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from ..utils.misc import detect_NaN
from .head.fine_matching import FineMatching
from .head.coarse_matching import CoarseMatching
from .neck.neck import CIM
from .backbone.resnet import ResNet18
from einops.einops import rearrange
import torch.nn.functional as F
import torch.nn as nn
import torch
torch.set_float32_matmul_precision("highest") # highest (defualt) high medium
class EDM(nn.Module):
def __init__(self, config):
super().__init__()
# Misc
self.config = config
self.local_resolution = self.config["local_resolution"]
self.bi_directional_refine = self.config["fine"]["bi_directional_refine"]
self.deploy = self.config["deploy"]
self.topk = config["coarse"]["topk"]
# Modules
self.backbone = ResNet18(config)
self.neck = CIM(config)
self.coarse_matching = CoarseMatching(config)
self.fine_matching = FineMatching(config)
def forward(self, data):
"""
Update:
data (dict): {
'image0': (torch.Tensor): (N, 1, H, W)
'image1': (torch.Tensor): (N, 1, H, W)
'mask0'(optional) : (torch.Tensor): (N, H, W) '0' indicates a padded position
'mask1'(optional) : (torch.Tensor): (N, H, W)
}
"""
if self.deploy:
image0, image1 = data.split(1, 1)
data = {"image0": image0, "image1": image1}
data.update(
{
"bs": data["image0"].size(0),
"hw0_i": data["image0"].shape[2:],
"hw1_i": data["image1"].shape[2:],
}
)
# 1. Feature Extraction
if data["hw0_i"] == data["hw1_i"]:
# faster & better BN convergence
feats = self.backbone(
torch.cat([data["image0"], data["image1"]], dim=0))
f8, f16, f32, f8_fine = feats
ms_feats = f8, f16, f32
feat_f0, feat_f1 = f8_fine.chunk(2)
else:
# handle different input shapes
# raise ValueError("image0 and image1 should have the same shape.")
feats0, feats1 = self.backbone(data["image0"]), self.backbone(
data["image1"]
)
f8_0, f16_0, f32_0, feat_f0 = feats0
f8_1, f16_1, f32_1, feat_f1 = feats1
ms_feats = f8_0, f16_0, f32_0, f8_1, f16_1, f32_1
mask_c0 = mask_c1 = None # mask is useful in training
if "mask0" in data:
mask_c0, mask_c1 = data["mask0"], data["mask1"]
# 2. Feature Interaction & Multi-Scale Fusion
feat_c0, feat_c1 = self.neck(ms_feats, mask_c0, mask_c1)
data.update(
{
"hw0_c": feat_c0.shape[2:],
"hw1_c": feat_c1.shape[2:],
"hw0_f": feat_c0.shape[2:] * self.config["local_resolution"],
"hw1_f": feat_c1.shape[2:] * self.config["local_resolution"],
}
)
feat_c0 = rearrange(feat_c0, "n c h w -> n (h w) c")
feat_c1 = rearrange(feat_c1, "n c h w -> n (h w) c")
feat_f0 = rearrange(feat_f0, "n c h w -> n (h w) c")
feat_f1 = rearrange(feat_f1, "n c h w -> n (h w) c")
# detect NaN during mixed precision training
if self.config["mp"] and (
torch.any(torch.isnan(feat_c0)) or torch.any(torch.isnan(feat_c1))
):
detect_NaN(feat_c0, feat_c1)
# 3. Coarse-Level Matching
conf_matrix = self.coarse_matching(
feat_c0,
feat_c1,
data,
mask_c0=(
mask_c0.view(mask_c0.size(0), -
1) if mask_c0 is not None else mask_c0
),
mask_c1=(
mask_c1.view(mask_c1.size(0), -
1) if mask_c1 is not None else mask_c1
),
)
if self.deploy:
k = self.topk
row_max_val, row_max_idx = torch.max(conf_matrix, dim=2)
topk_val, topk_idx = torch.topk(row_max_val, k, dim=1)
b_ids = (
torch.arange(conf_matrix.shape[0], device=conf_matrix.device)
.unsqueeze(1)
.repeat(1, k)
.flatten()
)
i_ids = topk_idx.flatten()
j_ids = row_max_idx[b_ids, i_ids].flatten()
mconf = conf_matrix[b_ids, i_ids, j_ids]
scale = data["hw0_i"][0] / data["hw0_c"][0]
scale0 = scale * \
data["scale0"][b_ids] if "scale0" in data else scale
scale1 = scale * \
data["scale1"][b_ids] if "scale1" in data else scale
mkpts0_c = (
torch.stack(
[
i_ids % data["hw0_c"][1],
torch.div(i_ids, data["hw0_c"][1],
rounding_mode="floor"),
],
dim=1,
)
* scale0
)
mkpts1_c = (
torch.stack(
[
j_ids % data["hw1_c"][1],
torch.div(j_ids, data["hw1_c"][1],
rounding_mode="floor"),
],
dim=1,
)
* scale1
)
data.update(
{
"mconf": mconf,
"mkpts0_c": mkpts0_c,
"mkpts1_c": mkpts1_c,
"b_ids": b_ids,
"i_ids": i_ids,
"j_ids": j_ids,
}
)
# 4. Fine-Level Matching
K0 = data["i_ids"].shape[0] // data["bs"]
K1 = data["j_ids"].shape[0] // data["bs"]
feat_f0 = feat_f0[data["b_ids"], data["i_ids"]
].reshape(data["bs"], K0, -1)
feat_f1 = feat_f1[data["b_ids"], data["j_ids"]
].reshape(data["bs"], K1, -1)
feat_c0 = feat_c0[data["b_ids"], data["i_ids"]
].reshape(data["bs"], K0, -1)
feat_c1 = feat_c1[data["b_ids"], data["j_ids"]
].reshape(data["bs"], K1, -1)
if self.bi_directional_refine:
# Bidirectional Refinement
offset, score = self.fine_matching(
torch.cat([feat_f0, feat_f1], dim=1),
torch.cat([feat_f1, feat_f0], dim=1),
torch.cat([feat_c0, feat_c1], dim=1),
torch.cat([feat_c1, feat_c0], dim=1),
data,
)
else:
offset, score = self.fine_matching(
feat_f0, feat_f1, feat_c0, feat_c1, data)
if self.deploy:
if self.bi_directional_refine:
fine_offset01, fine_offset10 = offset.chunk(2)
fine_score01, fine_score10 = score.unsqueeze(dim=1).chunk(2)
output = torch.cat(
[mkpts0_c, mkpts1_c, fine_offset01, fine_offset10, fine_score01, fine_score10, mconf.unsqueeze(dim=1)], 1) # [K, 11]
else:
output = torch.cat(
[mkpts0_c, mkpts1_c, offset, score, mconf.unsqueeze(dim=1)], 1)
return output
def load_state_dict(self, state_dict, *args, **kwargs):
for k in list(state_dict.keys()):
if k.startswith("matcher."):
state_dict[k.replace("matcher.", "", 1)] = state_dict.pop(k)
return super().load_state_dict(state_dict, *args, **kwargs)
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