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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detpro | detpro-main/mmdet/models/roi_heads/standard_roi_head_collect_reuse.py | import torch
from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
from ..builder import HEADS, build_head, build_roi_extractor
from .base_roi_head import BaseRoIHead
from .test_mixins import BBoxTestMixin, MaskTestMixin
import torch.nn as nn
import torch
import clip
import time
from mmcv.ops.roi_... | 40,123 | 46.484024 | 216 | py |
detpro | detpro-main/mmdet/models/roi_heads/standard_roi_head_save_proposal.py | import torch
from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
from ..builder import HEADS, build_head, build_roi_extractor
from .base_roi_head import BaseRoIHead
from .test_mixins import BBoxTestMixin, MaskTestMixin
import torch.nn as nn
import torch
import clip
import time
import mmcv
from m... | 32,326 | 44.724187 | 184 | py |
detpro | detpro-main/mmdet/models/roi_heads/cascade_roi_head.py | from re import S
import torch
from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
from ..builder import HEADS, build_head, build_roi_extractor
from .base_roi_head import BaseRoIHead
from .test_mixins import BBoxTestMixin, MaskTestMixin
import torch.nn as nn
import torch
import clip
import time
f... | 60,063 | 48.97005 | 259 | py |
detpro | detpro-main/mmdet/models/roi_heads/standard_roi_head_collect.py | import torch
from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
from ..builder import HEADS, build_head, build_roi_extractor
from .base_roi_head import BaseRoIHead
from .test_mixins import BBoxTestMixin, MaskTestMixin
import torch.nn as nn
import torch
import clip
import time
from mmcv.ops.roi_... | 40,108 | 46.466272 | 216 | py |
detpro | detpro-main/mmdet/models/roi_heads/base_roi_head.py | from abc import ABCMeta, abstractmethod
import torch.nn as nn
from ..builder import build_shared_head
class BaseRoIHead(nn.Module, metaclass=ABCMeta):
"""Base class for RoIHeads."""
def __init__(self,
bbox_roi_extractor=None,
bbox_head=None,
mask_roi_extra... | 3,060 | 27.607477 | 78 | py |
detpro | detpro-main/mmdet/models/roi_heads/standard_roi_head_text.py | import torch
from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
from ..builder import HEADS, build_head, build_roi_extractor
from .base_roi_head import BaseRoIHead
from .test_mixins import BBoxTestMixin, MaskTestMixin
import torch.nn as nn
import torch
import clip
import time
from mmcv.ops.roi_... | 24,771 | 43 | 161 | py |
detpro | detpro-main/mmdet/models/roi_heads/standard_roi_head_text_prompt.py | import torch
from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
from ..builder import HEADS, build_head, build_roi_extractor
from .base_roi_head import BaseRoIHead
from .test_mixins import BBoxTestMixin, MaskTestMixin
import torch.nn as nn
import torch
import clip
import time
from mmcv.ops.roi_... | 24,675 | 43.064286 | 161 | py |
detpro | detpro-main/mmdet/models/roi_heads/visualize.py | # -*- coding: utf-8 -*-
# @Time : 2021/8/22 10:41
# @Author : duyu
# @Email : abelazady@foxmail.com
# @File : visualize.py
# @Software: PyCharm
import mmcv
from mmcv.image import tensor2imgs
import os.path as osp
import warnings
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import pycocotool... | 8,228 | 32.864198 | 77 | py |
detpro | detpro-main/mmdet/models/roi_heads/test_mixins.py | import logging
import sys
import torch
from mmdet.core import (bbox2roi, bbox_mapping, merge_aug_bboxes,
merge_aug_masks, multiclass_nms)
logger = logging.getLogger(__name__)
if sys.version_info >= (3, 7):
from mmdet.utils.contextmanagers import completed
class BBoxTestMixin(object):
... | 11,818 | 42.452206 | 79 | py |
detpro | detpro-main/mmdet/models/roi_heads/standard_roi_head_prompt.py | import torch
from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
from ..builder import HEADS, build_head, build_roi_extractor
from .base_roi_head import BaseRoIHead
from .test_mixins import BBoxTestMixin, MaskTestMixin
import torch.nn as nn
import torch
import clip
import time
from mmcv.ops.roi_... | 46,906 | 48.901064 | 257 | py |
detpro | detpro-main/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py | from abc import ABCMeta, abstractmethod
import torch
import torch.nn as nn
from mmcv import ops
class BaseRoIExtractor(nn.Module, metaclass=ABCMeta):
"""Base class for RoI extractor.
