.
diff --git a/MeshAnything/miche/encode.py b/MeshAnything/miche/encode.py
new file mode 100644
index 0000000..e18c60d
--- /dev/null
+++ b/MeshAnything/miche/encode.py
@@ -0,0 +1,73 @@
+# -*- 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))
\ No newline at end of file
diff --git a/MeshAnything/miche/michelangelo/__init__.py b/MeshAnything/miche/michelangelo/__init__.py
new file mode 100644
index 0000000..40a96af
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/__init__.py
@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-
diff --git a/MeshAnything/miche/michelangelo/data/__init__.py b/MeshAnything/miche/michelangelo/data/__init__.py
new file mode 100644
index 0000000..40a96af
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/data/__init__.py
@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-
diff --git a/MeshAnything/miche/michelangelo/data/templates.json b/MeshAnything/miche/michelangelo/data/templates.json
new file mode 100644
index 0000000..f1a355f
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/data/templates.json
@@ -0,0 +1,69 @@
+{
+ "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 {}."
+ ]
+
+}
\ No newline at end of file
diff --git a/MeshAnything/miche/michelangelo/data/transforms.py b/MeshAnything/miche/michelangelo/data/transforms.py
new file mode 100644
index 0000000..0c8aa14
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/data/transforms.py
@@ -0,0 +1,407 @@
+# -*- 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
+
diff --git a/MeshAnything/miche/michelangelo/data/utils.py b/MeshAnything/miche/michelangelo/data/utils.py
new file mode 100644
index 0000000..af06ed0
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/data/utils.py
@@ -0,0 +1,59 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/graphics/__init__.py b/MeshAnything/miche/michelangelo/graphics/__init__.py
new file mode 100644
index 0000000..40a96af
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/graphics/__init__.py
@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-
diff --git a/MeshAnything/miche/michelangelo/graphics/primitives/__init__.py b/MeshAnything/miche/michelangelo/graphics/primitives/__init__.py
new file mode 100644
index 0000000..cb91087
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/graphics/primitives/__init__.py
@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+
+from .volume import generate_dense_grid_points
+
+from .mesh import (
+ MeshOutput,
+ save_obj,
+ savemeshtes2
+)
diff --git a/MeshAnything/miche/michelangelo/graphics/primitives/mesh.py b/MeshAnything/miche/michelangelo/graphics/primitives/mesh.py
new file mode 100644
index 0000000..3e5e8a5
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/graphics/primitives/mesh.py
@@ -0,0 +1,114 @@
+# -*- 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
+ )
+
+
+
diff --git a/MeshAnything/miche/michelangelo/graphics/primitives/volume.py b/MeshAnything/miche/michelangelo/graphics/primitives/volume.py
new file mode 100644
index 0000000..e8cb1d3
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/graphics/primitives/volume.py
@@ -0,0 +1,21 @@
+# -*- 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
+
diff --git a/MeshAnything/miche/michelangelo/models/__init__.py b/MeshAnything/miche/michelangelo/models/__init__.py
new file mode 100644
index 0000000..40a96af
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/__init__.py
@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-
diff --git a/MeshAnything/miche/michelangelo/models/asl_diffusion/__init__.py b/MeshAnything/miche/michelangelo/models/asl_diffusion/__init__.py
new file mode 100644
index 0000000..40a96af
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/asl_diffusion/__init__.py
@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-
diff --git a/MeshAnything/miche/michelangelo/models/asl_diffusion/asl_diffuser_pl_module.py b/MeshAnything/miche/michelangelo/models/asl_diffusion/asl_diffuser_pl_module.py
new file mode 100644
index 0000000..f86fcec
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/asl_diffusion/asl_diffuser_pl_module.py
@@ -0,0 +1,483 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/models/asl_diffusion/asl_udt.py b/MeshAnything/miche/michelangelo/models/asl_diffusion/asl_udt.py
new file mode 100644
index 0000000..755ab4d
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/asl_diffusion/asl_udt.py
@@ -0,0 +1,104 @@
