(self, opt)

(self, opt)

(self, module, module_name)

(self, opt)

(self, opt)

(self, channel, negative_slope=0.2, scale=2**0.5)

(self, *args, **kwargs)

(self, in_channels, out_channels, kernel_size,

(self, *args, **kwargs)

(self)

(self)

(self)

(self, rounding)

(self, rounding)

(self, rounding=torch.round)

(self)

(self)

(self)

(self)

(self)

(self)

(self, rounding=torch.round)

(self, differentiable=True)

(self, window=200)

(self, opt, start_iter=1, tb_logger=None)

(self, server_list_cfg, client_cfg, sys_path=None)

(self, db_paths, client_keys='default', readonly=True, lock=False, readahead=False, **kwargs)

(self, backend='disk', **kwargs)

(self, radius=50, sigma=0)

(self, dino_model='dinov2_giant', pretrained_path=r'pretrained_model/facebookdinov2_giant',img_size = 128)

(self, layer_name_list, vgg_type='vgg19', use_input_norm=Tr

(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.)

(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4., qkv_bias=

(self, input_resolution, dim, norm_layer=nn.LayerNorm)

(self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4., qkv_bias=True, qk_

(self, dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4., qkv_bias=True, qk_

(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None)

(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None)

(self, scale, num_feat)

(self, scale, num_feat, num_out_ch, input_resolution=None)

(self, img_size=64, patch_size=1, in_chans=3, embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6

(self, num_feat=64, res_scale=1, pytorch_init=False)

(self, normalized_shape)

(self, normalized_shape)

(self, dim, LayerNorm_type)

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(self, dim, num_heads, bias)

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(self, n_feat)

(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type, finetune_type=None)

(self, in_c=3, embed_dim=48, bias=False)

(self, inp_channels=3, out_channels=3, dim=48,

(self, normalized_shape)

(self, normalized_shape)

(self, dim, LayerNorm_type)

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(self, dim, num_heads, bias)

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(self, n_feat)

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(self, in_c=3, embed_dim=48, bias=False)

(self,prompt_dim=128,prompt_len=5,prompt_size = 96,lin_dim = 192)

(self, inp_channels=3, out_channels=3, dim = 48, num_blocks = [4,6,6,8],

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(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., dr

(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768)

( self, img_size=224, patch_size=16, in_chans=3, embed_dim=768,

(self, normalized_shape)

(self, normalized_shape)

(self, dim, LayerNorm_type)

(self, dim, ffn_expansion_factor, bias,finetune_type=None)

(self, dim, num_heads, bias)

(self, n_feat)

(self, n_feat)

(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type,finetune_type=None)

(self, in_c=3, embed_dim=48, bias=False)

(self, temperature)

(self, dino_dim=1536)

(self, dino_dim=1536, restore_dim=48, scale_factor=14, size=128)

(self, dim, num_heads, bias)

(self, inp_channels=3, out_channels=3, dim = 48, num_blocks = [4,6,6,8],

(self)

(self, opt)

(self, opt)

(self, opt)

(self, dataset, num_replicas, rank, ratio=1)

(self)

(self,mode="random",sigma=2)

(self,mode="random",gamma=2.)

(self,mode="random",var=50.)

(self,mode="random",q=85.)

(self,adderList)

(self, generator, num_prefetch_queue)

(self, num_prefetch_queue, **kwargs)

(self, loader, opt)

(self, opt, dataroot=None, augmentator=None,enlarge_ratio=1)

(self, opt, lq_path=None, gt_path=None, augmentator=None,enlarge_ratio=1)

(self, opt)