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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DSLA-DSLA | DSLA-DSLA/mmdet/models/roi_heads/mask_heads/feature_relay_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule, auto_fp16
from mmdet.models.builder import HEADS
@HEADS.register_module()
class FeatureRelayHead(BaseModule):
"""Feature Relay Head used in `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args:
in_... | 1,930 | 34.759259 | 78 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/roi_heads/mask_heads/global_context_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16, force_fp32
from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
@HEADS.register_module()
class GlobalContextHead(BaseMod... | 3,774 | 36.009804 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py | # Copyright (c) OpenMMLab. All rights reserved.
from warnings import warn
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, build_conv_layer, build_upsample_layer
from mmcv.ops.carafe import CARAFEPack
from mmcv.runner import BaseModule, ModuleList, ... | 17,449 | 41.251816 | 85 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16, force_fp32
from mmdet.models.builder import HEADS, build_loss
@HEADS.register_module()
class FusedSemanticHead(BaseModu... | 4,150 | 34.177966 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/roi_heads/mask_heads/mask_point_head.py | # Copyright (c) OpenMMLab. All rights reserved.
# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend/point_head/point_head.py # noqa
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
from mmcv.r... | 13,455 | 42.830619 | 126 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/ghm_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)... | 7,923 | 36.028037 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/mse_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 weighted_loss
@weighted_loss
def mse_loss(pred, target):
"""Warpper of mse loss."""
return F.mse_loss(pred, target, reduction='none')
@LOSSES.register_module... | 1,905 | 31.862069 | 78 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/dice_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 dice_loss(pred,
target,
weight=None,
eps=1e-3,
reduction='mean',
avg_factor=None):
"""Cal... | 4,340 | 34.008065 | 78 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/pisa_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.core import bbox_overlaps
@mmcv.jit(derivate=True, coderize=True)
def isr_p(cls_score,
bbox_pred,
bbox_targets,
rois,
sampling_results,
loss_cls,
bbox_coder,
k=2,
... | 7,216 | 38.010811 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/balanced_l1_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def balanced_l1_loss(pred,
target,
beta=1.0,... | 4,252 | 33.024 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/iou_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import warnings
import mmcv
import torch
import torch.nn as nn
from mmdet.core import bbox_overlaps
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def iou_loss(pred, target, linear=False... | 15,714 | 32.084211 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/smooth_l1_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Tensor)... | 4,635 | 30.537415 | 78 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/gfocal_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def quality_focal_loss(pred, target, beta=2.0):
r"""Quality Focal Loss (QFL) is fr... | 9,834 | 38.979675 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/varifocal_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
@mmcv.jit(derivate=True, coderize=True)
def varifocal_loss(pred,
target,
weight=None,
... | 5,365 | 38.748148 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import mmcv
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:
... | 3,103 | 29.431373 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/seesaw_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .accuracy import accuracy
from .cross_entropy_loss import cross_entropy
from .utils import weight_reduce_loss
def seesaw_ce_loss(cls_score,
labels,
... | 10,136 | 37.543726 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/ae_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
@mmcv.jit(derivate=True, coderize=True)
def ae_loss_per_image(tl_preds, br_preds, match):
"""Associative Embedding Loss in one image.
Associative Embedd... | 3,857 | 36.096154 | 143 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/accuracy.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
@mmcv.jit(coderize=True)
def accuracy(pred, target, topk=1, thresh=None):
"""Calculate accuracy according to the prediction and target.
