python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import (PLUGIN_LAYERS, Conv2d, ConvModule, caffe2_xavier_init,
normal_init, xavier_init)
from mmcv.cnn.bricks.transformer import (build_positional_encoding,
... | ViT-Adapter-main | segmentation/mmseg_custom/models/plugins/msdeformattn_pixel_decoder.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
from .msdeformattn_pixel_decoder import MSDeformAttnPixelDecoder
from .pixel_decoder import PixelDecoder, TransformerEncoderPixelDecoder
__all__ = [
'PixelDecoder', 'TransformerEncoderPixelDecoder',
'MSDeformAttnPixelDecoder'
]
| ViT-Adapter-main | segmentation/mmseg_custom/models/plugins/__init__.py |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.ops import point_sample
def get_uncertainty(mask_pred, labels):
"""Estimate uncertainty based on pred logits.
We estimate uncertainty as L1 distance between 0.0 and the logits
prediction in 'mask_pred' for the foreground class in `cla... | ViT-Adapter-main | segmentation/mmseg_custom/models/utils/point_sample.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
from .assigner import MaskHungarianAssigner
from .point_sample import get_uncertain_point_coords_with_randomness
from .positional_encoding import (LearnedPositionalEncoding,
SinePositionalEncoding)
from .transformer import (DetrTran... | ViT-Adapter-main | segmentation/mmseg_custom/models/utils/__init__.py |
# Copyright (c) OpenMMLab. All rights reserved.
import math
import warnings
from typing import Sequence
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import (Linear, build_activation_layer, build_conv_layer,
build_norm_layer, ... | ViT-Adapter-main | segmentation/mmseg_custom/models/utils/transformer.py |
# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING
from mmcv.runner import BaseModule
@POSITIONAL_ENCODING.register_module()
class SinePositionalEncoding(BaseModule):
"""Position encoding with sine and cosine ... | ViT-Adapter-main | segmentation/mmseg_custom/models/utils/positional_encoding.py |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import torch
import torch.nn.functional as F
from ..builder import MASK_ASSIGNERS, build_match_cost
try:
from scipy.optimize import linear_sum_assignment
except ImportError:
linear_sum_assignment = None
class AssignResu... | ViT-Adapter-main | segmentation/mmseg_custom/models/utils/assigner.py |
# --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# By Hangbo Bao
# B... | ViT-Adapter-main | segmentation/mmseg_custom/models/backbones/beit_baseline.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
from .beit_adapter import BEiTAdapter
from .beit_baseline import BEiTBaseline
from .vit_adapter import ViTAdapter
from .vit_baseline import ViTBaseline
from .uniperceiver_adapter import UniPerceiverAdapter
__all__ = ['ViTBaseline', 'ViTAdapter', 'BEiTAdapter',
... | ViT-Adapter-main | segmentation/mmseg_custom/models/backbones/__init__.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
import logging
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmseg.models.builder import BACKBONES
from ops.modules import MSDeformAttn
from timm.models.layers import trunc_normal_
from torch.nn.init import normal_
from .base.... | ViT-Adapter-main | segmentation/mmseg_custom/models/backbones/vit_adapter.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
import logging
import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmseg.models.builder import BACKBONES
from ops.modules import MSDeformAttn
from timm.models.layers import DropPath, trunc_normal_
from t... | ViT-Adapter-main | segmentation/mmseg_custom/models/backbones/beit_adapter.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
import logging
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmseg.models.builder import BACKBONES
from ops.modules import MSDeformAttn
from timm.models.layers import DropPath, trunc_normal_
from torch.nn.init import normal_
f... | ViT-Adapter-main | segmentation/mmseg_custom/models/backbones/uniperceiver_adapter.py |
import logging
from functools import partial
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from ops.modules import MSDeformAttn
from timm.models.layers import DropPath
_logger = logging.getLogger(__name__)
def get_reference_points(spatial_shapes, device):
reference_points_list = []
... | ViT-Adapter-main | segmentation/mmseg_custom/models/backbones/adapter_modules.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
import logging
import math
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner import load_checkpoint
from mmseg.models.builder import BACKBONES
from mmseg.utils import get_root_logger
from timm.models.layers import trunc_normal_
from .base.vit ... | ViT-Adapter-main | segmentation/mmseg_custom/models/backbones/vit_baseline.py |
# --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# By Hangbo Bao
# B... | ViT-Adapter-main | segmentation/mmseg_custom/models/backbones/base/beit.py |
import logging
import math
import torch
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.runner import load_checkpoint
from mmseg.utils import get_root_logger
from timm.models.layers import DropPath
from torch import nn
def window_partition(x, window_size):
"""
Args:
x: (... | ViT-Adapter-main | segmentation/mmseg_custom/models/backbones/base/uniperceiver.py |
"""Vision Transformer (ViT) in PyTorch.
