Spaces:
No application file
No application file
File size: 2,746 Bytes
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 |
import os
import cv2
import argparse
import numpy as np
import torch
import torchvision
from torchvision import datasets, transforms
from torch.autograd import Variable
from network_v0.model import PointModel
from datasets.hp_loader import PatchesDataset
from torch.utils.data import DataLoader
from evaluation.evaluate import evaluate_keypoint_net
def main():
parser = argparse.ArgumentParser(description="Testing")
parser.add_argument("--device", default=0, type=int, help="which gpu to run on.")
parser.add_argument("--test_dir", required=True, type=str, help="Test data path.")
opt = parser.parse_args()
torch.manual_seed(0)
use_gpu = torch.cuda.is_available()
if use_gpu:
torch.cuda.set_device(opt.device)
# Load data in 320x240
hp_dataset_320x240 = PatchesDataset(
root_dir=opt.test_dir, use_color=True, output_shape=(320, 240), type="all"
)
data_loader_320x240 = DataLoader(
hp_dataset_320x240,
batch_size=1,
pin_memory=False,
shuffle=False,
num_workers=4,
worker_init_fn=None,
sampler=None,
)
# Load data in 640x480
hp_dataset_640x480 = PatchesDataset(
root_dir=opt.test_dir, use_color=True, output_shape=(640, 480), type="all"
)
data_loader_640x480 = DataLoader(
hp_dataset_640x480,
batch_size=1,
pin_memory=False,
shuffle=False,
num_workers=4,
worker_init_fn=None,
sampler=None,
)
# Load model
model = PointModel(is_test=True)
ckpt = torch.load("./checkpoints/PointModel_v0.pth")
model.load_state_dict(ckpt["model_state"])
model = model.eval()
if use_gpu:
model = model.cuda()
print("Evaluating in 320x240, 300 points")
rep, loc, c1, c3, c5, mscore = evaluate_keypoint_net(
data_loader_320x240, model, output_shape=(320, 240), top_k=300
)
print("Repeatability: {0:.3f}".format(rep))
print("Localization Error: {0:.3f}".format(loc))
print("H-1 Accuracy: {:.3f}".format(c1))
print("H-3 Accuracy: {:.3f}".format(c3))
print("H-5 Accuracy: {:.3f}".format(c5))
print("Matching Score: {:.3f}".format(mscore))
print("\n")
print("Evaluating in 640x480, 1000 points")
rep, loc, c1, c3, c5, mscore = evaluate_keypoint_net(
data_loader_640x480, model, output_shape=(640, 480), top_k=1000
)
print("Repeatability: {0:.3f}".format(rep))
print("Localization Error: {0:.3f}".format(loc))
print("H-1 Accuracy: {:.3f}".format(c1))
print("H-3 Accuracy: {:.3f}".format(c3))
print("H-5 Accuracy: {:.3f}".format(c5))
print("Matching Score: {:.3f}".format(mscore))
print("\n")
if __name__ == "__main__":
main()
|