Args:
pred (torch.Tensor): The model prediction, shape (N, num_class)
targe... | 2,990 | 36.3875 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/focal_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss
from ..builder import LOSSES
from .utils import weight_reduce_loss
# This method is only for debugging
def py_sigmoid_focal_loss(pred,
... | 10,420 | 41.534694 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/cross_entropy_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
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',
a... | 9,696 | 37.480159 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/gaussian_focal_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0):
"""`Focal Loss <https://arxiv.org/abs/1708.0... | 3,312 | 34.623656 | 108 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/losses/kd_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def knowledge_distillation_kl_div_loss(pred,
so... | 2,912 | 31.730337 | 78 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/pvt.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import warnings
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import (Conv2d, build_activation_layer, build_norm_layer,
constant_init, normal_init, trunc_normal_init)
from mmcv.cnn.br... | 23,217 | 38.219595 | 89 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/hrnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import BaseModule, ModuleList, Sequential
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from .resnet import BasicBlock, Bot... | 23,106 | 38.164407 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/regnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import numpy as np
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .resnet import ResNet
from .resnext import Bottleneck
@BACKBONES.register_module()
class RegNet(ResNet):
"""RegNet... | 13,605 | 37.112045 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/mobilenet_v2.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from ..utils import InvertedResidual, make_divisible
@BACKBONES.register_module()... | 7,599 | 37.383838 | 78 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/swin.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from collections import OrderedDict
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_norm_layer, constant_init, trunc_normal_init
from mmcv.cnn.bric... | 30,138 | 38.448953 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/trident_resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import BaseModule
from torch.nn.modules.utils import _pair
from mmdet.models.backbones.resnet i... | 11,129 | 36.22408 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/detectors_resnext.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .detectors_resnet import Bottleneck as _Bottleneck
from .detectors_resnet import DetectoRS_ResNet
class Bottleneck(_Bottleneck):
expansion = 4
def __init_... | 3,920 | 30.620968 | 77 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer, build_plugin_layer
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
fro... | 23,838 | 34.421991 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/detectors_resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
kaiming_init)
from mmcv.runner import Sequential, load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
fr... | 12,736 | 34.980226 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/ssd_vgg.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmcv.cnn import VGG
from mmcv.runner import BaseModule
from ..builder import BACKBONES
from ..necks import ssd_neck
@BACKBONES.register_module()
class SSDVGG(VGG, BaseModule):
"""VGG Backbone network for single-shot-detec... | 4,705 | 35.48062 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/resnext.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottleneck(_Bottleneck):
expansion = 4
def __init__... | 5,712 | 35.858065 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/resnest.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import BaseModule
from ..builder import BACKBONES
from ..utils import ResLayer
fro... | 10,579 | 31.755418 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/csp_darknet.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from ..utils import CSPLayer
class Focus(n... | 10,543 | 35.996491 | 77 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/hourglass.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import BasicBlock
class HourglassModule(BaseModule):
"""Hourglass Modu... | 7,494 | 32.609865 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/res2net.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import Sequential
from ..builder import BACKBONES
from .resnet import Bottleneck as _Bottleneck
from .resnet impor... | 11,659 | 34.54878 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/models/backbones/darknet.py | # Copyright (c) OpenMMLab. All rights reserved.
# Copyright (c) 2019 Western Digital Corporation or its affiliates.
import warnings
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
class ResBlo... | 8,233 | 37.476636 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/datasets/custom.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
from collections import OrderedDict
import mmcv
import numpy as np
from mmcv.utils import print_log
from terminaltables import AsciiTable
from torch.utils.data import Dataset
from mmdet.core import eval_map, eval_recalls
from .build... | 14,679 | 36.641026 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/datasets/dataset_wrappers.py | # Copyright (c) OpenMMLab. All rights reserved.
import bisect
import collections
import copy
import math
from collections import defaultdict
import numpy as np
from mmcv.utils import build_from_cfg, print_log
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .builder import DATASETS, PIPELINES... | 16,052 | 36.683099 | 167 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/datasets/builder.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import platform
import random
import warnings
from functools import partial
import numpy as np
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import TORCH_VERSION, Registry, build_from_cfg, digit_version
from torch.uti... | 7,707 | 37.54 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/datasets/samplers/group_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import numpy as np
import torch
from mmcv.runner import get_dist_info
from torch.utils.data import Sampler
class GroupSampler(Sampler):
def __init__(self, dataset, samples_per_gpu=1):
assert hasattr(dataset, 'flag')
self.dataset = datas... | 5,384 | 35.14094 | 78 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/datasets/samplers/infinite_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import itertools
import numpy as np
import torch
from mmcv.runner import get_dist_info
from torch.utils.data.sampler import Sampler
class InfiniteGroupBatchSampler(Sampler):
"""Similar to `BatchSampler` warping a `GroupSampler. It is designed for
iteration-base... | 6,267 | 35.231214 | 110 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/datasets/samplers/distributed_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
class DistributedSampler(_DistributedSampler):
def __init__(self,
dataset,
num_replicas=None,
rank=None,
... | 1,358 | 32.146341 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/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 ..builder import PIPELINES
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tensor`.