A PyTorch implement of Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
- https:... | ViT-Adapter-main | segmentation/mmseg_custom/models/backbones/base/vit.py |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmseg.core import add_prefix
from mmseg.models import builder
from mmseg.models.builder import SEGMENTORS
from mmseg.models.segmentors.base import BaseSegmentor
from mmseg.ops import resize
@SEGMENT... | ViT-Adapter-main | segmentation/mmseg_custom/models/segmentors/encoder_decoder_mask2former.py |
# Copyright (c) OpenMMLab. All rights reserved.
from .encoder_decoder_mask2former import EncoderDecoderMask2Former
from .encoder_decoder_mask2former_aug import EncoderDecoderMask2FormerAug
__all__ = ['EncoderDecoderMask2Former', 'EncoderDecoderMask2FormerAug']
| ViT-Adapter-main | segmentation/mmseg_custom/models/segmentors/__init__.py |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmseg.core import add_prefix
from mmseg.models import builder
from mmseg.models.builder import SEGMENTORS
from mmseg.models.segmentors.base import BaseSegmentor
from mmseg.ops import resize
@SEGMENT... | ViT-Adapter-main | segmentation/mmseg_custom/models/segmentors/encoder_decoder_mask2former_aug.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import Conv2d, build_plugin_layer, kaiming_init
from mmcv.cnn.bricks.transformer import (build_positional_encoding,
build_transformer_layer_sequence)
from mmcv.runner import force_fp32
from mmseg.mo... | ViT-Adapter-main | segmentation/mmseg_custom/models/decode_heads/maskformer_head.py |
# Copyright (c) OpenMMLab. All rights reserved.
from .mask2former_head import Mask2FormerHead
from .maskformer_head import MaskFormerHead
__all__ = [
'MaskFormerHead',
'Mask2FormerHead',
]
| ViT-Adapter-main | segmentation/mmseg_custom/models/decode_heads/__init__.py |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import Conv2d, build_plugin_layer, caffe2_xavier_init
from mmcv.cnn.bricks.transformer import (build_positional_encoding,
build_transform... | ViT-Adapter-main | segmentation/mmseg_custom/models/decode_heads/mask2former_head.py |
# Copyright (c) ByteDance, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Mostly copy-paste from BEiT library:
https://github.com/microsoft/unilm/blob/master/beit/semantic_segmentation/mmcv_cus... | ViT-Adapter-main | segmentation/mmcv_custom/layer_decay_optimizer_constructor.py |
# Copyright (c) Open-MMLab. All rights reserved.
import io
import math
import os
import os.path as osp
import pkgutil
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import mmcv
import numpy as np
import torch
import torchvisio... | ViT-Adapter-main | segmentation/mmcv_custom/checkpoint.py |
import os.path as osp
import pkgutil
import time
from collections import OrderedDict
from importlib import import_module
import mmcv
import torch
from torch.utils import model_zoo
open_mmlab_model_urls = {
'vgg16_caffe': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/pretrain/third_party/vgg16_caffe-292e1171... | ViT-Adapter-main | segmentation/mmcv_custom/my_checkpoint.py |
