mmagic.models.editors.swinir.swinir_rstb
¶
Module Contents¶
Classes¶
Drop paths (Stochastic Depth) per sample (when applied in main path of |
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Multilayer Perceptron layer. |
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Window based multi-head self attention (W-MSA) |
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Swin Transformer Block. |
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A basic Swin Transformer layer for one stage. |
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Residual Swin Transformer Block (RSTB). |
- class mmagic.models.editors.swinir.swinir_rstb.DropPath(drop_prob: float = 0.0, scale_by_keep: bool = True)[source]¶
Bases:
torch.nn.Module
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
- class mmagic.models.editors.swinir.swinir_rstb.Mlp(in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0)[source]¶
Bases:
torch.nn.Module
Multilayer Perceptron layer.
- Parameters
in_features (int) – Number of input channels.
hidden_features (int | None, optional) – Number of hidden layer channels. Default: None
out_features (int | None, optional) – Number of output channels. Default: None
act_layer (nn.Module, optional) – Activation layer. Default: nn.GELU
drop (float, optional) – Dropout ratio of attention weight. Default: 0.0
- class mmagic.models.editors.swinir.swinir_rstb.WindowAttention(dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0)[source]¶
Bases:
torch.nn.Module
Window based multi-head self attention (W-MSA) module with relative position bias. It supports both of shifted and non-shifted window. :param dim: Number of input channels. :type dim: int :param window_size: The height and width of the window. :type window_size: tuple[int] :param num_heads: Number of attention heads. :type num_heads: int :param qkv_bias: If True, add a learnable bias to
query, key, value. Default: True
- Parameters
qk_scale (float | None, optional) – Override default qk scale of head_dim ** -0.5 if set
attn_drop (float, optional) – Dropout ratio of attention weight. Default: 0.0
proj_drop (float, optional) – Dropout ratio of output. Default: 0.0
- class mmagic.models.editors.swinir.swinir_rstb.SwinTransformerBlock(dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=nn.GELU, norm_layer=nn.LayerNorm)[source]¶
Bases:
torch.nn.Module
Swin Transformer Block. :param dim: Number of input channels. :type dim: int :param input_resolution: Input resolution. :type input_resolution: tuple[int] :param num_heads: Number of attention heads. :type num_heads: int :param window_size: Window size. :type window_size: int :param shift_size: Shift size for SW-MSA. :type shift_size: int :param mlp_ratio: Ratio of mlp hidden dim to embedding dim. :type mlp_ratio: float :param qkv_bias: If True, add a learnable bias
to query, key, value. Default: True
- Parameters
qk_scale (float | None, optional) – Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional) – Dropout rate. Default: 0.0
attn_drop (float, optional) – Attention dropout rate. Default: 0.0
drop_path (float, optional) – Stochastic depth rate. Default: 0.0
act_layer (nn.Module, optional) – Activation layer. Default: nn.GELU
norm_layer (nn.Module, optional) – Normalization layer. Default: nn.LayerNorm
- calculate_mask(x_size)[source]¶
Calculate attention mask for SW-MSA.
- Parameters
x_size (tuple[int]) – Resolution of input feature.
- Returns
Attention mask
- Return type
Tensor
- class mmagic.models.editors.swinir.swinir_rstb.BasicLayer(dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False)[source]¶
Bases:
torch.nn.Module
A basic Swin Transformer layer for one stage.
- Parameters
dim (int) – Number of input channels.
input_resolution (tuple[int]) – Input resolution.
depth (int) – Number of blocks.
num_heads (int) – Number of attention heads.
window_size (int) – Local window size.
mlp_ratio (float) – Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional) – If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional) – Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional) – Dropout rate. Default: 0.0
attn_drop (float, optional) – Attention dropout rate. Default: 0.0
drop_path (float | tuple[float], optional) – Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional) – Normalization layer. Default: nn.LayerNorm
downsample (nn.Module | None, optional) – Downsample layer at the end of the layer. Default: None
use_checkpoint (bool) – Whether to use checkpointing to save memory. Default: False.
- class mmagic.models.editors.swinir.swinir_rstb.RSTB(dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, img_size=224, patch_size=4, resi_connection='1conv')[source]¶
Bases:
torch.nn.Module
Residual Swin Transformer Block (RSTB).
- Parameters
dim (int) – Number of input channels.
input_resolution (tuple[int]) – Input resolution.
depth (int) – Number of blocks.
num_heads (int) – Number of attention heads.
window_size (int) – Local window size.
mlp_ratio (float) – Ratio of mlp hidden dim to embedding dim. Default: 4.0
qkv_bias (bool, optional) – If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional) – Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional) – Dropout rate. Default: 0.0
attn_drop (float, optional) – Attention dropout rate. Default: 0.0
drop_path (float | tuple[float], optional) – Stochastic depth rate. Default: 0.0
norm_layer (nn.Module, optional) – Normalization layer. Default: nn.LayerNorm
downsample (nn.Module | None, optional) – Downsample layer at the end of the layer. Default: None
use_checkpoint (bool) – Whether to use checkpointing to save memory. Default: False.
img_size (int) – Input image size. Default: 224
patch_size (int) – Patch size. Default: 4
resi_connection (string) – The convolutional block before residual connection. Default: ‘1conv’