repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
mmdetection | configs/hrnet/faster-rcnn_hrnetv2p-w18-1x_coco.py | .py | _base_ = './faster-rcnn_hrnetv2p-w32-1x_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pr... | 12 | 455 |
mmdetection | configs/hrnet/htc_x101-64x4d_fpn_16xb1-28e_coco.py | .py | _base_ = '../htc/htc_x101-64x4d_fpn_16xb1-20e_coco.py'
# learning policy
max_epochs = 28
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | 17 | 392 |
mmdetection | configs/hrnet/htc_hrnetv2p-w18_20e_coco.py | .py | _base_ = './htc_hrnetv2p-w32_20e_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', checkpoint='o... | 11 | 431 |
mmdetection | configs/hrnet/fcos_hrnetv2p-w40-gn-head_ms-640-800-4xb4-2x_coco.py | .py | _base_ = './fcos_hrnetv2p-w32-gn-head_ms-640-800-4xb4-2x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=di... | 12 | 480 |
mmdetection | configs/hrnet/faster-rcnn_hrnetv2p-w32_2x_coco.py | .py | _base_ = './faster-rcnn_hrnetv2p-w32-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | 17 | 386 |
mmdetection | configs/hrnet/cascade-mask-rcnn_hrnetv2p-w32_20e_coco.py | .py | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... | 52 | 1,528 |
mmdetection | configs/hrnet/htc_hrnetv2p-w40_28e_coco.py | .py | _base_ = './htc_hrnetv2p-w40_20e_coco.py'
# learning policy
max_epochs = 28
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epo... | 17 | 379 |
mmdetection | configs/hrnet/mask-rcnn_hrnetv2p-w40-2x_coco.py | .py | _base_ = './mask-rcnn_hrnetv2p-w40_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
b... | 17 | 384 |
mmdetection | configs/hrnet/cascade-rcnn_hrnetv2p-w40-20e_coco.py | .py | _base_ = './cascade-rcnn_hrnetv2p-w32-20e_coco.py'
# model settings
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=... | 13 | 482 |
mmdetection | configs/hrnet/fcos_hrnetv2p-w18-gn-head_4xb4-1x_coco.py | .py | _base_ = './fcos_hrnetv2p-w32-gn-head_4xb4-1x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pretrained', ... | 11 | 444 |
mmdetection | configs/swin/mask-rcnn_swin-t-p4-w7_fpn_amp-ms-crop-3x_coco.py | .py | _base_ = './mask-rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py'
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
| 4 | 170 |
mmdetection | configs/swin/retinanet_swin-t-p4-w7_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
bac... | 32 | 1,058 |
mmdetection | configs/swin/mask-rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
ty... | 100 | 3,297 |
mmdetection | configs/swin/mask-rcnn_swin-s-p4-w7_fpn_amp-ms-crop-3x_coco.py | .py | _base_ = './mask-rcnn_swin-t-p4-w7_fpn_amp-ms-crop-3x_coco.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
model = dict(
backbone=dict(
depths=[2, 2, 18, 2],
init_cfg=dict(type='Pretrained', checkpoint=pretrained)))
| 7 | 317 |
mmdetection | configs/swin/mask-rcnn_swin-t-p4-w7_fpn_1x_coco.py | .py | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
type... | 61 | 1,648 |
mmdetection | configs/fsaf/fsaf_r50_fpn_1x_coco.py | .py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='FSAF',
bbox_head=dict(
type='FSAFHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
reg_decoded_bbox=True,
# Only anchor-free branch is imple... | 48 | 1,459 |
mmdetection | configs/fsaf/fsaf_r101_fpn_1x_coco.py | .py | _base_ = './fsaf_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 192 |
mmdetection | configs/fsaf/fsaf_x101-64x4d_fpn_1x_coco.py | .py | _base_ = './fsaf_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | 15 | 414 |
mmdetection | configs/foveabox/fovea_r101_fpn_gn-head-align_ms-640-800-4xb4-2x_coco.py | .py | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
train_... | 35 | 1,042 |
mmdetection | configs/foveabox/fovea_r50_fpn_gn-head-align_ms-640-800-4xb4-2x_coco.py | .py | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
... | 31 | 901 |