Supported ty... | 13,291 | 32.821883 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/utils/contextmanagers.py | # Copyright (c) OpenMMLab. All rights reserved.
import asyncio
import contextlib
import logging
import os
import time
from typing import List
import torch
logger = logging.getLogger(__name__)
DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False))
@contextlib.asynccontextmanager
async def comple... | 4,125 | 32.544715 | 79 | py |
DSLA-DSLA | DSLA-DSLA/mmdet/utils/profiling.py | # Copyright (c) OpenMMLab. All rights reserved.
import contextlib
import sys
import time
import torch
if sys.version_info >= (3, 7):
@contextlib.contextmanager
def profile_time(trace_name,
name,
enabled=True,
stream=None,
end... | 1,336 | 31.609756 | 73 | py |
BS-Net | BS-Net-main/loaddata.py | import pandas as pd
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from PIL import Image
import random
from nyu_transform import *
import pdb
from scipy import io
class depthDataset(Dataset):
"""Face Landmarks dataset."""
def __init__(self, csv_fil... | 6,915 | 42.772152 | 115 | py |
BS-Net | BS-Net-main/sobel.py | import torch
import torch.nn as nn
import numpy as np
print(19//5)
class Sobel(nn.Module):
def __init__(self):
super(Sobel, self).__init__()
self.edge_conv=nn.Conv2d(1, 2, kernel_size=3, stride=1, padding=1, bias=False)
# edge_kx = np.array([[1, 0, -1], [2, 0, -2], [1, 0, -1]])
edge_... | 815 | 29.222222 | 86 | py |
BS-Net | BS-Net-main/test_iBims1.py | import warnings
warnings.filterwarnings("ignore")
import torch
import numpy as np
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import loaddata
import sobel
import os
import argparse
from models import modules as modules, net as net, dilation_resnet as resnet
from util import comput... | 11,843 | 41 | 140 | py |
BS-Net | BS-Net-main/util.py | import torch
from PIL import Image,ImageDraw,ImageFont
import matplotlib.pyplot as plt
import torch.nn as nn
import numpy as np
from skimage import feature
from scipy import ndimage
from sklearn.decomposition import PCA
import math
cmap = plt.cm.viridis
def lg10(x):
return torch.div(torch.log(x), math.log(10))
de... | 14,525 | 34.257282 | 120 | py |
BS-Net | BS-Net-main/nyu_transform.py | import torch
import numpy as np
from PIL import Image
import collections
try:
import accimage
except ImportError:
accimage = None
import random
import scipy.ndimage as ndimage
def _is_pil_image(img):
if accimage is not None:
return isinstance(img, (Image.Image, accimage.Image))
else:
... | 23,434 | 38.058333 | 137 | py |
BS-Net | BS-Net-main/metrics.py | import torch
import math
import numpy as np
def log10(x):
"""Convert a new tensor with the base-10 logarithm of the elements of x. """
return torch.log(x) / math.log(10)
class Result(object):
def __init__(self):
self.irmse, self.imae = 0, 0
self.mse, self.rmse, self.mae = 0, 0, 0
... | 4,037 | 37.09434 | 109 | py |
BS-Net | BS-Net-main/train.py | # -*- coding: UTF-8 -*-
import warnings
warnings.filterwarnings("ignore")
import argparse
import time
import os
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import loaddata
import random
import numpy as np
import util
from models import modules as m... | 6,175 | 35.544379 | 93 | py |
BS-Net | BS-Net-main/test_NYUDv2.py | import warnings
warnings.filterwarnings("ignore")
import time
import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import loaddata
import numpy as np
from metrics import AverageMeter, Result
from models import modules as modules, net as net, dilation_resnet as resnet
import torc... | 6,987 | 35.395833 | 133 | py |
BS-Net | BS-Net-main/models/dilation_resnet.py | """Dilated ResNet"""
import math
import torch
import torch.utils.model_zoo as model_zoo
import torch.nn as nn
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'BasicBlock', 'Bottleneck']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.... | 11,689 | 37.837209 | 162 | py |
BS-Net | BS-Net-main/models/modules.py | import torch
import torch.nn.functional as F
import torch.nn as nn
class _UpProjection(nn.Sequential):
def __init__(self, num_input_features, num_output_features):
super(_UpProjection, self).__init__()
