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Functions1,143 in github.com/devinxzhang/MFuser

⨍ Functions 1,143◇ Types & classes 269
Method__init__
(self, transformerlayers=None, num_layers=None, init_cfg=None)
mmseg/models/plugins/transformerlayers.py:840
Method__init__
(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.)
mmseg/models/plugins/transformerlayers.py:886
Method__init__
(self, embed_dims, num_heads, attn_drop=0.,
mmseg/models/plugins/transformerlayers.py:923
Method__init__
(self, attn_cfgs=None, ffn_cfgs=dict( type='FFN',
mmseg/models/plugins/transformerlayers.py:1012
Method__init__
(self, transformerlayers=None, num_layers=None, init_cfg=None)
mmseg/models/plugins/transformerlayers.py:1179
Method__init__
(self, in_channels=[256, 512, 1024, 2048], strides=[4, 8, 16, 32],
mmseg/models/plugins/msdeformattn_pixel_decoder.py:39
Method__init__
(self, in_channels, feat_channels, out_channels,
mmseg/models/plugins/pixel_decoder.py:135
Method__init__
Dice Loss, there are two forms of dice loss is supported: - the one proposed in `V-Net: Fully Convolutional Neural Networ
mmseg/models/losses/dice_loss.py:86
Method__init__
Module to calculate the accuracy. Args: topk (tuple, optional): The criterion used to calculate the accuracy. Def
mmseg/models/losses/accuracy.py:57
Method__init__
(self, weight=1., alpha=0.25, gamma=2, eps=1e-12)
mmseg/models/losses/match_loss.py:32
Method__init__
(self, weight=1.)
mmseg/models/losses/match_loss.py:112
Method__init__
(self, weight=1., pred_act=False, eps=1e-3)
mmseg/models/losses/match_loss.py:144
Method__init__
(self, use_sigmoid=False, use_mask=False, reduction='mean',
mmseg/models/losses/cross_entropy_loss.py:213
Method__init__
`Focal Loss <https://arxiv.org/abs/1708.02002>`_ Args: use_sigmoid (bool, optional): Whether to the prediction is
mmseg/models/losses/focal_loss.py:107
Method__init__
(self, weight=1., alpha=0.25, gamma=2, eps=1e-12)
mmseg/models/losses/match_costs.py:32
Method__init__
(self, weight=1.)
mmseg/models/losses/match_costs.py:112
Method__init__
(self, weight=1., pred_act=False, eps=1e-3)
mmseg/models/losses/match_costs.py:144
Method__init__
(self, weight=1., use_sigmoid=True)
mmseg/models/losses/match_costs.py:191
Method__init__
(self, reduction='mean', loss_weight=1.0)
mmseg/models/losses/l2_loss.py:10
Method__init__
(self, **cfg)
mmseg/models/uda/baseline.py:39
Method__init__
(self, root, transform, name=False)
mmseg/models/uda/photo_wct_batch.py:23
Method__init__
(self)
mmseg/models/uda/photo_wct_batch.py:62
Method__init__
(self, **cfg)
mmseg/models/uda/uda_decorator.py:26
Method__init__
(self, data_source)
mmseg/models/uda/sampler.py:19
Method__init__
(self, level)
mmseg/models/uda/vgg.py:191
Method__init__
(self, **cfg)
mmseg/models/uda/dacs.py:48
Method__init__
(self, **cfg)
mmseg/models/uda/stylization.py:39
Method__init__
(self, eva_clip, decode_head, class_names,
models/segmentors/mfuser_eva_clip.py:24
Method__init__
(self, backbone, text_encoder, decode_head,
models/segmentors/mfuser_clip.py:21
Method__init__
(self, backbone, text_encoder, decode_head,
models/segmentors/mfuser_siglip.py:21
Method__init__
(self, bpe_path: str = default_bpe())
models/backbones/utils.py:63
Method__init__
Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number
models/backbones/dino_v2.py:57
Method__init__
(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None, drop_path=0.)
models/backbones/clip/models.py:44
Method__init__
(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, drop_path_rate=0.)
