fTh`SrSSKJr SSKJr \R "\5r"SS\5rS/r g)z'Swin2SR Transformer model configuration)PretrainedConfig)loggingcr^\rSrSrSrSrSSSS.rSS S S S /S Q/S QSSSSSSSSSSSSSS4U4SjjrSrU=r $) Swin2SRConfiga This is the configuration class to store the configuration of a [`Swin2SRModel`]. It is used to instantiate a Swin Transformer v2 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Swin Transformer v2 [caidas/swin2sr-classicalsr-x2-64](https://huggingface.co/caidas/swin2sr-classicalsr-x2-64) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: image_size (`int`, *optional*, defaults to 64): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 1): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. num_channels_out (`int`, *optional*, defaults to `num_channels`): The number of output channels. If not set, it will be set to `num_channels`. embed_dim (`int`, *optional*, defaults to 180): Dimensionality of patch embedding. depths (`list(int)`, *optional*, defaults to `[6, 6, 6, 6, 6, 6]`): Depth of each layer in the Transformer encoder. num_heads (`list(int)`, *optional*, defaults to `[6, 6, 6, 6, 6, 6]`): Number of attention heads in each layer of the Transformer encoder. window_size (`int`, *optional*, defaults to 8): Size of windows. mlp_ratio (`float`, *optional*, defaults to 2.0): Ratio of MLP hidden dimensionality to embedding dimensionality. qkv_bias (`bool`, *optional*, defaults to `True`): Whether or not a learnable bias should be added to the queries, keys and values. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings and encoder. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. drop_path_rate (`float`, *optional*, defaults to 0.1): Stochastic depth rate. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. use_absolute_embeddings (`bool`, *optional*, defaults to `False`): Whether or not to add absolute position embeddings to the patch embeddings. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the layer normalization layers. upscale (`int`, *optional*, defaults to 2): The upscale factor for the image. 2/3/4/8 for image super resolution, 1 for denoising and compress artifact reduction img_range (`float`, *optional*, defaults to 1.0): The range of the values of the input image. resi_connection (`str`, *optional*, defaults to `"1conv"`): The convolutional block to use before the residual connection in each stage. upsampler (`str`, *optional*, defaults to `"pixelshuffle"`): The reconstruction reconstruction module. Can be 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None. Example: ```python >>> from transformers import Swin2SRConfig, Swin2SRModel >>> # Initializing a Swin2SR caidas/swin2sr-classicalsr-x2-64 style configuration >>> configuration = Swin2SRConfig() >>> # Initializing a model (with random weights) from the caidas/swin2sr-classicalsr-x2-64 style configuration >>> model = Swin2SRModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```swin2sr embed_dim num_heads num_layers) hidden_sizenum_attention_headsnum_hidden_layers@rN)rrrrrg@Tgg?geluFg{Gz?gh㈵>g?1conv pixelshufflec Z>[TU]"S0UD6 XlX lX0lUcUOUUlXPlX`l[U5Ul Xpl Xl Xl Xl XlXlXlXlXlUUlUUlUUlUUlUUlUUlg)N)super__init__ image_size patch_size num_channelsnum_channels_outr depthslenr r window_size mlp_ratioqkv_biashidden_dropout_probattention_probs_dropout_probdrop_path_rate hidden_actuse_absolute_embeddingslayer_norm_epsinitializer_rangeupscale img_rangeresi_connection upsampler)selfrrrrr r r r"r#r$r%r&r'r(r)r+r*r,r-r.r/kwargs __class__s i/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/swin2sr/configuration_swin2sr.pyrSwin2SRConfig.__init__gs2 "6"$$(0@0H N^" f+"&" #6 ,H),$'>$,!2 ".")r&r r'r r(r%rr-r+r*r#rrr r rr$r.r/r,r)r") __name__ __module__ __qualname____firstlineno____doc__ model_type attribute_mapr__static_attributes__ __classcell__)r2s@r3rrsnD LJ#*)M!$%( % -0#0#r5rN) r:configuration_utilsrutilsr get_loggerr6loggerr__all__rr5r3rDs<.3   H %#$#D  r5