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 |
|---|---|---|---|---|---|---|
mmyolo | mmyolo-main/mmyolo/datasets/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .transforms import * # noqa: F401,F403
from .utils import BatchShapePolicy, yolov5_collate
from .yolov5_coco import YOLOv5CocoDataset
from .yolov5_crowdhuman import YOLOv5CrowdHumanDataset
from .yolov5_dota import YOLOv5DOTADataset
from .yolov5_voc import YOLOv5VOCD... | 476 | 35.692308 | 68 | py |
mmyolo | mmyolo-main/mmyolo/datasets/yolov5_voc.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.datasets import VOCDataset
from mmyolo.datasets.yolov5_coco import BatchShapePolicyDataset
from ..registry import DATASETS
@DATASETS.register_module()
class YOLOv5VOCDataset(BatchShapePolicyDataset, VOCDataset):
"""Dataset for YOLOv5 VOC Dataset.
We... | 465 | 28.125 | 73 | py |
mmyolo | mmyolo-main/mmyolo/datasets/yolov5_dota.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmyolo.datasets.yolov5_coco import BatchShapePolicyDataset
from ..registry import DATASETS
try:
from mmrotate.datasets import DOTADataset
MMROTATE_AVAILABLE = True
except ImportError:
from mmengine.dataset import BaseDataset
DOTADataset = BaseDatase... | 922 | 29.766667 | 74 | py |
mmyolo | mmyolo-main/mmyolo/datasets/transforms/mix_img_transforms.py | # Copyright (c) OpenMMLab. All rights reserved.
import collections
import copy
from abc import ABCMeta, abstractmethod
from typing import Optional, Sequence, Tuple, Union
import mmcv
import numpy as np
from mmcv.transforms import BaseTransform
from mmdet.structures.bbox import autocast_box_type
from mmengine.dataset i... | 46,505 | 39.404865 | 79 | py |
mmyolo | mmyolo-main/mmyolo/datasets/transforms/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .mix_img_transforms import Mosaic, Mosaic9, YOLOv5MixUp, YOLOXMixUp
from .transforms import (LetterResize, LoadAnnotations, PPYOLOERandomCrop,
PPYOLOERandomDistort, RegularizeRotatedBox,
RemoveDataElement, YOLOv5CopyP... | 732 | 47.866667 | 77 | py |
mmyolo | mmyolo-main/mmyolo/datasets/transforms/transforms.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from copy import deepcopy
from typing import List, Sequence, Tuple, Union
import cv2
import mmcv
import numpy as np
import torch
from mmcv.transforms import BaseTransform, Compose
from mmcv.transforms.utils import cache_randomness
from mmdet.datasets.transfor... | 59,261 | 37.037227 | 79 | py |
mmyolo | mmyolo-main/mmyolo/deploy/object_detection.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Callable, Dict, Optional
import torch
from mmdeploy.codebase.base import CODEBASE, MMCodebase
from mmdeploy.codebase.mmdet.deploy import ObjectDetection
from mmdeploy.utils import Codebase, Task
from mmengine import Config
from mmengine.registry import... | 4,523 | 33.015038 | 78 | py |
mmyolo | mmyolo-main/mmyolo/deploy/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmdeploy.codebase.base import MMCodebase
from .models import * # noqa: F401,F403
from .object_detection import MMYOLO, YOLOObjectDetection
__all__ = ['MMCodebase', 'MMYOLO', 'YOLOObjectDetection']
| 253 | 30.75 | 57 | py |
mmyolo | mmyolo-main/mmyolo/deploy/models/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from . import dense_heads # noqa: F401,F403
| 93 | 30.333333 | 47 | py |
mmyolo | mmyolo-main/mmyolo/deploy/models/layers/bbox_nms.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdeploy.core import mark
from torch import Tensor
def _efficient_nms(
boxes: Tensor,
scores: Tensor,
max_output_boxes_per_class: int = 1000,
iou_threshold: float = 0.5,
score_threshold: float = 0.05,
pre_top_k: int = -1,
ke... | 3,931 | 33.491228 | 78 | py |
mmyolo | mmyolo-main/mmyolo/deploy/models/layers/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .bbox_nms import efficient_nms
__all__ = ['efficient_nms']
| 113 | 21.8 | 47 | py |
mmyolo | mmyolo-main/mmyolo/deploy/models/dense_heads/yolov5_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from functools import partial
from typing import List, Optional, Tuple
import torch
from mmdeploy.codebase.mmdet import get_post_processing_params
from mmdeploy.codebase.mmdet.models.layers import multiclass_nms
from mmdeploy.core import FUNCTION_REWRITER
fro... | 7,242 | 37.121053 | 79 | py |
mmyolo | mmyolo-main/mmyolo/deploy/models/dense_heads/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from . import yolov5_head # noqa: F401,F403
__all__ = ['yolov5_head']
| 120 | 23.2 | 47 | py |
mmyolo | mmyolo-main/mmyolo/engine/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .hooks import * # noqa: F401,F403
from .optimizers import * # noqa: F401,F403
| 133 | 32.5 | 47 | py |
mmyolo | mmyolo-main/mmyolo/engine/hooks/yolov5_param_scheduler_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional
import numpy as np
from mmengine.hooks import ParamSchedulerHook
from mmengine.runner import Runner
from mmyolo.registry import HOOKS
def linear_fn(lr_factor: float, max_epochs: int):
"""Generate linear function."""
