repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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MachineUnlearningPy | MachineUnlearningPy-master/lenskit/algorithms/funksvd.py | """
FunkSVD (biased MF).
"""
import logging
import time
import pandas as pd
import numpy as np
import numba as n
from . import basic
from .mf_common import BiasMFPredictor
from .. import util
_logger = logging.getLogger(__name__)
@n.jitclass([
('user_features', n.double[:, :]),
('item_features', n.double[... | 9,416 | 29.377419 | 98 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/algorithms/mf_common.py | """
Common utilities & implementations for matrix factorization.
"""
import pathlib
import logging
import numpy as np
import pandas as pd
from .. import util
from . import Predictor
_logger = logging.getLogger(__name__)
class MFPredictor(Predictor):
"""
Common predictor for matrix factorization.
Attr... | 4,358 | 27.122581 | 86 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/algorithms/als.py | import logging
from collections import namedtuple
import numpy as np
from numba import njit, prange
from . import basic
from .mf_common import BiasMFPredictor, MFPredictor
from ..matrix import sparse_ratings, _CSR
from .. import util
from ..math.solve import _dposv
_logger = logging.getLogger(__name__)
Context = na... | 11,409 | 35.222222 | 100 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/algorithms/user_knn.py | """
User-based k-NN collaborative filtering.
"""
from sys import intern
import pathlib
import logging
import pandas as pd
import numpy as np
from scipy import stats
from .. import util, matrix
from . import Predictor
_logger = logging.getLogger(__name__)
class UserUser(Predictor):
"""
User-user nearest-ne... | 7,625 | 33.506787 | 99 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/algorithms/hpf.py | import logging
import pandas as pd
from .mf_common import MFPredictor
_logger = logging.getLogger(__name__)
class HPF(MFPredictor):
"""
Hierarchical Poisson factorization, provided by hpfrec_.
.. _hpfrec: https://hpfrec.readthedocs.io/en/latest/
Args:
features(int): the number of features... | 1,322 | 24.442308 | 73 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/algorithms/__init__.py | """
LensKit algorithms.
The `lenskit.algorithms` package contains several example algorithms for carrying out recommender
experiments. These algorithm implementations are designed to mimic the characteristics of the
implementations provided by the original LensKit Java package. It also provides abstract base
classes... | 7,065 | 34.154229 | 99 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/algorithms/implicit.py | import logging
import inspect
import pandas as pd
from ..matrix import sparse_ratings
from . import Recommender
_logger = logging.getLogger(__name__)
class BaseRec(Recommender):
"""
Base class for Implicit-backed recommenders.
Args:
delegate(implicit.RecommenderBase):
The delegate a... | 3,209 | 28.449541 | 92 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/algorithms/basic.py | """
Basic utility algorithms and combiners.
"""
import logging
from collections.abc import Iterable, Sequence
import pandas as pd
import numpy as np
from .. import check
from . import Predictor, Recommender, CandidateSelector
_logger = logging.getLogger(__name__)
class Bias(Predictor):
"""
A user-item bia... | 11,732 | 32.144068 | 100 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/metrics/topn.py | """
Top-N evaluation metrics.
"""
import numpy as np
def precision(recs, truth):
"""
Compute recommendation precision.
"""
nrecs = len(recs)
if nrecs == 0:
return None
ngood = recs['item'].isin(truth.index).sum()
return ngood / nrecs
def recall(recs, truth):
"""
Compute... | 2,864 | 26.285714 | 100 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/metrics/__init__.py | """
Metrics for evaluating recommendations.
"""
| 48 | 11.25 | 39 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/metrics/predict.py | """
Prediction accuracy metrics.
"""
import numpy as np
import pandas as pd
def _check_missing(truth, missing):
"""
Check for missing truth values.
Args:
truth: the series of truth values
missing: what to do with missing values
"""
if missing == 'error' and truth.isna().any():
... | 1,952 | 24.038462 | 77 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/batch/_multi.py | import logging
import pathlib
import collections
import json
from copy import copy
import pandas as pd
from ..algorithms import Predictor
from .. import topn, util
from ._recommend import recommend
from ._predict import predict
try:
import fastparquet
except ImportError:
fastparquet = None
_logger = logging... | 14,040 | 33.927861 | 99 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/batch/_recommend.py | import logging
import warnings
import multiprocessing as mp
from multiprocessing.pool import Pool
import pandas as pd
import numpy as np
from ..algorithms import Recommender
from .. import util
_logger = logging.getLogger(__name__)
_rec_context = None
class MPRecContext:
def __init__(self, algo, candidates, si... | 3,470 | 33.366337 | 95 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/batch/__init__.py | """
Batch-run predictors and recommenders for evaluation.
