j vddlmZddlmcmZddlmZeGddejZdS)N)store_hyperparametersc<eZdZdZiZdddddddfd Zd ZxZS) SRVGGNetCompactaA compact VGG-style network structure for super-resolution. It is a compact network structure, which performs upsampling in the last layer and no convolution is conducted on the HR feature space. Args: num_in_ch (int): Channel number of inputs. Default: 3. num_out_ch (int): Channel number of outputs. Default: 3. num_feat (int): Channel number of intermediate features. Default: 64. num_conv (int): Number of convolution layers in the body network. Default: 16. upscale (int): Upsampling factor. Default: 4. act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu. @prelu) num_in_ch num_out_chnum_featnum_convupscaleact_typec t||_||_||_||_||_||_tj |_ |j tj ||ddd|dkrtj d}n8|dkrtj|}n|dkrtjd d }|j |t!|D]}|j tj ||ddd|dkrtj d}n8|dkrtj|}n|dkrtjd d }|j ||j tj |||z|zdddtj||_dS) NrreluT)inplacer )num_parameters leakyrelug?)negative_sloper)super__init__r r r rrrnn ModuleListbodyappendConv2dReLUPReLU LeakyReLUrange PixelShuffle upsampler) selfr r r rrr activation_ __class__s \C:\Projects\Historical\venv\Lib\site-packages\spandrel/architectures/Compact/__arch/SRVGG.pyrzSRVGGNetCompact.__init__s "$       MOO  9h1a@@AAA v  ...JJ  :::JJ  $ $S$GGGJ $$$x ) )A I  RYx1aCC D D D6!!WT222 W$$XX>>> [((\dKKK I  Z ( ( ( ( 8Z'-AG-KQPQSTUUVVV11c|}tdt|jD]}|j||}||}t j||jd}||z }|S)Nrnearest) scale_factormode)r"lenrr$F interpolater)r%xoutibases r)forwardzSRVGGNetCompact.forwardJsvq#di..)) $ $A$)A,s##CCnnS!!}QT\ JJJ t  r*)__name__ __module__ __qualname____doc__hyperparametersrr6 __classcell__)r(s@r)rr sw  O -2-2-2-2-2-2-2^       r*r) torch.nnrtorch.nn.functional functionalr0 spandrel.utilrModulerr*r)rCs//////IIIIIbiIIIIIr*