+# -*- 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
+
+
diff --git a/MeshAnything/miche/michelangelo/models/asl_diffusion/base.py b/MeshAnything/miche/michelangelo/models/asl_diffusion/base.py
new file mode 100644
index 0000000..a979197
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/asl_diffusion/base.py
@@ -0,0 +1,13 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/models/asl_diffusion/clip_asl_diffuser_pl_module.py b/MeshAnything/miche/michelangelo/models/asl_diffusion/clip_asl_diffuser_pl_module.py
new file mode 100644
index 0000000..866872e
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/asl_diffusion/clip_asl_diffuser_pl_module.py
@@ -0,0 +1,393 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/models/asl_diffusion/inference_utils.py b/MeshAnything/miche/michelangelo/models/asl_diffusion/inference_utils.py
new file mode 100644
index 0000000..967d5c5
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/asl_diffusion/inference_utils.py
@@ -0,0 +1,80 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/models/conditional_encoders/__init__.py b/MeshAnything/miche/michelangelo/models/conditional_encoders/__init__.py
new file mode 100644
index 0000000..f644ce0
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/conditional_encoders/__init__.py
@@ -0,0 +1,3 @@
+# -*- coding: utf-8 -*-
+
+from .clip import CLIPEncoder
diff --git a/MeshAnything/miche/michelangelo/models/conditional_encoders/clip.py b/MeshAnything/miche/michelangelo/models/conditional_encoders/clip.py
new file mode 100644
index 0000000..099b237
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/conditional_encoders/clip.py
@@ -0,0 +1,89 @@
+# -*- 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
+
+
+
+
+
+
+
+
+
+
diff --git a/MeshAnything/miche/michelangelo/models/conditional_encoders/encoder_factory.py b/MeshAnything/miche/michelangelo/models/conditional_encoders/encoder_factory.py
new file mode 100644
index 0000000..619530d
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/conditional_encoders/encoder_factory.py
@@ -0,0 +1,562 @@
+# -*- 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)
diff --git a/MeshAnything/miche/michelangelo/models/modules/__init__.py b/MeshAnything/miche/michelangelo/models/modules/__init__.py
new file mode 100644
index 0000000..0729b49
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/modules/__init__.py
@@ -0,0 +1,3 @@
+# -*- coding: utf-8 -*-
+
+from .checkpoint import checkpoint
diff --git a/MeshAnything/miche/michelangelo/models/modules/checkpoint.py b/MeshAnything/miche/michelangelo/models/modules/checkpoint.py
new file mode 100644
index 0000000..4fef818
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/modules/checkpoint.py
@@ -0,0 +1,69 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/models/modules/diffusion_transformer.py b/MeshAnything/miche/michelangelo/models/modules/diffusion_transformer.py
new file mode 100644
index 0000000..2b2c728
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/modules/diffusion_transformer.py
@@ -0,0 +1,218 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/models/modules/distributions.py b/MeshAnything/miche/michelangelo/models/modules/distributions.py
new file mode 100644
index 0000000..cf1cdcd
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/modules/distributions.py
@@ -0,0 +1,100 @@
+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)
+ )
diff --git a/MeshAnything/miche/michelangelo/models/modules/embedder.py b/MeshAnything/miche/michelangelo/models/modules/embedder.py
new file mode 100644
index 0000000..223de82
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/modules/embedder.py
@@ -0,0 +1,213 @@
+# -*- 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}")
diff --git a/MeshAnything/miche/michelangelo/models/modules/transformer_blocks.py b/MeshAnything/miche/michelangelo/models/modules/transformer_blocks.py
new file mode 100644
index 0000000..5b80103
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/modules/transformer_blocks.py
@@ -0,0 +1,286 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/models/modules/transformer_vit.py b/MeshAnything/miche/michelangelo/models/modules/transformer_vit.py
new file mode 100644
index 0000000..970ee43
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/modules/transformer_vit.py
@@ -0,0 +1,308 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/models/tsal/__init__.py b/MeshAnything/miche/michelangelo/models/tsal/__init__.py
