Spaces:
Sleeping
Sleeping
File size: 4,828 Bytes
a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb c74a070 a80d6bb |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from .score import peakiness_score
class BaseNet(nn.Module):
"""Helper class to construct a fully-convolutional network that
extract a l2-normalized patch descriptor.
"""
def __init__(self, inchan=3, dilated=True, dilation=1, bn=True, bn_affine=False):
super(BaseNet, self).__init__()
self.inchan = inchan
self.curchan = inchan
self.dilated = dilated
self.dilation = dilation
self.bn = bn
self.bn_affine = bn_affine
def _make_bn(self, outd):
return nn.BatchNorm2d(outd, affine=self.bn_affine)
def _add_conv(
self,
outd,
k=3,
stride=1,
dilation=1,
bn=True,
relu=True,
k_pool=1,
pool_type="max",
bias=False,
):
# as in the original implementation, dilation is applied at the end of layer, so it will have impact only from next layer
d = self.dilation * dilation
# if self.dilated:
# conv_params = dict(padding=((k-1)*d)//2, dilation=d, stride=1)
# self.dilation *= stride
# else:
# conv_params = dict(padding=((k-1)*d)//2, dilation=d, stride=stride)
conv_params = dict(
padding=((k - 1) * d) // 2, dilation=d, stride=stride, bias=bias
)
ops = nn.ModuleList([])
ops.append(nn.Conv2d(self.curchan, outd, kernel_size=k, **conv_params))
if bn and self.bn:
ops.append(self._make_bn(outd))
if relu:
ops.append(nn.ReLU(inplace=True))
self.curchan = outd
if k_pool > 1:
if pool_type == "avg":
ops.append(torch.nn.AvgPool2d(kernel_size=k_pool))
elif pool_type == "max":
ops.append(torch.nn.MaxPool2d(kernel_size=k_pool))
else:
print(f"Error, unknown pooling type {pool_type}...")
return nn.Sequential(*ops)
class Quad_L2Net(BaseNet):
"""Same than L2_Net, but replace the final 8x8 conv by 3 successive 2x2 convs."""
def __init__(self, dim=128, mchan=4, relu22=False, **kw):
BaseNet.__init__(self, **kw)
self.conv0 = self._add_conv(8 * mchan)
self.conv1 = self._add_conv(8 * mchan, bn=False)
self.bn1 = self._make_bn(8 * mchan)
self.conv2 = self._add_conv(16 * mchan, stride=2)
self.conv3 = self._add_conv(16 * mchan, bn=False)
self.bn3 = self._make_bn(16 * mchan)
self.conv4 = self._add_conv(32 * mchan, stride=2)
self.conv5 = self._add_conv(32 * mchan)
# replace last 8x8 convolution with 3 3x3 convolutions
self.conv6_0 = self._add_conv(32 * mchan)
self.conv6_1 = self._add_conv(32 * mchan)
self.conv6_2 = self._add_conv(dim, bn=False, relu=False)
self.out_dim = dim
self.moving_avg_params = nn.ParameterList(
[
Parameter(torch.tensor(1.0), requires_grad=False),
Parameter(torch.tensor(1.0), requires_grad=False),
Parameter(torch.tensor(1.0), requires_grad=False),
]
)
def forward(self, x):
# x: [N, C, H, W]
x0 = self.conv0(x)
x1 = self.conv1(x0)
x1_bn = self.bn1(x1)
x2 = self.conv2(x1_bn)
x3 = self.conv3(x2)
x3_bn = self.bn3(x3)
x4 = self.conv4(x3_bn)
x5 = self.conv5(x4)
x6_0 = self.conv6_0(x5)
x6_1 = self.conv6_1(x6_0)
x6_2 = self.conv6_2(x6_1)
# calculate score map
comb_weights = torch.tensor([1.0, 2.0, 3.0], device=x.device)
comb_weights /= torch.sum(comb_weights)
ksize = [3, 2, 1]
det_score_maps = []
for idx, xx in enumerate([x1, x3, x6_2]):
if self.training:
instance_max = torch.max(xx)
self.moving_avg_params[idx].data = (
self.moving_avg_params[idx] * 0.99 + instance_max.detach() * 0.01
)
else:
pass
alpha, beta = peakiness_score(
xx, self.moving_avg_params[idx].detach(), ksize=3, dilation=ksize[idx]
)
score_vol = alpha * beta
det_score_map = torch.max(score_vol, dim=1, keepdim=True)[0]
det_score_map = F.interpolate(
det_score_map, size=x.shape[2:], mode="bilinear", align_corners=True
)
det_score_map = comb_weights[idx] * det_score_map
det_score_maps.append(det_score_map)
det_score_map = torch.sum(torch.stack(det_score_maps, dim=0), dim=0)
# print([param.data for param in self.moving_avg_params])
return x6_2, det_score_map, x1, x3
|