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⨍ Functions 474◇ Types & classes 105

datasets/transform_utils.py:133

datasets/transform_utils.py:146

Apply augmentations. Parameters ---------- images : torch.Tensor Input tensor of shape ``[N, C, H, W]``.

datasets/augmentv2.py:195

Method__getitem__

preprocess_depth_davis.py:106

Method__getitem__

utils/data_utils.py:16

Method__getitem__

datasets/provider_vos.py:100

Method__getitem__

datasets/provider_davis.py:100

Method__getitem__

datasets/provider_co3d.py:96

Method__getitem__

datasets/provider_combined.py:111

Method__getitem__

datasets/provider_re10k_map.py:103

(self, opt, training=True, shuffle=False)

preprocess_depth_davis.py:46

(self, opt: Options, **model_kwargs)

preprocess_depth_davis.py:130

(self, viewpoint_stack, white_background)

utils/data_utils.py:12

(self, optimizer, warmup_iters: int, max_iters: int, initial_lr: float = 1e-10, last_iter: int = -1, min_lr: f

utils/general_utils.py:164

对 weight_decay 进行余弦“增加”调度，从 initial_wd -> final_wd。参数: - optimizer: 任何带有 'weight_decay' param_group 的 optimizer

utils/general_utils.py:196

(self, opt: Options, **model_kwargs)

model/encoder_model.py:29

(self, opt: Options, **model_kwargs)

model/splat_model_inference.py:14

(self, opt: Options, **kwargs)

model/model_utils.py:77

(self, opt: Options, transformer_dim: int, mlp_dim=None,

model/model_utils.py:116

(self, opt: Options, transformer_dim: int, mlp_dim=None, bias=True)

model/model_utils.py:372

(self, opt: Options, **model_kwargs)

model/model_utils.py:451

(self, opt: Options, **model_kwargs)

model/model_utils.py:525

(self, opt: Options, **model_kwargs)

model/splat_model.py:26

(self, model_name: str)

model/depth_wrapper.py:15

(self, n_sample=1, nr_mix=1)

model/mixture_model_utils.py:10

(self, drop_prob=None)

model/transformer_utils.py:50

(self, dim, eps=1e-5)

model/transformer_utils.py:69

( self, E_q: int, E_k: int, E_v: int, E_total: int, nheads: in

model/transformer_utils.py:167

(self, d_model, n_head, window_size=None, drop_path_rate=0.0)

model/transformer_utils.py:260

(self, d_model, n_head, window_size=None, drop_path_rate=0.0)

model/transformer_utils.py:302

(self, max_len, d_model)

model/transformer_utils.py:363

(self, width, layers, heads, window_size=None, block_cls=ResAttBlock, drop_path_rate=0.0)

model/transformer_utils.py:377

(self, width, layers, heads, window_size, token_len, block_cls, drop_path_rate=0.0)

model/transformer_utils.py:406

(self, input_res, in_channels, patch_size, width, layers, heads, window_size)

model/transformer_utils.py:423

(self, input_res, patch_size, width, layers, heads, window_size, encoder_dim=None, condition_len=576, conditio

model/transformer_utils.py:445

(self, token_len, width, layers, heads, window_size, encoder_dim=None)

model/transformer_utils.py:489

(self, d_model, d_model_out, scale_factor, resolution=None, view_num=1)

model/transformer_utils.py:520

(self, width, up_ratio, ch_decay=1, low_channels=64, window_size=None, opt: Options=None)

model/transformer_utils.py:545

(self, in_feature, out_feature)

model/depth_anything/depth_anything_v2/dpt.py:25

( self, encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024],

model/depth_anything/depth_anything_v2/dpt.py:154

Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number

model/depth_anything/depth_anything_v2/dinov2.py:45

Init. Args: features (int): number of features

model/depth_anything/depth_anything_v2/util/blocks.py:87

Init. Args: width (int): desired output width height (int): desired output height resize_target (bool, op

model/depth_anything/depth_anything_v2/util/transform.py:9

(self, mean, std)

model/depth_anything/depth_anything_v2/util/transform.py:129

model/depth_anything/depth_anything_v2/util/transform.py:143

(self, drop_prob=None)

model/depth_anything/depth_anything_v2/dinov2_layers/drop_path.py:30

( self, dim: int, num_heads: int, mlp_ratio: float = 4.0, qkv_bias: bo

model/depth_anything/depth_anything_v2/dinov2_layers/block.py:37

( self, dim: int, init_values: Union[float, Tensor] = 1e-5, inplace: bool = Fa

model/depth_anything/depth_anything_v2/dinov2_layers/layer_scale.py:17

( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bia

model/depth_anything/depth_anything_v2/dinov2_layers/attention.py:30

( self, in_features: int, hidden_features: Optional[int] = None, out_features:

model/depth_anything/depth_anything_v2/dinov2_layers/mlp.py:18

( self, in_features: int, hidden_features: Optional[int] = None, out_features:

