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
value |
|---|---|---|---|---|---|---|
mmyolo | mmyolo-main/configs/deploy/detection_onnxruntime_dynamic.py | _base_ = ['./base_dynamic.py']
codebase_config = dict(
type='mmyolo',
task='ObjectDetection',
model_type='end2end',
post_processing=dict(
score_threshold=0.05,
confidence_threshold=0.005,
iou_threshold=0.5,
max_output_boxes_per_class=200,
pre_top_k=5000,
k... | 440 | 26.5625 | 41 | py |
mmyolo | mmyolo-main/configs/deploy/detection_tensorrt-fp16_static-640x640.py | _base_ = ['./base_static.py']
onnx_config = dict(input_shape=(640, 640))
backend_config = dict(
type='tensorrt',
common_config=dict(fp16_mode=True, max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 640, 6... | 535 | 34.733333 | 104 | py |
mmyolo | mmyolo-main/configs/deploy/detection_tensorrt-fp16_dynamic-192x192-960x960.py | _base_ = ['./base_dynamic.py']
backend_config = dict(
type='tensorrt',
common_config=dict(fp16_mode=True, max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 192, 192],
opt_shape=[1, 3, ... | 493 | 34.285714 | 104 | py |
mmyolo | mmyolo-main/configs/deploy/model/yolov6_s-static.py | _base_ = '../../yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco.py'
test_pipeline = [
dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
dict(
type='LetterResize',
scale=_base_.img_scale,
allow_scale_up=False,
use_mini_pad=False,
),
dict(type='LoadAnno... | 618 | 29.95 | 77 | py |
mmyolo | mmyolo-main/configs/deploy/model/yolov5_s-static.py | _base_ = '../../yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py'
test_pipeline = [
dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
dict(
type='LetterResize',
scale=_base_.img_scale,
allow_scale_up=False,
use_mini_pad=False,
),
dict(type='LoadAnnot... | 617 | 29.9 | 77 | py |
mmyolo | mmyolo-main/configs/yolov6/yolov6_n_syncbn_fast_8xb32-300e_coco.py | _base_ = './yolov6_s_syncbn_fast_8xb32-300e_coco.py'
# ======================= Possible modified parameters =======================
# -----model related-----
# The scaling factor that controls the depth of the network structure
deepen_factor = 0.33
# The scaling factor that controls the width of the network structure
... | 839 | 37.181818 | 78 | py |
mmyolo | mmyolo-main/configs/yolov6/yolov6_t_syncbn_fast_8xb32-300e_coco.py | _base_ = './yolov6_s_syncbn_fast_8xb32-300e_coco.py'
# ======================= Possible modified parameters =======================
# -----model related-----
# The scaling factor that controls the depth of the network structure
deepen_factor = 0.33
# The scaling factor that controls the width of the network structure
... | 722 | 39.166667 | 78 | py |
mmyolo | mmyolo-main/configs/yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco.py | _base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py']
# ======================= Frequently modified parameters =====================
# -----data related-----
data_root = 'data/coco/' # Root path of data
# Path of train annotation file
train_ann_file = 'annotations/instances_train2017.json'
train_data_p... | 9,117 | 31.448399 | 93 | py |
mmyolo | mmyolo-main/configs/yolov6/yolov6_l_syncbn_fast_8xb32-300e_coco.py | _base_ = './yolov6_m_syncbn_fast_8xb32-300e_coco.py'
# ======================= Possible modified parameters =======================
# -----model related-----
# The scaling factor that controls the depth of the network structure
deepen_factor = 1
# The scaling factor that controls the width of the network structure
wid... | 1,076 | 36.137931 | 78 | py |
mmyolo | mmyolo-main/configs/yolov6/yolov6_m_syncbn_fast_8xb32-300e_coco.py | _base_ = './yolov6_s_syncbn_fast_8xb32-300e_coco.py'
# ======================= Possible modified parameters =======================
# -----model related-----
# The scaling factor that controls the depth of the network structure
deepen_factor = 0.6
# The scaling factor that controls the width of the network structure
w... | 2,097 | 32.301587 | 78 | py |