# Copyright (c) Shanghai AI Lab. All rights reserved.
from .checkpoint import load_checkpoint
from .customized_text import CustomizedTextLoggerHook
from .layer_decay_optimizer_constructor import LayerDecayOptimizerConstructor
from .my_checkpoint import my_load_checkpoint
__all__ = [
'LayerDecayOptimizerConstructor... | ViT-Adapter-main | segmentation/mmcv_custom/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import datetime
from collections import OrderedDict
import mmcv
import torch
from mmcv.runner import HOOKS, TextLoggerHo... | ViT-Adapter-main | segmentation/mmcv_custom/customized_text.py |
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True... | ViT-Adapter-main | segmentation/configs/_base_/default_runtime.py |
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 1024)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize',... | ViT-Adapter-main | segmentation/configs/_base_/datasets/cityscapes.py |
# dataset settings
dataset_type = 'NYUDepthV2Dataset'
data_root = 'data/nyu_depth_v2/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (480, 480)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=Tru... | ViT-Adapter-main | segmentation/configs/_base_/datasets/nyu_depth_v2.py |
# dataset settings
dataset_type = 'PascalContextDataset'
data_root = 'data/VOCdevkit/VOC2010/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (520, 520)
crop_size = (480, 480)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnno... | ViT-Adapter-main | segmentation/configs/_base_/datasets/pascal_context.py |
# dataset settings
dataset_type = 'LoveDADataset'
data_root = 'data/loveDA'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(... | ViT-Adapter-main | segmentation/configs/_base_/datasets/loveda.py |
# dataset settings
dataset_type = 'MapillaryDataset'
data_root = 'data/Mapillary/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (896, 896)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='MapillaryHa... | ViT-Adapter-main | segmentation/configs/_base_/datasets/mapillary_896x896.py |
_base_ = './cityscapes.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (1024, 1024)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dic... | ViT-Adapter-main | segmentation/configs/_base_/datasets/cityscapes_1024x1024.py |
_base_ = './pascal_voc12.py'
# dataset settings
data = dict(
train=dict(
ann_dir=['SegmentationClass', 'SegmentationClassAug'],
split=[
'ImageSets/Segmentation/train.txt',
'ImageSets/Segmentation/aug.txt'
]))
| ViT-Adapter-main | segmentation/configs/_base_/datasets/pascal_voc12_aug.py |
# dataset settings
dataset_type = 'PascalContextDataset59'
data_root = 'data/VOCdevkit/VOC2010/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (520, 520)
crop_size = (480, 480)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAn... | ViT-Adapter-main | segmentation/configs/_base_/datasets/pascal_context_59.py |
# dataset settings
dataset_type = 'HRFDataset'
data_root = 'data/HRF'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (2336, 3504)
crop_size = (256, 256)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type=... | ViT-Adapter-main | segmentation/configs/_base_/datasets/hrf.py |
# dataset settings
dataset_type = 'COCOStuffDataset'
data_root = 'data/coco_stuff164k'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize'... | ViT-Adapter-main | segmentation/configs/_base_/datasets/coco-stuff164k.py |
_base_ = './cityscapes.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (768, 768)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
dict(... | ViT-Adapter-main | segmentation/configs/_base_/datasets/cityscapes_768x768.py |
# dataset settings
dataset_type = 'COCOStuffDataset'
data_root = 'data/coco_stuff10k'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),... | ViT-Adapter-main | segmentation/configs/_base_/datasets/coco-stuff10k.py |
# dataset settings
dataset_type = 'PotsdamDataset'
data_root = 'data/potsdam'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dic... | ViT-Adapter-main | segmentation/configs/_base_/datasets/potsdam.py |
_base_ = './cityscapes.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (896, 896)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dict(... | ViT-Adapter-main | segmentation/configs/_base_/datasets/cityscapes_896x896.py |
# dataset settings
dataset_type = 'ADE20KDataset'
data_root = 'data/ade/ADEChallengeData2016'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_labe... | ViT-Adapter-main | segmentation/configs/_base_/datasets/ade20k.py |
# dataset settings
dataset_type = 'ChaseDB1Dataset'
data_root = 'data/CHASE_DB1'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (960, 999)
crop_size = (128, 128)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
d... | ViT-Adapter-main | segmentation/configs/_base_/datasets/chase_db1.py |
_base_ = './cityscapes.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (832, 832)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dict(... | ViT-Adapter-main | segmentation/configs/_base_/datasets/cityscapes_832x832.py |
_base_ = './cityscapes.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (769, 769)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)),
dict(... | ViT-Adapter-main | segmentation/configs/_base_/datasets/cityscapes_769x769.py |
# dataset settings
dataset_type = 'STAREDataset'
data_root = 'data/STARE'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (605, 700)
crop_size = (128, 128)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(typ... | ViT-Adapter-main | segmentation/configs/_base_/datasets/stare.py |