self.conv1 = nn.Conv2d(num_input_features, num_output_features,
k... | 10,669 | 38.227941 | 125 | py |
BS-Net | BS-Net-main/models/net.py | import torch.nn as nn
import models.modules as modules
class model(nn.Module):
def __init__(self, Encoder, num_features, block_channel):
super(model, self).__init__()
self.E = Encoder #(2048,8,10)
self.DCE = modules.DCE(num_features,num_features//2, sizes=(1, 2, 3, 6))
self.BUBF = ... | 763 | 35.380952 | 80 | py |
correlate | correlate-master/setup.py | """
Install tigramite
"""
from __future__ import print_function
import pathlib
import os
from setuptools import setup, Extension
from setuptools.command.build_ext import build_ext
# Handle building against numpy headers before installing numpy
class UseNumpyHeadersBuildExt(build_ext):
"""
Subclassed build_ex... | 4,316 | 34.677686 | 114 | py |
correlate | correlate-master/prediction/fully_connected.py | import math
import numpy as np
import torch
import torch.utils.data as data_utils
from sklearn.preprocessing import MinMaxScaler
from torch import nn
from torch.utils.tensorboard import SummaryWriter
from config import target_label, fully_connected_nn_prediction_on
writer = SummaryWriter()
epochs = 4115
lr = 0.0001... | 4,817 | 32.227586 | 108 | py |
DeepOnto | DeepOnto-main/src/deeponto/subs/bertsubs/pipeline_inter.py | # Copyright 2023 Jiaoyan Chen. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or ag... | 16,303 | 50.432177 | 152 | py |
DeepOnto | DeepOnto-main/src/deeponto/subs/bertsubs/pipeline_intra.py | # Copyright 2023 Jiaoyan Chen. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or ag... | 17,435 | 44.76378 | 120 | py |
DeepOnto | DeepOnto-main/src/deeponto/utils/logging.py | # Copyright 2021 Yuan He. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed ... | 2,609 | 34.27027 | 119 | py |
DeepOnto | DeepOnto-main/src/deeponto/align/bertmap/mapping_prediction.py | # Copyright 2021 Yuan He. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed ... | 15,548 | 49.980328 | 161 | py |
DeepOnto | DeepOnto-main/src/deeponto/align/bertmap/bert_classifier.py | # Copyright 2021 Yuan He. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed ... | 10,098 | 44.084821 | 150 | py |
ACE | ACE-main/example.py | import torch
import torch.nn.functional as F
import timm
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from ace import attack_confidence_estimation
def attack_example(file_name, true_label, transform, normalization):
image = Image.open(f'.... | 1,864 | 57.28125 | 149 | py |
ACE | ACE-main/ace.py | import torch
def softmax_response(logits):
return torch.nn.functional.softmax(logits, dim=1)
def attack_confidence_estimation(model, input, label, normalization, proxy=None, epsilon=0.005, epsilon_decay=0.5, max_iterations=15, confidence_score_function=softmax_response, device='cuda'):
input = input.to(devic... | 2,151 | 42.04 | 193 | py |
Fengshenbang-LM | Fengshenbang-LM-main/setup.py | from setuptools import setup, find_packages
setup(
name="fengshen",
version="0.0.1",
description="fengshen",
long_description="fengshen",
license="MIT Licence",
url="https://idea.edu.cn",
author="gaoxinyu",
author_email="gaoxinyu@idea.edu.cn",
packages=find_packages(),
include_... | 733 | 21.9375 | 69 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/strategies/megatron_deepspeed.py | # Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | 18,750 | 45.8775 | 120 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/pretrain_t5/pretrain_t5.py | import time
from builtins import print
import sys
import os
import torch
import argparse
import json
import pytorch_lightning as pl
from transformers import MT5Config, MT5Tokenizer
from pytorch_lightning import Trainer, loggers
from transformers import MT5ForConditionalGeneration
from pytorch_lightning.callbacks import... | 8,139 | 45.25 | 110 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/pretrain_t5/convert_ckpt_to_bin.py | import time
from builtins import print
import argparse
import torch
# os.environ["CUDA_VISIBLE_DEVICES"] = '3'
def get_time_str():
return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
def main():
total_parser = argparse.ArgumentParser("Pretrain Unsupervise.")
