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
value |
|---|---|---|---|---|---|---|
XFL | XFL-master/test/algorithm/framework/vertical/test_xgboost.py | # Copyright 2022 The XFL Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 33,748 | 48.053779 | 557 | py |
XFL | XFL-master/test/algorithm/framework/vertical/test_xgb.py | # Copyright 2022 The XFL Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 24,834 | 36.402108 | 117 | py |
XFL | XFL-master/test/algorithm/framework/vertical/test_xgb2.py | # Copyright 2022 The XFL Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 43,089 | 43.560496 | 118 | py |
XFL | XFL-master/test/algorithm/framework/local/test_local_data_statistic.py | # Copyright 2022 The XFL Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 5,100 | 34.671329 | 120 | py |
XFL | XFL-master/test/algorithm/framework/local/test_local_data_split.py | # Copyright 2022 The XFL Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | 8,194 | 45.039326 | 109 | py |
XFL | XFL-master/test/algorithm/framework/local/test_local_standard_scaler.py | # Copyright 2022 The XFL Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 3,677 | 32.436364 | 82 | py |
XFL | XFL-master/test/algorithm/framework/local/test_local_feature_preprocess.py | # Copyright 2022 The XFL Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 7,597 | 43.432749 | 119 | py |
XFL | XFL-master/test/algorithm/framework/local/test_local_normalization.py | # Copyright 2022 The XFL Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 6,376 | 36.075581 | 108 | py |
XFL | XFL-master/demo/horizontal/linear_regression/data_preprocess.py | import random
import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
bostonDataset = load_boston()
features = pd.DataFrame(bostonDataset['data'])
features.columns = bostonDataset['feature_names']
label = pd.DataFrame(bostonDataset['target'])
data = label.join(features)
num = len(data)
index ... | 746 | 28.88 | 70 | py |
XFL | XFL-master/demo/horizontal/linear_regression/2party/config/__init__.py | 0 | 0 | 0 | py | |
XFL | XFL-master/demo/horizontal/linear_regression/3party/config/__init__.py | 0 | 0 | 0 | py | |
XFL | XFL-master/demo/horizontal/nbafl/2party/config/__init__.py | 0 | 0 | 0 | py | |
XFL | XFL-master/demo/horizontal/nbafl/3party/config/__init__.py | 0 | 0 | 0 | py | |
XFL | XFL-master/demo/horizontal/chatglm/chatglm-demo/tokenization_chatglm.py | """Tokenization classes for ChatGLM."""
from typing import List, Optional, Union
import os
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.utils import logging, PaddingStrategy
from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
from typing import Dict
import ... | 17,047 | 37.396396 | 119 | py |
XFL | XFL-master/demo/horizontal/chatglm/chatglm-demo/configuration_chatglm.py | """ ChatGLM model configuration """
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class ChatGLMConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`~ChatGLMModel`].
It... | 4,276 | 40.125 | 122 | py |
XFL | XFL-master/demo/horizontal/chatglm/chatglm-demo/quantization.py | from torch.nn import Linear
from torch.nn.parameter import Parameter
import bz2
import torch
import base64
import ctypes
from transformers.utils import logging
from typing import List
from functools import partial
logger = logging.get_logger(__name__)
try:
from cpm_kernels.kernels.base import LazyKernelCModule,... | 15,054 | 73.529703 | 7,375 | py |
XFL | XFL-master/demo/horizontal/chatglm/chatglm-demo/modeling_chatglm.py | """ PyTorch ChatGLM model. """
import math
import copy
import os
import warnings
import re
import sys
import torch
import torch.utils.checkpoint
import torch.nn.functional as F
from torch import nn
from torch.nn import CrossEntropyLoss, LayerNorm
from torch.nn.utils import skip_init
from typing import Optional, Tuple... | 57,568 | 39.089833 | 121 | py |
XFL | XFL-master/demo/horizontal/logistic_regression/3party/config/__init__.py | 0 | 0 | 0 | py | |
XFL | XFL-master/docs/en/source/conf.py | # Copyright 2022 The XFL Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 3,375 | 32.76 | 79 | py |
XFL | XFL-master/docs/zh_CN/source/conf.py | # Copyright 2022 The XFL Authors. 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 3,378 | 32.79 | 79 | py |
benchmark | benchmark-main/setup.py | import contextlib
import os
import platform
import shutil
import sysconfig
from pathlib import Path
import setuptools
from setuptools.command import build_ext
PYTHON_INCLUDE_PATH_PLACEHOLDER = "<PYTHON_INCLUDE_PATH>"
IS_WINDOWS = platform.system() == "Windows"
IS_MAC = platform.system() == "Darwin"
