update SETN

exps/AA-NAS-test-API.py

@@ -11,11 +11,16 @@ from models     import CellStructure
 
 def get_unique_matrix(archs, consider_zero):
   UniquStrs = [arch.to_unique_str(consider_zero) for arch in archs]
-  print ('{:} create unique-string done'.format(time_string()))
+  print ('{:} create unique-string ({:}/{:}) done'.format(time_string(), len(set(UniquStrs)), len(UniquStrs)))
+  Unique2Index = dict()
+  for index, xstr in enumerate(UniquStrs):
+    if xstr not in Unique2Index: Unique2Index[xstr] = list()
+    Unique2Index[xstr].append( index )
   sm_matrix = torch.eye(len(archs)).bool()
-  for i, _ in enumerate(UniquStrs):
-    for j in range(i):
-      sm_matrix[i,j] = sm_matrix[j,i] = UniquStrs[i] == UniquStrs[j]
+  for _, xlist in Unique2Index.items():
+    for i in xlist:
+      for j in xlist:
+        sm_matrix[i,j] = True
   unique_ids, unique_num = [-1 for _ in archs], 0
   for i in range(len(unique_ids)):
     if unique_ids[i] > -1: continue

exps/algos/SETN.py

@@ -76,22 +76,22 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
 def get_best_arch(xloader, network, n_samples):
   with torch.no_grad():
     network.eval()
-    archs, valid_accs = [], []
+    archs, valid_accs = network.module.return_topK(n_samples), []
+    #print ('obtain the top-{:} architectures'.format(n_samples))
     loader_iter = iter(xloader)
-    for i in range(n_samples):
+    for i, sampled_arch in enumerate(archs):
+      network.module.set_cal_mode('dynamic', sampled_arch)
       try:
         inputs, targets = next(loader_iter)
       except:
         loader_iter = iter(xloader)
         inputs, targets = next(loader_iter)
 
-      sampled_arch = network.module.dync_genotype(False)
-      network.module.set_cal_mode('dynamic', sampled_arch)
       _, logits = network(inputs)
       val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5))
 
-      archs.append( sampled_arch )
       valid_accs.append( val_top1.item() )
+      #print ('--- {:}/{:} : {:} : {:}'.format(i, len(archs), sampled_arch, val_top1))
 
     best_idx = np.argmax(valid_accs)
     best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
@@ -221,11 +221,6 @@ def main(xargs):
     #logger.log('[{:}] Selec-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
     # check the best accuracy
     valid_accuracies[epoch] = valid_a_top1
-    if valid_a_top1 > valid_accuracies['best']:
-      valid_accuracies['best'] = valid_a_top1
-      genotypes['best']        = search_model.genotype()
-      find_best = True
-    else: find_best = False
 
     genotypes[epoch] = genotype
     logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
@@ -244,16 +239,17 @@ def main(xargs):
           'args' : deepcopy(args),
           'last_checkpoint': save_path,
           }, logger.path('info'), logger)
-    if find_best:
-      logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, valid_a_top1))
-      copy_checkpoint(model_base_path, model_best_path, logger)
     with torch.no_grad():
       logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
     # measure elapsed time
     epoch_time.update(time.time() - start_time)
     start_time = time.time()
 
-  logger.log('During searching, the best gentotype is : {:} , with the validation accuracy of {:.3f}%.'.format(genotypes['best'], valid_accuracies['best']))
+  #logger.log('During searching, the best gentotype is : {:} , with the validation accuracy of {:.3f}%.'.format(genotypes['best'], valid_accuracies['best']))
+  genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
+  network.module.set_cal_mode('dynamic', genotype)
+  valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion)
+  logger.log('Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.'.format(genotype, valid_a_top1))
   # sampling
   """
   with torch.no_grad():