File size: 6,247 Bytes
ba32b3e |
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 |
from torch import nn
import torch.nn.functional as F
import torch
from modules.util import Hourglass, make_coordinate_grid, kp2gaussian
from sync_batchnorm import SynchronizedBatchNorm3d as BatchNorm3d
class DenseMotionNetwork(nn.Module):
"""
Module that predicting a dense motion from sparse motion representation given by kp_source and kp_driving
"""
def __init__(self, block_expansion, num_blocks, max_features, num_kp, feature_channel, reshape_depth, compress,
estimate_occlusion_map=False):
super(DenseMotionNetwork, self).__init__()
# self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(feature_channel+1), max_features=max_features, num_blocks=num_blocks)
self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(compress+1), max_features=max_features, num_blocks=num_blocks)
self.mask = nn.Conv3d(self.hourglass.out_filters, num_kp + 1, kernel_size=7, padding=3)
self.compress = nn.Conv3d(feature_channel, compress, kernel_size=1)
self.norm = BatchNorm3d(compress, affine=True)
if estimate_occlusion_map:
# self.occlusion = nn.Conv2d(reshape_channel*reshape_depth, 1, kernel_size=7, padding=3)
self.occlusion = nn.Conv2d(self.hourglass.out_filters*reshape_depth, 1, kernel_size=7, padding=3)
else:
self.occlusion = None
self.num_kp = num_kp
def create_sparse_motions(self, feature, kp_driving, kp_source):
bs, _, d, h, w = feature.shape
identity_grid = make_coordinate_grid((d, h, w), type=kp_source['value'].type())
identity_grid = identity_grid.view(1, 1, d, h, w, 3)
coordinate_grid = identity_grid - kp_driving['value'].view(bs, self.num_kp, 1, 1, 1, 3)
k = coordinate_grid.shape[1]
# if 'jacobian' in kp_driving:
if 'jacobian' in kp_driving and kp_driving['jacobian'] is not None:
jacobian = torch.matmul(kp_source['jacobian'], torch.inverse(kp_driving['jacobian']))
jacobian = jacobian.unsqueeze(-3).unsqueeze(-3).unsqueeze(-3)
jacobian = jacobian.repeat(1, 1, d, h, w, 1, 1)
coordinate_grid = torch.matmul(jacobian, coordinate_grid.unsqueeze(-1))
coordinate_grid = coordinate_grid.squeeze(-1)
'''
if 'rot' in kp_driving:
rot_s = kp_source['rot']
rot_d = kp_driving['rot']
rot = torch.einsum('bij, bjk->bki', rot_s, torch.inverse(rot_d))
rot = rot.unsqueeze(-3).unsqueeze(-3).unsqueeze(-3).unsqueeze(-3)
rot = rot.repeat(1, k, d, h, w, 1, 1)
# print(rot.shape)
coordinate_grid = torch.matmul(rot, coordinate_grid.unsqueeze(-1))
coordinate_grid = coordinate_grid.squeeze(-1)
# print(coordinate_grid.shape)
'''
driving_to_source = coordinate_grid + kp_source['value'].view(bs, self.num_kp, 1, 1, 1, 3) # (bs, num_kp, d, h, w, 3)
#adding background feature
identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1, 1)
sparse_motions = torch.cat([identity_grid, driving_to_source], dim=1)
# sparse_motions = driving_to_source
return sparse_motions
def create_deformed_feature(self, feature, sparse_motions):
bs, _, d, h, w = feature.shape
feature_repeat = feature.unsqueeze(1).unsqueeze(1).repeat(1, self.num_kp+1, 1, 1, 1, 1, 1) # (bs, num_kp+1, 1, c, d, h, w)
feature_repeat = feature_repeat.view(bs * (self.num_kp+1), -1, d, h, w) # (bs*(num_kp+1), c, d, h, w)
sparse_motions = sparse_motions.view((bs * (self.num_kp+1), d, h, w, -1)) # (bs*(num_kp+1), d, h, w, 3)
sparse_deformed = F.grid_sample(feature_repeat, sparse_motions)
sparse_deformed = sparse_deformed.view((bs, self.num_kp+1, -1, d, h, w)) # (bs, num_kp+1, c, d, h, w)
return sparse_deformed
def create_heatmap_representations(self, feature, kp_driving, kp_source):
spatial_size = feature.shape[3:]
gaussian_driving = kp2gaussian(kp_driving, spatial_size=spatial_size, kp_variance=0.01)
gaussian_source = kp2gaussian(kp_source, spatial_size=spatial_size, kp_variance=0.01)
heatmap = gaussian_driving - gaussian_source
# adding background feature
zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1], spatial_size[2]).type(heatmap.type())
heatmap = torch.cat([zeros, heatmap], dim=1)
heatmap = heatmap.unsqueeze(2) # (bs, num_kp+1, 1, d, h, w)
return heatmap
def forward(self, feature, kp_driving, kp_source):
bs, _, d, h, w = feature.shape
feature = self.compress(feature)
feature = self.norm(feature)
feature = F.relu(feature)
out_dict = dict()
sparse_motion = self.create_sparse_motions(feature, kp_driving, kp_source)
deformed_feature = self.create_deformed_feature(feature, sparse_motion)
heatmap = self.create_heatmap_representations(deformed_feature, kp_driving, kp_source)
input = torch.cat([heatmap, deformed_feature], dim=2)
input = input.view(bs, -1, d, h, w)
# input = deformed_feature.view(bs, -1, d, h, w) # (bs, num_kp+1 * c, d, h, w)
prediction = self.hourglass(input)
mask = self.mask(prediction)
mask = F.softmax(mask, dim=1)
out_dict['mask'] = mask
mask = mask.unsqueeze(2) # (bs, num_kp+1, 1, d, h, w)
sparse_motion = sparse_motion.permute(0, 1, 5, 2, 3, 4) # (bs, num_kp+1, 3, d, h, w)
deformation = (sparse_motion * mask).sum(dim=1) # (bs, 3, d, h, w)
deformation = deformation.permute(0, 2, 3, 4, 1) # (bs, d, h, w, 3)
out_dict['deformation'] = deformation
if self.occlusion:
bs, c, d, h, w = prediction.shape
prediction = prediction.view(bs, -1, h, w)
occlusion_map = torch.sigmoid(self.occlusion(prediction))
out_dict['occlusion_map'] = occlusion_map
return out_dict
|