"""
from ._predict import predict
from ._recommend import recommend
from ._multi import MultiEval
| 157 | 18.75 | 53 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/batch/_predict.py | import logging
import multiprocessing as mp
from multiprocessing.pool import Pool
import pandas as pd
from .. import util
from .. import crossfold
_logger = logging.getLogger(__name__)
_rec_context = None
class MPRecContext:
def __init__(self, algo):
self.algo = algo
def __enter__(self):
g... | 3,635 | 34.300971 | 89 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/math/solve.py | """
Efficient solver routines.
"""
import numpy as np
import cffi
import numba as n
from numba.extending import get_cython_function_address
__ffi = cffi.FFI()
__uplo_U = np.array([ord('U')], dtype=np.int8)
__uplo_L = np.array([ord('L')], dtype=np.int8)
__trans_N = np.array([ord('N')], dtype=np.int8)
__trans_T = np... | 3,490 | 33.564356 | 91 | py |
MachineUnlearningPy | MachineUnlearningPy-master/lenskit/math/__init__.py | """
Mathematical helper routines.
"""
| 38 | 8.75 | 29 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_topn_recall.py | import numpy as np
import pandas as pd
from pytest import approx
from lenskit.topn import recall
def _test_recall(items, rel):
recs = pd.DataFrame({'item': items})
truth = pd.DataFrame({'item': rel}).set_index('item')
return recall(recs, truth)
def test_recall_empty_zero():
prec = _test_recall([],... | 2,502 | 25.347368 | 74 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_batch_sweep.py | import pathlib
import json
import pickle
import pandas as pd
import numpy as np
from lk_test_utils import ml_pandas, norm_path
from lenskit import batch, crossfold as xf
from lenskit.algorithms import Predictor
from lenskit.algorithms.basic import Bias, Popular
from pytest import mark
@mark.slow
@mark.parametrize... | 10,792 | 30.651026 | 94 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_check.py | from pytest import raises
import numpy as np
from lenskit.check import check_value, check_dimension
def test_check_value_passes():
check_value(True, "value should be true")
# it should complete successfully!
def test_check_value_fails():
with raises(ValueError):
check_value(False, "value shoul... | 1,251 | 25.638298 | 63 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_funksvd.py | import logging
import pickle
from pathlib import Path
import lenskit.algorithms.funksvd as svd
import pandas as pd
import numpy as np
from pytest import approx, mark
import lk_test_utils as lktu
_log = logging.getLogger(__name__)
simple_df = pd.DataFrame({'item': [1, 1, 2, 3],
'user': [1... | 6,346 | 29.514423 | 89 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_batch_predict.py | import pytest
import logging
from collections import namedtuple
import pandas as pd
import numpy as np
import lk_test_utils as lktu
from lenskit.algorithms.basic import Bias
import lenskit.batch as lkb
_log = logging.getLogger(__name__)
MLB = namedtuple('MLB', ['ratings', 'algo'])
@pytest.fixture
def mlb():
... | 4,039 | 29.37594 | 99 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/conftest.py | import logging
from pytest import fixture
_log = logging.getLogger('lenskit.tests')
@fixture(autouse=True)
def log_test(request):
_log.info('running test %s:%s', request.module.__name__, request.function.__name__)
def pytest_collection_modifyitems(items):
# add 'slow' to all 'eval' tests
for item in it... | 552 | 26.65 | 87 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_matrix_csr.py | import pickle
import numpy as np
import scipy.sparse as sps
import lenskit.matrix as lm
import lk_test_utils as lktu
from pytest import mark, approx, raises
@mark.parametrize('copy', [True, False])
def test_csr_from_sps(copy):
# initialize sparse matrix
mat = np.random.randn(10, 5)
mat[mat <= 0] = 0
... | 9,667 | 28.747692 | 87 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_als_explicit.py | import logging
import pickle
from lenskit.algorithms import als
import pandas as pd
import numpy as np
from pytest import approx, mark
import lk_test_utils as lktu
_log = logging.getLogger(__name__)
simple_df = pd.DataFrame({'item': [1, 1, 2, 3],
'user': [10, 12, 10, 13],
... | 4,743 | 29.216561 | 89 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_util.py | import time
import re
import pathlib
import numpy as np
import pandas as pd
from lenskit import util as lku
def test_stopwatch_instant():
w = lku.Stopwatch()
assert w.elapsed() > 0
def test_stopwatch_sleep():
w = lku.Stopwatch()
time.sleep(0.5)
assert w.elapsed() >= 0.45
def test_stopwatch_s... | 4,581 | 20.613208 | 79 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_implicit.py | import logging
import pickle
import pandas as pd
import numpy as np
from pytest import mark
try:
import implicit
have_implicit = True
except ImportError:
have_implicit = False
import lk_test_utils as lktu
from lenskit.algorithms.implicit import ALS, BPR
from lenskit import util
_log = logging.getLogger... | 3,075 | 24.421488 | 73 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_basic.py | from lenskit.algorithms import basic
from lenskit import util as lku
import pandas as pd
import numpy as np
import pickle
import lk_test_utils as lktu
from pytest import approx
simple_df = pd.DataFrame({'item': [1, 1, 2, 3],
'user': [10, 12, 10, 13],
'rating': [4.0... | 10,950 | 28.758152 | 83 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_topn_analysis.py | from pathlib import Path
import logging
import numpy as np
import pandas as pd
from pytest import approx