mmdetection | configs/foveabox/fovea_r50_fpn_4xb4-2x_coco.py | .py | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
# learning policy
max_epochs = 24
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
... | 16 | 379 |
mmdetection | configs/foveabox/fovea_r101_fpn_4xb4-1x_coco.py | .py | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 198 |
mmdetection | configs/foveabox/fovea_r101_fpn_4xb4-2x_coco.py | .py | _base_ = './fovea_r50_fpn_4xb4-2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 7 | 198 |
mmdetection | configs/foveabox/fovea_r50_fpn_gn-head-align_4xb4-2x_coco.py | .py | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
# learning policy
max_epochs = 24
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
... | 21 | 572 |
mmdetection | configs/foveabox/fovea_r50_fpn_4xb4-1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FOVEA',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 60 | 1,836 |
mmdetection | configs/foveabox/fovea_r101_fpn_gn-head-align_4xb4-2x_coco.py | .py | _base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
# lear... | 24 | 650 |
mmdetection | configs/nas_fpn/retinanet_r50_nasfpn_crop640-50e_coco.py | .py | _base_ = './retinanet_r50_fpn_crop640-50e_coco.py'
# model settings
model = dict(
# `pad_size_divisor=128` ensures the feature maps sizes
# in `NAS_FPN` won't mismatch.
data_preprocessor=dict(pad_size_divisor=128),
neck=dict(
_delete_=True,
type='NASFPN',
in_channels=[256, 512, ... | 17 | 480 |
mmdetection | configs/nas_fpn/retinanet_r50_fpn_crop640-50e_coco.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.6... | 79 | 2,521 |
mmdetection | configs/reid/reid_r50_8xb32-6e_mot20train80_test-mot20val20.py | .py | _base_ = ['./reid_r50_8xb32-6e_mot17train80_test-mot17val20.py']
model = dict(head=dict(num_classes=1701))
# data
data_root = 'data/MOT20/'
train_dataloader = dict(dataset=dict(data_root=data_root))
val_dataloader = dict(dataset=dict(data_root=data_root))
test_dataloader = val_dataloader
# train, val, test setting
tra... | 11 | 392 |
mmdetection | configs/reid/reid_r50_8xb32-6e_mot16train80_test-mot16val20.py | .py | _base_ = ['./reid_r50_8xb32-6e_mot17train80_test-mot17val20.py']
model = dict(head=dict(num_classes=371))
# data
data_root = 'data/MOT16/'
train_dataloader = dict(dataset=dict(data_root=data_root))
val_dataloader = dict(dataset=dict(data_root=data_root))
test_dataloader = val_dataloader
| 8 | 288 |
mmdetection | configs/reid/reid_r50_8xb32-6e_mot15train80_test-mot15val20.py | .py | _base_ = ['./reid_r50_8xb32-6e_mot17train80_test-mot17val20.py']
model = dict(head=dict(num_classes=368))
# data
data_root = 'data/MOT15/'
train_dataloader = dict(dataset=dict(data_root=data_root))
val_dataloader = dict(dataset=dict(data_root=data_root))
test_dataloader = val_dataloader
| 8 | 288 |
mmdetection | configs/reid/reid_r50_8xb32-6e_mot17train80_test-mot17val20.py | .py | _base_ = [
'../_base_/datasets/mot_challenge_reid.py', '../_base_/default_runtime.py'
]
model = dict(
type='BaseReID',
data_preprocessor=dict(
type='ReIDDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
backbone=dict(
ty... | 62 | 1,748 |
mmdetection | configs/dcnv2/mask-rcnn_r50-mdconv-c3-c5_fpn_amp-1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
# MMEngine support the following two ways, users can choose
# according to convenience
# optim_wrapper = di... | 11 | 393 |
mmdetection | configs/dcnv2/faster-rcnn_r50_fpn_mdpool_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
_delete_=True,
type='ModulatedDeformRoIPoolPack',
output_size=7,
o... | 13 | 417 |
mmdetection | configs/dcnv2/faster-rcnn_r50-mdconv-c3-c5_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 6 | 216 |
mmdetection | configs/dcnv2/faster-rcnn_r50-mdconv-group4-c3-c5_fpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 6 | 216 |