@contextlib.co... | 3,777 | 32.140351 | 81 | py |
benchmark | benchmark-main/.ycm_extra_conf.py | import os
import ycm_core
# These are the compilation flags that will be used in case there's no
# compilation database set (by default, one is not set).
# CHANGE THIS LIST OF FLAGS. YES, THIS IS THE DROID YOU HAVE BEEN LOOKING FOR.
flags = [
'-Wall',
'-Werror',
'-pedantic-errors',
'-std=c++0x',
'-fno-strict-aliasing'... | 3,640 | 30.387931 | 79 | py |
benchmark | benchmark-main/tools/compare.py | #!/usr/bin/env python3
import unittest
"""
compare.py - versatile benchmark output compare tool
"""
import argparse
from argparse import ArgumentParser
import json
import sys
import os
import gbench
from gbench import util, report
def check_inputs(in1, in2, flags):
"""
Perform checking on the user provided ... | 18,479 | 41.678984 | 513 | py |
benchmark | benchmark-main/tools/strip_asm.py | #!/usr/bin/env python3
"""
strip_asm.py - Cleanup ASM output for the specified file
"""
from argparse import ArgumentParser
import sys
import os
import re
def find_used_labels(asm):
found = set()
label_re = re.compile("\s*j[a-z]+\s+\.L([a-zA-Z0-9][a-zA-Z0-9_]*)")
for l in asm.splitlines():
m = la... | 4,419 | 28.078947 | 92 | py |
benchmark | benchmark-main/tools/gbench/report.py | """report.py - Utilities for reporting statistics about benchmark results
"""
import unittest
import os
import re
import copy
import random
from scipy.stats import mannwhitneyu, gmean
from numpy import array
class BenchmarkColor(object):
def __init__(self, name, code):
self.name = name
self.code... | 46,852 | 37.979201 | 140 | py |
benchmark | benchmark-main/tools/gbench/util.py | """util.py - General utilities for running, loading, and processing benchmarks
"""
import json
import os
import re
import subprocess
import sys
import tempfile
# Input file type enumeration
IT_Invalid = 0
IT_JSON = 1
IT_Executable = 2
_num_magic_bytes = 2 if sys.platform.startswith('win') else 4
def is_executable_... | 6,858 | 32.622549 | 133 | py |
benchmark | benchmark-main/tools/gbench/__init__.py | """Google Benchmark tooling"""
__author__ = 'Eric Fiselier'
__email__ = 'eric@efcs.ca'
__versioninfo__ = (0, 5, 0)
__version__ = '.'.join(str(v) for v in __versioninfo__) + 'dev'
__all__ = []
| 194 | 20.666667 | 63 | py |
benchmark | benchmark-main/bindings/python/google_benchmark/example.py | # Copyright 2020 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 3,858 | 27.167883 | 88 | py |
benchmark | benchmark-main/bindings/python/google_benchmark/__init__.py | # Copyright 2020 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 4,463 | 26.386503 | 90 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/utils/ExportToPX4/SIP2PX4.py | # ___SIPtoPX4___
# StructuralInspectionPlanner Path Exporter to PX4 Mission
#
# This script reads the output *.csv file of an inspection path generated by
# the StructuralInspectionPlanner Toolbox and exports a *.txt file that can
# be loaded at QGroudControl or any other service to import it as a PX4/Pixhawk
# aut... | 5,131 | 29.188235 | 158 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/utils/ExportToPX4/KML2PX4.py | # __KML2PX4__
# KML (.kml) to PX4/Pixhawk Mission File Parsing and Export
#
# This script accepts a *.kml file as an input and exports the
# corresponding PX4/Pixhawk autopilot mission to be loaded using
# QGroundControl or other compative software
#
# More Info on PX4/Pixhawk and ... | 6,553 | 38.245509 | 234 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/utils/ExportToRotorS/SIP2RotorS.py | # ___SIPtoRotorS___
# StructuralInspectionPlanner Path Exporter to RotorS missions
#
# This script reads the output *.csv file of an inspection path generated by
# the StructuralInspectionPlanner Toolbox and exports a *.txt file that can
# be loaded by the waypoint sampler of the RotorS simulator. Note that speed
#... | 3,188 | 34.043956 | 206 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/utils/Tools/LKH_Python_Interface/InvokeLKH.py | # __InvokeLKH__
