repo stringclasses 454
values | file_path stringlengths 5 201 | extension stringclasses 1
value | content stringlengths 8 509k | num_lines int64 3 16.9k | size_bytes int64 8 511k |
|---|---|---|---|---|---|
insightface | detection/scrfd/mmdet/models/detectors/fovea.py | .py | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class FOVEA(SingleStageDetector):
"""Implementation of `FoveaBox <https://arxiv.org/abs/1904.03797>`_"""
def __init__(self,
backbone,
neck,
bbox_head,
... | 18 | 552 |
insightface | detection/scrfd/mmdet/models/detectors/htc.py | .py | from ..builder import DETECTORS
from .cascade_rcnn import CascadeRCNN
@DETECTORS.register_module()
class HybridTaskCascade(CascadeRCNN):
"""Implementation of `HTC <https://arxiv.org/abs/1901.07518>`_"""
def __init__(self, **kwargs):
super(HybridTaskCascade, self).__init__(**kwargs)
@property
... | 16 | 450 |
insightface | detection/scrfd/mmdet/models/detectors/trident_faster_rcnn.py | .py | from ..builder import DETECTORS
from .faster_rcnn import FasterRCNN
@DETECTORS.register_module()
class TridentFasterRCNN(FasterRCNN):
"""Implementation of `TridentNet <https://arxiv.org/abs/1901.01892>`_"""
def __init__(self,
backbone,
rpn_head,
roi_head,
... | 67 | 2,662 |
insightface | detection/scrfd/mmdet/models/detectors/cascade_rcnn.py | .py | from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class CascadeRCNN(TwoStageDetector):
r"""Implementation of `Cascade R-CNN: Delving into High Quality Object
Detection <https://arxiv.org/abs/1906.09756>`_"""
def __init__(self,
backbone,
... | 38 | 1,288 |
insightface | detection/scrfd/mmdet/models/detectors/yolact.py | .py | import torch
from mmdet.core import bbox2result
from ..builder import DETECTORS, build_head
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class YOLACT(SingleStageDetector):
"""Implementation of `YOLACT <https://arxiv.org/abs/1904.02689>`_"""
def __init__(self,
b... | 147 | 6,114 |
insightface | detection/scrfd/mmdet/models/detectors/vfnet.py | .py | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class VFNet(SingleStageDetector):
"""Implementation of `VarifocalNet
(VFNet).<https://arxiv.org/abs/2008.13367>`_"""
def __init__(self,
backbone,
neck,
... | 19 | 568 |
insightface | detection/scrfd/mmdet/models/detectors/reppoints_detector.py | .py | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class RepPointsDetector(SingleStageDetector):
"""RepPoints: Point Set Representation for Object Detection.
This detector is the implementation of:
- RepPoints detector (https://arxiv.org/pdf... | 23 | 694 |
insightface | detection/scrfd/mmdet/models/detectors/mask_rcnn.py | .py | from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class MaskRCNN(TwoStageDetector):
"""Implementation of `Mask R-CNN <https://arxiv.org/abs/1703.06870>`_"""
def __init__(self,
backbone,
rpn_head,
roi_head,
... | 25 | 692 |
insightface | detection/scrfd/mmdet/models/detectors/scrfd.py | .py | from mmdet.core import bbox2result
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
import torch
@DETECTORS.register_module()
class SCRFD(SingleStageDetector):
def __init__(self,
backbone,
neck,
bbox_head,
train_cfg=... | 101 | 3,832 |
insightface | detection/scrfd/mmdet/models/detectors/paa.py | .py | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class PAA(SingleStageDetector):
"""Implementation of `PAA <https://arxiv.org/pdf/2007.08103.pdf>`_."""
def __init__(self,
backbone,
neck,
bbox_head,
... | 18 | 546 |
insightface | detection/scrfd/mmdet/models/detectors/rpn.py | .py | import mmcv
from mmcv.image import tensor2imgs
from mmdet.core import bbox_mapping
from ..builder import DETECTORS, build_backbone, build_head, build_neck
from .base import BaseDetector
@DETECTORS.register_module()
class RPN(BaseDetector):
"""Implementation of Region Proposal Network."""
def __init__(self,
... | 154 | 5,804 |
insightface | detection/scrfd/mmdet/models/detectors/single_stage.py | .py | import torch
import torch.nn as nn
from mmdet.core import bbox2result
from ..builder import DETECTORS, build_backbone, build_head, build_neck
from .base import BaseDetector
@DETECTORS.register_module()
class SingleStageDetector(BaseDetector):
"""Base class for single-stage detectors.
