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Functions1,036 in github.com/thygate/stable-diffusion-webui-depthmap-script

⨍ Functions 1,036◇ Types & classes 199↳ Endpoints 4
Method__init__
(self, data_dir_root, resize_shape)
dzoedepth/data/ddad.py:83
Method__init__
(self, config)
dzoedepth/data/ibims.py:35
Method__init__
(self)
dzoedepth/data/diml_indoor_test.py:35
Method__init__
(self, data_dir_root)
dzoedepth/data/diml_indoor_test.py:82
Method__init__
(self)
dzoedepth/trainers/loss.py:112
Method__init__
(self, ord_num, beta, discretization="SID")
dzoedepth/trainers/loss.py:139
Method__init__
(self, min_depth=1e-3, max_depth=10, depth_bins=64)
dzoedepth/trainers/loss.py:185
Method__init__
(self)
dzoedepth/trainers/loss.py:282
Method__init__
Base Trainer class for training a model.
dzoedepth/trainers/base_trainer.py:49
Method__init__
(self, config, model, train_loader, test_loader=None, device=None)
dzoedepth/trainers/zoedepth_nk_trainer.py:37
Method__init__
(self, config, model, train_loader, test_loader=None, device=None)
dzoedepth/trainers/zoedepth_trainer.py:40
Method__init__
(self)
dzoedepth/models/depth_model.py:36
Method__init__
(self, resize_mode="minimal", keep_aspect_ratio=True, img_size=384, do_resize=True)
dzoedepth/models/base_models/midas.py:176
Method__init__
Midas Base model used for multi-scale feature extraction. Args: midas (torch.nn.Module): Midas model. trainable (bool
dzoedepth/models/base_models/midas.py:190
Method__init__
ZoeDepth model. This is the version of ZoeDepth that has a single metric head Args: core (models.base_models.midas.MidasCore): Th
dzoedepth/models/zoedepth/zoedepth_v1.py:39
Method__init__
ViT-like transformer block Args: in_channels (int): Input channels patch_size (int, optional): patch size. Defaults t
dzoedepth/models/layers/patch_transformer.py:30
Method__init__
Conditional Log Binomial distribution Args: in_features (int): number of input channels in main feature condition_dim
dzoedepth/models/layers/dist_layers.py:73
Method__init__
Bin center regressor network. Bin centers are bounded on (min_depth, max_depth) interval. Args: in_features (int): input channels
dzoedepth/models/layers/localbins_layers.py:30
Method__init__
Bin center regressor network. Bin centers are unbounded Args: in_features (int): input channels n_bins (int, optional
dzoedepth/models/layers/localbins_layers.py:72
Method__init__
(self, in_features, prev_nbins, split_factor=2, mlp_dim=128, min_depth=1e-3, max_depth=10)
dzoedepth/models/layers/localbins_layers.py:122
Method__init__
Attractor layer for bin centers. Bin centers are unbounded
dzoedepth/models/layers/attractor.py:140
Method__init__
ZoeDepthNK model. This is the version of ZoeDepth that has two metric heads and uses a learned router to route to experts. Args:
dzoedepth/models/zoedepth_nk/zoedepth_nk_v1.py:41
Method__init__
(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm
lib/Resnext_torch.py:33
Method__init__
(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm
lib/Resnext_torch.py:79
Method__init__
(self)
lib/network_auxi.py:16
Method__init__
(self, backbone='resnet', depth=50, upfactors=[2, 2, 2, 2])
lib/network_auxi.py:72
Method__init__
(self, inchannels, midchannels=512)
lib/network_auxi.py:101
Method__init__
(self, inchannels, reduction=8)
lib/network_auxi.py:148
Method__init__
(self, inchannels, midchannels, outchannels, upfactor=2)
lib/network_auxi.py:192
Method__init__
(self, inchannels)
lib/network_auxi.py:289
Method__init__
(self, inchannels, outchannels)
lib/network_auxi.py:334
Method__init__
(self, channels)
lib/network_auxi.py:371
Method__init__
(self, inc, outc, ks=3, stride=1, dilation=1)
lib/spvcnn_classsification.py:11
Method__init__
(self, inc, outc, ks=3, stride=1)
lib/spvcnn_classsification.py:28
Method__init__