# Interface the TSP LKH Solver
#
# This script is a simple python interface to a compiled
# version of the LKH TSP Solver. It requires that the
# solver is compiled at the given directories.
#
#
# Example Syntax:
# python InvokeLKH.py
#
# This script is part of the "utils" section of the Struc... | 3,731 | 30.627119 | 111 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/utils/Tools/Airplane2p5D/Airplane2p5D.py | # __Airplane2p5D__
# 2.5D Airplane Point to Point Connections
# A Python script for simplified Aircraft Kinematics
#
# Computes Point - to - Point Aircraft connections based on
# 2D dubins curves for the XY-plane and constrained linear
# interpolation for the altitude component.
#
# For the dubins curve s... | 4,392 | 31.540741 | 129 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/utils/ExportToDJI/SIP2DJI.py | # ___SIPtoDJI___
# StructuralInspectionPlanner Path Exporter to DJI Missions
#
# This script reads the output *.csv file of an inspection path generated by
# the StructuralInspectionPlanner Toolbox and exports a *.awm file that can
# be loaded at the Groud Control Station of the DJI Drones. Note that all constraint... | 6,266 | 32.15873 | 156 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/utils/PathToTrajectory/PATH2TRAJ.py | # __PATH2TRAJ__
# SIP Path to Trajectory based on simple second-order linear model
# and an LQ controller with saturated control actions
#
# This script accepts an input file with the path and exports a
# trajectory based on second-order closed-loop simulation of simplified
# syste... | 15,749 | 39.076336 | 164 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/optec/interfaces/python/setup.py | #!/usr/bin/env python
##
## This file is part of qpOASES.
##
## qpOASES -- An Implementation of the Online Active Set Strategy.
## Copyright (C) 2007-2014 by Hans Joachim Ferreau, Andreas Potschka,
## Christian Kirches et al. All rights reserved.
##
## qpOASES is free software; you can redistribute it and/or
## ... | 2,182 | 31.58209 | 82 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/optec/interfaces/python/examples/example2.py | ##
## This file is part of qpOASES.
##
## qpOASES -- An Implementation of the Online Active Set Strategy.
## Copyright (C) 2007-2014 by Hans Joachim Ferreau, Andreas Potschka,
## Christian Kirches et al. All rights reserved.
##
## qpOASES is free software; you can redistribute it and/or
## modify it under the terms of ... | 2,876 | 30.615385 | 81 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/optec/interfaces/python/examples/example1.py | ##
## This file is part of qpOASES.
##
## qpOASES -- An Implementation of the Online Active Set Strategy.
## Copyright (C) 2007-2014 by Hans Joachim Ferreau, Andreas Potschka,
## Christian Kirches et al. All rights reserved.
##
## qpOASES is free software; you can redistribute it and/or
## modify it under the terms of ... | 2,227 | 27.935065 | 84 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/optec/interfaces/python/examples/example1b.py | ##
## This file is part of qpOASES.
##
## qpOASES -- An Implementation of the Online Active Set Strategy.
## Copyright (C) 2007-2014 by Hans Joachim Ferreau, Andreas Potschka,
## Christian Kirches et al. All rights reserved.
##
## qpOASES is free software; you can redistribute it and/or
## modify it under the terms of ... | 2,282 | 30.273973 | 81 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/optec/interfaces/python/examples/cython/setup.py | from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
ext_module = Extension(
"example1",
["example1.pyx"],
extra_compile_args=['-fopenmp'],
extra_link_args=['-fopenmp'],
)
setup(
name = 'Hello world app',
cmdclass = {'build_ext': bui... | 363 | 20.411765 | 41 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/optec/interfaces/python/tests/test_idhessian.py | """
This file is part of qpOASES.
qpOASES -- An Implementation of the Online Active Set Strategy.
Copyright (C) 2007-2009 by Hans Joachim Ferreau et al. All rights reserved.
qpOASES is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the ... | 3,128 | 31.936842 | 79 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/optec/interfaces/python/tests/test_examples.py | """
This file is part of qpOASES.
qpOASES -- An Implementation of the Online Active Set Strategy.
