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 |
|---|---|---|---|---|---|
mmdetection | projects/Detic_new/configs/detic_centernet2_swin-b_fpn_4x_lvis_coco_in21k.py | .py | # not support training, only for testing
_base_ = './detic_centernet2_swin-b_fpn_4x_lvis_in21k-lvis.py'
| 3 | 104 |
mmdetection | projects/Detic_new/configs/detic_centernet2_swin-b_fpn_4x_lvis-base_boxsup.py | .py | _base_ = './detic_centernet2_swin-b_fpn_4x_lvis_boxsup.py'
# 'lvis_v1_train_norare.json' is the annotations of lvis_v1
# removing the labels of 337 rare-class
train_dataloader = dict(
dataset=dict(
type='ClassBalancedDataset',
oversample_thr=1e-3,
dataset=dict(ann_file='annotations/lvis_v1_... | 10 | 342 |
mmdetection | projects/Detic_new/configs/detic_centernet2_r50_fpn_4x_lvis_boxsup.py | .py | _base_ = 'mmdet::_base_/default_runtime.py'
dataset_type = 'LVISV1Dataset'
custom_imports = dict(
imports=['projects.Detic_new.detic'], allow_failed_imports=False)
num_classes = 1203
lvis_cat_frequency_info = 'data/metadata/lvis_v1_train_cat_info.json'
# 'data/metadata/lvis_v1_clip_a+cname.npy' is pre-computed
# ... | 411 | 13,385 |
mmdetection | projects/Detic_new/configs/detic_centernet2_r50_fpn_4x_lvis_in21k-lvis.py | .py | _base_ = './detic_centernet2_r50_fpn_4x_lvis_boxsup.py'
dataset_type = ['LVISV1Dataset', 'ImageNetLVISV1Dataset']
image_size_det = (640, 640)
image_size_cls = (320, 320)
# backend = 'pillow'
backend_args = None
train_pipeline_det = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAn... | 92 | 2,674 |
mmdetection | projects/Detic_new/configs/detic_centernet2_swin-b_fpn_4x_lvis_boxsup.py | .py | _base_ = './detic_centernet2_r50_fpn_4x_lvis_boxsup.py'
model = dict(
backbone=dict(
_delete_=True,
type='SwinTransformer',
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=Non... | 79 | 2,113 |
mmdetection | projects/Detic_new/configs/detic_centernet2_r50_fpn_4x_lvis-base_boxsup.py | .py | _base_ = './detic_centernet2_r50_fpn_4x_lvis_boxsup.py'
# 'lvis_v1_train_norare.json' is the annotations of lvis_v1
# removing the labels of 337 rare-class
train_dataloader = dict(
dataset=dict(
type='ClassBalancedDataset',
oversample_thr=1e-3,
dataset=dict(ann_file='annotations/lvis_v1_tra... | 10 | 339 |
mmdetection | projects/DiffusionDet/diffusiondet/__init__.py | .py | from .diffusiondet import DiffusionDet
from .head import (DynamicConv, DynamicDiffusionDetHead,
SingleDiffusionDetHead, SinusoidalPositionEmbeddings)
from .loss import DiffusionDetCriterion, DiffusionDetMatcher
__all__ = [
'DiffusionDet', 'DynamicDiffusionDetHead', 'SingleDiffusionDetHead',
... | 11 | 420 |
mmdetection | projects/DiffusionDet/diffusiondet/loss.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Modified from https://github.com/ShoufaChen/DiffusionDet/blob/main/diffusiondet/loss.py # noqa