Args:
roi_layer (dict): Specify RoI layer type and arguments.
out_channels (int): Output channels of RoI laye... | 2,760 | 31.869048 | 79 | py |
detpro | detpro-main/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py | import torch
from mmcv.runner import force_fp32
from mmdet.models.builder import ROI_EXTRACTORS
from .base_roi_extractor import BaseRoIExtractor
@ROI_EXTRACTORS.register_module()
class SingleRoIExtractor(BaseRoIExtractor):
"""Extract RoI features from a single level feature map.
If there are multiple input ... | 3,911 | 38.12 | 79 | py |
detpro | detpro-main/mmdet/models/roi_heads/bbox_heads/bbox_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner import auto_fp16, force_fp32
from torch.nn.modules.utils import _pair
from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
from mmdet.models.builder import HEADS, build_loss
from mmdet.models.losses import accuracy
impo... | 13,733 | 39.633136 | 81 | py |
detpro | detpro-main/mmdet/models/roi_heads/bbox_heads/sabl_head.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, kaiming_init, normal_init, xavier_init
from mmcv.runner import force_fp32
from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
from mmdet.models.builder import HEADS, build_loss
from m... | 24,585 | 41.907504 | 79 | py |
detpro | detpro-main/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py | import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.models.builder import HEADS
from .bbox_head import BBoxHead
from mmcv.runner import auto_fp16, force_fp32
@HEADS.register_module()
class ConvFCBBoxHead(BBoxHead):
r"""More general bbox head, with shared conv and fc layers and two optio... | 9,617 | 34.230769 | 96 | py |
detpro | detpro-main/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py | import torch.nn as nn
from mmcv.cnn import ConvModule, normal_init, xavier_init
from mmdet.models.backbones.resnet import Bottleneck
from mmdet.models.builder import HEADS
from .bbox_head import BBoxHead
class BasicResBlock(nn.Module):
"""Basic residual block.
This block is a little different from the block... | 5,380 | 30.104046 | 78 | py |
detpro | detpro-main/mmdet/models/roi_heads/shared_heads/res_layer.py | import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import auto_fp16, load_checkpoint
from mmdet.models.backbones import ResNet
from mmdet.models.builder import SHARED_HEADS
from mmdet.models.utils import ResLayer as _ResLayer
from mmdet.utils import get_root_logger
@SHARED_HEADS.... | 2,454 | 30.474359 | 74 | py |
detpro | detpro-main/mmdet/models/roi_heads/mask_heads/grid_head.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, kaiming_init, normal_init
from mmdet.models.builder import HEADS, build_loss
@HEADS.register_module()
class GridHead(nn.Module):
def __init__(self,
grid_points=9,
... | 15,432 | 41.869444 | 79 | py |
detpro | detpro-main/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py | import torch.nn as nn
from mmcv.cnn import ConvModule, Linear, constant_init, xavier_init
from mmcv.runner import auto_fp16
from mmdet.models.builder import HEADS
from .fcn_mask_head import FCNMaskHead
@HEADS.register_module()
class CoarseMaskHead(FCNMaskHead):
"""Coarse mask head used in PointRend.