# dataset settings
dataset_type = 'DRIVEDataset'
data_root = 'data/DRIVE'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
img_scale = (584, 565)
crop_size = (64, 64)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type=... | ViT-Adapter-main | segmentation/configs/_base_/datasets/drive.py |
# dataset settings
dataset_type = 'PascalVOCDataset'
data_root = 'data/VOCdevkit/VOC2012'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resi... | ViT-Adapter-main | segmentation/configs/_base_/datasets/pascal_voc12.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/fcn_r50-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/pspnet_r50-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
backbone_norm_cfg = dict(type='LN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='SwinTransformer',
pretrain_img_size=224,
embed_dims=96,
patch_size=4,
... | ViT-Adapter-main | segmentation/configs/_base_/models/upernet_swin.py |
# model settings
backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True)
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='pretrain/jx_vit_large_p16_384-b3be5167.pth',
backbone=dict(
type='VisionTransformer',
img_size=(768, 768),
... | ViT-Adapter-main | segmentation/configs/_base_/models/setr_naive.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/nonlocal_r50-d8.py |
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='pretrain/vit-b16_p16_224-80ecf9dd.pth', # noqa
backbone=dict(
type='VisionTransformer',
img_size=224,
embed_dims=768,
num_layers=12,
num_heads=12,
out_indices=(... | ViT-Adapter-main | segmentation/configs/_base_/models/dpt_vit-b16.py |
# model_cfg
num_things_classes = 80
num_stuff_classes = 91
num_classes = num_things_classes + num_stuff_classes
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoderMask2Former',
pretrained=None,
backbone=dict(
type='XCiT',
patch_size=16,
embed_dim=384... | ViT-Adapter-main | segmentation/configs/_base_/models/mask2former_beit_cocostuff.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://msra/hrnetv2_w18',
backbone=dict(
type='HRNet',
norm_cfg=norm_cfg,
norm_eval=False,
extra=dict(
stage1=dict(
num_modul... | ViT-Adapter-main | segmentation/configs/_base_/models/fcn_hr18.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/isanet_r50-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/encnet_r50-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', eps=1e-03, requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='CGNet',
norm_cfg=norm_cfg,
in_channels=3,
num_channels=(32, 64, 128),
num_blocks=(3, 21),
dilations=(2, 4),
reductions=... | ViT-Adapter-main | segmentation/configs/_base_/models/cgnet.py |
# model_cfg
num_things_classes = 29
num_stuff_classes = 30
num_classes = num_things_classes + num_stuff_classes
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoderMask2Former',
pretrained=None,
backbone=dict(
type='XCiT',
patch_size=16,
embed_dim=384... | ViT-Adapter-main | segmentation/configs/_base_/models/mask2former_beit_pascal.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='ICNet',
backbone_cfg=dict(
type='ResNetV1c',
in_channels=3,
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | ViT-Adapter-main | segmentation/configs/_base_/models/icnet_r50-d8.py |
# model_cfg
num_things_classes = 100
num_stuff_classes = 50
num_classes = num_things_classes + num_stuff_classes
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoderMask2Former',
pretrained=None,
backbone=dict(
type='XCiT',
patch_size=16,
embed_dim=38... | ViT-Adapter-main | segmentation/configs/_base_/models/mask2former_beit.py |
# model settings
backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True)
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='pretrain/jx_vit_large_p16_384-b3be5167.pth',
backbone=dict(
type='VisionTransformer',
img_size=(768, 768),
... | ViT-Adapter-main | segmentation/configs/_base_/models/setr_mla.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/ccnet_r50-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/gcnet_r50-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='UNet',
in_channels=3,
base_channels=64,
num_stages=5,
strides=(1, 1, 1, 1, 1),
enc_num_convs=(2, 2, 2, 2, 2),
... | ViT-Adapter-main | segmentation/configs/_base_/models/deeplabv3_unet_s5-d16.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/dnl_r50-d8.py |
# --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# By Hangbo Bao
# B... | ViT-Adapter-main | segmentation/configs/_base_/models/upernet_beit.py |
# model_cfg
num_things_classes = 8
num_stuff_classes = 11
num_classes = num_things_classes + num_stuff_classes
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoderMask2Former',
pretrained=None,
backbone=dict(
type='XCiT',
patch_size=16,
embed_dim=384,... | ViT-Adapter-main | segmentation/configs/_base_/models/mask2former_beit_cityscapes.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='FastSCNN',
downsample_dw_channels=(32, 48),
global_in_channels=64,
global_block_channels=(64, 96, 128),
global_block_strides=(2, 2,... | ViT-Adapter-main | segmentation/configs/_base_/models/fast_scnn.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 1, 1),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/upernet_r50.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='UNet',
in_channels=3,
base_channels=64,
num_stages=5,
strides=(1, 1, 1, 1, 1),
enc_num_convs=(2, 2, 2, 2, 2),
