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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PseCo | PseCo-master/thirdparty/mmdetection/tools/dataset_converters/cityscapes.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import glob
import os.path as osp
import cityscapesscripts.helpers.labels as CSLabels
import mmcv
import numpy as np
import pycocotools.mask as maskUtils
def collect_files(img_dir, gt_dir):
suffix = 'leftImg8bit.png'
files = []
for img_file ... | 5,172 | 32.810458 | 75 | py |
PseCo | PseCo-master/thirdparty/mmdetection/tools/dataset_converters/pascal_voc.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import xml.etree.ElementTree as ET
import mmcv
import numpy as np
from mmdet.core import voc_classes
label_ids = {name: i for i, name in enumerate(voc_classes())}
def parse_xml(args):
xml_path, img_path = args
tree = ET.p... | 7,841 | 31.94958 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/tools/analysis_tools/analyze_results.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import mmcv
import numpy as np
from mmcv import Config, DictAction
from mmdet.core.evaluation import eval_map
from mmdet.core.visualization import imshow_gt_det_bboxes
from mmdet.datasets import build_dataset, get_loading_pipeline
... | 7,096 | 33.789216 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/tools/analysis_tools/eval_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import mmcv
from mmcv import Config, DictAction
from mmdet.datasets import build_dataset
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate metric of the '
'results saved in pkl format')
... | 3,118 | 35.694118 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/tools/analysis_tools/benchmark.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import time
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDistributedDataParallel
from mmcv.runner import init_dist, load_checkpoint, wrap_fp16_model
from mmdet.datasets import (b... | 4,843 | 32.638889 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/tools/analysis_tools/optimize_anchors.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Optimize anchor settings on a specific dataset.
This script provides two method to optimize YOLO anchors including k-means
anchor cluster and differential evolution. You can use ``--algorithm k-means``
and ``--algorithm differential_evolution`` to switch two method.
... | 13,161 | 34.477089 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/tools/analysis_tools/get_flops.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import numpy as np
import torch
from mmcv import Config, DictAction
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_ar... | 3,194 | 30.323529 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/tools/analysis_tools/analyze_logs.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def cal_train_time(log_dicts, args):
for i, log_dict in enumerate(log_dicts):
print(f'{"-" * 5}Analyze train time of {ar... | 6,300 | 33.812155 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/tools/analysis_tools/test_robustness.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import os.path as osp
import mmcv
import torch
from mmcv import DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
... | 15,421 | 38.341837 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/tools/analysis_tools/coco_error_analysis.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os
from argparse import ArgumentParser
from multiprocessing import Pool
import matplotlib.pyplot as plt
import numpy as np
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
def makeplot(rs, ps, outDir, class_name, iou_type):... | 12,389 | 35.441176 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/tools/analysis_tools/robustness_eval.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from argparse import ArgumentParser
import mmcv
import numpy as np
def print_coco_results(results):
def _print(result, ap=1, iouThr=None, areaRng='all', maxDets=100):
titleStr = 'Average Precision' if ap == 1 else 'Average Recall'
... | 8,112 | 31.194444 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/docs_zh-CN/stat.py | #!/usr/bin/env python
import functools as func
import glob
import os.path as osp
import re
import numpy as np
url_prefix = 'https://github.com/open-mmlab/mmdetection/blob/master/'
files = sorted(glob.glob('../configs/*/README.md'))
stats = []
titles = []
num_ckpts = 0
for f in files:
url = osp.dirname(f.replac... | 1,519 | 22.384615 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/docs_zh-CN/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 2,775 | 28.849462 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/.dev_scripts/convert_train_benchmark_script.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
def parse_args():
parser = argparse.ArgumentParser(
description='Convert benchmark model json to script')
parser.add_argument(
'txt_path', type=str, help='txt path output by benchmark_filter')
p... | 3,307 | 32.08 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/.dev_scripts/gather_test_benchmark_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import glob
import os.path as osp
import mmcv
from mmcv import Config
def parse_args():
parser = argparse.ArgumentParser(
description='Gather benchmarked models metric')
parser.add_argument('config', help='test config file path')
par... | 3,916 | 39.381443 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/.dev_scripts/benchmark_filter.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
def parse_args():
parser = argparse.ArgumentParser(description='Filter configs to train')
parser.add_argument(
'--basic-arch',
action='store_true',
help='to train models in basic arch')
... | 7,096 | 41.244048 | 92 | py |
PseCo | PseCo-master/thirdparty/mmdetection/.dev_scripts/gather_models.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import glob
import json
import os.path as osp
import shutil
import subprocess
from collections import OrderedDict
import mmcv
import torch
import yaml
def ordered_yaml_dump(data, stream=None, Dumper=yaml.SafeDumper, **kwds):
class OrderedDumper(Dum... | 9,899 | 34.869565 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/.dev_scripts/test_init_backbone.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Check out backbone whether successfully load pretrained checkpoint."""