Compare... | 3,233 | 34.152174 | 79 | py |
detpro | detpro-main/mmdet/models/roi_heads/mask_heads/maskiou_head.py | import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import Conv2d, Linear, MaxPool2d, kaiming_init, normal_init
from mmcv.runner import force_fp32
from torch.nn.modules.utils import _pair
from mmdet.models.builder import HEADS, build_loss
@HEADS.register_module()
class MaskIoUHead(nn.Module):
"""... | 7,332 | 38.213904 | 79 | py |
detpro | detpro-main/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import Conv2d, ConvModule, build_upsample_layer
from mmcv.ops.carafe import CARAFEPack
from mmcv.runner import auto_fp16, force_fp32
from torch.nn.modules.utils import _pair
from mmdet.core import mask_target
from mmdet... | 12,979 | 38.452888 | 79 | py |
detpro | detpro-main/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py | import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, kaiming_init
from mmcv.runner import auto_fp16, force_fp32
from mmdet.models.builder import HEADS
@HEADS.register_module()
class FusedSemanticHead(nn.Module):
r"""Multi-level fused semantic segmentation head.
.. code-bloc... | 3,610 | 32.435185 | 79 | py |
detpro | detpro-main/mmdet/models/roi_heads/mask_heads/mask_point_head.py | # 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, normal_init
from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
from mmdet.models.builder import HEADS, build... | 13,190 | 42.82392 | 126 | py |
detpro | detpro-main/mmdet/models/losses/ghm_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
def _expand_onehot_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero(
(labels >= 0) & (labels < label_channels), as_tuple... | 6,365 | 35.797688 | 79 | py |
detpro | detpro-main/mmdet/models/losses/mse_loss.py | 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()
class MSELoss(nn.Module):
"""MSELoss.
... | 1,463 | 28.28 | 78 | py |
detpro | detpro-main/mmdet/models/losses/pisa_loss.py | import torch
from mmdet.core import bbox_overlaps
def isr_p(cls_score,
bbox_pred,
bbox_targets,
rois,
sampling_results,
loss_cls,
bbox_coder,
k=2,
bias=0,
num_class=80):
"""Importance-based Sample Reweighting (ISR_P), posit... | 7,076 | 38.099448 | 79 | py |
detpro | detpro-main/mmdet/models/losses/balanced_l1_loss.py | import numpy as np
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def balanced_l1_loss(pred,
target,
beta=1.0,
alpha=0.5,
gamma=1.5,
reduction='mea... | 4,116 | 33.596639 | 79 | py |
detpro | detpro-main/mmdet/models/losses/iou_loss.py | import math
import torch
import torch.nn as nn
from mmdet.core import bbox_overlaps
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def iou_loss(pred, target, linear=False, eps=1e-6):
"""IoU loss.
Computing the IoU loss between a set of predicted bboxes and target bboxes.
T... | 13,888 | 31.225058 | 79 | py |
detpro | detpro-main/mmdet/models/losses/smooth_l1_loss.py | import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
beta... | 4,423 | 31.291971 | 78 | py |
detpro | detpro-main/mmdet/models/losses/gfocal_loss.py | import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def quality_focal_loss(pred, target, beta=2.0):
r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning
Qualified and Distributed Bounding Boxes for Dense Object D... | 7,318 | 38.349462 | 79 | py |
detpro | detpro-main/mmdet/models/losses/varifocal_loss.py | import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
def varifocal_loss(pred,
target,
weight=None,
alpha=0.75,
gamma=2.0,
iou_weighted=True,
... | 5,265 | 38.893939 | 79 | py |
detpro | detpro-main/mmdet/models/losses/utils.py | import functools
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:
Tensor: Reduced loss tensor.
"""
reduction_enum = ... | 3,003 | 29.343434 | 79 | py |
detpro | detpro-main/mmdet/models/losses/ae_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
def ae_loss_per_image(tl_preds, br_preds, match):
"""Associative Embedding Loss in one image.
Associative Embedding Loss including two parts: pull loss and push loss.