from lenskit.metrics.topn import _dcg
from lenskit import topn
_log = logging.getLogger(__name__)
def test_run_one():
rla = topn.RecListAnalysis()
rla.add_metric(topn.precision)
rla.add_metric(topn.... | 4,560 | 28.425806 | 83 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_knn_item_item.py | from lenskit import DataWarning
import lenskit.algorithms.item_knn as knn
from pathlib import Path
import logging
import os.path
import pickle
import pandas as pd
import numpy as np
from scipy import linalg as la
import pytest
from pytest import approx, mark
import lk_test_utils as lktu
_log = logging.getLogger(__... | 17,725 | 31.704797 | 100 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_knn_user_user.py | import lenskit.algorithms.user_knn as knn
from pathlib import Path
import logging
import pickle
import pandas as pd
import numpy as np
from scipy import sparse as sps
from pytest import approx, mark
import lk_test_utils as lktu
_log = logging.getLogger(__name__)
ml_ratings = lktu.ml_pandas.renamed.ratings
def t... | 8,382 | 29.933579 | 89 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_batch_recommend.py | import pytest
import os
import os.path
from collections import namedtuple
import logging
import pandas as pd
import numpy as np
import lk_test_utils as lktu
from lenskit.algorithms.basic import Bias, TopN
import lenskit.batch as lkb
MLB = namedtuple('MLB', ['ratings', 'algo'])
_log = logging.getLogger(__name__)
@... | 4,975 | 29.527607 | 100 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_hpf.py | import logging
import pickle
from lenskit.algorithms import hpf, basic
import pandas as pd
import numpy as np
from pytest import mark
import lk_test_utils as lktu
try:
import hpfrec
have_hpfrec = True
except ImportError:
have_hpfrec = False
_log = logging.getLogger(__name__)
simple_df = pd.DataFrame(... | 1,341 | 23.4 | 77 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_matrix.py | import scipy.sparse as sps
import scipy.linalg as sla
import numpy as np
import lenskit.matrix as lm
import lk_test_utils as lktu
from pytest import approx
def test_sparse_matrix():
ratings = lktu.ml_pandas.renamed.ratings
mat, uidx, iidx = lm.sparse_ratings(ratings)
assert mat.nrows == len(uidx)
... | 1,907 | 28.353846 | 60 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_matrix_mkl.py | import numpy as np
import scipy.sparse as sps
from pytest import mark, approx
import lenskit.matrix as lm
mkl_ops = lm.mkl_ops()
@mark.skipif(mkl_ops is None, reason='MKL not available')
def test_mkl_mult_vec():
for i in range(50):
m = np.random.randint(5, 100)
n = np.random.randint(5, 100)
... | 1,193 | 21.528302 | 57 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_crossfold.py | import itertools as it
import functools as ft
import pytest
import math
import numpy as np
import lk_test_utils as lktu
import lenskit.crossfold as xf
def test_partition_rows():
ratings = lktu.ml_pandas.renamed.ratings
splits = xf.partition_rows(ratings, 5)
splits = list(splits)
assert len(splits) ... | 11,496 | 32.616959 | 96 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_math_solve.py | import os
import numpy as np
import scipy.linalg as sla
from pytest import approx
from lenskit.math.solve import solve_tri, dposv
_runs = int(os.environ.get('RAND_TEST_ITERS', 10))
def test_solve_ltri():
for i in range(_runs):
size = np.random.randint(5, 50)
Af = np.random.randn(size, size)
... | 2,527 | 23.307692 | 56 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_topn_ndcg.py | import numpy as np
import pandas as pd
from pytest import approx
from lenskit.metrics.topn import _dcg, ndcg
import lk_test_utils as lktu
def test_dcg_empty():
"empty should be zero"
assert _dcg(np.array([])) == approx(0)
def test_dcg_zeros():
assert _dcg(np.zeros(10)) == approx(0)
def test_dcg_sing... | 2,744 | 28.202128 | 83 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_als_implicit.py | import logging
import pickle
from lenskit import topn
from lenskit.algorithms import als
import pandas as pd
import numpy as np
from pytest import mark
import lk_test_utils as lktu
_log = logging.getLogger(__name__)
simple_df = pd.DataFrame({'item': [1, 1, 2, 3],
'user': [10, 12, 10, 13]... | 3,923 | 28.283582 | 73 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_predict_metrics.py | import numpy as np
import pandas as pd
import os.path
from pytest import approx, raises, mark, skip
import lenskit.metrics.predict as pm
import lk_test_utils as lktu
def test_check_missing_empty():
pm._check_missing(pd.Series([]), 'error')
# should pass
assert True
def test_check_missing_has_values():... | 5,003 | 24.927461 | 91 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_topn_mrr.py | import numpy as np
import pandas as pd
from pytest import approx
from lenskit.topn import recip_rank
def _test_rr(items, rel):
recs = pd.DataFrame({'item': items})
truth = pd.DataFrame({'item': rel}).set_index('item')
return recip_rank(recs, truth)
def test_mrr_empty_zero():
rr = _test_rr([], [1, ... | 1,189 | 21.037037 | 61 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_baselines.py | import lenskit.algorithms.basic as bl
from lenskit import util as lku
import logging
import pickle
import pandas as pd
import numpy as np
from pytest import approx
import lk_test_utils as lktu
from lk_test_utils import ml_pandas
_log = logging.getLogger(__name__)
simple_df = pd.DataFrame({'item': [1, 1, 2, 3],
... | 8,090 | 30.119231 | 93 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/lk_test_utils.py | """
Test utilities for LKPY tests.