models/backbones/clip/models.py:70
Method__init__
(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.)
models/backbones/clip/models.py:82
Method__init__
( self, d_model, nhead, dropout=0.1)
models/backbones/clip/models.py:118
Method__init__
(self, input_resolution=224, patch_size=32, width=768,
models/backbones/clip/models.py:151
Method__init__
(self, context_length=77, vocab_size=49408, transformer_width=512,
models/backbones/clip/models.py:368
Method__init__
(self, context_length=22, vocab_size=49408, transformer_width=512,
models/backbones/clip/models.py:437
Method__init__
(self, transformer_width=256, transformer_heads=4, transfor
models/backbones/clip/models.py:539
Method__init__
( self, vocab_file, eos_token="</s>", unk_token="<unk>", pad_token="</
models/backbones/siglip/tokenization_siglip.py:88
Method__init__
( self, vocab_size=32000, hidden_size=768, intermediate_size=3072, num
models/backbones/siglip/configuration_siglip.py:83
Method__init__
( self, hidden_size=768, intermediate_size=3072, num_hidden_layers=12,
models/backbones/siglip/configuration_siglip.py:182
Method__init__
(self, is_causal: bool, sliding_window: Optional[int] = None)
models/backbones/siglip/modeling_siglip.py:132
Method__init__
(self, hidden_size, image_size, patch_size, num_channels)
models/backbones/siglip/modeling_siglip.py:352
Method__init__
(self, hidden_size, vocab_size, max_position_embeddings)
models/backbones/siglip/modeling_siglip.py:414
Method__init__
(self, hidden_size, num_attention_heads, attention_dropout)
models/backbones/siglip/modeling_siglip.py:470
Method__init__
(self, *args, **kwargs)
models/backbones/siglip/modeling_siglip.py:546
Method__init__
(self, hidden_size, hidden_act, num_attention_heads, intermediate_size, attention_dropout, layer_norm_eps)
models/backbones/siglip/modeling_siglip.py:691
Method__init__
(self, hidden_size, hidden_act, num_attention_heads, intermediate_size, attention_dropout, layer_norm_eps, num
models/backbones/siglip/modeling_siglip.py:732
Method__init__
(self, context_length=64, vocab_size=32000, hidden_size=768, intermediate_size=3072, num_hidden_layers=12, num
models/backbones/siglip/modeling_siglip.py:788
Method__init__
(self, hidden_size=768, intermediate_size=3072, num_hidden_layers=12, num_attention_heads=12, num_channels=3,
models/backbones/siglip/modeling_siglip.py:885
Method__init__
(self, hidden_size, num_attention_heads, layer_norm_eps, hidden_act, intermediate_size)
models/backbones/siglip/modeling_siglip.py:1070
Method__init__
(self, image_processor, tokenizer)
models/backbones/siglip/processing_siglip.py:46
Method__init__
( self, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImag
models/backbones/siglip/image_processing_siglip.py:82
Method__init__
(self, *args)
models/backbones/siglip/tokenization_utils.py:285
Method__init__
(self, **kwargs)
models/backbones/siglip/tokenization_utils.py:421
Method__init__
(self, drop_prob=None)
models/backbones/dino_layers/drop_path.py:29
Method__init__
( self, dim: int, num_heads: int, mlp_ratio: float = 4.0, qkv_bias: bo
models/backbones/dino_layers/block.py:44
Method__init__
( self, in_dim, out_dim, use_bn=False, nlayers=3, hidden_dim=2
models/backbones/dino_layers/dino_head.py:13
Method__init__
( self, dim: int, init_values: Union[float, Tensor] = 1e-5, inplace: bool = Fa
models/backbones/dino_layers/layer_scale.py:16
Method__init__
( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bia
models/backbones/dino_layers/attention.py:37
Method__init__
( self, in_features: int, hidden_features: Optional[int] = None, out_features:
models/backbones/dino_layers/mlp.py:17
Method__init__
( self, in_features: int, hidden_features: Optional[int] = None, out_features:
models/backbones/dino_layers/swiglu_ffn.py:55
Method__init__