retu... | 4,611 | 34.206107 | 79 | py |
mmyolo | mmyolo-main/mmyolo/engine/hooks/ppyoloe_param_scheduler_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional
from mmengine.hooks import ParamSchedulerHook
from mmengine.runner import Runner
from mmyolo.registry import HOOKS
@HOOKS.register_module()
class PPYOLOEParamSchedulerHook(ParamSchedulerHook):
"""A hook to update learning ra... | 3,781 | 37.989691 | 79 | py |
mmyolo | mmyolo-main/mmyolo/engine/hooks/yolox_mode_switch_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import Sequence
from mmengine.hooks import Hook
from mmengine.model import is_model_wrapper
from mmengine.runner import Runner
from mmyolo.registry import HOOKS
@HOOKS.register_module()
class YOLOXModeSwitchHook(Hook):
"""Switch the mode of... | 2,095 | 37.109091 | 79 | py |
mmyolo | mmyolo-main/mmyolo/engine/hooks/switch_to_deploy_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.hooks import Hook
from mmengine.runner import Runner
from mmyolo.registry import HOOKS
from mmyolo.utils import switch_to_deploy
@HOOKS.register_module()
class SwitchToDeployHook(Hook):
"""Switch to deploy mode before testing.
This hook converts... | 630 | 27.681818 | 76 | py |
mmyolo | mmyolo-main/mmyolo/engine/hooks/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .ppyoloe_param_scheduler_hook import PPYOLOEParamSchedulerHook
from .switch_to_deploy_hook import SwitchToDeployHook
from .yolov5_param_scheduler_hook import YOLOv5ParamSchedulerHook
from .yolox_mode_switch_hook import YOLOXModeSwitchHook
__all__ = [
'YOLOv5Para... | 416 | 36.909091 | 76 | py |
mmyolo | mmyolo-main/mmyolo/engine/optimizers/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .yolov5_optim_constructor import YOLOv5OptimizerConstructor
from .yolov7_optim_wrapper_constructor import YOLOv7OptimWrapperConstructor
__all__ = ['YOLOv5OptimizerConstructor', 'YOLOv7OptimWrapperConstructor']
| 264 | 43.166667 | 75 | py |
mmyolo | mmyolo-main/mmyolo/engine/optimizers/yolov5_optim_constructor.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch.nn as nn
from mmengine.dist import get_world_size
from mmengine.logging import print_log
from mmengine.model import is_model_wrapper
from mmengine.optim import OptimWrapper
from mmyolo.registry import (OPTIM_WRAPPER_CONSTRUCTORS,... | 5,201 | 38.112782 | 78 | py |
mmyolo | mmyolo-main/mmyolo/engine/optimizers/yolov7_optim_wrapper_constructor.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch.nn as nn
from mmengine.dist import get_world_size
from mmengine.logging import print_log
from mmengine.model import is_model_wrapper
from mmengine.optim import OptimWrapper
from mmyolo.models.dense_heads.yolov7_head import Implic... | 5,576 | 38.835714 | 78 | py |
mmyolo | mmyolo-main/mmyolo/utils/labelme_utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import json
import os.path
from mmengine.structures import InstanceData
class LabelmeFormat:
"""Predict results save into labelme file.
Base on https://github.com/wkentaro/labelme/blob/main/labelme/label_file.py
Args:
classes (tuple): Model classe... | 2,799 | 29.107527 | 79 | py |
mmyolo | mmyolo-main/mmyolo/utils/large_image.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence, Tuple
import torch
from mmcv.ops import batched_nms
from mmdet.structures import DetDataSample, SampleList
from mmengine.structures import InstanceData
def shift_rbboxes(bboxes: torch.Tensor, offset: Sequence[int]):
"""Shift rotated bbo... | 3,871 | 36.230769 | 79 | py |
mmyolo | mmyolo-main/mmyolo/utils/misc.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import urllib
import numpy as np
import torch
from mmengine.utils import scandir
from prettytable import PrettyTable
from mmyolo.models import RepVGGBlock
IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif',
'.tiff', '.... | 4,932 | 35.813433 | 175 | py |
mmyolo | mmyolo-main/mmyolo/utils/setup_env.py | # Copyright (c) OpenMMLab. All rights reserved.
import datetime
import warnings
from mmengine import DefaultScope
def register_all_modules(init_default_scope: bool = True):
"""Register all modules in mmdet into the registries.