mmyolo | mmyolo-main/configs/yolov6/yolov6_t_syncbn_fast_8xb32-400e_coco.py | _base_ = './yolov6_s_syncbn_fast_8xb32-400e_coco.py'
# ======================= Possible modified parameters =======================
# -----model related-----
# The scaling factor that controls the depth of the network structure
deepen_factor = 0.33
# The scaling factor that controls the width of the network structure
... | 722 | 39.166667 | 78 | py |
mmyolo | mmyolo-main/configs/yolov6/yolov6_s_fast_1xb12-40e_cat.py | _base_ = './yolov6_s_syncbn_fast_8xb32-400e_coco.py'
data_root = './data/cat/'
class_name = ('cat', )
num_classes = len(class_name)
metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
max_epochs = 40
train_batch_size_per_gpu = 12
train_num_workers = 4
num_last_epochs = 5
load_from = 'https://download.openmm... | 1,980 | 33.754386 | 172 | py |
mmyolo | mmyolo-main/configs/yolov6/yolov6_s_syncbn_fast_8xb32-300e_coco.py | _base_ = './yolov6_s_syncbn_fast_8xb32-400e_coco.py'
# ======================= Frequently modified parameters =====================
# -----train val related-----
# Base learning rate for optim_wrapper
max_epochs = 300 # Maximum training epochs
num_last_epochs = 15 # Last epoch number to switch training pipeline
# =... | 1,026 | 29.205882 | 78 | py |
mmyolo | mmyolo-main/configs/yolov6/yolov6_n_syncbn_fast_8xb32-400e_coco.py | _base_ = './yolov6_s_syncbn_fast_8xb32-400e_coco.py'
# ======================= Possible modified parameters =======================
# -----model related-----
# The scaling factor that controls the depth of the network structure
deepen_factor = 0.33
# The scaling factor that controls the width of the network structure
... | 839 | 37.181818 | 78 | py |
mmyolo | mmyolo-main/configs/yolov7/yolov7_e-p6_syncbn_fast_8x16b-300e_coco.py | _base_ = './yolov7_w-p6_syncbn_fast_8x16b-300e_coco.py'
model = dict(
backbone=dict(arch='E'),
neck=dict(
use_maxpool_in_downsample=True,
use_in_channels_in_downsample=True,
block_cfg=dict(
type='ELANBlock',
middle_ratio=0.4,
block_ratio=0.2,
... | 607 | 29.4 | 55 | py |
mmyolo | mmyolo-main/configs/yolov7/yolov7_w-p6_syncbn_fast_8x16b-300e_coco.py | _base_ = './yolov7_l_syncbn_fast_8x16b-300e_coco.py'
# ========================modified parameters========================
# -----data related-----
img_scale = (1280, 1280) # height, width
num_classes = 80 # Number of classes for classification
# Config of batch shapes. Only on val
# It means not used if batch_shape... | 6,232 | 33.060109 | 79 | py |
mmyolo | mmyolo-main/configs/yolov7/yolov7_tiny_syncbn_fast_8x16b-300e_coco.py | _base_ = './yolov7_l_syncbn_fast_8x16b-300e_coco.py'
# ========================modified parameters========================
# -----model related-----
# Data augmentation
max_translate_ratio = 0.1 # YOLOv5RandomAffine
scaling_ratio_range = (0.5, 1.6) # YOLOv5RandomAffine
mixup_prob = 0.05 # YOLOv5MixUp
randchoice_mo... | 3,236 | 31.69697 | 77 | py |
mmyolo | mmyolo-main/configs/yolov7/yolov7_tiny_fast_1xb12-40e_cat.py | _base_ = 'yolov7_tiny_syncbn_fast_8x16b-300e_coco.py'
data_root = './data/cat/'
class_name = ('cat', )
num_classes = len(class_name)
metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
anchors = [
[(68, 69), (154, 91), (143, 162)], # P3/8
[(242, 160), (189, 287), (391, 207)], # P4/16
[(353, 337... | 1,929 | 32.859649 | 178 | py |
mmyolo | mmyolo-main/configs/yolov7/yolov7_e2e-p6_syncbn_fast_8x16b-300e_coco.py | _base_ = './yolov7_w-p6_syncbn_fast_8x16b-300e_coco.py'
model = dict(
backbone=dict(arch='E2E'),
neck=dict(
use_maxpool_in_downsample=True,
use_in_channels_in_downsample=True,
block_cfg=dict(
type='EELANBlock',
num_elan_block=2,
middle_ratio=0.4,
... | 640 | 29.52381 | 55 | py |
mmyolo | mmyolo-main/configs/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco.py | _base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py']
# ========================Frequently modified parameters======================
# -----data related-----
data_root = 'data/coco/' # Root path of data
