0.0, dtype=pred_colors.dtype, device=pred_colors.device)
else:
kl_loss = posteriors.kl(dims=(1, 2))
kl_loss = torch.mean(kl_loss)
loss = vol_bce + near_bce * self.near_weight + color * self.color_weight + kl_loss * self.kl_weight
with torch.no_grad():
preds = logits >= 0
accuracy = (preds == labels).float()
accuracy = accuracy.mean()
psnr = compute_psnr(pred_colors, gt_colors)
log = {
"{}/total_loss".format(split): loss.clone().detach(),
"{}/near".format(split): near_bce.detach(),
"{}/far".format(split): vol_bce.detach(),
"{}/color".format(split): color.detach(),
"{}/kl".format(split): kl_loss.detach(),
"{}/psnr".format(split): psnr.detach(),
"{}/accuracy".format(split): accuracy
}
return loss, log
class ContrastKLNearFar(nn.Module):
def __init__(self,
contrast_weight: float = 1.0,
near_weight: float = 0.1,
kl_weight: float = 1.0,
num_near_samples: Optional[int] = None):
super().__init__()
self.labels = None
self.last_local_batch_size = None
self.contrast_weight = contrast_weight
self.near_weight = near_weight
self.kl_weight = kl_weight
self.num_near_samples = num_near_samples
self.geo_criterion = nn.BCEWithLogitsLoss()
def forward(self,
shape_embed: torch.FloatTensor,
text_embed: torch.FloatTensor,
image_embed: torch.FloatTensor,
logit_scale: torch.FloatTensor,
posteriors: Optional[DiagonalGaussianDistribution],
shape_logits: torch.FloatTensor,
shape_labels: torch.FloatTensor,
split: Optional[str] = "train", **kwargs):
local_batch_size = shape_embed.size(0)
if local_batch_size != self.last_local_batch_size:
self.labels = local_batch_size * misc.get_rank() + torch.arange(
local_batch_size, device=shape_embed.device
).long()
self.last_local_batch_size = local_batch_size
# normalized features
shape_embed = F.normalize(shape_embed, dim=-1, p=2)
text_embed = F.normalize(text_embed, dim=-1, p=2)
image_embed = F.normalize(image_embed, dim=-1, p=2)
# gather features from all GPUs
shape_embed_all, text_embed_all, image_embed_all = misc.all_gather_batch(
[shape_embed, text_embed, image_embed]
)
# cosine similarity as logits
logits_per_shape_text = logit_scale * shape_embed @ text_embed_all.t()
logits_per_text_shape = logit_scale * text_embed @ shape_embed_all.t()
logits_per_shape_image = logit_scale * shape_embed @ image_embed_all.t()
logits_per_image_shape = logit_scale * image_embed @ shape_embed_all.t()
contrast_loss = (F.cross_entropy(logits_per_shape_text, self.labels) +
F.cross_entropy(logits_per_text_shape, self.labels)) / 2 + \
(F.cross_entropy(logits_per_shape_image, self.labels) +
F.cross_entropy(logits_per_image_shape, self.labels)) / 2
# shape reconstruction
if self.num_near_samples is None:
num_vol = shape_logits.shape[1] // 2
else:
num_vol = shape_logits.shape[1] - self.num_near_samples
vol_logits = shape_logits[:, 0:num_vol]
vol_labels = shape_labels[:, 0:num_vol]
near_logits = shape_logits[:, num_vol:]
near_labels = shape_labels[:, num_vol:]
# occupancy loss
vol_bce = self.geo_criterion(vol_logits.float(), vol_labels.float())
near_bce = self.geo_criterion(near_logits.float(), near_labels.float())
if posteriors is None:
kl_loss = torch.tensor(0.0, dtype=vol_logits.dtype, device=vol_logits.device)
else:
kl_loss = posteriors.kl(dims=(1, 2))
kl_loss = torch.mean(kl_loss)
loss = vol_bce + near_bce * self.near_weight + kl_loss * self.kl_weight + contrast_loss * self.contrast_weight
# compute accuracy
with torch.no_grad():
pred = torch.argmax(logits_per_shape_text, dim=-1)
correct = pred.eq(self.labels).sum()
shape_text_acc = 100 * correct / local_batch_size
pred = torch.argmax(logits_per_shape_image, dim=-1)
correct = pred.eq(self.labels).sum()
shape_image_acc = 100 * correct / local_batch_size
preds = shape_logits >= 0
accuracy = (preds == shape_labels).float()
accuracy = accuracy.mean()
log = {
"{}/contrast".format(split): contrast_loss.clone().detach(),
"{}/near".format(split): near_bce.detach(),
"{}/far".format(split): vol_bce.detach(),
"{}/kl".format(split): kl_loss.detach(),
"{}/shape_text_acc".format(split): shape_text_acc,
"{}/shape_image_acc".format(split): shape_image_acc,
"{}/total_loss".format(split): loss.clone().detach(),
"{}/accuracy".format(split): accuracy,
}
if posteriors is not None:
log[f"{split}/mean"] = posteriors.mean.mean().detach()
log[f"{split}/std_mean"] = posteriors.std.mean().detach()
log[f"{split}/std_max"] = posteriors.std.max().detach()
return loss, log

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# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from typing import Optional
from einops import repeat
import math
from MeshAnything.miche.michelangelo.models.modules import checkpoint
from MeshAnything.miche.michelangelo.models.modules.embedder import FourierEmbedder
from MeshAnything.miche.michelangelo.models.modules.distributions import DiagonalGaussianDistribution
from MeshAnything.miche.michelangelo.models.modules.transformer_blocks import (
ResidualCrossAttentionBlock,
Transformer
)
from .tsal_base import ShapeAsLatentModule
class CrossAttentionEncoder(nn.Module):
def __init__(self, *,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
num_latents: int,
fourier_embedder: FourierEmbedder,
point_feats: int,
width: int,
heads: int,
layers: int,
init_scale: float = 0.25,
qkv_bias: bool = True,
flash: bool = False,
use_ln_post: bool = False,
use_checkpoint: bool = False):
super().__init__()
self.use_checkpoint = use_checkpoint
self.num_latents = num_latents
self.query = nn.Parameter(torch.randn((num_latents, width), device=device, dtype=dtype) * 0.02)
self.fourier_embedder = fourier_embedder
self.input_proj = nn.Linear(self.fourier_embedder.out_dim + point_feats, width, device=device, dtype=dtype)
self.cross_attn = ResidualCrossAttentionBlock(
device=device,
dtype=dtype,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
)
self.self_attn = Transformer(
device=device,
dtype=dtype,
n_ctx=num_latents,
width=width,
layers=layers,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
use_checkpoint=False
)
if use_ln_post:
self.ln_post = nn.LayerNorm(width, dtype=dtype, device=device)
else:
self.ln_post = None
def _forward(self, pc, feats):
"""
Args:
pc (torch.FloatTensor): [B, N, 3]
feats (torch.FloatTensor or None): [B, N, C]
Returns:
"""
bs = pc.shape[0]
data = self.fourier_embedder(pc)
if feats is not None:
data = torch.cat([data, feats], dim=-1)
data = self.input_proj(data)
query = repeat(self.query, "m c -> b m c", b=bs)
latents = self.cross_attn(query, data)
latents = self.self_attn(latents)
if self.ln_post is not None:
latents = self.ln_post(latents)
return latents, pc
def forward(self, pc: torch.FloatTensor, feats: Optional[torch.FloatTensor] = None):
"""
Args:
pc (torch.FloatTensor): [B, N, 3]
feats (torch.FloatTensor or None): [B, N, C]
Returns:
dict
"""
return checkpoint(self._forward, (pc, feats), self.parameters(), self.use_checkpoint)
class CrossAttentionDecoder(nn.Module):
def __init__(self, *,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
num_latents: int,
out_channels: int,
fourier_embedder: FourierEmbedder,
width: int,
heads: int,
init_scale: float = 0.25,
qkv_bias: bool = True,
flash: bool = False,
use_checkpoint: bool = False):
super().__init__()
self.use_checkpoint = use_checkpoint
self.fourier_embedder = fourier_embedder
self.query_proj = nn.Linear(self.fourier_embedder.out_dim, width, device=device, dtype=dtype)
self.cross_attn_decoder = ResidualCrossAttentionBlock(
device=device,
dtype=dtype,
n_data=num_latents,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash
)
self.ln_post = nn.LayerNorm(width, device=device, dtype=dtype)
self.output_proj = nn.Linear(width, out_channels, device=device, dtype=dtype)
def _forward(self, queries: torch.FloatTensor, latents: torch.FloatTensor):
queries = self.query_proj(self.fourier_embedder(queries))
x = self.cross_attn_decoder(queries, latents)
x = self.ln_post(x)
x = self.output_proj(x)
return x
def forward(self, queries: torch.FloatTensor, latents: torch.FloatTensor):
return checkpoint(self._forward, (queries, latents), self.parameters(), self.use_checkpoint)
class ShapeAsLatentPerceiver(ShapeAsLatentModule):
def __init__(self, *,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
num_latents: int,
point_feats: int = 0,
embed_dim: int = 0,
num_freqs: int = 8,
include_pi: bool = True,
width: int,
heads: int,
num_encoder_layers: int,
num_decoder_layers: int,
init_scale: float = 0.25,
qkv_bias: bool = True,
flash: bool = False,
use_ln_post: bool = False,
use_checkpoint: bool = False):
super().__init__()
self.use_checkpoint = use_checkpoint
self.num_latents = num_latents
self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi)
init_scale = init_scale * math.sqrt(1.0 / width)
self.encoder = CrossAttentionEncoder(
device=device,
dtype=dtype,
fourier_embedder=self.fourier_embedder,
num_latents=num_latents,
point_feats=point_feats,
width=width,
heads=heads,
layers=num_encoder_layers,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
use_ln_post=use_ln_post,
use_checkpoint=use_checkpoint
)
self.embed_dim = embed_dim
if embed_dim > 0:
# VAE embed
self.pre_kl = nn.Linear(width, embed_dim * 2, device=device, dtype=dtype)
self.post_kl = nn.Linear(embed_dim, width, device=device, dtype=dtype)
self.latent_shape = (num_latents, embed_dim)
else:
self.latent_shape = (num_latents, width)
self.transformer = Transformer(
device=device,
dtype=dtype,
n_ctx=num_latents,
width=width,
layers=num_decoder_layers,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
use_checkpoint=use_checkpoint
)
# geometry decoder
self.geo_decoder = CrossAttentionDecoder(
device=device,
dtype=dtype,
fourier_embedder=self.fourier_embedder,
out_channels=1,
num_latents=num_latents,
width=width,
heads=heads,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
use_checkpoint=use_checkpoint
)
def encode(self,
pc: torch.FloatTensor,
feats: Optional[torch.FloatTensor] = None,
sample_posterior: bool = True):
"""
Args:
pc (torch.FloatTensor): [B, N, 3]
feats (torch.FloatTensor or None): [B, N, C]
sample_posterior (bool):
Returns:
latents (torch.FloatTensor)
center_pos (torch.FloatTensor or None):
posterior (DiagonalGaussianDistribution or None):
"""
latents, center_pos = self.encoder(pc, feats)
posterior = None
if self.embed_dim > 0:
moments = self.pre_kl(latents)
posterior = DiagonalGaussianDistribution(moments, feat_dim=-1)
if sample_posterior:
latents = posterior.sample()
else:
latents = posterior.mode()
return latents, center_pos, posterior
def decode(self, latents: torch.FloatTensor):
latents = self.post_kl(latents)
return self.transformer(latents)
def query_geometry(self, queries: torch.FloatTensor, latents: torch.FloatTensor):
logits = self.geo_decoder(queries, latents).squeeze(-1)
return logits
def forward(self,
pc: torch.FloatTensor,
feats: torch.FloatTensor,
volume_queries: torch.FloatTensor,
sample_posterior: bool = True):
"""
Args:
pc (torch.FloatTensor): [B, N, 3]
feats (torch.FloatTensor or None): [B, N, C]
volume_queries (torch.FloatTensor): [B, P, 3]
sample_posterior (bool):
Returns:
logits (torch.FloatTensor): [B, P]
center_pos (torch.FloatTensor): [B, M, 3]
posterior (DiagonalGaussianDistribution or None).
"""
latents, center_pos, posterior = self.encode(pc, feats, sample_posterior=sample_posterior)
latents = self.decode(latents)
logits = self.query_geometry(volume_queries, latents)
return logits, center_pos, posterior
class AlignedShapeLatentPerceiver(ShapeAsLatentPerceiver):
def __init__(self, *,
device: Optional[torch.device],
dtype: Optional[torch.dtype],
num_latents: int,
point_feats: int = 0,
embed_dim: int = 0,
num_freqs: int = 8,
include_pi: bool = True,
width: int,
heads: int,
num_encoder_layers: int,
num_decoder_layers: int,
init_scale: float = 0.25,
qkv_bias: bool = True,
flash: bool = False,
use_ln_post: bool = False,
use_checkpoint: bool = False):
super().__init__(
device=device,
dtype=dtype,
num_latents=1 + num_latents,
point_feats=point_feats,
embed_dim=embed_dim,
num_freqs=num_freqs,
include_pi=include_pi,
width=width,
heads=heads,
num_encoder_layers=num_encoder_layers,
num_decoder_layers=num_decoder_layers,
init_scale=init_scale,
qkv_bias=qkv_bias,
flash=flash,
use_ln_post=use_ln_post,
use_checkpoint=use_checkpoint
)
self.width = width
def encode(self,
pc: torch.FloatTensor,
feats: Optional[torch.FloatTensor] = None,
sample_posterior: bool = True):
"""
Args:
pc (torch.FloatTensor): [B, N, 3]
feats (torch.FloatTensor or None): [B, N, c]
sample_posterior (bool):
Returns:
shape_embed (torch.FloatTensor)
kl_embed (torch.FloatTensor):
posterior (DiagonalGaussianDistribution or None):
"""
shape_embed, latents = self.encode_latents(pc, feats)
kl_embed, posterior = self.encode_kl_embed(latents, sample_posterior)
return shape_embed, kl_embed, posterior
def encode_latents(self,
pc: torch.FloatTensor,
feats: Optional[torch.FloatTensor] = None):
x, _ = self.encoder(pc, feats)
shape_embed = x[:, 0]
latents = x[:, 1:]
return shape_embed, latents
def encode_kl_embed(self, latents: torch.FloatTensor, sample_posterior: bool = True):
posterior = None
if self.embed_dim > 0:
moments = self.pre_kl(latents)
posterior = DiagonalGaussianDistribution(moments, feat_dim=-1)
if sample_posterior:
kl_embed = posterior.sample()
else:
kl_embed = posterior.mode()
else:
kl_embed = latents
return kl_embed, posterior
def forward(self,
pc: torch.FloatTensor,
feats: torch.FloatTensor,
volume_queries: torch.FloatTensor,
sample_posterior: bool = True):
"""
Args:
pc (torch.FloatTensor): [B, N, 3]
feats (torch.FloatTensor or None): [B, N, C]
volume_queries (torch.FloatTensor): [B, P, 3]
sample_posterior (bool):
Returns:
shape_embed (torch.FloatTensor): [B, projection_dim]
logits (torch.FloatTensor): [B, M]
posterior (DiagonalGaussianDistribution or None).
"""
shape_embed, kl_embed, posterior = self.encode(pc, feats, sample_posterior=sample_posterior)
latents = self.decode(kl_embed)
logits = self.query_geometry(volume_queries, latents)
return shape_embed, logits, posterior

