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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Pedestron | Pedestron-master/mmdet/core/evaluation/eval_hooks.py | import os
import os.path as osp
import mmcv
import numpy as np
import torch
import torch.distributed as dist
from mmcv.runner import Hook, obj_from_dict
from mmcv.parallel import scatter, collate
from pycocotools.cocoeval import COCOeval
from torch.utils.data import Dataset
from .coco_utils import results2json, fast_... | 8,060 | 37.203791 | 77 | py |
Pedestron | Pedestron-master/mmdet/core/post_processing/merge_augs.py | import torch
import numpy as np
from mmdet.ops import nms
from ..bbox import bbox_mapping_back
def merge_aug_proposals(aug_proposals, img_metas, rpn_test_cfg):
"""Merge augmented proposals (multiscale, flip, etc.)
Args:
aug_proposals (list[Tensor]): proposals from different testing
sche... | 3,317 | 33.206186 | 78 | py |
Pedestron | Pedestron-master/mmdet/core/post_processing/bbox_nms.py | import torch
from mmdet.ops.nms import nms_wrapper
def multiclass_nms(multi_bboxes,
multi_scores,
score_thr,
nms_cfg,
max_num=-1,
score_factors=None):
"""NMS for multi-class bboxes.
Args:
multi_bboxes (Ten... | 2,277 | 34.046154 | 75 | py |
Pedestron | Pedestron-master/mmdet/core/mask/mask_target.py | import torch
import numpy as np
import mmcv
def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_list,
cfg):
cfg_list = [cfg for _ in range(len(pos_proposals_list))]
mask_targets = map(mask_target_single, pos_proposals_list,
pos_assigned_gt_inds_list, ... | 1,427 | 37.594595 | 77 | py |
Pedestron | Pedestron-master/mmdet/core/my_mmcv/runner/mean_teacher_runner.py | from mmcv.runner import Runner
import logging
import os.path as osp
import time
import mmcv
import torch
from mmcv.runner import hooks
from mmcv.runner.log_buffer import LogBuffer
from mmdet.core.my_mmcv.runner.hooks.mean_teacher_optimizer import OptimizerHook
from mmcv.runner.hooks import (Hook, LrUpdaterHook, Chec... | 6,235 | 35.899408 | 94 | py |
Pedestron | Pedestron-master/mmdet/core/my_mmcv/runner/hooks/mean_teacher_optimizer.py | from torch.nn.utils import clip_grad
from mmcv.runner.hooks.hook import Hook
class OptimizerHook(Hook):
def __init__(self, grad_clip=None, mean_teacher=None):
self.grad_clip = grad_clip
self.mean_teacher = mean_teacher
def clip_grads(self, params):
clip_grad.clip_grad_norm_(
... | 1,009 | 33.827586 | 129 | py |
Pedestron | Pedestron-master/mmdet/core/fp16/hooks.py | import copy
import torch
import torch.nn as nn
from mmcv.runner import OptimizerHook
from .utils import cast_tensor_type
from ..utils.dist_utils import allreduce_grads
class Fp16OptimizerHook(OptimizerHook):
"""FP16 optimizer hook.
The steps of fp16 optimizer is as follows.
1. Scale the loss value.
... | 4,481 | 34.291339 | 79 | py |
Pedestron | Pedestron-master/mmdet/core/fp16/utils.py | from collections import abc
import numpy as np
import torch
def cast_tensor_type(inputs, src_type, dst_type):
if isinstance(inputs, torch.Tensor):
return inputs.to(dst_type)
elif isinstance(inputs, str):
return inputs
elif isinstance(inputs, np.ndarray):
return inputs
elif isi... | 664 | 26.708333 | 74 | py |
Pedestron | Pedestron-master/mmdet/core/fp16/decorators.py | import functools
from inspect import getfullargspec
import torch
from .utils import cast_tensor_type
def auto_fp16(apply_to=None, out_fp32=False):
"""Decorator to enable fp16 training automatically.
This decorator is useful when you write custom modules and want to support
mixed precision training. If ... | 6,211 | 37.583851 | 79 | py |
Pedestron | Pedestron-master/mmdet/core/bbox/bbox_target.py | import torch
from .transforms import bbox2delta
from ..utils import multi_apply
def bbox_target(pos_bboxes_list,
neg_bboxes_list,
pos_gt_bboxes_list,
pos_gt_labels_list,
cfg,
reg_classes=1,
target_means=[.0, .0, .0, .0],
... | 2,799 | 36.837838 | 78 | py |
Pedestron | Pedestron-master/mmdet/core/bbox/geometry.py | import torch
def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False):
"""Calculate overlap between two set of bboxes.
If ``is_aligned`` is ``False``, then calculate the ious between each bbox
of bboxes1 and bboxes2, otherwise the ious between each aligned pair of
bboxes1 and bboxes2.
