def forward(self, x, out_feat_keys=None): feat = x # we first apply sobel filter feat = self.sobel(feat) out_feats = get_trunk_forward_outputs_module_list( feat, out_feat_keys, self._feature_blocks, self.all_feat_names) return out_feats
def forward(self, x, out_feat_keys=None): feat = x # In case of LAB image, we take only "L" channel as input. Split the data # along the channel dimension into [L, AB] and keep only L channel. feat = torch.split(feat, [1, 2], dim=1)[0] out_feats = get_trunk_forward_outputs_module_list( feat, out_feat_keys, self._feature_blocks, self.all_feat_names) return out_feats
def forward(self, x, out_feat_keys=None): feat = x out_feats = get_trunk_forward_outputs_module_list( feat, out_feat_keys, self._feature_blocks, self.all_feat_names, ) return out_feats