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import logging
import os
import cv2
import torch
from copy import deepcopy
import torch.nn.functional as F
from torchvision.transforms import ToTensor
import math
from alnet import ALNet
from soft_detect import DKD
import time
configs = {
"alike-t": {
"c1": 8,
"c2": 16,
"c3": 32,
"c4": 64,
"dim": 64,
"single_head": True,
"radius": 2,
"model_path": os.path.join(os.path.split(__file__)[0], "models", "alike-t.pth"),
},
"alike-s": {
"c1": 8,
"c2": 16,
"c3": 48,
"c4": 96,
"dim": 96,
"single_head": True,
"radius": 2,
"model_path": os.path.join(os.path.split(__file__)[0], "models", "alike-s.pth"),
},
"alike-n": {
"c1": 16,
"c2": 32,
"c3": 64,
"c4": 128,
"dim": 128,
"single_head": True,
"radius": 2,
"model_path": os.path.join(os.path.split(__file__)[0], "models", "alike-n.pth"),
},
"alike-l": {
"c1": 32,
"c2": 64,
"c3": 128,
"c4": 128,
"dim": 128,
"single_head": False,
"radius": 2,
"model_path": os.path.join(os.path.split(__file__)[0], "models", "alike-l.pth"),
},
}
class ALike(ALNet):
def __init__(
self,
# ================================== feature encoder
c1: int = 32,
c2: int = 64,
c3: int = 128,
c4: int = 128,
dim: int = 128,
single_head: bool = False,
# ================================== detect parameters
radius: int = 2,
top_k: int = 500,
scores_th: float = 0.5,
n_limit: int = 5000,
device: str = "cpu",
model_path: str = "",
):
super().__init__(c1, c2, c3, c4, dim, single_head)
self.radius = radius
self.top_k = top_k
self.n_limit = n_limit
self.scores_th = scores_th
self.dkd = DKD(
radius=self.radius,
top_k=self.top_k,
scores_th=self.scores_th,
n_limit=self.n_limit,
)
self.device = device
if model_path != "":
state_dict = torch.load(model_path, self.device)
self.load_state_dict(state_dict)
self.to(self.device)
self.eval()
logging.info(f"Loaded model parameters from {model_path}")
logging.info(
f"Number of model parameters: {sum(p.numel() for p in self.parameters() if p.requires_grad) / 1e3}KB"
)
def extract_dense_map(self, image, ret_dict=False):
# ====================================================
# check image size, should be integer multiples of 2^5
# if it is not a integer multiples of 2^5, padding zeros
device = image.device
b, c, h, w = image.shape
h_ = math.ceil(h / 32) * 32 if h % 32 != 0 else h
w_ = math.ceil(w / 32) * 32 if w % 32 != 0 else w
if h_ != h:
h_padding = torch.zeros(b, c, h_ - h, w, device=device)
image = torch.cat([image, h_padding], dim=2)
if w_ != w:
w_padding = torch.zeros(b, c, h_, w_ - w, device=device)
image = torch.cat([image, w_padding], dim=3)
# ====================================================
scores_map, descriptor_map = super().forward(image)
# ====================================================
if h_ != h or w_ != w:
descriptor_map = descriptor_map[:, :, :h, :w]
scores_map = scores_map[:, :, :h, :w] # Bx1xHxW
# ====================================================
# BxCxHxW
descriptor_map = torch.nn.functional.normalize(descriptor_map, p=2, dim=1)
if ret_dict:
return {
"descriptor_map": descriptor_map,
"scores_map": scores_map,
}
else:
return descriptor_map, scores_map
def forward(self, img, image_size_max=99999, sort=False, sub_pixel=False):
"""
:param img: np.array HxWx3, RGB
:param image_size_max: maximum image size, otherwise, the image will be resized
:param sort: sort keypoints by scores
:param sub_pixel: whether to use sub-pixel accuracy
:return: a dictionary with 'keypoints', 'descriptors', 'scores', and 'time'
"""
H, W, three = img.shape
assert three == 3, "input image shape should be [HxWx3]"
# ==================== image size constraint
image = deepcopy(img)
max_hw = max(H, W)
if max_hw > image_size_max:
ratio = float(image_size_max / max_hw)
image = cv2.resize(image, dsize=None, fx=ratio, fy=ratio)
# ==================== convert image to tensor
image = (
torch.from_numpy(image)
.to(self.device)
.to(torch.float32)
.permute(2, 0, 1)[None]
/ 255.0
)
# ==================== extract keypoints
start = time.time()
with torch.no_grad():
descriptor_map, scores_map = self.extract_dense_map(image)
keypoints, descriptors, scores, _ = self.dkd(
scores_map, descriptor_map, sub_pixel=sub_pixel
)
keypoints, descriptors, scores = keypoints[0], descriptors[0], scores[0]
keypoints = (keypoints + 1) / 2 * keypoints.new_tensor([[W - 1, H - 1]])
if sort:
indices = torch.argsort(scores, descending=True)
keypoints = keypoints[indices]
descriptors = descriptors[indices]
scores = scores[indices]
end = time.time()
return {
"keypoints": keypoints.cpu().numpy(),
"descriptors": descriptors.cpu().numpy(),
"scores": scores.cpu().numpy(),
"scores_map": scores_map.cpu().numpy(),
"time": end - start,
}
if __name__ == "__main__":
import numpy as np
from thop import profile
net = ALike(c1=32, c2=64, c3=128, c4=128, dim=128, single_head=False)
image = np.random.random((640, 480, 3)).astype(np.float32)
flops, params = profile(net, inputs=(image, 9999, False), verbose=False)
print("{:<30} {:<8} GFLops".format("Computational complexity: ", flops / 1e9))
print("{:<30} {:<8} KB".format("Number of parameters: ", params / 1e3))
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