A... | 2,163 | 32.8125 | 79 | py |
Pedestron | Pedestron-master/mmdet/core/bbox/transforms.py | import mmcv
import numpy as np
import torch
def bbox2delta(proposals, gt, means=[0, 0, 0, 0], stds=[1, 1, 1, 1]):
assert proposals.size() == gt.size()
proposals = proposals.float()
gt = gt.float()
px = (proposals[..., 0] + proposals[..., 2]) * 0.5
py = (proposals[..., 1] + proposals[..., 3]) * 0.... | 15,256 | 34.31713 | 146 | py |
Pedestron | Pedestron-master/mmdet/core/bbox/assigners/assign_result.py | import torch
class AssignResult(object):
def __init__(self, num_gts, gt_inds, max_overlaps, labels=None):
self.num_gts = num_gts
self.gt_inds = gt_inds
self.max_overlaps = max_overlaps
self.labels = labels
def add_gt_(self, gt_labels):
self_inds = torch.arange(
... | 664 | 32.25 | 77 | py |
Pedestron | Pedestron-master/mmdet/core/bbox/assigners/approx_max_iou_assigner.py | import torch
from .max_iou_assigner import MaxIoUAssigner
from ..geometry import bbox_overlaps
class ApproxMaxIoUAssigner(MaxIoUAssigner):
"""Assign a corresponding gt bbox or background to each bbox.
Each proposals will be assigned with `-1`, `0`, or a positive integer
indicating the ground truth index... | 4,950 | 41.316239 | 79 | py |
Pedestron | Pedestron-master/mmdet/core/bbox/assigners/max_iou_assigner.py | import torch
from .base_assigner import BaseAssigner
from .assign_result import AssignResult
from ..geometry import bbox_overlaps
class MaxIoUAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each bbox.
Each proposals will be assigned with `-1`, `0`, or a positive integer
indica... | 6,462 | 41.24183 | 79 | py |
Pedestron | Pedestron-master/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py | import numpy as np
import torch
from .random_sampler import RandomSampler
class InstanceBalancedPosSampler(RandomSampler):
def _sample_pos(self, assign_result, num_expected, **kwargs):
pos_inds = torch.nonzero(assign_result.gt_inds > 0)
if pos_inds.numel() != 0:
pos_inds = pos_inds.s... | 1,765 | 41.047619 | 77 | py |
Pedestron | Pedestron-master/mmdet/core/bbox/samplers/base_sampler.py | from abc import ABCMeta, abstractmethod
import torch
from .sampling_result import SamplingResult
class BaseSampler(metaclass=ABCMeta):
def __init__(self,
num,
pos_fraction,
neg_pos_ub=-1,
add_gt_as_proposals=True,
**kwargs):
... | 2,753 | 33.860759 | 78 | py |
Pedestron | Pedestron-master/mmdet/core/bbox/samplers/random_sampler.py | import numpy as np
import torch
from .base_sampler import BaseSampler
class RandomSampler(BaseSampler):
def __init__(self,
num,
pos_fraction,
neg_pos_ub=-1,
add_gt_as_proposals=True,
**kwargs):
super(RandomSampler, self... | 1,858 | 33.425926 | 77 | py |
Pedestron | Pedestron-master/mmdet/core/bbox/samplers/ohem_sampler.py | import torch
from .base_sampler import BaseSampler
from ..transforms import bbox2roi
class OHEMSampler(BaseSampler):
def __init__(self,
num,
pos_fraction,
context,
neg_pos_ub=-1,
add_gt_as_proposals=True,
**kwa... | 2,769 | 36.432432 | 77 | py |
Pedestron | Pedestron-master/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py | import numpy as np
import torch
from .random_sampler import RandomSampler
class IoUBalancedNegSampler(RandomSampler):
"""IoU Balanced Sampling
arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019)
Sampling proposals according to their IoU. `floor_fraction` of needed RoIs
are sampled from proposal... | 5,869 | 42.80597 | 79 | py |
Pedestron | Pedestron-master/mmdet/core/bbox/samplers/sampling_result.py | import torch
class SamplingResult(object):
def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result,
gt_flags):
self.pos_inds = pos_inds
self.neg_inds = neg_inds
self.pos_bboxes = bboxes[pos_inds]
self.neg_bboxes = bboxes[neg_inds]
self.pos_... | 790 | 30.64 | 76 | py |
Pedestron | Pedestron-master/mmdet/core/bbox/samplers/pseudo_sampler.py | import torch
from .base_sampler import BaseSampler
from .sampling_result import SamplingResult
class PseudoSampler(BaseSampler):
def __init__(self, **kwargs):
pass
def _sample_pos(self, **kwargs):
raise NotImplementedError
def _sample_neg(self, **kwargs):
raise NotImplementedEr... | 829 | 29.740741 | 79 | py |
Pedestron | Pedestron-master/mmdet/core/utils/dist_utils.py | from collections import OrderedDict
import torch.distributed as dist
from torch._utils import (_flatten_dense_tensors, _unflatten_dense_tensors,
_take_tensors)
from mmcv.runner import OptimizerHook
def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
if bucket_size_mb > 0:
... | 2,439 | 34.362319 | 97 | py |
Pedestron | Pedestron-master/mmdet/core/anchor/anchor_target.py | import torch
from ..bbox import assign_and_sample, build_assigner, PseudoSampler, bbox2delta
from ..utils import multi_apply
def anchor_target(anchor_list,
valid_flag_list,
gt_bboxes_list,
img_metas,
target_means,
target_stds,
... | 7,556 | 37.953608 | 79 | py |
Pedestron | Pedestron-master/mmdet/core/anchor/guided_anchor_target.py | import torch
from ..bbox import build_assigner, build_sampler, PseudoSampler
from ..utils import unmap, multi_apply
def calc_region(bbox, ratio, featmap_size=None):
"""Calculate a proportional bbox region.
