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import torch
import os
import torchvision.transforms as tfm
import py3_wget
from matching import BaseMatcher, THIRD_PARTY_DIR, WEIGHTS_DIR
from matching.utils import resize_to_divisible, add_to_path
# add_to_path(THIRD_PARTY_DIR.joinpath("DeDoDe"))
# from DeDoDe import (
# dedode_detector_L,
# dedode_descriptor_B,
# )
add_to_path(THIRD_PARTY_DIR.joinpath("affine-steerers"))
from affine_steerers.utils import build_affine
from affine_steerers.matchers.dual_softmax_matcher import DualSoftMaxMatcher, MaxSimilarityMatcher
from affine_steerers import dedode_detector_L, dedode_descriptor_B, dedode_descriptor_G
class AffSteererMatcher(BaseMatcher):
detector_path_L = WEIGHTS_DIR.joinpath("dedode_detector_C4_affsteerers.pth")
descriptor_path_equi_G = WEIGHTS_DIR.joinpath("descriptor_aff_equi_G.pth")
descriptor_path_steer_G = WEIGHTS_DIR.joinpath("descriptor_aff_steer_G.pth")
descriptor_path_equi_B = WEIGHTS_DIR.joinpath("descriptor_aff_equi_B.pth")
descriptor_path_steer_B = WEIGHTS_DIR.joinpath("descriptor_aff_steer_B.pth")
steerer_path_equi_G = WEIGHTS_DIR.joinpath("steerer_aff_equi_G.pth")
steerer_path_steer_G = WEIGHTS_DIR.joinpath("steerer_aff_steer_G.pth")
steerer_path_equi_B = WEIGHTS_DIR.joinpath("steerer_aff_equi_B.pth")
steerer_path_steer_B = WEIGHTS_DIR.joinpath("steerer_aff_steer_B.pth")
dino_patch_size = 14
STEERER_TYPES = ["equi_G", "steer_G", "equi_B", "steer_B"]
def __init__(
self,
device="cpu",
max_num_keypoints=10_000,
steerer_type="equi_G",
match_threshold=0.01,
*args,
**kwargs,
):
super().__init__(device, **kwargs)
if self.device != "cuda": # only cuda devices work due to autocast in cuda in upstream.
raise ValueError("Only device 'cuda' supported for AffineSteerers.")
WEIGHTS_DIR.mkdir(exist_ok=True)
self.steerer_type = steerer_type
if self.steerer_type not in self.STEERER_TYPES:
raise ValueError(f'unsupported type for aff-steerer: {steerer_type}. Must choose from {self.STEERER_TYPES}.')
# download detector / descriptor / steerer
self.download_weights()
self.max_keypoints = max_num_keypoints
self.threshold = match_threshold
self.normalize = tfm.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.detector, self.descriptor, self.steerer, self.matcher = self.build_matcher()
def download_weights(self):
if not AffSteererMatcher.detector_path_L.exists():
print("Downloading dedode_detector_C4.pth")
py3_wget.download_file(
"https://github.com/georg-bn/affine-steerers/releases/download/weights/dedode_detector_C4.pth",
AffSteererMatcher.detector_path_L,
)
# download descriptors
if self.steerer_type == "equi_G" and not AffSteererMatcher.descriptor_path_equi_G.exists():
print("Downloading descriptor_aff_equi_G.pth")
py3_wget.download_file(
"https://github.com/georg-bn/affine-steerers/releases/download/weights/descriptor_aff_equi_G.pth",
AffSteererMatcher.descriptor_path_equi_G,
)
if self.steerer_type == "steer_G" and not AffSteererMatcher.descriptor_path_steer_G.exists():
print("Downloading descriptor_aff_steer_G.pth")
py3_wget.download_file(
"https://github.com/georg-bn/affine-steerers/releases/download/weights/descriptor_aff_steer_G.pth",
AffSteererMatcher.descriptor_path_steer_G,
)
if self.steerer_type == "equi_B" and not AffSteererMatcher.descriptor_path_equi_B.exists():
print("Downloading descriptor_aff_equi_B.pth")
py3_wget.download_file(
"https://github.com/georg-bn/affine-steerers/releases/download/weights/descriptor_aff_equi_B.pth",
AffSteererMatcher.descriptor_path_equi_B,
)
if self.steerer_type == "steer_B" and not AffSteererMatcher.descriptor_path_steer_B.exists():
print("Downloading descriptor_aff_steer_B.pth")
py3_wget.download_file(
"https://github.com/georg-bn/affine-steerers/releases/download/weights/descriptor_aff_steer_B.pth",
AffSteererMatcher.descriptor_path_steer_B,
)
# download steerers
if self.steerer_type == "equi_G" and not AffSteererMatcher.steerer_path_equi_G.exists():
print("Downloading steerer_aff_equi_G.pth")
py3_wget.download_file(
"https://github.com/georg-bn/affine-steerers/releases/download/weights/steerer_aff_equi_G.pth",
AffSteererMatcher.steerer_path_equi_G,
)
if self.steerer_type == "steer_G" and not AffSteererMatcher.steerer_path_steer_G.exists():
print("Downloading steerer_aff_steer_G.pth")
py3_wget.download_file(
"https://github.com/georg-bn/affine-steerers/releases/download/weights/steerer_aff_steer_G.pth",
AffSteererMatcher.steerer_path_steer_G,
)
if self.steerer_type == "equi_B" and not AffSteererMatcher.steerer_path_equi_B.exists():