Pull loss makes embedding vectors from sa... | 3,757 | 36.207921 | 143 | py |
detpro | detpro-main/mmdet/models/losses/accuracy.py | import torch.nn as nn
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)
target (torch.Tensor): The target of each prediction, shape (N, )
topk (int | tupl... | 2,905 | 36.74026 | 79 | py |
detpro | detpro-main/mmdet/models/losses/focal_loss.py | 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,
target,
weigh... | 6,417 | 39.620253 | 79 | py |
detpro | detpro-main/mmdet/models/losses/cross_entropy_loss.py | 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',
avg_factor=None,
class_weight=N... | 7,394 | 35.608911 | 79 | py |
detpro | detpro-main/mmdet/models/losses/gaussian_focal_loss.py | import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0):
"""`Focal Loss <https://arxiv.org/abs/1708.02002>`_ for targets in gaussian
distribution.
Args:
pred (torch.Tensor): The predic... | 3,211 | 34.688889 | 108 | py |
detpro | detpro-main/mmdet/models/backbones/hrnet.py | import torch.nn as nn
from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
kaiming_init)
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.utils import get_root_logger
from ..builder import BACKBONES
from .resnet import BasicB... | 20,359 | 36.843866 | 79 | py |
detpro | detpro-main/mmdet/models/backbones/regnet.py | 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 backbone.
More details can be found in `paper <https://arxi... | 12,269 | 36.638037 | 79 | py |
detpro | detpro-main/mmdet/models/backbones/trident_resnet.py | 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, kaiming_init
from torch.nn.modules.utils import _pair
from mmdet.models.backbones.resnet import Bottleneck, ResNet
from mmdet.models.builder import BACKBONES
... | 10,863 | 36.078498 | 79 | py |
detpro | detpro-main/mmdet/models/backbones/detectors_resnext.py | 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__(self,
inplanes,
... | 3,872 | 30.487805 | 77 | py |
detpro | detpro-main/mmdet/models/backbones/resnet.py | import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (build_conv_layer, build_norm_layer, build_plugin_layer,
constant_init, kaiming_init)
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.utils import get_root_logger
fr... | 23,377 | 34.207831 | 79 | py |
detpro | detpro-main/mmdet/models/backbones/detectors_resnet.py | import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer, constant_init
from ..builder import BACKBONES
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottleneck(_Bottleneck):
r"""Bottleneck for the ResNet backbone in `Detec... | 10,517 | 33.372549 | 78 | py |
detpro | detpro-main/mmdet/models/backbones/ssd_vgg.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import VGG, constant_init, kaiming_init, normal_init, xavier_init
from mmcv.runner import load_checkpoint
from mmdet.utils import get_root_logger
from ..builder import BACKBONES
@BACKBONES.register_module()
class SSDVGG(VGG):
"""VGG... | 5,882 | 33.605882 | 79 | py |
detpro | detpro-main/mmdet/models/backbones/resnext.py | 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__(self,
inplanes,
... | 5,664 | 35.785714 | 79 | py |
detpro | detpro-main/mmdet/models/backbones/resnest.py | 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 ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNetV1d
class RS... | 10,352 | 31.556604 | 79 | py |
detpro | detpro-main/mmdet/models/backbones/hourglass.py | import torch.nn as nn
from mmcv.cnn import ConvModule
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import BasicBlock
class HourglassModule(nn.Module):
"""Hourglass Module for HourglassNet backbone.
Generate module recursively and use BasicBlock as the base unit.
