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Functions937 in github.com/VCIP-RGBD/DFormer

⨍ Functions 937◇ Types & classes 246
↓ 1 callersMethodresize_abs_pos_embed
(self, state_dict)
mmseg/models/backbones/mae.py:207
↓ 1 callersMethodrestore_checkpoint
(self)
utils/engine/engine.py:158
↓ 1 callersMethodrun
There are four evaluation modes: 1.only eval a .pth model: -e *.pth 2.only eval a certain epoch: -e epoch 3.eval all epochs in
utils/engine/evaluator.py:56
↓ 1 callersMethodsave_checkpoint
(self, path)
utils/engine/engine.py:101
↓ 1 callersMethodscale_process
(self, img, ori_shape, crop_size, stride_rate, device=None)
utils/engine/evaluator.py:226
↓ 1 callersMethodscale_process
(self, img, ori_shape, crop_size, stride_rate, device=None)
utils/engine/dist_test.py:188
↓ 1 callersMethodscale_process_rgbX
(self, img, modal_x, ori_shape, crop_size, stride_rate, device=None)
utils/engine/evaluator.py:339
↓ 1 callersMethodscaled_l2
(x, codewords, scale)
mmseg/ops/encoding.py:31
↓ 1 callersFunctionset_seed
(seed)
utils/train.py:88
↓ 1 callersMethodsimple_test
Placeholder for single image test.
mmseg/models/segmentors/base.py:52
↓ 1 callersFunctionsingle_gpu_test
Test with single GPU by progressive mode. Args: model (nn.Module): Model to be tested. data_loader (utils.data.Dataloader): Pytor
mmseg/apis/test.py:33
↓ 1 callersMethodslide_inference
Inference by sliding-window with overlap. If h_crop > h_img or w_crop > w_img, the small patch will be used to decode without padding
mmseg/models/segmentors/encoder_decoder.py:146
↓ 1 callersFunctiontotal_intersect_and_union
Calculate Total Intersection and Union. Args: results (list[ndarray] | list[str]): List of prediction segmentation maps or li
mmseg/core/evaluation/metrics.py:80
↓ 1 callersMethodtrain
Convert the model into training mode while keep normalization layer freezed.
mmseg/models/backbones/unet.py:426
↓ 1 callersFunctiontversky_loss
(pred, target, valid_mask, alpha=0.3, beta=0.7, smooth=1, class_weight=None, ignore_index=255)
mmseg/models/losses/tversky_loss.py:15
↓ 1 callersFunctiontversky_loss
(pred, target, valid_mask, alpha=0.3, beta=0.7, smooth=1, class_weight=None, ignore_index=255)
models/losses/tversky_loss.py:15
↓ 1 callersMethodupdate_iteration
(self, epoch, iteration)
utils/engine/engine.py:97
↓ 1 callersMethodwhole_inference
Inference with full image.
mmseg/models/segmentors/encoder_decoder.py:192
↓ 1 callersMethodwindow_reverse
Args: windows: (num_windows*B, window_size, window_size, C) H (int): Height of image W (int): Width of im
mmseg/models/backbones/swin.py:247
FunctionDFormer_Base
(pretrained=False, drop_path_rate=0.1, **kwargs)
models/encoders/DFormer.py:360
FunctionDFormer_Large
(pretrained=False, drop_path_rate=0.1, **kwargs)
models/encoders/DFormer.py:376
FunctionDFormer_Small
(pretrained=False, **kwargs)
models/encoders/DFormer.py:346
FunctionDFormer_Tiny
(pretrained=False, **kwargs)
models/encoders/DFormer.py:331
FunctionDFormerv2_B
(pretrained=False, **kwargs)
models/encoders/DFormerv2.py:661
FunctionDFormerv2_L
(pretrained=False, **kwargs)
models/encoders/DFormerv2.py:674
FunctionDFormerv2_S
(pretrained=False, **kwargs)
models/encoders/DFormerv2.py:650
Method__call__
(self, rgb, gt, modal_x)
utils/dataloader/dataloader.py:43
Method__call__
(self, rgb, gt, modal_x)
utils/dataloader/dataloader.py:88
Method__call__
Call function to load images into results. Args: results (dict): A result dict contains the file name of the imag
mmseg/apis/inference.py:45
Method__enter__
(self)
utils/engine/engine.py:182
Method__exit__
(self, type, value, tb)
utils/engine/engine.py:185
Method__getitem__
(self, index)
utils/dataloader/RGBXDataset.py:139
Method__init__
