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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DDOD | DDOD-main/mmdet/models/dense_heads/retina_head.py | import torch.nn as nn
from mmcv.cnn import ConvModule
from ..builder import HEADS
from .anchor_head import AnchorHead
@HEADS.register_module()
class RetinaHead(AnchorHead):
r"""An anchor-based head used in `RetinaNet
<https://arxiv.org/pdf/1708.02002.pdf>`_.
The head contains two subnetworks. The first ... | 4,003 | 33.817391 | 76 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/ga_rpn_head.py | import copy
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv import ConfigDict
from mmcv.ops import nms
from ..builder import HEADS
from .guided_anchor_head import GuidedAnchorHead
@HEADS.register_module()
class GARPNHead(GuidedAnchorHead):
"""Guided-Anchor-based RPN ... | 7,039 | 38.774011 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/deformable_detr_head.py | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import Linear, bias_init_with_prob, constant_init
from mmcv.runner import force_fp32
from mmdet.core import multi_apply
from mmdet.models.utils.transformer import inverse_sigmoid
from ..builder import HEADS
from .detr_head im... | 13,680 | 42.022013 | 98 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/ga_retina_head.py | import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import MaskedConv2d
from ..builder import HEADS
from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
@HEADS.register_module()
class GARetinaHead(GuidedAnchorHead):
"""Guided-Anchor-based RetinaNet head."""
def __init__(self,
... | 3,875 | 33.300885 | 77 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/ld_head.py | import torch
from mmcv.runner import force_fp32
from mmdet.core import (bbox2distance, bbox_overlaps, distance2bbox,
multi_apply, reduce_mean)
from ..builder import HEADS, build_loss
from .gfl_head import GFLHead
@HEADS.register_module()
class LDHead(GFLHead):
"""Localization distillation... | 10,641 | 39.618321 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/ssd_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmcv.runner import force_fp32
from mmdet.core import (build_anchor_generator, build_assigner,
build_bbox_coder, build_sampler, multi_apply)
from ..builder import... | 14,425 | 40.693642 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/fcos_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import Scale
from mmcv.runner import force_fp32
from mmdet.core import distance2bbox, multi_apply, multiclass_nms, reduce_mean
from ..builder import HEADS, build_loss
from .anchor_free_head import AnchorFreeHead
INF = 1e8
@HEADS.regist... | 29,400 | 44.302003 | 113 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/yolo_head.py | # Copyright (c) 2019 Western Digital Corporation or its affiliates.
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import (ConvModule, bias_init_with_prob, constant_init, is_norm,
normal_init)
from mmcv.runner import force_fp32
from mmdet.core i... | 27,175 | 42.621188 | 106 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/centripetal_head.py | import torch.nn as nn
from mmcv.cnn import ConvModule, normal_init
from mmcv.ops import DeformConv2d
from mmdet.core import multi_apply
from ..builder import HEADS, build_loss
from .corner_head import CornerHead
@HEADS.register_module()
class CentripetalHead(CornerHead):
"""Head of CentripetalNet: Pursuing High-... | 19,763 | 45.285714 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/paa_head.py | import numpy as np
import torch
from mmcv.runner import force_fp32
from mmdet.core import multi_apply, multiclass_nms
from mmdet.core.bbox.iou_calculators import bbox_overlaps
from mmdet.models import HEADS
from mmdet.models.dense_heads import ATSSHead
EPS = 1e-12
try:
import sklearn.mixture as skm
except ImportE... | 29,827 | 43.255193 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/retina_sepbn_head.py | import torch.nn as nn
from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
from ..builder import HEADS
from .anchor_head import AnchorHead
@HEADS.register_module()
class RetinaSepBNHead(AnchorHead):
""""RetinaHead with separate BN.