Args:
init_default_scope (bool): Whether initialize the mmdet default sco... | 1,863 | 43.380952 | 93 | py |
mmyolo | mmyolo-main/mmyolo/utils/collect_env.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmdet
from mmengine.utils import get_git_hash
from mmengine.utils.dl_utils import collect_env as collect_base_env
import mmyolo
def collect_env() -> dict:
"""Collect the information of the running environments."""
env_info = collect_base_env(... | 606 | 26.590909 | 70 | py |
mmyolo | mmyolo-main/mmyolo/utils/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .misc import is_metainfo_lower, switch_to_deploy
from .setup_env import register_all_modules
__all__ = [
'register_all_modules', 'collect_env', 'switch_to_deploy',
'is_metainfo_lower'
]
| 285 | 27.6 | 62 | py |
mmyolo | mmyolo-main/mmyolo/utils/boxam_utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import bisect
import copy
import warnings
from pathlib import Path
from typing import Callable, List, Optional, Tuple, Union
import cv2
import numpy as np
import torch
import torch.nn as nn
import torchvision
from mmcv.transforms import Compose
from mmdet.evaluation impo... | 19,429 | 36.875244 | 79 | py |
DALLE-pytorch | DALLE-pytorch-main/train_dalle.py | import argparse
from pathlib import Path
import time
from glob import glob
import os
import shutil
import torch
import wandb # Quit early if user doesn't have wandb installed.
from torch.nn.utils import clip_grad_norm_
from torch.optim import Adam
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.util... | 23,672 | 33.967504 | 199 | py |
DALLE-pytorch | DALLE-pytorch-main/setup.py | from setuptools import setup, find_packages
exec(open('dalle_pytorch/version.py').read())
setup(
name = 'dalle-pytorch',
packages = find_packages(),
include_package_data = True,
version = __version__,
license='MIT',
description = 'DALL-E - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmai... | 1,149 | 23.468085 | 65 | py |
DALLE-pytorch | DALLE-pytorch-main/generate.py | import argparse
from pathlib import Path
from tqdm import tqdm
# torch
import torch
from einops import repeat
# vision imports
from PIL import Image
from torchvision.utils import make_grid, save_image
# dalle related classes and utils
from dalle_pytorch import __version__
from dalle_pytorch import DiscreteVAE, O... | 4,695 | 31.611111 | 286 | py |
DALLE-pytorch | DALLE-pytorch-main/train_vae.py | import math
from math import sqrt
import argparse
from pathlib import Path
# torch
import torch
from torch.optim import Adam
from torch.optim.lr_scheduler import ExponentialLR
# vision imports
from torchvision import transforms as T
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolde... | 9,727 | 29.117647 | 168 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/reversible.py | import torch
import torch.nn as nn
from operator import itemgetter
from torch.autograd.function import Function
from torch.utils.checkpoint import get_device_states, set_device_states
# for routing arguments into the functions of the reversible layer
def route_args(router, args, depth):
routed_args = [(dict(), dic... | 5,390 | 33.120253 | 165 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/dalle_pytorch.py | from math import log2, sqrt
import torch
from torch import nn, einsum
import torch.nn.functional as F
import numpy as np
from axial_positional_embedding import AxialPositionalEmbedding
from einops import rearrange
from dalle_pytorch import distributed_utils
from dalle_pytorch.vae import OpenAIDiscreteVAE, VQGanVAE
fr... | 23,608 | 34.13244 | 170 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/vae.py | import io
import sys
import os
import requests
import PIL
import warnings
import hashlib
import urllib
import yaml
from pathlib import Path
from tqdm import tqdm
from math import sqrt, log
from packaging import version
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel, GumbelVQ
import importlib
... | 7,674 | 31.939914 | 149 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/distributed_utils.py | """
Utility functions for optional distributed execution.
To use,
1. set the `BACKENDS` to the ones you want to make available,
2. in the script, wrap the argument parser with `wrap_arg_parser`,
3. in the script, set and use the backend by calling
`set_backend_from_args`.
You can check whether a backend is in use ... | 2,839 | 28.278351 | 79 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/version.py | __version__ = '1.6.6'
| 22 | 10.5 | 21 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/transformer.py | from collections import deque
from collections.abc import Iterable
from functools import partial
from itertools import islice, cycle
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange
from dalle_pytorch.reversible import ReversibleSequence, SequentialSequence
from d... | 13,131 | 36.413105 | 180 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/tokenizer.py | # take from https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py
# to give users a quick easy start to training DALL-E without doing BPE
import torch
import youtokentome as yttm
from tokenizers import Tokenizer
from tokenizers.processors import ByteLevel
from transformers import BertTokenizer
import htm... | 9,432 | 34.329588 | 120 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/__init__.py | from dalle_pytorch.dalle_pytorch import DALLE, CLIP, DiscreteVAE
from dalle_pytorch.vae import OpenAIDiscreteVAE, VQGanVAE
from pkg_resources import get_distribution
from dalle_pytorch.version import __version__
| 213 | 34.666667 | 64 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/attention.py | from inspect import isfunction
from math import ceil
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, repeat
from rotary_embedding_torch import apply_rotary_emb
# helpers
def exists(val):
return val is not None
def uniq(arr):
return{el: True for el in ... | 14,131 | 34.418546 | 165 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/loader.py | from pathlib import Path
from random import randint, choice
import PIL
from torch.utils.data import Dataset
from torchvision import transforms as T
class TextImageDataset(Dataset):
def __init__(self,
folder,
text_len=256,
image_size=128,
trunca... | 3,558 | 33.221154 | 112 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/distributed_backends/dummy_backend.py | from .distributed_backend import DistributedBackend
class DummyBackend(DistributedBackend):
"""Acts like a distributed backend.