# This work is licensed under the CC-BY-NC 4.0 License.
# Users should be careful about adopting the... | 342 | 14,481 |
mmdetection | projects/DiffusionDet/diffusiondet/diffusiondet.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models import SingleStageDetector
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
@MODELS.register_module()
class DiffusionDet(SingleStageDetector):
"""Implementation of `DiffusionDet <>`_"""
def __... | 27 | 920 |
mmdetection | projects/DiffusionDet/diffusiondet/head.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Modified from https://github.com/ShoufaChen/DiffusionDet/blob/main/diffusiondet/detector.py # noqa
# Modified from https://github.com/ShoufaChen/DiffusionDet/blob/main/diffusiondet/head.py # noq... | 1,035 | 43,032 |
mmdetection | projects/DiffusionDet/model_converters/diffusiondet_resnet_to_mmdet.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
import numpy as np
import torch
from mmengine.fileio import load
def convert(src, dst):
if src.endswith('pth'):
src_model = torch.load(src)
else:
src_model = load(src)
dst_state_dict = Ord... | 89 | 3,395 |
mmdetection | projects/DiffusionDet/configs/diffusiondet_r50_fpn_500-proposals_1-step_crop-ms-480-800-450k_coco.py | .py | _base_ = [
'mmdet::_base_/datasets/coco_detection.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
custom_imports = dict(
imports=['projects.DiffusionDet.diffusiondet'], allow_failed_imports=False)
# model settings
model = dict(
type='DiffusionDet',
data_prep... | 186 | 6,186 |
mmdetection | projects/EfficientDet/convert_tf_to_pt.py | .py | import argparse
import numpy as np
import torch
from tensorflow.python.training import py_checkpoint_reader
torch.set_printoptions(precision=20)
def tf2pth(v):
if v.ndim == 4:
return np.ascontiguousarray(v.transpose(3, 2, 0, 1))
elif v.ndim == 2:
return np.ascontiguousarray(v.transpose())
... | 627 | 26,971 |
mmdetection | projects/EfficientDet/efficientdet/utils.py | .py | import math
from typing import Tuple, Union
import torch
import torch.nn as nn
from mmcv.cnn.bricks import Swish, build_norm_layer
from torch.nn import functional as F
from torch.nn.init import _calculate_fan_in_and_fan_out, trunc_normal_
from mmdet.registry import MODELS
from mmdet.utils import OptConfigType
def v... | 155 | 4,897 |
mmdetection | projects/EfficientDet/efficientdet/efficientdet_head.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple
import torch
import torch.nn as nn
from mmcv.cnn.bricks import Swish, build_norm_layer
from mmengine.model import bias_init_with_prob
from torch import Tensor
from mmdet.models.dense_heads.anchor_head import AnchorHead
from mmdet.models.ut... | 262 | 10,986 |
mmdetection | projects/EfficientDet/efficientdet/huber_loss.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
import torch.nn as nn
from torch import Tensor
from mmdet.models.losses.utils import weighted_loss
from mmdet.registry import MODELS
@weighted_loss
def huber_loss(pred: Tensor, target: Tensor, beta: float = 1.0) -> Tensor:
... | 92 | 2,888 |
mmdetection | projects/EfficientDet/efficientdet/__init__.py | .py | from .bifpn import BiFPN
from .efficientdet import EfficientDet
from .efficientdet_head import EfficientDetSepBNHead
from .huber_loss import HuberLoss
from .tensorflow.anchor_generator import YXYXAnchorGenerator
from .tensorflow.coco_90class import Coco90Dataset
from .tensorflow.coco_90metric import Coco90Metric
from .... | 17 | 696 |
mmdetection | projects/EfficientDet/efficientdet/efficientdet.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.detectors.single_stage import SingleStageDetector
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
@MODELS.register_module()
class EfficientDet(SingleStageDetector):
def __init__(self,
... | 26 | 896 |
mmdetection | projects/EfficientDet/efficientdet/bifpn.py | .py | from typing import List
import torch
import torch.nn as nn
from mmcv.cnn.bricks import Swish
from mmengine.model import BaseModule
from mmdet.registry import MODELS
from mmdet.utils import MultiConfig, OptConfigType
from .utils import DepthWiseConvBlock, DownChannelBlock, MaxPool2dSamePadding
class BiFPNStage(nn.Mo... | 307 | 12,443 |
mmdetection | projects/EfficientDet/efficientdet/tensorflow/anchor_generator.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Tuple, Union
import torch
from torch import Tensor
from mmdet.models.task_modules.prior_generators.anchor_generator import \
AnchorGenerator
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes
DeviceT... | 110 | 4,261 |
mmdetection | projects/EfficientDet/efficientdet/tensorflow/yxyx_bbox_coder.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import numpy as np
import torch
from mmdet.models.task_modules.coders.delta_xywh_bbox_coder import \
DeltaXYWHBBoxCoder
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor
@TASK_UTILS.register_mod... | 370 | 15,371 |
mmdetection | projects/EfficientDet/efficientdet/tensorflow/coco_90metric.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import datetime
import itertools
import os.path as osp
import tempfile
from collections import OrderedDict
from typing import Dict, List, Optional, Sequence, Union
import numpy as np
from mmengine.evaluator import BaseMetric