"""
import os
import os.path
import tempfile
import pathlib
import logging
from contextlib import contextmanager
import pandas as pd
import pytest
_log = logging.getLogger('lktu')
ml_dir = os.path.join(os.path.dirname(__file__), '../ml-latest-small')
class Renamer:
def __ini... | 2,954 | 22.267717 | 83 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_topn_utils.py | from lenskit import topn
from lenskit.algorithms import CandidateSelector
import pandas as pd
import numpy as np
import lk_test_utils as lktu
def test_unrated():
ratings = lktu.ml_pandas.renamed.ratings
unrate = topn.UnratedCandidates(ratings)
cs = unrate(100)
items = ratings.item.unique()
rate... | 1,241 | 24.346939 | 55 | py |
MachineUnlearningPy | MachineUnlearningPy-master/tests/test_topn_precision.py | import numpy as np
import pandas as pd
from pytest import approx
from lenskit.topn import precision
def _test_prec(items, rel):
recs = pd.DataFrame({'item': items})
truth = pd.DataFrame({'item': rel}).set_index('item')
return precision(recs, truth)
def test_precision_empty_none():
prec = _test_pre... | 2,386 | 25.522222 | 73 | py |
MachineUnlearningPy | MachineUnlearningPy-master/unlearn/visualization.py | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df = pd.read_csv('output_matrix.csv')
time_mean = df.groupby("n").mean().values
ns = df.groupby("n").mean().index.get_level_values(0)
plt.plot(ns,time_mean[:,0],label="native learning")
plt.plot(ns,time_mean[:,1],label="unlearn supported learning"... | 550 | 33.4375 | 100 | py |
MachineUnlearningPy | MachineUnlearningPy-master/unlearn/basic.py | import sys
sys.path.insert(0,'../.')
from lenskit import batch, topn, util
from lenskit import crossfold as xf
from lenskit.algorithms import Recommender, als, item_knn as knn
import pandas as pd
import matplotlib
import time
ratings = pd.read_csv('../ml-100k/u.data', sep='\t',
names=['user',... | 984 | 21.386364 | 77 | py |
MachineUnlearningPy | MachineUnlearningPy-master/doc/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 6,129 | 31.263158 | 79 | py |
GWE | GWE-master/convAE/make_char_feat_dict_nopool.py | import cPickle as pkl
import pdb
import sys
import numpy as np
import tensorflow as tf
sys.path.insert(0, './models/')
from conv_ae_char_nopool import Model
checkpoint_filename = './checkpoints/conv_ae_char_nopool.ckpt-100'
char_bitmap_dict_pkl_filename = '../data/char_dict.pkl'
char_feat_dict_filename = '../data/ch... | 1,455 | 25.472727 | 77 | py |
GWE | GWE-master/convAE/tsne_feature_nopool.py | # -*- coding: utf-8 -*-
import cPickle as pkl
import pdb
import sys
import numpy as np
#from PIL import Image
import tensorflow as tf
from tsne import bh_sne
sys.path.insert(0, './models/')
from conv_ae_char_nopool import Model
def save_collection_img(img_filename, n_row, n_col, img_size, offset, imgs):
image=Ima... | 3,809 | 25.643357 | 82 | py |
GWE | GWE-master/convAE/train_conv_ae_char_nopool.py | import cPickle as pkl
import pdb
import random
import sys
import time
import numpy as np
from PIL import Image
import tensorflow as tf
sys.path.insert(0, './models/')
from conv_ae_char_nopool import Model
def save_collection_img(img_filename, n_row, n_col, img_size, offset, imgs):
image=Image.new("RGB", (n_col*img... | 5,361 | 29.99422 | 112 | py |
GWE | GWE-master/convAE/models/conv_ae_char_nopool.py | import os
import numpy as np
import tensorflow as tf
def unpool(updates, ksize=[1, 2, 2, 1]):
original_shape = updates.get_shape()
original_shape = tuple([i.__int__() for i in original_shape])
new_size = tf.shape(updates)[1:3]
new_size *= tf.constant(np.array([ksize[1], ksize[2]]).astype('int32'))
... | 14,915 | 38.356201 | 108 | py |
battery-historian | battery-historian-master/scripts/historian.py | #!/usr/bin/python
"""Legacy Historian script for analyzing Android bug reports."""