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# -*- coding: utf-8 -*-
from typing import List, Tuple, Dict, Optional
from omegaconf import DictConfig
import torch
from torch.optim import lr_scheduler
import pytorch_lightning as pl
from typing import Union
from functools import partial
from MeshAnything.miche.michelangelo.utils import instantiate_from_config
from .inference_utils import extract_geometry
from .tsal_base import (
ShapeAsLatentModule,
Latent2MeshOutput,
Point2MeshOutput
)
class ShapeAsLatentPLModule(pl.LightningModule):
def __init__(self, *,
module_cfg,
loss_cfg,
optimizer_cfg: Optional[DictConfig] = None,
ckpt_path: Optional[str] = None,
ignore_keys: Union[Tuple[str], List[str]] = ()):
super().__init__()
self.sal: ShapeAsLatentModule = instantiate_from_config(module_cfg, device=None, dtype=None)
self.loss = instantiate_from_config(loss_cfg)
self.optimizer_cfg = optimizer_cfg
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
self.save_hyperparameters()
@property
def latent_shape(self):
return self.sal.latent_shape
@property
def zero_rank(self):
if self._trainer:
zero_rank = self.trainer.local_rank == 0
else:
zero_rank = True
return zero_rank
def init_from_ckpt(self, path, ignore_keys=()):
state_dict = torch.load(path, map_location="cpu")["state_dict"]
keys = list(state_dict.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del state_dict[k]
missing, unexpected = self.load_state_dict(state_dict, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
print(f"Unexpected Keys: {unexpected}")
def configure_optimizers(self) -> Tuple[List, List]:
lr = self.learning_rate
# optimizers = [torch.optim.AdamW(self.sal.parameters(), lr=lr, betas=(0.9, 0.99), weight_decay=1e-4)]
# optimizers = [torch.optim.AdamW(self.sal.parameters(), lr=lr, betas=(0.9, 0.99), weight_decay=1e-3)]
if self.optimizer_cfg is None:
optimizers = [torch.optim.AdamW(self.sal.parameters(), lr=lr, betas=(0.9, 0.99), weight_decay=1e-3)]
schedulers = []
else:
optimizer = instantiate_from_config(self.optimizer_cfg.optimizer, params=self.sal.parameters())
scheduler_func = instantiate_from_config(
self.optimizer_cfg.scheduler,
max_decay_steps=self.trainer.max_steps,
lr_max=lr
)
scheduler = {
"scheduler": lr_scheduler.LambdaLR(optimizer, lr_lambda=scheduler_func.schedule),
"interval": "step",
"frequency": 1
}
optimizers = [optimizer]
schedulers = [scheduler]
return optimizers, schedulers
def forward(self,
pc: torch.FloatTensor,
feats: torch.FloatTensor,
volume_queries: torch.FloatTensor):
logits, center_pos, posterior = self.sal(pc, feats, volume_queries)
return posterior, logits
def encode(self, surface: torch.FloatTensor, sample_posterior=True):
pc = surface[..., 0:3]
feats = surface[..., 3:6]
latents, center_pos, posterior = self.sal.encode(
pc=pc, feats=feats, sample_posterior=sample_posterior
)
return latents
def decode(self,
z_q,
bounds: Union[Tuple[float], List[float], float] = 1.1,
octree_depth: int = 7,
num_chunks: int = 10000) -> List[Latent2MeshOutput]:
latents = self.sal.decode(z_q) # latents: [bs, num_latents, dim]
outputs = self.latent2mesh(latents, bounds=bounds, octree_depth=octree_depth, num_chunks=num_chunks)
return outputs
def training_step(self, batch: Dict[str, torch.FloatTensor],
batch_idx: int, optimizer_idx: int = 0) -> torch.FloatTensor:
"""
Args:
batch (dict): the batch sample, and it contains:
- surface (torch.FloatTensor): [bs, n_surface, (3 + input_dim)]
- geo_points (torch.FloatTensor): [bs, n_pts, (3 + 1)]
batch_idx (int):
optimizer_idx (int):
Returns:
loss (torch.FloatTensor):
"""
pc = batch["surface"][..., 0:3]
feats = batch["surface"][..., 3:]
volume_queries = batch["geo_points"][..., 0:3]
volume_labels = batch["geo_points"][..., -1]
posterior, logits = self(
pc=pc, feats=feats, volume_queries=volume_queries
)
aeloss, log_dict_ae = self.loss(posterior, logits, volume_labels, split="train")
self.log_dict(log_dict_ae, prog_bar=True, logger=True, batch_size=logits.shape[0],
sync_dist=False, rank_zero_only=True)
return aeloss
def validation_step(self, batch: Dict[str, torch.FloatTensor], batch_idx: int) -> torch.FloatTensor:
pc = batch["surface"][..., 0:3]
feats = batch["surface"][..., 3:]
volume_queries = batch["geo_points"][..., 0:3]
volume_labels = batch["geo_points"][..., -1]
posterior, logits = self(
pc=pc, feats=feats, volume_queries=volume_queries,
)
aeloss, log_dict_ae = self.loss(posterior, logits, volume_labels, split="val")
self.log_dict(log_dict_ae, prog_bar=True, logger=True, batch_size=logits.shape[0],
sync_dist=False, rank_zero_only=True)
return aeloss
def point2mesh(self,
pc: torch.FloatTensor,
feats: torch.FloatTensor,
bounds: Union[Tuple[float], List[float]] = (-1.25, -1.25, -1.25, 1.25, 1.25, 1.25),
octree_depth: int = 7,
num_chunks: int = 10000) -> List[Point2MeshOutput]:
"""
Args:
pc:
feats:
bounds:
octree_depth:
num_chunks:
Returns:
mesh_outputs (List[MeshOutput]): the mesh outputs list.
"""
outputs = []
device = pc.device
bs = pc.shape[0]
# 1. point encoder + latents transformer
latents, center_pos, posterior = self.sal.encode(pc, feats)
latents = self.sal.decode(latents) # latents: [bs, num_latents, dim]
geometric_func = partial(self.sal.query_geometry, latents=latents)
# 2. decode geometry
mesh_v_f, has_surface = extract_geometry(
geometric_func=geometric_func,
device=device,
batch_size=bs,
bounds=bounds,
octree_depth=octree_depth,
num_chunks=num_chunks,
disable=not self.zero_rank
)
# 3. decode texture
for i, ((mesh_v, mesh_f), is_surface) in enumerate(zip(mesh_v_f, has_surface)):
if not is_surface:
outputs.append(None)
continue
out = Point2MeshOutput()
out.mesh_v = mesh_v
out.mesh_f = mesh_f
out.pc = torch.cat([pc[i], feats[i]], dim=-1).cpu().numpy()
if center_pos is not None:
out.center = center_pos[i].cpu().numpy()
outputs.append(out)
return outputs
def latent2mesh(self,
latents: torch.FloatTensor,
bounds: Union[Tuple[float], List[float], float] = 1.1,
octree_depth: int = 7,
num_chunks: int = 10000) -> List[Latent2MeshOutput]:
"""
Args:
latents: [bs, num_latents, dim]
bounds:
octree_depth:
num_chunks:
Returns:
mesh_outputs (List[MeshOutput]): the mesh outputs list.
"""
outputs = []
geometric_func = partial(self.sal.query_geometry, latents=latents)
# 2. decode geometry
device = latents.device
mesh_v_f, has_surface = extract_geometry(
geometric_func=geometric_func,
device=device,
batch_size=len(latents),
bounds=bounds,
octree_depth=octree_depth,
num_chunks=num_chunks,
disable=not self.zero_rank
)
# 3. decode texture
for i, ((mesh_v, mesh_f), is_surface) in enumerate(zip(mesh_v_f, has_surface)):
if not is_surface:
outputs.append(None)
continue
out = Latent2MeshOutput()
out.mesh_v = mesh_v
out.mesh_f = mesh_f
outputs.append(out)
return outputs