new file mode 100644
index 0000000..40a96af
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/tsal/__init__.py
@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-
diff --git a/MeshAnything/miche/michelangelo/models/tsal/asl_pl_module.py b/MeshAnything/miche/michelangelo/models/tsal/asl_pl_module.py
new file mode 100644
index 0000000..b4439e5
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/tsal/asl_pl_module.py
@@ -0,0 +1,395 @@
+# -*- 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
+
diff --git a/MeshAnything/miche/michelangelo/models/tsal/clip_asl_module.py b/MeshAnything/miche/michelangelo/models/tsal/clip_asl_module.py
new file mode 100644
index 0000000..ba69ba6
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/tsal/clip_asl_module.py
@@ -0,0 +1,118 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/models/tsal/inference_utils.py b/MeshAnything/miche/michelangelo/models/tsal/inference_utils.py
new file mode 100644
index 0000000..fde3e2d
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/tsal/inference_utils.py
@@ -0,0 +1,80 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/models/tsal/loss.py b/MeshAnything/miche/michelangelo/models/tsal/loss.py
new file mode 100644
index 0000000..be9ebd9
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/tsal/loss.py
@@ -0,0 +1,303 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/models/tsal/sal_perceiver.py b/MeshAnything/miche/michelangelo/models/tsal/sal_perceiver.py
new file mode 100644
index 0000000..34390d0
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/tsal/sal_perceiver.py
@@ -0,0 +1,423 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/models/tsal/sal_pl_module.py b/MeshAnything/miche/michelangelo/models/tsal/sal_pl_module.py
new file mode 100644
index 0000000..3502ca2
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/tsal/sal_pl_module.py
@@ -0,0 +1,290 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/models/tsal/tsal_base.py b/MeshAnything/miche/michelangelo/models/tsal/tsal_base.py
new file mode 100644
index 0000000..8ea052f
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/models/tsal/tsal_base.py
@@ -0,0 +1,120 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/utils/__init__.py b/MeshAnything/miche/michelangelo/utils/__init__.py
new file mode 100644
index 0000000..6e9efc9
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/utils/__init__.py
@@ -0,0 +1,3 @@
+# -*- coding: utf-8 -*-
+
+from .misc import instantiate_from_config
diff --git a/MeshAnything/miche/michelangelo/utils/eval.py b/MeshAnything/miche/michelangelo/utils/eval.py
new file mode 100644
index 0000000..954b9ae
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/utils/eval.py
@@ -0,0 +1,12 @@
+# -*- 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
+
diff --git a/MeshAnything/miche/michelangelo/utils/io.py b/MeshAnything/miche/michelangelo/utils/io.py
new file mode 100644
index 0000000..e651e5a
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/utils/io.py
@@ -0,0 +1,47 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/utils/misc.py b/MeshAnything/miche/michelangelo/utils/misc.py
new file mode 100644
index 0000000..2194eeb
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/utils/misc.py
@@ -0,0 +1,83 @@
+# -*- 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
diff --git a/MeshAnything/miche/michelangelo/utils/visualizers/__init__.py b/MeshAnything/miche/michelangelo/utils/visualizers/__init__.py
new file mode 100644
index 0000000..40a96af
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/utils/visualizers/__init__.py
@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-
diff --git a/MeshAnything/miche/michelangelo/utils/visualizers/color_util.py b/MeshAnything/miche/michelangelo/utils/visualizers/color_util.py
new file mode 100644
index 0000000..7983243
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/utils/visualizers/color_util.py
@@ -0,0 +1,43 @@
+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
+
diff --git a/MeshAnything/miche/michelangelo/utils/visualizers/html_util.py b/MeshAnything/miche/michelangelo/utils/visualizers/html_util.py
new file mode 100644
index 0000000..f90fe6c
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/utils/visualizers/html_util.py
@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+import io
+import base64
+import numpy as np
