repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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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/__init__.py | from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .centripetal_head import CentripetalHead
from .cor... | 2,256 | 42.403846 | 76 | 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/make_divisible.py | def make_divisible(value, divisor, min_value=None, min_ratio=0.9):
"""Make divisible function.
This function rounds the channel number to the nearest value that can be
divisible by the divisor. It is taken from the original tf repo. It ensures
that all layers have a channel number that is divisible by ... | 1,231 | 43 | 116 | 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/__init__.py | from .builder import build_linear_layer, build_transformer
from .gaussian_target import gaussian_radius, gen_gaussian_target
from .inverted_residual import InvertedResidual
from .make_divisible import make_divisible
from .normed_predictor import NormedConv2d, NormedLinear
from .positional_encoding import (LearnedPositi... | 989 | 46.142857 | 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/double_roi_head.py | from ..builder import HEADS
from .standard_roi_head import StandardRoIHead
@HEADS.register_module()
class DoubleHeadRoIHead(StandardRoIHead):
"""RoI head for Double Head RCNN.
https://arxiv.org/abs/1904.06493
"""
def __init__(self, reg_roi_scale_factor, **kwargs):
super(DoubleHeadRoIHead, se... | 1,202 | 34.382353 | 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/base_roi_head.py | from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
from ..builder import build_shared_head
class BaseRoIHead(BaseModule, metaclass=ABCMeta):
"""Base class for RoIHeads."""
def __init__(self,
bbox_roi_extractor=None,
bbox_head=None,
... | 3,149 | 29.582524 | 78 | 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/__init__.py | from .base_roi_head import BaseRoIHead
from .bbox_heads import (BBoxHead, ConvFCBBoxHead, DIIHead,
DoubleConvFCBBoxHead, SABLHead, SCNetBBoxHead,
Shared2FCBBoxHead, Shared4Conv1FCBBoxHead)
from .cascade_roi_head import CascadeRoIHead
from .double_roi_head import DoubleH... | 1,913 | 50.72973 | 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/pisa_roi_head.py | from mmdet.core import bbox2roi
from ..builder import HEADS
from ..losses.pisa_loss import carl_loss, isr_p
from .standard_roi_head import StandardRoIHead
@HEADS.register_module()
class PISARoIHead(StandardRoIHead):
r"""The RoI head for `Prime Sample Attention in Object Detection
<https://arxiv.org/abs/1904.0... | 6,608 | 40.30625 | 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/__init__.py | from .base_roi_extractor import BaseRoIExtractor
from .generic_roi_extractor import GenericRoIExtractor
from .single_level_roi_extractor import SingleRoIExtractor
__all__ = ['BaseRoIExtractor', 'SingleRoIExtractor', 'GenericRoIExtractor']
| 240 | 39.166667 | 75 | 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/roi_extractors/generic_roi_extractor.py | from mmcv.cnn.bricks import build_plugin_layer
from mmcv.runner import force_fp32
from mmdet.models.builder import ROI_EXTRACTORS
from .base_roi_extractor import BaseRoIExtractor
@ROI_EXTRACTORS.register_module()
class GenericRoIExtractor(BaseRoIExtractor):
"""Extract RoI features from all level feature maps lev... | 3,223 | 37.380952 | 79 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/bbox_heads/scnet_bbox_head.py | from mmdet.models.builder import HEADS
from .convfc_bbox_head import ConvFCBBoxHead
@HEADS.register_module()
class SCNetBBoxHead(ConvFCBBoxHead):
"""BBox head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
This inherits ``ConvFCBBoxHead`` with modified forward() function, allow us
to get intermediate s... | 2,259 | 28.350649 | 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/__init__.py | from .bbox_head import BBoxHead
from .convfc_bbox_head import (ConvFCBBoxHead, Shared2FCBBoxHead,
Shared4Conv1FCBBoxHead)
from .dii_head import DIIHead
from .double_bbox_head import DoubleConvFCBBoxHead
from .sabl_head import SABLHead
from .scnet_bbox_head import SCNetBBoxHead
__all__ = ... | 476 | 33.071429 | 76 | 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/shared_heads/__init__.py | from .res_layer import ResLayer
__all__ = ['ResLayer']
| 56 | 13.25 | 31 | 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/coarse_mask_head.py | from mmcv.cnn import ConvModule, Linear
from mmcv.runner import ModuleList, 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.
Compared with standard ``FCNMaskHead``, ``Coa... | 3,503 | 34.04 | 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/scnet_semantic_head.py | from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from .fused_semantic_head import FusedSemanticHead
@HEADS.register_module()
class SCNetSemanticHead(FusedSemanticHead):
"""Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args:
conv_to_res ... | 950 | 32.964286 | 72 | 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/__init__.py | from .coarse_mask_head import CoarseMaskHead
from .fcn_mask_head import FCNMaskHead
from .feature_relay_head import FeatureRelayHead
from .fused_semantic_head import FusedSemanticHead
from .global_context_head import GlobalContextHead
from .grid_head import GridHead
from .htc_mask_head import HTCMaskHead
from .mask_poi... | 699 | 37.888889 | 70 | py |
DDOD | DDOD-main/mmdet/models/roi_heads/mask_heads/htc_mask_head.py | from mmcv.cnn import ConvModule
from mmdet.models.builder import HEADS
from .fcn_mask_head import FCNMaskHead
@HEADS.register_module()
class HTCMaskHead(FCNMaskHead):
def __init__(self, with_conv_res=True, *args, **kwargs):
super(HTCMaskHead, self).__init__(*args, **kwargs)
self.with_conv_res = ... | 1,234 | 30.666667 | 78 | 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/roi_heads/mask_heads/scnet_mask_head.py | from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from .fcn_mask_head import FCNMaskHead
@HEADS.register_module()
class SCNetMaskHead(FCNMaskHead):
"""Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args:
conv_to_res (bool, optional): if T... | 931 | 32.285714 | 72 | 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/__init__.py | from .accuracy import Accuracy, accuracy
from .ae_loss import AssociativeEmbeddingLoss
from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy, mask_cross_entropy)
from .focal_loss import Focal... | 1,629 | 51.580645 | 77 | 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/__init__.py | from .darknet import Darknet
from .detectors_resnet import DetectoRS_ResNet
from .detectors_resnext import DetectoRS_ResNeXt
from .hourglass import HourglassNet
from .hrnet import HRNet
from .mobilenet_v2 import MobileNetV2
from .regnet import RegNet
from .res2net import Res2Net
from .resnest import ResNeSt
from .resne... | 657 | 31.9 | 66 | 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/deepfashion.py | from .builder import DATASETS
from .coco import CocoDataset
@DATASETS.register_module()
class DeepFashionDataset(CocoDataset):
CLASSES = ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants', 'bag',
'neckwear', 'headwear', 'eyeglass', 'belt', 'footwear', 'hair',
'skin', 'face')
| 317 | 27.909091 | 78 | py |
DDOD | DDOD-main/mmdet/datasets/voc.py | from collections import OrderedDict
from mmcv.utils import print_log
from mmdet.core import eval_map, eval_recalls
from .builder import DATASETS
from .xml_style import XMLDataset
@DATASETS.register_module()
class VOCDataset(XMLDataset):
CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',... | 3,766 | 39.074468 | 79 | py |
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