mmdetection | configs/dcnv2/mask-rcnn_r50-mdconv-c3-c5_fpn_1x_coco.py | .py | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 6 | 212 |
mmdetection | configs/queryinst/queryinst_r101_fpn_300-proposals_crop-ms-480-800-3x_coco.py | .py | _base_ = './queryinst_r50_fpn_300-proposals_crop-ms-480-800-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 228 |
mmdetection | configs/queryinst/queryinst_r101_fpn_ms-480-800-3x_coco.py | .py | _base_ = './queryinst_r50_fpn_ms-480-800-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 8 | 209 |
mmdetection | configs/queryinst/queryinst_r50_fpn_300-proposals_crop-ms-480-800-3x_coco.py | .py | _base_ = './queryinst_r50_fpn_ms-480-800-3x_coco.py'
num_proposals = 300
model = dict(
rpn_head=dict(num_proposals=num_proposals),
test_cfg=dict(
_delete_=True,
rpn=None,
rcnn=dict(max_per_img=num_proposals, mask_thr_binary=0.5)))
# augmentation strategy originates from DETR.
train_pipe... | 46 | 1,896 |
mmdetection | configs/queryinst/queryinst_r50_fpn_ms-480-800-3x_coco.py | .py | _base_ = './queryinst_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
... | 33 | 967 |
mmdetection | configs/queryinst/queryinst_r50_fpn_1x_coco.py | .py | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
num_stages = 6
num_proposals = 100
model = dict(
type='QueryInst',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.... | 156 | 5,345 |
mmdetection | configs/pafpn/faster-rcnn_r50_pafpn_1x_coco.py | .py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
neck=dict(
type='PAFPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5))
| 9 | 200 |
numpy | tools/write_release.py | .py | """
Standalone script for writing release doc::
python tools/write_release <version>
Example::
python tools/write_release.py 1.7.0
Needs to be run from the root of the repository and assumes
that the output is in `release` and wheels and sdist in
`release/installers`.
Translation from rst to md markdown re... | 69 | 1,722 |
numpy | tools/check_installed_files.py | .py | """
Check if all the test and .pyi files are installed after building.
Examples::
$ python check_installed_files.py install_dirname
install_dirname:
the relative path to the directory where NumPy is installed after
building and running `meson install`.
Notes
=====
The script wil... | 119 | 3,858 |
numpy | tools/check_openblas_version.py | .py | """
Checks related to the OpenBLAS version used in CI.
Options:
1. Check that the BLAS used at build time is (a) scipy-openblas, and (b) its version is
higher than a given minimum version. Note: this method only seems to give
the first 3 version components, so 0.3.30.0.7 gets translated to 0.3.30 when reading
... | 108 | 3,523 |
numpy | tools/get_submodule_paths.py | .py | import glob
import os.path
def get_submodule_paths():
'''
Get paths to submodules so that we can exclude them from things like
check_test_name.py, check_unicode.py, etc.
'''
root_directory = os.path.dirname(os.path.dirname(__file__))
gitmodule_file = os.path.join(root_directory, '.gitmodules')... | 32 | 1,083 |
numpy | tools/changelog.py | .py | #!/usr/bin/env python3
"""
Script to generate contributor and pull request lists
This script generates contributor and pull request lists for release
changelogs using Github v3 protocol. Use requires an authentication token in
order to have sufficient bandwidth, you can get one following the directions at
`<https://he... | 188 | 5,769 |
numpy | tools/refguide_check.py | .py | #!/usr/bin/env python3
"""
refguide_check.py [OPTIONS] [-- ARGS]
- Check for a NumPy submodule whether the objects in its __all__ dict
correspond to the objects included in the reference guide.
- Check docstring examples
- Check example blocks in RST files
Example of usage::
$ python tools/refguide_check.py
N... | 673 | 19,123 |
numpy | tools/functions_missing_types.py | .py | #!/usr/bin/env python
"""Find the functions in a module missing type annotations.