In RetinaHead, conv/norm layers are shared across different FPN... | 4,510 | 37.228814 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/anchor_free_head.py | from abc import abstractmethod
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import force_fp32
from mmdet.core import multi_apply
from ..builder import HEADS, build_loss
from .base_dense_head import BaseDenseHead
from .dense_test_mixins import BBoxTestMixin
@HEADS.register_modu... | 13,512 | 38.627566 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/pisa_ssd_head.py | import torch
from mmdet.core import multi_apply
from ..builder import HEADS
from ..losses import CrossEntropyLoss, SmoothL1Loss, carl_loss, isr_p
from .ssd_head import SSDHead
# TODO: add loss evaluator for SSD
@HEADS.register_module()
class PISASSDHead(SSDHead):
def loss(self,
cls_scores,
... | 5,551 | 38.657143 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/embedding_rpn_head.py | import torch
import torch.nn as nn
from mmcv.runner import BaseModule
from mmdet.models.builder import HEADS
from ...core import bbox_cxcywh_to_xyxy
@HEADS.register_module()
class EmbeddingRPNHead(BaseModule):
"""RPNHead in the `Sparse R-CNN <https://arxiv.org/abs/2011.12450>`_ .
Unlike traditional RPNHead,... | 4,581 | 38.5 | 78 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/autoassign_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import bias_init_with_prob, normal_init
from mmcv.runner import force_fp32
from mmdet.core import distance2bbox, multi_apply
from mmdet.core.bbox import bbox_overlaps
from mmdet.models import HEADS
from mmdet.models.dense_heads.atss_head ... | 22,565 | 42.563707 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/ddod_fcos_head.py | import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init, constant_init
from mmcv.runner import force_fp32
from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler,
images_to_levels, multi_apply, multiclass_nms,
... | 35,459 | 43.491844 | 107 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/pisa_retinanet_head.py | import torch
from mmcv.runner import force_fp32
from mmdet.core import images_to_levels
from ..builder import HEADS
from ..losses import carl_loss, isr_p
from .retina_head import RetinaHead
@HEADS.register_module()
class PISARetinaHead(RetinaHead):
"""PISA Retinanet Head.
The head owns the same structure wi... | 6,220 | 39.135484 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/gfl_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, Scale
from mmcv.runner import force_fp32
from mmdet.core import (anchor_inside_flags, bbox2distance, bbox_overlaps,
build_assigner, build_sampler, distance2bbox,
images_to... | 28,109 | 42.312789 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/corner_head.py | from logging import warning
from math import ceil, log
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, bias_init_with_prob
from mmcv.ops import CornerPool, batched_nms
from mmcv.runner import BaseModule
from mmdet.core import multi_apply
from ..builder import HEADS, build_loss
from ..utils import ... | 46,890 | 43.530864 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/yolact_head.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, ModuleList, force_fp32
from mmdet.core import build_sampler, fast_nms, images_to_levels, multi_apply
from ..builder import HEADS, build_loss
from .anchor_head import... | 43,107 | 41.638971 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/base_dense_head.py | from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
class BaseDenseHead(BaseModule, metaclass=ABCMeta):
"""Base class for DenseHeads."""
def __init__(self, init_cfg=None):
super(BaseDenseHead, self).__init__(init_cfg)
@abstractmethod
def loss(self, **kwargs):
... | 2,934 | 36.151899 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/ddod_head.py | import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
from mmcv.runner import force_fp32
from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler,
images_to_levels, multi_apply, multiclass_nms,
reduc... | 35,360 | 43.423367 | 107 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/free_anchor_retina_head.py | import torch
import torch.nn.functional as F
from mmdet.core import bbox_overlaps
from ..builder import HEADS
from .retina_head import RetinaHead
EPS = 1e-12
@HEADS.register_module()
class FreeAnchorRetinaHead(RetinaHead):
"""FreeAnchor RetinaHead used in https://arxiv.org/abs/1909.02466.