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# -*- coding: utf-8 -*-
import torch.nn as nn
from typing import Tuple, List, Optional
class Point2MeshOutput(object):
def __init__(self):
self.mesh_v = None
self.mesh_f = None
self.center = None
self.pc = None
class Latent2MeshOutput(object):
def __init__(self):
self.mesh_v = None
self.mesh_f = None
class AlignedMeshOutput(object):
def __init__(self):
self.mesh_v = None
self.mesh_f = None
self.surface = None
self.image = None
self.text: Optional[str] = None
self.shape_text_similarity: Optional[float] = None
self.shape_image_similarity: Optional[float] = None
class ShapeAsLatentPLModule(nn.Module):
latent_shape: Tuple[int]
def encode(self, surface, *args, **kwargs):
raise NotImplementedError
def decode(self, z_q, *args, **kwargs):
raise NotImplementedError
def latent2mesh(self, latents, *args, **kwargs) -> List[Latent2MeshOutput]:
raise NotImplementedError
def point2mesh(self, *args, **kwargs) -> List[Point2MeshOutput]:
raise NotImplementedError
class ShapeAsLatentModule(nn.Module):
latent_shape: Tuple[int, int]
def __init__(self, *args, **kwargs):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
def decode(self, *args, **kwargs):
raise NotImplementedError
def query_geometry(self, *args, **kwargs):
raise NotImplementedError
class AlignedShapeAsLatentPLModule(nn.Module):
latent_shape: Tuple[int]
def set_shape_model_only(self):
raise NotImplementedError
def encode(self, surface, *args, **kwargs):
raise NotImplementedError
def decode(self, z_q, *args, **kwargs):
raise NotImplementedError
def latent2mesh(self, latents, *args, **kwargs) -> List[Latent2MeshOutput]:
raise NotImplementedError
def point2mesh(self, *args, **kwargs) -> List[Point2MeshOutput]:
raise NotImplementedError
class AlignedShapeAsLatentModule(nn.Module):
shape_model: ShapeAsLatentModule
latent_shape: Tuple[int, int]
def __init__(self, *args, **kwargs):
super().__init__()
def set_shape_model_only(self):
raise NotImplementedError
def encode_image_embed(self, *args, **kwargs):
raise NotImplementedError
def encode_text_embed(self, *args, **kwargs):
raise NotImplementedError
def encode_shape_embed(self, *args, **kwargs):
raise NotImplementedError
class TexturedShapeAsLatentModule(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
def decode(self, *args, **kwargs):
raise NotImplementedError
def query_geometry(self, *args, **kwargs):
raise NotImplementedError
def query_color(self, *args, **kwargs):
raise NotImplementedError

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MeshAnything/miche/michelangelo/utils/__init__.py Normal file
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# -*- coding: utf-8 -*-
from .misc import instantiate_from_config

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MeshAnything/miche/michelangelo/utils/eval.py Normal file
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# -*- coding: utf-8 -*-
import torch
def compute_psnr(x, y, data_range: float = 2, eps: float = 1e-7):
mse = torch.mean((x - y) ** 2)
psnr = 10 * torch.log10(data_range / (mse + eps))
return psnr

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MeshAnything/miche/michelangelo/utils/io.py Normal file
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# -*- coding: utf-8 -*-
import os
import io
import tarfile
import json
import numpy as np
import numpy.lib.format
def mkdir(path):
os.makedirs(path, exist_ok=True)
return path
def npy_loads(data):
stream = io.BytesIO(data)
return np.lib.format.read_array(stream)
def npz_loads(data):
return np.load(io.BytesIO(data))
def json_loads(data):
return json.loads(data)
def load_json(filepath):
with open(filepath, "r") as f:
data = json.load(f)
return data
def write_json(filepath, data):
with open(filepath, "w") as f:
json.dump(data, f, indent=2)
def extract_tar(tar_path, tar_cache_folder):
with tarfile.open(tar_path, "r") as tar:
tar.extractall(path=tar_cache_folder)
tar_uids = sorted(os.listdir(tar_cache_folder))
print(f"extract tar: {tar_path} to {tar_cache_folder}")
return tar_uids

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MeshAnything/miche/michelangelo/utils/misc.py Normal file
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# -*- coding: utf-8 -*-
import importlib
import torch
import torch.distributed as dist
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, package=None), cls)
def get_obj_from_config(config):
if "target" not in config:
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])
def instantiate_from_config(config, **kwargs):
if "target" not in config:
raise KeyError("Expected key `target` to instantiate.")
cls = get_obj_from_str(config["target"])
params = config.get("params", dict())
# params.update(kwargs)
# instance = cls(**params)
kwargs.update(params)
instance = cls(**kwargs)
return instance
def is_dist_avail_and_initialized():
if not dist.is_available():
return False
if not dist.is_initialized():
return False
return True
def get_rank():
if not is_dist_avail_and_initialized():
return 0
return dist.get_rank()
def get_world_size():
if not is_dist_avail_and_initialized():
return 1
return dist.get_world_size()
def all_gather_batch(tensors):
"""
Performs all_gather operation on the provided tensors.
"""
# Queue the gathered tensors
world_size = get_world_size()
# There is no need for reduction in the single-proc case
if world_size == 1:
return tensors
tensor_list = []
output_tensor = []
for tensor in tensors:
tensor_all = [torch.ones_like(tensor) for _ in range(world_size)]
dist.all_gather(
tensor_all,
tensor,
async_op=False # performance opt
)
tensor_list.append(tensor_all)
for tensor_all in tensor_list:
output_tensor.append(torch.cat(tensor_all, dim=0))
return output_tensor

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MeshAnything/miche/michelangelo/utils/visualizers/__init__.py Normal file
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# -*- coding: utf-8 -*-

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MeshAnything/miche/michelangelo/utils/visualizers/color_util.py Normal file
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import numpy as np
import matplotlib.pyplot as plt
# Helper functions
def get_colors(inp, colormap="viridis", normalize=True, vmin=None, vmax=None):
colormap = plt.cm.get_cmap(colormap)
if normalize:
vmin = np.min(inp)
vmax = np.max(inp)
norm = plt.Normalize(vmin, vmax)
return colormap(norm(inp))[:, :3]
def gen_checkers(n_checkers_x, n_checkers_y, width=256, height=256):
# tex dims need to be power of two.
array = np.ones((width, height, 3), dtype='float32')
# width in texels of each checker
checker_w = width / n_checkers_x
checker_h = height / n_checkers_y
for y in range(height):
for x in range(width):
color_key = int(x / checker_w) + int(y / checker_h)
if color_key % 2 == 0:
array[x, y, :] = [1., 0.874, 0.0]
else:
array[x, y, :] = [0., 0., 0.]
return array
def gen_circle(width=256, height=256):
xx, yy = np.mgrid[:width, :height]
circle = (xx - width / 2 + 0.5) ** 2 + (yy - height / 2 + 0.5) ** 2
array = np.ones((width, height, 4), dtype='float32')
array[:, :, 0] = (circle <= width)
array[:, :, 1] = (circle <= width)
array[:, :, 2] = (circle <= width)
array[:, :, 3] = circle <= width
return array

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MeshAnything/miche/michelangelo/utils/visualizers/html_util.py Normal file
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# -*- coding: utf-8 -*-
import io
import base64
import numpy as np
from PIL import Image
def to_html_frame(content):
html_frame = f"""
<html>
<body>
{content}
</body>
</html>
"""
return html_frame
def to_single_row_table(caption: str, content: str):
table_html = f"""
<table border = "1">
<caption>{caption}</caption>
<tr>
<td>{content}</td>
</tr>
</table>
"""
return table_html
def to_image_embed_tag(image: np.ndarray):
# Convert np.ndarray to bytes
img = Image.fromarray(image)
raw_bytes = io.BytesIO()
img.save(raw_bytes, "PNG")
# Encode bytes to base64
image_base64 = base64.b64encode(raw_bytes.getvalue()).decode("utf-8")
image_tag = f"""
<img src="data:image/png;base64,{image_base64}" alt="Embedded Image">
"""
return image_tag