Copyright (C) 2007-2009 by Hans Joachim Ferreau et al. All rights reserved.
qpOASES is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the ... | 12,207 | 36.106383 | 166 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/optec/interfaces/python/tests/test_testbench.py | """
This file is part of qpOASES.
qpOASES -- An Implementation of the Online Active Set Strategy.
Copyright (C) 2007-2009 by Hans Joachim Ferreau et al. All rights reserved.
qpOASES is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the ... | 9,796 | 28.42042 | 132 | py |
StructuralInspectionPlanner | StructuralInspectionPlanner-master/optec/interfaces/python/tests/__init__.py | 1 | 0 | 0 | py | |
ultrasound-nerve-segmentation | ultrasound-nerve-segmentation-master/data.py | from __future__ import print_function
import os
import numpy as np
from skimage.io import imsave, imread
data_path = 'raw/'
image_rows = 420
image_cols = 580
def create_train_data():
train_data_path = os.path.join(data_path, 'train')
images = os.listdir(train_data_path)
total = len(images) / 2
im... | 2,446 | 25.031915 | 87 | py |
ultrasound-nerve-segmentation | ultrasound-nerve-segmentation-master/submission.py | from __future__ import print_function
import numpy as np
from skimage.transform import resize
from data import image_cols, image_rows
def prep(img):
img = img.astype('float32')
img = (img > 0.5).astype(np.uint8) # threshold
img = resize(img, (image_cols, image_rows), preserve_range=True)
return img
... | 1,643 | 24.292308 | 68 | py |
ultrasound-nerve-segmentation | ultrasound-nerve-segmentation-master/train.py | from __future__ import print_function
import os
from skimage.transform import resize
from skimage.io import imsave
import numpy as np
from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose
from keras.optimizers import Adam
from keras.callbacks import ModelChec... | 5,352 | 33.75974 | 107 | py |
deep-video-mvs | deep-video-mvs-master/setup.py | from setuptools import setup
setup(
name='deep-video-mvs',
version='1.0',
description='Deep Video Multi-View Stereo',
author='Arda Düzceker',
packages=['dvmvs']
)
| 184 | 17.5 | 47 | py |
deep-video-mvs | deep-video-mvs-master/dataset/utils.py | import re
import numpy as np
def write_point_cloud(ply_filename, points):
formatted_points = []
for point in points:
formatted_points.append("%f %f %f %d %d %d 0\n" % (point[0], point[1], point[2], point[3], point[4], point[5]))
out_file = open(ply_filename, "w")
out_file.write('''ply
fo... | 2,575 | 22.633028 | 119 | py |
deep-video-mvs | deep-video-mvs-master/dataset/build_point_cloud.py | import os
import sys
import numpy as np
import cv2
from path import Path
from tqdm import tqdm
sys.path.append('.')
from utils import depth_image_to_point_cloud, write_point_cloud
def build_point_cloud(dataset_folder, scene_name, is_test=True):
scene_folder = Path(dataset_folder) / scene_name
poses = np.fr... | 1,662 | 32.938776 | 101 | py |
deep-video-mvs | deep-video-mvs-master/dataset/rgbdscenes-export/rgbdscenes-export.py | import os
import shutil
from functools import partial
from multiprocessing.pool import Pool
import cv2
import numpy as np
from path import Path
from scipy.spatial.transform.rotation import Rotation
def process_scene(scene_no, input_directory, output_folder):
image_filenames = sorted((input_directory / 'imgs' / "... | 2,918 | 35.037037 | 136 | py |
deep-video-mvs | deep-video-mvs-master/dataset/tum-rgbd-export/tum-rgbd-export.py | import os
import shutil
from functools import partial
from multiprocessing.pool import Pool
import cv2
import numpy as np
from path import Path
from scipy.spatial.transform import Rotation
def get_closest_index(target_timestamp, other_timestamps):
differences = np.abs(other_timestamps - target_timestamp)
ret... | 4,003 | 38.643564 | 127 | py |
deep-video-mvs | deep-video-mvs-master/dataset/augmented-iclnuim-export/iclnuim-export.py | import os
import shutil
from functools import partial
from multiprocessing.pool import Pool
import cv2
import numpy as np
from path import Path
def process_scene(input_directory, output_folder):
# For K, https://github.com/intel-isl/Open3D/issues/540
K = np.array([[525.0, 0.0, 320.0],
[0.0,... | 2,725 | 30.697674 | 127 | py |
deep-video-mvs | deep-video-mvs-master/dataset/7scenes-export/7scenes-export-depth.py | import shutil
import cv2
import numpy as np