Used as a stand-in replacement to obtain a non-distributed program.
"""
# We define this so we can use `super().__init__` but want this to
# throw an error upon import.... | 1,222 | 22.075472 | 79 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/distributed_backends/distributed_backend.py | """
An abstract backend for distributed deep learning.
Provides several standard utility methods under a common API.
Please check the documentation of the class `DistributedBackend` for
details to implement a new backend.
"""
from importlib import import_module
class DistributedBackend:
"""An abstract backend c... | 5,671 | 30.687151 | 79 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/distributed_backends/deepspeed_backend.py | import json
import os
import torch
from .distributed_backend import DistributedBackend
class DeepSpeedBackend(DistributedBackend):
"""Distributed backend using the DeepSpeed engine."""
BACKEND_MODULE_NAME = 'deepspeed'
BACKEND_NAME = 'DeepSpeed'
def wrap_arg_parser(self, parser):
if not se... | 5,987 | 33.813953 | 78 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/distributed_backends/horovod_backend.py | import torch
from .distributed_backend import DistributedBackend
class HorovodBackend(DistributedBackend):
"""Distributed backend using Horovod."""
BACKEND_MODULE_NAME = 'horovod.torch'
BACKEND_NAME = 'Horovod'
def wrap_arg_parser(self, parser):
return parser
def check_batch_size(self,... | 1,703 | 27.881356 | 71 | py |
DALLE-pytorch | DALLE-pytorch-main/dalle_pytorch/distributed_backends/__init__.py | from .deepspeed_backend import DeepSpeedBackend
from .distributed_backend import DistributedBackend
from .dummy_backend import DummyBackend
from .horovod_backend import HorovodBackend
| 184 | 36 | 51 | py |
covid-vax-stance | covid-vax-stance-main/classifier/classifier_predict.py | # Libraries
import torch
from torchtext.data import Field, TabularDataset, Iterator
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoTokenizer, BertForSequenceClassification
import os
import csv
import time
import argparse
import warnings
torch.manual_seed(42)
warnings.filterwarning... | 5,168 | 33.46 | 176 | py |
libgpuarray | libgpuarray-master/setup.py | import sys
import os
import versioneer
import distutils.command.clean
import shutil
have_cython = False
try:
import Cython
if Cython.__version__ < '0.25':
raise Exception('cython is too old or not installed '
'(at least 0.25 required)')
from Cython.Build import cythonize
... | 5,763 | 34.801242 | 146 | py |
libgpuarray | libgpuarray-master/versioneer.py |
# Version: 0.18
"""The Versioneer - like a rocketeer, but for versions.
The Versioneer
==============
* like a rocketeer, but for versions!
* https://github.com/warner/python-versioneer
* Brian Warner
* License: Public Domain
* Compatible With: python2.6, 2.7, 3.2, 3.3, 3.4, 3.5, 3.6, and pypy
* [![Latest Version]
... | 68,610 | 36.65697 | 79 | py |
libgpuarray | libgpuarray-master/src/head.py | # Used to generate the string tables to embed the cluda headers.
