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import torch
import lightning.pytorch as pl
from .dataloader import Demo_Dataset, Demo_Remesh_Dataset, Correspondence_Demo_Dataset
from torch.utils.data import DataLoader
from partfield.model.UNet.model import ResidualUNet3D
from partfield.model.triplane import TriplaneTransformer, get_grid_coord #, sample_from_planes, Voxel2Triplane
from partfield.model.model_utils import VanillaMLP
import torch.nn.functional as F
import torch.nn as nn
import os
import trimesh
import skimage
import numpy as np
import h5py
import torch.distributed as dist
from partfield.model.PVCNN.encoder_pc import TriPlanePC2Encoder, sample_triplane_feat
import json
import gc
import time
from plyfile import PlyData, PlyElement
class Model(pl.LightningModule):
def __init__(self, cfg):
super().__init__()
self.save_hyperparameters()
self.cfg = cfg
self.automatic_optimization = False
self.triplane_resolution = cfg.triplane_resolution
self.triplane_channels_low = cfg.triplane_channels_low
self.triplane_transformer = TriplaneTransformer(
input_dim=cfg.triplane_channels_low * 2,
transformer_dim=1024,
transformer_layers=6,
transformer_heads=8,
triplane_low_res=32,
triplane_high_res=128,
triplane_dim=cfg.triplane_channels_high,
)
self.sdf_decoder = VanillaMLP(input_dim=64,
output_dim=1,
out_activation="tanh",
n_neurons=64, #64
n_hidden_layers=6) #6
self.use_pvcnn = cfg.use_pvcnnonly
self.use_2d_feat = cfg.use_2d_feat
if self.use_pvcnn:
self.pvcnn = TriPlanePC2Encoder(
cfg.pvcnn,
device="cuda",
shape_min=-1,
shape_length=2,
use_2d_feat=self.use_2d_feat) #.cuda()
self.logit_scale = nn.Parameter(torch.tensor([1.0], requires_grad=True))
self.grid_coord = get_grid_coord(256)
self.mse_loss = torch.nn.MSELoss()
self.l1_loss = torch.nn.L1Loss(reduction='none')
if cfg.regress_2d_feat:
self.feat_decoder = VanillaMLP(input_dim=64,
output_dim=192,
out_activation="GELU",
n_neurons=64, #64
n_hidden_layers=6) #6
def predict_dataloader(self):
if self.cfg.remesh_demo:
dataset = Demo_Remesh_Dataset(self.cfg)
elif self.cfg.correspondence_demo:
dataset = Correspondence_Demo_Dataset(self.cfg)
else:
dataset = Demo_Dataset(self.cfg)
dataloader = DataLoader(dataset,
num_workers=self.cfg.dataset.val_num_workers,
batch_size=self.cfg.dataset.val_batch_size,
shuffle=False,
pin_memory=True,
drop_last=False)
return dataloader
@torch.no_grad()
def predict_step(self, batch, batch_idx):
save_dir = f"{self.cfg.result_name}"
os.makedirs(save_dir, exist_ok=True)
uid = batch['uid'][0]
view_id = 0
starttime = time.time()
if uid == "car" or uid == "complex_car":
# if uid == "complex_car":
print("Skipping this for now.")
print(uid)
return
### Skip if model already processed
if os.path.exists(f'{save_dir}/part_feat_{uid}_{view_id}.npy') or os.path.exists(f'{save_dir}/part_feat_{uid}_{view_id}_batch.npy'):
print("Already processed "+uid)
return
N = batch['pc'].shape[0]
assert N == 1
if self.use_2d_feat:
print("ERROR. Dataloader not implemented with input 2d feat.")
exit()
else:
pc_feat = self.pvcnn(batch['pc'], batch['pc'])
planes = pc_feat
planes = self.triplane_transformer(planes)
sdf_planes, part_planes = torch.split(planes, [64, planes.shape[2] - 64], dim=2)
if self.cfg.is_pc:
tensor_vertices = batch['pc'].reshape(1, -1, 3).cuda().to(torch.float16)
point_feat = sample_triplane_feat(part_planes, tensor_vertices) # N, M, C
point_feat = point_feat.cpu().detach().numpy().reshape(-1, 448)
np.save(f'{save_dir}/part_feat_{uid}_{view_id}.npy', point_feat)
print(f"Exported part_feat_{uid}_{view_id}.npy")
###########
from sklearn.decomposition import PCA
data_scaled = point_feat / np.linalg.norm(point_feat, axis=-1, keepdims=True)
pca = PCA(n_components=3)
data_reduced = pca.fit_transform(data_scaled)
data_reduced = (data_reduced - data_reduced.min()) / (data_reduced.max() - data_reduced.min())
colors_255 = (data_reduced * 255).astype(np.uint8)
points = batch['pc'].squeeze().detach().cpu().numpy()
if colors_255 is None:
colors_255 = np.full_like(points, 255) # Default to white color (255,255,255)
else:
assert colors_255.shape == points.shape, "Colors must have the same shape as points"
# Convert to structured array for PLY format
vertex_data = np.array(
[(*point, *color) for point, color in zip(points, colors_255)],
dtype=[("x", "f4"), ("y", "f4"), ("z", "f4"), ("red", "u1"), ("green", "u1"), ("blue", "u1")]
)
# Create PLY element
el = PlyElement.describe(vertex_data, "vertex")
# Write to file
filename = f'{save_dir}/feat_pca_{uid}_{view_id}.ply'
PlyData([el], text=True).write(filename)
print(f"Saved PLY file: {filename}")
############
else:
use_cuda_version = True
if use_cuda_version:
def sample_points(vertices, faces, n_point_per_face):