import copy
import os
from os.path import dirname, exists, join
import pytest
from mmcv import Config, ProgressBar
from mmcv.runner import _load_checkpoint
from mmdet.models import build_detector
... | 6,625 | 35.406593 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/.dev_scripts/benchmark_inference_fps.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import mmcv
from mmcv import Config, DictAction
from mmcv.runner import init_dist
from tools.analysis_tools.benchmark import measure_inferense_speed
def parse_args():
parser = argparse.ArgumentParser(
descript... | 3,626 | 37.178947 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/.dev_scripts/gather_train_benchmark_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import glob
import os.path as osp
import mmcv
from gather_models import get_final_results
try:
import xlrd
except ImportError:
xlrd = None
try:
import xlutils
from xlutils.copy import copy
except ImportError:
xlutils = None
def pars... | 5,843 | 37.701987 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/.dev_scripts/batch_test_list.py | # Copyright (c) OpenMMLab. All rights reserved.
# yapf: disable
atss = dict(
config='configs/atss/atss_r50_fpn_1x_coco.py',
checkpoint='atss_r50_fpn_1x_coco_20200209-985f7bd0.pth',
eval='bbox',
metric=dict(bbox_mAP=39.4),
)
autoassign = dict(
config='configs/autoassign/autoassign_r50_fpn_8x2_1x_coco... | 12,707 | 34.3 | 117 | py |
PseCo | PseCo-master/thirdparty/mmdetection/.dev_scripts/benchmark_test_image.py | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import os.path as osp
from argparse import ArgumentParser
from mmcv import Config
from mmdet.apis import inference_detector, init_detector, show_result_pyplot
from mmdet.utils import get_root_logger
def parse_args():
parser = ArgumentParser()
pa... | 3,674 | 34.679612 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/.dev_scripts/convert_test_benchmark_script.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmcv import Config
def parse_args():
parser = argparse.ArgumentParser(
description='Convert benchmark model list to script')
parser.add_argument('config', help='test config file path')
parser.add_... | 3,604 | 29.041667 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/demo/video_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import mmcv
from mmdet.apis import inference_detector, init_detector
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection video demo')
parser.add_argument('video', help='Video file')
parser.add_argument('co... | 1,974 | 30.854839 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/demo/create_result_gif.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import mmcv
import numpy as np
try:
import imageio
except ImportError:
imageio = None
def parse_args():
parser = argparse.ArgumentParser(d... | 4,930 | 29.067073 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/demo/webcam_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import torch
from mmdet.apis import inference_detector, init_detector
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection webcam demo')
parser.add_argument('config', help='test config file path')
parser.ad... | 1,308 | 26.270833 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/demo/image_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParser
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector, show_result_pyplot)
def parse_args():
parser = ArgumentParser()
parser.add_argument('img', help='Imag... | 1,719 | 32.72549 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch... | 423 | 27.266667 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 201 | 27.857143 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
loss_cls=dict(
_delete_=True,
type='GHMC',
bins=30,
momentum=0.75,
use_sigmoid=True,
loss_weight=1.0),
loss_bbox=dict(
_delete_=True,... | 532 | 25.65 | 60 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch... | 423 | 27.266667 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 216 | 35.166667 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 212 | 34.5 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 217 | 35.333333 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 216 | 35.166667 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
_delete_=True,
type='DeformRoIPoolPack',
output_size=7,
output_cha... | 408 | 30.461538 | 56 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 210 | 34.166667 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 228 | 37.166667 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
sty... | 557 | 31.823529 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 221 | 36 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 222 | 36.166667 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 214 | 34.833333 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 216 | 35.166667 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 215 | 35 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 211 | 34.333333 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