# Usage: python head.py <file>
# This will output <file>.c
def wrt(f, n, b):
f.write(b',')
n += 1
if n > 10:
f.write(b'\n')
n = 0
else:
f.write(b' ')
f.write(b"0x%02x" % (b,))
return n
def convert(src, ds... | 953 | 23.461538 | 78 | py |
libgpuarray | libgpuarray-master/src/gen_types.py | import sys
from mako import exceptions
from mako.template import Template
TYPEMAP = {}
i = 0
def add_type(name, C, sz):
global i
TYPEMAP[i] = ("ga_"+name, sz), name, C
i+=1
add_type("bool", "uint8_t", 1)
add_type("byte", "int8_t", 1)
add_type("ubyte", "uint8_t", 1)
for name, sz in [("short", 2), ("int... | 4,777 | 19.245763 | 108 | py |
libgpuarray | libgpuarray-master/pygpu/reduction.py | import math
import re
from mako.template import Template
import numpy
from . import gpuarray
from .tools import ScalarArg, ArrayArg, check_args, prod, lru_cache
from .dtypes import parse_c_arg_backend
def parse_c_args(arguments):
return tuple(parse_c_arg_backend(arg, ScalarArg, ArrayArg)
for a... | 10,284 | 31.140625 | 77 | py |
libgpuarray | libgpuarray-master/pygpu/_array.py | from __future__ import division
import numpy as np
from .elemwise import elemwise1, elemwise2, ielemwise2, compare, arg, GpuElemwise, as_argument
from .reduction import reduce1
from .dtypes import dtype_to_ctype, get_np_obj, get_common_dtype
from . import gpuarray
class ndgpuarray(gpuarray.GpuArray):
"""
Ext... | 10,320 | 33.986441 | 94 | py |
libgpuarray | libgpuarray-master/pygpu/_version.py |
# This file helps to compute a version number in source trees obtained from
# git-archive tarball (such as those provided by githubs download-from-tag
# feature). Distribution tarballs (built by setup.py sdist) and build
# directories (produced by setup.py build) will contain a much shorter file
# that just contains t... | 18,503 | 34.516315 | 79 | py |
libgpuarray | libgpuarray-master/pygpu/tools.py | import functools
import six
from six.moves import reduce
from heapq import nsmallest
from operator import itemgetter, mul
import numpy
from .dtypes import dtype_to_ctype, _fill_dtype_registry
from .gpuarray import GpuArray
_fill_dtype_registry()
def as_argument(obj, name):
if isinstance(obj, GpuArray):
... | 6,395 | 27.681614 | 76 | py |
libgpuarray | libgpuarray-master/pygpu/elemwise.py | import numpy
from .dtypes import dtype_to_ctype, get_common_dtype
from . import gpuarray
from ._elemwise import GpuElemwise, arg
__all__ = ['GpuElemwise', 'arg', 'as_argument',
'elemwise1', 'elemwise2', 'ielemwise2', 'compare']
def _dtype(o):
if hasattr(o, 'dtype'):
return o.dtype
return ... | 3,200 | 30.07767 | 78 | py |
libgpuarray | libgpuarray-master/pygpu/__init__.py | def get_include():
import os.path
p = os.path.dirname(__file__)
assert os.path.exists(os.path.join(p, 'gpuarray_api.h'))
return p
from . import gpuarray, elemwise, reduction
from .gpuarray import (init, set_default_context, get_default_context,
array, zeros, empty, asarray, ascon... | 792 | 30.72 | 71 | py |
libgpuarray | libgpuarray-master/pygpu/dtypes.py | """Type mapping helpers."""
from __future__ import division
import numpy as np
from . import gpuarray
__copyright__ = "Copyright (C) 2011 Andreas Kloeckner"
__license__ = """
Permission is hereby granted, free of charge, to any person
obtaining a copy of this software and associated documentation
files (the "Softwa... | 5,921 | 28.172414 | 75 | py |
libgpuarray | libgpuarray-master/pygpu/operations.py | from six.moves import range
from .gpuarray import _split, _concatenate, dtype_to_typecode
from .dtypes import upcast
from . import asarray
def atleast_1d(*arys):
res = []
for ary in arys:
ary = asarray(ary)
if len(ary.shape) == 0:
result = ary.reshape((1,))
else:
... | 4,070 | 27.468531 | 79 | py |
libgpuarray | libgpuarray-master/pygpu/basic.py | from string import Template
from .gpuarray import GpuArray, GpuKernel, SIZE, dtype_to_ctype
import numpy
def _generate_kernel(ctx, cols, dtype, upper=True):
tmpl = Template("""
#include "cluda.h"
KERNEL void extract_tri(GLOBAL_MEM ${ctype} *a, ga_size a_off, ga_uint N) {
a = (GLOBAL_MEM ${ctype} *)... | 2,407 | 29.871795 | 79 | py |
libgpuarray | libgpuarray-master/pygpu/tests/test_collectives.py | from __future__ import print_function
import os
import sys
import unittest
from six.moves import range
from six import PY3
import pickle
import numpy as np
from pygpu import gpuarray
from pygpu.collectives import COMM_ID_BYTES, GpuCommCliqueId, GpuComm
from pygpu.tests.support import (check_all, gen_gpuarray, conte... | 12,098 | 38.410423 | 100 | py |
libgpuarray | libgpuarray-master/pygpu/tests/main.py | import os
import nose.plugins.builtin
from nose.config import Config
from nose.plugins.manager import PluginManager
from numpy.testing.nosetester import NoseTester
from numpy.testing.noseclasses import KnownFailure, NumpyTestProgram
class NoseTester(NoseTester):
"""
Nose test runner.