Args:
num... | 11,149 | 40.143911 | 94 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/guided_anchor_head.py | import torch
import torch.nn as nn
from mmcv.ops import DeformConv2d, MaskedConv2d
from mmcv.runner import BaseModule, force_fp32
from mmdet.core import (anchor_inside_flags, build_anchor_generator,
build_assigner, build_bbox_coder, build_sampler,
calc_region, images_to_... | 37,004 | 42.079162 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/yolof_head.py | import torch
import torch.nn as nn
from mmcv.cnn import (ConvModule, bias_init_with_prob, constant_init, is_norm,
normal_init)
from mmcv.runner import force_fp32
from mmdet.core import anchor_inside_flags, multi_apply, reduce_mean, unmap
from ..builder import HEADS
from .anchor_head import Anchor... | 17,350 | 40.709135 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/sabl_retina_head.py | import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import force_fp32
from mmdet.core import (build_anchor_generator, build_assigner,
build_bbox_coder, build_sampler, images_to_levels,
multi_apply, multiclass_nms, unmap)... | 27,226 | 42.70305 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/fovea_head.py | import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import DeformConv2d
from mmcv.runner import BaseModule
from mmdet.core import multi_apply, multiclass_nms
from ..builder import HEADS
from .anchor_free_head import AnchorFreeHead
INF = 1e8
class FeatureAlign(BaseModule):
def __ini... | 14,776 | 41.340974 | 79 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/atss_iou_head.py | import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
from mmcv.runner import force_fp32
from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler,
images_to_levels, multi_apply, multiclass_nms,
reduc... | 29,973 | 42.757664 | 95 | py |
DDOD | DDOD-main/mmdet/models/dense_heads/dense_test_mixins.py | import sys
from inspect import signature
import torch
from mmdet.core import bbox_mapping_back, merge_aug_proposals, multiclass_nms
if sys.version_info >= (3, 7):
from mmdet.utils.contextmanagers import completed
class BBoxTestMixin(object):
"""Mixin class for testing det bboxes via DenseHead."""
def ... | 8,288 | 40.238806 | 79 | py |
DDOD | DDOD-main/mmdet/models/utils/se_layer.py | import mmcv
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
class SELayer(BaseModule):
"""Squeeze-and-Excitation Module.
Args:
channels (int): The input (and output) channels of the SE layer.
ratio (int): Squeeze ratio in SELayer, the intermediate chan... | 2,127 | 35.689655 | 79 | py |
DDOD | DDOD-main/mmdet/models/utils/gaussian_target.py | from math import sqrt
import torch
import torch.nn.functional as F
def gaussian2D(radius, sigma=1, dtype=torch.float32, device='cpu'):
"""Generate 2D gaussian kernel.
Args:
radius (int): Radius of gaussian kernel.
sigma (int): Sigma of gaussian function. Default: 1.
dtype (torch.dtyp... | 8,345 | 30.141791 | 79 | py |
DDOD | DDOD-main/mmdet/models/utils/normed_predictor.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import CONV_LAYERS
from .builder import LINEAR_LAYERS
@LINEAR_LAYERS.register_module(name='NormedLinear')
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Args:
tempeature (float, optional): Tempeature term. D... | 2,950 | 32.534091 | 77 | py |
DDOD | DDOD-main/mmdet/models/utils/res_layer.py | from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import BaseModule, Sequential
from torch import nn as nn
class ResLayer(Sequential):
"""ResLayer to build ResNet style backbone.
Args:
block (nn.Module): block used to build ResLayer.
inplanes (int): inplanes of block.
... | 6,344 | 32.394737 | 79 | py |
DDOD | DDOD-main/mmdet/models/utils/transformer.py | import math
import warnings
import torch
import torch.nn as nn
from mmcv.cnn import build_activation_layer, build_norm_layer, xavier_init
from mmcv.cnn.bricks.registry import (TRANSFORMER_LAYER,
TRANSFORMER_LAYER_SEQUENCE)
from mmcv.cnn.bricks.transformer import (BaseTransformerLa... | 32,701 | 40.134591 | 79 | py |
DDOD | DDOD-main/mmdet/models/utils/positional_encoding.py | import math
import torch
import torch.nn as nn
from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING
from mmcv.runner import BaseModule
@POSITIONAL_ENCODING.register_module()
class SinePositionalEncoding(BaseModule):
"""Position encoding with sine and cosine functions.
See `End-to-End Object Detection... | 6,520 | 39.006135 | 79 | py |
DDOD | DDOD-main/mmdet/models/utils/inverted_residual.py | import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from .se_layer import SELayer
class InvertedResidual(BaseModule):
"""Inverted Residual Block.