from path import Path
scenes = [("7scenes_chess", "01", "02"),
("7scenes_fire", "01", "02"),
("7scenes_heads", "02"),
("7scenes_office", "01", "03"),
("7scenes_pumpkin", "03", "06"),
("7scenes_redkitchen", "01", "07"),
... | 1,434 | 33.166667 | 84 | py |
deep-video-mvs | deep-video-mvs-master/dataset/7scenes-export/7scenes-export-color.py | import os
import shutil
from multiprocessing.pool import Pool
import cv2
import numpy as np
from functools import partial
from path import Path
def process_scene(input_directory, output_folder):
K = np.array([[525.0, 0.0, 320.0],
[0.0, 525.0, 240.0],
[0.0, 0.0, 1.0]])
prin... | 2,596 | 31.4625 | 136 | py |
deep-video-mvs | deep-video-mvs-master/dataset/scannet-export/scannet-export.py | import os
import random
import numpy as np
from multiprocessing import Pool
import copy
import os
import struct
import zlib
from itertools import groupby
import cv2
import imageio
import numpy as np
import torch
COMPRESSION_TYPE_COLOR = {-1: 'unknown', 0: 'raw', 1: 'png', 2: 'jpeg'}
COMPRESSION_TYPE_DEPTH = {-1: 'unkn... | 12,059 | 40.586207 | 124 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/keyframe_buffer.py | from collections import deque
import numpy as np
from dvmvs.utils import is_pose_available, pose_distance
class KeyframeBuffer:
def __init__(self, buffer_size, keyframe_pose_distance, optimal_t_score, optimal_R_score, store_return_indices):
self.buffer = deque([], maxlen=buffer_size)
self.keyfra... | 6,058 | 45.607692 | 146 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/losses.py | from __future__ import division
import torch
from torch import nn
class LossMeter(object):
def __init__(self):
self.count = 0.0
self.sum = 0.0
self.avg = 0.0
self.item_average = 0.0
def update(self, loss, count):
self.sum += loss
self.count += count
s... | 3,529 | 41.53012 | 146 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/errors.py | import numpy as np
def compute_errors(gt, pred, max_depth=np.inf):
valid1 = gt >= 0.5
valid2 = gt <= max_depth
valid = valid1 & valid2
gt = gt[valid]
pred = pred[valid]
n_valid = np.float32(len(gt))
if n_valid == 0:
return np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np... | 1,369 | 34.128205 | 126 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/simulate_keyframe_buffer.py | import numpy as np
from path import Path
from dvmvs.keyframe_buffer import KeyframeBuffer, SimpleBuffer
def simulate_keyframe_buffer(test_dataset_path, output_folder, n_measurement_frames):
test_dataset_path = Path(test_dataset_path)
scene_folders = sorted(test_dataset_path.listdir())
test_keyframe_buff... | 5,005 | 42.155172 | 139 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/utils.py | from __future__ import division
import os
import zipfile
import cv2
import kornia
import numpy as np
import torch
from path import Path
from pytorch3d import structures, renderer
from dvmvs.errors import compute_errors
# GEOMETRIC UTILS
def pose_distance(reference_pose, measurement_pose):
"""
:param refere... | 19,349 | 47.014888 | 156 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/layers.py | import torch
def down_conv_layer(input_channels, output_channels, kernel_size):
return torch.nn.Sequential(
torch.nn.Conv2d(
input_channels,
output_channels,
kernel_size,
padding=(kernel_size - 1) // 2,
stride=1,
bias=False),
... | 1,984 | 29.075758 | 84 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/config.py | import time
class Config:
# training settings
train_image_width = 256
train_image_height = 256
train_min_depth = 0.25
train_max_depth = 20.0
train_n_depth_levels = 64
train_minimum_pose_distance = 0.125
train_maximum_pose_distance = 0.325
train_crawl_step = 3
train_subsequence_... | 1,830 | 34.211538 | 106 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/dataset_loader.py | import copy
import random
from functools import partial
from multiprocessing import Manager
from multiprocessing.pool import Pool
import cv2
import numpy as np
import torch
from kornia import adjust_brightness, adjust_gamma, adjust_contrast
from path import Path
from torch.utils.data import Dataset, DataLoader
from d... | 21,202 | 38.192237 | 128 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/convlstm.py | import torch
import torch.nn as nn
from dvmvs.utils import warp_frame_depth
class MVSLayernormConvLSTMCell(nn.Module):