This class enables ... | 4,535 | 34.162791 | 79 | py |
libgpuarray | libgpuarray-master/pygpu/tests/test_basic.py | import pygpu
from pygpu.basic import (tril, triu)
from unittest import TestCase
from .support import (guard_devsup, gen_gpuarray, context)
import numpy
def test_tril():
for dtype in ['float32','float64']:
for shape in [(10, 5), (5, 10), (10, 10)]:
for order in ['c', 'f']:
for ... | 2,609 | 29.705882 | 68 | py |
libgpuarray | libgpuarray-master/pygpu/tests/test_reduction.py | import numpy
from nose.tools import assert_raises
from pygpu import gpuarray, ndgpuarray as elemary
from pygpu.reduction import ReductionKernel
from .support import (guard_devsup, check_meta_content, context, gen_gpuarray,
dtypes_no_complex_big, dtypes_no_complex)
def test_red_array_basic():
... | 4,645 | 30.181208 | 78 | py |
libgpuarray | libgpuarray-master/pygpu/tests/test_blas.py | from itertools import product
import numpy
from nose.plugins.skip import SkipTest
from .support import (guard_devsup, gen_gpuarray, context)
try:
import scipy.linalg.blas
try:
fblas = scipy.linalg.blas.fblas
except AttributeError:
fblas = scipy.linalg.blas
except ImportError as e:
rai... | 9,194 | 36.684426 | 83 | py |
libgpuarray | libgpuarray-master/pygpu/tests/support.py | from __future__ import print_function
import os
import sys
import numpy
from nose.plugins.skip import SkipTest
from pygpu import gpuarray
if numpy.__version__ < '1.6.0':
skip_single_f = True
else:
skip_single_f = False
dtypes_all = ["float32", "float64",
"int8", "int16", "uint8", "uint16",
... | 5,147 | 29.461538 | 76 | py |
libgpuarray | libgpuarray-master/pygpu/tests/test_tools.py | from pygpu.tools import check_args
from .support import context, gen_gpuarray
def test_check_args_simple():
ac, ag = gen_gpuarray((50,), 'float32', ctx=context)
bc, bg = gen_gpuarray((50,), 'float32', ctx=context)
n, nd, dims, strs, offsets = check_args((ag, bg))
assert n == 50
assert nd == 1
... | 3,878 | 31.596639 | 72 | py |
libgpuarray | libgpuarray-master/pygpu/tests/test_gpu_ndarray.py | from __future__ import print_function
import unittest
import copy
from six.moves import range
from six import PY3
import pickle
import numpy
from nose.tools import assert_raises
import pygpu
from pygpu.gpuarray import GpuArray, GpuKernel
from .support import (guard_devsup, check_meta, check_flags, check_all,
... | 26,790 | 31.162065 | 79 | py |
libgpuarray | libgpuarray-master/pygpu/tests/__init__.py | 0 | 0 | 0 | py | |
libgpuarray | libgpuarray-master/pygpu/tests/test_operations.py | import numpy
import pygpu
from .support import (gen_gpuarray, context, SkipTest)
def test_array_split():
xc, xg = gen_gpuarray((8,), 'float32', ctx=context)
rc = numpy.array_split(xc, 3)
rg = pygpu.array_split(xg, 3)
assert len(rc) == len(rg)
for pc, pg in zip(rc, rg):
numpy.testing.asse... | 2,636 | 25.636364 | 77 | py |
libgpuarray | libgpuarray-master/pygpu/tests/test_elemwise.py | import operator
import numpy
from mako.template import Template
from unittest import TestCase
from pygpu import gpuarray, ndgpuarray as elemary
from pygpu.dtypes import dtype_to_ctype, get_common_dtype
from pygpu.elemwise import as_argument, ielemwise2
from pygpu._elemwise import GpuElemwise, arg
from six import PY2
... | 11,580 | 32.37464 | 79 | py |
libgpuarray | libgpuarray-master/doc/conf.py | # -*- Coding: utf-8 -*-
#
# gpuarray documentation build configuration file, created by
# sphinx-quickstart on Wed Nov 21 16:23:37 2012.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# Al... | 9,714 | 30.852459 | 80 | py |
GENESIM | GENESIM-master/example.py | """
This is an example script that will apply k-cross-validation on all datasets with a load function in
`data.load_datasets` and for all implemented tree constructors, ensemble techniques and GENESIM. In the end,
a confusion matrices will be stored at path `output/dataset_name_CVk.png` and the average model complexity... | 11,794 | 53.105505 | 153 | py |
GENESIM | GENESIM-master/decisiontree.py | """
Contains the decisiontree object used throughout this project.
Written by Gilles Vandewiele in commission of IDLab - INTEC from University Ghent.
"""
from copy import deepcopy, copy
import sklearn
from graphviz import Source
import matplotlib.pyplot as plt
import numpy as np
import json
import operator
from pand... | 24,478 | 42.02109 | 152 | py |
GENESIM | GENESIM-master/RFTest.py | from sklearn.cross_validation import StratifiedKFold
from sklearn.metrics import confusion_matrix
from constructors.ensemble import RFClassification
from data.load_all_datasets import load_all_datasets
import numpy as np
from decisiontree import DecisionTree
from refined_rf import RefinedRandomForest
rf = RFClassi... | 3,492 | 35.385417 | 91 | py |
GENESIM | GENESIM-master/__init__.py | 0 | 0 | 0 | py | |
GENESIM | GENESIM-master/constructors/genesim.py | """
Contains the code for the innovative algorithm called GENESIM
Written by Gilles Vandewiele in commission of IDLab - INTEC from University Ghent.