Args:
in_channels (int): The input channels of this Module.
out_channels (int): The output chan... | 4,046 | 31.637097 | 78 | py |
DDOD | DDOD-main/mmdet/models/utils/builder.py | import torch.nn as nn
from mmcv.utils import Registry, build_from_cfg
TRANSFORMER = Registry('Transformer')
LINEAR_LAYERS = Registry('linear layers')
def build_transformer(cfg, default_args=None):
"""Builder for Transformer."""
return build_from_cfg(cfg, TRANSFORMER, default_args)
LINEAR_LAYERS.register_mo... | 1,487 | 30.659574 | 78 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/standard_roi_head.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
@HEADS.register_module()
class StandardRoIHead(BaseRoIHead, BBoxTestMixin,... | 16,014 | 41.935657 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/grid_roi_head.py | import numpy as np
import torch
from mmdet.core import bbox2result, bbox2roi
from ..builder import HEADS, build_head, build_roi_extractor
from .standard_roi_head import StandardRoIHead
@HEADS.register_module()
class GridRoIHead(StandardRoIHead):
"""Grid roi head for Grid R-CNN.
https://arxiv.org/abs/1811.12... | 6,913 | 39.670588 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/scnet_roi_head.py | import torch
import torch.nn.functional as F
from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes,
merge_aug_masks, multiclass_nms)
from ..builder import HEADS, build_head, build_roi_extractor
from .cascade_roi_head import CascadeRoIHead
@HEADS.register_module()
class... | 23,548 | 40.902135 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/sparse_roi_head.py | import torch
from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh
from mmdet.core.bbox.samplers import PseudoSampler
from ..builder import HEADS
from .cascade_roi_head import CascadeRoIHead
@HEADS.register_module()
class SparseRoIHead(CascadeRoIHead):
r"""The RoIHead for `Sparse R-CNN: End-to-End Ob... | 14,515 | 44.504702 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/cascade_roi_head.py | import numpy as np
import torch
import torch.nn as nn
from mmcv.runner import ModuleList
from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, build_assigner,
build_sampler, merge_aug_bboxes, merge_aug_masks,
multiclass_nms)
from ..builder import HEADS, build_head... | 22,191 | 42.685039 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/trident_roi_head.py | import torch
from mmcv.ops import batched_nms
from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes,
multiclass_nms)
from mmdet.models.roi_heads.standard_roi_head import StandardRoIHead
from ..builder import HEADS
@HEADS.register_module()
class TridentRoIHead(StandardR... | 5,273 | 42.95 | 78 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/dynamic_roi_head.py | import numpy as np
import torch
from mmdet.core import bbox2roi
from mmdet.models.losses import SmoothL1Loss
from ..builder import HEADS
from .standard_roi_head import StandardRoIHead
EPS = 1e-15
@HEADS.register_module()
class DynamicRoIHead(StandardRoIHead):
"""RoI head for `Dynamic R-CNN <https://arxiv.org/ab... | 6,606 | 41.625806 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/point_rend_roi_head.py | # Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend # noqa
import logging
import os
import numpy as np
import torch
import torch.nn.functional as F
from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
from mmdet.core import bbox2roi, bbox_mapping, merge_aug_mask... | 18,733 | 46.427848 | 101 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/mask_scoring_roi_head.py | import torch
from mmdet.core import bbox2roi
from ..builder import HEADS, build_head
from .standard_roi_head import StandardRoIHead
@HEADS.register_module()
class MaskScoringRoIHead(StandardRoIHead):
"""Mask Scoring RoIHead for Mask Scoring RCNN.
https://arxiv.org/abs/1903.00241
"""
def __init__(se... | 5,182 | 44.867257 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/htc_roi_head.py | import torch
import torch.nn.functional as F
from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes,
merge_aug_masks, multiclass_nms)
from ..builder import HEADS, build_head, build_roi_extractor
from .cascade_roi_head import CascadeRoIHead
@HEADS.register_module()
class... | 25,519 | 43.075993 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/test_mixins.py | import logging
import sys
import numpy as np
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 BBoxTe... | 12,275 | 42.225352 | 79 | py |
DDOD | DDOD-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
from mmcv.runner import BaseModule
class BaseRoIExtractor(BaseModule, metaclass=ABCMeta):
"""Base class for RoI extractor.