(self, embed_dim=128, num_heads=4, initial_value=2, heads_range=6)
utils/demo_geometry_prior.py:22
Method__init__
(self, beta=0.6)
utils/train.py:63
Method__init__
(self, num_classes: int, ignore_label: int, device)
utils/metrics_new.py:7
Method__init__
(self, gamma=0, weight=None, reduction="mean", ignore_index=255)
utils/loss_opr.py:14
Method__init__
(self, ignore_index=255, reduction="mean", weight=None)
utils/loss_opr.py:67
Method__init__
(self, delta=0.2, ignore_index=0, reduction="mean")
utils/loss_opr.py:87
Method__init__
(self, ignore_label, gamma=2.0, alpha=0.25, reduction="mean")
utils/loss_opr.py:109
Method__init__
(self, ignore_label, reduction="mean", thresh=0.6, min_kept=256, down_ratio=1, use_weight=False)
utils/loss_opr.py:138
Method__init__
(self, start_lr, lr_power, total_iters)
utils/lr_policy.py:13
Method__init__
(self, start_lr, lr_power, total_iters, warmup_steps)
utils/lr_policy.py:23
Method__init__
(self, lr_stages)
utils/lr_policy.py:37
Method__init__
(self, start_lr, end_lr, warm_iters)
utils/lr_policy.py:50
Method__init__
(self, setting, split_name, preprocess=None, file_length=None)
utils/dataloader/RGBXDataset.py:113
Method__init__
(self, norm_mean, norm_std, sign=False, config=None)
utils/dataloader/dataloader.py:37
Method__init__
(self, norm_mean, norm_std, sign=False, config=None)
utils/dataloader/dataloader.py:82
Method__init__
( self, dataset, class_num, norm_mean, norm_std, network,
utils/engine/evaluator.py:19
Method__init__
(self)
utils/engine/engine.py:23
Method__init__
(self, custom_parser=None)
utils/engine/engine.py:37
Method__init__
( self, dataset, class_num, image_mean, image_std, network,
utils/engine/dist_test.py:20
Method__init__
(self, channels, num_codes)
mmseg/ops/encoding.py:18
Method__init__
(self, size=None, scale_factor=None, mode="nearest", align_corners=None)
mmseg/ops/wrappers.py:29
Method__init__
( self, init_kwargs=None, interval=50, log_checkpoint=False, log_check
mmseg/core/hook/wandblogger_hook.py:86
Method__init__
(self, context, thresh=None, min_kept=100000)
mmseg/core/seg/sampler/ohem_pixel_sampler.py:25
Method__init__
(self, **kwargs)
mmseg/core/seg/sampler/base_pixel_sampler.py:8
Method__init__
(self, optimizer_cfg, paramwise_cfg)
mmseg/core/optimizers/layer_decay_optimizer_constructor.py:190
Method__init__
(self, *args, by_epoch=False, efficient_test=False, pre_eval=False, **kwargs)
mmseg/core/evaluation/eval_hooks.py:74
Method__init__
(self, init_cfg=None)
mmseg/models/segmentors/base.py:16
Method__init__
( self, backbone, decode_head, neck=None, auxiliary_head=None,
mmseg/models/segmentors/encoder_decoder.py:22
Method__init__
( self, num_stages, backbone, decode_head, neck=None, auxiliar
mmseg/models/segmentors/cascade_encoder_decoder.py:20
Method__init__
(self, in_channels=[1024, 1024, 1024, 1024], out_channels=256, norm_cfg=None, act_cfg=None)
mmseg/models/necks/mla_neck.py:9
Method__init__
( self, in_channels, out_channels, num_outs, start_level=0, en
mmseg/models/necks/fpn.py:67
Method__init__
( self, low_channels, high_channels, out_channels, conv_cfg=None,
mmseg/models/necks/ic_neck.py:36
Method__init__
( self, in_channels=(512, 1024, 2048), mid_channels=512, start_level=0,
mmseg/models/necks/jpu.py:43
Method__init__
(self, embed_dim, rescales=[4, 2, 1, 0.5], norm_cfg=dict(type="SyncBN", requires_grad=True))
mmseg/models/necks/featurepyramid.py:22
Method__init__
(self, in_channels, out_channels, scales=[0.5, 1, 2, 4], norm_cfg=None, act_cfg=None)
mmseg/models/necks/multilevel_neck.py:25
Method__init__
(self, radix, groups)
mmseg/models/backbones/resnest.py:24
Method__init__
( self, in_channels, channels, kernel_size, stride=1, padding=
mmseg/models/backbones/resnest.py:59
Method__init__
Bottleneck block for ResNeSt.
