repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/builder.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import random
from distutils.version import LooseVersion
from functools import partial
import numpy as np
import torch
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import Registry, build_from_cfg
from torch.utils... | 4,471 | 34.776 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/cifar.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path
import pickle
import numpy as np
import torch.distributed as dist
from mmcv.runner import get_dist_info
from mmcls.datasets.disentangle_data.multi_task import MultiTask
from mmcls.datasets.pipelines.compose import Compose
from .base_dataset imp... | 10,938 | 33.507886 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/imagenet.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import numpy as np
from .base_dataset import BaseDataset
from mmcls.datasets.disentangle_data.multi_task import MultiTask
from .builder import DATASETS
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
... | 37,785 | 32.203866 | 146 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/disentangle_data/dsprites.py | # Copyright (c) OpenMMLab. All rights reserved.
import codecs
import numpy as np
import os
import os.path as osp
import torch
import torch.distributed as dist
from mmcv.runner import get_dist_info, master_only
from numpy import random
from mmcls.datasets.builder import DATASETS
from mmcls.datasets.utils import (downlo... | 2,668 | 31.156627 | 121 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/disentangle_data/multi_task.py | import numpy as np
from mmcls.core.evaluation import precision_recall_f1, support
from mmcls.datasets.base_dataset import BaseDataset
from mmcls.datasets.builder import DATASETS
from mmcls.models.losses import accuracy
from tqdm import tqdm
@DATASETS.register_module()
class MultiTask(BaseDataset):
def evaluate(se... | 4,638 | 43.605769 | 85 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/disentangle_data/shape3d.py | # Copyright (c) OpenMMLab. All rights reserved.
import codecs
import os
import os.path as osp
import numpy as np
import torch
import torch.distributed as dist
from mmcv.runner import get_dist_info, master_only
from .multi_task import MultiTask
from mmcls.datasets.builder import DATASETS
from mmcls.datasets.utils impo... | 2,826 | 32.258824 | 86 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/disentangle_data/__init__.py | from .dsprites import dSprites
from .shape3d import Shape3D | 59 | 29 | 30 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/disentangle_data/mpi3d.py | # Copyright (c) OpenMMLab. All rights reserved.
import codecs
import numpy as np
import os
import os.path as osp
import torch
import torch.distributed as dist
from mmcv.runner import get_dist_info, master_only
from numpy import random
from mmcls.datasets.builder import DATASETS
from mmcls.datasets.utils import (downlo... | 3,167 | 32 | 121 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/samplers/distributed_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
class DistributedSampler(_DistributedSampler):
def __init__(self,
dataset,
num_replicas=None,
rank=None,
shuffle=... | 1,433 | 31.590909 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/samplers/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .distributed_sampler import DistributedSampler
__all__ = ['DistributedSampler']
| 134 | 26 | 51 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/pipelines/loading.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import mmcv
import numpy as np
from ..builder import PIPELINES
@PIPELINES.register_module()
class LoadImageFromFile(object):
"""Load an image from file.
Required keys are "img_prefix" and "img_info" (a dict that must contain the
key ... | 2,607 | 35.732394 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/pipelines/compose.py | # Copyright (c) OpenMMLab. All rights reserved.
from collections.abc import Sequence
from mmcv.utils import build_from_cfg
from ..builder import PIPELINES
@PIPELINES.register_module()
class Compose(object):
"""Compose a data pipeline with a sequence of transforms.
Args:
transforms (list[dict | call... | 1,339 | 29.454545 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/pipelines/auto_augment.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import inspect
import random
from numbers import Number
from typing import Sequence
import mmcv
import numpy as np
from ..builder import PIPELINES
from .compose import Compose
# Default hyperparameters for all Ops
_HPARAMS_DEFAULT = dict(pad_val=128)
def ... | 37,110 | 39.338043 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/pipelines/formating.py | # Copyright (c) OpenMMLab. All rights reserved.
from collections.abc import Sequence
import mmcv
import numpy as np
import torch
from mmcv.parallel import DataContainer as DC
from PIL import Image
from ..builder import PIPELINES
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tens... | 5,129 | 27.342541 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/pipelines/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .auto_augment import (AutoAugment, AutoContrast, Brightness,
ColorTransform, Contrast, Cutout, Equalize, Invert,
Posterize, RandAugment, Rotate, Sharpness, Shear,
Solarize, SolarizeAdd, ... | 1,228 | 52.434783 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/pipelines/transforms.py | # Copyright (c) OpenMMLab. All rights reserved.
