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Experiment svd #52

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256ee4f
added experiment scripts to view output files
pfvaldez Jan 20, 2023
fd7e7aa
Added SVD face algo script
pfvaldez Jan 20, 2023
b00fdc5
Update experiment_SVDface script
pfvaldez Dec 9, 2023
e46c308
Added functionality to continue SVD transformation from checkpoint
pfvaldez Dec 17, 2023
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169 changes: 169 additions & 0 deletions .gitignore
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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
.pybuilder/
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version

# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock

# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock

# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml

# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

# Pyre type checker
.pyre/

# pytype static type analyzer
.pytype/

# Cython debug symbols
cython_debug/

# Data
data/

# Models
models/
*.pt

# D2-Net
d2-net/

# results
results/

# scratch
scratch/
2 changes: 2 additions & 0 deletions README.md
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Expand Up @@ -192,3 +192,5 @@ python train.py --save-path /path/to/fast-model.pt --net 'Fast_Quad_L2Net_ConfCF
```

Note that you can fully configure the training (i.e. select the data sources, change the batch size, learning rate, number of epochs etc.). One easy way to improve the model is to train for more epochs, e.g. `--epochs 50`. For more details about all parameters, run `python train.py --help`.


147 changes: 147 additions & 0 deletions experiment_SVDface.py
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import os
import time
import csv
import cv2
import torch
import numpy as np


SEED = 123
np.random.seed(SEED)

'''
-------------------------------------------
| |
| (x1, y1) |
| ------------------------ |
| | | |
| | | |
| | ROI | |
| | | |
| | | |
| | | |
| ------------------------ |
| (x2, y2) |
| |
| |
| |
-------------------------------------------
'''

def compute_svd(data_dir, N, tau, device):
'''
Input:
data_dir: directory of the image
N: size of the ROI
tau: relaxation of singular values
device: CPU or GPU
'''
## Load Image
img= cv2.imread(data_dir, 0)
img=img.astype(float)
print(img.shape)
height, width= img.shape
## Create a one matrix for I2 representation
SF= torch.ones(height, width)

img_t= torch.from_numpy(img)
img_t.to(device)
ROI_number= 0
for i in range (1, height):
for j in range(1, width):

ROI= img_t[i:i+N, j:j+N]
# print(ROI.shape)
u, s, v_t = torch.linalg.svd(ROI, full_matrices= False) ## SVD: ROI-> [mxn]; u-> [mxm]; s-> [mxn]; v_t-> [nxn]
## relaxation of singular values
s+= tau

## Normalize the largest singular values
index_max= torch.argmax(s)
s_sum= torch.sum(s)
SF[i,j]= s[index_max]/s_sum

ROI_number+= 1

svd_img= SF.cpu().detach().numpy()
## Scale to 0 -255
svd_img_scaled = ((svd_img - svd_img.min()) * (1/(svd_img.max() - svd_img.min()) * 255)).astype('uint8')

return svd_img_scaled


def main():
start = time.time()
torch.manual_seed(0)
## To do: Use pytorch SVD on GPU
## SVDFace Hyperparameters
# N= 10
# tau= 80

## SVD Face paper settings
N= 3
# tau= 5
# tau= 10
tau= 20

data_dir= './data/oxbuild_images-v1/'

for filename in os.listdir(data_dir):
# for filename in ls_data[0]:
filename= data_dir.split('/')[-1]
imagename= filename.split('.')[0]
print(imagename)
dir_filename= data_dir+'{}' .format(filename)
print(dir_filename)

## Compute SVD Transform, CUDA for pytorch
use_cuda= torch.cuda.is_available()
device= torch.device("cuda:0" if use_cuda else "cpu")
svd_img_scaled= compute_svd(dir_filename, N, tau, device)

# Writing the SVDFace image
filename1 = './results/oxbuild_images-v1_svd_n3t20/{}_SVDtau{}.jpg' .format(imagename, tau)
cv2.imwrite(filename1, svd_img_scaled)
print('Image saved: {}' .format(filename1))

end = time.time()
print('Time elapsed:\t',end - start)

## Uncomment to resume from a checkpoint (csv file)
## SVD Face paper settings
N= 3
tau= 20

data_dir= './data/oxbuild_images-v1/'
todo_dir= './results/to_do.csv'

## Read the csv file
ls_data= []
with open(todo_dir, newline='') as f:
reader = csv.reader(f)
ls_data = list(reader)
print(len(ls_data[0]))

for filename in ls_data[0]:
imagename= filename.split('.')[0]
print(imagename)
dir_filename= data_dir+'{}' .format(filename)
print(dir_filename)

## Compute SVD Transform, CUDA for pytorch
use_cuda= torch.cuda.is_available()
device= torch.device("cuda:0" if use_cuda else "cpu")

svd_img_scaled= compute_svd(dir_filename, N, tau, device)

# Writing the SVDFace image
filename1 = './results/oxbuild_images-v1_svd_n3t20/{}_SVDtau{}.jpg' .format(imagename, tau)
cv2.imwrite(filename1, svd_img_scaled)
print('Image saved: {}' .format(filename1))

end = time.time()
print('Time elapsed:\t',end - start)


if __name__ == '__main__':
main()
10 changes: 10 additions & 0 deletions experiment_load.py
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##Load npz file .r2d2
import numpy as np

target_file= "/home/pfvaldez/Development/r2d2/d2-net/hpatches_sequences/hpatches-sequences-release/i_ajuntament/1.ppm.r2d2"
npz= np.load(target_file, mmap_mode='r')
# npz= np.load(cache_file)
# print(npz)

d = dict(zip(("data1{}".format(k) for k in npz), (npz[k] for k in npz)))
print(d)
12 changes: 12 additions & 0 deletions experiment_loadcache.py
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import numpy as np

cache_file= "/home/pfvaldez/Development/r2d2/d2-net/hpatches_sequences/cache/superpoint.npy"
data = np.load(cache_file, allow_pickle=True)
print(data)
# f = io.open(cache_file, mode="r", encoding="utf-8")
# file_list = []
# for line in f.readlines():
# file_list.append(line.strip()) # strips newline character at the end of the line
# for i in sorted(file_list):
# print(i)
# f.close()
8 changes: 6 additions & 2 deletions extract.py
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Expand Up @@ -2,7 +2,7 @@
# CC BY-NC-SA 3.0
# Available only for non-commercial use


import time
import os, pdb
from PIL import Image
import numpy as np
Expand Down Expand Up @@ -163,7 +163,7 @@ def extract_keypoints(args):
parser.add_argument("--model", type=str, required=True, help='model path')

parser.add_argument("--images", type=str, required=True, nargs='+', help='images / list')
parser.add_argument("--tag", type=str, default='r2d2', help='output file tag')
parser.add_argument("--tag", type=str, default='i_r2d2', help='output file tag') # Changed tag to `i_r2d2` to track expriments

parser.add_argument("--top-k", type=int, default=5000, help='number of keypoints')

Expand All @@ -179,5 +179,9 @@ def extract_keypoints(args):
parser.add_argument("--gpu", type=int, nargs='+', default=[0], help='use -1 for CPU')
args = parser.parse_args()

start_time= time.time()
extract_keypoints(args)
end_time= time.time()
print("Finished extract, time in seconds =", end_time - start_time)


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