# Copyright 2016 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ht... | 52,562 | 31.87242 | 167 | py |
battery-historian | battery-historian-master/scripts/kernel_trace.py | #!/usr/bin/python
"""Historian script for converting the timestamps in kernel trace to UTC.
TO USE:
kernel_trace.py --bugreport=<path to bugreport> --trace=<path to trace file>
--device=<device type hammerhead/shamu/flounder/flounder_lte>
"""
# Copyright 2016 Google Inc. All rights reserved.
#
# Licensed under the... | 6,942 | 29.186957 | 78 | py |
3DG-STFM | 3DG-STFM-master/train_rgbd_t_s.py | import math
import argparse
import pprint
from distutils.util import strtobool
from pathlib import Path
from loguru import logger as loguru_logger
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_only
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.callbac... | 5,270 | 41.168 | 111 | py |
3DG-STFM | 3DG-STFM-master/train_rgb.py | import math
import argparse
import pprint
from distutils.util import strtobool
from pathlib import Path
from loguru import logger as loguru_logger
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_only
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.callbac... | 5,007 | 40.04918 | 111 | py |
3DG-STFM | 3DG-STFM-master/test_rgbd.py | import pytorch_lightning as pl
import argparse
import pprint
from loguru import logger as loguru_logger
from src.config.default import get_cfg_defaults
from src.utils.profiler import build_profiler
from src.lightning.data import MultiSceneDataModule, RGBDDataModule
from src.lightning.lightning_loftr import PL_LoFTR,P... | 2,657 | 37.521739 | 111 | py |
3DG-STFM | 3DG-STFM-master/test_rgb.py | import pytorch_lightning as pl
import argparse
import pprint
from loguru import logger as loguru_logger
from src.config.default import get_cfg_defaults
from src.utils.profiler import build_profiler
from src.lightning.data import RGBDataModule
from src.lightning.lightning_loftr import PL_LoFTR_RGB
def parse_args():
... | 2,622 | 37.014493 | 111 | py |
3DG-STFM | 3DG-STFM-master/demo.py | import os
import torch
import cv2
import numpy as np
import matplotlib.cm as cm
import matplotlib.colors
from src.loftr import default_cfg, LoFTR_RGBD, LoFTR_RGB
import matplotlib.pyplot as plt
def make_matching_figure(
img0, img1, mkpts0, mkpts1, color,
kpts0=None, kpts1=None, text=[], dpi=75, path=... | 4,874 | 33.574468 | 106 | py |
3DG-STFM | 3DG-STFM-master/train_rgbd.py | import math
import argparse
import pprint
from distutils.util import strtobool
from pathlib import Path
from loguru import logger as loguru_logger
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_only
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.callbac... | 5,134 | 40.41129 | 111 | py |
3DG-STFM | 3DG-STFM-master/src/__init__.py | 0 | 0 | 0 | py | |
3DG-STFM | 3DG-STFM-master/src/config/default.py | from yacs.config import CfgNode as CN
_CN = CN()
############## ↓ LoFTR Pipeline ↓ ##############
_CN.LOFTR = CN()
_CN.LOFTR.BACKBONE_TYPE = 'ResNetFPN'
_CN.LOFTR.RESOLUTION = (8, 2) # options: [(8, 2), (16, 4)]
_CN.LOFTR.FINE_WINDOW_SIZE = 5 # window_size in fine_level, must be odd
_CN.LOFTR.FINE_CONCAT_COARSE_... | 7,068 | 40.339181 | 133 | py |
3DG-STFM | 3DG-STFM-master/src/datasets/sampler.py | import torch
from torch.utils.data import Sampler, ConcatDataset
class RandomConcatSampler(Sampler):
""" Random sampler for ConcatDataset. At each epoch, `n_samples_per_subset` samples will be draw from each subset
in the ConcatDataset. If `subset_replacement` is ``True``, sampling within each subset will be ... | 4,293 | 54.051282 | 164 | py |
3DG-STFM | 3DG-STFM-master/src/datasets/megadepth.py | import os.path as osp
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from loguru import logger
import cv2
from src.utils.dataset import read_megadepth_gray, read_megadepth_depth, read_megadepth_rgb
class MegaDepth_RGB_Dataset(Dataset):
def __init__(self,
... | 12,808 | 46.6171 | 129 | py |
3DG-STFM | 3DG-STFM-master/src/datasets/scannet.py | from os import path as osp
from typing import Dict
from unicodedata import name
import numpy as np
import torch
import torch.utils as utils
from numpy.linalg import inv
from src.utils.dataset import (
read_scannet_rgb,
read_scannet_gray,
read_scannet_depth,
read_scannet_pose,
read_scannet_intrinsic... | 11,203 | 43.995984 | 130 | py |
3DG-STFM | 3DG-STFM-master/src/lightning/lightning_loftr.py |
from collections import defaultdict
import pprint
from loguru import logger
from pathlib import Path
import torch
import numpy as np
import pytorch_lightning as pl
from matplotlib import pyplot as plt
from src.loftr import LoFTR_RGB,LoFTR_RGBD,LoFTR_RGBD_teacher,LoFTR_RGB_student
from src.loftr.utils.supervision imp... | 40,636 | 45.021518 | 129 | py |
3DG-STFM | 3DG-STFM-master/src/lightning/data.py | import os
import math
from collections import abc
from loguru import logger