The bbox center are fixed and the new h' and w' is h * ratio and w * ratio.
Args:
bbox (T... | 12,146 | 41.472028 | 79 | py |
Pedestron | Pedestron-master/mmdet/core/anchor/anchor_generator.py | import torch
class AnchorGenerator(object):
def __init__(self, base_size, scales, ratios, scale_major=True, ctr=None):
self.base_size = base_size
self.scales = torch.Tensor(scales)
self.ratios = torch.Tensor(ratios)
self.scale_major = scale_major
self.ctr = ctr
sel... | 3,117 | 35.682353 | 78 | py |
Pedestron | Pedestron-master/mmdet/models/builder.py | from torch import nn
from mmdet.utils import build_from_cfg
from .registry import (BACKBONES, NECKS, ROI_EXTRACTORS, SHARED_HEADS, HEADS,
LOSSES, DETECTORS)
def build(cfg, registry, default_args=None):
if isinstance(cfg, list):
modules = [
build_from_cfg(cfg_, registry,... | 959 | 20.818182 | 78 | py |
Pedestron | Pedestron-master/mmdet/models/detectors/two_stage.py | import torch
import torch.nn as nn
from .base import BaseDetector
from .test_mixins import RPNTestMixin, BBoxTestMixin, MaskTestMixin
from .. import builder
from ..registry import DETECTORS
from mmdet.core import bbox2roi, bbox2result, build_assigner, build_sampler
@DETECTORS.register_module
class TwoStageDetector(B... | 9,289 | 37.547718 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/detectors/base.py | import logging
from abc import ABCMeta, abstractmethod
import mmcv
import numpy as np
import torch.nn as nn
import pycocotools.mask as maskUtils
from mmdet.core import tensor2imgs, get_classes, auto_fp16
class BaseDetector(nn.Module):
"""Base class for detectors"""
__metaclass__ = ABCMeta
def __init__... | 4,967 | 31.051613 | 77 | py |
Pedestron | Pedestron-master/mmdet/models/detectors/single_stage.py | import torch.nn as nn
from .base import BaseDetector
from .. import builder
from ..registry import DETECTORS
from mmdet.core import bbox2result
@DETECTORS.register_module
class SingleStageDetector(BaseDetector):
def __init__(self,
backbone,
neck=None,
bbox_head... | 2,475 | 32.459459 | 78 | py |
Pedestron | Pedestron-master/mmdet/models/detectors/cascade_rcnn.py | from __future__ import division
import torch
import torch.nn as nn
from .base import BaseDetector
from .test_mixins import RPNTestMixin
from .. import builder
from ..registry import DETECTORS
from mmdet.core import (build_assigner, bbox2roi, bbox2result, build_sampler,
merge_aug_masks)
@DETE... | 15,878 | 40.786842 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/detectors/grid_rcnn.py | from .two_stage import TwoStageDetector
from ..registry import DETECTORS
import torch
from .. import builder
from mmdet.core import bbox2roi, bbox2result, build_assigner, build_sampler
@DETECTORS.register_module
class GridRCNN(TwoStageDetector):
"""Grid R-CNN.
This detector is the implementation of:
- ... | 8,293 | 39.262136 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/detectors/mgan.py | import torch
import torch.nn as nn
from .base import BaseDetector
from .test_mixins import RPNTestMixin, BBoxTestMixin
from .. import builder
from ..registry import DETECTORS
from mmdet.core import bbox2roi, bbox2result, build_assigner, build_sampler
@DETECTORS.register_module
class MGAN(BaseDetector, RPNTestMixin, ... | 4,332 | 33.11811 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/detectors/htc.py | import torch
import torch.nn.functional as F
from .cascade_rcnn import CascadeRCNN
from .. import builder
from ..registry import DETECTORS
from mmdet.core import (bbox2roi, bbox2result, build_assigner, build_sampler,
merge_aug_masks)
@DETECTORS.register_module
class HybridTaskCascade(CascadeR... | 17,522 | 43.138539 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/detectors/mask_scoring_rcnn.py | import torch
from mmdet.core import bbox2roi, build_assigner, build_sampler
from .two_stage import TwoStageDetector
from .. import builder
from ..registry import DETECTORS
@DETECTORS.register_module
class MaskScoringRCNN(TwoStageDetector):
"""Mask Scoring RCNN.
https://arxiv.org/abs/1903.00241
"""
... | 8,496 | 41.914141 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/plugins/non_local.py | import torch
import torch.nn as nn
from mmcv.cnn import constant_init, normal_init
from ..utils import ConvModule
class NonLocal2D(nn.Module):
"""Non-local module.