Args:
roi_layer (dict): Specify RoI layer type and arguments.
out_channel... | 2,954 | 32.579545 | 78 | py |
DDOD | DDOD-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 ... | 4,781 | 40.582609 | 79 | py |
DDOD | DDOD-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 BaseModule, 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 a... | 24,638 | 42.075175 | 79 | py |
DDOD | DDOD-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
from mmcv.runner import BaseModule, force_fp32
from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
from mmdet.models.builder import HEADS, build_loss
from mmdet.models.losses import ac... | 25,025 | 41.85274 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/bbox_heads/dii_head.py | import torch
import torch.nn as nn
from mmcv.cnn import (bias_init_with_prob, build_activation_layer,
build_norm_layer)
from mmcv.cnn.bricks.transformer import FFN, MultiheadAttention
from mmcv.runner import auto_fp16, force_fp32
from mmdet.core import multi_apply
from mmdet.models.builder import... | 18,979 | 43.976303 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py | import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.models.builder import HEADS
from mmdet.models.utils import build_linear_layer
from .bbox_head import BBoxHead
@HEADS.register_module()
class ConvFCBBoxHead(BBoxHead):
r"""More general bbox head, with shared conv and fc layers and two optional
s... | 7,903 | 34.603604 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py | import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, ModuleList
from mmdet.models.backbones.resnet import Bottleneck
from mmdet.models.builder import HEADS
from .bbox_head import BBoxHead
class BasicResBlock(BaseModule):
"""Basic residual block.
This block is a little di... | 5,685 | 30.94382 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/shared_heads/res_layer.py | import warnings
import torch.nn as nn
from mmcv.runner import BaseModule, auto_fp16
from mmdet.models.backbones import ResNet
from mmdet.models.builder import SHARED_HEADS
from mmdet.models.utils import ResLayer as _ResLayer
@SHARED_HEADS.register_module()
class ResLayer(BaseModule):
def __init__(self,
... | 2,537 | 30.725 | 76 | py |
DDOD | DDOD-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
from mmcv.runner import BaseModule
from mmdet.models.builder import HEADS, build_loss
@HEADS.register_module()
class GridHead(BaseModule):
def __init__(self,
grid_points=9,
... | 15,531 | 41.787879 | 79 | py |
DDOD | DDOD-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
from mmcv.runner import BaseModule, force_fp32
from torch.nn.modules.utils import _pair
from mmdet.models.builder import HEADS, build_loss
@HEADS.register_module()
class MaskIoUHead(BaseModule):
"""Mask IoU Head.... | 7,334 | 39.081967 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/mask_heads/feature_relay_head.py | 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_channels (int, optional): number of input channe... | 1,882 | 34.528302 | 78 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/mask_heads/global_context_head.py | 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(BaseModule):
"""Global context head used in `SCNet ... | 3,726 | 35.90099 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py | 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, auto_fp16, force_fp32
from torch.nn.modules.util... | 17,282 | 41.051095 | 85 | py |
DDOD | DDOD-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
from mmcv.runner import BaseModule, auto_fp16, force_fp32
from mmdet.models.builder import HEADS
@HEADS.register_module()
class FusedSemanticHead(BaseModule):
r"""Multi-level fused semantic segmentation head.
.. code-block... | 3,654 | 33.158879 | 79 | py |
DDOD | DDOD-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
from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
from mmcv.runner import BaseModule
from mmdet.models.build... | 13,407 | 42.816993 | 126 | py |
DDOD | DDOD-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 |
DDOD | DDOD-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.
... | 1,857 | 31.596491 | 78 | py |
DDOD | DDOD-main/mmdet/models/losses/pisa_loss.py | 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,
bias=0,
num_class=80):
"... | 7,168 | 37.961957 | 79 | py |
DDOD | DDOD-main/mmdet/models/losses/balanced_l1_loss.py | 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,
alpha=0.5,
... | 4,168 | 33.454545 | 79 | py |
DDOD | DDOD-main/mmdet/models/losses/iou_loss.py | import math
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, eps=1e-6):
"""IoU loss.
Computing the IoU loss betwee... | 14,560 | 31.574944 | 79 | py |
DDOD | DDOD-main/mmdet/models/losses/smooth_l1_loss.py | 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): The prediction.
target (torch.Tensor):... | 4,515 | 31.257143 | 78 | py |
DDOD | DDOD-main/mmdet/models/losses/gfocal_loss.py | 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 from `Generalized Focal Loss: Learning
Qualifi... | 7,410 | 38.21164 | 79 | py |
DDOD | DDOD-main/mmdet/models/losses/varifocal_loss.py | 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,
alpha=0.75,
gamma=2.0,... | 5,317 | 38.686567 | 79 | py |
DDOD | DDOD-main/mmdet/models/losses/utils.py | 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:
Tensor: Reduced loss tensor.
