File size: 5,266 Bytes
f53b39e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 |
import torch
import torch.nn.functional as F
from utils.utils import bilinear_sampler, coords_grid
try:
import alt_cuda_corr
except:
# alt_cuda_corr is not compiled
pass
class CorrBlock2:
def __init__(self, fmap1, fmap2, args):
self.num_levels = args.corr_levels
self.radius = args.corr_radius
self.args = args
self.corr_pyramid = []
# all pairs correlation
for i in range(self.num_levels):
corr = CorrBlock2.corr(fmap1, fmap2, 1)
batch, h1, w1, dim, h2, w2 = corr.shape
corr = corr.reshape(batch*h1*w1, dim, h2, w2)
fmap2 = F.interpolate(fmap2, scale_factor=0.5, mode='bilinear', align_corners=False)
self.corr_pyramid.append(corr)
def __call__(self, coords, dilation=None):
r = self.radius
coords = coords.permute(0, 2, 3, 1)
batch, h1, w1, _ = coords.shape
if dilation is None:
dilation = torch.ones(batch, 1, h1, w1, device=coords.device)
# print(dilation.max(), dilation.mean(), dilation.min())
out_pyramid = []
for i in range(self.num_levels):
corr = self.corr_pyramid[i]
device = coords.device
dx = torch.linspace(-r, r, 2*r+1, device=device)
dy = torch.linspace(-r, r, 2*r+1, device=device)
delta = torch.stack(torch.meshgrid(dy, dx), axis=-1)
delta_lvl = delta.view(1, 2*r+1, 2*r+1, 2)
delta_lvl = delta_lvl * dilation.view(batch * h1 * w1, 1, 1, 1)
centroid_lvl = coords.reshape(batch*h1*w1, 1, 1, 2) / 2**i
coords_lvl = centroid_lvl + delta_lvl
corr = bilinear_sampler(corr, coords_lvl)
corr = corr.view(batch, h1, w1, -1)
out_pyramid.append(corr)
out = torch.cat(out_pyramid, dim=-1)
out = out.permute(0, 3, 1, 2).contiguous().float()
return out
@staticmethod
def corr(fmap1, fmap2, num_head):
batch, dim, h1, w1 = fmap1.shape
h2, w2 = fmap2.shape[2:]
fmap1 = fmap1.view(batch, num_head, dim // num_head, h1*w1)
fmap2 = fmap2.view(batch, num_head, dim // num_head, h2*w2)
corr = fmap1.transpose(2, 3) @ fmap2
corr = corr.reshape(batch, num_head, h1, w1, h2, w2).permute(0, 2, 3, 1, 4, 5)
return corr / torch.sqrt(torch.tensor(dim).float())
class CorrBlock:
def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
self.num_levels = num_levels
self.radius = radius
self.corr_pyramid = []
# all pairs correlation
corr = CorrBlock.corr(fmap1, fmap2)
batch, h1, w1, dim, h2, w2 = corr.shape
corr = corr.reshape(batch*h1*w1, dim, h2, w2)
self.corr_pyramid.append(corr)
for i in range(self.num_levels-1):
corr = F.avg_pool2d(corr, 2, stride=2)
self.corr_pyramid.append(corr)
def __call__(self, coords):
r = self.radius
coords = coords.permute(0, 2, 3, 1)
batch, h1, w1, _ = coords.shape
out_pyramid = []
for i in range(self.num_levels):
corr = self.corr_pyramid[i]
dx = torch.linspace(-r, r, 2*r+1)
dy = torch.linspace(-r, r, 2*r+1)
delta = torch.stack(torch.meshgrid(dy, dx), axis=-1).to(coords.device)
centroid_lvl = coords.reshape(batch*h1*w1, 1, 1, 2) / 2**i
delta_lvl = delta.view(1, 2*r+1, 2*r+1, 2)
coords_lvl = centroid_lvl + delta_lvl
corr = bilinear_sampler(corr, coords_lvl)
corr = corr.view(batch, h1, w1, -1)
out_pyramid.append(corr)
out = torch.cat(out_pyramid, dim=-1)
return out.permute(0, 3, 1, 2).contiguous().float()
@staticmethod
def corr(fmap1, fmap2):
batch, dim, ht, wd = fmap1.shape
fmap1 = fmap1.view(batch, dim, ht*wd)
fmap2 = fmap2.view(batch, dim, ht*wd)
corr = torch.matmul(fmap1.transpose(1,2), fmap2)
corr = corr.view(batch, ht, wd, 1, ht, wd)
return corr / torch.sqrt(torch.tensor(dim).float())
class AlternateCorrBlock:
def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
self.num_levels = num_levels
self.radius = radius
self.pyramid = [(fmap1, fmap2)]
for i in range(self.num_levels):
fmap1 = F.avg_pool2d(fmap1, 2, stride=2)
fmap2 = F.avg_pool2d(fmap2, 2, stride=2)
self.pyramid.append((fmap1, fmap2))
def __call__(self, coords):
coords = coords.permute(0, 2, 3, 1)
B, H, W, _ = coords.shape
dim = self.pyramid[0][0].shape[1]
corr_list = []
for i in range(self.num_levels):
r = self.radius
fmap1_i = self.pyramid[0][0].permute(0, 2, 3, 1).contiguous()
fmap2_i = self.pyramid[i][1].permute(0, 2, 3, 1).contiguous()
coords_i = (coords / 2**i).reshape(B, 1, H, W, 2).contiguous()
corr, = alt_cuda_corr.forward(fmap1_i, fmap2_i, coords_i, r)
corr_list.append(corr.squeeze(1))
corr = torch.stack(corr_list, dim=1)
corr = corr.reshape(B, -1, H, W)
return corr / torch.sqrt(torch.tensor(dim).float())
|