# Path of train annotation file
train_ann_file = 'annotations/instances_train2017.json'
train_data_p... | 10,602 | 31.624615 | 78 | py |
mmyolo | mmyolo-main/configs/yolov7/yolov7_d-p6_syncbn_fast_8x16b-300e_coco.py | _base_ = './yolov7_w-p6_syncbn_fast_8x16b-300e_coco.py'
model = dict(
backbone=dict(arch='D'),
neck=dict(
use_maxpool_in_downsample=True,
use_in_channels_in_downsample=True,
block_cfg=dict(
type='ELANBlock',
middle_ratio=0.4,
block_ratio=0.2,
... | 672 | 29.590909 | 55 | py |
mmyolo | mmyolo-main/configs/yolov7/yolov7_x_syncbn_fast_8x16b-300e_coco.py | _base_ = './yolov7_l_syncbn_fast_8x16b-300e_coco.py'
model = dict(
backbone=dict(arch='X'),
neck=dict(
in_channels=[640, 1280, 1280],
out_channels=[160, 320, 640],
block_cfg=dict(
type='ELANBlock',
middle_ratio=0.4,
block_ratio=0.4,
num_bl... | 464 | 28.0625 | 67 | py |
mmyolo | mmyolo-main/configs/razor/subnets/yolov5_s_spos_shufflenetv2_syncbn_8xb16-300e_coco.py | _base_ = [
'mmrazor::_base_/nas_backbones/spos_shufflenet_supernet.py',
'../../yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
]
checkpoint_file = 'https://download.openmmlab.com/mmrazor/v1/spos/spos_shufflenetv2_subnet_8xb128_in1k_flops_0.33M_acc_73.87_20211222-1f0a0b4d_v3.pth' # noqa
fix_subnet = 'https... | 1,156 | 37.566667 | 165 | py |
mmyolo | mmyolo-main/configs/razor/subnets/yolov6_l_attentivenas_a6_d12_syncbn_fast_8xb32-300e_coco.py | _base_ = [
'mmrazor::_base_/nas_backbones/attentive_mobilenetv3_supernet.py',
'../../yolov6/yolov6_l_syncbn_fast_8xb32-300e_coco.py'
]
checkpoint_file = 'https://download.openmmlab.com/mmrazor/v1/bignas/attentive_mobilenet_subnet_8xb256_in1k_flops-0.93G_acc-80.81_20221229_200440-73d92cc6.pth' # noqa
fix_subne... | 1,363 | 36.888889 | 166 | py |
mmyolo | mmyolo-main/configs/razor/subnets/rtmdet_tiny_ofa_lat31_syncbn_16xb16-300e_coco.py | _base_ = [
'mmrazor::_base_/nas_backbones/ofa_mobilenetv3_supernet.py',
'../../rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco.py'
]
checkpoint_file = 'https://download.openmmlab.com/mmrazor/v1/ofa/ofa_mobilenet_subnet_8xb256_in1k_note8_lat%4031ms_top1%4072.8_finetune%4025.py_20221214_0939-981a8b2a.pth' # noqa
fi... | 4,154 | 32.24 | 179 | py |
mmyolo | mmyolo-main/configs/yolov8/yolov8_n_syncbn_fast_8xb16-500e_coco.py | _base_ = './yolov8_s_syncbn_fast_8xb16-500e_coco.py'
deepen_factor = 0.33
widen_factor = 0.25
model = dict(
backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
bbox_head=dict(head_module=dict(widen_factor=widen_factor)))... | 321 | 31.2 | 74 | py |
mmyolo | mmyolo-main/configs/yolov8/yolov8_x_mask-refine_syncbn_fast_8xb16-500e_coco.py | _base_ = './yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco.py'
# This config use refining bbox and `YOLOv5CopyPaste`.
# Refining bbox means refining bbox by mask while loading annotations and
# transforming after `YOLOv5RandomAffine`
deepen_factor = 1.00
widen_factor = 1.25
model = dict(
backbone=dict(deepen_f... | 505 | 35.142857 | 74 | py |
mmyolo | mmyolo-main/configs/yolov8/yolov8_n_mask-refine_syncbn_fast_8xb16-500e_coco.py | _base_ = './yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco.py'
# This config will refine bbox by mask while loading annotations and
# transforming after `YOLOv5RandomAffine`
deepen_factor = 0.33
widen_factor = 0.25
model = dict(
backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
neck=di... | 445 | 33.307692 | 74 | py |
mmyolo | mmyolo-main/configs/yolov8/yolov8_s_syncbn_fast_8xb16-500e_coco.py | _base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py']
# ========================Frequently modified parameters======================
# -----data related-----
data_root = 'data/coco/' # Root path of data
# Path of train annotation file
train_ann_file = 'annotations/instances_train2017.json'
train_data_p... | 11,071 | 32.050746 | 78 | py |
mmyolo | mmyolo-main/configs/yolov8/yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco.py | _base_ = './yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco.py'