To use it run
./functions_missing_types.py <module>
and it will print out a list of functions in the module that don't
have types.
"""
import argparse
import ast
import importlib
import os
NUMPY_ROOT = os.path.dirname(os.path.join(
... | 126 | 2,982 |
numpy | tools/check_python_h_first.py | .py | #!/usr/bin/env python
"""Check that Python.h is included before any stdlib headers.
May be a bit overzealous, but it should get the job done.
"""
import argparse
import fnmatch
import os.path
import re
import subprocess
import sys
from get_submodule_paths import get_submodule_paths
HEADER_PATTERN = re.compile(
r... | 255 | 8,545 |
numpy | tools/linter.py | .py | import os
import subprocess
import sys
from argparse import ArgumentParser
CWD = os.path.abspath(os.path.dirname(__file__))
class DiffLinter:
def __init__(self) -> None:
self.repository_root = os.path.realpath(os.path.join(CWD, ".."))
def run_ruff(self, fix: bool) -> tuple[int, str]:
"""
... | 92 | 2,609 |
numpy | tools/swig/test/testSuperTensor.py | .py | #!/usr/bin/env python3
import sys
import unittest
import numpy as np
major, minor = [int(d) for d in np.__version__.split(".")[:2]]
if major == 0:
BadListError = TypeError
else:
BadListError = ValueError
import SuperTensor
######################################################################
class SuperTe... | 396 | 17,096 |
numpy | tools/swig/test/testFlat.py | .py | #!/usr/bin/env python3
import struct
import sys
import unittest
import numpy as np
major, minor = [int(d) for d in np.__version__.split(".")[:2]]
if major == 0:
BadListError = TypeError
else:
BadListError = ValueError
import Flat
######################################################################
class ... | 198 | 7,084 |
numpy | tools/swig/test/setup.py | .py | #!/usr/bin/env python3
from distutils.core import Extension, setup
import numpy
# Obtain the numpy include directory.
numpy_include = numpy.get_include()
# Array extension module
_Array = Extension("_Array",
["Array_wrap.cxx",
"Array1.cxx",
"Array2.cxx",
... | 67 | 1,951 |
numpy | tools/swig/test/testVector.py | .py | #!/usr/bin/env python3
import sys
import unittest
import numpy as np
major, minor = [int(d) for d in np.__version__.split(".")[:2]]
if major == 0:
BadListError = TypeError
else:
BadListError = ValueError
import Vector
######################################################################
class VectorTestCa... | 380 | 15,145 |
numpy | tools/swig/test/testFortran.py | .py | #!/usr/bin/env python3
import sys
import unittest
import numpy as np
major, minor = [int(d) for d in np.__version__.split(".")[:2]]
if major == 0:
BadListError = TypeError
else:
BadListError = ValueError
import Fortran
######################################################################
class FortranTest... | 162 | 5,943 |
numpy | tools/swig/test/testMatrix.py | .py | #!/usr/bin/env python3
import sys
import unittest
import numpy as np
major, minor = [int(d) for d in np.__version__.split(".")[:2]]
if major == 0:
BadListError = TypeError
else:
BadListError = ValueError
import Matrix
######################################################################
class MatrixTestCa... | 361 | 14,439 |
numpy | tools/swig/test/testFarray.py | .py | #!/usr/bin/env python3
import os
import sys
import unittest
from distutils.util import get_platform
import numpy as np
major, minor = [int(d) for d in np.__version__.split(".")[:2]]
if major == 0:
BadListError = TypeError
else:
BadListError = ValueError
# Add the distutils-generated build directory to the p... | 161 | 5,123 |
numpy | tools/swig/test/testArray.py | .py | #!/usr/bin/env python3
import sys
import unittest
import numpy as np
major, minor = [int(d) for d in np.__version__.split(".")[:2]]
if major == 0:
BadListError = TypeError
else:
BadListError = ValueError
import Array
######################################################################
class Array1TestCas... | 391 | 13,051 |
numpy | tools/swig/test/testTensor.py | .py | #!/usr/bin/env python3
import sys
import unittest
from math import sqrt
import numpy as np
major, minor = [int(d) for d in np.__version__.split(".")[:2]]
if major == 0:
BadListError = TypeError
else:
BadListError = ValueError
import Tensor
####################################################################... | 401 | 16,541 |
numpy | tools/c_coverage/c_coverage_report.py | .py | #!/usr/bin/env python3
"""
A script to create C code-coverage reports based on the output of
valgrind's callgrind tool.
