data/beta/RoseTTAFold__network__Refine_module.patch

--- Refine_module_old.py	2021-08-04 07:49:26.000000000 -0400
+++ Refine_module.py	2021-08-04 07:46:27.000000000 -0400
@@ -129,47 +129,61 @@
         self.norm_state = LayerNorm(SE3_param['l0_out_features'])
         self.pred_lddt = nn.Linear(SE3_param['l0_out_features'], 1)
 
-    def forward(self, node, edge, seq1hot, idx, use_transf_checkpoint=False, eps=1e-4):
+    def forward(self, node, edge, seq1hot, idx, use_transf_checkpoint=False, eps=1e-4,
+                mirror_mode="serial"):       
+        
+        def run(xyz, state, node, edge, idx, seq1hot):
+          best_xyz = xyz
+          best_lddt = torch.zeros((xyz.shape[0], xyz.shape[1], 1), device=xyz.device)
+          prev_lddt = 0.0
+          no_impr = 0
+          no_impr_best = 0
+          for i_iter in range(200):
+              for i_m in range(self.n_module):
+                  if use_transf_checkpoint:
+                      xyz, state = checkpoint.checkpoint(create_custom_forward(self.refine_net[i_m], top_k=64), node.float(), edge.float(), xyz.detach().float(), state.float(), seq1hot, idx)
+                  else:
+                      xyz, state = self.refine_net[i_m](node.float(), edge.float(), xyz.detach().float(), state.float(), seq1hot, idx, top_k=64)
+              #
+              lddt = self.pred_lddt(self.norm_state(state)) 
+              lddt = torch.clamp(lddt, 0.0, 1.0)[...,0]
+              print (f"SE(3) iteration {i_iter} {lddt.mean(-1).cpu().numpy()}")
+              if lddt.mean(-1).max() <= prev_lddt+eps:
+                  no_impr += 1
+              else:
+                  no_impr = 0
+              if lddt.mean(-1).max() <= best_lddt.mean(-1).max()+eps:
+                  no_impr_best += 1
+              else:
+                  no_impr_best = 0
+              if no_impr > 10 or no_impr_best > 20:
+                  break
+              if lddt.mean(-1).max() > best_lddt.mean(-1).max():
+                  best_lddt = lddt
+                  best_xyz = xyz
+              prev_lddt = lddt.mean(-1).max()
+          
+          return best_xyz, best_lddt
+        
         edge = self.proj_edge(edge)
-
         xyz, state = self.regen_net(seq1hot, idx, node, edge)
-       
-        # DOUBLE IT w/ Mirror images
-        xyz = torch.cat([xyz, xyz*torch.tensor([1,1,-1], dtype=xyz.dtype, device=xyz.device)])
-        state = torch.cat([state, state])
-        node = torch.cat([node, node])
-        edge = torch.cat([edge, edge])
-        idx = torch.cat([idx, idx])
-        seq1hot = torch.cat([seq1hot, seq1hot])
 
-        best_xyz = xyz
-        best_lddt = torch.zeros((xyz.shape[0], xyz.shape[1], 1), device=xyz.device)
-        prev_lddt = 0.0
-        no_impr = 0
-        no_impr_best = 0
-        for i_iter in range(200):
-            for i_m in range(self.n_module):
-                if use_transf_checkpoint:
-                    xyz, state = checkpoint.checkpoint(create_custom_forward(self.refine_net[i_m], top_k=64), node.float(), edge.float(), xyz.detach().float(), state.float(), seq1hot, idx)
-                else:
-                    xyz, state = self.refine_net[i_m](node.float(), edge.float(), xyz.detach().float(), state.float(), seq1hot, idx, top_k=64)
-            #
-            lddt = self.pred_lddt(self.norm_state(state)) 
-            lddt = torch.clamp(lddt, 0.0, 1.0)[...,0]
-            print (f"SE(3) iteration {i_iter} {lddt.mean(-1).cpu().numpy()}")
-            if lddt.mean(-1).max() <= prev_lddt+eps:
-                no_impr += 1
-            else:
-                no_impr = 0
-            if lddt.mean(-1).max() <= best_lddt.mean(-1).max()+eps:
-                no_impr_best += 1
-            else:
-                no_impr_best = 0
-            if no_impr > 10 or no_impr_best > 20:
-                break
-            if lddt.mean(-1).max() > best_lddt.mean(-1).max():
-                best_lddt = lddt
-                best_xyz = xyz
-            prev_lddt = lddt.mean(-1).max()
-        pick = best_lddt.mean(-1).argmax()
-        return best_xyz[pick][None], best_lddt[pick][None]
+        # DOUBLE IT w/ Mirror images
+        if mirror_mode == "parallel":
+          xyz = torch.cat([xyz, xyz*torch.tensor([1,1,-1], dtype=xyz.dtype, device=xyz.device)])
+          state = torch.cat([state, state])
+          node = torch.cat([node, node])
+          edge = torch.cat([edge, edge])
+          idx = torch.cat([idx, idx])
+          seq1hot = torch.cat([seq1hot, seq1hot])
+          best_xyz, best_lddt = run(xyz, state, node, edge, idx, seq1hot)
+          pick = best_lddt.mean(-1).argmax()
+          return best_xyz[pick][None], best_lddt[pick][None]
+        else:
+          best_xyz_A, best_lddt_A = run(xyz, state, node, edge, idx, seq1hot)
+          xyz = xyz * torch.tensor([1,1,-1], dtype=xyz.dtype, device=xyz.device)
+          best_xyz_B, best_lddt_B = run(xyz, state, node, edge, idx, seq1hot)
+          if best_lddt_B.mean() > best_lddt_A.mean():
+            return best_xyz_B, best_lddt_B
+          else:
+            return best_xyz_A, best_lddt_A
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