Single-stage detectors ... | 161 | 6,160 |
insightface | detection/scrfd/mmdet/models/detectors/two_stage.py | .py | import torch
import torch.nn as nn
# from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
from ..builder import DETECTORS, build_backbone, build_head, build_neck
from .base import BaseDetector
@DETECTORS.register_module()
class TwoStageDetector(BaseDetector):
"""Base class for two-stage de... | 211 | 7,423 |
insightface | detection/scrfd/mmdet/models/detectors/fcos.py | .py | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class FCOS(SingleStageDetector):
"""Implementation of `FCOS <https://arxiv.org/abs/1904.01355>`_"""
def __init__(self,
backbone,
neck,
bbox_head,
... | 18 | 545 |
insightface | detection/scrfd/mmdet/models/detectors/fsaf.py | .py | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class FSAF(SingleStageDetector):
"""Implementation of `FSAF <https://arxiv.org/abs/1903.00621>`_"""
def __init__(self,
backbone,
neck,
bbox_head,
... | 18 | 545 |
insightface | detection/scrfd/mmdet/models/detectors/grid_rcnn.py | .py | from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class GridRCNN(TwoStageDetector):
"""Grid R-CNN.
This detector is the implementation of:
- Grid R-CNN (https://arxiv.org/abs/1811.12030)
- Grid R-CNN Plus: Faster and Better (https://arxiv.org/abs/190... | 30 | 815 |
insightface | detection/scrfd/mmdet/models/detectors/faster_rcnn.py | .py | from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class FasterRCNN(TwoStageDetector):
"""Implementation of `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_"""
def __init__(self,
backbone,
rpn_head,
roi_hea... | 25 | 698 |
insightface | detection/scrfd/mmdet/models/detectors/base.py | .py | from abc import ABCMeta, abstractmethod
from collections import OrderedDict
import mmcv
import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
from mmcv.runner import auto_fp16
from mmcv.utils import print_log
from mmdet.utils import get_root_logger
class BaseDetector(nn.Module, meta... | 356 | 14,099 |
insightface | detection/scrfd/mmdet/models/detectors/point_rend.py | .py | from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class PointRend(TwoStageDetector):
"""PointRend: Image Segmentation as Rendering
This detector is the implementation of
`PointRend <https://arxiv.org/abs/1912.08193>`_.
"""
def __init__(self,
... | 30 | 773 |
insightface | detection/scrfd/mmdet/models/detectors/atss.py | .py | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class ATSS(SingleStageDetector):
"""Implementation of `ATSS <https://arxiv.org/abs/1912.02424>`_."""
def __init__(self,
backbone,
neck,
bbox_head,
... | 18 | 546 |
insightface | detection/scrfd/mmdet/models/detectors/cornernet.py | .py | import torch
from mmdet.core import bbox2result, bbox_mapping_back
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class CornerNet(SingleStageDetector):
"""CornerNet.
This detector is the implementation of the paper `CornerNet: Detecting
Objects... | 96 | 3,578 |
insightface | detection/scrfd/mmdet/models/losses/mse_loss.py | .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.
... | 50 | 1,463 |
insightface | detection/scrfd/mmdet/models/losses/ghm_loss.py | .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... | 173 | 6,365 |
insightface | detection/scrfd/mmdet/models/losses/utils.py | .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 = ... | 100 | 3,077 |
insightface | detection/scrfd/mmdet/models/losses/cross_entropy_loss.py | .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... | 202 | 7,394 |
insightface | detection/scrfd/mmdet/models/losses/balanced_l1_loss.py | .py | import numpy as np
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def balanced_l1_loss(pred,
target,
beta=1.0,
alpha=0.5,
gamma=1.5,
reduction='mea... | 119 | 4,116 |
insightface | detection/scrfd/mmdet/models/losses/ae_loss.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
def ae_loss_per_image(tl_preds, br_preds, match):
"""Associative Embedding Loss in one image.