"""
import os
import re
import sys
from xml.sax.saxutils import escape, quoteattr
try:
import pygments
if tuple(int(x) for x in pygments.__version__.split('.')) < (0, 11):
raise Impo... | 182 | 5,988 |
numpy | tools/ci/push_docs_to_repo.py | .py | #!/usr/bin/env python3
import argparse
import os
import shutil
import subprocess
import sys
import tempfile
parser = argparse.ArgumentParser(
description='Upload files to a remote repo, replacing existing content'
)
parser.add_argument('dir', help='directory of which content will be uploaded')
parser.add_argument... | 76 | 2,597 |
numpy | tools/ci/test_all_newsfragments_used.py | .py | #!/usr/bin/env python3
import os
import sys
import toml
def main():
path = toml.load("pyproject.toml")["tool"]["towncrier"]["directory"]
fragments = os.listdir(path)
fragments.remove("README.rst")
fragments.remove("template.rst")
if fragments:
print("The following files were not found ... | 24 | 445 |
numpy | tools/ci/check_c_api_usage.py | .py | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import os
import re
import sys
import tempfile
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path
from re import Pattern
"""
Borrow-ref C API linter (Python version).
- Recursively scans source files under... | 266 | 8,593 |
numpy | benchmarks/asv_pip_nopep517.py | .py | """
This file is used by asv_compare.conf.json.tpl.
"""
import subprocess
import sys
# pip ignores '--global-option' when pep517 is enabled therefore we disable it.
cmd = [sys.executable, '-mpip', 'wheel', '--no-use-pep517']
try:
output = subprocess.check_output(cmd, stderr=subprocess.STDOUT, text=True)
except Exc... | 18 | 499 |
numpy | benchmarks/benchmarks/bench_records.py | .py | import numpy as np
from .common import Benchmark
class Records(Benchmark):
def setup(self):
self.l50 = np.arange(1000)
self.fields_number = 10000
self.arrays = [self.l50 for _ in range(self.fields_number)]
self.formats = [self.l50.dtype.str for _ in range(self.fields_number)]
... | 41 | 1,391 |
numpy | benchmarks/benchmarks/bench_ufunc_strides.py | .py | import numpy as np
from .common import Benchmark, get_data
UFUNCS = [obj for obj in np._core.umath.__dict__.values() if
isinstance(obj, np.ufunc)]
UFUNCS_UNARY = [uf for uf in UFUNCS if "O->O" in uf.types]
class _AbstractBinary(Benchmark):
params = []
param_names = ['ufunc', 'stride_in0', 'stride_i... | 230 | 7,396 |
numpy | benchmarks/benchmarks/bench_reduce.py | .py | import numpy as np
from .common import TYPES1, Benchmark, get_squares
class AddReduce(Benchmark):
def setup(self):
self.squares = get_squares().values()
def time_axis_0(self):
[np.add.reduce(a, axis=0) for a in self.squares]
def time_axis_1(self):
[np.add.reduce(a, axis=1) for a... | 138 | 3,220 |
numpy | benchmarks/benchmarks/bench_ufunc.py | .py | import itertools
import operator
from packaging import version
import numpy as np
from .common import DLPACK_TYPES, TYPES1, Benchmark, get_squares_
ufuncs = ['abs', 'absolute', 'add', 'arccos', 'arccosh', 'arcsin', 'arcsinh',
'arctan', 'arctan2', 'arctanh', 'bitwise_and', 'bitwise_count', 'bitwise_not',
... | 618 | 18,218 |
numpy | benchmarks/benchmarks/bench_array_coercion.py | .py | import numpy as np
from .common import Benchmark
class ArrayCoercionSmall(Benchmark):