# This config use refining bbox and `YOLOv5CopyPaste`.
# Refining bbox means refining bbox by mask while loading annotations and
# transforming after `YOLOv5RandomAffine`
# ========================modified parameters======================
deepen_factor ... | 2,061 | 30.242424 | 73 | py |
mmyolo | mmyolo-main/configs/yolov8/yolov8_s_fast_1xb12-40e_cat.py | _base_ = 'yolov8_s_syncbn_fast_8xb16-500e_coco.py'
data_root = './data/cat/'
class_name = ('cat', )
num_classes = len(class_name)
metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
close_mosaic_epochs = 5
max_epochs = 40
train_batch_size_per_gpu = 12
train_num_workers = 4
load_from = 'https://download.ope... | 1,854 | 34 | 172 | py |
mmyolo | mmyolo-main/configs/yolov8/yolov8_m_mask-refine_syncbn_fast_8xb16-500e_coco.py | _base_ = './yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco.py'
# This config use refining bbox and `YOLOv5CopyPaste`.
# Refining bbox means refining bbox by mask while loading annotations and
# transforming after `YOLOv5RandomAffine`
# ========================modified parameters======================
deepen_factor ... | 2,719 | 30.627907 | 77 | py |
mmyolo | mmyolo-main/configs/yolov8/yolov8_x_syncbn_fast_8xb16-500e_coco.py | _base_ = './yolov8_l_syncbn_fast_8xb16-500e_coco.py'
deepen_factor = 1.00
widen_factor = 1.25
model = dict(
backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
bbox_head=dict(head_module=dict(widen_factor=widen_factor)))... | 321 | 31.2 | 74 | py |
mmyolo | mmyolo-main/configs/yolov8/yolov8_m_syncbn_fast_8xb16-500e_coco.py | _base_ = './yolov8_s_syncbn_fast_8xb16-500e_coco.py'
# ========================modified parameters======================
deepen_factor = 0.67
widen_factor = 0.75
last_stage_out_channels = 768
affine_scale = 0.9
mixup_prob = 0.1
# =======================Unmodified in most cases==================
img_scale = _base_.im... | 2,255 | 28.298701 | 67 | py |
mmyolo | mmyolo-main/configs/yolov8/yolov8_s_mask-refine_syncbn_fast_8xb16-500e_coco.py | _base_ = './yolov8_s_syncbn_fast_8xb16-500e_coco.py'
# This config will refine bbox by mask while loading annotations and
# transforming after `YOLOv5RandomAffine`
# ========================modified parameters======================
use_mask2refine = True
min_area_ratio = 0.01 # YOLOv5RandomAffine
# ================... | 2,701 | 31.166667 | 79 | py |
mmyolo | mmyolo-main/configs/yolov8/yolov8_l_syncbn_fast_8xb16-500e_coco.py | _base_ = './yolov8_m_syncbn_fast_8xb16-500e_coco.py'
# ========================modified parameters======================
deepen_factor = 1.00
widen_factor = 1.00
last_stage_out_channels = 512
mixup_prob = 0.15
# =======================Unmodified in most cases==================
pre_transform = _base_.pre_transform
mo... | 1,211 | 29.3 | 67 | py |
mmyolo | mmyolo-main/docs/en/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/3.x/configs'
files = sorted(glob.glob('../../configs/*/README.md'))
stats = []
titles = []
num_ckpts = 0
for f in files:
url = osp.dirname(f... | 1,529 | 21.835821 | 74 | py |
mmyolo | mmyolo-main/docs/en/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... | 3,414 | 28.439655 | 79 | py |
mmyolo | mmyolo-main/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/mmyolo/blob/main/'
files = sorted(glob.glob('../configs/*/README.md'))
stats = []
titles = []
num_ckpts = 0
for f in files:
url = osp.dirname(f.replace('../'... | 1,505 | 21.477612 | 74 | py |
mmyolo | mmyolo-main/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... | 3,434 | 28.110169 | 79 | py |
mmyolo | mmyolo-main/mmyolo/registry.py | # Copyright (c) OpenMMLab. All rights reserved.
"""MMYOLO provides 17 registry nodes to support using modules across projects.
Each node is a child of the root registry in MMEngine.