"""
import copy
import multiprocessing
from collections import Counter
from pandas import DataFrame, concat
import numpy as np
import time
import sys
from sklearn.cr... | 36,137 | 49.542657 | 182 | py |
GENESIM | GENESIM-master/constructors/inTrees.py | """
inTrees / STEL
--------------
Merges different decision trees in an ensemble together in an ordered rule list
Written by Gilles Vandewiele in commission of IDLab - INTEC from University Ghent.
Reference:
Houtao Deng
"Interpreting Tree Ensembles with inTrees"
"""
import sys
im... | 10,783 | 33.899676 | 157 | py |
GENESIM | GENESIM-master/constructors/treeconstructor.py | """
Contains wrappers around well-known decision tree induction algorithms: C4.5, CART, QUEST and GUIDE.
Written by Gilles Vandewiele in commission of IDLab - INTEC from University Ghent.
"""
import pandas as pd
import numpy as np
from sklearn.cross_validation import StratifiedKFold
from sklearn.metrics import accura... | 23,107 | 38.772806 | 151 | py |
GENESIM | GENESIM-master/constructors/__init__.py | """
Contains implementations for different classifiers: decision tree induction algorithms, ensemble techniques and
GENESIM: GENetic Extraction of a Single, Interpretable Model
Written by Gilles Vandewiele in commission of IDLab - INTEC from University Ghent.
""" | 264 | 43.166667 | 111 | py |
GENESIM | GENESIM-master/constructors/ensemble.py | """
Contains wrappers around well-known ensemble techniques: Random Forest and XGBoost.
Written by Gilles Vandewiele in commission of IDLab - INTEC from University Ghent.
"""
import time
from bayes_opt import BayesianOptimization
from sklearn.cross_validation import cross_val_score
from sklearn.ensemble import AdaBoo... | 12,560 | 38.5 | 121 | py |
GENESIM | GENESIM-master/constructors/ISM.py | """
Interpretable Single Model
--------------------------
Merges different decision trees in an ensemble together in a single, interpretable decision tree
Written by Gilles Vandewiele in commission of IDLab - INTEC from University Ghent.
Reference:
Van Assche, Anneleen, and Hendrik Blocke... | 12,135 | 45.676923 | 141 | py |
GENESIM | GENESIM-master/data/load_datasets.py | """Contains data set loading functions. If you want the test script to include a new dataset, a new function must
be written in this module that returns a pandas Dataframe, the feature column names, the label column name and the
dataset name.
Written by Gilles Vandewiele in commission of IDLab - INTEC from University ... | 16,311 | 47.692537 | 143 | py |
GENESIM | GENESIM-master/data/__init__.py | """
Contains the data files and two python files that are responsible for loading them in easily. In `data.load_datasets`,
a load function for each dataset must be written. In `data.load_all_datasets` python introspection is used to easily
load in all datasets with a load function.
Written by Gilles Vandewiele in comm... | 370 | 52 | 118 | py |
GENESIM | GENESIM-master/data/load_all_datasets.py | """
Uses python introspection to call all function in `data.load_datasets`
Written by Gilles Vandewiele in commission of IDLab - INTEC from University Ghent.