from torch.utils.data.dataset import Dataset
from tqdm import tqdm
from os import path as osp
from pathlib import Path
from joblib import Parallel, delayed
import pytorch_lightning as pl
from torch import distributed as dist
from torch.utils.d... | 27,968 | 44.626427 | 132 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/__init__.py | from .loftr import LoFTR_RGB,LoFTR_RGBD,LoFTR_RGBD_teacher,LoFTR_RGB_student
from .utils.cvpr_ds_config import default_cfg
| 123 | 40.333333 | 76 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/loftr.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from einops.einops import rearrange, repeat
from .backbone import build_backbone_rgb,build_backbone_rgbd
from .utils.position_encoding import PositionEncodingSine
from .loftr_module import LocalFeatureTransformer, FinePreprocess
from .utils.coarse_matc... | 14,794 | 45.671924 | 154 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/backbone/__init__.py | from .resnet_fpn import ResNetFPN_8_2_RGB,ResNetFPN_8_2_RGBD
def build_backbone_rgb(config):
if config['backbone_type'] == 'ResNetFPN':
if config['resolution'] == (8, 2):
return ResNetFPN_8_2_RGB(config['resnetfpn'])
else:
raise ValueError(f"LOFTR.BACKBONE_TYPE {config['backbone_ty... | 624 | 40.666667 | 89 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/backbone/resnet_fpn.py | import torch.nn as nn
import torch.nn.functional as F
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution without padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False)
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with pad... | 6,772 | 33.380711 | 96 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/loftr_module/linear_attention.py | """
Linear Transformer proposed in "Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention"
Modified from: https://github.com/idiap/fast-transformers/blob/master/fast_transformers/attention/linear_attention.py
"""
import torch
from torch.nn import Module, Dropout
def elu_feature_map(x):
re... | 2,794 | 33.085366 | 117 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/loftr_module/fine_preprocess.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from einops.einops import rearrange, repeat
class FinePreprocess(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.cat_c_feat = config['fine_concat_coarse_feat']
self.W = self.con... | 5,006 | 43.705357 | 109 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/loftr_module/transformer.py | import copy
import torch
import torch.nn as nn
from .linear_attention import LinearAttention, FullAttention
class LoFTREncoderLayer(nn.Module):
def __init__(self,
d_model,
nhead,
attention='linear'):
super(LoFTREncoderLayer, self).__init__()
self... | 3,657 | 34.514563 | 105 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/loftr_module/__init__.py | from .transformer import LocalFeatureTransformer
from .fine_preprocess import FinePreprocess
| 93 | 30.333333 | 48 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/utils/supervision.py | from math import log
from loguru import logger
import torch
from einops import repeat
from kornia.utils import create_meshgrid
from .geometry import warp_kpts
############## ↓ Coarse-Level supervision ↓ ##############
@torch.no_grad()
def mask_pts_at_padded_regions(grid_pt, mask):
"""For megadepth dataset,... | 5,724 | 36.418301 | 110 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/utils/cvpr_ds_config.py | from yacs.config import CfgNode as CN
def lower_config(yacs_cfg):
if not isinstance(yacs_cfg, CN):
return yacs_cfg
return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()}
_CN = CN()
_CN.BACKBONE_TYPE = 'ResNetFPN'
_CN.RESOLUTION = (8, 2) # options: [(8, 2), (16, 4)]
_CN.FINE_WINDOW_SIZE = ... | 1,516 | 29.34 | 84 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/utils/position_encoding.py | import math
import torch
from torch import nn
class PositionEncodingSine(nn.Module):
"""
This is a sinusoidal position encoding that generalized to 2-dimensional images
"""
def __init__(self, d_model, max_shape=(256, 256)):
"""
Args:
max_shape (tuple): for 1/8 featmap, the... | 1,235 | 33.333333 | 104 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/utils/fine_matching.py | import math
import torch
import torch.nn as nn
from kornia.geometry.subpix import dsnt
from kornia.utils.grid import create_meshgrid
class FineMatching(nn.Module):
"""FineMatching with s2d paradigm"""
def __init__(self):
super().__init__()
def forward(self, feat_f0, feat_f1, data):
"""
... | 5,385 | 37.471429 | 113 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/utils/supervision_homography.py | from math import log
from loguru import logger
import torch
from einops import repeat
from kornia.utils import create_meshgrid
from .geometry import warp_kpts,warp_kpts_homo
############## ↓ Coarse-Level supervision ↓ ##############
@torch.no_grad()
def mask_pts_at_padded_regions(grid_pt, mask):