More details can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from mmengine.registry import DATA_S... | 4,331 | 40.653846 | 79 | py |
mmyolo | mmyolo-main/mmyolo/version.py | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.5.0'
from typing import Tuple
short_version = __version__
def parse_version_info(version_str: str) -> Tuple:
"""Parse version info of MMYOLO."""
version_info = []
for x in version_str.split('.'):
if x.isdigit():
versio... | 609 | 24.416667 | 56 | py |
mmyolo | mmyolo-main/mmyolo/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmdet
import mmengine
from mmengine.utils import digit_version
from .version import __version__, version_info
mmcv_minimum_version = '2.0.0rc4'
mmcv_maximum_version = '2.1.0'
mmcv_version = digit_version(mmcv.__version__)
mmengine_minimum_version = '... | 1,464 | 35.625 | 76 | py |
mmyolo | mmyolo-main/mmyolo/testing/_utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from os.path import dirname, exists, join
import numpy as np
from mmengine.config import Config
def _get_config_directory():
"""Find the predefined detector config directory."""
try:
# Assume we are running in the source mmyolo repo
... | 1,637 | 29.333333 | 78 | py |
mmyolo | mmyolo-main/mmyolo/testing/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from ._utils import get_detector_cfg
__all__ = ['get_detector_cfg']
| 117 | 22.6 | 47 | py |
mmyolo | mmyolo-main/mmyolo/models/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .data_preprocessors import * # noqa: F401,F403
from .dense_heads import * # noqa: F401,F403
from .detectors import * # noqa: F401,F403
from .layers import * # noqa: F401,F403
from .losses import * # noqa: F401,F403
fro... | 446 | 39.636364 | 52 | py |
mmyolo | mmyolo-main/mmyolo/models/data_preprocessors/data_preprocessor.py | # Copyright (c) OpenMMLab. All rights reserved.
import random
from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
from mmdet.models import BatchSyncRandomResize
from mmdet.models.data_preprocessors import DetDataPreprocessor
from mmengine import MessageHub, is_list_of
from mmen... | 11,943 | 38.419142 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/data_preprocessors/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .data_preprocessor import (PPYOLOEBatchRandomResize,
PPYOLOEDetDataPreprocessor,
YOLOv5DetDataPreprocessor,
YOLOXBatchSyncRandomResize)
__all__ = [
'YOLOv5DetDataPrep... | 424 | 37.636364 | 62 | py |
mmyolo | mmyolo-main/mmyolo/models/detectors/yolo_detector.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.models.detectors.single_stage import SingleStageDetector
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from mmengine.dist import get_world_size
from mmengine.logging import print_log
from mmyolo.registry import MODELS
@MODELS... | 2,138 | 38.611111 | 76 | py |
mmyolo | mmyolo-main/mmyolo/models/detectors/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .yolo_detector import YOLODetector
__all__ = ['YOLODetector']
| 116 | 22.4 | 47 | py |
mmyolo | mmyolo-main/mmyolo/models/plugins/cbam.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.utils import OptMultiConfig
from mmengine.model import BaseModule
from mmyolo.registry import MODELS
class ChannelAttention(BaseModule):
"""ChannelAttention.
Args:
channels ... | 3,949 | 31.916667 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/plugins/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .cbam import CBAM
__all__ = ['CBAM']
| 91 | 17.4 | 47 | py |
mmyolo | mmyolo-main/mmyolo/models/necks/yolox_pafpn.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmdet.models.backbones.csp_darknet import CSPLayer
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.registry import MODELS
from .base_yolo_neck... | 5,747 | 32.225434 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/necks/yolov8_pafpn.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Union
import torch.nn as nn
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.registry import MODELS
from .. import CSPLayerWithTwoConv
from ..utils import make_divisible, make_round
from .yolov5_pafpn import YOLOv5PAFPN
@MODELS.r... | 3,716 | 35.087379 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/necks/yolov6_pafpn.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.registry import MODELS
from ..layers import BepC3StageBlock, RepStageBlock
from ..utils import make_round
from .base... | 10,763 | 36.636364 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/necks/yolov5_pafpn.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Union
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.models.backbones.csp_darknet import CSPLayer
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.registry import MODELS
from ..utils import make_divis... | 6,273 | 35.476744 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/necks/cspnext_pafpn.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Sequence
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmdet.models.backbones.csp_darknet import CSPLayer
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.registry import MODELS
from... | 6,750 | 32.420792 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/necks/yolov7_pafpn.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.registry import MODELS