(self, **kwargs)
lib/spvcnn_classsification.py:75
Method__init__
(self, backbone='resnet50')
lib/multi_depth_model_woauxi.py:7
Method__init__
(self, inplanes, planes, stride=1, downsample=None)
lib/Resnet.py:26
Method__init__
(self, inplanes, planes, stride=1, downsample=None)
lib/Resnet.py:58
Method__init__
Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features
dmidas/midas_net_custom.py:16
Method__init__
Init. Args: features (int): number of features
dmidas/blocks.py:250
Method__init__
Init. Args: features (int): number of features
dmidas/blocks.py:289
Method__init__
Init. Args: features (int): number of features
dmidas/blocks.py:326
Method__init__
Init. Args: features (int): number of features
dmidas/blocks.py:386
Method__init__
Init. Args: width (int): desired output width height (int): desired output height resize_target (bool, op
dmidas/transforms.py:52
Method__init__
(self, mean, std)
dmidas/transforms.py:201
Method__init__
(self)
dmidas/transforms.py:215
Method__init__
Init. Args: path (str, optional): Path to saved model. Defaults to None. features (int, optional): Number of features
dmidas/midas_net.py:16
Method__init__
(self, path=None, non_negative=True, **kwargs)
dmidas/dpt_depth.py:143
Method__init__
( self, in_chs, out_chs, kernel_size=1, stride=1, pad=0, dilation=1, groups=1, bn_weig
dmidas/backbones/levit.py:62
Method__init__
(self, start_index=1)
dmidas/backbones/utils.py:16
Method__init__
(self, in_features, start_index=1)
dmidas/backbones/utils.py:29
Method__init__
(self, dim0, dim1)
dmidas/backbones/utils.py:43
Method__init__
( self, in_channels, out_channels, kernel_size, st
dmidas/backbones/next_vit.py:76
Method__init__
(self, in_channels, out_channels, stride=1)
dmidas/backbones/next_vit.py:107
Method__init__
(self, out_channels, head_dim)
dmidas/backbones/next_vit.py:134
Method__init__
(self, in_channels, out_channels, stride=1, path_dropout=0, drop=0, head_dim=32, mlp_ratio=3)
dmidas/backbones/next_vit.py:177
Method__init__
(self, dim, out_dim=None, head_dim=32, qkv_bias=True, qk_scale=None, attn_drop=0, proj_drop=0
dmidas/backbones/next_vit.py:214
Method__init__
( self, in_channels, out_channels, path_dropout, stride=1, sr_ratio=1, mlp_ratio=2, he
dmidas/backbones/next_vit.py:280
Method__init__
(self, stem_chs, depths, path_dropout, attn_drop=0, drop=0, num_classes=1000, strides=[1, 2,
dmidas/backbones/next_vit.py:339
Method__init__
(self, values)
src/core.py:64
Method__init__
(self)
src/gradio_args_transport.py:6
Method__init__
(self)
src/depthmap_generation.py:41
Method__init__
(self, root_dir, name, patchsinfo, rgb_image, scale=1)
src/depthmap_generation.py:563
Method__init__
Saves default value as a member (called "df") of a member of this enum
src/common_constants.py:14
Method__init__
(self, in_feature, out_feature)
ddepth_anything_v2/depth_anything_v2/dpt.py:25
Method__init__
( self, encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024],
ddepth_anything_v2/depth_anything_v2/dpt.py:154
Method__init__
Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number
ddepth_anything_v2/depth_anything_v2/dinov2.py:45
Method__init__
Init. Args: features (int): number of features
ddepth_anything_v2/depth_anything_v2/util/blocks.py:87
Method__init__
Init. Args: width (int): desired output width height (int): desired output height resize_target (bool, op
ddepth_anything_v2/depth_anything_v2/util/transform.py:9
Method__init__
(self, mean, std)
ddepth_anything_v2/depth_anything_v2/util/transform.py:129
Method__init__
(self)
ddepth_anything_v2/depth_anything_v2/util/transform.py:143
Method__init__
(self, drop_prob=None)
ddepth_anything_v2/depth_anything_v2/dinov2_layers/drop_path.py:30
Method__init__
( self, dim: int, num_heads: int, mlp_ratio: float = 4.0, qkv_bias: bo
