Add more algorithms

lib/nas_infer_model/DXYs/construct_utils.py

@@ -0,0 +1,60 @@
+import torch
+import torch.nn.functional as F
+
+
+def drop_path(x, drop_prob):
+  if drop_prob > 0.:
+    keep_prob = 1. - drop_prob
+    mask = x.new_zeros(x.size(0), 1, 1, 1)
+    mask = mask.bernoulli_(keep_prob)
+    x = torch.div(x, keep_prob)
+    x.mul_(mask)
+  return x
+
+
+def return_alphas_str(basemodel):
+  if hasattr(basemodel, 'alphas_normal'):
+    string = 'normal [{:}] : \n-->>{:}'.format(basemodel.alphas_normal.size(), F.softmax(basemodel.alphas_normal, dim=-1) )
+  else: string = ''
+  if hasattr(basemodel, 'alphas_reduce'):
+    string = string + '\nreduce : {:}'.format( F.softmax(basemodel.alphas_reduce, dim=-1) )
+
+  if hasattr(basemodel, 'get_adjacency'):
+    adjacency = basemodel.get_adjacency()
+    for i in range( len(adjacency) ):
+      weight = F.softmax( basemodel.connect_normal[str(i)], dim=-1 )
+      adj = torch.mm(weight, adjacency[i]).view(-1)
+      adj = ['{:3.3f}'.format(x) for x in adj.cpu().tolist()]
+      string = string + '\nnormal--{:}-->{:}'.format(i, ', '.join(adj))
+    for i in range( len(adjacency) ):
+      weight = F.softmax( basemodel.connect_reduce[str(i)], dim=-1 )
+      adj = torch.mm(weight, adjacency[i]).view(-1)
+      adj = ['{:3.3f}'.format(x) for x in adj.cpu().tolist()]
+      string = string + '\nreduce--{:}-->{:}'.format(i, ', '.join(adj))
+
+  if hasattr(basemodel, 'alphas_connect'):
+    weight = F.softmax(basemodel.alphas_connect, dim=-1).cpu()
+    ZERO = ['{:.3f}'.format(x) for x in weight[:,0].tolist()]
+    IDEN = ['{:.3f}'.format(x) for x in weight[:,1].tolist()]
+    string = string + '\nconnect [{:}] : \n ->{:}\n ->{:}'.format( list(basemodel.alphas_connect.size()), ZERO, IDEN )
+  else:
+    string = string + '\nconnect = None'
+  
+  if hasattr(basemodel, 'get_gcn_out'):
+    outputs = basemodel.get_gcn_out(True)
+    for i, output in enumerate(outputs):
+      string = string + '\nnormal:[{:}] : {:}'.format(i, F.softmax(output, dim=-1) )
+
+  return string
+
+
+def remove_duplicate_archs(all_archs):
+  archs = []
+  str_archs = ['{:}'.format(x) for x in all_archs]
+  for i, arch_x in enumerate(str_archs):
+    choose = True
+    for j in range(i):
+      if arch_x == str_archs[j]:
+        choose = False; break
+    if choose: archs.append(all_archs[i])
+  return archs