"""
import data.load_datasets
from inspect import getmembers, isfunction
def load_all_datasets():
"""
Uses python introspection to call all function i... | 696 | 26.88 | 114 | py |
WiFi-CSI-Sensing-Benchmark | WiFi-CSI-Sensing-Benchmark-main/NTU_Fi_model.py | import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, reduce, repeat
from einops.layers.torch import Rearrange, Reduce
class NTU_Fi_MLP(nn.Module):
def __init__(self, num_classes):
super(NTU_Fi_MLP,self).__init__()
self.fc = nn.Sequentia... | 13,031 | 32.674419 | 128 | py |
WiFi-CSI-Sensing-Benchmark | WiFi-CSI-Sensing-Benchmark-main/dataset.py | import numpy as np
import glob
import scipy.io as sio
import torch
from torch.utils.data import Dataset, DataLoader
def UT_HAR_dataset(root_dir):
data_list = glob.glob(root_dir+'/UT_HAR/data/*.csv')
label_list = glob.glob(root_dir+'/UT_HAR/label/*.csv')
WiFi_data = {}
for data_dir in data_list:
... | 3,086 | 29.564356 | 105 | py |
WiFi-CSI-Sensing-Benchmark | WiFi-CSI-Sensing-Benchmark-main/UT_HAR_model.py | import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, reduce, repeat
from einops.layers.torch import Rearrange, Reduce
class UT_HAR_MLP(nn.Module):
def __init__(self):
super(UT_HAR_MLP,self).__init__()
self.fc = nn.Sequential(
... | 12,505 | 32.52815 | 128 | py |
WiFi-CSI-Sensing-Benchmark | WiFi-CSI-Sensing-Benchmark-main/run.py | import numpy as np
import torch
import torch.nn as nn
import argparse
from util import load_data_n_model
def train(model, tensor_loader, num_epochs, learning_rate, criterion, device):
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr = learning_rate)
for epoch in range(num_epochs... | 3,185 | 33.258065 | 140 | py |
WiFi-CSI-Sensing-Benchmark | WiFi-CSI-Sensing-Benchmark-main/self_supervised_model.py | import torch
import torch.nn as nn
from einops import rearrange, reduce, repeat
from einops.layers.torch import Rearrange, Reduce
import torch.nn.functional as F
class MLP_Parrallel(nn.Module):
def __init__(self):
super(MLP_Parrallel, self).__init__()
self.encoder_1 = MLP_encoder()
self.en... | 20,995 | 30.763994 | 128 | py |
WiFi-CSI-Sensing-Benchmark | WiFi-CSI-Sensing-Benchmark-main/util.py | from dataset import *
from UT_HAR_model import *
from NTU_Fi_model import *
from widar_model import *
from self_supervised_model import *
import torch
def load_data_n_model(dataset_name, model_name, root):
classes = {'UT_HAR_data':7,'NTU-Fi-HumanID':14,'NTU-Fi_HAR':6,'Widar':22}
if dataset_name == 'UT_HAR_data... | 10,985 | 41.416988 | 140 | py |
WiFi-CSI-Sensing-Benchmark | WiFi-CSI-Sensing-Benchmark-main/widar_model.py | import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, reduce, repeat
from einops.layers.torch import Rearrange, Reduce
class Widar_MLP(nn.Module):
def __init__(self, num_classes):
super(Widar_MLP,self).__init__()
self.fc = nn.Sequential(... | 12,936 | 32.866492 | 128 | py |
WiFi-CSI-Sensing-Benchmark | WiFi-CSI-Sensing-Benchmark-main/self_supervised.py | import torch
import torch.optim as optim
import random
import torch.nn as nn
from util import load_unsupervised_data_n_model
import argparse
from torch.autograd import Variable
class EntLoss(nn.Module):
def __init__(self, args, lam1, lam2, pqueue=None):
super(EntLoss, self).__init__()
self.lam1 = ... | 8,839 | 39.365297 | 139 | py |
deficient-efficient | deficient-efficient-master/darts_experiments.py | import json
#settings = ['ACDC_%i'%n for n in [6, 12]] +\
# ['SepHashed_%.2f'%s for s in [0.09, 0.20, 0.38]] +\
settings = ['Generic_%.2f'%s for s in [0.03, 0.06, 0.12]] +\
['Tucker_%.2f'%s for s in [0.24, 0.37, 0.54]] +\
['TensorTrain_%.2f'%s for s in [0.27, 0.41, 0.59]] +\
... | 1,152 | 37.433333 | 96 | py |
deficient-efficient | deficient-efficient-master/main.py | ''''Writing everything into one script..'''
from __future__ import print_function
import os
import imp
import sys
import time
import json
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.nn.functional as F
import torchvision
impo... | 25,508 | 39.426307 | 141 | py |
deficient-efficient | deficient-efficient-master/count.py | '''Count parameters or mult-adds in models.'''
from __future__ import print_function
import math
import torch
import argparse
from torch.autograd import Variable
from models.wide_resnet import WideResNet, WRN_50_2
from models.darts import DARTS
from models.MobileNetV2 import MobileNetV2
from funcs import what_conv_blo... | 12,725 | 38.156923 | 138 | py |
deficient-efficient | deficient-efficient-master/funcs.py | import torch
import torch.nn.functional as F
from models import *
from models.wide_resnet import parse_options
def distillation(y, teacher_scores, labels, T, alpha):
return F.kl_div(F.log_softmax(y/T, dim=1), F.softmax(teacher_scores/T, dim=1)) * (T*T * 2. * alpha)\
+ F.cross_entropy(y, labels) * (1. - ... | 2,477 | 26.533333 | 112 | py |
deficient-efficient | deficient-efficient-master/wrn_experiments.py | import json
#settings = ['ACDC_%i'%n for n in [15, 48, 64]] +\
# ['SepHashed_%.2f'%s for s in [0.05, 0.2, 0.5]] +\
settings = ['Generic_%.2f'%s for s in [0.03, 0.1, 0.24]] +\
['Tucker_%.2f'%s for s in [0.21, 0.41, 0.67]] +\
['TensorTrain_%.2f'%s for s in [0.23, 0.44, 0.7]] +\
... | 1,201 | 39.066667 | 114 | py |
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