( self, img_size: Union[int, Tuple[int, int]] = 224, patch_size: Union[int, Tuple[int,
models/backbones/dino_layers/patch_embed.py:37
Method__init__
( self, local_loss=False, gather_with_grad=False, cache_labels
models/backbones/eva_clip/loss.py:72
Method__init__
( self, dim, pt_seq_len, ft_seq_len=None, custom_freqs = None,
models/backbones/eva_clip/rope.py:80
Method__init__
(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None)
models/backbones/eva_clip/modified_resnet.py:59
Method__init__
(self, layers, output_dim, heads, image_size=224, width=64)
models/backbones/eva_clip/modified_resnet.py:103
Method__init__
( self, d_model, d_state=8, d_conv=3, expand=1, dt_rank="auto"
models/backbones/eva_clip/adapter_module.py:61
Method__init__
(self, bpe_path: str = default_bpe(), special_tokens=None)
models/backbones/eva_clip/tokenizer.py:73
Method__init__
(self, tokenizer_name:str)
models/backbones/eva_clip/tokenizer.py:190
Method__init__
(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0)
models/backbones/eva_clip/transform.py:15
Method__init__
( self, model_name, embed_dim, image_size=224, poo
models/backbones/eva_clip/timm_model.py:33
Method__init__
( self, model_name_or_path: str, output_dim: int, tokenizer_n
models/backbones/eva_clip/hf_model.py:77
Method__init__
(self, *args, **kwargs)
models/backbones/eva_clip/transformer.py:42
Method__init__
(self, dim, init_values=1e-5, inplace=False)
models/backbones/eva_clip/transformer.py:71
Method__init__
(self, prob, exclude_first_token=True)
models/backbones/eva_clip/transformer.py:84
Method__init__
( self, dim, num_heads=8, qkv_bias=True, scaled_co
models/backbones/eva_clip/transformer.py:248
Method__init__
( self, d_model: int, n_head: int, mlp_ratio: float = 4.0,
models/backbones/eva_clip/transformer.py:344
Method__init__
( self, width: int, layers: int, heads: int, mlp_r
models/backbones/eva_clip/transformer.py:394
Method__init__
( self, d_model: int, n_head: int, mlp_ratio: float = 4.0,
models/backbones/eva_clip/transformer.py:448
Method__init__
( self, width: int, layers: int, heads: int, mlp_r
models/backbones/eva_clip/transformer.py:490
Method__init__
( self, image_size: int, patch_size: int, width: int,
models/backbones/eva_clip/transformer.py:525
Method__init__
( self, context_length: int = 77, vocab_size: int = 49408, wid
models/backbones/eva_clip/transformer.py:647
Method__init__
(self, drop_prob=None)
models/backbones/eva_clip/eva_vit_model.py:41
Method__init__
(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0., norm
models/backbones/eva_clip/eva_vit_model.py:86
Method__init__
( self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., w
models/backbones/eva_clip/eva_vit_model.py:111
Method__init__
(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., dr
models/backbones/eva_clip/eva_vit_model.py:251
Method__init__
(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768)
models/backbones/eva_clip/eva_vit_model.py:310
Method__init__
(self, window_size, num_heads)
models/backbones/eva_clip/eva_vit_model.py:335
Method__init__
(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, nu
models/backbones/eva_clip/eva_vit_model.py:373
Method__init__
( self, embed_dim: int, vision_cfg: CLIPVisionCfg, text_cfg: C
models/backbones/eva_clip/model.py:276
Method__iter__
(self)
mmseg/models/uda/sampler.py:22
Method__len__
Total number of samples of data.
mmseg/datasets/custom.py:126
Method__len__
The length is multiplied by ``times``
mmseg/datasets/dataset_wrappers.py:50
Method__len__
(self)
mmseg/datasets/ug_dataset.py:110
Method__len__
(self)
mmseg/datasets/uda_dataset.py:120
Method__len__
(self)
mmseg/models/uda/photo_wct_batch.py:43
Method__len__
(self)
mmseg/models/uda/sampler.py:25
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