import inspect
import math
import random
from numbers import Number
from typing import Sequence
import mmcv
import numpy as np
from ..builder import PIPELINES
from .compose import Compose
try:
import albumentations
except ImportError:
albumentations = None
@P... | 41,925 | 38.330206 | 117 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/utils/logger.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
from mmcv.utils import get_logger
def get_root_logger(log_file=None, log_level=logging.INFO):
return get_logger('mmcls', log_file, log_level)
| 212 | 22.666667 | 59 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/utils/collect_env.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.utils import collect_env as collect_base_env
from mmcv.utils import get_git_hash
import mmcls
def collect_env():
"""Collect the information of the running environments."""
env_info = collect_base_env()
env_info['MMClassification'] = mmcls.__versio... | 476 | 25.5 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/utils/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .logger import get_root_logger
__all__ = ['collect_env', 'get_root_logger']
| 167 | 27 | 47 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/multi_task/simplecnn64_1x128_dsprite.py | _base_ = [
'../_base_/datasets/dsprite.py',
'../_base_/default_runtime.py'
]
# optimizer
optimizer = dict(type='Adam', lr=1e-4, betas=(0.9, 0.999), weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='step', step=[10, 15])
runner = dict(type='EpochBasedRunner',... | 722 | 25.777778 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/multi_task/resnet18_vib_1x128_dsprite.py | _base_ = [
'../_base_/models/resnet18_vib_dsprite.py',
'../_base_/datasets/dsprite.py',
'../_base_/schedules/dsprite_bs128.py',
'../_base_/default_runtime.py'
]
checkpoint_config = dict(interval=5) | 214 | 29.714286 | 47 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/multi_task/simplecnn64_simplecnn64_1x128_dsprite.py | _base_ = [
'../_base_/datasets/dsprite.py',
'../_base_/default_runtime.py'
]
# optimizer
optimizer = dict(type='Adam', lr=1e-4, betas=(0.9, 0.999), weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='step', step=[10, 15])
runner = dict(type='EpochBasedRunner',... | 2,356 | 31.736111 | 128 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/multi_task/resnet18_1x128_shape3d.py | _base_ = [
'../_base_/models/resnet18_shape3d.py',
'../_base_/datasets/shape3d.py',
'../_base_/schedules/shape3d_bs128.py',
'../_base_/default_runtime.py'
]
checkpoint_config = dict(interval=5) | 210 | 29.142857 | 44 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/multi_task/resnet18_vib_1x128_shape3d.py | _base_ = [
'../_base_/models/resnet18_vib_shape3d.py',
'../_base_/datasets/shape3d.py',
'../_base_/schedules/shape3d_bs128.py',
'../_base_/default_runtime.py'
]
checkpoint_config = dict(interval=5) | 214 | 29.714286 | 47 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/multi_task/resnet18_1x128_dsprite.py | _base_ = [
'../_base_/models/resnet18_dsprite.py',
'../_base_/datasets/dsprite.py',
'../_base_/schedules/dsprite_bs128.py',
'../_base_/default_runtime.py'
]
checkpoint_config = dict(interval=5) | 210 | 29.142857 | 44 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/multi_task/simplecnn64_1x128_shape3d.py | _base_ = [
'../_base_/datasets/shape3d.py',
'../_base_/default_runtime.py'
]
# optimizer
optimizer = dict(type='Adam', lr=1e-4, betas=(0.9, 0.999), weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='step', step=[3])
runner = dict(type='EpochBasedRunner', max_... | 721 | 24.785714 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/default_runtime.py | # checkpoint saving
checkpoint_config = dict(interval=20)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
work... | 342 | 19.176471 | 44 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/models/mobilenet_v2_1x.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='MobileNetV2', widen_factor=1.0),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1280,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
... | 346 | 25.692308 | 60 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/models/resnet18_shape3d.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=18,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='MultiTaskLinearClsHead',
num_classes=[1... | 430 | 24.352941 | 60 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/models/wide-resnet28-10.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='WideResNet_CIFAR',
depth=28,
stem_channels=16,
base_channels=16 * 10,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
out_channel=640,
... | 568 | 24.863636 | 60 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/models/resnet18_cifar.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=18,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