from ..layers import MaxPoolAndStrideConvBlock, RepVGGBlock, SPPFCSPBlock
from .base_yolo_neck import Base... | 7,846 | 35.16129 | 77 | py |
mmyolo | mmyolo-main/mmyolo/models/necks/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .base_yolo_neck import BaseYOLONeck
from .cspnext_pafpn import CSPNeXtPAFPN
from .ppyoloe_csppan import PPYOLOECSPPAFPN
from .yolov5_pafpn import YOLOv5PAFPN
from .yolov6_pafpn import YOLOv6CSPRepPAFPN, YOLOv6RepPAFPN
from .yolov7_pafpn import YOLOv7PAFPN
from .yolov... | 558 | 33.9375 | 74 | py |
mmyolo | mmyolo-main/mmyolo/models/necks/base_yolo_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import List, Union
import torch
import torch.nn as nn
from mmdet.utils import ConfigType, OptMultiConfig
from mmengine.model import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from mmyolo.registry impo... | 11,105 | 41.389313 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/necks/ppyoloe_csppan.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.models.backbones.csp_resnet import CSPResLayer
from mmyolo.models.necks import BaseYOLONeck
from mmyolo.registry import MODELS
... | 7,704 | 34.506912 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/layers/yolo_bricks.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import (ConvModule, DepthwiseSeparableConvModule, MaxPool2d,
build_norm_layer)
from mmdet.models.layers.csp_layer import \
Da... | 54,506 | 35.073461 | 156 | py |
mmyolo | mmyolo-main/mmyolo/models/layers/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .ema import ExpMomentumEMA
from .yolo_bricks import (BepC3StageBlock, CSPLayerWithTwoConv,
DarknetBottleneck, EELANBlock, EffectiveSELayer,
ELANBlock, ImplicitA, ImplicitM,
MaxPoolAndStride... | 808 | 46.588235 | 74 | py |
mmyolo | mmyolo-main/mmyolo/models/layers/ema.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional
import torch
import torch.nn as nn
from mmdet.models.layers import ExpMomentumEMA as MMDET_ExpMomentumEMA
from torch import Tensor
from mmyolo.registry import MODELS
@MODELS.register_module()
class ExpMomentumEMA(MMDET_ExpMoment... | 3,886 | 39.072165 | 78 | py |
mmyolo | mmyolo-main/mmyolo/models/dense_heads/rtmdet_head.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Sequence, Tuple
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, is_norm
from mmdet.models.task_modules.samplers import PseudoSampler
from mmdet.structures.bbox import distance2bbox
from mmdet.utils import (ConfigType, Instance... | 15,054 | 39.799458 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/dense_heads/yolov8_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import List, Sequence, Tuple, Union
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.models.utils import multi_apply
from mmdet.utils import (ConfigType, OptConfigType, OptInstanceList,
OptMult... | 16,795 | 41.307305 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/dense_heads/yolov6_head.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Sequence, Tuple, Union
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.models.utils import multi_apply
from mmdet.utils import (ConfigType, OptConfigType, OptInstanceList,
OptMultiConfig)
fro... | 15,037 | 39.643243 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/dense_heads/yolox_head.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Sequence, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmdet.models.task_modules.samplers import PseudoSampler
from mmdet.models.utils... | 22,508 | 42.706796 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/dense_heads/rtmdet_ins_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import List, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, is_norm
from mmcv.ops import batched_nms
from mmdet.models.utils import filter_scores_and_topk
from... | 30,484 | 40.990358 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/dense_heads/yolov5_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import math
from typing import List, Optional, Sequence, Tuple, Union
import torch
import torch.nn as nn
from mmdet.models.dense_heads.base_dense_head import BaseDenseHead
from mmdet.models.utils import filter_scores_and_topk, multi_apply
from mmdet.structure... | 38,981 | 42.750842 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/dense_heads/yolov7_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import List, Optional, Sequence, Tuple, Union
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.models.utils import multi_apply
from mmdet.utils import ConfigType, OptInstanceList
from mmengine.dist import get_dist_info... | 17,391 | 41.94321 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/dense_heads/rtmdet_rotated_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
from typing import List, Optional, Sequence, Tuple
import torch
import torch.nn as nn
from mmdet.models.utils import filter_scores_and_topk
from mmdet.structures.bbox import HorizontalBoxes, distance2bbox
from mmdet.structures.bbox.transforms ... | 26,337 | 40.024922 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/dense_heads/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .ppyoloe_head import PPYOLOEHead, PPYOLOEHeadModule
from .rtmdet_head import RTMDetHead, RTMDetSepBNHeadModule
from .rtmdet_ins_head import RTMDetInsSepBNHead, RTMDetInsSepBNHeadModule
