Spaces:
Running
Running
File size: 5,882 Bytes
ec9a6bc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
import os
import numpy as np
import torch
import torch.nn.functional as F
import config
def compute_gradient_volume(weight_volume, voxel_size):
"""
:param weight_volume: (C, X, Y, Z)
"""
sobel_x = torch.zeros((3, 3, 3), dtype = torch.float32, device = config.device)
sobel_x[0] = torch.tensor([[-1,-2,-1], [-2,-4,-2], [-1,-2,-1]], dtype = torch.float32)
sobel_x[2] = -sobel_x[0]
sobel_z = sobel_x.permute((1, 2, 0))
sobel_y = sobel_x.permute((2, 0, 1))
# normalize
sobel_x = sobel_x / (16 * 2 * voxel_size[0])
sobel_y = sobel_y / (16 * 2 * voxel_size[1])
sobel_z = sobel_z / (16 * 2 * voxel_size[2])
# sobel_x = torch.zeros((3, 3, 3), dtype = torch.float32, device = config.device)
# sobel_x[0] = torch.tensor([[0, 0, 0], [0, -1, 0], [0, 0, 0]], dtype = torch.float32)
# sobel_x[2] = -sobel_x[0]
# sobel_z = sobel_x.permute((1, 2, 0))
# sobel_y = sobel_x.permute((2, 0, 1))
#
# # normalize
# sobel_x = sobel_x / (2 * voxel_size[0])
# sobel_y = sobel_y / (2 * voxel_size[1])
# sobel_z = sobel_z / (2 * voxel_size[2])
sobel_filter = torch.cat((sobel_x.unsqueeze(0), sobel_y.unsqueeze(0), sobel_z.unsqueeze(0)), dim = 0)
sobel_filter = sobel_filter.unsqueeze(1)
grad_volume = F.conv3d(input = weight_volume.unsqueeze(1), weight = sobel_filter, padding = 1)
return grad_volume # [J, 3, X, Y, Z]
class CanoBlendWeightVolume:
def __init__(self, data_path):
if not os.path.exists(data_path):
raise FileNotFoundError('# CanoBlendWeightVolume is not found from %s' % data_path)
data = np.load(data_path)
diff_weight_volume = data['diff_weight_volume']
diff_weight_volume = diff_weight_volume.transpose((3, 0, 1, 2))[None]
# base_weight_volume = base_weight_volume.transpose((3, 2, 1, 0))[None]
self.diff_weight_volume = torch.from_numpy(diff_weight_volume).to(torch.float32).to(config.device)
self.res_x, self.res_y, self.res_z = self.diff_weight_volume.shape[2:]
self.joint_num = self.diff_weight_volume.shape[1]
self.ori_weight_volume = torch.from_numpy(data['ori_weight_volume'].transpose((3, 0, 1, 2))[None]).to(torch.float32).to(config.device)
if 'sdf_volume' in data:
smpl_sdf_volume = data['sdf_volume']
if len(smpl_sdf_volume.shape) == 3:
smpl_sdf_volume = smpl_sdf_volume[..., None]
smpl_sdf_volume = smpl_sdf_volume.transpose((3, 0, 1, 2))[None]
self.smpl_sdf_volume = torch.from_numpy(smpl_sdf_volume).to(torch.float32).to(config.device)
self.volume_bounds = torch.from_numpy(data['volume_bounds']).to(torch.float32).to(config.device)
self.center = torch.from_numpy(data['center']).to(torch.float32).to(config.device)
self.smpl_bounds = torch.from_numpy(data['smpl_bounds']).to(torch.float32).to(config.device)
volume_len = self.volume_bounds[1] - self.volume_bounds[0]
self.voxel_size = volume_len / torch.tensor([self.res_x-1, self.res_y-1, self.res_z-1]).to(volume_len)
# self.base_gradient_volume = compute_gradient_volume(self.diff_weight_volume[0], self.voxel_size) # [joint_num, 3, X, Y, Z]
def forward_weight(self, pts, requires_scale = True, volume_type = 'diff'):
"""
:param pts: (B, N, 3)
:param requires_scale: bool, scale pts to [0, 1]
:return: (B, N, 24)
"""
if requires_scale:
pts = (pts - self.volume_bounds[None, None, 0]) / (self.volume_bounds[1] - self.volume_bounds[0])[None, None]
B, N, _ = pts.shape
grid = 2 * pts - 1
grid = grid[..., [2, 1, 0]]
grid = grid[:, :, None, None]
weight_volume = self.diff_weight_volume if volume_type == 'diff' else self.ori_weight_volume
base_w = F.grid_sample(weight_volume.expand(B, -1, -1, -1, -1),
grid,
mode = 'bilinear',
padding_mode = 'border',
align_corners = True)
base_w = base_w[:, :, :, 0, 0].permute(0, 2, 1)
return base_w
def forward_weight_grad(self, pts, requires_scale = True):
"""
:param pts: (B, N, 3)
:param requires_scale: bool, scale pts to [0, 1]
:return: (B, N, 24)
"""
if requires_scale:
pts = (pts - self.volume_bounds[None, None, 0]) / (self.volume_bounds[1] - self.volume_bounds[0])[None, None]
B, N, _ = pts.shape
grid = 2 * pts - 1
grid = grid.reshape(-1, 3)[:, [2, 1, 0]]
grid = grid[None, :, None, None]
base_g = F.grid_sample(self.base_gradient_volume.view(self.joint_num * 3, self.res_x, self.res_y, self.res_z)[None].expand(B, -1, -1, -1, -1),
grid,
mode = 'nearest',
padding_mode = 'border',
align_corners = True)
base_g = base_g[:, :, :, 0, 0].permute(0, 2, 1).reshape(B, N, -1, 3)
return base_g
def forward_sdf(self, pts, requires_scale = True):
if requires_scale:
pts = (pts - self.volume_bounds[None, None, 0]) / (self.volume_bounds[1] - self.volume_bounds[0])[None, None]
B, N, _ = pts.shape
grid = 2 * pts - 1
grid = grid.reshape(-1, 3)[:, [2, 1, 0]]
grid = grid[None, :, None, None]
sdf = F.grid_sample(self.smpl_sdf_volume.expand(B, -1, -1, -1, -1),
grid,
padding_mode = 'border',
align_corners = True)
sdf = sdf[:, :, :, 0, 0].permute(0, 2, 1)
return sdf
|