model/depth_anything/depth_anything_v2/dinov2_layers/swiglu_ffn.py:46

( self, img_size: Union[int, Tuple[int, int]] = 224, patch_size: Union[int, Tuple[int,

model/depth_anything/depth_anything_v2/dinov2_layers/patch_embed.py:38

(self, model_name: str, modulation_dim: int = None, freeze: bool = True)

encoders/dinov2_wrapper.py:28

(self, multiple)

encoders/dinov2/hub/utils.py:24

(self, *, backbone: nn.Module, linear_head: nn.Module, layers: int = 4)

encoders/dinov2/hub/classifiers.py:53

( self, in_channels, conv_layer=None, act_layer=nn.ReLU, channels=96,

encoders/dinov2/hub/depth/decode_heads.py:61

( self, in_channels, out_channels, kernel_size, stride=1, padd

encoders/dinov2/hub/depth/decode_heads.py:352

(self, scale_factor, mode, align_corners=False)

encoders/dinov2/hub/depth/decode_heads.py:500

(self, features)

encoders/dinov2/hub/depth/decode_heads.py:513

(self, in_channels=768, out_channels=[96, 192, 384, 768], readout_type="ignore", patch_size=16)

encoders/dinov2/hub/depth/decode_heads.py:539

(self, in_channels, act_layer, norm_layer, stride=1, dilation=1)

encoders/dinov2/hub/depth/decode_heads.py:610

(self, in_channels, act_layer, norm_layer, expand=False, align_corners=True)

encoders/dinov2/hub/depth/decode_heads.py:656

( self, embed_dims=768, post_process_channels=[96, 192, 384, 768], readout_typ

encoders/dinov2/hub/depth/decode_heads.py:704

(self, backbone, decode_head)

encoders/dinov2/hub/depth/encoder_decoder.py:40

(self, drop_prob=None)

encoders/dinov2/layers/drop_path.py:29

(self, *args, **kwargs)

encoders/dinov2/layers/block.py:126

( self, in_dim, out_dim, use_bn=False, nlayers=3, hidden_dim=2

encoders/dinov2/layers/dino_head.py:13

( self, dim: int, init_values: Union[float, Tensor] = 1e-5, inplace: bool = Fa

encoders/dinov2/layers/layer_scale.py:16

( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bia

encoders/dinov2/layers/attention.py:37

( self, in_features: int, hidden_features: Optional[int] = None, out_features:

encoders/dinov2/layers/mlp.py:17

( self, in_features: int, hidden_features: Optional[int] = None, out_features:

encoders/dinov2/layers/swiglu_ffn.py:55

( self, img_size: Union[int, Tuple[int, int]] = 224, patch_size: Union[int, Tuple[int,

encoders/dinov2/layers/patch_embed.py:37

Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number

encoders/dinov2/models/vision_transformer.py:54

(self, raster_settings)

gaussian_renderer_dynamic/diff_gaussian_rasterization.py:239

(self, raster_settings)

submodules/diff-gaussian-rasterization-orth/diff_gaussian_rasterization_kiui_orth/__init__.py:175

(self, opt, training=True, shuffle=False, nearby_range=3)

datasets/provider_vos.py:12

(self, opt, training=True, shuffle=False, nearby_range=4)

datasets/provider_davis.py:12

(self, opt: Options, training=True, shuffle=False,

datasets/provider_co3d.py:17

Init. Args: width (int): desired output width height (int): desired output height resize_target (bool, op

datasets/transform_utils.py:9

(self, mean, std)

datasets/transform_utils.py:129

datasets/transform_utils.py:143

Combined dataset that includes Co3D, Re10k and DAVIS datasets. Args: opt: Configuration object that includes:

datasets/provider_combined.py:13

(self, opt, shuffle=False, training=True,

datasets/provider_re10k_map.py:16

(self, p=1, xflip=0, yflip=0, rotate_int=0, translate_int=0, translate_int_max=0.125, scale=0,

datasets/augmentv2.py:155

preprocess_depth_davis.py:103

utils/data_utils.py:36

datasets/provider_vos.py:97

datasets/provider_davis.py:97

datasets/provider_co3d.py:86

datasets/provider_combined.py:108

datasets/provider_re10k_map.py:100

Function_count_params

model/model_utils.py:38

Function_count_params

model/splat_model.py:22

model/model_utils.py:484

Retrieve a random tensor by name, sampling if necessary.

datasets/augmentv2.py:230

Method_init_weights

encoders/dinov2/layers/dino_head.py:30

Methodattn_residual_func

model/depth_anything/depth_anything_v2/dinov2_layers/block.py:83

Methodattn_residual_func

(x: Tensor, attn_bias=None)

model/depth_anything/depth_anything_v2/dinov2_layers/block.py:213

Methodattn_residual_func

encoders/dinov2/layers/block.py:96

Methodattn_residual_func

(x: Tensor, mod: Tensor)

encoders/dinov2/layers/block.py:130

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