"""For me... | 5,957 | 36.708861 | 111 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/utils/geometry.py | import torch
import cv2
@torch.no_grad()
def warp_kpts_homo(kpts0, M):
""" Warp kpts0 from I0 to I1 with Homography M
Args:
kpts0 (torch.Tensor): [N, L, 2] - <x, y>,
M (torch.Tensor):
Returns:
warped_keypoints0 (torch.Tensor): [N, L, 2] <x0_hat, y1_hat>
"""
#kpts0_long = kpt... | 2,838 | 34.936709 | 113 | py |
3DG-STFM | 3DG-STFM-master/src/loftr/utils/coarse_matching.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from einops.einops import rearrange
INF = 1e9
def mask_border(m, b: int, v):
""" Mask borders with value
Args:
m (torch.Tensor): [N, H0, W0, H1, W1]
b (int)
v (m.dtype)
"""
if b <= 0:
return
m[:, :b... | 20,002 | 41.289641 | 120 | py |
3DG-STFM | 3DG-STFM-master/src/optimizers/__init__.py | import torch
from torch.optim.lr_scheduler import MultiStepLR, CosineAnnealingLR, ExponentialLR
def build_optimizer(model, config):
name = config.TRAINER.OPTIMIZER
lr = config.TRAINER.TRUE_LR
if name == "adam":
return torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=lr, ... | 1,665 | 35.217391 | 135 | py |
3DG-STFM | 3DG-STFM-master/src/utils/plotting.py | import bisect
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import os
def _compute_conf_thresh(data):
dataset_name = data['dataset_name'][0].lower()
if dataset_name == 'scannet':
thr = 5e-4
elif dataset_name == 'megadepth':
thr = 1e-4
else:
raise ValueErro... | 5,723 | 35.227848 | 106 | py |
3DG-STFM | 3DG-STFM-master/src/utils/comm.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
[Copied from detectron2]
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import functools
import logging
import numpy as np
import pickle
import torch
import torch.distributed as di... | 7,776 | 28.236842 | 100 | py |
3DG-STFM | 3DG-STFM-master/src/utils/dataloader.py | import numpy as np
# --- PL-DATAMODULE ---
def get_local_split(items: list, world_size: int, rank: int, seed: int):
""" The local rank only loads a split of the dataset. """
n_items = len(items)
items_permute = np.random.RandomState(seed).permutation(items)
if n_items % world_size == 0:
padde... | 876 | 35.541667 | 109 | py |
3DG-STFM | 3DG-STFM-master/src/utils/misc.py | import os
import contextlib
import joblib
from typing import Union
from loguru import _Logger, logger
from itertools import chain
import torch
from yacs.config import CfgNode as CN
from pytorch_lightning.utilities import rank_zero_only
def lower_config(yacs_cfg):
if not isinstance(yacs_cfg, CN):
return y... | 3,512 | 33.441176 | 128 | py |
3DG-STFM | 3DG-STFM-master/src/utils/augment.py | import albumentations as A
class DarkAug(object):
"""
Extreme dark augmentation aiming at Aachen Day-Night
"""
def __init__(self) -> None:
self.augmentor = A.Compose([
A.RandomBrightnessContrast(p=0.75, brightness_limit=(-0.6, 0.0), contrast_limit=(-0.5, 0.3)),
A.Blur(... | 1,578 | 27.196429 | 105 | py |
3DG-STFM | 3DG-STFM-master/src/utils/dataset.py | import io
from loguru import logger
import cv2
import numpy as np
import h5py
import torch
from numpy.linalg import inv
import os
try:
# for internel use only
from .client import MEGADEPTH_CLIENT, SCANNET_CLIENT
except Exception:
MEGADEPTH_CLIENT = SCANNET_CLIENT = None
# --- DATA IO ---
def load_array_... | 8,671 | 31.479401 | 111 | py |
3DG-STFM | 3DG-STFM-master/src/utils/metrics.py | import torch
import cv2
import numpy as np
from collections import OrderedDict
from loguru import logger
from kornia.geometry.epipolar import numeric
from kornia.geometry.conversions import convert_points_to_homogeneous
import random
# --- METRICS ---
def relative_pose_error(T_0to1, R, t, ignore_gt_t_thr=0.0):
# a... | 16,290 | 35.042035 | 119 | py |
3DG-STFM | 3DG-STFM-master/src/utils/profiler.py | import torch
from pytorch_lightning.profiler import SimpleProfiler, PassThroughProfiler
from contextlib import contextmanager
from pytorch_lightning.utilities import rank_zero_only
class InferenceProfiler(SimpleProfiler):
"""
This profiler records duration of actions with cuda.synchronize()
Use this in te... | 1,199 | 29 | 81 | py |
3DG-STFM | 3DG-STFM-master/src/losses/loftr_loss.py | from loguru import logger
import torch
import torch.nn as nn
import torch.nn.functional as F
class LoFTRLoss(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config # config under the global namespace
self.loss_config = config['loftr']['loss']
self.match_ty... | 20,436 | 46.30787 | 179 | py |
3DG-STFM | 3DG-STFM-master/configs/loftr/indoor/loftr_ds_dense.py | from src.config.default import _CN as cfg
cfg.LOFTR.MATCH_COARSE.MATCH_TYPE = 'dual_softmax'
cfg.LOFTR.MATCH_COARSE.SPARSE_SPVS = False
cfg.TRAINER.MSLR_MILESTONES = [3, 6, 9, 12, 17, 20, 23, 26, 29]
| 203 | 24.5 | 63 | py |
3DG-STFM | 3DG-STFM-master/configs/loftr/indoor/scannet/loftr_ds_eval.py | """ A config only for reproducing the ScanNet evaluation results.
