spandrel.architectures.Uformer.Uformer

class documentation

class Uformer(nn.Module): (source)

Constructor: Uformer(img_size, in_chans, dd_in, embed_dim, ...)

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Method __init__ Undocumented
Method extra_repr Undocumented
Method flops Undocumented
Method forward Undocumented
Method no_weight_decay Undocumented
Method no_weight_decay_keywords Undocumented
Class Variable hyperparameters Undocumented
Instance Variable conv Undocumented
Instance Variable dd_in Undocumented
Instance Variable decoderlayer_0 Undocumented
Instance Variable decoderlayer_1 Undocumented
Instance Variable decoderlayer_2 Undocumented
Instance Variable decoderlayer_3 Undocumented
Instance Variable dowsample_0 Undocumented
Instance Variable dowsample_1 Undocumented
Instance Variable dowsample_2 Undocumented
Instance Variable dowsample_3 Undocumented
Instance Variable embed_dim Undocumented
Instance Variable encoderlayer_0 Undocumented
Instance Variable encoderlayer_1 Undocumented
Instance Variable encoderlayer_2 Undocumented
Instance Variable encoderlayer_3 Undocumented
Instance Variable input_proj Undocumented
Instance Variable mlp Undocumented
Instance Variable mlp_ratio Undocumented
Instance Variable num_dec_layers Undocumented
Instance Variable num_enc_layers Undocumented
Instance Variable output_proj Undocumented
Instance Variable patch_norm Undocumented
Instance Variable pos_drop Undocumented
Instance Variable reso Undocumented
Instance Variable token_projection Undocumented
Instance Variable upsample_0 Undocumented
Instance Variable upsample_1 Undocumented
Instance Variable upsample_2 Undocumented
Instance Variable upsample_3 Undocumented
Instance Variable win_size Undocumented
Method _init_weights Undocumented
def __init__(self, *, img_size=256, in_chans=3, dd_in=3, embed_dim=32, depths=[2, 2, 2, 2, 2, 2, 2, 2, 2], num_heads=[1, 2, 4, 8, 16, 16, 8, 4, 2], win_size=8, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1, norm_layer=nn.LayerNorm, patch_norm=True, use_checkpoint=False, token_projection='linear', token_mlp='leff', dowsample=Downsample, upsample=Upsample, shift_flag=True, modulator=False, cross_modulator=False): (source)

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def extra_repr(self) -> str: (source)

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def flops(self): (source)

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def forward(self, x, mask=None): (source)

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@torch.jit.ignore
def no_weight_decay(self): (source)

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@torch.jit.ignore
def no_weight_decay_keywords(self): (source)

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hyperparameters: dict = (source)

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conv = (source)

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dd_in = (source)

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decoderlayer_0 = (source)

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decoderlayer_1 = (source)

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decoderlayer_2 = (source)

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decoderlayer_3 = (source)

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dowsample_0 = (source)

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dowsample_1 = (source)

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dowsample_2 = (source)

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dowsample_3 = (source)

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embed_dim = (source)

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encoderlayer_0 = (source)

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encoderlayer_1 = (source)

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encoderlayer_2 = (source)

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encoderlayer_3 = (source)

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input_proj = (source)

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mlp = (source)

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mlp_ratio = (source)

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num_dec_layers = (source)

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num_enc_layers = (source)

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output_proj = (source)

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patch_norm = (source)

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pos_drop = (source)

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reso = (source)

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token_projection = (source)

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upsample_0 = (source)

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upsample_1 = (source)

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upsample_2 = (source)

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upsample_3 = (source)

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win_size = (source)

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def _init_weights(self, m): (source)

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