# More detailed benchmarks for array coercion,
# some basic benchmarks are in `bench_core.py`.
params = [[range(3), [1], 1, np.array([5], dtype=np.int64), np.int64(5)]]
param_names = ['array_like']
int64 = np.dt... | 55 | 1,665 |
numpy | benchmarks/benchmarks/bench_clip.py | .py | import numpy as np
from .common import Benchmark
class ClipFloat(Benchmark):
param_names = ["dtype", "size"]
params = [
[np.float32, np.float64, np.longdouble],
[100, 100_000]
]
def setup(self, dtype, size):
rnd = np.random.RandomState(994584855)
self.array = rnd.rand... | 36 | 926 |
numpy | benchmarks/benchmarks/bench_shape_base.py | .py | import numpy as np
from .common import Benchmark
class Block(Benchmark):
params = [1, 10, 100]
param_names = ['size']
def setup(self, n):
self.a_2d = np.ones((2 * n, 2 * n))
self.b_1d = np.ones(2 * n)
self.b_2d = 2 * self.a_2d
self.a = np.ones(3 * n)
self.b = np.... | 171 | 5,211 |
numpy | benchmarks/benchmarks/bench_ma.py | .py | import numpy as np
from .common import Benchmark
class MA(Benchmark):
def setup(self):
self.l100 = range(100)
self.t100 = ([True] * 100)
def time_masked_array(self):
np.ma.masked_array()
def time_masked_array_l100(self):
np.ma.masked_array(self.l100)
def time_masked... | 313 | 9,962 |
numpy | benchmarks/benchmarks/bench_core.py | .py | import numpy as np
from .common import Benchmark
class Core(Benchmark):
def setup(self):
self.l100 = range(100)
self.l50 = range(50)
self.float_l1000 = [float(i) for i in range(1000)]
self.float64_l1000 = [np.float64(i) for i in range(1000)]
self.int_l1000 = list(range(100... | 356 | 9,231 |
numpy | benchmarks/benchmarks/bench_scalar.py | .py | import numpy as np
from .common import TYPES1, Benchmark
class ScalarMath(Benchmark):
# Test scalar math, note that each of these is run repeatedly to offset
# the function call overhead to some degree.
params = [TYPES1]
param_names = ["type"]
def setup(self, typename):
self.num = np.dty... | 81 | 2,338 |
numpy | benchmarks/benchmarks/bench_io.py | .py | from io import SEEK_SET, BytesIO, StringIO
import numpy as np
from .common import Benchmark, get_squares, get_squares_
class Copy(Benchmark):
params = ["int8", "int16", "float32", "float64",
"complex64", "complex128"]
param_names = ['type']
def setup(self, typename):
dtype = np.dt... | 281 | 8,665 |
numpy | benchmarks/benchmarks/__init__.py | .py | import os
import sys
from . import common
def show_cpu_features():
from numpy.lib._utils_impl import _opt_info
info = _opt_info()
info = "NumPy CPU features: " + (info or 'nothing enabled')
# ASV wrapping stdout & stderr, so we assume having a tty here
if 'SHELL' in os.environ and sys.platform !=... | 56 | 1,852 |
numpy | benchmarks/benchmarks/bench_datetime.py | .py | import numpy as np
from .common import Benchmark
class DatetimeAsString(Benchmark):
"""ISO string formatting from datetime64 — exercises set_datetimestruct_days
on every element for every output unit.