Args:
... | 6,452 | 31.427136 | 79 | py |
detpro | detpro-main/mmdet/models/backbones/res2net.py | 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, constant_init,
kaiming_init)
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.utils import get_root_log... | 12,675 | 35.011364 | 79 | py |
detpro | detpro-main/mmdet/models/backbones/darknet.py | # Copyright (c) 2019 Western Digital Corporation or its affiliates.
import logging
import torch.nn as nn
from mmcv.cnn import ConvModule, constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
class ResBlock(nn.Module):
... | 7,574 | 36.875 | 79 | py |
detpro | detpro-main/mmdet/datasets/custom.py | import os.path as osp
import warnings
from collections import OrderedDict
import mmcv
import numpy as np
from torch.utils.data import Dataset
from mmdet.core import eval_map, eval_recalls
from .builder import DATASETS
from .pipelines import Compose
@DATASETS.register_module()
class CustomDataset(Dataset):
"""Cu... | 11,608 | 34.72 | 93 | py |
detpro | detpro-main/mmdet/datasets/dataset_wrappers.py | import bisect
import math
from collections import defaultdict
import numpy as np
from mmcv.utils import print_log
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .builder import DATASETS
from .coco import CocoDataset
@DATASETS.register_module()
class ConcatDataset(_ConcatDataset):
"""A... | 11,088 | 38.183746 | 167 | py |
detpro | detpro-main/mmdet/datasets/lvis.py | import itertools
import logging
import os.path as osp
import tempfile
import warnings
from collections import OrderedDict
import numpy as np
from mmcv.utils import print_log
from terminaltables import AsciiTable
from .builder import DATASETS
from .coco import CocoDataset
from torch import distributed as dist
import j... | 52,205 | 57.856821 | 157 | py |
detpro | detpro-main/mmdet/datasets/builder.py | import copy
import platform
import random
from functools import partial
import numpy as np
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import Registry, build_from_cfg
from torch.utils.data import DataLoader
from .samplers import DistributedGroupSampler, DistributedSampler, ... | 5,291 | 35.75 | 79 | py |
detpro | detpro-main/mmdet/datasets/samplers/group_sampler.py | from __future__ import division
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 = dataset
self.... | 5,073 | 34.236111 | 78 | py |
detpro | detpro-main/mmdet/datasets/samplers/distributed_sampler.py | import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
class DistributedSampler(_DistributedSampler):
def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
super().__init__(dataset, num_replicas=num_replicas, rank=rank)
self.shuffle = s... | 1,104 | 32.484848 | 79 | py |
detpro | detpro-main/mmdet/datasets/pipelines/zip.py | import os
from mmcv import FileClient, BaseStorageBackend
from zipfile import ZipFile
import torch
import io
class ZipBackend(BaseStorageBackend):
"""
Only single image directory is supported
"""
def __init__(self, zip_file_name=None):
if zip_file_name is not None:
self.zip_f... | 2,057 | 38.576923 | 103 | py |
detpro | detpro-main/mmdet/datasets/pipelines/formating.py | 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 types are: :class:`numpy.ndarray`, :class:`torch.T... | 12,378 | 32.276882 | 79 | py |
detpro | detpro-main/mmdet/utils/contextmanagers.py | 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 completed(trace_name='',
name='',
... | 4,077 | 32.42623 | 79 | py |
detpro | detpro-main/mmdet/utils/profiling.py | 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_stream=None):
"""Print time spent by CP... | 1,288 | 31.225 | 73 | py |
gt4py | gt4py-main/src/gt4py/cartesian/backend/pyext_builder.py | # GT4Py - GridTools Framework