Associative Embedding Loss including two parts: pull loss and push loss.
Pull loss makes embedding vectors from sa... | 101 | 3,757 |
insightface | detection/scrfd/mmdet/models/losses/gfocal_loss.py | .py | import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def quality_focal_loss(pred, target, beta=2.0):
r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning
Qualified and Distributed Bounding Boxes for Dense Object D... | 186 | 7,318 |
insightface | detection/scrfd/mmdet/models/losses/varifocal_loss.py | .py | import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
def varifocal_loss(pred,
target,
weight=None,
alpha=0.75,
gamma=2.0,
iou_weighted=True,
... | 132 | 5,265 |
insightface | detection/scrfd/mmdet/models/losses/__init__.py | .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... | 29 | 1,493 |
insightface | detection/scrfd/mmdet/models/losses/smooth_l1_loss.py | .py | import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning target of the prediction.
beta... | 137 | 4,423 |
insightface | detection/scrfd/mmdet/models/losses/iou_loss.py | .py | import math
import torch
import torch.nn as nn
from mmdet.core import bbox_overlaps
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def iou_loss(pred, target, eps=1e-6):
"""IoU loss.
Computing the IoU loss between a set of predicted bboxes and target bboxes.
The loss is cal... | 421 | 13,571 |
insightface | detection/scrfd/mmdet/models/losses/pisa_loss.py | .py | import torch
from mmdet.core import bbox_overlaps
def isr_p(cls_score,
bbox_pred,
bbox_targets,
rois,
sampling_results,
loss_cls,
bbox_coder,
k=2,
bias=0,
num_class=80):
"""Importance-based Sample Reweighting (ISR_P), posit... | 181 | 7,076 |
insightface | detection/scrfd/mmdet/models/losses/gaussian_focal_loss.py | .py | import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0):
"""`Focal Loss <https://arxiv.org/abs/1708.02002>`_ for targets in gaussian
distribution.
Args:
pred (torch.Tensor): The predic... | 90 | 3,211 |
insightface | detection/scrfd/mmdet/models/losses/accuracy.py | .py | import torch.nn as nn
def accuracy(pred, target, topk=1, thresh=None):
"""Calculate accuracy according to the prediction and target.
Args:
pred (torch.Tensor): The model prediction, shape (N, num_class)
target (torch.Tensor): The target of each prediction, shape (N, )
topk (int | tupl... | 77 | 2,905 |
insightface | detection/scrfd/mmdet/models/losses/focal_loss.py | .py | import torch.nn as nn
import torch.nn.functional as F
from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss
from ..builder import LOSSES
from .utils import weight_reduce_loss
# This method is only for debugging
def py_sigmoid_focal_loss(pred,
target,
weigh... | 158 | 6,417 |
insightface | detection/scrfd/mmdet/models/utils/gaussian_target.py | .py | from math import sqrt
import torch
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.dtype): Dtype of gaussian tensor. De... | 186 | 5,784 |
insightface | detection/scrfd/mmdet/models/utils/transformer.py | .py | import torch
import torch.nn as nn
from mmcv.cnn import (Linear, build_activation_layer, build_norm_layer,
xavier_init)
from .builder import TRANSFORMER
class MultiheadAttention(nn.Module):
"""A warpper for torch.nn.MultiheadAttention.
This module implements MultiheadAttention with res... | 745 | 32,038 |
insightface | detection/scrfd/mmdet/models/utils/res_layer.py | .py | from mmcv.cnn import build_conv_layer, build_norm_layer
from torch import nn as nn
class ResLayer(nn.Sequential):
"""ResLayer to build ResNet style backbone.
Args:
block (nn.Module): block used to build ResLayer.
inplanes (int): inplanes of block.
planes (int): planes of block.
... | 103 | 3,639 |
insightface | detection/scrfd/mmdet/models/utils/positional_encoding.py | .py | import math
import torch
import torch.nn as nn
from mmcv.cnn import uniform_init
from .builder import POSITIONAL_ENCODING
@POSITIONAL_ENCODING.register_module()
class SinePositionalEncoding(nn.Module):
"""Position encoding with sine and cosine functions.