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MeshAnything/miche/michelangelo/utils/visualizers/pythreejs_viewer.py Normal file
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import numpy as np
from ipywidgets import embed
import pythreejs as p3s
import uuid
from .color_util import get_colors, gen_circle, gen_checkers
EMBED_URL = "https://cdn.jsdelivr.net/npm/@jupyter-widgets/html-manager@1.0.1/dist/embed-amd.js"
class PyThreeJSViewer(object):
def __init__(self, settings, render_mode="WEBSITE"):
self.render_mode = render_mode
self.__update_settings(settings)
self._light = p3s.DirectionalLight(color='white', position=[0, 0, 1], intensity=0.6)
self._light2 = p3s.AmbientLight(intensity=0.5)
self._cam = p3s.PerspectiveCamera(position=[0, 0, 1], lookAt=[0, 0, 0], fov=self.__s["fov"],
aspect=self.__s["width"] / self.__s["height"], children=[self._light])
self._orbit = p3s.OrbitControls(controlling=self._cam)
self._scene = p3s.Scene(children=[self._cam, self._light2], background=self.__s["background"]) # "#4c4c80"
self._renderer = p3s.Renderer(camera=self._cam, scene=self._scene, controls=[self._orbit],
width=self.__s["width"], height=self.__s["height"],
antialias=self.__s["antialias"])
self.__objects = {}
self.__cnt = 0
def jupyter_mode(self):
self.render_mode = "JUPYTER"
def offline(self):
self.render_mode = "OFFLINE"
def website(self):
self.render_mode = "WEBSITE"
def __get_shading(self, shading):
shad = {"flat": True, "wireframe": False, "wire_width": 0.03, "wire_color": "black",
"side": 'DoubleSide', "colormap": "viridis", "normalize": [None, None],
"bbox": False, "roughness": 0.5, "metalness": 0.25, "reflectivity": 1.0,
"line_width": 1.0, "line_color": "black",
"point_color": "red", "point_size": 0.01, "point_shape": "circle",
"text_color": "red"
}
for k in shading:
shad[k] = shading[k]
return shad
def __update_settings(self, settings={}):
sett = {"width": 600, "height": 600, "antialias": True, "scale": 1.5, "background": "#ffffff",
"fov": 30}
for k in settings:
sett[k] = settings[k]
self.__s = sett
def __add_object(self, obj, parent=None):
if not parent: # Object is added to global scene and objects dict
self.__objects[self.__cnt] = obj
self.__cnt += 1
self._scene.add(obj["mesh"])
else: # Object is added to parent object and NOT to objects dict
parent.add(obj["mesh"])
self.__update_view()
if self.render_mode == "JUPYTER":
return self.__cnt - 1
elif self.render_mode == "WEBSITE":
return self
def __add_line_geometry(self, lines, shading, obj=None):
lines = lines.astype("float32", copy=False)
mi = np.min(lines, axis=0)
ma = np.max(lines, axis=0)
geometry = p3s.LineSegmentsGeometry(positions=lines.reshape((-1, 2, 3)))
material = p3s.LineMaterial(linewidth=shading["line_width"], color=shading["line_color"])
# , vertexColors='VertexColors'),
lines = p3s.LineSegments2(geometry=geometry, material=material) # type='LinePieces')
line_obj = {"geometry": geometry, "mesh": lines, "material": material,
"max": ma, "min": mi, "type": "Lines", "wireframe": None}
if obj:
return self.__add_object(line_obj, obj), line_obj
else:
return self.__add_object(line_obj)
def __update_view(self):
if len(self.__objects) == 0:
return
ma = np.zeros((len(self.__objects), 3))
mi = np.zeros((len(self.__objects), 3))
for r, obj in enumerate(self.__objects):
ma[r] = self.__objects[obj]["max"]
mi[r] = self.__objects[obj]["min"]
ma = np.max(ma, axis=0)
mi = np.min(mi, axis=0)
diag = np.linalg.norm(ma - mi)
mean = ((ma - mi) / 2 + mi).tolist()
scale = self.__s["scale"] * (diag)
self._orbit.target = mean
self._cam.lookAt(mean)
self._cam.position = [mean[0], mean[1], mean[2] + scale]
self._light.position = [mean[0], mean[1], mean[2] + scale]
self._orbit.exec_three_obj_method('update')
self._cam.exec_three_obj_method('updateProjectionMatrix')
def __get_bbox(self, v):
m = np.min(v, axis=0)
M = np.max(v, axis=0)
# Corners of the bounding box
v_box = np.array([[m[0], m[1], m[2]], [M[0], m[1], m[2]], [M[0], M[1], m[2]], [m[0], M[1], m[2]],
[m[0], m[1], M[2]], [M[0], m[1], M[2]], [M[0], M[1], M[2]], [m[0], M[1], M[2]]])
f_box = np.array([[0, 1], [1, 2], [2, 3], [3, 0], [4, 5], [5, 6], [6, 7], [7, 4],
[0, 4], [1, 5], [2, 6], [7, 3]], dtype=np.uint32)
return v_box, f_box
def __get_colors(self, v, f, c, sh):
coloring = "VertexColors"
if type(c) == np.ndarray and c.size == 3: # Single color
colors = np.ones_like(v)
colors[:, 0] = c[0]
colors[:, 1] = c[1]
colors[:, 2] = c[2]
# print("Single colors")
elif type(c) == np.ndarray and len(c.shape) == 2 and c.shape[1] == 3: # Color values for
if c.shape[0] == f.shape[0]: # faces
colors = np.hstack([c, c, c]).reshape((-1, 3))
coloring = "FaceColors"
# print("Face color values")
elif c.shape[0] == v.shape[0]: # vertices
colors = c
# print("Vertex color values")
else: # Wrong size, fallback
print("Invalid color array given! Supported are numpy arrays.", type(c))
colors = np.ones_like(v)
colors[:, 0] = 1.0
colors[:, 1] = 0.874
colors[:, 2] = 0.0
elif type(c) == np.ndarray and c.size == f.shape[0]: # Function values for faces
normalize = sh["normalize"][0] != None and sh["normalize"][1] != None
cc = get_colors(c, sh["colormap"], normalize=normalize,
vmin=sh["normalize"][0], vmax=sh["normalize"][1])
# print(cc.shape)
colors = np.hstack([cc, cc, cc]).reshape((-1, 3))
coloring = "FaceColors"
# print("Face function values")
elif type(c) == np.ndarray and c.size == v.shape[0]: # Function values for vertices
normalize = sh["normalize"][0] != None and sh["normalize"][1] != None
colors = get_colors(c, sh["colormap"], normalize=normalize,
vmin=sh["normalize"][0], vmax=sh["normalize"][1])
# print("Vertex function values")
else:
colors = np.ones_like(v)
colors[:, 0] = 1.0
colors[:, 1] = 0.874
colors[:, 2] = 0.0
# No color
if c is not None:
print("Invalid color array given! Supported are numpy arrays.", type(c))
return colors, coloring
def __get_point_colors(self, v, c, sh):
v_color = True
if c is None: # No color given, use global color
# conv = mpl.colors.ColorConverter()
colors = sh["point_color"] # np.array(conv.to_rgb(sh["point_color"]))
v_color = False
elif isinstance(c, str): # No color given, use global color
# conv = mpl.colors.ColorConverter()
colors = c # np.array(conv.to_rgb(c))
v_color = False
elif type(c) == np.ndarray and len(c.shape) == 2 and c.shape[0] == v.shape[0] and c.shape[1] == 3:
# Point color
colors = c.astype("float32", copy=False)
elif isinstance(c, np.ndarray) and len(c.shape) == 2 and c.shape[0] == v.shape[0] and c.shape[1] != 3:
# Function values for vertices, but the colors are features
c_norm = np.linalg.norm(c, ord=2, axis=-1)
normalize = sh["normalize"][0] != None and sh["normalize"][1] != None
colors = get_colors(c_norm, sh["colormap"], normalize=normalize,
vmin=sh["normalize"][0], vmax=sh["normalize"][1])
colors = colors.astype("float32", copy=False)
elif type(c) == np.ndarray and c.size == v.shape[0]: # Function color
normalize = sh["normalize"][0] != None and sh["normalize"][1] != None
colors = get_colors(c, sh["colormap"], normalize=normalize,
vmin=sh["normalize"][0], vmax=sh["normalize"][1])
colors = colors.astype("float32", copy=False)
# print("Vertex function values")
else:
print("Invalid color array given! Supported are numpy arrays.", type(c))
colors = sh["point_color"]
v_color = False
return colors, v_color
def add_mesh(self, v, f, c=None, uv=None, n=None, shading={}, texture_data=None, **kwargs):
shading.update(kwargs)
sh = self.__get_shading(shading)
mesh_obj = {}
# it is a tet
if v.shape[1] == 3 and f.shape[1] == 4:
f_tmp = np.ndarray([f.shape[0] * 4, 3], dtype=f.dtype)
for i in range(f.shape[0]):
f_tmp[i * 4 + 0] = np.array([f[i][1], f[i][0], f[i][2]])
f_tmp[i * 4 + 1] = np.array([f[i][0], f[i][1], f[i][3]])
f_tmp[i * 4 + 2] = np.array([f[i][1], f[i][2], f[i][3]])
f_tmp[i * 4 + 3] = np.array([f[i][2], f[i][0], f[i][3]])
f = f_tmp
if v.shape[1] == 2:
v = np.append(v, np.zeros([v.shape[0], 1]), 1)
# Type adjustment vertices
v = v.astype("float32", copy=False)
# Color setup
colors, coloring = self.__get_colors(v, f, c, sh)
# Type adjustment faces and colors
c = colors.astype("float32", copy=False)
# Material and geometry setup
ba_dict = {"color": p3s.BufferAttribute(c)}
if coloring == "FaceColors":
verts = np.zeros((f.shape[0] * 3, 3), dtype="float32")
for ii in range(f.shape[0]):
# print(ii*3, f[ii])
verts[ii * 3] = v[f[ii, 0]]
verts[ii * 3 + 1] = v[f[ii, 1]]
verts[ii * 3 + 2] = v[f[ii, 2]]
v = verts
else:
f = f.astype("uint32", copy=False).ravel()
ba_dict["index"] = p3s.BufferAttribute(f, normalized=False)
ba_dict["position"] = p3s.BufferAttribute(v, normalized=False)
if uv is not None:
uv = (uv - np.min(uv)) / (np.max(uv) - np.min(uv))
if texture_data is None:
texture_data = gen_checkers(20, 20)
tex = p3s.DataTexture(data=texture_data, format="RGBFormat", type="FloatType")
material = p3s.MeshStandardMaterial(map=tex, reflectivity=sh["reflectivity"], side=sh["side"],
roughness=sh["roughness"], metalness=sh["metalness"],
flatShading=sh["flat"],
polygonOffset=True, polygonOffsetFactor=1, polygonOffsetUnits=5)
ba_dict["uv"] = p3s.BufferAttribute(uv.astype("float32", copy=False))
else:
material = p3s.MeshStandardMaterial(vertexColors=coloring, reflectivity=sh["reflectivity"],
side=sh["side"], roughness=sh["roughness"], metalness=sh["metalness"],
flatShading=sh["flat"],
polygonOffset=True, polygonOffsetFactor=1, polygonOffsetUnits=5)
if type(n) != type(None) and coloring == "VertexColors": # TODO: properly handle normals for FaceColors as well
ba_dict["normal"] = p3s.BufferAttribute(n.astype("float32", copy=False), normalized=True)
geometry = p3s.BufferGeometry(attributes=ba_dict)
if coloring == "VertexColors" and type(n) == type(None):
geometry.exec_three_obj_method('computeVertexNormals')
elif coloring == "FaceColors" and type(n) == type(None):
geometry.exec_three_obj_method('computeFaceNormals')
# Mesh setup
mesh = p3s.Mesh(geometry=geometry, material=material)
# Wireframe setup
mesh_obj["wireframe"] = None
if sh["wireframe"]:
wf_geometry = p3s.WireframeGeometry(mesh.geometry) # WireframeGeometry
wf_material = p3s.LineBasicMaterial(color=sh["wire_color"], linewidth=sh["wire_width"])
wireframe = p3s.LineSegments(wf_geometry, wf_material)
mesh.add(wireframe)
mesh_obj["wireframe"] = wireframe
# Bounding box setup
if sh["bbox"]:
v_box, f_box = self.__get_bbox(v)
_, bbox = self.add_edges(v_box, f_box, sh, mesh)
mesh_obj["bbox"] = [bbox, v_box, f_box]
# Object setup
mesh_obj["max"] = np.max(v, axis=0)
mesh_obj["min"] = np.min(v, axis=0)
mesh_obj["geometry"] = geometry
mesh_obj["mesh"] = mesh
mesh_obj["material"] = material
mesh_obj["type"] = "Mesh"
mesh_obj["shading"] = sh
mesh_obj["coloring"] = coloring
mesh_obj["arrays"] = [v, f, c] # TODO replays with proper storage or remove if not needed
return self.__add_object(mesh_obj)
def add_lines(self, beginning, ending, shading={}, obj=None, **kwargs):
shading.update(kwargs)
if len(beginning.shape) == 1:
if len(beginning) == 2:
beginning = np.array([[beginning[0], beginning[1], 0]])
else:
if beginning.shape[1] == 2:
beginning = np.append(
beginning, np.zeros([beginning.shape[0], 1]), 1)
if len(ending.shape) == 1:
if len(ending) == 2:
ending = np.array([[ending[0], ending[1], 0]])
else:
if ending.shape[1] == 2:
ending = np.append(
ending, np.zeros([ending.shape[0], 1]), 1)
sh = self.__get_shading(shading)
lines = np.hstack([beginning, ending])
lines = lines.reshape((-1, 3))
return self.__add_line_geometry(lines, sh, obj)
def add_edges(self, vertices, edges, shading={}, obj=None, **kwargs):
shading.update(kwargs)
if vertices.shape[1] == 2:
vertices = np.append(
vertices, np.zeros([vertices.shape[0], 1]), 1)
sh = self.__get_shading(shading)
lines = np.zeros((edges.size, 3))
cnt = 0
for e in edges:
lines[cnt, :] = vertices[e[0]]
lines[cnt + 1, :] = vertices[e[1]]
cnt += 2
return self.__add_line_geometry(lines, sh, obj)
def add_points(self, points, c=None, shading={}, obj=None, **kwargs):
shading.update(kwargs)
if len(points.shape) == 1:
if len(points) == 2:
points = np.array([[points[0], points[1], 0]])
else:
if points.shape[1] == 2:
points = np.append(
points, np.zeros([points.shape[0], 1]), 1)
sh = self.__get_shading(shading)
points = points.astype("float32", copy=False)
mi = np.min(points, axis=0)
ma = np.max(points, axis=0)
g_attributes = {"position": p3s.BufferAttribute(points, normalized=False)}
m_attributes = {"size": sh["point_size"]}
if sh["point_shape"] == "circle": # Plot circles
tex = p3s.DataTexture(data=gen_circle(16, 16), format="RGBAFormat", type="FloatType")
m_attributes["map"] = tex
m_attributes["alphaTest"] = 0.5
m_attributes["transparency"] = True
else: # Plot squares
pass
colors, v_colors = self.__get_point_colors(points, c, sh)
if v_colors: # Colors per point
m_attributes["vertexColors"] = 'VertexColors'
g_attributes["color"] = p3s.BufferAttribute(colors, normalized=False)
else: # Colors for all points
m_attributes["color"] = colors
material = p3s.PointsMaterial(**m_attributes)
geometry = p3s.BufferGeometry(attributes=g_attributes)
points = p3s.Points(geometry=geometry, material=material)
point_obj = {"geometry": geometry, "mesh": points, "material": material,
"max": ma, "min": mi, "type": "Points", "wireframe": None}
if obj:
return self.__add_object(point_obj, obj), point_obj
else:
return self.__add_object(point_obj)
def remove_object(self, obj_id):
if obj_id not in self.__objects:
print("Invalid object id. Valid ids are: ", list(self.__objects.keys()))
return
self._scene.remove(self.__objects[obj_id]["mesh"])
del self.__objects[obj_id]
self.__update_view()
def reset(self):
for obj_id in list(self.__objects.keys()).copy():
self._scene.remove(self.__objects[obj_id]["mesh"])
del self.__objects[obj_id]
self.__update_view()
def update_object(self, oid=0, vertices=None, colors=None, faces=None):
obj = self.__objects[oid]
if type(vertices) != type(None):
if obj["coloring"] == "FaceColors":
f = obj["arrays"][1]
verts = np.zeros((f.shape[0] * 3, 3), dtype="float32")
for ii in range(f.shape[0]):
# print(ii*3, f[ii])
verts[ii * 3] = vertices[f[ii, 0]]
verts[ii * 3 + 1] = vertices[f[ii, 1]]
verts[ii * 3 + 2] = vertices[f[ii, 2]]
v = verts
else:
v = vertices.astype("float32", copy=False)
obj["geometry"].attributes["position"].array = v
# self.wireframe.attributes["position"].array = v # Wireframe updates?
obj["geometry"].attributes["position"].needsUpdate = True
# obj["geometry"].exec_three_obj_method('computeVertexNormals')
if type(colors) != type(None):
colors, coloring = self.__get_colors(obj["arrays"][0], obj["arrays"][1], colors, obj["shading"])
colors = colors.astype("float32", copy=False)
obj["geometry"].attributes["color"].array = colors
obj["geometry"].attributes["color"].needsUpdate = True
if type(faces) != type(None):
if obj["coloring"] == "FaceColors":
print("Face updates are currently only possible in vertex color mode.")
return
f = faces.astype("uint32", copy=False).ravel()
print(obj["geometry"].attributes)
obj["geometry"].attributes["index"].array = f
# self.wireframe.attributes["position"].array = v # Wireframe updates?
obj["geometry"].attributes["index"].needsUpdate = True
# obj["geometry"].exec_three_obj_method('computeVertexNormals')
# self.mesh.geometry.verticesNeedUpdate = True
# self.mesh.geometry.elementsNeedUpdate = True
# self.update()
if self.render_mode == "WEBSITE":
return self
# def update(self):
# self.mesh.exec_three_obj_method('update')
# self.orbit.exec_three_obj_method('update')
# self.cam.exec_three_obj_method('updateProjectionMatrix')
# self.scene.exec_three_obj_method('update')
def add_text(self, text, shading={}, **kwargs):
shading.update(kwargs)
sh = self.__get_shading(shading)
tt = p3s.TextTexture(string=text, color=sh["text_color"])
sm = p3s.SpriteMaterial(map=tt)
text = p3s.Sprite(material=sm, scaleToTexture=True)
self._scene.add(text)
# def add_widget(self, widget, callback):
# self.widgets.append(widget)
# widget.observe(callback, names='value')
# def add_dropdown(self, options, default, desc, cb):
# widget = widgets.Dropdown(options=options, value=default, description=desc)
# self.__widgets.append(widget)
# widget.observe(cb, names="value")
# display(widget)
# def add_button(self, text, cb):
# button = widgets.Button(description=text)
# self.__widgets.append(button)
# button.on_click(cb)
# display(button)
def to_html(self, imports=True, html_frame=True):
# Bake positions (fixes centering bug in offline rendering)
if len(self.__objects) == 0:
return
ma = np.zeros((len(self.__objects), 3))
mi = np.zeros((len(self.__objects), 3))
for r, obj in enumerate(self.__objects):
ma[r] = self.__objects[obj]["max"]
mi[r] = self.__objects[obj]["min"]
ma = np.max(ma, axis=0)
mi = np.min(mi, axis=0)
diag = np.linalg.norm(ma - mi)
mean = (ma - mi) / 2 + mi
for r, obj in enumerate(self.__objects):
v = self.__objects[obj]["geometry"].attributes["position"].array
v -= mean
v += np.array([0.0, .9, 0.0]) #! to move the obj to the center of window
scale = self.__s["scale"] * (diag)
self._orbit.target = [0.0, 0.0, 0.0]
self._cam.lookAt([0.0, 0.0, 0.0])
# self._cam.position = [0.0, 0.0, scale]
self._cam.position = [0.0, 0.5, scale * 1.3] #! show four complete meshes in the window
self._light.position = [0.0, 0.0, scale]
state = embed.dependency_state(self._renderer)
# Somehow these entries are missing when the state is exported in python.
# Exporting from the GUI works, so we are inserting the missing entries.
for k in state:
if state[k]["model_name"] == "OrbitControlsModel":
state[k]["state"]["maxAzimuthAngle"] = "inf"
state[k]["state"]["maxDistance"] = "inf"
state[k]["state"]["maxZoom"] = "inf"
state[k]["state"]["minAzimuthAngle"] = "-inf"
tpl = embed.load_requirejs_template
if not imports:
embed.load_requirejs_template = ""
s = embed.embed_snippet(self._renderer, state=state, embed_url=EMBED_URL)
# s = embed.embed_snippet(self.__w, state=state)
embed.load_requirejs_template = tpl
if html_frame:
s = "<html>\n<body>\n" + s + "\n</body>\n</html>"
# Revert changes
for r, obj in enumerate(self.__objects):
v = self.__objects[obj]["geometry"].attributes["position"].array
v += mean
self.__update_view()
return s
def save(self, filename=""):
if filename == "":
uid = str(uuid.uuid4()) + ".html"
else:
filename = filename.replace(".html", "")
uid = filename + '.html'
with open(uid, "w") as f:
f.write(self.to_html())
print("Plot saved to file %s." % uid)