See https://arxiv.org/abs/1711.07971 for details.
Args:
in_channels (int): Channels of the input feature map.
reduction (in... | 3,709 | 31.26087 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/plugins/generalized_attention.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
from mmcv.cnn import kaiming_init
class GeneralizedAttention(nn.Module):
"""GeneralizedAttention module.
See 'An Empirical Study of Spatial Attention Mechanisms in Deep Networks'
(https://arxiv.org/abs/1711... | 15,139 | 38.324675 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/necks/csp_neck.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from mmcv.cnn import xavier_init
from mmdet.core import auto_fp16
from ..registry import NECKS
from ..utils import ConvModule
import cv2
@NECKS.register_module
class CSPNeck(nn.Module):
def __init__(self,
... | 3,042 | 29.128713 | 83 | py |
Pedestron | Pedestron-master/mmdet/models/necks/fpn.py | import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import xavier_init
from mmdet.core import auto_fp16
from ..registry import NECKS
from ..utils import ConvModule
@NECKS.register_module
class FPN(nn.Module):
def __init__(self,
in_channels,
out_channels,
... | 5,284 | 35.958042 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/necks/bfp.py | import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import xavier_init
from ..plugins import NonLocal2D
from ..registry import NECKS
from ..utils import ConvModule
@NECKS.register_module
class BFP(nn.Module):
"""BFP (Balanced Feature Pyrmamids)
BFP takes multi-level features as inputs and ga... | 3,598 | 33.941748 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/necks/hrfpn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from mmcv.cnn.weight_init import caffe2_xavier_init
from ..utils import ConvModule
from ..registry import NECKS
@NECKS.register_module
class HRFPN(nn.Module):
"""HRFPN (High Resolution Feature Pyrmami... | 3,244 | 32.112245 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/roi_extractors/single_level.py | from __future__ import division
import torch
import torch.nn as nn
from mmdet import ops
from mmdet.core import force_fp32
from ..registry import ROI_EXTRACTORS
@ROI_EXTRACTORS.register_module
class SingleRoIExtractor(nn.Module):
"""Extract RoI features from a single level feature map.
If there are mulitpl... | 3,186 | 33.641304 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/anchor_heads/rpn_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import normal_init
from mmdet.core import delta2bbox
from mmdet.ops import nms
from .anchor_head import AnchorHead
from ..registry import HEADS
@HEADS.register_module
class RPNHead(AnchorHead):
def __init__(self, in_channels, **kwa... | 4,050 | 37.580952 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/anchor_heads/anchor_head.py | from __future__ import division
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from mmdet.core import (AnchorGenerator, anchor_target, delta2bbox,
multi_apply, multiclass_nms, force_fp32)
from ..builder import build_loss
from ..registry import HEADS
@H... | 11,132 | 40.081181 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/anchor_heads/retina_head.py | import numpy as np
import torch.nn as nn
from mmcv.cnn import normal_init
from .anchor_head import AnchorHead
from ..registry import HEADS
from ..utils import bias_init_with_prob, ConvModule
@HEADS.register_module
class RetinaHead(AnchorHead):
def __init__(self,
num_classes,
in... | 2,866 | 33.130952 | 76 | py |
Pedestron | Pedestron-master/mmdet/models/anchor_heads/ga_rpn_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import normal_init
from mmdet.core import delta2bbox
from mmdet.ops import nms
from .guided_anchor_head import GuidedAnchorHead
from ..registry import HEADS
@HEADS.register_module
class GARPNHead(GuidedAnchorHead):
"""Guided-Anchor-... | 5,332 | 40.664063 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/anchor_heads/ga_retina_head.py | import torch.nn as nn
from mmcv.cnn import normal_init
from .guided_anchor_head import GuidedAnchorHead, FeatureAdaption
from ..registry import HEADS
from ..utils import bias_init_with_prob, ConvModule
from mmdet.ops import MaskedConv2d
@HEADS.register_module
class GARetinaHead(GuidedAnchorHead):
"""Guided-Ancho... | 4,013 | 36.166667 | 78 | py |
Pedestron | Pedestron-master/mmdet/models/anchor_heads/csp_head.py | import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from mmdet.core import multi_apply, multiclass_nms, csp_height2bbox, csp_heightwidth2bbox, force_fp32
from ..builder import build_loss
from ..registry import HEADS
from ..utils import bias_init_with_prob, Scale, ConvModule
import cv2
import numpy as ... | 16,638 | 39.385922 | 113 | py |
Pedestron | Pedestron-master/mmdet/models/anchor_heads/ssd_head.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import xavier_init
from mmdet.core import AnchorGenerator, anchor_target, multi_apply
from .anchor_head import AnchorHead
from ..losses import smooth_l1_loss
from ..registry import HEADS
# TODO: add loss evaluator for... | 7,708 | 38.737113 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/anchor_heads/fcos_head.py | import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from mmdet.core import multi_apply, multiclass_nms, distance2bbox, force_fp32
from ..builder import build_loss
from ..registry import HEADS