from .rtmdet_rotated_head import (RTMDetRotatedHead,
... | 1,048 | 48.952381 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/dense_heads/ppyoloe_head.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.models.utils import multi_apply
from mmdet.utils import (ConfigType, OptConfigType, OptInstanceList,
OptMultiConfig, reduce_me... | 15,834 | 41.226667 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/utils/misc.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Sequence, Union
import torch
from mmdet.structures.bbox.transforms import get_box_tensor
from torch import Tensor
def make_divisible(x: float,
widen_factor: float = 1.0,
divisor: int = 8) -> int:
... | 3,851 | 38.306122 | 76 | py |
mmyolo | mmyolo-main/mmyolo/models/utils/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .misc import gt_instances_preprocess, make_divisible, make_round
__all__ = ['make_divisible', 'make_round', 'gt_instances_preprocess']
| 189 | 37 | 69 | py |
mmyolo | mmyolo-main/mmyolo/models/task_modules/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .assigners import BatchATSSAssigner, BatchTaskAlignedAssigner
from .coders import YOLOv5BBoxCoder, YOLOXBBoxCoder
__all__ = [
'YOLOv5BBoxCoder', 'YOLOXBBoxCoder', 'BatchATSSAssigner',
'BatchTaskAlignedAssigner'
]
| 275 | 29.666667 | 66 | py |
mmyolo | mmyolo-main/mmyolo/models/task_modules/assigners/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch
import torch.nn.functional as F
from torch import Tensor
def select_candidates_in_gts(priors_points: Tensor,
gt_bboxes: Tensor,
eps: float = 1e-9) -> Tensor:
"""Select ... | 4,202 | 36.864865 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/task_modules/assigners/batch_dsl_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.structures.bbox import BaseBoxes
from mmdet.utils import ConfigType
from torch import Tensor
from mmyolo.registry import TASK_UTILS
INF = 100000000
EPS = 1.0e-7
def... | 10,901 | 38.934066 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/task_modules/assigners/batch_yolov7_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.structures.bbox import bbox_cxcywh_to_xyxy, bbox_overlaps
def _cat_multi_level_tensor_in_place(*multi_level_tensor, place_hold_var):
"""concat multi-level tens... | 14,354 | 40.608696 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/task_modules/assigners/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .batch_atss_assigner import BatchATSSAssigner
from .batch_dsl_assigner import BatchDynamicSoftLabelAssigner
from .batch_task_aligned_assigner import BatchTaskAlignedAssigner
from .utils import (select_candidates_in_gts, select_highest_overlaps,
yo... | 529 | 39.769231 | 70 | py |
mmyolo | mmyolo-main/mmyolo/models/task_modules/assigners/batch_atss_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.utils import ConfigType
from torch import Tensor
from mmyolo.registry import TASK_UTILS
from .utils import (select_candidates_in_gts, select_highest_overlaps,
... | 14,471 | 41.564706 | 81 | py |
mmyolo | mmyolo-main/mmyolo/models/task_modules/assigners/batch_task_aligned_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from mmyolo.models.losses import bbox_overlaps
from mmyolo.registry import TASK_UTILS
from .utils import (select_candidates_in_gts, select_high... | 13,143 | 41.128205 | 78 | py |
mmyolo | mmyolo-main/mmyolo/models/task_modules/coders/distance_point_bbox_coder.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Union
import torch
from mmdet.models.task_modules.coders import \
DistancePointBBoxCoder as MMDET_DistancePointBBoxCoder
from mmdet.structures.bbox import bbox2distance, distance2bbox
from mmyolo.registry import TASK_UTILS
@T... | 2,948 | 35.8625 | 78 | py |
mmyolo | mmyolo-main/mmyolo/models/task_modules/coders/yolox_bbox_coder.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Union
import torch
from mmdet.models.task_modules.coders.base_bbox_coder import BaseBBoxCoder
from mmyolo.registry import TASK_UTILS
@TASK_UTILS.register_module()
class YOLOXBBoxCoder(BaseBBoxCoder):
"""YOLOX BBox coder.
This decoder decode... | 1,477 | 31.130435 | 78 | py |
mmyolo | mmyolo-main/mmyolo/models/task_modules/coders/distance_angle_point_coder.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Union
import torch
from mmyolo.registry import TASK_UTILS
try:
from mmrotate.models.task_modules.coders import \
DistanceAnglePointCoder as MMROTATE_DistanceAnglePointCoder
MMROTATE_AVAILABLE = True
except ImportEr... | 3,512 | 35.978947 | 78 | py |
mmyolo | mmyolo-main/mmyolo/models/task_modules/coders/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .distance_angle_point_coder import DistanceAnglePointCoder
from .distance_point_bbox_coder import DistancePointBBoxCoder
from .yolov5_bbox_coder import YOLOv5BBoxCoder
from .yolox_bbox_coder import YOLOXBBoxCoder
__all__ = [
'YOLOv5BBoxCoder', 'YOLOXBBoxCoder', ... | 378 | 33.454545 | 66 | py |
mmyolo | mmyolo-main/mmyolo/models/task_modules/coders/yolov5_bbox_coder.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Union
import torch
from mmdet.models.task_modules.coders.base_bbox_coder import BaseBBoxCoder
from mmyolo.registry import TASK_UTILS
@TASK_UTILS.register_module()
class YOLOv5BBoxCoder(BaseBBoxCoder):
"""YOLOv5 BBox coder.