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model:
target: MeshAnything.miche.michelangelo.models.tsal.asl_pl_module.AlignedShapeAsLatentPLModule
params:
shape_module_cfg:
target: MeshAnything.miche.michelangelo.models.tsal.sal_perceiver.AlignedShapeLatentPerceiver
params:
num_latents: 256
embed_dim: 64
point_feats: 3 # normal
num_freqs: 8
include_pi: false
heads: 12
width: 768
num_encoder_layers: 8
num_decoder_layers: 16
use_ln_post: true
init_scale: 0.25
qkv_bias: false
use_checkpoint: true
aligned_module_cfg:
target: MeshAnything.miche.michelangelo.models.tsal.clip_asl_module.CLIPAlignedShapeAsLatentModule
params:
clip_model_version: "./checkpoints/clip/clip-vit-large-patch14"
loss_cfg:
target: MeshAnything.miche.michelangelo.models.tsal.loss.ContrastKLNearFar
params:
contrast_weight: 0.1
near_weight: 0.1
kl_weight: 0.001
optimizer_cfg:
optimizer:
target: torch.optim.AdamW
params:
betas: [0.9, 0.99]
eps: 1.e-6
weight_decay: 1.e-2
scheduler:
target: MeshAnything.miche.michelangelo.utils.trainings.lr_scheduler.LambdaWarmUpCosineFactorScheduler
params:
warm_up_steps: 5000
f_start: 1.e-6
f_min: 1.e-3
f_max: 1.0