"""
params = [
[10_000, 1_000_000],
['datetime64[D]', 'datetime64[s]', 'datetime64[m... | 83 | 2,796 |
numpy | benchmarks/benchmarks/bench_import.py | .py | from subprocess import call
from sys import executable
from timeit import default_timer
from .common import Benchmark
class Import(Benchmark):
timer = default_timer
def execute(self, command):
call((executable, '-c', command))
def time_numpy(self):
self.execute('import numpy')
def ... | 35 | 838 |
numpy | benchmarks/benchmarks/bench_strings.py | .py | import operator
import numpy as np
from .common import Benchmark
_OPERATORS = {
'==': operator.eq,
'!=': operator.ne,
'<': operator.lt,
'<=': operator.le,
'>': operator.gt,
'>=': operator.ge,
}
class StringComparisons(Benchmark):
# Basic string comparison speed tests
params = [
... | 44 | 1,233 |
numpy | benchmarks/benchmarks/bench_searchsorted.py | .py | import numpy as np
from .common import Benchmark
class SearchSorted(Benchmark):
params = [
[100, 10_000, 1_000_000, 100_000_000], # array sizes
[1, 10, 100_000], # number of query elements
['ordered', 'random'], # query order
[False, True], ... | 36 | 1,209 |
numpy | benchmarks/benchmarks/bench_random.py | .py | import numpy as np
from .common import Benchmark
try:
from numpy.random import Generator
except ImportError:
pass
class Random(Benchmark):
params = ['normal', 'uniform', 'weibull 1', 'binomial 10 0.5',
'poisson 10']
def setup(self, name):
items = name.split()
name = it... | 187 | 5,377 |
numpy | benchmarks/benchmarks/bench_trim_zeros.py | .py | import numpy as np
from .common import Benchmark
_FLOAT = np.dtype('float64')
_COMPLEX = np.dtype('complex128')
_INT = np.dtype('int64')
_BOOL = np.dtype('bool')
class TrimZeros(Benchmark):
param_names = ["dtype", "size"]
params = [
[_INT, _FLOAT, _COMPLEX, _BOOL],
[3000, 30_000, 300_000]
... | 28 | 607 |
numpy | benchmarks/benchmarks/bench_indexing.py | .py | import shutil
from os.path import join as pjoin
from tempfile import mkdtemp
import numpy as np
from numpy import array, float32, memmap
from .common import TYPES1, Benchmark, get_indexes_, get_indexes_rand_, get_square_
class Indexing(Benchmark):
params = [TYPES1 + ["object", "O,i"],
["indexes_",... | 181 | 4,845 |
numpy | benchmarks/benchmarks/bench_ndindex.py | .py | from itertools import product
import numpy as np
from .common import Benchmark
class NdindexBenchmark(Benchmark):
"""
Benchmark comparing numpy.ndindex() and itertools.product()
for different multi-dimensional shapes.
"""
# Fix: Define each dimension separately, not as tuples
# ASV will pas... | 55 | 1,629 |
numpy | benchmarks/benchmarks/bench_app.py | .py | import numpy as np
from .common import Benchmark
class LaplaceInplace(Benchmark):
params = ['inplace', 'normal']
param_names = ['update']
def setup(self, update):
N = 150
Niter = 1000
dx = 0.1
dy = 0.1
dx2 = (dx * dx)
dy2 = (dy * dy)
def num_updat... | 86 | 2,669 |
numpy | benchmarks/benchmarks/bench_overrides.py | .py | from .common import Benchmark
try:
from numpy._core.overrides import array_function_dispatch
except ImportError:
# Don't fail at import time with old Numpy versions
def array_function_dispatch(*args, **kwargs):
def wrap(*args, **kwargs):
return None
return wrap
import numpy as ... | 68 | 1,786 |
numpy | benchmarks/benchmarks/common.py | .py | import random
from functools import lru_cache
from pathlib import Path
import numpy as np
# Various pre-crafted datasets/variables for testing
# !!! Must not be changed -- only appended !!!
# while testing numpy we better not rely on numpy to produce random
# sequences
random.seed(1)
# but will seed it nevertheless
n... | 216 | 5,558 |
numpy | benchmarks/benchmarks/bench_creation.py | .py | import numpy as np
from .common import TYPES1, Benchmark, get_squares_
class MeshGrid(Benchmark):
""" Benchmark meshgrid generation
"""
params = [[16, 32],
[2, 3, 4],
['ij', 'xy'], TYPES1]
param_names = ['size', 'ndims', 'ind', 'ndtype']
timeout = 10
def setup(sel... | 75 | 2,015 |
numpy | benchmarks/benchmarks/bench_manipulate.py | .py | from collections import deque
import numpy as np
from .common import TYPES1, Benchmark
class BroadcastArrays(Benchmark):
params = [[(16, 32), (128, 256), (512, 1024)],
TYPES1]
param_names = ['shape', 'ndtype']
timeout = 10
def setup(self, shape, ndtype):
self.xarg = np.random.... | 112 | 3,178 |
numpy | benchmarks/benchmarks/bench_lib.py | .py | """Benchmarks for `numpy.lib`."""