ddepth_anything_v2/depth_anything_v2/dinov2_layers/block.py:37
Method__init__
( self, dim: int, init_values: Union[float, Tensor] = 1e-5, inplace: bool = Fa
ddepth_anything_v2/depth_anything_v2/dinov2_layers/layer_scale.py:17
Method__init__
( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bia
ddepth_anything_v2/depth_anything_v2/dinov2_layers/attention.py:30
Method__init__
( self, in_features: int, hidden_features: Optional[int] = None, out_features:
ddepth_anything_v2/depth_anything_v2/dinov2_layers/mlp.py:18
Method__init__
( self, in_features: int, hidden_features: Optional[int] = None, out_features:
ddepth_anything_v2/depth_anything_v2/dinov2_layers/swiglu_ffn.py:46
Method__init__
( self, img_size: Union[int, Tuple[int, int]] = 224, patch_size: Union[int, Tuple[int,
ddepth_anything_v2/depth_anything_v2/dinov2_layers/patch_embed.py:38
Method__init__
(self, filelist_path, mode, size=(518, 518))
ddepth_anything_v2/metric_depth/dataset/kitti.py:10
Method__init__
(self, filelist_path, mode, size=(518, 518))
ddepth_anything_v2/metric_depth/dataset/vkitti2.py:10
Method__init__
Init. Args: width (int): desired output width height (int): desired output height resize_target (bool, op
ddepth_anything_v2/metric_depth/dataset/transform.py:54
Method__init__
(self, mean, std)
ddepth_anything_v2/metric_depth/dataset/transform.py:211
Method__init__
(self)
ddepth_anything_v2/metric_depth/dataset/transform.py:225
Method__init__
(self, size)
ddepth_anything_v2/metric_depth/dataset/transform.py:251
Method__init__
(self, filelist_path, mode, size=(518, 518))
ddepth_anything_v2/metric_depth/dataset/hypersim.py:27
Method__init__
(self, in_feature, out_feature)
ddepth_anything_v2/metric_depth/depth_anything_v2/dpt.py:25
Method__init__
( self, encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024],
ddepth_anything_v2/metric_depth/depth_anything_v2/dpt.py:153
Method__init__
Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number
ddepth_anything_v2/metric_depth/depth_anything_v2/dinov2.py:45
Method__init__
Init. Args: features (int): number of features
ddepth_anything_v2/metric_depth/depth_anything_v2/util/blocks.py:87
Method__init__
Init. Args: width (int): desired output width height (int): desired output height resize_target (bool, op
ddepth_anything_v2/metric_depth/depth_anything_v2/util/transform.py:9
Method__init__
(self, mean, std)
ddepth_anything_v2/metric_depth/depth_anything_v2/util/transform.py:129
Method__init__
(self)
ddepth_anything_v2/metric_depth/depth_anything_v2/util/transform.py:143
Method__init__
(self, drop_prob=None)
ddepth_anything_v2/metric_depth/depth_anything_v2/dinov2_layers/drop_path.py:30
Method__init__
( self, dim: int, num_heads: int, mlp_ratio: float = 4.0, qkv_bias: bo
ddepth_anything_v2/metric_depth/depth_anything_v2/dinov2_layers/block.py:37
Method__init__
( self, dim: int, init_values: Union[float, Tensor] = 1e-5, inplace: bool = Fa
ddepth_anything_v2/metric_depth/depth_anything_v2/dinov2_layers/layer_scale.py:17
Method__init__
( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bia
ddepth_anything_v2/metric_depth/depth_anything_v2/dinov2_layers/attention.py:30
Method__init__
( self, in_features: int, hidden_features: Optional[int] = None, out_features:
ddepth_anything_v2/metric_depth/depth_anything_v2/dinov2_layers/mlp.py:18
Method__init__
( self, in_features: int, hidden_features: Optional[int] = None, out_features:
ddepth_anything_v2/metric_depth/depth_anything_v2/dinov2_layers/swiglu_ffn.py:46
Method__init__
( self, img_size: Union[int, Tuple[int, int]] = 224, patch_size: Union[int, Tuple[int,
ddepth_anything_v2/metric_depth/depth_anything_v2/dinov2_layers/patch_embed.py:38
Method__init__
(self, lambd=0.5)
ddepth_anything_v2/metric_depth/util/loss.py:6
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