See `End-to-End Object Detection with Transformers
... | 151 | 5,800 |
insightface | detection/scrfd/mmdet/models/utils/builder.py | .py | from mmcv.utils import Registry, build_from_cfg
TRANSFORMER = Registry('Transformer')
POSITIONAL_ENCODING = Registry('Position encoding')
def build_transformer(cfg, default_args=None):
"""Builder for Transformer."""
return build_from_cfg(cfg, TRANSFORMER, default_args)
def build_positional_encoding(cfg, de... | 15 | 445 |
insightface | detection/scrfd/mmdet/models/utils/__init__.py | .py | from .builder import build_positional_encoding, build_transformer
from .gaussian_target import gaussian_radius, gen_gaussian_target
from .positional_encoding import (LearnedPositionalEncoding,
SinePositionalEncoding)
from .res_layer import ResLayer
from .transformer import (FFN, Multih... | 17 | 824 |
insightface | detection/scrfd/mmdet/models/necks/nasfcos_fpn.py | .py | import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, caffe2_xavier_init
from mmcv.ops.merge_cells import ConcatCell
from ..builder import NECKS
@NECKS.register_module()
class NASFCOS_FPN(nn.Module):
"""FPN structure in NASFPN.
Implementation of paper `NAS-FCOS: Fast Neural ... | 162 | 6,164 |
insightface | detection/scrfd/mmdet/models/necks/fpn_carafe.py | .py | import torch.nn as nn
from mmcv.cnn import ConvModule, build_upsample_layer, xavier_init
from mmcv.ops.carafe import CARAFEPack
from ..builder import NECKS
@NECKS.register_module()
class FPN_CARAFE(nn.Module):
"""FPN_CARAFE is a more flexible implementation of FPN. It allows more
choice for upsample methods ... | 268 | 10,671 |
insightface | detection/scrfd/mmdet/models/necks/rfp.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import constant_init, kaiming_init, xavier_init
from ..builder import NECKS, build_backbone
from .fpn import FPN
class ASPP(nn.Module):
"""ASPP (Atrous Spatial Pyramid Pooling)
This is an implementation of the ASPP module used ... | 129 | 4,592 |
insightface | detection/scrfd/mmdet/models/necks/bfp.py | .py | import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, xavier_init
from mmcv.cnn.bricks import NonLocal2d
import numpy as np
from ..builder import NECKS
@NECKS.register_module()
class BFP(nn.Module):
"""BFP (Balanced Feature Pyrmamids)
BFP takes multi-level features as inputs ... | 105 | 3,754 |
insightface | detection/scrfd/mmdet/models/necks/nas_fpn.py | .py | import torch.nn as nn
from mmcv.cnn import ConvModule, caffe2_xavier_init
from mmcv.ops.merge_cells import GlobalPoolingCell, SumCell
from ..builder import NECKS
@NECKS.register_module()
class NASFPN(nn.Module):
"""NAS-FPN.
Implementation of `NAS-FPN: Learning Scalable Feature Pyramid Architecture
for O... | 161 | 6,539 |
insightface | detection/scrfd/mmdet/models/necks/yolo_neck.py | .py | # Copyright (c) 2019 Western Digital Corporation or its affiliates.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from ..builder import NECKS
class DetectionBlock(nn.Module):
"""Detection block in YOLO neck.
Let out_channels = n, the DetectionBlock conta... | 137 | 5,089 |
insightface | detection/scrfd/mmdet/models/necks/__init__.py | .py | from .bfp import BFP
from .channel_mapper import ChannelMapper
from .fpn import FPN
from .fpn_carafe import FPN_CARAFE
from .hrfpn import HRFPN
from .nas_fpn import NASFPN
from .nasfcos_fpn import NASFCOS_FPN
from .pafpn import PAFPN
from .rfp import RFP
from .yolo_neck import YOLOV3Neck
from .lfpn import LFPN
__all__... | 18 | 455 |
insightface | detection/scrfd/mmdet/models/necks/lfpn.py | .py | import warnings
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, xavier_init
from mmcv.runner import auto_fp16
from ..builder import NECKS
@NECKS.register_module()
class LFPN(nn.Module):
r"""Feature Pyramid Network.
