update README

README.md

@@ -22,6 +22,8 @@ from utils import get_model_infos
 flop, param  = get_model_infos(net, (1,3,32,32))
 ```
 
+2. Different NAS-searched architectures are defined [here](https://github.com/D-X-Y/NAS-Projects/blob/master/lib/nas_infer_model/DXYs/genotypes.py).
+
 
 ## [Network Pruning via Transformable Architecture Search](https://arxiv.org/abs/1905.09717)
 In this paper, we proposed a differentiable searching strategy for transformable architectures, i.e., searching for the depth and width of a deep neural network.

exps/vis/random-nn.py

@@ -0,0 +1 @@
+from graphviz import Digraph

exps/vis/show-results.py

@@ -0,0 +1,67 @@
+# python ./vis-exps/show-results.py
+import os, sys
+from pathlib import Path
+import torch
+import numpy as np
+from collections import OrderedDict
+lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
+if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
+
+from aa_nas_api   import AANASBenchAPI
+
+api = AANASBenchAPI('./output/AA-NAS-BENCH-4/simplifies/C16-N5-final-infos.pth')
+
+def plot_results_nas(dataset, xset, file_name, y_lims):
+  import matplotlib
+  matplotlib.use('agg')
+  import matplotlib.pyplot as plt
+  root = Path('./output/cell-search-tiny-vis').resolve()
+  print ('root path : {:}'.format( root ))
+  root.mkdir(parents=True, exist_ok=True)
+  checkpoints = ['./output/cell-search-tiny/R-EA-cifar10/results.pth',
+                 './output/cell-search-tiny/REINFORCE-cifar10/results.pth',
+                 './output/cell-search-tiny/RAND-cifar10/results.pth',
+                 './output/cell-search-tiny/BOHB-cifar10/results.pth'
+                ]
+  legends, indexes = ['REA', 'REINFORCE', 'RANDOM', 'BOHB'], None
+  All_Accs = OrderedDict()
+  for legend, checkpoint in zip(legends, checkpoints):
+    all_indexes = torch.load(checkpoint, map_location='cpu')
+    accuracies  = []
+    for x in all_indexes:
+      info = api.arch2infos[ x ]
+      _, accy = info.get_metrics(dataset, xset, None, False)
+      accuracies.append( accy )
+    if indexes is None: indexes = list(range(len(all_indexes)))
+    All_Accs[legend] = sorted(accuracies)
+  
+  color_set = ['r', 'b', 'g', 'c', 'm', 'y', 'k']
+  dpi, width, height = 300, 3400, 2600
+  LabelSize, LegendFontsize = 26, 26
+  figsize = width / float(dpi), height / float(dpi)
+  fig = plt.figure(figsize=figsize)
+  x_axis = np.arange(0, 600)
+  plt.xlim(0, max(indexes))
+  plt.ylim(y_lims[0], y_lims[1])
+  interval_x, interval_y = 100, y_lims[2]
+  plt.xticks(np.arange(0, max(indexes), interval_x), fontsize=LegendFontsize)
+  plt.yticks(np.arange(y_lims[0],y_lims[1], interval_y), fontsize=LegendFontsize)
+  plt.grid()
+  plt.xlabel('The index of runs', fontsize=LabelSize)
+  plt.ylabel('The accuracy (%)', fontsize=LabelSize)
+
+  for idx, legend in enumerate(legends):
+    plt.plot(indexes, All_Accs[legend], color=color_set[idx], linestyle='-', label='{:}'.format(legend), lw=2)
+    print ('{:} : mean = {:}, std = {:}'.format(legend, np.mean(All_Accs[legend]), np.std(All_Accs[legend])))
+  plt.legend(loc=4, fontsize=LegendFontsize)
+  save_path = root / '{:}-{:}-{:}'.format(dataset, xset, file_name)
+  print('save figure into {:}\n'.format(save_path))
+  fig.savefig(str(save_path), dpi=dpi, bbox_inches='tight', format='pdf')
+
+
+if __name__ == '__main__':
+  plot_results_nas('cifar10', 'ori-test', 'nas-com.pdf', (85,95, 1))
+  plot_results_nas('cifar100', 'x-valid', 'nas-com.pdf', (55,75, 3))
+  plot_results_nas('cifar100', 'x-test' , 'nas-com.pdf', (55,75, 3))
+  plot_results_nas('ImageNet16-120', 'x-valid', 'nas-com.pdf', (35,50, 3))
+  plot_results_nas('ImageNet16-120', 'x-test' , 'nas-com.pdf', (35,50, 3))

lib/models/cell_searchs/genotypes.py

@@ -74,20 +74,15 @@ class Structure:
       nodes[i+1] = sum(sums) > 0
     return nodes[len(self.nodes)]
 
-  def to_unique_str(self, consider_zero=False):
+  def to_unique_str(self):
     # this is used to identify the isomorphic cell, which rerquires the prior knowledge of operation
     # two operations are special, i.e., none and skip_connect
     nodes = {0: '0'}
     for i_node, node_info in enumerate(self.nodes):
       cur_node = []
       for op, xin in node_info:
-        if consider_zero:
-          if op == 'none' or nodes[xin] == '#': x = '#' # zero
-          elif op == 'skip_connect': x = nodes[xin]
-          else: x = '('+nodes[xin]+')' + '@{:}'.format(op)
-        else:
-          if op == 'skip_connect': x = nodes[xin]
-          else: x = '('+nodes[xin]+')' + '@{:}'.format(op)
+        if op == 'skip_connect': x = nodes[xin]
+        else: x = '('+nodes[xin]+')' + '@{:}'.format(op)
         cur_node.append(x)
       nodes[i_node+1] = '+'.join( sorted(cur_node) )
     return nodes[ len(self.nodes) ]