... | 406 | 22.941176 | 60 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/models/resnet18.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=18,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_... | 423 | 22.555556 | 60 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/models/wide-resnet28-2.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='WideResNet_CIFAR',
depth=28,
stem_channels=16,
base_channels=16 * 2,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
out_channel=128,
... | 567 | 24.818182 | 60 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/models/resnet50.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_... | 424 | 22.611111 | 60 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/models/resnet18_dsprite.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
in_channels=1,
depth=18,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='MultiTaskLinearClsHead',... | 450 | 24.055556 | 60 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/models/shufflenet_v2_1x.py | # model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='ShuffleNetV2', widen_factor=1.0),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1024,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
... | 347 | 25.769231 | 60 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/dsprite_bs128.py | # optimizer
optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='step', step=[10,15])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 242 | 33.714286 | 71 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/shape3d_bs128.py | # optimizer
optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='step', step=[3])
runner = dict(type='EpochBasedRunner', max_epochs=5)
| 237 | 33 | 71 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/imagenet_bs256_coslr_300e.py | # optimizer
optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='CosineAnnealing', min_lr=0)
runner = dict(type='EpochBasedRunner', max_epochs=300)
| 250 | 34.857143 | 71 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/imagenet_bs1024_adamw_swin.py | paramwise_cfg = dict(
norm_decay_mult=0.0,
bias_decay_mult=0.0,
custom_keys={
'.absolute_pos_embed': dict(decay_mult=0.0),
'.relative_position_bias_table': dict(decay_mult=0.0)
})
# for batch in each gpu is 128, 8 gpu
# lr = 5e-4 * 128 * 8 / 512 = 0.001
optimizer = dict(
type='AdamW... | 765 | 23.709677 | 61 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/imagenet_bs2048_coslr.py | # optimizer
optimizer = dict(
type='SGD', lr=0.8, momentum=0.9, weight_decay=0.0001, nesterov=True)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='CosineAnnealing',
min_lr=0,
warmup='linear',
warmup_iters=2500,
warmup_ratio=0.25)
runner = dict(type='EpochBase... | 346 | 25.692308 | 73 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/imagenet_bs1024_coslr.py | # optimizer
optimizer = dict(
type='SGD', lr=0.5, momentum=0.9, weight_decay=0.0001, nesterov=True)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='CosineAnnealing',
min_lr=0,
warmup='linear',
warmup_iters=2500,
warmup_ratio=0.25)
runner = dict(type='EpochBase... | 346 | 25.692308 | 73 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/imagenet_bs256_coslr.py | # optimizer
optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='CosineAnnealing', min_lr=0)
runner = dict(type='EpochBasedRunner', max_epochs=150)
| 250 | 34.857143 | 71 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/cifar10_bs128.py | # optimizer
optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='step', step=[100, 150])
runner = dict(type='EpochBasedRunner', max_epochs=200)
| 246 | 34.285714 | 71 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/imagenet_bs256_epochstep.py | # optimizer
optimizer = dict(type='SGD', lr=0.045, momentum=0.9, weight_decay=0.00004)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='step', gamma=0.98, step=1)
runner = dict(type='EpochBasedRunner', max_epochs=300)
| 252 | 35.142857 | 74 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/imagenet_bs256_coslr_mobilenetv2.py | # optimizer
optimizer = dict(type='SGD', lr=0.05, momentum=0.9, weight_decay=0.00004)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='CosineAnnealing', min_lr=0)
runner = dict(type='EpochBasedRunner', max_epochs=200)
| 252 | 35.142857 | 73 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py | # optimizer
optimizer = dict(
type='SGD',
lr=0.5,
momentum=0.9,
weight_decay=0.00004,
paramwise_cfg=dict(norm_decay_mult=0))
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='poly',
min_lr=0,
by_epoch=False,
warmup='constant',
warmup_iters=5000,
... | 377 | 20 | 54 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/imagenet_bs256.py | # optimizer
optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='step', step=[30, 60, 90])
runner = dict(type='EpochBasedRunner', max_epochs=100)
| 248 | 34.571429 | 71 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/imagenet_bs2048_AdamW.py | # optimizer