from mmengine.fileio import dump, get_local_pa... | 541 | 22,717 |
mmdetection | projects/EfficientDet/efficientdet/tensorflow/trans_max_iou_assigner.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
from mmengine.structures import InstanceData
from mmdet.models.task_modules.assigners.assign_result import AssignResult
from mmdet.models.task_modules.assigners.max_iou_assigner import MaxIoUAssigner
from mmdet.registry import TA... | 111 | 5,094 |
mmdetection | projects/EfficientDet/efficientdet/tensorflow/coco_90class.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
from typing import List, Union
from mmengine.fileio import get_local_path
from mmdet.datasets.base_det_dataset import BaseDetDataset
from mmdet.registry import DATASETS
from .api_wrappers import COCO
@DATASETS.register_module()
class ... | 199 | 8,204 |
mmdetection | projects/EfficientDet/efficientdet/tensorflow/api_wrappers/__init__.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from .coco_api import COCO, COCOeval, COCOPanoptic
__all__ = ['COCO', 'COCOeval', 'COCOPanoptic']
| 5 | 147 |
mmdetection | projects/EfficientDet/efficientdet/tensorflow/api_wrappers/coco_api.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
# This file add snake case alias for coco api
import warnings
from collections import defaultdict
from typing import List, Optional, Union
import pycocotools
from pycocotools.coco import COCO as _COCO
from pycocotools.cocoeval import COCOeval as _COCOeval
class COCO(_... | 146 | 5,118 |
mmdetection | projects/EfficientDet/configs/efficientdet_effb3_bifpn_8xb16-crop896-300e_coco-90cls.py | .py | _base_ = [
'mmdet::_base_/datasets/coco_detection.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
custom_imports = dict(
imports=['projects.EfficientDet.efficientdet'], allow_failed_imports=False)
image_size = 896
batch_augments = [
dict(type='BatchFixedSizePad',... | 172 | 5,356 |
mmdetection | projects/EfficientDet/configs/efficientdet_effb0_bifpn_8xb16-crop512-300e_coco.py | .py | _base_ = [
'mmdet::_base_/datasets/coco_detection.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
custom_imports = dict(
imports=['projects.EfficientDet.efficientdet'], allow_failed_imports=False)
image_size = 512
batch_augments = [
dict(type='BatchFixedSizePad',... | 172 | 5,349 |
mmdetection | projects/EfficientDet/configs/efficientdet_effb3_bifpn_8xb16-crop896-300e_coco.py | .py | _base_ = [
'mmdet::_base_/datasets/coco_detection.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
custom_imports = dict(
imports=['projects.EfficientDet.efficientdet'], allow_failed_imports=False)
image_size = 896
batch_augments = [
dict(type='BatchFixedSizePad',... | 172 | 5,352 |
mmdetection | projects/EfficientDet/configs/tensorflow/efficientdet_effb0_bifpn_8xb16-crop512-300e_coco_tf.py | .py | _base_ = [
'mmdet::_base_/datasets/coco_detection.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
custom_imports = dict(
imports=['projects.EfficientDet.efficientdet'], allow_failed_imports=False)
image_size = 512
batch_augments = [
dict(type='BatchFixedSizePad',... | 172 | 5,366 |
mmdetection | projects/XDecoder/demo.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser
from mmengine.config import Config
from mmengine.logging import print_log
from mmdet.apis import DetInferencer
from projects.XDecoder.xdecoder.inference import (
ImageCaptionInferencer, RefImageCaptionInferencer,
TextToImageRe... | 100 | 2,971 |
mmdetection | projects/XDecoder/xdecoder/transformer_decoder.py | .py | import torch
from torch import nn
from torch.nn import functional as F
from mmdet.registry import MODELS
from .language_model import LanguageEncoder
from .transformer_blocks import (MLP, Conv2d, CrossAttentionLayer, FFNLayer,
PositionEmbeddingSine, SelfAttentionLayer)
from .utils impor... | 440 | 18,307 |
mmdetection | projects/XDecoder/xdecoder/utils.py | .py | import logging
from contextlib import contextmanager
from functools import wraps
import torch
from mmcv.cnn.bricks.wrappers import obsolete_torch_version
from torch.nn import functional as F
TORCH_VERSION = tuple(int(x) for x in torch.__version__.split('.')[:2])
def is_lower_torch_version(version=(1, 10)):
"""C... | 216 | 7,057 |
mmdetection | projects/XDecoder/xdecoder/focalnet.py | .py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from mmcv.cnn.bricks import DropPath
from mmdet.registry import MODELS
# modified from https://github.com/microsoft/X-Decoder/blob/main/xdecoder/backbone/focal_dw.py # noqa
@MODELS.register_module()
class ... | 523 | 17,364 |
mmdetection | projects/XDecoder/xdecoder/language_model.py | .py | import os
from collections import OrderedDict
import torch
from mmcv.cnn.bricks import DropPath
from torch import nn
from transformers import CLIPTokenizer
from .utils import get_prompt_templates
# modified from https://github.com/microsoft/X-Decoder/blob/main/xdecoder/language/vlpencoder.py # noqa
class LanguageE... | 252 | 8,639 |
mmdetection | projects/XDecoder/xdecoder/transformer_blocks.py | .py | import copy
import math
from typing import Optional
import torch
import torch.nn.functional as F
from torch import Tensor, nn
# modified from https://github.com/microsoft/X-Decoder/blob/main/xdecoder/body/transformer_blocks.py # noqa
"""Transformer class.