This decoder deco... | 1,895 | 32.857143 | 78 | py |
mmyolo | mmyolo-main/mmyolo/models/losses/iou_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from mmdet.models.losses.utils import weight_reduce_loss
from mmdet.structures.bbox import HorizontalBoxes
from mmyolo.registry import MODELS
def bbox_overlaps(pred: torch.Tensor,... | 8,786 | 36.712446 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/losses/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .iou_loss import IoULoss, bbox_overlaps
__all__ = ['IoULoss', 'bbox_overlaps']
| 133 | 25.8 | 47 | py |
mmyolo | mmyolo-main/mmyolo/models/backbones/yolov7_backbone.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple, Union
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.models.backbones.csp_darknet import Focus
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.registry import MODELS
from ..layers import MaxPoolA... | 11,081 | 37.748252 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/backbones/efficient_rep.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple, Union
import torch
import torch.nn as nn
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.models.layers.yolo_bricks import SPPFBottleneck
from mmyolo.registry import MODELS
from ..layers import BepC3StageBlock, RepStageBloc... | 11,355 | 38.430556 | 78 | py |
mmyolo | mmyolo-main/mmyolo/models/backbones/csp_resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple, Union
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.models.backbones import BaseBackbone
from mmyolo.models.layers.yolo_bricks import CSPResLayer
from mmyolo.registry ... | 6,791 | 38.952941 | 78 | py |
mmyolo | mmyolo-main/mmyolo/models/backbones/base_backbone.py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import List, Sequence, Union
import torch
import torch.nn as nn
from mmcv.cnn import build_plugin_layer
from mmdet.utils import ConfigType, OptMultiConfig
from mmengine.model import BaseModule
from torch.nn.modules.batc... | 7,920 | 34.048673 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/backbones/cspnext.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import List, Sequence, Union
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmdet.models.backbones.csp_darknet import CSPLayer
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from mmyolo... | 7,258 | 37.611702 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/backbones/csp_darknet.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple, Union
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmdet.models.backbones.csp_darknet import CSPLayer, Focus
from mmdet.utils import ConfigType, OptMultiConfig
from mmyolo.registry ... | 17,158 | 39.091121 | 79 | py |
mmyolo | mmyolo-main/mmyolo/models/backbones/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .base_backbone import BaseBackbone
from .csp_darknet import YOLOv5CSPDarknet, YOLOv8CSPDarknet, YOLOXCSPDarknet
from .csp_resnet import PPYOLOECSPResNet
from .cspnext import CSPNeXt
from .efficient_rep import YOLOv6CSPBep, YOLOv6EfficientRep
from .yolov7_backbone imp... | 527 | 36.714286 | 77 | py |
mmyolo | mmyolo-main/mmyolo/datasets/yolov5_coco.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional
from mmdet.datasets import BaseDetDataset, CocoDataset
from ..registry import DATASETS, TASK_UTILS
class BatchShapePolicyDataset(BaseDetDataset):
"""Dataset with the batch shape policy that makes paddings with least
pixels duri... | 2,311 | 34.030303 | 76 | py |
mmyolo | mmyolo-main/mmyolo/datasets/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Sequence
import numpy as np
import torch
from mmengine.dataset import COLLATE_FUNCTIONS
from ..registry import TASK_UTILS
@COLLATE_FUNCTIONS.register_module()
def yolov5_collate(data_batch: Sequence,
use_ms_training: bool = ... | 4,075 | 34.443478 | 79 | py |
mmyolo | mmyolo-main/mmyolo/datasets/yolov5_crowdhuman.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.datasets import CrowdHumanDataset
from ..registry import DATASETS
from .yolov5_coco import BatchShapePolicyDataset
@DATASETS.register_module()
class YOLOv5CrowdHumanDataset(BatchShapePolicyDataset, CrowdHumanDataset):
"""Dataset for YOLOv5 CrowdHuman Dat... | 485 | 29.375 | 76 | py |
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