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import torch
from torch import nn, Tensor
from transformers import AutoModelForCausalLM, AutoConfig, AutoModel
from MeshAnything.miche.encode import load_model
from MeshAnything.models.shape_opt import ShapeOPTConfig
from einops.layers.torch import Rearrange
from einops import rearrange, repeat, reduce, pack, unpack
import torch.nn.functional as F
class NoiseResistantDecoder(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.pad_id = -1
self.num_quantizers = 3
self.discrete_num = 128
self.codebook_size = args.codebook_size
self.codebook_dim = args.codebook_dim
config = AutoConfig.from_pretrained("bert-base-uncased")
config.num_hidden_layers = 6
self.decoder= AutoModel.from_config(config=config).to_bettertransformer().encoder
self.n_embd = self.decoder.config.hidden_size
self.pos_embedding = nn.Embedding(18000, self.n_embd)
self.layernorm = nn.LayerNorm(self.n_embd)
self.point_layernorm = nn.LayerNorm(self.n_embd)
self.cond_length = 257
self.cond_dim = 768
self.point_pe = nn.Embedding(self.cond_length, self.n_embd)
self.cond_proj = nn.Linear(self.cond_dim, self.n_embd)
self.cond_head_proj = nn.Linear(self.cond_dim, self.n_embd)
self.project_down_codebook = nn.Linear(self.codebook_dim * 3, self.n_embd)
self.to_coor_logits = nn.Sequential(
nn.Linear(self.n_embd, self.discrete_num * 9),
Rearrange('... (v c) -> ... v c', v = 9)
)
def process_point_feature(self, encode_feature):
point_feature = torch.zeros(encode_feature.shape[0], self.cond_length, self.n_embd, device=self.cond_head_proj.weight.device, dtype=self.cond_head_proj.weight.dtype)
point_feature[:, 0] = self.cond_head_proj(encode_feature[:, 0])
point_feature[:, 1:] = self.cond_proj(encode_feature[:, 1:])
point_feature = self.point_layernorm(point_feature + self.point_pe.weight[None, :point_feature.shape[1]])
return point_feature
def forward(self, input_ids, input_embeds, point_feature = None):
input_ids = input_ids.reshape(input_ids.shape[0], -1)
point_feature = self.process_point_feature(point_feature)
face_embeds = rearrange(input_embeds, 'b (nf nv) d -> b nf (nv d)', nv = 3)
face_embeds = self.project_down_codebook(face_embeds)
face_mask = reduce(input_ids != self.pad_id, 'b (nf nv q) -> b nf', 'all', nv = 3, q = self.num_quantizers)
face_embeds[~face_mask] = 0
face_embeds = self.layernorm(face_embeds + self.pos_embedding.weight[None, :face_embeds.shape[1]])
outputs = self.decoder(
hidden_states=torch.concatenate([point_feature, face_embeds], dim=1),
)
decoded = outputs.last_hidden_state[:, self.cond_length:] # batch x nfaces x dim
decoded = decoded.masked_fill(~face_mask.unsqueeze(-1), 0.)
# batch x nfaces x 9 -> batch x nfaces x 3 x 3
pred_face_logits = self.to_coor_logits(decoded) # batch x nfaces x 9 x ndiscrete
pred_face_coords = rearrange(pred_face_logits.argmax(dim = -1), '... (v c) -> ... v c', v = 3)
continuous_coors = undiscretize(
pred_face_coords,
num_discrete = self.discrete_num,
low = -0.5,
high = 0.5
)
continuous_coors = continuous_coors.masked_fill(~rearrange(face_mask, 'b nf -> b nf 1 1'), float('nan'))
return continuous_coors
class MeshAnything(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.point_encoder = load_model(ckpt_path=None)
self.tokenizer = NoiseResistantDecoder(args)
self.num_quantizers = 3
self.face_per_token = self.num_quantizers * 3
self.cond_length = 257
self.cond_dim = 768
self.max_length = args.n_max_triangles * self.face_per_token + 2 + self.cond_length
self.config = ShapeOPTConfig.from_pretrained(
args.llm,
n_positions=18259,
max_position_embeddings=18259,
vocab_size=self.tokenizer.codebook_size + 3,
_attn_implementation="flash_attention_2"
)
self.bos_token_id = 0
self.eos_token_id = 1
self.pad_token_id = 2
self.config.bos_token_id = self.bos_token_id
self.config.eos_token_id = self.eos_token_id
self.config.pad_token_id = self.pad_token_id
self.config.quantize_codebook_dim = self.tokenizer.codebook_dim
self.config.face_per_token = self.face_per_token
self.config._attn_implementation="flash_attention_2"
self.config.cond_length = self.cond_length
if self.config.word_embed_proj_dim != self.config.hidden_size:
self.config.word_embed_proj_dim = self.config.hidden_size
self.transformer = AutoModelForCausalLM.from_config(
config=self.config, use_flash_attention_2 = True
)
self.transformer.to_bettertransformer()
self.transformer.model.decoder.quantize_codebooks = nn.Parameter(torch.zeros(1, self.tokenizer.codebook_size, self.tokenizer.codebook_dim))
self.cond_head_proj = nn.Linear(self.cond_dim, self.config.word_embed_proj_dim)
self.cond_proj = nn.Linear(self.cond_dim * 2, self.config.word_embed_proj_dim)
self.eval()
def process_point_feature(self, point_feature):
encode_feature = torch.zeros(point_feature.shape[0], self.cond_length, self.config.word_embed_proj_dim,
device=self.cond_head_proj.weight.device, dtype=self.cond_head_proj.weight.dtype)
encode_feature[:, 0] = self.cond_head_proj(point_feature[:, 0])
shape_latents = self.point_encoder.to_shape_latents(point_feature[:, 1:])
encode_feature[:, 1:] = self.cond_proj(torch.cat([point_feature[:, 1:], shape_latents], dim=-1))
return encode_feature
@torch.no_grad()
def forward(self, pc_normal, sampling=False) -> dict:
batch_size = pc_normal.shape[0]
point_feature = self.point_encoder.encode_latents(pc_normal)
processed_point_feature = self.process_point_feature(point_feature)
generate_length = self.max_length - self.cond_length
net_device = next(self.parameters()).device
outputs = torch.ones(batch_size, generate_length).long().to(net_device) * self.eos_token_id
if not sampling:
results = self.transformer.generate(
inputs_embeds=processed_point_feature,
max_new_tokens=generate_length, # all faces plus two
num_beams=1,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
)
else:
results = self.transformer.generate(
inputs_embeds = processed_point_feature,
max_new_tokens=generate_length, # all faces plus two
do_sample=True,
top_k=50,
top_p=0.95,
bos_token_id = self.bos_token_id,
eos_token_id = self.eos_token_id,
pad_token_id = self.pad_token_id,
)
assert results.shape[1] <= generate_length # B x ID bos is not included since it's predicted
outputs[:, :results.shape[1]] = results
# batch x ntokens ====> batch x ntokens x D
outputs = outputs[:, 1: -1]
outputs[outputs == self.bos_token_id] = self.tokenizer.pad_id
outputs[outputs == self.eos_token_id] = self.tokenizer.pad_id
outputs[outputs == self.pad_token_id] = self.tokenizer.pad_id
outputs[outputs != self.tokenizer.pad_id] -= 3
code_embed = self.get_codes(outputs)
decoder_output = self.tokenizer(outputs, code_embed, point_feature=point_feature)
return decoder_output
def get_codes(self, indices):
indices = indices.reshape(indices.shape[0], -1)
indices = rearrange(indices, 'b (n q) -> b n q', q=self.num_quantizers)
batch, quantize_dim = indices.shape[0], indices.shape[-1]
# may also receive indices in the shape of 'b h w q' (accept_image_fmap)
indices, ps = pack([indices], 'b * q')
# because of quantize dropout, one can pass in indices that are coarse
# and the network should be able to reconstruct
if quantize_dim < self.num_quantizers:
indices = F.pad(indices, (0, self.num_quantizers - quantize_dim), value = -1)
# take care of quantizer dropout
mask = indices == -1.
indices = indices.masked_fill(mask, 0) # have it fetch a dummy code to be masked out later
# dummy implementation for shared codebook
all_codes = self.transformer.model.decoder.quantize_codebooks[0][indices]
all_codes = all_codes.permute(2, 0, 1, 3)
# mask out any codes that were dropout-ed
all_codes = all_codes.masked_fill(rearrange(mask, 'b n q -> q b n 1'), 0.)
# if (accept_image_fmap = True) then return shape (quantize, batch, height, width, dimension)
codes, = unpack(all_codes, ps, 'q b * d')
codes_summed = reduce(codes, 'q ... -> ...', 'sum')
return codes_summed
def undiscretize(
t,
low,
high,
num_discrete
) -> Tensor:
t = t.float()
t /= num_discrete
return t * (high - low) + low

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from transformers import AutoModelForCausalLM, AutoConfig, OPTConfig
from transformers.models.opt.modeling_opt import OPTForCausalLM, OPTModel, OPTDecoder, OPTLearnedPositionalEmbedding, OPTDecoderLayer
from typing import List, Optional, Tuple, Union
from einops import repeat
from transformers.modeling_outputs import (
CausalLMOutputWithPast,
)
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.utils import replace_return_docstrings, logging
from transformers.modeling_outputs import BaseModelOutputWithPast
# from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
class ShapeOPTConfig(OPTConfig):
model_type = "shape_opt"
class ShapeOPT(OPTForCausalLM):
config_class = ShapeOPTConfig
def __init__(self, config: ShapeOPTConfig):
super(OPTForCausalLM, self).__init__(config)
self.model = ShapeOPTModel(config)
self.lm_head = nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def tie_weights(self):
"""
Tie the weights between the input embeddings and the output embeddings.
If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning the
weights instead.
"""
if getattr(self.config, "is_encoder_decoder", False) and getattr(self.config, "tie_encoder_decoder", False):
if hasattr(self, self.base_model_prefix):
self = getattr(self, self.base_model_prefix)
self._tie_encoder_decoder_weights(self.encoder, self.decoder, self.base_model_prefix)
for module in self.modules():
if hasattr(module, "_tie_weights"):
module._tie_weights()
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class="OPTConfig")
def forward(
self,
input_ids: torch.LongTensor = None,
face_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, OPTForCausalLM
>>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious. I'm just a little bit of a weirdo."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
face_ids = face_ids,
attention_mask=attention_mask,
head_mask=head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.lm_head(outputs[0]).contiguous()
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class ShapeOPTModel(OPTModel):
config_class = ShapeOPTConfig
def __init__(self, config: ShapeOPTConfig):
super(OPTModel,self).__init__(config)
self.decoder = ShapeOPTDecoder(config)
# Initialize weights and apply final processing
self.post_init()
class ShapeOPTDecoder(OPTDecoder):
config_class = ShapeOPTConfig
def __init__(self, config: ShapeOPTConfig):
super(OPTDecoder,self).__init__(config)
self.config = config
self.dropout = config.dropout
self.layerdrop = config.layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.word_embed_proj_dim, self.padding_idx) # not used
self.hidden_size = config.hidden_size
self.word_embed_proj_dim = config.word_embed_proj_dim
self.extra_embeds = nn.Embedding(3, config.word_embed_proj_dim) #padding_idx=self.padding_idx)
self.input_layer = nn.Linear(config.quantize_codebook_dim, config.word_embed_proj_dim)
self.embed_positions = OPTLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size)
self.token_embed_positions = OPTFacePositionalEmbedding(config.face_per_token + 3, config.word_embed_proj_dim) #padding_idx=self.padding_idx)
self.face_per_token = config.face_per_token
self.cond_length = config.cond_length
self.cond_embed = nn.Embedding(2, config.word_embed_proj_dim)
if config.word_embed_proj_dim != config.hidden_size:
self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False)
else:
self.project_out = None
if config.word_embed_proj_dim != config.hidden_size:
self.project_in = nn.Linear(config.word_embed_proj_dim, config.hidden_size, bias=False)
else:
self.project_in = None
# Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
# with checkpoints that have been fine-tuned before transformers v4.20.1
# see https://github.com/facebookresearch/metaseq/pull/164
if config.do_layer_norm_before and not config._remove_final_layer_norm:
self.final_layer_norm = nn.LayerNorm(
config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine
)
else:
self.final_layer_norm = None
self.layers = nn.ModuleList([OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)])
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def embed_with_vae(self, input_ids):
inputs_embeds = repeat(torch.zeros(input_ids.shape, device=input_ids.device), 'b n -> b n d',
d=self.word_embed_proj_dim).clone().detach()
idx_in_extra = torch.isin(input_ids, torch.LongTensor([0, 1, 2]).to(input_ids.device))
inputs_embeds[idx_in_extra] += self.extra_embeds(input_ids[idx_in_extra])
self.quantize_codebooks = self.quantize_codebooks.to(input_ids.device)
inputs_embeds[~idx_in_extra] += self.input_layer(self.quantize_codebooks[0][input_ids[~idx_in_extra] - 3])
return inputs_embeds
def forward(
self,
input_ids: torch.LongTensor = None,
face_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
# OPT Decoder
# print("used my Trans")
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Transformer Decoder
if input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
inputs_embeds = self.embed_with_vae(input_ids) # nothing to do with position
face_embeds = self.token_embed_positions(attention_mask[:, self.cond_length:], face_ids, input_ids,
self.face_per_token)
inputs_embeds += face_embeds
cond_embed_query = torch.ones((inputs_embeds.shape[0], inputs_embeds.shape[1]), device=inputs_embeds.device,
dtype=inputs_embeds.dtype).long()
inputs_embeds = inputs_embeds + self.cond_embed(cond_embed_query)
elif inputs_embeds is not None:
# assert self.cond and not self.training
total_length = inputs_embeds.shape[1] # B x length x embeding
cond_embed_query = torch.zeros((inputs_embeds.shape[0], total_length), device=inputs_embeds.device,
dtype=inputs_embeds.dtype).long()
inputs_embeds = inputs_embeds + self.cond_embed(cond_embed_query)
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
batch_size, seq_length = inputs_embeds.shape[:2] # seq_length not used since mask_seq_length is not used
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
# required mask seq length can be calculated via length of past
mask_seq_length = past_key_values_length + seq_length # not used since attention mask is input
# embed positions
if self._use_flash_attention_2:
# 2d mask is passed through the layers
assert attention_mask is not None
causal_attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
attention_mask = (
torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
if attention_mask is None
else attention_mask
)
else:
raise ValueError("Only flash_attention_2 is supported in MeshAnything")
pos_embeds = self.embed_positions(attention_mask, past_key_values_length)
if self.project_in is not None:
inputs_embeds = self.project_in(inputs_embeds)
hidden_states = inputs_embeds + pos_embeds
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
# check if head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != (len(self.layers)):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop:
continue
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_attention_mask,
head_mask[idx] if head_mask is not None else None,
None,
output_attentions,
use_cache,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if self.final_layer_norm is not None:
hidden_states = self.final_layer_norm(hidden_states)
if self.project_out is not None:
hidden_states = self.project_out(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class OPTFacePositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
super().__init__(num_embeddings, embedding_dim)
def forward(self, attention_mask=None, face_ids = None, input_ids = None, face_per_token = None):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
if face_ids is not None:
return super().forward(face_ids)
assert input_ids.shape[1] == 1
idx_in_extra = torch.isin(input_ids, torch.LongTensor([0, 1, 2]).to(input_ids.device))
cur_ids = input_ids.clone().detach()
cur_index = (attention_mask.sum(dim=1, keepdim=True) - 2) % face_per_token + 3
cur_ids[~idx_in_extra]=cur_index[~idx_in_extra]
return super().forward(cur_ids)
AutoConfig.register("shape_opt", ShapeOPTConfig)
AutoModelForCausalLM.register(ShapeOPTConfig, ShapeOPT)