This is an implementation of paper `Feature Pyramid N... | 177 | 7,431 |
insightface | detection/scrfd/mmdet/models/necks/pafpn.py | .py | import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import auto_fp16
from ..builder import NECKS
from .fpn import FPN
@NECKS.register_module()
class PAFPN(FPN):
"""Path Aggregation Network for Instance Segmentation.
This is an implementation of the `PAFPN i... | 143 | 5,713 |
insightface | detection/scrfd/mmdet/models/necks/hrfpn.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, caffe2_xavier_init
from torch.utils.checkpoint import checkpoint
from ..builder import NECKS
@NECKS.register_module()
class HRFPN(nn.Module):
"""HRFPN (High Resolution Feature Pyrmamids)
paper: `High-Resoluti... | 103 | 3,481 |
insightface | detection/scrfd/mmdet/models/necks/channel_mapper.py | .py | import torch.nn as nn
from mmcv.cnn import ConvModule, xavier_init
from ..builder import NECKS
@NECKS.register_module()
class ChannelMapper(nn.Module):
r"""Channel Mapper to reduce/increase channels of backbone features.
This is used to reduce/increase channels of backbone features.
Args:
in_ch... | 75 | 2,765 |
insightface | detection/scrfd/mmdet/models/necks/fpn.py | .py | import warnings
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, xavier_init
from mmcv.runner import auto_fp16
from ..builder import NECKS
@NECKS.register_module()
class FPN(nn.Module):
r"""Feature Pyramid Network.
This is an implementation of paper `Feature Pyramid Ne... | 222 | 9,466 |
insightface | detection/scrfd/tools/browse_dataset.py | .py | import argparse
import os
from pathlib import Path
import mmcv
from mmcv import Config
from mmdet.datasets.builder import build_dataset
def parse_args():
parser = argparse.ArgumentParser(description='Browse a dataset')
parser.add_argument('config', help='train config file path')
parser.add_argument(
... | 69 | 1,887 |
insightface | detection/scrfd/tools/train.py | .py | import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import get_git_hash
from mmdet import __version__
from mmdet.apis import set_random_seed, train_detector... | 183 | 6,624 |
insightface | detection/scrfd/tools/benchmark.py | .py | import argparse
import os
import os.path as osp
import pickle
import numpy as np
import datetime
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, ini... | 117 | 3,617 |
insightface | detection/scrfd/tools/get_flops.py | .py | import argparse
import torch
from mmcv import Config
from mmdet.models import build_detector
try:
from mmcv.cnn import get_model_complexity_info
except ImportError:
raise ImportError('Please upgrade mmcv to >0.6.2')
def parse_args():
parser = argparse.ArgumentParser(description='Train a detector')
... | 76 | 2,239 |
insightface | detection/scrfd/tools/test_widerface.py | .py | import argparse
import os
import os.path as osp
import pickle
import numpy as np
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_che... | 257 | 9,954 |
insightface | detection/scrfd/tools/print_config.py | .py | import argparse
from mmcv import Config, DictAction
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config file path')
parser.add_argument(
'--options', nargs='+', action=DictAction, help='arguments in dict')
args = ... | 27 | 591 |
insightface | detection/scrfd/tools/publish_model.py | .py | import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output checkpoint filename')
args = par... | 40 | 1,125 |
insightface | detection/scrfd/tools/scrfd2onnx.py | .py | import argparse
import os.path as osp
import numpy as np
import onnx
import os
#import onnxruntime as rt
import torch
from mmdet.core import (build_model_from_cfg, generate_inputs_and_wrap_model,
preprocess_example_input)
#from mmdet.models import build
def pytorch2onnx(config_path,
... | 202 | 6,430 |
insightface | detection/scrfd/tools/scrfd.py | .py | # -*- coding: utf-8 -*-
# @Organization : insightface.ai
# @Author : Jia Guo
# @Time : 2021-05-04
# @Function :
from __future__ import division
import datetime
import numpy as np
import onnx
import onnxruntime
import os
import os.path as osp
import cv2
import sys
def softmax(z):
assert len(... | 338 | 12,400 |
insightface | detection/scrfd/tools/test.py | .py | import argparse