from ..utils import bias_init_with_prob, Scale, ConvModule
INF = 1e8
@HEADS.register_module
class FCOSHead(n... | 15,870 | 39.590793 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/anchor_heads/guided_anchor_head.py | from __future__ import division
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from mmdet.core import (AnchorGenerator, anchor_target, anchor_inside_flags,
ga_loc_target, ga_shape_target, delta2bbox,
multi_apply, multiclass_nms, f... | 24,865 | 39.763934 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/bbox_heads/mgan_head.py | import torch.nn as nn
from ..registry import HEADS
from ..utils import ConvModule
from mmdet.core import auto_fp16
@HEADS.register_module
class MGANHead(nn.Module):
def __init__(self,
num_convs=2,
roi_feat_size=7,
in_channels=512,
conv_out_chan... | 1,503 | 27.377358 | 71 | py |
Pedestron | Pedestron-master/mmdet/models/bbox_heads/bbox_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.core import (delta2bbox, multiclass_nms, bbox_target, force_fp32,
auto_fp16)
from ..builder import build_loss
from ..losses import accuracy
from ..registry import HEADS
@HEADS.register_module
class BBoxHead(nn.Modul... | 8,861 | 36.710638 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/bbox_heads/cascade_ped_head.py | import torch.nn as nn
from .bbox_head import BBoxHead
from ..registry import HEADS
from ..utils import ConvModule
from ..bbox_heads.convfc_bbox_head import ConvFCBBoxHead
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.core import (delta2bbox, multiclass_nms, bbox_target, force_fp32,
... | 2,870 | 34.012195 | 95 | py |
Pedestron | Pedestron-master/mmdet/models/bbox_heads/convfc_bbox_head.py | import torch.nn as nn
from .bbox_head import BBoxHead
from ..registry import HEADS
from ..utils import ConvModule
@HEADS.register_module
class ConvFCBBoxHead(BBoxHead):
"""More general bbox head, with shared conv and fc layers and two optional
separated branches.
/-> cls conv... | 7,011 | 36.698925 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/shared_heads/res_layer.py | import logging
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from mmdet.core import auto_fp16
from ..backbones import ResNet, make_res_layer
from ..registry import SHARED_HEADS
@SHARED_HEADS.register_module
class ResLayer(nn.Module):
def __init__... | 2,236 | 29.643836 | 74 | py |
Pedestron | Pedestron-master/mmdet/models/utils/weight_init.py | import numpy as np
import torch.nn as nn
def xavier_init(module, gain=1, bias=0, distribution='normal'):
assert distribution in ['uniform', 'normal']
if distribution == 'uniform':
nn.init.xavier_uniform_(module.weight, gain=gain)
else:
nn.init.xavier_normal_(module.weight, gain=gain)
i... | 1,455 | 29.978723 | 71 | py |
Pedestron | Pedestron-master/mmdet/models/utils/norm.py | import torch.nn as nn
norm_cfg = {
# format: layer_type: (abbreviation, module)
'BN': ('bn', nn.BatchNorm2d),
'SyncBN': ('bn', nn.SyncBatchNorm),
'GN': ('gn', nn.GroupNorm),
# and potentially 'SN'
}
def build_norm_layer(cfg, num_features, postfix=''):
""" Build normalization layer
Args:
... | 1,684 | 29.089286 | 74 | py |
Pedestron | Pedestron-master/mmdet/models/utils/scale.py | import torch
import torch.nn as nn
class Scale(nn.Module):
def __init__(self, scale=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
def forward(self, x):
return x * self.scale
| 266 | 19.538462 | 73 | py |
Pedestron | Pedestron-master/mmdet/models/utils/conv_ws.py | import torch.nn as nn
import torch.nn.functional as F
def conv_ws_2d(input,
weight,
bias=None,
stride=1,
padding=0,
dilation=1,
groups=1,
eps=1e-5):
c_in = weight.size(0)
weight_flat = weight.view(c_in, -1... | 1,335 | 27.425532 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/utils/conv_module.py | import warnings
import torch.nn as nn
from mmcv.cnn import kaiming_init, constant_init
from .conv_ws import ConvWS2d
from .norm import build_norm_layer
conv_cfg = {
'Conv': nn.Conv2d,
'ConvWS': ConvWS2d,
# TODO: octave conv
}
def build_conv_layer(cfg, *args, **kwargs):
""" Build convolution layer
... | 5,543 | 32.804878 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/losses/ghm_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..registry import LOSSES
def _expand_binary_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero(labels >= 1).squeeze()
if inds.numel() > 0:
bin... | 6,156 | 35.64881 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/losses/mse_loss.py | import torch.nn as nn
import torch.nn.functional as F
from .utils import weighted_loss
from ..registry import LOSSES
mse_loss = weighted_loss(F.mse_loss)
@LOSSES.register_module
class MSELoss(nn.Module):
def __init__(self, reduction='mean', loss_weight=1.0):
super().__init__()
self.reduction = ... | 632 | 23.346154 | 66 | py |
Pedestron | Pedestron-master/mmdet/models/losses/balanced_l1_loss.py | import numpy as np
import torch
import torch.nn as nn
from .utils import weighted_loss
from ..registry import LOSSES
@weighted_loss
def balanced_l1_loss(pred,
target,
beta=1.0,
alpha=0.5,
gamma=1.5,
reduction='me... | 1,884 | 25.928571 | 73 | py |
Pedestron | Pedestron-master/mmdet/models/losses/iou_loss.py | import torch
import torch.nn as nn
from mmdet.core import bbox_overlaps
from .utils import weighted_loss
from ..registry import LOSSES
@weighted_loss
def iou_loss(pred, target, eps=1e-6):
"""IoU loss.