def __init__(self, input_dim, hidden_dim, kernel_size, activation_function=None):
super(MVSLayernormConvLSTMCell, self).__init__()
self.activation_function = activation_functio... | 2,554 | 38.307692 | 116 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/__init__.py | 0 | 0 | 0 | py | |
deep-video-mvs | deep-video-mvs-master/dvmvs/train.py | import torch
import torchvision
from tqdm import tqdm
from dvmvs.config import Config
from dvmvs.losses import LossMeter
from dvmvs.utils import save_checkpoint, save_optimizer, freeze_batchnorm
def switch_mode(model, mode):
if mode == 'train':
for module in model:
module.train()
... | 8,579 | 56.583893 | 153 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/mvdepthnet/encoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from dvmvs.utils import freeze_batchnorm
def down_conv_layer(input_channels, output_channels, kernel_size):
return nn.Sequential(
nn.Conv2d(
input_channels,
output_channels,
... | 3,979 | 28.051095 | 77 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/mvdepthnet/run-testing.py | import cv2
import numpy as np
import torch
from path import Path
from tqdm import tqdm
from dvmvs.baselines.mvdepthnet.decoder import Decoder
from dvmvs.baselines.mvdepthnet.encoder import Encoder
from dvmvs.config import Config
from dvmvs.dataset_loader import PreprocessImage, load_image
from dvmvs.utils import cost_... | 9,324 | 48.078947 | 138 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/mvdepthnet/decoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from dvmvs.utils import freeze_batchnorm
def down_conv_layer(input_channels, output_channels, kernel_size):
return nn.Sequential(
nn.Conv2d(
input_channels,
output_channels,
kernel_size,
padd... | 3,920 | 28.044444 | 80 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/base_model.py | import collections
from abc import ABCMeta, abstractmethod
from torch import nn
def dict_update(d, u):
"""Improved update for nested dictionaries.
Arguments:
d: The dictionary to be updated.
u: The update dictionary.
Returns:
The updated dictionary.
"""
d = d.copy()
... | 1,752 | 24.042857 | 75 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/utils.py | import torch
def reorder_desc(desc, batch_sz):
"""Reorders Descriptors"""
b, c, h, w = desc.shape
desc = desc.view(-1, batch_sz, c, h, w)
desc = desc.transpose(1, 0)
return desc
def pose_square(pose):
"""Converts pose matrix of size 3x4 to a square matrix of size 4x4"""
pose_sh = pose.... | 1,006 | 24.175 | 73 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/superpoint.py | import torch
import torchvision
from .base_model import BaseModel
def simple_nms(scores, radius):
"""Performs non maximum suppression on the heatmap using max-pooling.
This method does not suppress contiguous points that have the same score.
Arguments:
scores: the score heatmap, with shape `[B, H... | 9,883 | 38.536 | 153 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/densedepth.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from .base_model import BaseModel
from .resnet_s2d import resnet50
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
... | 13,604 | 37.109244 | 160 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/run-testing.py | import argparse
import cv2
import numpy as np
import torch.backends.cudnn as cudnn
import torch.utils.data
from path import Path
from tqdm import tqdm
from dvmvs.baselines.deltas import superpoint, triangulation, densedepth
from dvmvs.baselines.deltas.utils import *
from dvmvs.config import Config
from dvmvs.dataset_... | 15,377 | 46.462963 | 170 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/resnet_s2d.py | import torch.nn as nn
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=di... | 9,946 | 36.394737 | 106 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/deltas/triangulation.py | import numpy as np
import torch
from torch import svd
from .base_model import BaseModel
def homogeneous_to_euclidean(points):
"""Converts homogeneous points to euclidean
Args:
points numpy array or torch tensor of shape (N, M + 1): N homogeneous points of dimension M
Returns:
numpy arra... | 22,576 | 37.527304 | 157 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/gpmvs/encoder.py | import torch
import torch.nn as nn
from dvmvs.utils import freeze_batchnorm
def down_conv_layer(input_channels, output_channels, kernel_size):
return nn.Sequential(