_delete_=True,
type='ModulatedDeformRoIPoolPack',
output_size=7,
o... | 417 | 31.153846 | 56 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 591 | 28.6 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/htc/htc_r50_fpn_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 140 | 27.2 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='HybridTaskCascade',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen... | 8,333 | 34.164557 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 591 | 28.6 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 1,489 | 32.863636 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/htc/htc_r50_fpn_1x_coco.py | _base_ = './htc_without_semantic_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[8]),
semanti... | 1,998 | 34.070175 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/htc/htc_r101_fpn_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 295 | 28.6 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(type='Pretrained',
checkpoint='torchvis... | 340 | 36.888889 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 217 | 30.142857 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='RepPointsDetector',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
... | 2,065 | 29.382353 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/reppoints/reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
model = dict(bbox_head=dict(transform_method='partial_minmax'))
| 126 | 41.333333 | 63 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py | _base_ = './reppoints_moment_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(neck=dict(norm_cfg=norm_cfg), bbox_head=dict(norm_cfg=norm_cfg))
optimizer = dict(lr=0.01)
| 215 | 42.2 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
model = dict(
bbox_head=dict(transform_method='minmax', use_grid_points=True),
# training and testing settings
train_cfg=dict(
init=dict(
assigner=dict(
_delete_=True,
type='MaxIoUAssigner',
... | 452 | 31.357143 | 68 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 148 | 36.25 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
model = dict(bbox_head=dict(transform_method='minmax', use_grid_points=True))
| 140 | 46 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | 562 | 32.117647 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/reppoints/reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
model = dict(bbox_head=dict(transform_method='minmax'))
| 118 | 38.666667 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
model = dict(
type='GFL',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | 585 | 29.842105 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py | _base_ = './gfl_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
# multi-scale training
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'),
... | 788 | 33.304348 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
model = dict(
type='GFL',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | 461 | 26.176471 | 76 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='ResNet',
depth=101,
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',
init_cfg=... | 406 | 28.071429 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/gfl/gfl_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='GFL',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=di... | 1,739 | 29 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=F... | 529 | 32.125 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py | _base_ = 'tridentnet_r50_caffe_1x_coco.py'
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(133... | 756 | 31.913043 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py | _base_ = 'tridentnet_r50_caffe_mstrain_1x_coco.py'
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 138 | 26.8 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='TridentFasterRCNN',
backbone=dict(
type='TridentResNet',
trident_dilations=(1, 2, 3),
... | 1,868 | 32.375 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ssd/ssd512_coco.py | _base_ = 'ssd300_coco.py'
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 256, 256, 256),
... | 2,650 | 32.1375 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ssd/ssd300_coco.py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_p... | 2,190 | 31.701493 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='SingleStageDetector',
backbone=dict(
type='MobileNetV2',
out_indices=(4, 7),
norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
init_cfg=dict(type='TruncNormal', layer='C... | 4,756 | 31.582192 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
cudnn_benchmark = True