# Generate random barycentric coordinates
# borrowed from Kaolin https://github.com/NVIDIAGameWorks/kaolin/blob/master/kaolin/ops/mesh/trianglemesh.py#L43
n_f = faces.shape[0]
u = torch.sqrt(torch.rand((n_f, n_point_per_face, 1),
device=vertices.device,
dtype=vertices.dtype))
v = torch.rand((n_f, n_point_per_face, 1),
device=vertices.device,
dtype=vertices.dtype)
w0 = 1 - u
w1 = u * (1 - v)
w2 = u * v
face_v_0 = torch.index_select(vertices, 0, faces[:, 0].reshape(-1))
face_v_1 = torch.index_select(vertices, 0, faces[:, 1].reshape(-1))
face_v_2 = torch.index_select(vertices, 0, faces[:, 2].reshape(-1))
points = w0 * face_v_0.unsqueeze(dim=1) + w1 * face_v_1.unsqueeze(dim=1) + w2 * face_v_2.unsqueeze(dim=1)
return points
def sample_and_mean_memory_save_version(part_planes, tensor_vertices, n_point_per_face):
n_sample_each = self.cfg.n_sample_each # we iterate over this to avoid OOM
n_v = tensor_vertices.shape[1]
n_sample = n_v // n_sample_each + 1
all_sample = []
for i_sample in range(n_sample):
sampled_feature = sample_triplane_feat(part_planes, tensor_vertices[:, i_sample * n_sample_each: i_sample * n_sample_each + n_sample_each,])
assert sampled_feature.shape[1] % n_point_per_face == 0
sampled_feature = sampled_feature.reshape(1, -1, n_point_per_face, sampled_feature.shape[-1])
sampled_feature = torch.mean(sampled_feature, axis=-2)
all_sample.append(sampled_feature)
return torch.cat(all_sample, dim=1)
if self.cfg.vertex_feature:
tensor_vertices = batch['vertices'][0].reshape(1, -1, 3).to(torch.float32)
point_feat = sample_and_mean_memory_save_version(part_planes, tensor_vertices, 1)
else:
n_point_per_face = self.cfg.n_point_per_face
tensor_vertices = sample_points(batch['vertices'][0], batch['faces'][0], n_point_per_face)
tensor_vertices = tensor_vertices.reshape(1, -1, 3).to(torch.float32)
point_feat = sample_and_mean_memory_save_version(part_planes, tensor_vertices, n_point_per_face) # N, M, C
#### Take mean feature in the triangle
print("Time elapsed for feature prediction: " + str(time.time() - starttime))
point_feat = point_feat.reshape(-1, 448).cpu().numpy()
np.save(f'{save_dir}/part_feat_{uid}_{view_id}_batch.npy', point_feat)
print(f"Exported part_feat_{uid}_{view_id}.npy")
###########
from sklearn.decomposition import PCA
data_scaled = point_feat / np.linalg.norm(point_feat, axis=-1, keepdims=True)
pca = PCA(n_components=3)
data_reduced = pca.fit_transform(data_scaled)
data_reduced = (data_reduced - data_reduced.min()) / (data_reduced.max() - data_reduced.min())
colors_255 = (data_reduced * 255).astype(np.uint8)
V = batch['vertices'][0].cpu().numpy()
F = batch['faces'][0].cpu().numpy()
if self.cfg.vertex_feature:
colored_mesh = trimesh.Trimesh(vertices=V, faces=F, vertex_colors=colors_255, process=False)
else:
colored_mesh = trimesh.Trimesh(vertices=V, faces=F, face_colors=colors_255, process=False)
colored_mesh.export(f'{save_dir}/feat_pca_{uid}_{view_id}.ply')
############
torch.cuda.empty_cache()
else:
### Mesh input (obj file)
V = batch['vertices'][0].cpu().numpy()
F = batch['faces'][0].cpu().numpy()
##### Loop through faces #####
num_samples_per_face = self.cfg.n_point_per_face
all_point_feats = []
for face in F:
# Get the vertices of the current face
v0, v1, v2 = V[face]
# Generate random barycentric coordinates
u = np.random.rand(num_samples_per_face, 1)
v = np.random.rand(num_samples_per_face, 1)
is_prob = (u+v) >1
u[is_prob] = 1 - u[is_prob]
v[is_prob] = 1 - v[is_prob]
w = 1 - u - v
# Calculate points in Cartesian coordinates
points = u * v0 + v * v1 + w * v2
tensor_vertices = torch.from_numpy(points.copy()).reshape(1, -1, 3).cuda().to(torch.float32)
point_feat = sample_triplane_feat(part_planes, tensor_vertices) # N, M, C
#### Take mean feature in the triangle
point_feat = torch.mean(point_feat, axis=1).cpu().detach().numpy()
all_point_feats.append(point_feat)
##############################
all_point_feats = np.array(all_point_feats).reshape(-1, 448)
point_feat = all_point_feats
np.save(f'{save_dir}/part_feat_{uid}_{view_id}.npy', point_feat)
print(f"Exported part_feat_{uid}_{view_id}.npy")
###########
from sklearn.decomposition import PCA
data_scaled = point_feat / np.linalg.norm(point_feat, axis=-1, keepdims=True)
pca = PCA(n_components=3)
data_reduced = pca.fit_transform(data_scaled)
data_reduced = (data_reduced - data_reduced.min()) / (data_reduced.max() - data_reduced.min())
colors_255 = (data_reduced * 255).astype(np.uint8)
colored_mesh = trimesh.Trimesh(vertices=V, faces=F, face_colors=colors_255, process=False)
colored_mesh.export(f'{save_dir}/feat_pca_{uid}_{view_id}.ply')
############
print("Time elapsed: " + str(time.time()-starttime))
return |