total_parser.add_argument('--ckpt_pa... | 1,071 | 27.210526 | 68 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/pretrain_t5/finetune_t5.py | import time
from builtins import print
import sys
import os
import torch
import argparse
import pytorch_lightning as pl
from pytorch_lightning import Trainer, loggers
from transformers import MT5ForConditionalGeneration
from pytorch_lightning.callbacks import LearningRateMonitor
# os.environ["CUDA_VISIBLE_DEVICES"] = '... | 6,184 | 41.655172 | 110 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/stable_diffusion_dreambooth/train.py | # -*- encoding: utf-8 -*-
'''
Copyright 2022 The International Digital Economy Academy (IDEA). CCNL team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.o... | 11,678 | 41.162455 | 118 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/zen2_finetune/fengshen_token_level_ft_task.py | # coding=utf-8
# Copyright 2021 The IDEA Authors. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by a... | 28,463 | 40.920471 | 163 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/zen2_finetune/fengshen_sequence_level_ft_task.py | # coding=utf-8
# Copyright 2021 The IDEA Authors. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by a... | 27,189 | 40.830769 | 130 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/classification/finetune_classification.py | # coding=utf-8
# Copyright 2021 The IDEA Authors. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by a... | 15,787 | 39.482051 | 117 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/DAVAE/generate.py | # -*- encoding: utf-8 -*-
'''
Copyright 2022 The International Digital Economy Academy (IDEA). CCNL team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.o... | 1,595 | 42.135135 | 157 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/disco_project/disco.py | import os
import sys
# sys.path.insert(0, f'{PROJECT_DIR}/guided-diffusion') # 加在前面，不再读取库文件的东西。
import subprocess
import io
import torch.nn as nn
from torch.nn import functional as F
import torch
import torchvision.transforms.functional as TF
import torchvision.transforms as T
import math
import requests
import cv2
f... | 29,225 | 38.709239 | 150 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/resample.py | from abc import ABC, abstractmethod
import numpy as np
import torch as th
import torch.distributed as dist
def create_named_schedule_sampler(name, diffusion):
"""
Create a ScheduleSampler from a library of pre-defined samplers.
:param name: the name of the sampler.
:param diffusion: the diffusion ob... | 5,689 | 35.709677 | 87 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/losses.py | """
Helpers for various likelihood-based losses. These are ported from the original
Ho et al. diffusion models codebase:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/utils.py
"""
import numpy as np
import torch as th
def normal_kl(mean1, logvar1, mean2, logvar... | 2,502 | 32.824324 | 109 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/nn.py | """
Various utilities for neural networks.
"""
import math
import torch as th
import torch.nn as nn
# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
def forward(self, x):
return x * th.sigmoid(x)
class GroupNorm32(nn.GroupNorm):
def forward(self, x):
return super(... | 5,835 | 29.554974 | 99 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/fp16_util.py | """
Helpers to train with 16-bit precision.
"""
import numpy as np
import torch as th
import torch.nn as nn
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
from . import logger
INITIAL_LOG_LOSS_SCALE = 20.0
def convert_module_to_f16(ll):
"""
Convert primitive modules to float16.
... | 7,955 | 32.56962 | 114 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/unet.py | from abc import abstractmethod
import math
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from .fp16_util import convert_module_to_f16, convert_module_to_f32
from .nn import (
checkpoint,
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
... | 34,109 | 33.94877 | 124 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/gaussian_diffusion.py | """
This code started out as a PyTorch port of Ho et al's diffusion models:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py
Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules.