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(... | 2,488 | 29.728395 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
cudnn_benchmark = True
# model settings
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
type='RetinaNet',
backbone=dict(
type='ResNet',
depth=50,
... | 2,478 | 29.9875 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/paa/paa_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PAA',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=di... | 2,120 | 28.873239 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/paa/paa_r101_fpn_mstrain_3x_coco.py | _base_ = './paa_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 199 | 27.571429 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/paa/paa_r50_fpn_2x_coco.py | _base_ = './paa_r50_fpn_1x_coco.py'
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 122 | 29.75 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/paa/paa_r101_fpn_1x_coco.py | _base_ = './paa_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 191 | 26.428571 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/paa/paa_r50_fpn_1.5x_coco.py | _base_ = './paa_r50_fpn_1x_coco.py'
lr_config = dict(step=[12, 16])
runner = dict(type='EpochBasedRunner', max_epochs=18)
| 122 | 29.75 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/paa/paa_r50_fpn_mstrain_3x_coco.py | _base_ = './paa_r50_fpn_1x_coco.py'
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, 640), (1333, 800)],
... | 747 | 34.619048 | 77 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/paa/paa_r101_fpn_2x_coco.py | _base_ = './paa_r101_fpn_1x_coco.py'
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 123 | 30 | 53 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/yolact/yolact_r50_1x8_coco.py | _base_ = '../_base_/default_runtime.py'
# model settings
img_size = 550
model = dict(
type='YOLACT',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1, # do not freeze stem
norm_cfg=dict(type='BN', requires_grad=Tru... | 5,103 | 30.701863 | 79 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/yolact/yolact_r101_1x8_coco.py | _base_ = './yolact_r50_1x8_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 192 | 23.125 | 61 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/yolact/yolact_r50_8x8_coco.py | _base_ = 'yolact_r50_1x8_coco.py'
optimizer = dict(type='SGD', lr=8e-3, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=0.1,
step=[20, 42, 49, 52])
| 320 | 25.75 | 70 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py | _base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
# model settings
model = dict(
type='CornerNet',
backbone=dict(
type='HourglassNet',
downsample_times=5,
num_stacks=2,
stage_channels=[256, 256, 384, 384, 384, 512],
stage_blocks=[2, ... | 3,404 | 31.122642 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py | _base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
# model settings
model = dict(
type='CornerNet',
backbone=dict(
type='HourglassNet',
downsample_times=5,
num_stacks=2,
stage_channels=[256, 256, 384, 384, 384, 512],
stage_blocks=[2, ... | 3,404 | 31.122642 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py | _base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
# model settings
model = dict(
type='CornerNet',
backbone=dict(
type='HourglassNet',
downsample_times=5,
num_stacks=2,
stage_channels=[256, 256, 384, 384, 384, 512],
stage_blocks=[2, ... | 3,404 | 31.122642 | 78 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
fp16 = dict(loss_scale=512.)
| 242 | 29.375 | 74 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
fp16 = dict(loss_scale=512.)
| 240 | 29.125 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
# fp16 settings
fp16 = dict(loss_scale=512.)
| 98 | 23.75 | 52 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
# fp16 settings
fp16 = dict(loss_scale=512.)
| 102 | 24.75 | 56 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# fp16 settings
fp16 = dict(loss_scale=512.)
| 98 | 23.75 | 52 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
# model settings
model = dict(
type='PointRend',
roi_head=dict(
type='PointRendRoIHead',
mask_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='concat',
roi_layer=dict(
... | 1,453 | 31.311111 | 75 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 161 | 31.4 | 56 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/detectors/detectors_htc_r101_20e_coco.py | _base_ = '../htc/htc_r101_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_deform=True),
stage_with_sac=(False, True, True, True),
output_img=True),
neck=dict(
type='RFP',
rfp_s... | 920 | 30.758621 | 57 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_def... | 1,053 | 30.939394 | 72 | py |
PseCo | PseCo-master/thirdparty/mmdetection/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_def... | 382 | 28.461538 | 72 | py |
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