"""
reduc... | 3,055 | 29.257426 | 79 | py |
DDOD | DDOD-main/mmdet/models/losses/seesaw_loss.py | 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,
label_weights,
... | 10,088 | 37.507634 | 79 | py |
DDOD | DDOD-main/mmdet/models/losses/ae_loss.py | 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 Embedding Loss including two parts: pull loss and push... | 3,809 | 35.990291 | 143 | py |
DDOD | DDOD-main/mmdet/models/losses/accuracy.py | 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)
target (torch.Tensor): The target of each prediction,... | 2,942 | 36.253165 | 79 | py |
DDOD | DDOD-main/mmdet/models/losses/focal_loss.py | 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,
target,
... | 7,517 | 40.307692 | 79 | py |
DDOD | DDOD-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... | 7,910 | 35.795349 | 79 | py |
DDOD | DDOD-main/mmdet/models/losses/gaussian_focal_loss.py | 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/1708.02002>`_ for targets in gaussian
distribution... | 3,264 | 34.48913 | 108 | py |
DDOD | DDOD-main/mmdet/models/losses/kd_loss.py | 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,
soft_label,
... | 2,864 | 31.556818 | 78 | py |
DDOD | DDOD-main/mmdet/models/backbones/hrnet.py | 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, Bottleneck
class HRModule(BaseModule):
"""Hig... | 21,648 | 37.316814 | 79 | py |
DDOD | DDOD-main/mmdet/models/backbones/regnet.py | 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 backbone.
More details can be found in `pa... | 13,555 | 37.078652 | 79 | py |
DDOD | DDOD-main/mmdet/models/backbones/mobilenet_v2.py | 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()
class MobileNetV2(BaseModule):
"""MobileNet... | 7,549 | 37.324873 | 78 | py |
DDOD | DDOD-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
from mmcv.runner import BaseModule
from torch.nn.modules.utils import _pair
from mmdet.models.backbones.resnet import Bottleneck, ResNet
from mmdet.models.build... | 11,081 | 36.187919 | 79 | py |
DDOD | DDOD-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 |
DDOD | DDOD-main/mmdet/models/backbones/resnet.py | 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
from ..utils import ResLayer
class BasicBlock(Bas... | 23,788 | 34.400298 | 79 | py |
DDOD | DDOD-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,
kaiming_init)
from mmcv.runner import Sequential, load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.utils import get_root_logger
from ..bui... | 12,686 | 34.94051 | 79 | py |
DDOD | DDOD-main/mmdet/models/backbones/ssd_vgg.py | 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-detection.
Args:
depth (int): Depth of v... | 4,655 | 35.375 | 79 | py |
DDOD | DDOD-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 |
DDOD | DDOD-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 mmcv.runner import BaseModule
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import Bottleneck as _Bottleneck
from ... | 10,531 | 31.708075 | 79 | py |
DDOD | DDOD-main/mmdet/models/backbones/hourglass.py | 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 Module for HourglassNet backbone.
Generate modu... | 7,282 | 33.03271 | 79 | py |
DDOD | DDOD-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
from mmcv.runner import Sequential
from ..builder import BACKBONES
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottle2neck(_Bottleneck):
e... | 11,611 | 34.510703 | 79 | py |
DDOD | DDOD-main/mmdet/models/backbones/darknet.py | # 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 ResBlock(BaseModule):
"""The basic residual block ... | 8,183 | 37.422535 | 79 | py |
DDOD | DDOD-main/mmdet/datasets/custom.py | 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 .builder import DATASETS
from .pipelines import Compos... | 13,174 | 35.395028 | 79 | py |
DDOD | DDOD-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... | 11,072 | 38.127208 | 167 | py |
DDOD | DDOD-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, ... | 5,284 | 35.701389 | 79 | py |
DDOD | DDOD-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.... | 5,368 | 35.033557 | 78 | py |
DDOD | DDOD-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,
seed=0):
... | 1,310 | 31.775 | 79 | py |
DDOD | DDOD-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... | 11,981 | 31.827397 | 79 | py |
DDOD | DDOD-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 |
DDOD | DDOD-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 |
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