nn.Conv2d(
input_channels,
output_channels,
kernel_size,
padding=(kernel_size - 1) // 2,
... | 2,588 | 26.542553 | 66 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/gpmvs/run-testing.py | from copy import deepcopy
import cv2
import numpy as np
import torch
from path import Path
from scipy.linalg import expm
from tqdm import tqdm
from dvmvs.baselines.gpmvs.decoder import Decoder
from dvmvs.baselines.gpmvs.encoder import Encoder
from dvmvs.baselines.gpmvs.gplayer import GPlayer
from dvmvs.config import ... | 10,783 | 45.283262 | 153 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/gpmvs/decoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from dvmvs.utils import freeze_batchnorm
def down_conv_layer(input_channels, output_channels, kernel_size):
return nn.Sequential(
nn.Conv2d(
input_channels,
output_channels,
kernel_size,
padd... | 3,920 | 28.044444 | 80 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/gpmvs/gplayer.py | import math
import torch
from dvmvs.utils import freeze_batchnorm
class GPlayer(torch.nn.Module):
def __init__(self, device):
super(GPlayer, self).__init__()
self.gamma2 = torch.nn.Parameter(torch.randn(1).to(device).float(), requires_grad=True)
self.ell = torch.nn.Parameter(torch.randn(... | 1,637 | 37.093023 | 134 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/dpsnet/run-testing.py | import cv2
import numpy as np
import torch
from path import Path
from tqdm import tqdm
from dvmvs.baselines.dpsnet.dpsnet import PSNet
from dvmvs.config import Config
from dvmvs.dataset_loader import PreprocessImage, load_image
from dvmvs.utils import save_results, InferenceTimer, visualize_predictions
def predict()... | 7,607 | 46.849057 | 138 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/baselines/dpsnet/dpsnet.py | from __future__ import print_function
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.utils.data
from torch.autograd import Variable
from dvmvs.utils import freeze_batchnorm
pixel_coords = None
def set_id_grid(depth):
globa... | 16,381 | 40.684478 | 157 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/pairnet/run-training.py | import datetime
import itertools
import os
import numpy as np
from path import Path
from tensorboardX import SummaryWriter
from torch.backends import cudnn
from torch.utils.data import DataLoader
from dvmvs.dataset_loader import MVSDataset
from dvmvs.losses import LossMeter, update_losses
from dvmvs.pairnet.model imp... | 12,924 | 45.160714 | 132 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/pairnet/model.py | from collections import OrderedDict
import torch
from torchvision import models
from torchvision.ops import FeaturePyramidNetwork
from dvmvs.config import Config
from dvmvs.layers import conv_layer, depth_layer_3x3
fpn_output_channels = 32
hyper_channels = 32
class StandardLayer(torch.nn.Module):
def __init__(... | 14,393 | 46.193443 | 127 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/pairnet/run-testing.py | import cv2
import numpy as np
import torch
from path import Path
from tqdm import tqdm
from dvmvs.config import Config
from dvmvs.dataset_loader import PreprocessImage, load_image
from dvmvs.pairnet.model import FeatureExtractor, FeatureShrinker, CostVolumeEncoder, CostVolumeDecoder
from dvmvs.utils import cost_volume... | 10,187 | 50.715736 | 138 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/pairnet/run-testing-online.py | import cv2
import numpy as np
import torch
from path import Path
from tqdm import tqdm
from dvmvs.config import Config
from dvmvs.dataset_loader import PreprocessImage, load_image
from dvmvs.keyframe_buffer import KeyframeBuffer
from dvmvs.pairnet.model import FeatureExtractor, FeatureShrinker, CostVolumeEncoder, Cost... | 9,280 | 48.897849 | 138 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/fusionnet/run-training.py | import datetime
import itertools
import os
import numpy as np
from path import Path
from tensorboardX import SummaryWriter
from torch.backends import cudnn
from torch.utils.data import DataLoader
from dvmvs.dataset_loader import MVSDataset
from dvmvs.fusionnet.model import *
from dvmvs.losses import LossMeter, update... | 13,088 | 44.290657 | 144 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/fusionnet/model.py | from collections import OrderedDict
import torch
from torchvision import models