Copy-paste from torch.nn.Transformer with modifications:
... | 474 | 15,434 |
mmdetection | projects/XDecoder/xdecoder/__init__.py | .py | from .focalnet import FocalNet
from .pixel_decoder import XTransformerEncoderPixelDecoder
from .transformer_decoder import XDecoderTransformerDecoder
from .unified_head import XDecoderUnifiedhead
from .xdecoder import XDecoder
__all__ = [
'XDecoder', 'FocalNet', 'XDecoderUnifiedhead',
'XTransformerEncoderPixel... | 11 | 361 |
mmdetection | projects/XDecoder/xdecoder/unified_head.py | .py | import copy
from typing import Sequence
import torch
from mmengine.structures import InstanceData, PixelData
from torch import nn
from torch.nn import functional as F
from mmdet.evaluation.functional import INSTANCE_OFFSET
from mmdet.registry import MODELS
from .utils import (is_lower_torch_version, retry_if_cuda_oom... | 364 | 16,236 |
mmdetection | projects/XDecoder/xdecoder/pixel_decoder.py | .py | from typing import Callable, Optional, Union
from torch import nn
from torch.nn import functional as F
from mmdet.registry import MODELS
from .transformer_blocks import (Conv2d, PositionEmbeddingSine,
TransformerEncoder, TransformerEncoderLayer,
get_no... | 215 | 7,396 |
mmdetection | projects/XDecoder/xdecoder/xdecoder.py | .py | from torch import Tensor
from mmdet.models.detectors.single_stage import SingleStageDetector
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
@MODELS.register_module()
class XDecoder(SingleStageDetector):
def __init__(sel... | 37 | 1,367 |
mmdetection | projects/XDecoder/xdecoder/inference/__init__.py | .py | from .image_caption import ImageCaptionInferencer, RefImageCaptionInferencer
from .texttoimage_regionretrieval_inferencer import \
TextToImageRegionRetrievalInferencer
__all__ = [
'ImageCaptionInferencer', 'RefImageCaptionInferencer',
'TextToImageRegionRetrievalInferencer'
]
| 9 | 289 |
mmdetection | projects/XDecoder/xdecoder/inference/image_caption.py | .py | import copy
import os.path as osp
from typing import Iterable, List, Optional, Tuple, Union
import mmcv
import mmengine
import numpy as np
import torch
from mmengine.dataset import Compose
from rich.progress import track
from mmdet.apis.det_inferencer import DetInferencer, InputsType, PredType
from mmdet.utils import... | 309 | 11,525 |
mmdetection | projects/XDecoder/xdecoder/inference/texttoimage_regionretrieval_inferencer.py | .py | import copy
from typing import Iterable, Optional, Union
import torch
from mmengine.dataset import Compose
from rich.progress import track
from mmdet.apis.det_inferencer import DetInferencer, InputsType
from mmdet.utils import ConfigType
class TextToImageRegionRetrievalInferencer(DetInferencer):
def _init_pipe... | 227 | 8,487 |
mmdetection | projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-instance_coco.py | .py | _base_ = [
'_base_/xdecoder-tiny_open-vocab-instance.py',
'mmdet::_base_/datasets/coco_instance.py'
]
test_pipeline = [
dict(
type='LoadImageFromFile',
imdecode_backend='pillow',
backend_args=_base_.backend_args),
dict(
type='ResizeShortestEdge', scale=800, max_size=1333... | 28 | 773 |
mmdetection | projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-ref-seg_refcoco.py | .py | _base_ = [
'_base_/xdecoder-tiny_ref-seg.py', 'mmdet::_base_/datasets/refcoco.py'
]
| 4 | 88 |
mmdetection | projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-semseg_ade20k.py | .py | _base_ = [
'_base_/xdecoder-tiny_open-vocab-semseg.py',
'mmdet::_base_/datasets/ade20k_semantic.py'
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='Resize', scale=(2560, 640), keep_ratio=True),
dict(
type='LoadAnnotations',
with_bbox=... | 51 | 2,389 |
mmdetection | projects/XDecoder/configs/xdecoder-tiny_zeroshot_caption_coco2014.py | .py | _base_ = [
'_base_/xdecoder-tiny_caption.py', 'mmdet::_base_/datasets/coco_caption.py'
]
test_pipeline = [
dict(
type='LoadImageFromFile',
imdecode_backend='pillow',
backend_args=_base_.backend_args),
dict(type='ResizeShortestEdge', scale=224, backend='pillow'),
dict(
ty... | 19 | 539 |
mmdetection | projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-panoptic_ade20k.py | .py | _base_ = [