#
# Copyright (c) 2014-2023, ETH Zurich
# All rights reserved.
#
# This file is part of the GT4Py project and the GridTools framework.
# GT4Py is free software: you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the
# Free Software ... | 12,263 | 32.508197 | 113 | py |
gt4py | gt4py-main/docs/user/cartesian/conf.py | # GT4Py - GridTools Framework
#
# Copyright (c) 2014-2023, ETH Zurich
# All rights reserved.
#
# This file is part of the GT4Py project and the GridTools framework.
# GT4Py is free software: you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the
# Free Software ... | 7,909 | 30.895161 | 123 | py |
snpy | snpy-master/snpy-bootstrap.py | #!/usr/bin/env python
## WARNING: This file is generated
#!/usr/bin/env python
"""Create a "virtual" Python installation
"""
# If you change the version here, change it in setup.py
# and docs/conf.py as well.
virtualenv_version = "1.6.1"
import base64
import sys
import os
import optparse
import re
import shutil
impo... | 93,894 | 41.505659 | 244 | py |
freqtrade-develop | freqtrade-develop/setup.py | from setuptools import setup
# Requirements used for submodules
plot = ['plotly>=4.0']
hyperopt = [
'scipy',
'scikit-learn<=1.1.3',
'scikit-optimize>=0.7.0',
'filelock',
]
freqai = [
'scikit-learn',
'joblib',
'catboost; platform_machine != "aarch64"',
'lightgbm',
'xgboost',
't... | 2,330 | 17.5 | 75 | py |
freqtrade-develop | freqtrade-develop/freqtrade/constants.py | # pragma pylint: disable=too-few-public-methods
"""
bot constants
"""
from typing import Any, Dict, List, Literal, Tuple
from freqtrade.enums import CandleType, PriceType, RPCMessageType
DOCS_LINK = "https://www.freqtrade.io/en/stable"
DEFAULT_CONFIG = 'config.json'
PROCESS_THROTTLE_SECS = 5 # sec
HYPEROPT_EPOCH =... | 29,640 | 41.223647 | 96 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/utils.py | import logging
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict
import numpy as np
import pandas as pd
import rapidjson
from freqtrade.configuration import TimeRange
from freqtrade.constants import Config
from freqtrade.data.dataprovider import DataProvider
from freqtrade.... | 7,382 | 36.477157 | 96 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/freqai_interface.py | import logging
import threading
import time
from abc import ABC, abstractmethod
from collections import deque
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Literal, Optional, Tuple
import datasieve.transforms as ds
import numpy as np
import pandas as pd
import psu... | 45,555 | 44.239325 | 100 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/data_kitchen.py | import copy
import inspect
import logging
import random
import shutil
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
import psutil
from datasieve.pipeline import Pipeline
from pandas ... | 42,787 | 42.572301 | 100 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/data_drawer.py | import collections
import importlib
import logging
import re
import shutil
import threading
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, Tuple, TypedDict
import numpy as np
import pandas as pd
import psutil
import rapidjson
from joblib import dump, load
from... | 30,099 | 41.877493 | 100 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/base_models/BasePyTorchClassifier.py | import logging
from time import time
from typing import Any, Dict, List, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
import torch
from pandas import DataFrame
from torch.nn import functional as F
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.base_models.BasePy... | 8,283 | 36.826484 | 98 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/base_models/BasePyTorchModel.py | import logging
from abc import ABC, abstractmethod
import torch
from freqtrade.freqai.freqai_interface import IFreqaiModel
from freqtrade.freqai.torch.PyTorchDataConvertor import PyTorchDataConvertor
logger = logging.getLogger(__name__)
class BasePyTorchModel(IFreqaiModel, ABC):
"""
Base class for PyTorch... | 1,144 | 30.805556 | 86 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/prediction_models/XGBoostRegressor.py | import logging
from typing import Any, Dict
from xgboost import XGBRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.tensorboard import TBCallback
logger = logging.getLogger(__name__)
class X... | 2,104 | 37.272727 | 98 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py | from typing import Any, Dict
import torch
from freqtrade.freqai.base_models.BasePyTorchClassifier import BasePyTorchClassifier
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.torch.PyTorchDataConvertor import (DefaultPyTorchDataConvertor,
... | 3,634 | 38.51087 | 95 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/prediction_models/ReinforcementLearner.py | import logging
from pathlib import Path