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<p align="center">
<h3 align="center"><strong>MeshAnything:<br> Artist-Created Mesh Generation<br> with Autoregressive Transformers</strong></h3>
<p align="center">
<a href="https://buaacyw.github.io/">Yiwen Chen</a><sup>1,2*</sup>,
<a href="https://tonghe90.github.io/">Tong He</a><sup>2†</sup>,
<a href="https://dihuang.me/">Di Huang</a><sup>2</sup>,
<a href="https://ywcmaike.github.io/">Weicai Ye</a><sup>2</sup>,
<a href="https://ch3cook-fdu.github.io/">Sijin Chen</a><sup>3</sup>,
<a href="https://me.kiui.moe/">Jiaxiang Tang</a><sup>4</sup><br>
<a href="https://chenxin.tech/">Xin Chen</a><sup>5</sup>,
<a href="https://caizhongang.github.io/">Zhongang Cai</a><sup>6</sup>,
<a href="https://scholar.google.com.hk/citations?user=jZH2IPYAAAAJ&hl=en">Lei Yang</a><sup>6</sup>,
<a href="https://www.skicyyu.org/">Gang Yu</a><sup>7</sup>,
<a href="https://guosheng.github.io/">Guosheng Lin</a><sup>1†</sup>,
<a href="https://icoz69.github.io/">Chi Zhang</a><sup>8†</sup>
<br>
<sup>*</sup>Work done during a research internship at Shanghai AI Lab.
<br>
<sup>†</sup>Corresponding authors.
<br>
<sup>1</sup>S-Lab, Nanyang Technological University,
<sup>2</sup>Shanghai AI Lab,
<br>
<sup>3</sup>Fudan University,
<sup>4</sup>Peking University,
<sup>5</sup>University of Chinese Academy of Sciences,
<br>
<sup>6</sup>SenseTime Research,
<sup>7</sup>Stepfun,
<sup>8</sup>Westlake University
</p>
<div align="center">
<a href='https://arxiv.org/abs/2311.14521'><img src='https://img.shields.io/badge/arXiv-2311.14521-b31b1b.svg'></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<a href='https://buaacyw.github.io/mesh-anything/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<a href='https://github.com/buaacyw/MeshAnything/blob/master/LICENSE.txt'><img src='https://img.shields.io/badge/License-SLab-blue'></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<a href="https://huggingface.co/spaces/Yiwen-ntu/MeshAnything"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Gradio%20Demo-Huggingface-orange"></a>;
</div>
<p align="center">
<img src="demo/demo_video.gif" alt="Demo GIF" width="1000px" />
</p>
## Release
- [6/17] 🔥🔥 We released the 350m version of **MeshAnything**.
## Contents
- [Release](#release)
- [Contents](#contents)
- [Installation](#installation)
- [Usage](#usage)
- [Important Notes](#important-notes)
- [TODO](#todo)
## Installation
Our environment has been tested on Ubuntu 22, CUDA 11.8 with A100, A800 and A6000.
1. Clone our repo and create conda environment
```
git clone https://github.com/buaacyw/MeshAnything.git && cd MeshAnything
conda create -n MeshAnything python==3.10.13
conda activate MeshAnything
pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
pip install flash-attn --no-build-isolation
```
## Usage
```
# Gradio Demo
python app.py
# Command line inference
# For mesh
# inference for folder
python main.py --input_dir examples --out_dir mesh_output --input_type mesh
# inference for single file
python main.py --input_dir examples/wand.ply --out_dir mesh_output --input_type mesh
# Preprocess with Marching Cubes first
python main.py --input_dir examples --out_dir mesh_output --input_type mesh --mc
# For point cloud
# Note: if you want to use your own point cloud, please make sure the normal is included.
# The file format should be a .npy file with shape (N, 6), where N is the number of points. The first 3 columns are the coordinates, and the last 3 columns are the normal.
# inference for folder
python main.py --input_dir pc_examples --out_dir pc_output --input_type pc_normal
# inference for single file
python main.py --input_dir pc_examples/mouse.npy --out_dir pc_output --input_type pc_normal
```
## Important Notes
- The input mesh will be normalized to a unit bounding box. The up vector of the input mesh should be +Y for better results.
- Limited by computational resources, MeshAnything is trained on meshes with fewer than 800 faces and cannot generate meshes with more than 800 faces. The shape of the input mesh should be sharp enough; otherwise, it will be challenging to represent it with only 800 faces. Thus, feed-forward image-to-3D methods may often produce bad results due to insufficient shape quality.
- It takes about 7GB and 30s to generate a mesh on an A6000 GPU.
- Please refer to https://huggingface.co/spaces/Yiwen-ntu/MeshAnything/tree/main/examples for more examples.
## TODO
The repo is still being under construction, thanks for your patience.
- [ ] Release of training code.
- [ ] Release of larger model.
## Acknowledgement
Our code is based on these wonderful repos:
* [MeshGPT](https://nihalsid.github.io/mesh-gpt/)
* [meshgpt-pytorch](https://github.com/lucidrains/meshgpt-pytorch)
* [Michelangelo](https://github.com/NeuralCarver/Michelangelo)
* [transformers](https://github.com/huggingface/transformers)
* [vector-quantize-pytorch](https://github.com/lucidrains/vector-quantize-pytorch)

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import os
import torch
import trimesh
from accelerate.utils import set_seed
from accelerate import Accelerator
import numpy as np
import gradio as gr
from main import get_args, load_model
from mesh_to_pc import process_mesh_to_pc
import time
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from PIL import Image
import io
args = get_args()
model = load_model(args)
device = torch.device('cuda')
accelerator = Accelerator(
mixed_precision="fp16",
)
model = accelerator.prepare(model)
model.eval()
print("Model loaded to device")
def wireframe_render(mesh):
views = [
(90, 20), (270, 20)
]
mesh.vertices = mesh.vertices[:, [0, 2, 1]]
bounding_box = mesh.bounds
center = mesh.centroid
scale = np.ptp(bounding_box, axis=0).max()
fig = plt.figure(figsize=(10, 10))
# Function to render and return each view as an image
def render_view(mesh, azimuth, elevation):
ax = fig.add_subplot(111, projection='3d')
ax.set_axis_off()
# Extract vertices and faces for plotting
vertices = mesh.vertices
faces = mesh.faces
# Plot faces
ax.add_collection3d(Poly3DCollection(
vertices[faces],
facecolors=(0.8, 0.5, 0.2, 1.0), # Brownish yellow
edgecolors='k',
linewidths=0.5,
))
# Set limits and center the view on the object
ax.set_xlim(center[0] - scale / 2, center[0] + scale / 2)
ax.set_ylim(center[1] - scale / 2, center[1] + scale / 2)
ax.set_zlim(center[2] - scale / 2, center[2] + scale / 2)
# Set view angle
ax.view_init(elev=elevation, azim=azimuth)
# Save the figure to a buffer
buf = io.BytesIO()
plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0, dpi=300)
plt.clf()
buf.seek(0)
return Image.open(buf)
# Render each view and store in a list
images = [render_view(mesh, az, el) for az, el in views]
# Combine images horizontally
widths, heights = zip(*(i.size for i in images))
total_width = sum(widths)
max_height = max(heights)
combined_image = Image.new('RGBA', (total_width, max_height))
x_offset = 0
for img in images:
combined_image.paste(img, (x_offset, 0))
x_offset += img.width
# Save the combined image
save_path = f"combined_mesh_view_{int(time.time())}.png"
combined_image.save(save_path)
plt.close(fig)
return save_path
@torch.no_grad()
def do_inference(input_3d, sample_seed=0, do_sampling=False, do_marching_cubes=False):
set_seed(sample_seed)
print("Seed value:", sample_seed)
input_mesh = trimesh.load(input_3d)
pc_list, mesh_list = process_mesh_to_pc([input_mesh], marching_cubes = do_marching_cubes)
mesh = mesh_list[0]
mesh.merge_vertices()
mesh.update_faces(mesh.unique_faces())
mesh.fix_normals()
if mesh.visual.vertex_colors is not None:
orange_color = np.array([255, 165, 0, 255], dtype=np.uint8)
mesh.visual.vertex_colors = np.tile(orange_color, (mesh.vertices.shape[0], 1))
else:
orange_color = np.array([255, 165, 0, 255], dtype=np.uint8)
mesh.visual.vertex_colors = np.tile(orange_color, (mesh.vertices.shape[0], 1))
input_save_name = f"processed_input_{int(time.time())}.obj"
mesh.export(input_save_name)
input_render_res = wireframe_render(mesh)
pc_normal = pc_list[0] # 4096, 6
pc_coor = pc_normal[:, :3]
normals = pc_normal[:, 3:]
bounds = np.array([pc_coor.min(axis=0), pc_coor.max(axis=0)])
pc_coor = pc_coor - (bounds[0] + bounds[1])[None, :] / 2
pc_coor = pc_coor / np.abs(pc_coor).max() * 0.9995
assert (np.linalg.norm(normals, axis=-1) > 0.99).all(), "normals should be unit vectors, something wrong"
normalized_pc_normal = np.concatenate([pc_coor, normals], axis=-1, dtype=np.float16)
input = torch.tensor(normalized_pc_normal, dtype=torch.float16, device=device)[None]
print("Data loaded")
# with accelerator.autocast():
with accelerator.autocast():
outputs = model(input, do_sampling)
print("Model inference done")
recon_mesh = outputs[0]
recon_mesh = recon_mesh[~torch.isnan(recon_mesh[:, 0, 0])] # nvalid_face x 3 x 3
vertices = recon_mesh.reshape(-1, 3).cpu()
vertices_index = np.arange(len(vertices)) # 0, 1, ..., 3 x face
triangles = vertices_index.reshape(-1, 3)
artist_mesh = trimesh.Trimesh(vertices=vertices, faces=triangles, force="mesh",
merge_primitives=True)
artist_mesh.merge_vertices()
artist_mesh.update_faces(artist_mesh.unique_faces())
artist_mesh.fix_normals()
if artist_mesh.visual.vertex_colors is not None:
orange_color = np.array([255, 165, 0, 255], dtype=np.uint8)
artist_mesh.visual.vertex_colors = np.tile(orange_color, (artist_mesh.vertices.shape[0], 1))
else:
orange_color = np.array([255, 165, 0, 255], dtype=np.uint8)
artist_mesh.visual.vertex_colors = np.tile(orange_color, (artist_mesh.vertices.shape[0], 1))
num_faces = len(artist_mesh.faces)
brown_color = np.array([165, 42, 42, 255], dtype=np.uint8)
face_colors = np.tile(brown_color, (num_faces, 1))
artist_mesh.visual.face_colors = face_colors
# add time stamp to avoid cache
save_name = f"output_{int(time.time())}.obj"
artist_mesh.export(save_name)
output_render = wireframe_render(artist_mesh)
return input_save_name, input_render_res, save_name, output_render
_HEADER_ = '''
<h2><b>Official ? Gradio Demo</b></h2><h2><a href='https://github.com/buaacyw/MeshAnything' target='_blank'><b>MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers</b></a></h2>
**MeshAnything** converts any 3D representation into meshes created by human artists, i.e., Artist-Created Meshes (AMs).
Code: <a href='https://github.com/buaacyw/MeshAnything' target='_blank'>GitHub</a>. Arxiv Paper: <a href='https://buaacyw.github.io/mesh-anything/' target='_blank'>ArXiv</a>.
??????**Important Notes:**
- Gradio doesn't support interactive wireframe rendering currently. For interactive mesh visualization, please use download the obj file and open it with MeshLab or https://3dviewer.net/.
- The input mesh will be normalized to a unit bounding box. The up vector of the input mesh should be +Y for better results. Click **Preprocess with Marching Cubes** if the input mesh is a manually created mesh.
- Limited by computational resources, MeshAnything is trained on meshes with fewer than 800 faces and cannot generate meshes with more than 800 faces. The shape of the input mesh should be sharp enough; otherwise, it will be challenging to represent it with only 800 faces. Thus, feed-forward image-to-3D methods may often produce bad results due to insufficient shape quality.
- For point cloud input, please refer to our github repo <a href='https://github.com/buaacyw/MeshAnything' target='_blank'>GitHub</a>.
'''
_CITE_ = r"""
If MeshAnything is helpful, please help to ? the <a href='https://github.com/buaacyw/MeshAnything' target='_blank'>Github Repo</a>. Thanks!
---
? **License**
S-Lab-1.0 LICENSE. Please refer to the [LICENSE file](https://github.com/buaacyw/GaussianEditor/blob/master/LICENSE.txt) for details.
? **Contact**
If you have any questions, feel free to open a discussion or contact us at <b>yiwen002@e.ntu.edu.sg</b>.
"""
output_model_obj = gr.Model3D(
label="Processed Input Mesh (OBJ Format)",
clear_color=[1, 1, 1, 1],
)
preprocess_model_obj = gr.Model3D(
label="Generated Mesh (OBJ Format)",
clear_color=[1, 1, 1, 1],
)
input_image_render = gr.Image(
label="Wireframe Render of Processed Input Mesh",
)
output_image_render = gr.Image(
label="Wireframe Render of Generated Mesh",
)
with (gr.Blocks() as demo):
gr.Markdown(_HEADER_)
with gr.Row(variant="panel"):
with gr.Column():
with gr.Row():
input_3d = gr.Model3D(
label="Input Mesh",
clear_color=[1,1,1,1],
)
with gr.Row():
with gr.Group():
do_marching_cubes = gr.Checkbox(label="Preprocess with Marching Cubes", value=False)
do_sampling = gr.Checkbox(label="Random Sampling", value=False)
sample_seed = gr.Number(value=0, label="Seed Value", precision=0)
with gr.Row():
submit = gr.Button("Generate", elem_id="generate", variant="primary")
with gr.Row(variant="panel"):
mesh_examples = gr.Examples(
examples=[
os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
],
inputs=input_3d,
outputs=[preprocess_model_obj, input_image_render, output_model_obj, output_image_render],
fn=do_inference,
cache_examples = False,
examples_per_page=10
)
with gr.Column():
with gr.Row():
input_image_render.render()
with gr.Row():
with gr.Tab("OBJ"):
preprocess_model_obj.render()
with gr.Row():
output_image_render.render()
with gr.Row():
with gr.Tab("OBJ"):
output_model_obj.render()
with gr.Row():
gr.Markdown('''Try click random sampling and different <b>Seed Value</b> if the result is unsatisfying''')
gr.Markdown(_CITE_)
mv_images = gr.State()
submit.click(
fn=do_inference,
inputs=[input_3d, sample_seed, do_sampling, do_marching_cubes],
outputs=[preprocess_model_obj, input_image_render, output_model_obj, output_image_render],
)
demo.launch(share=True)