'_base_/xdecoder-tiny_open-vocab-panoptic.py',
'mmdet::_base_/datasets/ade20k_panoptic.py'
]
model = dict(test_cfg=dict(mask_thr=0.4))
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='Resize', scale=(2560, 640), keep_ratio=True),
dict(type='... | 52 | 2,517 |
mmdetection | projects/XDecoder/configs/xdecoder-tiny_zeroshot_ref-caption.py | .py | _base_ = 'xdecoder-tiny_zeroshot_caption_coco2014.py'
model = dict(head=dict(task='ref-caption'))
grounding_scale = 512
test_pipeline = [
dict(type='LoadImageFromFile', imdecode_backend='pillow'),
dict(type='ResizeShortestEdge', scale=224, backend='pillow'),
dict(
type='PackDetInputs',
me... | 18 | 517 |
mmdetection | projects/XDecoder/configs/xdecoder-tiny_zeroshot_text-image-retrieval.py | .py | _base_ = 'xdecoder-tiny_zeroshot_caption_coco2014.py'
model = dict(head=dict(task='retrieval'))
grounding_scale = 512
test_pipeline = [
dict(
type='LoadImageFromFile',
imdecode_backend='pillow',
backend_args=_base_.backend_args),
dict(
type='ResizeShortestEdge',
scale=... | 25 | 632 |
mmdetection | projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-panoptic_coco.py | .py | _base_ = [
'_base_/xdecoder-tiny_open-vocab-panoptic.py',
'mmdet::_base_/datasets/coco_panoptic.py'
]
model = dict(test_cfg=dict(mask_thr=0.4))
test_pipeline = [
dict(
type='LoadImageFromFile',
imdecode_backend='pillow',
backend_args=_base_.backend_args),
dict(
type='Re... | 28 | 773 |
mmdetection | projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-ref-seg_refcoco+.py | .py | _base_ = [
'_base_/xdecoder-tiny_ref-seg.py', 'mmdet::_base_/datasets/refcoco+.py'
]
| 4 | 89 |
mmdetection | projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-instance_ade20k.py | .py | _base_ = [
'_base_/xdecoder-tiny_open-vocab-instance.py',
'mmdet::_base_/datasets/ade20k_instance.py'
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='Resize', scale=(2560, 640), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True, with_mask... | 21 | 641 |
mmdetection | projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-semseg_coco.py | .py | _base_ = '_base_/xdecoder-tiny_open-vocab-semseg.py'
dataset_type = 'CocoSegDataset'
data_root = 'data/coco/'
test_pipeline = [
dict(
type='LoadImageFromFile', imdecode_backend='pillow',
backend_args=None),
dict(
type='ResizeShortestEdge', scale=800, max_size=1333, backend='pillow'),
... | 69 | 2,929 |
mmdetection | projects/XDecoder/configs/xdecoder-tiny_zeroshot_open-vocab-ref-seg_refcocog.py | .py | _base_ = [
'_base_/xdecoder-tiny_ref-seg.py', 'mmdet::_base_/datasets/refcocog.py'
]
| 4 | 89 |
mmdetection | projects/XDecoder/configs/_base_/xdecoder-tiny_open-vocab-panoptic.py | .py | _base_ = 'xdecoder-tiny_open-vocab-semseg.py'
model = dict(
head=dict(task='panoptic'), test_cfg=dict(mask_thr=0.8, overlap_thr=0.8))
| 5 | 139 |
mmdetection | projects/XDecoder/configs/_base_/xdecoder-tiny_open-vocab-instance.py | .py | _base_ = 'xdecoder-tiny_open-vocab-semseg.py'
model = dict(head=dict(task='instance'), test_cfg=dict(max_per_img=100))
| 4 | 120 |
mmdetection | projects/XDecoder/configs/_base_/xdecoder-tiny_open-vocab-semseg.py | .py | _base_ = 'mmdet::_base_/default_runtime.py'
custom_imports = dict(
imports=['projects.XDecoder.xdecoder'], allow_failed_imports=False)
model = dict(
type='XDecoder',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | 30 | 980 |
mmdetection | projects/XDecoder/configs/_base_/xdecoder-tiny_ref-seg.py | .py | _base_ = 'xdecoder-tiny_open-vocab-semseg.py'
model = dict(head=dict(task='ref-seg'))
| 4 | 87 |
mmdetection | projects/XDecoder/configs/_base_/xdecoder-tiny_caption.py | .py | _base_ = 'xdecoder-tiny_open-vocab-semseg.py'
model = dict(head=dict(task='caption'))
| 4 | 87 |
mmdetection | projects/example_largemodel/fsdp_utils.py | .py | from typing import Sequence, Union
import torch.nn as nn
from mmdet.models.backbones.swin import SwinBlock
from mmdet.models.layers.transformer.deformable_detr_layers import \
DeformableDetrTransformerEncoderLayer
# TODO: The new version of configs does not support passing a module list,
# so for now, it can o... | 39 | 1,202 |
mmdetection | projects/example_largemodel/__init__.py | .py | from .fsdp_utils import checkpoint_check_fn, layer_auto_wrap_policy