import os
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
fro... | 209 | 8,042 |
insightface | detection/scrfd/tools/convert_crowdhuman.py | .py | from __future__ import print_function
import cv2
import argparse
import os
import os.path as osp
import shutil
import numpy as np
import json
def parse_args():
parser = argparse.ArgumentParser(
description='convert crowdhuman dataset to scrfd format')
parser.add_argument('--raw', help='raw dataset dir'... | 93 | 3,626 |
insightface | detection/scrfd/configs/scrfd_crowdhuman/scrfd_crowdhuman_2.5g_bnkps.py | .py | lr_mult = 4
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=2500,
warmup_ratio=0.001,
step=[55*lr_mult, 68*lr_mult])
total_epochs = 80*lr_mult
checkpoint_config = dict(inte... | 190 | 6,447 |
insightface | detection/scrfd/configs/_base_/default_runtime.py | .py | checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
| 15 | 319 |
insightface | detection/scrfd/configs/_base_/schedules/schedule_20e.py | .py | # optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[16, 19])
total_epochs = 20
| 12 | 284 |
insightface | detection/scrfd/configs/_base_/schedules/schedule_1x.py | .py | # optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
total_epochs = 12
| 12 | 283 |
insightface | detection/scrfd/configs/_base_/schedules/schedule_2x.py | .py | # optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[16, 22])
total_epochs = 24
| 12 | 284 |
insightface | detection/scrfd/configs/_base_/schedules/schedule_retinaface_sgd.py | .py | # optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[55, 68])
total_epochs = 80
| 12 | 284 |
insightface | detection/scrfd/configs/_base_/datasets/retinaface.py | .py | dataset_type = 'RetinaFaceDataset'
data_root = 'data/retinaface/'
train_root = data_root+'train/'
val_root = data_root+'val/'
#img_norm_cfg = dict(
# mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_norm_cfg = dict(
mean=[127.5, 127.5, 127.5], std=[128.0, 128.0, 128.0], to_rgb=True)
... | 86 | 3,109 |
insightface | detection/scrfd/configs/_base_/datasets/coco_detection.py | .py | dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True... | 49 | 1,692 |
insightface | detection/scrfd/configs/_base_/datasets/cityscapes_instance.py | .py | dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize', i... | 56 | 1,944 |
insightface | detection/scrfd/configs/_base_/datasets/deepfashion.py | .py | # dataset settings
dataset_type = 'DeepFashionDataset'
data_root = 'data/DeepFashion/In-shop/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
d... | 54 | 1,888 |
insightface | detection/scrfd/configs/_base_/datasets/coco_instance_semantic.py | .py | dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
dict(type='Resize'... | 54 | 1,903 |
insightface | detection/scrfd/configs/_base_/datasets/coco_instance.py | .py | dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img_scale=(1333, 800),... | 49 | 1,718 |
insightface | detection/scrfd/configs/_base_/datasets/lvis_v0.5_instance.py | .py | _base_ = 'coco_instance.py'
dataset_type = 'LVISV05Dataset'
data_root = 'data/lvis_v0.5/'
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
_delete_=True,
type='ClassBalancedDataset',
oversample_thr=1e-3,
dataset=dict(
type=dataset_type,
a... | 24 | 767 |
insightface | detection/scrfd/configs/_base_/datasets/lvis_v1_instance.py | .py | _base_ = 'coco_instance.py'
dataset_type = 'LVISV1Dataset'
data_root = 'data/lvis_v1/'
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
_delete_=True,
type='ClassBalancedDataset',
oversample_thr=1e-3,
dataset=dict(
type=dataset_type,
ann_... | 24 | 717 |
insightface | detection/scrfd/configs/_base_/datasets/cityscapes_detection.py | .py | dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize', img_scale=[(2048,... | 56 | 1,918 |
insightface | detection/scrfd/configs/_base_/datasets/voc0712.py | .py | # dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1000... | 56 | 1,916 |
insightface | detection/scrfd/configs/_base_/datasets/wider_face.py | .py | # dataset settings
dataset_type = 'WIDERFaceDataset'
data_root = 'data/WIDERFace/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PhotoMetric... | 64 | 2,011 |