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import os, argparse, importlib
import torch
import time
import trimesh
import numpy as np
from MeshAnything.models.meshanything import MeshAnything
import datetime
from accelerate import Accelerator
from accelerate.utils import set_seed
from accelerate.utils import DistributedDataParallelKwargs
from safetensors import safe_open
from mesh_to_pc import process_mesh_to_pc
from huggingface_hub import hf_hub_download
class Dataset:
def __init__(self, input_type, input_list, mc=False):
super().__init__()
self.data = []
if input_type == 'pc_normal':
for input_path in input_list:
# load npy
cur_data = np.load(input_path)
# sample 4096
assert cur_data.shape[0] >= 4096, "input pc_normal should have at least 4096 points"
idx = np.random.choice(cur_data.shape[0], 4096, replace=False)
cur_data = cur_data[idx]
self.data.append({'pc_normal': cur_data, 'uid': input_path.split('/')[-1].split('.')[0]})
elif input_type == 'mesh':
mesh_list = []
for input_path in input_list:
# load ply
cur_data = trimesh.load(input_path)
mesh_list.append(cur_data)
if mc:
print("First Marching Cubes and then sample point cloud, need several minutes...")
pc_list, _ = process_mesh_to_pc(mesh_list, marching_cubes=mc)
for input_path, cur_data in zip(input_list, pc_list):
self.data.append({'pc_normal': cur_data, 'uid': input_path.split('/')[-1].split('.')[0]})
print(f"dataset total data samples: {len(self.data)}")
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
data_dict = {}
data_dict['pc_normal'] = self.data[idx]['pc_normal']
# normalize pc coor
pc_coor = data_dict['pc_normal'][:, :3]
normals = data_dict['pc_normal'][:, 3:]
bounds = np.array([pc_coor.min(axis=0), pc_coor.max(axis=0)])
pc_coor = pc_coor - (bounds[0] + bounds[1])[None, :] / 2
pc_coor = pc_coor / np.abs(pc_coor).max() * 0.9995
assert (np.linalg.norm(normals, axis=-1) > 0.99).all(), "normals should be unit vectors, something wrong"
data_dict['pc_normal'] = np.concatenate([pc_coor, normals], axis=-1, dtype=np.float16)
data_dict['uid'] = self.data[idx]['uid']
return data_dict
def get_args():
parser = argparse.ArgumentParser("MeshAnything", add_help=False)
parser.add_argument('--llm', default="facebook/opt-350m", type=str)
parser.add_argument('--input_dir', default=None, type=str)
parser.add_argument('--input_path', default=None, type=str)
parser.add_argument('--out_dir', default="inference_out", type=str)
parser.add_argument('--pretrained_weights', default="MeshAnything_350m.pth", type=str)
parser.add_argument(
'--input_type',
choices=['mesh','pc_normal'],
default='pc',
help="Type of the asset to process (default: pc)"
)
parser.add_argument("--codebook_size", default=8192, type=int)
parser.add_argument("--codebook_dim", default=1024, type=int)
parser.add_argument("--n_max_triangles", default=800, type=int)
parser.add_argument("--batchsize_per_gpu", default=1, type=int)
parser.add_argument("--seed", default=0, type=int)
parser.add_argument("--mc", default=False, action="store_true")
parser.add_argument("--sampling", default=False, action="store_true")
args = parser.parse_args()
return args
def load_model(args):
model = MeshAnything(args)
print("load model over!!!")
ckpt_path = hf_hub_download(
repo_id="Yiwen-ntu/MeshAnything",
filename="MeshAnything_350m.pth",
)
tensors = {}
with safe_open(ckpt_path, framework="pt", device=0) as f:
for k in f.keys():
tensors[k] = f.get_tensor(k)
model.load_state_dict(tensors, strict=True)
print("load weights over!!!")
return model
if __name__ == "__main__":
args = get_args()
cur_time = datetime.datetime.now().strftime("%d_%H-%M-%S")
checkpoint_dir = os.path.join(args.out_dir, cur_time)
os.makedirs(checkpoint_dir, exist_ok=True)
kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(
mixed_precision="fp16",
project_dir=checkpoint_dir,
kwargs_handlers=[kwargs]
)
model = load_model(args)
# create dataset
if args.input_dir is not None:
input_list = sorted(os.listdir(args.input_dir))
# only ply, obj or npy
if args.input_type == 'pc_normal':
input_list = [os.path.join(args.input_dir, x) for x in input_list if x.endswith('.npy')]
else:
input_list = [os.path.join(args.input_dir, x) for x in input_list if x.endswith('.ply') or x.endswith('.obj') or x.endswith('.npy')]
set_seed(args.seed)
dataset = Dataset(args.input_type, input_list, args.mc)
elif args.input_path is not None:
set_seed(args.seed)
dataset = Dataset(args.input_type, [args.input_path], args.mc)
else:
raise ValueError("input_dir or input_path must be provided.")
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batchsize_per_gpu,
drop_last = False,
shuffle = False,
)
if accelerator.state.num_processes > 1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
dataloader, model = accelerator.prepare(dataloader, model)
begin_time = time.time()
print("Generation Start!!!")
with accelerator.autocast():
for curr_iter, batch_data_label in enumerate(dataloader):
curr_time = time.time()
outputs = model(batch_data_label['pc_normal'], sampling=args.sampling)
batch_size = outputs.shape[0]
device = outputs.device
for batch_id in range(batch_size):
recon_mesh = outputs[batch_id]
recon_mesh = recon_mesh[~torch.isnan(recon_mesh[:, 0, 0])] # nvalid_face x 3 x 3
vertices = recon_mesh.reshape(-1, 3).cpu()
vertices_index = np.arange(len(vertices)) # 0, 1, ..., 3 x face
triangles = vertices_index.reshape(-1, 3)
scene_mesh = trimesh.Trimesh(vertices=vertices, faces=triangles, force="mesh",
merge_primitives=True)
scene_mesh.merge_vertices()
scene_mesh.update_faces(scene_mesh.unique_faces())
scene_mesh.fix_normals()
save_path = os.path.join(checkpoint_dir, f'{batch_data_label["uid"][batch_id]}_gen.obj')
num_faces = len(scene_mesh.faces)
brown_color = np.array([255, 165, 0, 255], dtype=np.uint8)
face_colors = np.tile(brown_color, (num_faces, 1))
scene_mesh.visual.face_colors = face_colors
scene_mesh.export(save_path)
print(f"{save_path} Over!!")
end_time = time.time()
print(f"Total time: {end_time - begin_time}")

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import mesh2sdf.core
import numpy as np
import skimage.measure
import trimesh
def normalize_vertices(vertices, scale=0.9):
bbmin, bbmax = vertices.min(0), vertices.max(0)
center = (bbmin + bbmax) * 0.5
scale = 2.0 * scale / (bbmax - bbmin).max()
vertices = (vertices - center) * scale
return vertices, center, scale
def export_to_watertight(normalized_mesh, octree_depth: int = 7):
"""
Convert the non-watertight mesh to watertight.
Args:
input_path (str): normalized path
octree_depth (int):
Returns:
mesh(trimesh.Trimesh): watertight mesh
"""
size = 2 ** octree_depth
level = 2 / size
scaled_vertices, to_orig_center, to_orig_scale = normalize_vertices(normalized_mesh.vertices)
sdf = mesh2sdf.core.compute(scaled_vertices, normalized_mesh.faces, size=size)
vertices, faces, normals, _ = skimage.measure.marching_cubes(np.abs(sdf), level)
# watertight mesh
vertices = vertices / size * 2 - 1 # -1 to 1
vertices = vertices / to_orig_scale + to_orig_center
# vertices = vertices / to_orig_scale + to_orig_center
mesh = trimesh.Trimesh(vertices, faces, normals=normals)
return mesh
def process_mesh_to_pc(mesh_list, marching_cubes = False, sample_num = 4096):
# mesh_list : list of trimesh
pc_normal_list = []
return_mesh_list = []
for mesh in mesh_list:
if marching_cubes:
mesh = export_to_watertight(mesh)
print("MC over!")
return_mesh_list.append(mesh)
points, face_idx = mesh.sample(sample_num, return_index=True)
normals = mesh.face_normals[face_idx]
pc_normal = np.concatenate([points, normals], axis=-1, dtype=np.float16)
pc_normal_list.append(pc_normal)
print("process mesh success")
return pc_normal_list, return_mesh_list

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trimesh==4.2.3
accelerate==0.28.0
mesh2sdf==1.1.0
einops==0.7.0
einx==0.1.3
optimum==1.18.0
omegaconf==2.3.0
opencv-python==4.9.0.80
transformers==4.39.3
huggingface_hub
matplotlib
gradio
spaces