Computing the IoU loss between a set of predicted bboxes and target bboxes.
The loss is calculated as n... | 4,339 | 30.911765 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/losses/smooth_l1_loss.py | import torch
import torch.nn as nn
from .utils import weighted_loss
from ..registry import LOSSES
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
assert beta > 0
assert pred.size() == target.size() and target.numel() > 0
diff = torch.abs(pred - target)
loss = torch.where(diff < beta, 0.5 *... | 1,288 | 27.021739 | 73 | py |
Pedestron | Pedestron-master/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 = ... | 2,982 | 29.438776 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/losses/accuracy.py | import torch.nn as nn
def accuracy(pred, target, topk=1):
assert isinstance(topk, (int, tuple))
if isinstance(topk, int):
topk = (topk, )
return_single = True
else:
return_single = False
maxk = max(topk)
_, pred_label = pred.topk(maxk, dim=1)
pred_label = pred_label.t(... | 801 | 24.0625 | 69 | py |
Pedestron | Pedestron-master/mmdet/models/losses/focal_loss.py | import torch.nn as nn
import torch.nn.functional as F
from mmdet.ops import sigmoid_focal_loss as _sigmoid_focal_loss
from .utils import weight_reduce_loss
from ..registry import LOSSES
# This method is only for debugging
def py_sigmoid_focal_loss(pred,
target,
wei... | 2,784 | 32.554217 | 76 | py |
Pedestron | Pedestron-master/mmdet/models/losses/cross_entropy_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import weight_reduce_loss
from ..registry import LOSSES
def cross_entropy(pred, label, weight=None, reduction='mean', avg_factor=None):
# element-wise losses
loss = F.cross_entropy(pred, label, reduction='none')
# apply weigh... | 3,386 | 31.567308 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/backbones/hrnet.py | import logging
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from ..registry import BACKBONES
from ..utils import build_norm_layer, build_conv_layer
from .resnet import BasicBlock, Bottleneck
class HRM... | 18,403 | 36.946392 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/backbones/resnet.py | import logging
import torch.nn as nn
import torch.utils.checkpoint as cp
from torch.nn.modules.batchnorm import _BatchNorm
from mmcv.cnn import constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from mmdet.ops import DeformConv, ModulatedDeformConv, ContextBlock
from mmdet.models.plugins import Gene... | 17,451 | 32.05303 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/backbones/vgg.py | import logging
import torch.nn as nn
from mmcv.cnn import (VGG, constant_init, kaiming_init,
normal_init)
from mmcv.runner import load_checkpoint
from ..registry import BACKBONES
@BACKBONES.register_module
class VGG(VGG):
def __init__(self,
depth=16,
with_... | 1,449 | 28 | 74 | py |
Pedestron | Pedestron-master/mmdet/models/backbones/ssd_vgg.py | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import (VGG, xavier_init, constant_init, kaiming_init,
normal_init)
from mmcv.runner import load_checkpoint
from ..registry import BACKBONES
@BACKBONES.register_module
class SSDVGG(VGG):
extra_s... | 4,657 | 33.503704 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/backbones/resnext.py | import math
import torch.nn as nn
from mmdet.ops import DeformConv, ModulatedDeformConv
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
from ..registry import BACKBONES
from ..utils import build_conv_layer, build_norm_layer
class Bottleneck(_Bottleneck):
def __init__(self, inplanes, pl... | 7,841 | 34.008929 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/backbones/senet.py | from __future__ import print_function, division, absolute_import
from collections import OrderedDict
import math
from ..registry import BACKBONES
import torch.nn as nn
from torch.utils import model_zoo
from mmcv.runner import load_checkpoint
import logging
"""
https://github.com/Cadene/pretrained-models.pytorch/blob/ma... | 13,521 | 35.349462 | 112 | py |
Pedestron | Pedestron-master/mmdet/models/backbones/mobilenet.py | import logging
from mmcv.runner import load_checkpoint
import torch
import torch.nn as nn
from mmcv.cnn import (constant_init, kaiming_init, normal_init)
from ..registry import BACKBONES
model_urls = {
'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
}
def _make_divisible(v, div... | 6,210 | 35.110465 | 107 | py |
Pedestron | Pedestron-master/mmdet/models/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 kaiming_init, normal_init
from ..builder import build_loss
from ..registry import HEADS
from ..utils import ConvModule
@HEADS.register_module
class GridHead(nn.Module):
def __init__(self,
... | 15,299 | 41.5 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/mask_heads/maskiou_head.py | import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import kaiming_init, normal_init
from mmdet.core import force_fp32
from ..builder import build_loss
from ..registry import HEADS
@HEADS.register_module
class MaskIoUHead(nn.Module):
"""Mask IoU Head.