mmseg/models/backbones/resnest.py:166
Method__init__
( self, in_channels, out_channels, stride, norm_cfg=None, act_
mmseg/models/backbones/stdc.py:30
Method__init__
( self, in_channels, out_channels, scale_factor=4, norm_cfg=dict(type=
mmseg/models/backbones/stdc.py:153
Method__init__
( self, backbone_cfg, last_in_channels=(1024, 512), out_channels=128,
mmseg/models/backbones/stdc.py:356
Method__init__
( self, embed_dims, num_heads, attn_drop=0.0, proj_drop=0.0, d
mmseg/models/backbones/mit.py:108
Method__init__
( self, embed_dims, num_heads, feedforward_channels, drop_rate=0.0,
mmseg/models/backbones/mit.py:235
Method__init__
( self, in_channels=3, embed_dims=64, num_stages=4, num_layers=[3, 4,
mmseg/models/backbones/mit.py:335
Method__init__
( self, in_channels, out_channels, num_convs=2, stride=1, dila
mmseg/models/backbones/unet.py:45
Method__init__
( self, in_channels, out_channels, with_cp=False, norm_cfg=dict(type="
mmseg/models/backbones/unet.py:111
Method__init__
( self, in_channels, out_channels, with_cp=False, norm_cfg=dict(type="
mmseg/models/backbones/unet.py:182
Method__init__
( self, detail_channels=(64, 64, 128), in_channels=3, conv_cfg=None, n
mmseg/models/backbones/bisenetv2.py:32
Method__init__
( self, in_channels=3, out_channels=16, conv_cfg=None, norm_cfg=dict(t
mmseg/models/backbones/bisenetv2.py:132
Method__init__
( self, in_channels=3, out_channels=16, conv_cfg=None, norm_cfg=dict(t
mmseg/models/backbones/bisenetv2.py:347
Method__init__
(self, semantic_channels=(16, 32, 64, 128), in_channels=3, exp_ratio=6, init_cfg=None)
mmseg/models/backbones/bisenetv2.py:410
Method__init__
( self, out_channels=128, align_corners=False, conv_cfg=None, norm_cfg
mmseg/models/backbones/bisenetv2.py:474
Method__init__
( self, in_channels=3, detail_channels=(64, 64, 128), semantic_channels=(16, 3
mmseg/models/backbones/bisenetv2.py:601
Method__init__
(self, channel, reduction=16, with_cp=False)
mmseg/models/backbones/cgnet.py:27
Method__init__
( self, in_channels, out_channels, dilation=2, reduction=16, s
mmseg/models/backbones/cgnet.py:81
Method__init__
(self, num_downsampling)
mmseg/models/backbones/cgnet.py:162
Method__init__
( self, embed_dims, num_heads, feedforward_channels, drop_rate=0.0,
mmseg/models/backbones/vit.py:47
Method__init__
( self, arch="small", conv_cfg=None, norm_cfg=dict(type="BN"), out_ind
mmseg/models/backbones/mobilenet_v3.py:74
Method__init__
(self, inplanes, planes, groups=1, base_width=4, base_channels=64, **kwargs)
mmseg/models/backbones/resnext.py:19
Method__init__
( self, in_channels=3, num_channels=(64, 64, 64, 128), conv_cfg=None,
mmseg/models/backbones/bisenetv1.py:25
Method__init__
( self, in_channels, out_channel, conv_cfg=None, norm_cfg=dict(type="B
mmseg/models/backbones/bisenetv1.py:104
Method__init__
( self, backbone_cfg, context_channels=(128, 256, 512), align_corners=False,
mmseg/models/backbones/bisenetv1.py:163
Method__init__
( self, in_channels, out_channels, conv_cfg=None, norm_cfg=dict(type="
mmseg/models/backbones/bisenetv1.py:246
Method__init__
( self, num_branches, blocks, num_blocks, in_channels, num_cha
mmseg/models/backbones/hrnet.py:21
Method__init__
( self, embed_dims, num_heads, window_size, bias="qv_bias", qk
mmseg/models/backbones/beit.py:47
Method__init__
( self, embed_dims, num_heads, feedforward_channels, attn_drop_rate=0.
mmseg/models/backbones/beit.py:177
Method__init__
( self, in_channels, dw_channels, out_channels, conv_cfg=None,
mmseg/models/backbones/fast_scnn.py:32
Method__init__
( self, in_channels=64, block_channels=(64, 96, 128), out_channels=128,
mmseg/models/backbones/fast_scnn.py:121
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