__all__ = ['checkpoint_check_fn', 'layer_auto_wrap_policy']
| 4 | 129 |
mmdetection | projects/example_largemodel/dino-5scale_swin-l_fsdp_8xb2-12e_coco.py | .py | from mmengine.config import read_base
with read_base():
from mmdet.configs.dino.dino_5scale_swin_l_8xb2_12e_coco import * # noqa
from projects.example_largemodel import (checkpoint_check_fn,
layer_auto_wrap_policy)
# The checkpoint needs to be controlled by the checkpoin... | 19 | 759 |
mmdetection | projects/example_largemodel/dino-5scale_swin-l_deepspeed_8xb2-12e_coco.py | .py | from mmengine.config import read_base
with read_base():
from mmdet.configs.dino.dino_5scale_swin_l_8xb2_12e_coco import * # noqa
model.update(dict(encoder=dict(num_cp=6))) # noqa
runner_type = 'FlexibleRunner'
strategy = dict(
type='DeepSpeedStrategy',
gradient_clipping=0.1,
fp16=dict(
enab... | 45 | 1,237 |
mmdetection | projects/SparseInst/configs/sparseinst_r50_iam_8xb8-ms-270k_coco.py | .py | _base_ = [
'mmdet::_base_/datasets/coco_instance.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
custom_imports = dict(
imports=['projects.SparseInst.sparseinst'], allow_failed_imports=False)
model = dict(
type='SparseInst',
data_preprocessor=dict(
t... | 147 | 4,133 |
mmdetection | projects/SparseInst/sparseinst/__init__.py | .py | from .decoder import BaseIAMDecoder, GroupIAMDecoder, GroupIAMSoftDecoder
from .encoder import PyramidPoolingModule
from .loss import SparseInstCriterion, SparseInstMatcher
from .sparseinst import SparseInst
__all__ = [
'BaseIAMDecoder', 'GroupIAMDecoder', 'GroupIAMSoftDecoder',
'PyramidPoolingModule', 'Sparse... | 11 | 376 |
mmdetection | projects/SparseInst/sparseinst/encoder.py | .py | # Copyright (c) Tianheng Cheng and its affiliates. All Rights Reserved
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmengine.model.weight_init import caffe2_xavier_init, kaiming_init
from mmdet.registry import MODELS
class PyramidPoolingModule(nn.Module):
def __init__(self,
... | 103 | 3,806 |
mmdetection | projects/SparseInst/sparseinst/loss.py | .py | # Copyright (c) Tianheng Cheng and its affiliates. All Rights Reserved
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy.optimize import linear_sum_assignment
from torch.cuda.amp import autocast
from mmdet.registry import MODELS, TASK_UTILS
from mmdet.utils import reduce_mean
def compute... | 250 | 9,212 |
mmdetection | projects/SparseInst/sparseinst/decoder.py | .py | # Copyright (c) Tianheng Cheng and its affiliates. All Rights Reserved
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmengine.model.weight_init import caffe2_xavier_init, kaiming_init
from torch.nn import init
from mmdet.registry import MODELS
def _make_stack_3x3_convs(num_con... | 401 | 13,792 |
mmdetection | projects/SparseInst/sparseinst/sparseinst.py | .py | # Copyright (c) Tianheng Cheng and its affiliates. All Rights Reserved
from typing import List, Tuple, Union
import torch
import torch.nn.functional as F
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.models import BaseDetector
from mmdet.models.utils import unpack_gt_instances
from ... | 207 | 7,972 |
mmdetection | projects/gradio_demo/launch.py | .py | # Modified from MMPretrain
import gradio as gr
import torch
from mmengine.logging import MMLogger
from mmdet.apis import DetInferencer
from projects.XDecoder.xdecoder.inference import (
ImageCaptionInferencer, RefImageCaptionInferencer,
TextToImageRegionRetrievalInferencer)
logger = MMLogger('mmdetection', lo... | 624 | 21,555 |
mmdetection | projects/ConvNeXt-V2/configs/mask-rcnn_convnext-v2-b_fpn_lsj-3x-fcmae_coco.py | .py | _base_ = [
'mmdet::_base_/models/mask-rcnn_r50_fpn.py',
'mmdet::_base_/datasets/coco_instance.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
# please install the mmpretrain
# import mmpretrain.models to trigger register_module in mmpretrain
custom_imports = dict(
... | 93 | 2,856 |
mmdetection | projects/AlignDETR/align_detr/utils.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, List, Optional
class KeysRecorder:
"""Wrap object to record its `__getitem__` keys in the history.