import string
from asv_runner.benchmarks.mark import SkipNotImplemented
import numpy as np
from .common import Benchmark
class Pad(Benchmark):
"""Benchmarks for `numpy.pad`.
When benchmarking the pad function it is useful to cover scenarios where
the ratio between th... | 256 | 9,917 |
numpy | benchmarks/benchmarks/bench_itemselection.py | .py | import numpy as np
from .common import TYPES1, Benchmark
class Take(Benchmark):
params = [
[(1000, 1), (2, 1000, 1), (1000, 3)],
["raise", "wrap", "clip"],
TYPES1 + ["O", "i,O"]]
param_names = ["shape", "mode", "dtype"]
def setup(self, shape, mode, dtype):
self.arr = np.o... | 62 | 1,730 |
numpy | benchmarks/benchmarks/bench_function_base.py | .py | import numpy as np
from .common import Benchmark
try:
# SkipNotImplemented is available since 6.0
from asv_runner.benchmarks.mark import SkipNotImplemented
except ImportError:
SkipNotImplemented = NotImplementedError
class Linspace(Benchmark):
def setup(self):
self.d = np.array([1, 2, 3])
... | 424 | 11,682 |
numpy | benchmarks/benchmarks/bench_linalg.py | .py | import numpy as np
from .common import TYPES1, Benchmark, get_indexes_rand, get_squares_
class Eindot(Benchmark):
def setup(self):
self.a = np.arange(60000.0).reshape(150, 400)
self.ac = self.a.copy()
self.at = self.a.T
self.atc = self.a.T.copy()
self.b = np.arange(240000.... | 276 | 9,060 |
numpy | benchmarks/benchmarks/bench_polynomial.py | .py | import numpy as np
from .common import Benchmark
class Polynomial(Benchmark):
def setup(self):
self.polynomial_degree2 = np.polynomial.Polynomial(np.array([1, 2]))
self.array3 = np.linspace(0, 1, 3)
self.array1000 = np.linspace(0, 1, 10_000)
self.float64 = np.float64(1.0)
de... | 28 | 803 |
numpy | benchmarks/benchmarks/bench_alloc_cache.py | .py | """Benchmarks for the NumPy small-allocation cache.
NumPy caches data allocations smaller than 1024 bytes (up to 7 per size
bucket) to avoid repeated malloc/free calls. For float64 arrays this
means arrays with fewer than 128 elements hit the cache.
These benchmarks measure tight create-and-discard loops so that the... | 46 | 1,353 |
numpy | doc/preprocess.py | .py | #!/usr/bin/env python3
import os
from string import Template
def main():
doxy_gen(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
def doxy_gen(root_path):
"""
Generate Doxygen configuration file.
"""
confs = doxy_config(root_path)
build_path = os.path.join(root_path, "doc", "b... | 52 | 1,565 |
numpy | doc/postprocess.py | .py | #!/usr/bin/env python3
"""
Post-processes HTML and Latex files output by Sphinx.
"""
def main():
import argparse
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('mode', help='file mode', choices=('html', 'tex'))
parser.add_argument('file', nargs='+', help='input file(s)')
... | 51 | 1,306 |
numpy | doc/conftest.py | .py | """
Pytest configuration and fixtures for the Numpy test suite.
"""
import doctest
import matplotlib
import pytest
import numpy
matplotlib.use('agg', force=True)
# Ignore matplotlib output such as `<matplotlib.image.AxesImage at
# 0x7f956908c280>`. doctest monkeypatching inspired by
# https://github.com/wooyek/pyte... | 34 | 943 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.