from typing import Any, Dict, Type
import torch as th
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment
from freqtrade... | 6,732 | 41.08125 | 99 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py | from typing import Any, Dict
import torch
from freqtrade.freqai.base_models.BasePyTorchRegressor import BasePyTorchRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.torch.PyTorchDataConvertor import (DefaultPyTorchDataConvertor,
... | 3,320 | 37.616279 | 95 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/prediction_models/XGBoostRegressorMultiTarget.py | import logging
from typing import Any, Dict
from xgboost import XGBRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
log... | 2,596 | 37.191176 | 94 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/prediction_models/XGBoostClassifier.py | import logging
from typing import Any, Dict, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
from pandas.api.types import is_integer_dtype
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBClassifier
from freqtrade.freqai.base_models.BaseClassif... | 3,338 | 36.516854 | 91 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py | from typing import Any, Dict, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
import torch
from freqtrade.freqai.base_models.BasePyTorchRegressor import BasePyTorchRegressor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.torch.PyTorchDataConvertor import (Def... | 5,895 | 39.108844 | 96 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/prediction_models/XGBoostRFClassifier.py | import logging
from typing import Any, Dict, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
from pandas.api.types import is_integer_dtype
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBRFClassifier
from freqtrade.freqai.base_models.BaseClass... | 3,345 | 36.595506 | 91 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/prediction_models/XGBoostRFRegressor.py | import logging
from typing import Any, Dict
from xgboost import XGBRFRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.tensorboard import TBCallback
logger = logging.getLogger(__name__)
class... | 2,110 | 37.381818 | 98 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/torch/PyTorchTrainerInterface.py | from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List
import pandas as pd
import torch
from torch import nn
class PyTorchTrainerInterface(ABC):
@abstractmethod
def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]) -> None:
"""
:param ... | 2,026 | 36.537037 | 93 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/torch/PyTorchTransformerModel.py | import math
import torch
from torch import nn
"""
The architecture is based on the paper “Attention Is All You Need”.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Lukasz Kaiser, and Illia Polosukhin. 2017.
"""
class PyTorchTransformerModel(nn.Module):
"""
A transf... | 3,605 | 37.361702 | 98 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/torch/PyTorchDataConvertor.py | from abc import ABC, abstractmethod
from typing import Optional
import pandas as pd
import torch
class PyTorchDataConvertor(ABC):
"""
This class is responsible for converting `*_features` & `*_labels` pandas dataframes
to pytorch tensors.
"""
@abstractmethod
def convert_x(self, df: pd.DataFr... | 2,028 | 28.838235 | 88 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/torch/datasets.py | import torch
class WindowDataset(torch.utils.data.Dataset):
def __init__(self, xs, ys, window_size):
self.xs = xs
self.ys = ys
self.window_size = window_size
def __len__(self):
return len(self.xs) - self.window_size
def __getitem__(self, index):
idx_rev = len(self... | 666 | 32.35 | 88 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/torch/PyTorchMLPModel.py | import logging
import torch
from torch import nn
logger = logging.getLogger(__name__)
class PyTorchMLPModel(nn.Module):
"""
A multi-layer perceptron (MLP) model implemented using PyTorch.
This class mainly serves as a simple example for the integration of PyTorch model's
to freqai. It is not optim... | 3,762 | 37.793814 | 98 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/torch/PyTorchModelTrainer.py | import logging
import math
from pathlib import Path
from typing import Any, Dict, List, Optional
import pandas as pd
import torch
from torch import nn
from torch.optim import Optimizer
from torch.utils.data import DataLoader, TensorDataset
from freqtrade.freqai.torch.PyTorchDataConvertor import PyTorchDataConvertor
f... | 8,903 | 37.713043 | 99 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/tensorboard/tensorboard.py | import logging
from pathlib import Path
from typing import Any
from torch.utils.tensorboard import SummaryWriter
from xgboost import callback
from freqtrade.freqai.tensorboard.base_tensorboard import (BaseTensorBoardCallback,