Args:
obj (object): Any object that supports `__getitem__`.
keys (List): List of keys already recorded. Default to None.
... | 35 | 1,014 |
mmdetection | projects/AlignDETR/align_detr/align_detr_head.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Dict, List, Tuple, Union
import torch
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.models.dense_heads import DINOHead
from mmdet.registry import MODELS
from mmdet.structures.bbox import (bbox_cxcywh_to_xyxy, bb... | 509 | 24,490 |
mmdetection | projects/AlignDETR/align_detr/mixed_hungarian_assigner.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Union
import torch
from mmengine import ConfigDict
from mmengine.structures import InstanceData
from scipy.optimize import linear_sum_assignment
from torch import Tensor
from mmdet.models.task_modules import AssignResult, BaseAssigner
... | 163 | 6,986 |
mmdetection | projects/AlignDETR/configs/align_detr-4scale_r50_8xb2-24e_coco.py | .py | _base_ = './align_detr-4scale_r50_8xb2-12e_coco.py'
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.0001,
by_epoch=False,
begin=0,
end=2000),
dict(
t... | 20 | 449 |
mmdetection | projects/AlignDETR/configs/align_detr-4scale_r50_8xb2-12e_coco.py | .py | _base_ = [
'../../../configs/_base_/datasets/coco_detection.py',
'../../../configs/_base_/default_runtime.py'
]
custom_imports = dict(
imports=['projects.AlignDETR.align_detr'], allow_failed_imports=False)
model = dict(
type='DINO',
num_queries=900, # num_matching_queries
with_box_refine=True,... | 186 | 6,551 |
mmdetection | projects/HDINO/__init__.py | .py | from .h_dino import HDINO
from .h_dino_head import HybridDINOHead
__all__ = ['HDINO', 'HybridDINOHead']
| 5 | 105 |
mmdetection | projects/HDINO/h_dino.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch
from torch import Tensor, nn
from torch.nn.init import normal_
from mmdet.models.detectors import DINO, DeformableDETR
from mmdet.models.detectors.deformable_detr import \
MultiScaleDeformableAttention
from mmdet.registry import... | 150 | 6,094 |
mmdetection | projects/HDINO/h_dino_head.py | .py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List
from torch import Tensor
from mmdet.models.dense_heads.dino_head import DINOHead
from mmdet.models.utils import multi_apply
from mmdet.registry import MODELS
from mmdet.utils import InstanceList, OptInstanceList
@MODELS.register_module()
... | 113 | 5,098 |
mmdetection | projects/HDINO/h-dino-4scale_r50_8xb2-12e_coco.py | .py | _base_ = [
'../../configs/_base_/datasets/coco_detection.py',
'../../configs/_base_/default_runtime.py'
]
custom_imports = dict(imports=['projects.HDINO'], allow_failed_imports=False)
model = dict(
type='HDINO',
num_queries=1800, # num_total_queries: 900+900
with_box_refine=True,
as_two_stage... | 169 | 5,865 |
mmdetection | configs/pascal_voc/ssd512_voc0712.py | .py | _base_ = 'ssd300_voc0712.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),... | 83 | 3,059 |
mmdetection | configs/pascal_voc/faster-rcnn_r50_fpn_1x_voc0712.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# training schedule, voc dataset is repeated 3 times, in
# `_base_/datasets/voc0712.py`, so the actual epoch = 4 * 3 = 12
max_epoch... | 36 | 1,040 |
mmdetection | configs/pascal_voc/faster-rcnn_r50_fpn_1x_voc0712-cocofmt.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
METAINFO = {
'classes':
('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'dinin... | 101 | 3,378 |
mmdetection | configs/pascal_voc/faster-rcnn_r50-caffe-c4_ms-18k_voc0712.py | .py | _base_ = [
'../_base_/models/faster-rcnn_r50-caffe-c4.py',
'../_base_/schedules/schedule_1x.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile', backend_arg... | 87 | 2,857 |
mmdetection | configs/pascal_voc/ssd300_voc0712.py | .py | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/voc0712.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
bbox_head=dict(
num_classes=20, anchor_generator=dict(basesize_ratio_range=(0.2,
... | 103 | 3,578 |
mmdetection | configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py | .py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=20))