This head predicts the IoU of predicted ... | 7,254 | 37.796791 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/mask_heads/fcn_mask_head.py | import mmcv
import numpy as np
import pycocotools.mask as mask_util
import torch
import torch.nn as nn
from ..builder import build_loss
from ..registry import HEADS
from ..utils import ConvModule
from mmdet.core import mask_target, force_fp32, auto_fp16
@HEADS.register_module
class FCNMaskHead(nn.Module):
def _... | 6,971 | 37.733333 | 79 | py |
Pedestron | Pedestron-master/mmdet/models/mask_heads/fused_semantic_head.py | import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import kaiming_init
from mmdet.core import auto_fp16, force_fp32
from ..registry import HEADS
from ..utils import ConvModule
@HEADS.register_module
class FusedSemanticHead(nn.Module):
"""Multi-level fused semantic segmentation head.
in_1 ->... | 3,552 | 32.205607 | 79 | py |
Pedestron | Pedestron-master/mmdet/datasets/custom.py | import os.path as osp
import mmcv
import numpy as np
from mmcv.parallel import DataContainer as DC
from torch.utils.data import Dataset
from .registry import DATASETS
from .transforms import (ImageTransform, BboxTransform, MaskTransform,
SegMapTransform, Numpy2Tensor)
from .utils import to_te... | 14,115 | 37.994475 | 108 | py |
Pedestron | Pedestron-master/mmdet/datasets/utils.py | from collections import Sequence
import matplotlib.pyplot as plt
import mmcv
import numpy as np
import torch
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tensor`.
Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
:class:`Sequence`, :class:`int` and :cl... | 2,178 | 30.57971 | 72 | py |
Pedestron | Pedestron-master/mmdet/datasets/dataset_wrappers.py | import numpy as np
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .registry import DATASETS
@DATASETS.register_module
class ConcatDataset(_ConcatDataset):
"""A wrapper of concatenated dataset.
Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
concat the group flag for... | 1,639 | 28.285714 | 78 | py |
Pedestron | Pedestron-master/mmdet/datasets/transforms.py | import mmcv
import numpy as np
import torch
__all__ = [
'ImageTransform', 'BboxTransform', 'MaskTransform', 'SegMapTransform',
'Numpy2Tensor'
]
class ImageTransform(object):
"""Preprocess an image.
1. rescale the image to expected size
2. normalize the image
3. flip the image (if needed)
... | 4,454 | 29.101351 | 79 | py |
Pedestron | Pedestron-master/mmdet/datasets/loader/sampler.py | from __future__ import division
import math
import torch
import numpy as np
from mmcv.runner.utils import get_dist_info
from torch.utils.data import Sampler
from torch.utils.data import DistributedSampler as _DistributedSampler
class DistributedSampler(_DistributedSampler):
def __init__(self, dataset, num_repl... | 5,668 | 34.21118 | 78 | py |
Pedestron | Pedestron-master/mmdet/datasets/loader/build_loader.py | import platform
from functools import partial
from mmcv.runner import get_dist_info
from mmcv.parallel import collate
from torch.utils.data import DataLoader
from .sampler import GroupSampler, DistributedGroupSampler, DistributedSampler
if platform.system() != 'Windows':
# https://github.com/pytorch/pytorch/issu... | 1,559 | 30.836735 | 78 | py |
Pedestron | Pedestron-master/mmdet/ops/dcn/functions/deform_pool.py | import torch
from torch.autograd import Function
from .. import deform_pool_cuda
class DeformRoIPoolingFunction(Function):
@staticmethod
def forward(ctx,
data,
rois,
offset,
spatial_scale,
out_size,
out_channels,... | 2,370 | 32.871429 | 78 | py |
Pedestron | Pedestron-master/mmdet/ops/dcn/functions/deform_conv.py | import torch
from torch.autograd import Function
from torch.nn.modules.utils import _pair
from .. import deform_conv_cuda
class DeformConvFunction(Function):
@staticmethod
def forward(ctx,
input,
offset,
weight,
stride=1,
paddin... | 7,291 | 39.065934 | 79 | py |
Pedestron | Pedestron-master/mmdet/ops/dcn/modules/deform_pool.py | from torch import nn
from ..functions.deform_pool import deform_roi_pooling
class DeformRoIPooling(nn.Module):
def __init__(self,
spatial_scale,
out_size,
out_channels,
no_trans,
group_size=1,
part_size=None,
... | 7,058 | 39.803468 | 79 | py |
Pedestron | Pedestron-master/mmdet/ops/dcn/modules/deform_conv.py | import math
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair
from ..functions.deform_conv import deform_conv, modulated_deform_conv
class DeformConv(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size,
... | 5,198 | 31.905063 | 78 | py |
Pedestron | Pedestron-master/mmdet/ops/masked_conv/functions/masked_conv.py | import math
import torch
from torch.autograd import Function
from torch.nn.modules.utils import _pair
from .. import masked_conv2d_cuda
class MaskedConv2dFunction(Function):
@staticmethod
def forward(ctx, features, mask, weight, bias, padding=0, stride=1):
assert mask.dim() == 3 and mask.size(0) == 1... | 2,333 | 39.947368 | 79 | py |
Pedestron | Pedestron-master/mmdet/ops/masked_conv/modules/masked_conv.py | import torch.nn as nn
from ..functions.masked_conv import masked_conv2d
class MaskedConv2d(nn.Conv2d):
"""A MaskedConv2d which inherits the official Conv2d.