insightface | detection/scrfd/configs/_base_/models/rpn_r50_fpn.py | .py | # model settings
model = dict(
type='RPN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style... | 61 | 1,699 |
insightface | detection/scrfd/configs/_base_/models/faster_rcnn_r50_fpn.py | .py | model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch'... | 112 | 3,415 |
insightface | detection/scrfd/configs/_base_/models/faster_rcnn_r50_caffe_c4.py | .py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2,... | 117 | 3,481 |
insightface | detection/scrfd/configs/_base_/models/ssd300.py | .py | # model settings
input_size = 300
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 50 | 1,395 |
insightface | detection/scrfd/configs/_base_/models/rpn_r50_caffe_c4.py | .py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=dict(type... | 59 | 1,655 |
insightface | detection/scrfd/configs/_base_/models/retinanet_r50_fpn.py | .py | # model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 61 | 1,657 |
insightface | detection/scrfd/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py | .py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
strides=(1, 2, 2, 1),
dilations=(1, 1, 1, 2),
out_indic... | 108 | 3,266 |
insightface | detection/scrfd/configs/_base_/models/fast_rcnn_r50_fpn.py | .py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 63 | 1,932 |
insightface | detection/scrfd/configs/_base_/models/mask_rcnn_r50_fpn.py | .py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 125 | 3,858 |
insightface | detection/scrfd/configs/_base_/models/cascade_rcnn_r50_fpn.py | .py | # model settings
model = dict(
type='CascadeRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 184 | 6,001 |
insightface | detection/scrfd/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py | .py | # model settings
model = dict(
type='CascadeRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 201 | 6,610 |
insightface | detection/scrfd/configs/_base_/models/mask_rcnn_r50_caffe_c4.py | .py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, )... | 128 | 3,840 |
insightface | detection/scrfd/configs/scrfd/scrfd_500m.py | .py | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict(grad_clip=None)
lr_mult = 8
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1500,
warmup_ratio=0.001,
step=[55*lr_mult, 68*lr_mult])
total_epochs = 80*lr_mult
checkpoint_config = dict(inte... | 219 | 7,394 |
insightface | detection/scrfd/configs/scrfd/base_2.5g.py | .py | _base_ = [
#'../_base_/datasets/retinaface.py',
'../_base_/schedules/schedule_retinaface_sgd.py', '../_base_/default_runtime.py'
]
dataset_type = 'RetinaFaceDataset'
data_root = 'data/retinaface/'
train_root = data_root+'train/'
val_root = data_root+'val/'
#img_norm_cfg = dict(
# mean=[123.675, 116.28, 103.5... | 186 | 6,040 |
insightface | detection/scrfd/configs/scrfd/scrfd_500m_bnkps.py | .py | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict(grad_clip=None)
lr_mult = 8
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1500,
warmup_ratio=0.001,
step=[55*lr_mult, 68*lr_mult])
total_epochs = 80*lr_mult
checkpoint_config = dict(inte... | 219 | 7,395 |
insightface | detection/scrfd/configs/scrfd/scrfd_1gbn.py | .py | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict(grad_clip=None)
lr_mult = 8
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1500,
warmup_ratio=0.001,
step=[55*lr_mult, 68*lr_mult])
total_epochs = 80*lr_mult
checkpoint_config = dict(inte... | 219 | 7,359 |
insightface | detection/scrfd/configs/scrfd/scrfd_1g.py | .py | optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict(grad_clip=None)
lr_mult = 8
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1500,
warmup_ratio=0.001,
step=[55*lr_mult, 68*lr_mult])
total_epochs = 80*lr_mult
checkpoint_config = dict(inte... | 218 | 7,303 |
insightface | detection/scrfd/configs/scrfd/scrfd_34g.py | .py | optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict(grad_clip=None)
lr_mult = 8
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1500,
warmup_ratio=0.001,
step=[55*lr_mult, 68*lr_mult])
total_epochs = 80*lr_mult
checkpoint_config = dict(inte... | 226 | 7,563 |
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