"""
import enum
import math... | 50,680 | 37.482156 | 185 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/respace.py | import numpy as np
import torch as th
from .gaussian_diffusion import GaussianDiffusion
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the origin... | 5,192 | 39.255814 | 85 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/pretrain_erlangshen_deberta_v2/pretrain_deberta.py | from dataclasses import dataclass
from transformers import (
DebertaV2Config,
DebertaV2ForMaskedLM,
AutoTokenizer,
)
from pytorch_lightning import (
LightningModule,
Trainer,
)
from pytorch_lightning.callbacks import (
LearningRateMonitor,
)
import argparse
import torch
import os
import numpy as... | 8,886 | 37.97807 | 119 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/tcbert/example.py | import argparse
from fengshen.pipelines.tcbert import TCBertPipelines
from pytorch_lightning import seed_everything
def main():
seed_everything(123)
total_parser = argparse.ArgumentParser("Topic Classification")
total_parser = TCBertPipelines.piplines_args(total_parser)
args = total_parser.parse_args()... | 3,693 | 41.45977 | 94 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/ziya_inference/hf_quantizatin_inference.py | """
这是基于hugging face社区开源的框架accelerate制定的基础量化推理方案
该框架主要实现了int8、int4量化，以及cpu或者disk offload
实现了用低存储，小设备运行大模型
具体可以见wiki：http://wiki.team.idea.edu.cn/pages/viewpage.action?pageId=31464125
"""
import time
from transformers import AutoModelForCausalLM, AutoTokenizer
import bitsandbytes as bnb
from bitsandbytes.nn import Linea... | 2,638 | 35.652778 | 114 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/qa_t5/finetune_t5_cmrc.py | # -*- encoding: utf-8 -*-
'''
Copyright 2022 The International Digital Economy Academy (IDEA). CCNL team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.o... | 17,183 | 37.101996 | 100 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/qa_t5/qa_dataset.py | # -*- encoding: utf-8 -*-
'''
Copyright 2022 The International Digital Economy Academy (IDEA). CCNL team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.o... | 6,086 | 31.37766 | 96 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/hubert/pretrain_hubert.py | import fengshen.data.hubert.hubert_dataset as datasets
from fengshen.data.universal_datamodule import UniversalDataModule
from transformers import HubertConfig, HubertModel
# from transformers.models.hubert.modeling_hubert import _compute_mask_indices
import argparse
from fairseq.data import Dictionary
from pytorch_lig... | 11,643 | 39.430556 | 109 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/pretrain_erlangshen_bert/pretrain_erlangshen.py | from dataclasses import dataclass
from transformers import (
MegatronBertConfig,
MegatronBertForPreTraining,
AutoTokenizer,
)
from pytorch_lightning import (
LightningModule,
Trainer,
)
from pytorch_lightning.callbacks import (
LearningRateMonitor,
)
import argparse
import torch
import os
import... | 9,575 | 39.235294 | 120 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/wenzhong_qa/finetune_medicalQA.py | from transformers import GPT2LMHeadModel
from data.task_dataloader.medicalQADataset import GPT2QADataModel
from transformers.optimization import get_linear_schedule_with_warmup
from pytorch_lightning import Trainer, loggers
from pytorch_lightning.callbacks import ModelCheckpoint
import pytorch_lightning as pl
import ar... | 7,423 | 40.943503 | 110 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/wenzhong_qa/finetune_wenzhong.py | # sys.path.append('./')
import os
import torch
import argparse
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning import Trainer, loggers
from transformers.optimization import get_linear_schedule_with_warmup
from transformers import GPT2LMHeadModel
from fengshe... | 6,611 | 41.935065 | 114 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/finetune_bart_qg/utils.py | # -*- encoding: utf-8 -*-
'''
Copyright 2022 The International Digital Economy Academy (IDEA). CCNL team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.o... | 2,208 | 30.112676 | 96 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/finetune_bart_qg/finetune_bart.py | # -*- encoding: utf-8 -*-
'''
Copyright 2022 The International Digital Economy Academy (IDEA). CCNL team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.o... | 17,301 | 39.237209 | 141 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/zen1_finetune/fengshen_token_level_ft_task.py | # coding=utf-8
# Copyright 2021 The IDEA Authors. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by a... | 26,317 | 39.614198 | 163 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/zen1_finetune/fengshen_sequence_level_ft_task.py | # coding=utf-8
# Copyright 2021 The IDEA Authors. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by a... | 24,857 | 39.684124 | 130 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/pretrain_taiyi_clip/pretrain.py | from pytorch_lightning import (
LightningModule,
Trainer,
)
from pytorch_lightning.callbacks import (
LearningRateMonitor,
)
from fengshen.models.clip import (
TaiyiCLIPModel,
TaiyiCLIPProcessor,
)
from fengshen.models.model_utils import (
add_module_args,
configure_optimizers,
get_total... | 12,711 | 40.139159 | 113 | py |
Fengshenbang-LM | Fengshenbang-LM-main/fengshen/examples/pretrain_taiyi_clip/test.py | from pytorch_lightning import (
Trainer,
)
from fengshen.models.model_utils import (
add_module_args,
)
import argparse
from fengshen.data.universal_datamodule import UniversalDataModule
from fengshen.utils.universal_checkpoint import UniversalCheckpoint
from fengshen.examples.pretrain_taiyi_clip.pretrain impor... | 1,404 | 36.972973 | 97 | py |
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