# In ClassyVision, the lr is set to 0.003 for bs4096.
# In this implementation(bs2048), lr = 0.003 / 4096 * (32bs * 64gpus) = 0.0015
optimizer = dict(type='AdamW', lr=0.0015, weight_decay=0.3)
optimizer_config = dict(grad_clip=dict(max_norm=1.0))
# specific to vit pretrain
paramwise_cfg = dict(
custom_... | 642 | 29.619048 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/imagenet_bs256_140e.py | # optimizer
optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(policy='step', step=[40, 80, 120])
runner = dict(type='EpochBasedRunner', max_epochs=140)
| 249 | 34.714286 | 71 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/imagenet_bs4096_AdamW.py | # optimizer
optimizer = dict(type='AdamW', lr=0.003, weight_decay=0.3)
optimizer_config = dict(grad_clip=dict(max_norm=1.0))
# specific to vit pretrain
paramwise_cfg = dict(
custom_keys={
'.backbone.cls_token': dict(decay_mult=0.0),
'.backbone.pos_embed': dict(decay_mult=0.0)
})
# learning poli... | 508 | 25.789474 | 58 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/schedules/imagenet_bs2048.py | # optimizer
optimizer = dict(
type='SGD', lr=0.8, momentum=0.9, weight_decay=0.0001, nesterov=True)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=2500,
warmup_ratio=0.25,
step=[30, 60, 90])
runner = dict(type='EpochBasedR... | 344 | 25.538462 | 73 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/datasets/imagenet_bs256_randaug.py | # dataset settings
_base_ = ['./pipelines/rand_aug.py']
dataset_type = 'ImageNet'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', size=224),
dict(type='RandomFlip', flip_prob=... | 1,703 | 31.769231 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/datasets/imagenet_bs64.py | # dataset settings
dataset_type = 'ImageNet'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', size=224),
dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
dic... | 1,389 | 33.75 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/datasets/cifar10_bs128_2task.py | # dataset settings
dataset_type = 'CIFAR10_2Task'
img_norm_cfg = dict(
mean=[125.307, 122.961, 113.8575],
std=[51.5865, 50.847, 51.255],
to_rgb=False)
train_pipeline = [
dict(type='RandomCrop', size=32, padding=4),
dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
dict(type='Normal... | 1,072 | 28.805556 | 67 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/datasets/cifar10_bs128.py | # dataset settings
_base_ = ['./pipelines/rand_aug_cifar.py']
dataset_type = 'CIFAR10'
img_norm_cfg = dict(
mean=[125.307, 122.961, 113.8575],
std=[51.5865, 50.847, 51.255],
to_rgb=False)
train_pipeline = [
dict(type='RandomCrop', size=32, padding=4),
dict(type='RandomFlip', flip_prob=0.5, direct... | 1,710 | 28 | 67 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/datasets/imagenet_bs64_randaug.py | # dataset settings
_base_ = ['./pipelines/rand_aug.py']
dataset_type = 'ImageNet'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', size=224),
dict(type='RandomFlip', flip_prob=... | 1,702 | 31.75 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/datasets/dsprite.py | # dataset settings
dataset_type = 'dSprites'
multi_task = True
img_norm_cfg = dict(
mean=[0.5],
std=[0.5],
to_rgb=False)
train_pipeline = [
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='ToTensor', keys=['gt_label']),
dict(type='Collect', keys=['... | 925 | 25.457143 | 54 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/datasets/imagenet_bs32_randaug.py | # dataset settings
_base_ = ['./pipelines/rand_aug.py']
dataset_type = 'ImageNet'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', size=224),
dict(type='RandomFlip', flip_prob=... | 1,702 | 31.75 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/datasets/imagenet_bs256.py | # dataset settings
dataset_type = 'ImageNet'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', size=224),
dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
dic... | 1,390 | 33.775 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/datasets/shape3d.py | # dataset settings
dataset_type = 'Shape3D'
multi_task = True
img_norm_cfg = dict(
mean=[127.0, 127.0, 127.0],
std=[127.0, 127.0, 127.0],
to_rgb=False)
train_pipeline = [
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='ToTensor', keys=['gt_label']),
... | 956 | 26.342857 | 54 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/datasets/pipelines/rand_aug.py | # Refers to `_RAND_INCREASING_TRANSFORMS` in pytorch-image-models
rand_increasing_policies = [
dict(type='AutoContrast'),
dict(type='Equalize'),
dict(type='Invert'),
dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, ... | 1,429 | 32.255814 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/_base_/datasets/pipelines/rand_aug_cifar.py | img_norm_cfg = dict(
mean=[125.307, 122.961, 113.8575],
std=[51.5865, 50.847, 51.255],
to_rgb=False)
rand_increasing_policies = [
dict(type='AutoContrast'),
dict(type='Brightness', magnitude_key='magnitude',
magnitude_range=(0.05, 0.95)),
dict(type='ColorTransform', magnitude_key='magni... | 1,863 | 36.28 | 73 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/cifar10-kf/wideresnet28-2_mobilenetv2_b128x1_cifar10_softtar_kf.py | _base_ = [
'../_base_/datasets/cifar10_bs128.py'
]
# 93.61
# checkpoint saving
checkpoint_config = dict(interval=10)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl... | 3,043 | 26.423423 | 65 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/cifar10-kf/wideresnet28-2_wideresnet28-2_b128x1_cifar10_softtar_kf.py | _base_ = [
'../_base_/datasets/cifar10_bs128.py'
]
# 93.58
# checkpoint saving
checkpoint_config = dict(interval=10)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl... | 3,275 | 26.529412 | 65 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/cifar10-kf/wideresnet28-2_resnet18_b128x1_cifar10_softtar_kf.py | _base_ = [
'../_base_/datasets/cifar10_bs128.py'
]
# checkpoint saving
checkpoint_config = dict(interval=10)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_l... | 3,091 | 26.607143 | 65 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/imagenet-kd/resnet50_resnet18_b32x8_imagenet_softtar_kd.py | _base_ = [
'../_base_/datasets/imagenet_bs32_randaug.py',
'../_base_/schedules/imagenet_bs256_coslr.py'
]
# checkpoint saving
checkpoint_config = dict(interval=10)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
... | 1,872 | 25.380282 | 64 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/imagenet-kd/resnet18_resnet18_b32x8_imagenet_softtar_kd.py | _base_ = [
'../_base_/datasets/imagenet_bs32_randaug.py',
'../_base_/schedules/imagenet_bs256_coslr.py'
]
# checkpoint saving
checkpoint_config = dict(interval=10)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
... | 1,871 | 25.366197 | 64 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/cifar10-kd/wideresnet28-2_resnet18_b128x1_cifar10.py | _base_ = [
'../_base_/datasets/cifar10_bs128.py'
]
# checkpoint saving
checkpoint_config = dict(interval=10)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_l... | 1,992 | 24.883117 | 76 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/cifar10-kd/wideresnet28-2_wideresnet28-2_b128x1_cifar10.py | _base_ = [
'../_base_/datasets/cifar10_bs128.py'
]
# checkpoint saving
checkpoint_config = dict(interval=10)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_l... | 2,153 | 25.268293 | 76 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/cifar10-kd/wideresnet28-10_wideresnet28-2_b128x1_cifar10.py | _base_ = [
'../_base_/datasets/cifar10_bs128.py'
]
# checkpoint saving
checkpoint_config = dict(interval=10)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_l... | 2,184 | 25.325301 | 76 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/cifar10-kd/wideresnet28-10_mobilenetv2_b128x1_cifar10.py | _base_ = [
'../_base_/datasets/cifar10_bs128.py'
]
# checkpoint saving
checkpoint_config = dict(interval=10)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_l... | 1,987 | 25.506667 | 76 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/cifar10-kd/wideresnet28-2_mobilenetv2_b128x1_cifar10.py | _base_ = [
'../_base_/datasets/cifar10_bs128.py'
]
# checkpoint saving
checkpoint_config = dict(interval=10)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_l... | 1,986 | 25.493333 | 76 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/cifar10-kd/wideresnet28-10_resnet18_b128x1_cifar10.py | _base_ = [
'../_base_/datasets/cifar10_bs128.py'
]
# checkpoint saving
checkpoint_config = dict(interval=10)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_l... | 2,023 | 24.948718 | 76 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/imagenet-kf/resnet18_resnet18_b32x8_imagenet_softtar_kf.py | _base_ = [
'../_base_/datasets/imagenet_bs32_randaug.py',
'../_base_/schedules/imagenet_bs256_coslr.py'
]
# checkpoint saving
checkpoint_config = dict(interval=10)
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
... | 2,876 | 27.205882 | 103 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/imagenet-kf/resnet18_mbnv2_b32x8_imagenet_softtar_kf.py | _base_ = [
'../_base_/datasets/imagenet_bs32_randaug.py',
'../_base_/schedules/imagenet_bs256_coslr_mobilenetv2.py'
]
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend=... | 2,802 | 27.896907 | 136 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/imagenet-kf/resnet18_mbnv2_b32x8_imagenet_softtar_kf_tmp.py | _base_ = [
'../_base_/datasets/imagenet_bs32_randaug.py',
'../_base_/schedules/imagenet_bs256_coslr_mobilenetv2.py'
]
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend=... | 2,802 | 27.896907 | 136 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/configs/imagenet-kf/resnet50_resnet18_b32x8_imagenet_softtar_kf.py | _base_ = [
'../_base_/datasets/imagenet_bs32_randaug.py',
'../_base_/schedules/imagenet_bs256_coslr.py'
]
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log... | 2,796 | 26.693069 | 64 | py |
Detecting-Cyberbullying-Across-SMPs | Detecting-Cyberbullying-Across-SMPs-master/models.py | import tflearn
import numpy as np
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_1d, global_max_pool
from tflearn.layers.merge_ops import merge
from tflearn.layers.estimator import regression
imp... | 5,025 | 39.208 | 115 | py |
mmda | mmda-main/setup.py | from setuptools import setup
setup()
| 38 | 8.75 | 28 | py |
mmda | mmda-main/examples/title_abstract.py | import pathlib
import sys
from mmda.parsers.pdfplumber_parser import PDFPlumberParser
from mmda.predictors.heuristic_predictors.dictionary_word_predictor import DictionaryWordPredictor
from mmda.predictors.lp_predictors import LayoutParserPredictor
from mmda.predictors.hf_predictors.vila_predictor import IVILAPredicto... | 1,510 | 31.847826 | 98 | py |
mmda | mmda-main/examples/section_nesting_prediction/main.py | """
Tests for SectionNestingPredictor
@rauthur
"""
import pathlib
import unittest
from copy import deepcopy
from mmda.parsers.pdfplumber_parser import PDFPlumberParser
from mmda.predictors.hf_predictors.vila_predictor import IVILAPredictor
from mmda.predictors.lp_predictors import LayoutParserPredictor
from mmda.pre... | 2,720 | 28.576087 | 79 | py |
mmda | mmda-main/examples/vlue_evaluation/main.py | """Compare VILA predictors to other models on VLUE."""
import argparse
import csv
import os
from collections import defaultdict
from dataclasses import dataclass
from statistics import mean, stdev
from typing import Callable, Dict, List
from mmda.eval.vlue import (LabeledDoc, PredictedDoc, grobid_prediction,
... | 12,105 | 34.19186 | 86 | py |
mmda | mmda-main/examples/bibliography_extraction/main.py | from collections import defaultdict
from dataclasses import dataclass
from typing import Iterable, List, Optional
from mmda.eval.metrics import box_overlap
from mmda.parsers.pdfplumber_parser import PDFPlumberParser
from mmda.predictors.heuristic_predictors.grobid_citation_predictor import (
get_title,
)
from mmda... | 4,148 | 27.417808 | 86 | py |
mmda | mmda-main/examples/vila_for_scidoc_parsing/main.py | import argparse
import contextlib
import json
import os
import re
import urllib.request
from tempfile import NamedTemporaryFile
from typing import Dict, Generator, List, Optional
from layoutparser.elements import Layout, Rectangle, TextBlock
from layoutparser.visualization import draw_box
from PIL.Image import Image
f... | 5,773 | 28.309645 | 83 | py |
mmda | mmda-main/src/mmda/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/mmda/eval/vlue.py | import json
import random
import string
from dataclasses import dataclass
from typing import Protocol
from mmda.eval import s2
from mmda.eval.metrics import levenshtein
from mmda.parsers.grobid_parser import GrobidHeaderParser
@dataclass(frozen=True)
class LabeledDoc:
id: str
title: str
abstract: str
... | 2,577 | 24.524752 | 81 | py |
mmda | mmda-main/src/mmda/eval/s2.py | from dataclasses import dataclass
from typing import Optional
import requests
_API_FIELDS = ["title", "abstract", "url"]
_API_URL = "https://api.semanticscholar.org/graph/v1/paper/{}?fields={}"
@dataclass
class PaperMetadata:
id: str
url: str
title: str
abstract: Optional[str]
def get_paper_metada... | 743 | 21.545455 | 75 | py |
mmda | mmda-main/src/mmda/eval/metrics.py | from mmda.types.box import Box
def levenshtein(
s1: str,
s2: str,
case_sensitive: bool = True,
strip_spaces: bool = False,
normalize: bool = False,
) -> int:
"""See https://en.wikipedia.org/wiki/Levenshtein_distance.
Args:
s1 (str): String 1 for comparison
s2 (str): String ... | 2,829 | 24.044248 | 82 | py |
mmda | mmda-main/src/mmda/eval/__init__.py | 0 | 0 | 0 | py | |
mmda | mmda-main/src/mmda/rasterizers/rasterizer.py | from typing import Iterable, Protocol
from mmda.types.image import PILImage
try:
import pdf2image
except ImportError:
pass
class Rasterizer(Protocol):
def rasterize(self, input_pdf_path: str, dpi: int, **kwargs) -> Iterable[PILImage]:
"""Given an input PDF return a List[Image]
Args:
... | 978 | 31.633333 | 95 | py |
mmda | mmda-main/src/mmda/rasterizers/__init__.py | from mmda.rasterizers.rasterizer import PDF2ImageRasterizer
__all__ = [
'PDF2ImageRasterizer'
] | 100 | 19.2 | 59 | py |
mmda | mmda-main/src/mmda/predictors/lp_predictors.py | from typing import Union, List, Dict, Any, Optional
from tqdm import tqdm
import layoutparser as lp
from mmda.types import Document, Box, BoxGroup, Metadata
from mmda.types.names import ImagesField, PagesField
from mmda.predictors.base_predictors.base_predictor import BasePredictor
class LayoutParserPredictor(BaseP... | 3,464 | 31.083333 | 77 | py |
mmda | mmda-main/src/mmda/predictors/tesseract_predictors.py | import csv
import io
import itertools
from dataclasses import dataclass
from typing import Dict, Iterable, Tuple
import pytesseract
from mmda.predictors.base_predictors.base_predictor import BasePredictor
from mmda.types.annotation import BoxGroup
from mmda.types.box import Box
from mmda.types.document import Document... | 3,199 | 29.47619 | 85 | py |
mmda | mmda-main/src/mmda/predictors/__init__.py | # flake8: noqa
from necessary import necessary
with necessary(["tokenizers"], soft=True) as TOKENIZERS_AVAILABLE:
if TOKENIZERS_AVAILABLE:
from mmda.predictors.heuristic_predictors.whitespace_predictor import WhitespacePredictor
from mmda.predictors.heuristic_predictors.dictionary_word_predictor im... | 934 | 41.5 | 106 | py |
mmda | mmda-main/src/mmda/predictors/sklearn_predictors/svm_word_predictor.py | """
SVM Word Predictor
Given a list of tokens, predict which tokens were originally part of the same word.
This does this in two phases: First, it uses a whitespace tokenizer to inform
whether tokens were originally part of the same word. Second, it uses a SVM
classifier to predict whether hyphenated segments should ... | 23,546 | 38.376254 | 148 | py |
mmda | mmda-main/src/mmda/predictors/sklearn_predictors/base_sklearn_predictor.py | from abc import abstractmethod
from typing import Any, Dict, List, Union
from mmda.predictors.base_predictors.base_predictor import BasePredictor
from mmda.types.document import Document
class BaseSklearnPredictor(BasePredictor):
REQUIRED_BACKENDS = ["sklearn", "numpy", "scipy", "tokenizers"]
@classmethod
... | 392 | 27.071429 | 72 | py |
mmda | mmda-main/src/mmda/predictors/xgb_predictors/citation_link_predictor.py | from scipy.stats import rankdata
import numpy as np
import os
import pandas as pd
from typing import List, Dict, Tuple
import xgboost as xgb
from mmda.types.document import Document
from mmda.featurizers.citation_link_featurizers import CitationLink, featurize
class CitationLinkPredictor:
def __init__(self, artif... | 1,620 | 34.23913 | 85 | py |
mmda | mmda-main/src/mmda/predictors/xgb_predictors/section_nesting_predictor.py | """
SectionNestingPredictor -- Use token-level predictions for "Section" to predict the
parent-child relationships between sections.
Adapted from https://github.com/rauthur/section-annotations-gold
@rauthur
"""
import json
import logging
import re
from collections import OrderedDict
from copy import deepcopy
f... | 13,683 | 26.813008 | 117 | py |
mmda | mmda-main/src/mmda/predictors/xgb_predictors/__init__.py | 0 | 0 | 0 | py |
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