levit.py [407:424]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                                      resolution=resolution),
                        ), drop_path))
            if do[0] == 'Subsample':
                #('Subsample',key_dim, num_heads, attn_ratio, mlp_ratio, stride)
                resolution_ = (resolution - 1) // do[5] + 1
                self.blocks.append(
                    AttentionSubsample(
                        *embed_dim[i:i + 2], key_dim=do[1], num_heads=do[2],
                        attn_ratio=do[3],
                        activation=attention_activation,
                        stride=do[5],
                        resolution=resolution,
                        resolution_=resolution_))
                resolution = resolution_
                if do[4] > 0:  # mlp_ratio
                    h = int(embed_dim[i + 1] * do[4])
                    self.blocks.append(
                        Residual(torch.nn.Sequential(
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



levit_c.py [356:373]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                                      resolution=resolution),
                        ), drop_path))
            if do[0] == 'Subsample':
                #('Subsample',key_dim, num_heads, attn_ratio, mlp_ratio, stride)
                resolution_ = (resolution - 1) // do[5] + 1
                self.blocks.append(
                    AttentionSubsample(
                        *embed_dim[i:i + 2], key_dim=do[1], num_heads=do[2],
                        attn_ratio=do[3],
                        activation=attention_activation,
                        stride=do[5],
                        resolution=resolution,
                        resolution_=resolution_))
                resolution = resolution_
                if do[4] > 0:  # mlp_ratio
                    h = int(embed_dim[i + 1] * do[4])
                    self.blocks.append(
                        Residual(torch.nn.Sequential(
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -