repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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KnowledgeFactor | KnowledgeFactor-main/cls/tools/deployment/pytorch2onnx.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from functools import partial
import mmcv
import numpy as np
import onnxruntime as rt
import torch
from mmcv.onnx import register_extra_symbolics
from mmcv.runner import load_checkpoint
from mmcls.models import build_classifier
torch.manual_seed(3)
de... | 7,488 | 32.137168 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/deployment/mmcls_handler.py | # Copyright (c) OpenMMLab. All rights reserved.
import base64
import os
import mmcv
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmcls.apis import inference_model, init_model
class MMclsHandler(BaseHandler):
def initialize(self, context):
properties = context.system_propertie... | 1,650 | 30.75 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/deployment/pytorch2torchscript.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from functools import partial
import mmcv
import numpy as np
import torch
from mmcv.runner import load_checkpoint
from torch import nn
from mmcls.models import build_classifier
torch.manual_seed(3)
def _demo_mm_inputs(i... | 4,363 | 30.171429 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/convert_models/mobilenetv2_to_mmcls.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
import torch
def convert_conv1(model_key, model_weight, state_dict, converted_names):
if model_key.find('features.0.0') >= 0:
new_key = model_key.replace('features.0.0', 'backbone.conv1.conv')
else:
... | 4,732 | 33.801471 | 75 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/convert_models/vgg_to_mmcls.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
from collections import OrderedDict
import torch
def get_layer_maps(layer_num, with_bn):
layer_maps = {'conv': {}, 'bn': {}}
if with_bn:
if layer_num == 11:
layer_idxs = [0, 4, 8, 11, 15, 18, 22, 25]
elif la... | 4,084 | 33.618644 | 75 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/convert_models/publish_model.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import datetime
import os
import subprocess
import torch
from mmcv import digit_version
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input ch... | 1,742 | 30.125 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/convert_models/shufflenetv2_to_mmcls.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
import torch
def convert_conv1(model_key, model_weight, state_dict, converted_names):
if model_key.find('conv1.0') >= 0:
new_key = model_key.replace('conv1.0', 'backbone.conv1.conv')
else:
new_... | 4,137 | 35.298246 | 74 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/analysis_tools/analysis_para.py | import argparse
import torch
from mmcv import Config
from prettytable import PrettyTable
from mmcls.models.builder import build_classifier
def count_parameters(model):
table = PrettyTable(["Modules", "Parameters"])
total_params = 0
for name, parameter in model.named_parameters():
if not parameter... | 953 | 22.268293 | 67 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/apis/inference.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import mmcv
import numpy as np
import torch
from mmcv.parallel import collate, scatter
from mmcv.runner import load_checkpoint
from mmcls.datasets.pipelines import Compose
from mmcls.models import build_classifier
def init_model(config, checkpoint=None... | 3,971 | 35.777778 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/apis/multitask_test.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import pickle
import shutil
import tempfile
import time
import mmcv
import numpy as np
import torch
import torch.distributed as dist
from mmcv.image import tensor2imgs
from mmcv.runner import get_dist_info
def multitask_single_gpu_test(model,
... | 7,703 | 36.398058 | 94 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/apis/test.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import pickle
import shutil
import tempfile
import time
import mmcv
import numpy as np
import torch
import torch.distributed as dist
from mmcv.image import tensor2imgs
from mmcv.runner import get_dist_info
def single_gpu_test(model,
... | 7,129 | 34.829146 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/apis/train.py | # Copyright (c) OpenMMLab. All rights reserved.
import random
import warnings
import numpy as np
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import DistSamplerSeedHook, build_optimizer, build_runner
from mmcls.core import DistOptimizerHook
from mmcls.datasets impo... | 5,723 | 33.481928 | 83 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/evaluation/multilabel_eval_metrics.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import numpy as np
import torch
def average_performance(pred, target, thr=None, k=None):
"""Calculate CP, CR, CF1, OP, OR, OF1, where C stands for per-class
average, O stands for overall average, P stands for precision, R stands for
recall a... | 2,900 | 38.739726 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/evaluation/eval_metrics.py | # Copyright (c) OpenMMLab. All rights reserved.
from numbers import Number
import numpy as np
import torch
def calculate_confusion_matrix(pred, target):
"""Calculate confusion matrix according to the prediction and target.
Args:
pred (torch.Tensor | np.array): The model prediction with shape (N, C).... | 10,811 | 42.421687 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/evaluation/eval_hooks.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
from mmcv.runner import Hook
from torch.utils.data import DataLoader
class EvalHook(Hook):
"""Evaluation hook.
Args:
dataloader (DataLoader): A PyTorch dataloader.
interval (int): Evaluation interval (by epo... | 3,967 | 36.433962 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/evaluation/multitask_eval_hooks.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
from mmcv.runner import Hook
from torch.utils.data import DataLoader
class MultiTaskEvalHook(Hook):
"""Evaluation hook.
Args:
dataloader (DataLoader): A PyTorch dataloader.
interval (int): Evaluation interv... | 4,076 | 36.75 | 86 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/evaluation/mean_ap.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
def average_precision(pred, target):
r"""Calculate the average precision for a single class.
AP summarizes a precision-recall curve as the weighted mean of maximum
precisions obtained for any r'>r, where r is the recall:
... | 2,414 | 31.2 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/fp16/hooks.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import torch
import torch.nn as nn
from mmcv.runner import OptimizerHook
from mmcv.utils.parrots_wrapper import _BatchNorm
from ..utils import allreduce_grads
from .utils import cast_tensor_type
class Fp16OptimizerHook(OptimizerHook):
"""FP16 optimizer... | 4,548 | 33.992308 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/fp16/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
from collections import abc
import numpy as np
import torch
def cast_tensor_type(inputs, src_type, dst_type):
if isinstance(inputs, torch.Tensor):
return inputs.to(dst_type)
elif isinstance(inputs, str):
return inputs
elif isinstance(inputs,... | 712 | 27.52 | 74 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/fp16/decorators.py | # Copyright (c) OpenMMLab. All rights reserved.
import functools
from inspect import getfullargspec
import torch
from .utils import cast_tensor_type
def auto_fp16(apply_to=None, out_fp32=False):
"""Decorator to enable fp16 training automatically.
This decorator is useful when you write custom modules and w... | 6,259 | 37.641975 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/export/test.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import numpy as np
import onnxruntime as ort
import torch
from mmcls.models.classifiers import BaseClassifier
class ONNXRuntimeClassifier(BaseClassifier):
"""Wrapper for classifier's inference with ONNXRuntime."""
def __init__(self, onnx_file,... | 3,439 | 34.463918 | 71 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/utils/kd_hook.py | import torch
from mmcv.parallel import is_module_wrapper
from mmcv.runner import (HOOKS, OPTIMIZER_BUILDERS, OPTIMIZERS,
DefaultOptimizerConstructor, Hook, OptimizerHook)
from mmcv.utils import build_from_cfg
@OPTIMIZER_BUILDERS.register_module()
class KDOptimizerBuilder(DefaultOptimizerConst... | 1,003 | 37.615385 | 74 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/utils/dist_utils.py | # Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
import torch.distributed as dist
from mmcv.runner import OptimizerHook
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
def _allreduce_coalesced(tensors, world_size,... | 1,904 | 31.844828 | 73 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/utils/visualize.py | import os.path as osp
from mmcv.utils import TORCH_VERSION, digit_version
from mmcv.runner.dist_utils import master_only
from mmcv.runner.hooks import HOOKS
from mmcv.runner.hooks.logger.base import LoggerHook
from collections import OrderedDict
import numpy as np
@HOOKS.register_module()
class TensorboardVisLogge... | 3,792 | 34.12037 | 110 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/necks/gap.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import NECKS
@NECKS.register_module()
class GlobalAveragePooling(nn.Module):
"""Global Average Pooling neck.
Note that we use `view` to remove extra channel after pooling. We do not
use `squeeze` as it will... | 1,492 | 31.456522 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/classifiers/base.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
import mmcv
import torch
import torch.distributed as dist
from mmcv.runner import BaseModule
from mmcls.core.visualization import imshow_infos
# TODO import `auto_fp16` from mmc... | 7,775 | 35 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/classifiers/image.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import numpy as np
import warnings
from re import S
import torch.nn as nn
import torch.nn.functional as F
from ..builder import CLASSIFIERS, build_backbone, build_head, build_neck
from ..utils.augment import Augments
from .base import BaseClassifier
@CLASSI... | 5,829 | 37.355263 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/classifiers/kf.py | import copy
import numpy as np
import torch
import torch.nn.functional as F
import warnings
from shutil import ExecError
from torch import nn
from mmcls.models.losses.kd_loss import (InfoMax_loss, InfoMin_loss)
from ..builder import (CLASSIFIERS, build_backbone, build_head, build_loss,
build_nec... | 12,546 | 38.332288 | 113 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/classifiers/kd.py | import copy
import warnings
from shutil import ExecError
import torch
import torch.nn.functional as F
from torch import nn
from ..builder import (CLASSIFIERS, build_backbone, build_head, build_loss,
build_neck)
from ..utils.augment import Augments
from .base import BaseClassifier
@CLASSIFIERS... | 7,148 | 37.643243 | 82 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/embed.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner.base_module import BaseModule
from .helpers import to_2tuple
class PatchEmbed(BaseModule):
"""Image to Patch Embedding.
We use a conv layer to implement... | 9,624 | 36.893701 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/se_layer.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from .make_divisible import make_divisible
class SELayer(BaseModule):
"""Squeeze-and-Excitation Module.
Args:
channels (int): The input (and output) ch... | 2,989 | 38.866667 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/inverted_residual.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from .se_layer import SELayer
# class InvertedResidual(nn.Module):
class InvertedResidual(BaseModule):
"""Inverted Residual Block.
Args:
in_channels ... | 3,688 | 31.078261 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/attention.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn.bricks.transformer import build_dropout
from mmcv.cnn.utils.weight_init import trunc_normal_
from mmcv.runner.base_module import BaseModule
from ..builder import ATTENTION
from .helpers impo... | 11,410 | 38.213058 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/helpers.py | # Copyright (c) OpenMMLab. All rights reserved.
import collections.abc
import warnings
from distutils.version import LooseVersion
from itertools import repeat
import torch
def is_tracing() -> bool:
if LooseVersion(torch.__version__) >= LooseVersion('1.6.0'):
on_trace = torch.jit.is_tracing()
# In... | 1,127 | 25.232558 | 73 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/channel_shuffle.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
def channel_shuffle(x, groups):
"""Channel Shuffle operation.
This function enables cross-group information flow for multiple groups
convolution layers.
Args:
x (Tensor): The input tensor.
groups (int): The number of groups... | 889 | 28.666667 | 74 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/augment/identity.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn.functional as F
from .builder import AUGMENT
@AUGMENT.register_module(name='Identity')
class Identity(object):
"""Change gt_label to one_hot encoding and keep img as the same.
Args:
num_classes (int): The number of classes.
prob... | 857 | 26.677419 | 70 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/augment/cutmix.py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import numpy as np
import torch
import torch.nn.functional as F
from .builder import AUGMENT
class BaseCutMixLayer(object, metaclass=ABCMeta):
"""Base class for CutMixLayer.
Args:
alpha (float): Parameters for B... | 5,453 | 37.680851 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/augment/mixup.py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import numpy as np
import torch
import torch.nn.functional as F
from .builder import AUGMENT
class BaseMixupLayer(object, metaclass=ABCMeta):
"""Base class for MixupLayer.
Args:
alpha (float): Parameters for Bet... | 1,674 | 27.87931 | 76 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/augment/augments.py | # Copyright (c) OpenMMLab. All rights reserved.
import random
import numpy as np
from .builder import build_augment
class Augments(object):
"""Data augments.
We implement some data augmentation methods, such as mixup, cutmix.
Args:
augments_cfg (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict`)... | 2,799 | 36.837838 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/label_smooth_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
import torch.nn as nn
from ..builder import LOSSES
from .cross_entropy_loss import CrossEntropyLoss
from .utils import convert_to_one_hot
@LOSSES.register_module()
class LabelSmoothLoss(nn.Module):
r"""Intializer for the label smoothed... | 6,591 | 38.238095 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/asymmetric_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weight_reduce_loss
def asymmetric_loss(pred,
target,
weight=None,
gamma_pos=1.0,
gamma_neg=4.0,
... | 3,887 | 33.40708 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import torch
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
... | 3,827 | 30.377049 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/accuracy.py | # Copyright (c) OpenMMLab. All rights reserved.
from numbers import Number
import numpy as np
import torch
import torch.nn as nn
def accuracy_numpy(pred, target, topk=1, thrs=0.):
if isinstance(thrs, Number):
thrs = (thrs, )
res_single = True
elif isinstance(thrs, tuple):
res_single =... | 4,342 | 32.152672 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/focal_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
def sigmoid_focal_loss(pred,
target,
weight=None,
gamma=2.0,
... | 4,089 | 34.565217 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/cross_entropy_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
def cross_entropy(pred,
label,
weight=None,
reduction='mean',
avg_factor=Non... | 6,753 | 34.547368 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/kd_loss.py | import re
from numpy import inf
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
@LOSSES.register_module()
class Logits(nn.Module):
'''
Do Deep Nets Really Need to be Deep?
http://papers.nips.cc/paper/5484-do-deep-nets-really-need-to-be-deep.pdf
'''
... | 1,876 | 26.602941 | 96 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/mobilenet_v2.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.utils import make_divisible
from ..builder import BACKBONES
from .base_backbon... | 9,588 | 35.184906 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (ConvModule, build_conv_layer, build_norm_layer,
constant_init)
from mmcv.utils.parrots_wrapper import _BatchNorm
from ..builder import BACKBONES
from .base_backbone impo... | 26,579 | 33.474708 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/tsn.py | from re import S
import torch.nn as nn
import torch
from ..builder import BACKBONES, build_backbone
from .base_backbone import BaseBackbone
import torch.nn.functional as F
@BACKBONES.register_module()
class TSN_backbone(BaseBackbone):
def __init__(self, backbone, in_channels, out_channels):
super().__init... | 860 | 25.90625 | 76 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/disentangle.py | import torch
import torch.nn as nn
from ..builder import BACKBONES
class Flatten3D(nn.Module):
def forward(self, x):
x = x.view(x.size()[0], -1)
return x
@BACKBONES.register_module()
class SimpleConv64(nn.Module):
def __init__(self,
latent_dim=10,
num_chann... | 2,028 | 27.577465 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/resnet_cifar.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .resnet import ResNet
@BACKBONES.register_module()
class ResNet_CIFAR(ResNet):
"""ResNet backbone for CIFAR.
Compared to standard ResNet, it uses... | 3,707 | 44.219512 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/shufflenet_v2.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule, constant_init, normal_init
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.utils import channel_shuffle
from ..b... | 10,408 | 33.92953 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/wideresnet.py | import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (build_conv_layer, build_norm_layer)
from .resnet import ResNet, WideBasicBlock
from ..builder import BACKBONES
@BACKBONES.register_module()
class WideResNet_CIFAR(ResNet):
"""Wide ResNet-50-2 model from
`"Wide Residual Networks"... | 2,163 | 33.349206 | 75 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/heads/cls_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn.functional as F
from mmcls.models.losses import Accuracy
from ..builder import HEADS, build_loss
from ..utils import is_tracing
from .base_head import BaseHead
@HEADS.register_module()
class ClsHead(BaseHead):
"""classification head.
... | 2,636 | 31.9625 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/heads/multi_label_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn.functional as F
from ..builder import HEADS, build_loss
from ..utils import is_tracing
from .base_head import BaseHead
@HEADS.register_module()
class MultiLabelClsHead(BaseHead):
"""Classification head for multilabel task.
Args:
... | 1,887 | 28.046154 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/heads/multitask_linear_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..builder import HEADS
from .cls_head import ClsHead
@HEADS.register_module()
class MultiTaskLinearClsHead(ClsHead):
"""Linear classifier head.
Args:
num_classes (int): Number of categories exc... | 2,253 | 30.746479 | 74 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/heads/linear_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..builder import HEADS
from .cls_head import ClsHead
@HEADS.register_module()
class LinearClsHead(ClsHead):
"""Linear classifier head.
Args:
num_classes (int): Number of categories excluding th... | 3,723 | 30.559322 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/base_dataset.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from abc import ABCMeta, abstractmethod
import mmcv
import numpy as np
from torch.utils.data import Dataset
from mmcls.core.evaluation import precision_recall_f1, support
from mmcls.models.losses import accuracy
from .pipelines import Compose
class BaseDat... | 7,191 | 33.576923 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/dataset_wrappers.py | # Copyright (c) OpenMMLab. All rights reserved.
import bisect
import math
from collections import defaultdict
import numpy as np
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .builder import DATASETS
@DATASETS.register_module()
class ConcatDataset(_ConcatDataset):
"""A wrapper of con... | 6,092 | 34.219653 | 167 | py |
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/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/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/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/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_/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/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/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/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/hf_predictors/vila_predictor.py | # This file rewrites the PDFPredictor classes in
# https://github.com/allenai/VILA/blob/dd242d2fcbc5fdcf05013174acadb2dc896a28c3/src/vila/predictors.py#L1
# to reduce the dependency on the VILA package.
from typing import List, Union, Dict, Any, Tuple
from abc import abstractmethod
from dataclasses import dataclass
im... | 11,250 | 35.060897 | 105 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/mention_predictor.py | import itertools
import os.path
import string
from typing import Dict, Iterator, List, Optional
from optimum.onnxruntime import ORTModelForTokenClassification
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer, BatchEncoding
from mmda.types.annotation import Annotation, SpanGroup
fro... | 9,459 | 40.130435 | 132 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/token_classification_predictor.py | from typing import List, Union, Dict, Any, Tuple, Optional, Sequence
from abc import abstractmethod
from tqdm import tqdm
from vila.predictors import (
SimplePDFPredictor,
LayoutIndicatorPDFPredictor,
HierarchicalPDFPredictor,
)
from mmda.types.metadata import Metadata
from mmda.types.names import Blocks... | 5,137 | 34.191781 | 157 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/span_group_classification_predictor.py | """
@kylel
"""
from typing import List, Any, Tuple, Optional, Sequence
from collections import defaultdict
import numpy as np
import torch
import transformers
from smashed.interfaces.simple import (
TokenizerMapper,
UnpackingMapper,
FixedBatchSizeMapper,
FromTokenizerListCollatorMapper,
Python2... | 14,554 | 39.543175 | 99 | py |
mmda | mmda-main/src/mmda/predictors/hf_predictors/base_hf_predictor.py | from abc import abstractmethod
from typing import Union, List, Dict, Any
from transformers import AutoTokenizer, AutoConfig, AutoModel
from mmda.types.document import Document
from mmda.predictors.base_predictors.base_predictor import BasePredictor
class BaseHFPredictor(BasePredictor):
REQUIRED_BACKENDS = ["tra... | 1,206 | 32.527778 | 72 | py |
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