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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import distributions
class Conv1d_BN_Act(nn.Sequential):
def __init__(
self,
a,
b,
ks=1,
stride=1,
pad=0,
dilation=1,
groups=1,
bn_weight_init=1,
act=None,
drop=None,
):
super().__init__()
self.inp_channel = a
self.out_channel = b
self.ks = ks
self.pad = pad
self.stride = stride
self.dilation = dilation
self.groups = groups
self.add_module(
"c", nn.Conv1d(a, b, ks, stride, pad, dilation, groups, bias=False)
)
bn = nn.BatchNorm1d(b)
nn.init.constant_(bn.weight, bn_weight_init)
nn.init.constant_(bn.bias, 0)
self.add_module("bn", bn)
if act != None:
self.add_module("a", act)
if drop != None:
self.add_module("d", nn.Dropout(drop))
class RealNVP(nn.Module):
"""RealNVP: a flow-based generative model
`Density estimation using Real NVP
arXiv: <https://arxiv.org/abs/1605.08803>`_.
Code is modified from `the mmpose implementation of RLE
<https://github.com/open-mmlab/mmpose/blob/main/mmpose/models/utils/realnvp.py>`_.
"""
@staticmethod
def get_scale_net(channel):
"""Get the scale model in a single invertable mapping."""
return nn.Sequential(
nn.Linear(2, channel),
nn.GELU(),
nn.Linear(channel, channel),
nn.GELU(),
nn.Linear(channel, 2),
nn.Tanh(),
)
@staticmethod
def get_trans_net(channel):
"""Get the translation model in a single invertable mapping."""
return nn.Sequential(
nn.Linear(2, channel),
nn.GELU(),
nn.Linear(channel, channel),
nn.GELU(),
nn.Linear(channel, 2),
)
@property
def prior(self):
"""The prior distribution."""
return distributions.MultivariateNormal(self.loc, self.cov)
def __init__(self, channel=64):
super(RealNVP, self).__init__()
self.channel = channel
self.register_buffer("loc", torch.zeros(2))
self.register_buffer("cov", torch.eye(2))
self.register_buffer(
"mask", torch.tensor([[0, 1], [1, 0]] * 3, dtype=torch.float32)
)
self.s = torch.nn.ModuleList(
[self.get_scale_net(self.channel) for _ in range(len(self.mask))]
)
self.t = torch.nn.ModuleList(
[self.get_trans_net(self.channel) for _ in range(len(self.mask))]
)
self.init_weights()
def init_weights(self):
"""Initialization model weights."""
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight, gain=0.01)
def backward_p(self, x):
"""Apply mapping form the data space to the latent space and calculate
the log determinant of the Jacobian matrix."""
log_det_jacob, z = x.new_zeros(x.shape[0]), x
for i in reversed(range(len(self.t))):
z_ = self.mask[i] * z
s = self.s[i](z_) * (1 - self.mask[i]) # torch.exp(s): betas
t = self.t[i](z_) * (1 - self.mask[i]) # gammas
z = (1 - self.mask[i]) * (z - t) * torch.exp(-s) + z_
log_det_jacob -= s.sum(dim=1)
return z, log_det_jacob
def log_prob(self, x):
"""Calculate the log probability of given sample in data space."""
z, log_det = self.backward_p(x)
return self.prior.log_prob(z) + log_det
def soft_argmax(x, temperature=1.0):
L = x.shape[1]
assert L % 2 # L is odd to ensure symmetry
idx = torch.arange(0, L, 1, device=x.device).repeat(x.shape[0], 1)
scale_x = x / temperature
out = F.softmax(scale_x, dim=1) * idx
out = out.sum(dim=1, keepdim=True)
return out
class FineMatching(nn.Module):
def __init__(self, config, act_layer=nn.GELU):
super(FineMatching, self).__init__()
self.config = config
self.block_dims = self.config["backbone"]["block_dims"]
self.local_resolution = self.config["local_resolution"]
self.drop = self.config["fine"]["droprate"]
self.coord_length = self.config["fine"]["coord_length"]
self.bi_directional_refine = self.config["fine"]["bi_directional_refine"]
self.sigma_selection = self.config["fine"]["sigma_selection"]
self.mconf_thr = self.config["coarse"]["mconf_thr"]
self.sigma_thr = self.config["fine"]["sigma_thr"]
self.border_rm = self.config["coarse"]["border_rm"] * \
self.local_resolution
self.deploy = self.config["deploy"]
# network
self.query_encoder = nn.Sequential(
Conv1d_BN_Act(
self.block_dims[-1],
self.block_dims[-1],
act=act_layer(),
drop=self.drop,
),
Conv1d_BN_Act(
self.block_dims[-1],
self.block_dims[-1],
act=act_layer(),
drop=self.drop,
),
)
self.reference_encoder = nn.Sequential(
Conv1d_BN_Act(
self.block_dims[-1],
self.block_dims[-1],
act=act_layer(),
drop=self.drop,
),
Conv1d_BN_Act(
self.block_dims[-1],
self.block_dims[-1],
act=act_layer(),
drop=self.drop,
),
)
self.merge_qr = nn.Sequential(
Conv1d_BN_Act(
self.block_dims[-1] * 2,
self.block_dims[-1] * 2,
act=act_layer(),
drop=self.drop,
),
Conv1d_BN_Act(
self.block_dims[-1] * 2,
self.block_dims[-1] * 2,
act=act_layer(),
drop=self.drop,
),
)
self.x_head = nn.Sequential(
Conv1d_BN_Act(
self.block_dims[-1] * 2,
self.block_dims[-1] * 2,
act=act_layer(),
drop=self.drop,
),
nn.Conv1d(self.block_dims[-1] * 2,
self.coord_length + 2, kernel_size=1),
)
self.y_head = nn.Sequential(
Conv1d_BN_Act(
self.block_dims[-1] * 2,
self.block_dims[-1] * 2,
act=act_layer(),
drop=self.drop,
),
nn.Conv1d(self.block_dims[-1] * 2,
self.coord_length + 2, kernel_size=1),
)
self.flow = RealNVP()
self.init_params()
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv1d):
nn.init.kaiming_normal_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, feat_f0, feat_f1, feat_c0, feat_c1, data={}):
q = self.query_encoder(
feat_f0.permute(0, 2, 1).contiguous()
+ feat_c0.permute(0, 2, 1).contiguous()
)
r = self.reference_encoder(
feat_f1.permute(0, 2, 1).contiguous()
+ feat_c1.permute(0, 2, 1).contiguous()
)
out = self.merge_qr(torch.cat([q, r], dim=1))
x = self.x_head(out).permute(0, 2, 1).contiguous()
y = self.y_head(out).permute(0, 2, 1).contiguous()
if self.bi_directional_refine:
x01, x10 = x.chunk(2, dim=1)
x01 = x01.reshape(-1, self.coord_length + 2)
x10 = x10.reshape(-1, self.coord_length + 2)
x_out = torch.cat([x01, x10])
y01, y10 = y.chunk(2, dim=1)
y01 = y01.reshape(-1, self.coord_length + 2)
y10 = y10.reshape(-1, self.coord_length + 2)
y_out = torch.cat([y01, y10])
else:
x_out = x.reshape(-1, self.coord_length + 2)
y_out = y.reshape(-1, self.coord_length + 2)
x_cls = x_out[:, : self.coord_length + 1]
coord_x = soft_argmax(x_cls) / self.coord_length - \
0.5 # range [-0.5, +0.5]
x_sigma = x_out[:, -1:].sigmoid()
y_cls = y_out[:, : self.coord_length + 1]
coord_y = soft_argmax(y_cls) / self.coord_length - 0.5
y_sigma = y_out[:, -1:].sigmoid()
coord = torch.cat([coord_x, coord_y], dim=1)
sigma = torch.cat([x_sigma, y_sigma], dim=1)