from torchvision.ops import FeaturePyramidNetwork
from dvmvs.config import Config
from dvmvs.convlstm import MVSLayernormConvLSTMCell
from dvmvs.layers import conv_layer, depth_layer_3x3
fpn_output_channels = 32
hyper_channels = 32
cla... | 15,992 | 46.316568 | 127 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/fusionnet/run-testing.py | import cv2
import numpy as np
import torch
from dvmvs.config import Config
from dvmvs.dataset_loader import PreprocessImage, load_image
from dvmvs.fusionnet.model import FeatureExtractor, FeatureShrinker, CostVolumeEncoder, LSTMFusion, CostVolumeDecoder
from dvmvs.utils import cost_volume_fusion, save_results, visualiz... | 12,702 | 53.055319 | 149 | py |
deep-video-mvs | deep-video-mvs-master/dvmvs/fusionnet/run-testing-online.py | import cv2
import numpy as np
import torch
from path import Path
from tqdm import tqdm
from dvmvs.config import Config
from dvmvs.dataset_loader import PreprocessImage, load_image
from dvmvs.fusionnet.model import FeatureExtractor, FeatureShrinker, CostVolumeEncoder, LSTMFusion, CostVolumeDecoder
from dvmvs.keyframe_b... | 12,118 | 50.351695 | 149 | py |
deep-video-mvs | deep-video-mvs-master/sample-data/run-tsdf-reconstruction.py | ################################################################################################
### This implementation is taken and adapted from https://github.com/andyzeng/tsdf-fusion-python
### Copyright (c) 2018 Andy Zeng
#############################################################################################... | 32,077 | 47.383107 | 150 | py |
uncertainty-project | uncertainty-project-master/Code/core/losses.py | '''
losses.py
Includes the losses / error functions for different models and tasks
'''
import sys
from data import predict_label
import numpy as np
from scipy.integrate import nquad
from scipy.stats import multivariate_normal
# === LOSSES FOR AMP WITH THE ORDER PARAMETERS
def mse(x : np.ndarray, xhat : np.ndarr... | 2,392 | 30.486842 | 131 | py |
uncertainty-project | uncertainty-project-master/Code/core/utility.py | # utility.py
# various useful functions
from typing import List
import numpy as np
import scipy.optimize as opt4
from scipy.optimize import minimize_scalar, root_scalar
from scipy.special import erf, erfc
from datetime import datetime
# FUNCTION OF THE LOGIT MODEL
sigmoid = np.vectorize(lambda x : 1. / (1. + np.exp(... | 3,849 | 33.375 | 112 | py |
uncertainty-project | uncertainty-project-master/Code/core/data.py | '''
data.py
File containing the functions to generate the data
So far, only data w/ gaussian covariance matrix
NOTE : For now, all
'''
from typing import Tuple
import numpy as np
import sys
from numpy.core.fromnumeric import size
sys.path.append('..')
sys.path.append('core')
import core.utility as utility
def iid_... | 3,045 | 31.752688 | 99 | py |
uncertainty-project | uncertainty-project-master/Code/core/erm.py | '''
erm.py
Compute exact ERM on logistic or ridge regression w/ sklearn
'''
from typing import List, Dict
import pandas as pd
from sklearn.linear_model import LogisticRegression, Ridge
from data import *
import losses
def get_errors(w0 : List[float], X : List[List[float]], y : List[float], w : List[float], task :... | 2,178 | 34.145161 | 158 | py |
uncertainty-project | uncertainty-project-master/Code/core/calibration.py | # calibration.py
# Functions to compute (empirically and theoretically) the calibration
import numpy as np
from scipy.integrate import nquad, quad
import scipy.linalg
from scipy.special import erfc, erfcinv
import scipy.stats as stats
from core import *
import data
from overlaps import *
import utility
def compute_b... | 4,936 | 36.976923 | 131 | py |
uncertainty-project | uncertainty-project-master/Code/core/gamp.py | # gamp.py
# File containing the code for GAMP with sign perceptron and gaussian prior
from typing import List
import numpy as np
from scipy.special import erfc
from scipy.integrate import nquad
import matplotlib.pyplot as plt
import pandas as pd
import data
from models.bayes_optimal import BayesOptimal
from models.a... | 3,045 | 26.196429 | 150 | py |
uncertainty-project | uncertainty-project-master/Code/core/__init__.py | # __all__ = ['bo_state_evolution', 'calibration' 'data', 'erm', 'gamp', 'gcm_erm', 'losses', 'overlaps', 'utility']
| 116 | 57.5 | 115 | py |
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