We remove border matches by default, but the originally implemented
`remove_border()` has a bug, leading to only two sides of
all borders are actually removed. However, the [bug fix](https://github.com/zju3dv/LoFTR/commit/e9146c8144dea5f3cbdd98b225f3e14... | 685 | 41.875 | 137 | py |
3DG-STFM | 3DG-STFM-master/configs/loftr/outdoor/loftr_ds_dense.py | from src.config.default import _CN as cfg
cfg.LOFTR.MATCH_COARSE.MATCH_TYPE = 'dual_softmax'
cfg.LOFTR.MATCH_COARSE.SPARSE_SPVS = False
cfg.TRAINER.CANONICAL_LR = 8e-3
cfg.TRAINER.WARMUP_STEP = 1875 # 3 epochs
cfg.TRAINER.WARMUP_RATIO = 0.1
cfg.TRAINER.MSLR_MILESTONES = [8, 12, 16, 20, 24]
# pose estimation
cfg.TRA... | 461 | 26.176471 | 50 | py |
3DG-STFM | 3DG-STFM-master/configs/data/scannet_mini_trainval.py | from configs.data.base import cfg
TRAIN_BASE_PATH = "data/scannet_mini/index"
cfg.DATASET.TRAINVAL_DATA_SOURCE = "ScanNet"
cfg.DATASET.TRAIN_DATA_ROOT = "data/scannet_mini/train"
cfg.DATASET.TRAIN_NPZ_ROOT = f"{TRAIN_BASE_PATH}/scene_data/train"
cfg.DATASET.TRAIN_LIST_PATH = f"{TRAIN_BASE_PATH}/scene_data/train_list/... | 907 | 49.444444 | 101 | py |
3DG-STFM | 3DG-STFM-master/configs/data/base.py | """
The data config will be the last one merged into the main config.
Setups in data configs will override all existed setups!
"""
from yacs.config import CfgNode as CN
_CN = CN()
_CN.DATASET = CN()
_CN.TRAINER = CN()
# training data config
_CN.DATASET.TRAIN_DATA_ROOT = None
_CN.DATASET.TRAIN_POSE_ROOT = None
_CN.DAT... | 949 | 25.388889 | 65 | py |
3DG-STFM | 3DG-STFM-master/configs/data/megadepth_trainval_640.py | from configs.data.base import cfg
TRAIN_BASE_PATH = "data/megadepth/index"
cfg.DATASET.TRAINVAL_DATA_SOURCE = "MegaDepth"
cfg.DATASET.TRAIN_DATA_ROOT = "data/megadepth/train"
cfg.DATASET.TRAIN_NPZ_ROOT = f"{TRAIN_BASE_PATH}/scene_info_0.1_0.7"
cfg.DATASET.TRAIN_LIST_PATH = f"{TRAIN_BASE_PATH}/trainvaltest_list/train_... | 1,022 | 43.478261 | 107 | py |
3DG-STFM | 3DG-STFM-master/configs/data/scannet_test_1500.py | from configs.data.base import cfg
TEST_BASE_PATH = "inference/scannet_test_1500"
cfg.DATASET.TEST_DATA_SOURCE = "ScanNet"
cfg.DATASET.TEST_DATA_ROOT = "data/scannet/test"
cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}"
cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/scannet_test.txt"
cfg.DATASET.TEST_INTRINSIC_PATH =... | 398 | 32.25 | 68 | py |
3DG-STFM | 3DG-STFM-master/configs/data/scannet_test_mini.py | from configs.data.base import cfg
TEST_BASE_PATH = "data/scannet_mini/index"
cfg.DATASET.TEST_DATA_SOURCE = "ScanNet"
cfg.DATASET.TEST_DATA_ROOT = "data/scannet_mini/train"
cfg.DATASET.TEST_NPZ_ROOT = f"{TEST_BASE_PATH}/scene_data/train"
cfg.DATASET.TEST_LIST_PATH = f"{TEST_BASE_PATH}/scene_data/train_list/scannet_al... | 437 | 38.818182 | 86 | py |
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