+from PIL import Image
+
+
+def to_html_frame(content):
+
+ html_frame = f"""
+
+
+ {content}
+
+
+ """
+
+ return html_frame
+
+
+def to_single_row_table(caption: str, content: str):
+
+ table_html = f"""
+
+ {caption}
+
+ | {content} |
+
+
+ """
+
+ 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"""
+ 
+ """
+
+ return image_tag
diff --git a/MeshAnything/miche/michelangelo/utils/visualizers/pythreejs_viewer.py b/MeshAnything/miche/michelangelo/utils/visualizers/pythreejs_viewer.py
new file mode 100644
index 0000000..b3ce0f8
--- /dev/null
+++ b/MeshAnything/miche/michelangelo/utils/visualizers/pythreejs_viewer.py
@@ -0,0 +1,534 @@
+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 = "\n\n" + s + "\n\n"
+
+ # 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)
diff --git a/MeshAnything/miche/shapevae-256.yaml b/MeshAnything/miche/shapevae-256.yaml
new file mode 100644
index 0000000..cc1c1aa
--- /dev/null
+++ b/MeshAnything/miche/shapevae-256.yaml
@@ -0,0 +1,46 @@
+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
diff --git a/MeshAnything/models/meshanything.py b/MeshAnything/models/meshanything.py
new file mode 100644
index 0000000..f990694
--- /dev/null
+++ b/MeshAnything/models/meshanything.py
@@ -0,0 +1,223 @@
+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
diff --git a/MeshAnything/models/shape_opt.py b/MeshAnything/models/shape_opt.py
new file mode 100644
index 0000000..7b0e479
--- /dev/null
+++ b/MeshAnything/models/shape_opt.py
@@ -0,0 +1,464 @@
+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)
+
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..004d2fe
--- /dev/null
+++ b/README.md
@@ -0,0 +1,123 @@
+
+
MeshAnything:
Artist-Created Mesh Generation
with Autoregressive Transformers
+
+
+ Yiwen Chen1,2*,
+ Tong He2†,
+ Di Huang2,
+ Weicai Ye2,
+ Sijin Chen3,
+ Jiaxiang Tang4
+ Xin Chen5,
+ Zhongang Cai6,
+ Lei Yang6,
+ Gang Yu7,
+ Guosheng Lin1†,
+ Chi Zhang8†
+
+ *Work done during a research internship at Shanghai AI Lab.
+
+ †Corresponding authors.
+
+ 1S-Lab, Nanyang Technological University,
+ 2Shanghai AI Lab,
+
+ 3Fudan University,
+ 4Peking University,
+ 5University of Chinese Academy of Sciences,
+
+ 6SenseTime Research,
+ 7Stepfun,
+ 8Westlake University
+
+
+
+
+
+
+
+ 
+
+
+
+## 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)
diff --git a/app.py b/app.py
new file mode 100644
index 0000000..e882dc9
--- /dev/null
+++ b/app.py
@@ -0,0 +1,262 @@
+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_ = '''
+Official ? Gradio Demo
+
+**MeshAnything** converts any 3D representation into meshes created by human artists, i.e., Artist-Created Meshes (AMs).
+
+Code: GitHub. Arxiv Paper: ArXiv.
+
+??????**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 GitHub.
+'''
+
+
+_CITE_ = r"""
+If MeshAnything is helpful, please help to ? the Github Repo. 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 yiwen002@e.ntu.edu.sg.
+
+"""
+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 Seed Value 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)
\ No newline at end of file
diff --git a/demo/demo_video.gif b/demo/demo_video.gif
new file mode 100644
index 0000000..77a0fca
Binary files /dev/null and b/demo/demo_video.gif differ
diff --git a/examples/screwdriver.obj b/examples/screwdriver.obj
new file mode 100644
index 0000000..c24a8d0
--- /dev/null
+++ b/examples/screwdriver.obj
@@ -0,0 +1,26718 @@
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diff --git a/examples/wand.obj b/examples/wand.obj
new file mode 100644
index 0000000..bb0825a
--- /dev/null
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diff --git a/main.py b/main.py
new file mode 100644
index 0000000..9305d76
--- /dev/null
+++ b/main.py
@@ -0,0 +1,177 @@
+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}")
\ No newline at end of file
diff --git a/mesh_to_pc.py b/mesh_to_pc.py
new file mode 100644
index 0000000..bbc7047
--- /dev/null
+++ b/mesh_to_pc.py
@@ -0,0 +1,58 @@
+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
+
diff --git a/pc_examples/mouse.npy b/pc_examples/mouse.npy
new file mode 100644
index 0000000..4d044d6
Binary files /dev/null and b/pc_examples/mouse.npy differ
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..90f22b3
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,13 @@
+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
\ No newline at end of file
+ 
+ 
+
;
+
+