# training schedule, voc dataset is repeated 3 times, in
# `_base_/datasets/voc0712.py`, so the actual epoch = 4 * 3 = 12
max_epochs = 4
train_cfg =... | 35 | 1,022 |
mmdetection | configs/boxinst/boxinst_r50_fpn_ms-90k_coco.py | .py | _base_ = '../common/ms-90k_coco.py'
# model settings
model = dict(
type='BoxInst',
data_preprocessor=dict(
type='BoxInstDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32,
mask_stride=4,
pa... | 94 | 2,693 |
mmdetection | configs/boxinst/boxinst_r101_fpn_ms-90k_coco.py | .py | _base_ = './boxinst_r50_fpn_ms-90k_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 9 | 217 |
mmdetection | configs/conditional_detr/conditional-detr_r50_8xb2-50e_coco.py | .py | _base_ = ['../detr/detr_r50_8xb2-150e_coco.py']
model = dict(
type='ConditionalDETR',
num_queries=300,
decoder=dict(
num_layers=6,
layer_cfg=dict(
self_attn_cfg=dict(
_delete_=True,
embed_dims=256,
num_heads=8,
attn_... | 43 | 1,321 |
mmdetection | configs/strongsort/yolox_x_8xb4-80e_crowdhuman-mot20train_test-mot20test.py | .py | _base_ = ['./yolox_x_8xb4-80e_crowdhuman-mot17halftrain_test-mot17halfval.py']
data_root = 'data/MOT20/'
img_scale = (1600, 896) # width, height
# model settings
model = dict(
data_preprocessor=dict(batch_augments=[
dict(type='BatchSyncRandomResize', random_size_range=(640, 1152))
]))
train_pipelin... | 109 | 3,935 |
mmdetection | configs/strongsort/strongsort_yolox_x_8xb4-80e_crowdhuman-mot20train_test-mot20test.py | .py | _base_ = [
'./strongsort_yolox_x_8xb4-80e_crowdhuman-mot17halftrain'
'_test-mot17halfval.py'
]
img_scale = (1600, 896) # width, height
model = dict(
data_preprocessor=dict(
type='TrackDataPreprocessor',
pad_size_divisor=32,
batch_augments=[
dict(type='BatchSyncRandomRe... | 45 | 1,318 |
mmdetection | configs/strongsort/yolox_x_8xb4-80e_crowdhuman-mot17halftrain_test-mot17halfval.py | .py | _base_ = ['../yolox/yolox_x_8xb8-300e_coco.py']
data_root = 'data/MOT17/'
img_scale = (1440, 800) # width, height
batch_size = 4
# model settings
model = dict(
bbox_head=dict(num_classes=1),
test_cfg=dict(nms=dict(iou_threshold=0.7)),
init_cfg=dict(
type='Pretrained',
checkpoint= # noqa... | 189 | 5,827 |
mmdetection | configs/strongsort/strongsort_yolox_x_8xb4-80e_crowdhuman-mot17halftrain_test-mot17halfval.py | .py | _base_ = [
'./yolox_x_8xb4-80e_crowdhuman-mot17halftrain_test-mot17halfval.py', # noqa: E501
]
dataset_type = 'MOTChallengeDataset'
detector = _base_.model
detector.pop('data_preprocessor')
del _base_.model
model = dict(
type='StrongSORT',
data_preprocessor=dict(
type='TrackDataPreprocessor',
... | 131 | 4,114 |
mmdetection | configs/pvt/retinanet_pvtv2-b4_fpn_1x_coco.py | .py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 8, 27, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b4.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
# optimi... | 21 | 701 |
mmdetection | configs/pvt/retinanet_pvt-s_fpn_1x_coco.py | .py | _base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
model = dict(
backbone=dict(
num_layers=[3, 4, 6, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_small.pth')))
| 7 | 237 |
mmdetection | configs/pvt/retinanet_pvtv2-b2_fpn_1x_coco.py | .py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 4, 6, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b2.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
| 9 | 311 |
mmdetection | configs/pvt/retinanet_pvt-l_fpn_1x_coco.py | .py | _base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
model = dict(
backbone=dict(
num_layers=[3, 8, 27, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_large.pth')))
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = ... | 9 | 349 |
mmdetection | configs/pvt/retinanet_pvt-m_fpn_1x_coco.py | .py | _base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
model = dict(
backbone=dict(
num_layers=[3, 4, 18, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_medium.pth')))
| 7 | 239 |
mmdetection | configs/pvt/retinanet_pvtv2-b1_fpn_1x_coco.py | .py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b1.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
| 8 | 278 |
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