if data.get("target_uv", None) is not None:
gt_uv = data["target_uv"]
mask = data["target_uv_weight"].clone()
if mask.sum() == 0:
mask[0] = True
mask_coord = coord[mask]
mask_gt_uv = gt_uv[mask]
mask_sigma = sigma[mask]
mask_sigma = torch.clamp(mask_sigma, 1e-6, 1 - 1e-6)
bar_mu = (mask_coord - mask_gt_uv) / mask_sigma
log_phi = self.flow.log_prob(bar_mu).unsqueeze(-1)
nf_loss = torch.log(mask_sigma) - log_phi
data.update(
{
"pred_coord": coord,
"pred_score": 1.0 - torch.mean(sigma, dim=-1).flatten(),
"mask_coord": mask_coord,
"mask_sigma": mask_sigma,
"nf_loss": nf_loss,
}
)
else:
data.update(
{
"pred_coord": coord,
"pred_score": 1.0 - torch.mean(sigma, dim=-1).flatten(),
}
)
if not self.deploy:
self.final_matching_selection(data)
return data["pred_coord"], data["pred_score"]
@torch.no_grad()
def final_matching_selection(self, data):
offset = data["pred_coord"] * self.local_resolution
if self.bi_directional_refine:
fine_offset01, fine_offset10 = torch.clamp(
offset, -self.local_resolution / 2, self.local_resolution / 2
).chunk(2)
else:
fine_offset01 = torch.clamp(
offset, -self.local_resolution / 2, self.local_resolution / 2
)
h0, w0 = data["hw0_i"]
h1, w1 = data["hw1_i"]
scale0 = data["scale0"][data["b_ids"]] if "scale0" in data else 1.0
scale1 = data["scale1"][data["b_ids"]] if "scale1" in data else 1.0
scale0_w = scale0[:, 0] if "scale0" in data else 1.0
scale0_h = scale0[:, 1] if "scale0" in data else 1.0
scale1_w = scale1[:, 0] if "scale1" in data else 1.0
scale1_h = scale1[:, 1] if "scale1" in data else 1.0
# Filter by mconf and border
mkpts0_f = data["mkpts0_c"]
mkpts1_f = data["mkpts1_c"] + fine_offset01 * scale1
mask = (
(data["mconf"] > self.mconf_thr)
& (mkpts0_f[:, 0] >= self.border_rm)
& (mkpts0_f[:, 0] <= w0 * scale0_w - self.border_rm)
& (mkpts0_f[:, 1] >= self.border_rm)
& (mkpts0_f[:, 1] <= h0 * scale0_h - self.border_rm)
& (mkpts1_f[:, 0] >= self.border_rm)
& (mkpts1_f[:, 0] <= w1 * scale1_w - self.border_rm)
& (mkpts1_f[:, 1] >= self.border_rm)
& (mkpts1_f[:, 1] <= h1 * scale1_h - self.border_rm)
)
if self.bi_directional_refine:
mkpts0_f_ = data["mkpts0_c"] + fine_offset10 * scale0
mkpts1_f_ = data["mkpts1_c"]
mask_ = (
(data["mconf"] > self.mconf_thr)
& (mkpts0_f_[:, 0] >= self.border_rm)
& (mkpts0_f_[:, 0] <= w0 * scale0_w - self.border_rm)
& (mkpts0_f_[:, 1] >= self.border_rm)
& (mkpts0_f_[:, 1] <= h0 * scale0_h - self.border_rm)
& (mkpts1_f_[:, 0] >= self.border_rm)
& (mkpts1_f_[:, 0] <= w1 * scale1_w - self.border_rm)
& (mkpts1_f_[:, 1] >= self.border_rm)
& (mkpts1_f_[:, 1] <= h1 * scale1_h - self.border_rm)
)
if self.bi_directional_refine:
mkpts0_f = torch.cat([mkpts0_f, mkpts0_f_])
mkpts1_f = torch.cat([mkpts1_f, mkpts1_f_])
mask = torch.cat([mask, mask_])
data["mconf"] = torch.cat([data["mconf"], data["mconf"]])
data["b_ids"] = torch.cat([data["b_ids"], data["b_ids"]])
# Filter by sigma
if self.bi_directional_refine and self.sigma_selection:
# Retain the more confident matching pair with a smaller sigma (more significant) in the bi-directional matching pairs
pred_score01, pred_score10 = data["pred_score"].chunk(2)
pred_score_mask = pred_score01 > pred_score10
pred_score_mask = torch.cat([pred_score_mask, ~pred_score_mask])
pred_score_mask &= data["pred_score"] > self.sigma_thr
mask &= pred_score_mask
data.update(
{
# "gt_mask": data["mconf"] == 0,
"m_bids": data["b_ids"][mask],
"mkpts0_f": mkpts0_f[mask],
"mkpts1_f": mkpts1_f[mask],
"mconf": data["mconf"][mask],
}
)
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