... | ViT-Adapter-main | segmentation/configs/_base_/models/pspnet_unet_s5-d16.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='MixVisionTransformer',
in_channels=3,
embed_dims=32,
num_stages=4,
num_layers=[2, 2, 2, 2],
num_heads=[1, 2, 5, 8],
... | ViT-Adapter-main | segmentation/configs/_base_/models/segformer_mit-b0.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='UNet',
in_channels=3,
base_channels=64,
num_stages=5,
strides=(1, 1, 1, 1, 1),
enc_num_convs=(2, 2, 2, 2, 2),
... | ViT-Adapter-main | segmentation/configs/_base_/models/fcn_unet_s5-d16.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
dilations=(1, 1, 2, 4),
strides=(1, 2, 2, 2),
out_indices=... | ViT-Adapter-main | segmentation/configs/_base_/models/fastfcn_r50-d32_jpu_psp.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/danet_r50-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='CascadeEncoderDecoder',
num_stages=2,
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1... | ViT-Adapter-main | segmentation/configs/_base_/models/ocrnet_r50-d8.py |
# model_cfg
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='XCiT',
patch_size=16,
embed_dim=384,
depth=12,
num_heads=8,
mlp_ratio=4,
qkv_bias=True,
use_abs_pos_emb=Tr... | ViT-Adapter-main | segmentation/configs/_base_/models/maskformer_beit.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='CascadeEncoderDecoder',
num_stages=2,
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1... | ViT-Adapter-main | segmentation/configs/_base_/models/pointrend_r50.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/psanet_r50-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='BiSeNetV2',
detail_channels=(64, 64, 128),
semantic_channels=(16, 32, 64, 128),
semantic_expansion_ratio=6,
bga_channels=128,... | ViT-Adapter-main | segmentation/configs/_base_/models/bisenetv2.py |
# model_cfg
num_things_classes = 1
num_stuff_classes = 5
num_classes = num_things_classes + num_stuff_classes
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoderMask2Former',
pretrained=None,
backbone=dict(
type='BEiT',
patch_size=16,
embed_dim=384,
... | ViT-Adapter-main | segmentation/configs/_base_/models/mask2former_beit_potsdam.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='CascadeEncoderDecoder',
num_stages=2,
pretrained='open-mmlab://msra/hrnetv2_w18',
backbone=dict(
type='HRNet',
norm_cfg=norm_cfg,
norm_eval=False,
extra=dict(
stage1=dict(
... | ViT-Adapter-main | segmentation/configs/_base_/models/ocrnet_hr18.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/deeplabv3plus_r50-d8.py |
# model settings
backbone_norm_cfg = dict(type='LN')
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='PCPVT',
init_cfg=dict(
type='Pretrained', checkpoint='pretrained/pcpvt_small.pth'),
in_channels=3,
embed_d... | ViT-Adapter-main | segmentation/configs/_base_/models/twins_pcpvt-s_upernet.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/dmnet_r50-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='pretrain/jx_vit_base_p16_224-80ecf9dd.pth',
backbone=dict(
type='VisionTransformer',
img_size=(512, 512),
patch_size=16,
in_channels=3,
embed_dims=768,... | ViT-Adapter-main | segmentation/configs/_base_/models/upernet_vit-b16_ln_mln.py |
# model settings
norm_cfg = dict(type='SyncBN', eps=0.001, requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='MobileNetV3',
arch='large',
out_indices=(1, 3, 16),
norm_cfg=norm_cfg),
decode_head=dict(
type='LRASPPHead',
in_channels=(1... | ViT-Adapter-main | segmentation/configs/_base_/models/lraspp_m-v3-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/deeplabv3_r50-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 1, 1),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/fpn_r50.py |
# model settings
backbone_norm_cfg = dict(type='LN')
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='PCPVT',
init_cfg=dict(
type='Pretrained', checkpoint='pretrained/pcpvt_small.pth'),
in_channels=3,
embed_d... | ViT-Adapter-main | segmentation/configs/_base_/models/twins_pcpvt-s_fpn.py |
# model_cfg
num_things_classes = 0
num_stuff_classes = 2
num_classes = num_things_classes + num_stuff_classes
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoderMask2Former',
pretrained=None,
backbone=dict(
type='BEiT',
patch_size=16,
embed_dim=384,
... | ViT-Adapter-main | segmentation/configs/_base_/models/mask2former_beit_chase_db1.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/ann_r50-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='BiSeNetV1',
in_channels=3,
context_channels=(128, 256, 512),
spatial_channels=(64, 64, 64, 128),
out_indices=(0, 1, 2),
out_channels=256,
... | ViT-Adapter-main | segmentation/configs/_base_/models/bisenetv1_r18-d32.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/apcnet_r50-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | ViT-Adapter-main | segmentation/configs/_base_/models/emanet_r50-d8.py |
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained=None,
backbone=dict(
type='ERFNet',
in_channels=3,
enc_downsample_channels=(16, 64, 128),
enc_stage_non_bottlenecks=(5, 8),
enc_non_bottleneck_dilations... | ViT-Adapter-main | segmentation/configs/_base_/models/erfnet_fcn.py |
# model settings
backbone_norm_cfg = dict(type='LN', eps=1e-6, requires_grad=True)
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='pretrain/jx_vit_large_p16_384-b3be5167.pth',
backbone=dict(
type='VisionTransformer',
img_size=(768, 768),
... | ViT-Adapter-main | segmentation/configs/_base_/models/setr_pup.py |
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