The masked forward doesn't implement the backward function and only
supports the stride parameter to be 1 currently.
"""
def __init__(self,... | 1,010 | 31.612903 | 76 | py |
Pedestron | Pedestron-master/mmdet/ops/sigmoid_focal_loss/functions/sigmoid_focal_loss.py | from torch.autograd import Function
from torch.autograd.function import once_differentiable
from .. import sigmoid_focal_loss_cuda
class SigmoidFocalLossFunction(Function):
@staticmethod
def forward(ctx, input, target, gamma=2.0, alpha=0.25):
ctx.save_for_backward(input, target)
num_classes ... | 1,081 | 29.914286 | 77 | py |
Pedestron | Pedestron-master/mmdet/ops/sigmoid_focal_loss/modules/sigmoid_focal_loss.py | from torch import nn
from ..functions.sigmoid_focal_loss import sigmoid_focal_loss
# TODO: remove this module
class SigmoidFocalLoss(nn.Module):
def __init__(self, gamma, alpha):
super(SigmoidFocalLoss, self).__init__()
self.gamma = gamma
self.alpha = alpha
def forward(self, logits,... | 670 | 25.84 | 74 | py |
Pedestron | Pedestron-master/mmdet/ops/gcb/context_block.py | import torch
from mmcv.cnn import constant_init, kaiming_init
from torch import nn
def last_zero_init(m):
if isinstance(m, nn.Sequential):
constant_init(m[-1], val=0)
else:
constant_init(m, val=0)
class ContextBlock(nn.Module):
def __init__(self,
inplanes,
... | 3,766 | 34.87619 | 76 | py |
Pedestron | Pedestron-master/mmdet/ops/roi_align/gradcheck.py | import numpy as np
import torch
from torch.autograd import gradcheck
import os.path as osp
import sys
sys.path.append(osp.abspath(osp.join(__file__, '../../')))
from roi_align import RoIAlign # noqa: E402
feat_size = 15
spatial_scale = 1.0 / 8
img_size = feat_size / spatial_scale
num_imgs = 2
num_rois = 20
batch_in... | 866 | 27.9 | 76 | py |
Pedestron | Pedestron-master/mmdet/ops/roi_align/functions/roi_align.py | from torch.autograd import Function
from .. import roi_align_cuda
class RoIAlignFunction(Function):
@staticmethod
def forward(ctx, features, rois, out_size, spatial_scale, sample_num=0):
if isinstance(out_size, int):
out_h = out_size
out_w = out_size
elif isinstance(o... | 2,113 | 33.096774 | 79 | py |
Pedestron | Pedestron-master/mmdet/ops/roi_align/modules/roi_align.py | from torch.nn.modules.module import Module
from ..functions.roi_align import RoIAlignFunction
class RoIAlign(Module):
def __init__(self, out_size, spatial_scale, sample_num=0):
super(RoIAlign, self).__init__()
self.out_size = out_size
self.spatial_scale = float(spatial_scale)
sel... | 535 | 30.529412 | 74 | py |
Pedestron | Pedestron-master/mmdet/ops/roi_pool/gradcheck.py | import torch
from torch.autograd import gradcheck
import os.path as osp
import sys
sys.path.append(osp.abspath(osp.join(__file__, '../../')))
from roi_pool import RoIPool # noqa: E402
feat = torch.randn(4, 16, 15, 15, requires_grad=True).cuda()
rois = torch.Tensor([[0, 0, 0, 50, 50], [0, 10, 30, 43, 55],
... | 500 | 30.3125 | 66 | py |
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