print("Downloading steerer_aff_equi_B.pth")
py3_wget.download_file(
"https://github.com/georg-bn/affine-steerers/releases/download/weights/steerer_aff_equi_B.pth",
AffSteererMatcher.steerer_path_equi_B,
)
if self.steerer_type == "steer_B" and not AffSteererMatcher.steerer_path_steer_B.exists():
print("Downloading steerer_aff_steer_B.pth")
py3_wget.download_file(
"https://github.com/georg-bn/affine-steerers/releases/download/weights/steerer_aff_steer_B.pth",
AffSteererMatcher.steerer_path_steer_B,
)
def build_matcher(self):
detector = dedode_detector_L(weights=torch.load(self.detector_path_L, map_location=self.device))
if "G" in self.steerer_type:
descriptor_path = self.descriptor_path_equi_G if 'equi' in self.steerer_type else self.descriptor_path_steer_G
descriptor = dedode_descriptor_G(
weights=torch.load(descriptor_path, map_location=self.device)
)
else:
descriptor_path = self.descriptor_path_equi_B if 'equi' in self.steerer_type else self.descriptor_path_steer_B
descriptor = dedode_descriptor_B(
weights=torch.load(self.descriptor_path, map_location=self.device)
)
if "G" in self.steerer_type:
steerer_path = self.steerer_path_equi_G if 'equi' in self.steerer_type else self.steerer_path_steer_G
else:
steerer_path = self.steerer_path_equi_B if 'equi' in self.steerer_type else self.steerer_path_steer_B
assert steerer_path.exists(), f"could not find steerer weights at {steerer_path}. Please check that they exist."
steerer = self.load_steerer(
steerer_path
).to(self.device).eval()
steerer.use_prototype_affines = True
if 'steer' not in self.steerer_type:
steerer.prototype_affines = torch.stack(
[
build_affine(
angle_1=0.,
dilation_1=1.,
dilation_2=1.,
angle_2=r * 2 * torch.pi / 8
)
for r in range(8)
], # + ... more affines
dim=0,
).to(self.device)
matcher = MaxSimilarityMatcher(
steerer=steerer,
normalize=False,
inv_temp=5,
threshold=self.threshold
)
if self.device == "cpu":
detector = detector.to(torch.float32)
descriptor = descriptor.to(torch.float32)
steerer = steerer.to(torch.float32)
return detector, descriptor, steerer, matcher
@staticmethod
def load_steerer(steerer_path, checkpoint=False, prototypes=True, feat_dim=256):
from affine_steerers.steerers import SteererSpread
if checkpoint:
sd = torch.load(steerer_path, map_location="cpu")["steerer"]
else:
sd = torch.load(steerer_path, map_location="cpu")
nbr_prototypes = 0
if prototypes and "prototype_affines" in sd:
nbr_prototypes = sd["prototype_affines"].shape[0]
steerer = SteererSpread(
feat_dim=feat_dim,
max_order=4,
normalize=True,
normalize_only_higher=False,
fix_order_1_scalings=False,
max_determinant_scaling=None,
block_diag_rot=False,
block_diag_optimal_scalings=False,
learnable_determinant_scaling=True,
learnable_basis=True,
learnable_reference_direction=False,
use_prototype_affines=prototypes and "prototype_affines" in sd,
prototype_affines_init=[
torch.eye(2)
for i in range(nbr_prototypes)
]
)
steerer.load_state_dict(sd)
return steerer
def preprocess(self, img):
# ensure that the img has the proper w/h to be compatible with patch sizes
_, h, w = img.shape
orig_shape = h, w
img = resize_to_divisible(img, self.dino_patch_size)
img = self.normalize(img).unsqueeze(0).to(self.device)
return img, orig_shape
def _forward(self, img0, img1):
img0, img0_orig_shape = self.preprocess(img0)
img1, img1_orig_shape = self.preprocess(img1)
batch_0 = {"image": img0}
detections_0 = self.detector.detect(batch_0, num_keypoints=self.max_keypoints)
keypoints_0, P_0 = detections_0["keypoints"], detections_0["confidence"]
batch_1 = {"image": img1}
detections_1 = self.detector.detect(batch_1, num_keypoints=self.max_keypoints)
keypoints_1, P_1 = detections_1["keypoints"], detections_1["confidence"]
description_0 = self.descriptor.describe_keypoints(batch_0, keypoints_0)["descriptions"]
description_1 = self.descriptor.describe_keypoints(batch_1, keypoints_1)["descriptions"]
matches_0, matches_1, _ = self.matcher.match(
keypoints_0,
description_0,
keypoints_1,
description_1,
)
H0, W0, H1, W1 = *img0.shape[-2:], *img1.shape[-2:]
mkpts0, mkpts1 = self.matcher.to_pixel_coords(matches_0, matches_1, H0, W0, H1, W1)
# dedode sometimes requires reshaping an image to fit vit patch size evenly, so we need to
# rescale kpts to the original img
mkpts0 = self.rescale_coords(mkpts0, *img0_orig_shape, H0, W0)
mkpts1 = self.rescale_coords(mkpts1, *img1_orig_shape, H1, W1)
return mkpts0, mkpts1, keypoints_0, keypoints_1, description_0, description_1
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