BaseTensorboardLogger)
logger =... | 1,926 | 29.587302 | 84 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/tensorboard/__init__.py | # ensure users can still use a non-torch freqai version
try:
from freqtrade.freqai.tensorboard.tensorboard import TensorBoardCallback, TensorboardLogger
TBLogger = TensorboardLogger
TBCallback = TensorBoardCallback
except ModuleNotFoundError:
from freqtrade.freqai.tensorboard.base_tensorboard import (Ba... | 587 | 35.75 | 95 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/tensorboard/base_tensorboard.py | import logging
from pathlib import Path
from typing import Any
from xgboost.callback import TrainingCallback
logger = logging.getLogger(__name__)
class BaseTensorboardLogger:
def __init__(self, logdir: Path, activate: bool = True):
pass
def log_scalar(self, tag: str, scalar_value: Any, step: int):... | 690 | 19.323529 | 69 | py |
freqtrade-develop | freqtrade-develop/freqtrade/freqai/RL/BaseReinforcementLearningModel.py | import copy
import importlib
import logging
from abc import abstractmethod
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Tuple, Type, Union
import gymnasium as gym
import numpy as np
import numpy.typing as npt
import pandas as pd
import torch as th
i... | 21,133 | 43.492632 | 100 | py |
freqtrade-develop | freqtrade-develop/tests/freqai/conftest.py | import platform
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict
from unittest.mock import MagicMock
import pytest
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.freqai.data_drawer import FreqaiDataDrawer
from freqtrade.... | 9,232 | 34.786822 | 100 | py |
freqtrade-develop | freqtrade-develop/tests/freqai/test_freqai_interface.py | import logging
import platform
import shutil
import sys
from pathlib import Path
from unittest.mock import MagicMock
import pytest
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import RunMode
from freqtrade.freqai.data_kitchen import FreqaiData... | 23,601 | 41.221825 | 99 | py |
SMedBERT | SMedBERT-main/pretraining_args.py | # -----------ARGS---------------------
pretrain_train_path = "data/train.txt"
pretrain_dev_path = "data/dev.txt"
max_seq_length = 512
do_train = True
do_lower_case = True
train_batch_size = 4
eval_batch_size = 16
learning_rate = 1e-6 # 1e-4
num_train_epochs = 2
warmup_proportion = 0.1
no_cuda = False
loca... | 681 | 12.372549 | 61 | py |
SMedBERT | SMedBERT-main/run_re.py | from __future__ import absolute_import, division, print_function
import json
# import pretraining_args as args
import csv
import logging
import os
# import random
import sys
from glob import glob
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
... | 49,201 | 42.930357 | 211 | py |
SMedBERT | SMedBERT-main/run_CMedMRC.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. 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/LICENS... | 92,085 | 48.217531 | 2,317 | py |
SMedBERT | SMedBERT-main/run_IR.py | from __future__ import absolute_import, division, print_function
import json
# import pretraining_args as args
import csv
import logging
import os
# import random
import sys
from glob import glob
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
... | 57,956 | 43.823666 | 257 | py |
SMedBERT | SMedBERT-main/run_pretraining_stream.py | from __future__ import absolute_import, division, print_function
import json
import csv
import logging
import os
import sys
from glob import glob
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset, Dataset)
from torc... | 44,018 | 41.65407 | 164 | py |
SMedBERT | SMedBERT-main/run_seq_cls.py | from __future__ import absolute_import, division, print_function
import json
# import pretraining_args as args
import csv
import logging
import os
# import random
import sys
from glob import glob
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
... | 51,851 | 42.720067 | 257 | py |
SMedBERT | SMedBERT-main/run_ner.py | from __future__ import absolute_import, division, print_function
import json
# import pretraining_args as args
import csv
import logging
import os
# import random
import sys
from glob import glob
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
... | 45,681 | 43.962598 | 211 | py |
SMedBERT | SMedBERT-main/run_pretraining.py | from __future__ import absolute_import, division, print_function
import json
#import pretraining_args as args
import csv
import logging
import os
#import random
import sys
from glob import glob
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
... | 40,722 | 45.014689 | 165 | py |
SMedBERT | SMedBERT-main/pytorch_pretrained_bert/useless.py | # coding=utf-8
# dsafdsafdsa
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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 ma... | 121,914 | 54.390731 | 197 | py |
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