From 95ec4d328e704e5d4bbbda79e31d862bca374803 Mon Sep 17 00:00:00 2001
From: D-X-Y <280835372@qq.com>
Date: Sat, 21 Dec 2019 11:13:08 +1100
Subject: [PATCH] update NAS-Bench-102

---
 .gitignore             |  3 ++
 NAS-Bench-102.md       | 19 ++++++---
 lib/nas_102_api/api.py | 93 +++++++++++++++++++++++++++---------------
 3 files changed, 76 insertions(+), 39 deletions(-)

diff --git a/.gitignore b/.gitignore
index 7173302..fce4354 100644
--- a/.gitignore
+++ b/.gitignore
@@ -115,3 +115,6 @@ GPU-*.sh
 cal.sh
 aaa
 cx.sh
+
+NAS-Bench-102-v1_0.pth
+lib/NAS-Bench-102-v1_0.pth
diff --git a/NAS-Bench-102.md b/NAS-Bench-102.md
index b086fe8..d4e2213 100644
--- a/NAS-Bench-102.md
+++ b/NAS-Bench-102.md
@@ -6,11 +6,16 @@ Each edge here is associated with an operation selected from a predefined operat
 For it to be applicable for all NAS algorithms, the search space defined in NAS-Bench-102 includes 4 nodes and 5 associated operation options, which generates 15,625 neural cell candidates in total.
 
 In this Markdown file, we provide:
-- Detailed instruction to reproduce NAS-Bench-102.
-- 10 NAS algorithms evaluated in our paper.
+- [How to Use NAS-Bench-102](#how-to-use-nas-bench-102)
+- [Instruction to re-generate NAS-Bench-102](#instruction-to-re-generate-nas-bench-102)
+- [10 NAS algorithms evaluated in our paper](#to-reproduce-10-baseline-nas-algorithms-in-nas-bench-102)
 
 Note: please use `PyTorch >= 1.2.0` and `Python >= 3.6.0`.
 
+The data file of NAS-Bench-102 can be downloaded from [Google Drive](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) or [Baidu-Wangpan].
+
+The training and evaluation data used in NAS-Bench-102 can be downloaded from [Google Drive](https://drive.google.com/open?id=1L0Lzq8rWpZLPfiQGd6QR8q5xLV88emU7) or [Baidu-Wangpan].
+
 ## How to Use NAS-Bench-102
 
 1. Creating an API instance from a file:
@@ -35,8 +40,8 @@ api.show(2)
 
 # show the mean loss and accuracy of an architecture
 info = api.query_meta_info_by_index(1)
-loss, accuracy = info.get_metrics('cifar10', 'train')
-flops, params, latency = info.get_comput_costs('cifar100')
+res_metrics = info.get_metrics('cifar10', 'train')
+cost_metrics = info.get_comput_costs('cifar100')
 
 # get the detailed information
 results = api.query_by_index(1, 'cifar100')
@@ -55,7 +60,8 @@ index = api.query_index_by_arch('|nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1
 api.show(index)
 ```
 
-5. For other usages, please see `lib/aa_nas_api/api.py`
+5. For other usages, please see `lib/nas_102_api/api.py`
+
 
 ### Detailed Instruction
 
@@ -98,8 +104,10 @@ print(archRes.get_metrics('cifar10-valid', 'x-valid', None,  True)) # print loss
 ```
 from nas_102_api import NASBench102API as API
 api = API('NAS-Bench-102-v1_0.pth')
+api.show(-1)  # show info of all architectures
 ```
 
+
 ## Instruction to Re-Generate NAS-Bench-102
 
 1. generate the meta file for NAS-Bench-102 using the following script, where `NAS-BENCH-102` indicates the name and `4` indicates the maximum number of nodes in a cell.
@@ -139,6 +147,7 @@ CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/NAS-Bench-102/train-a-net.sh resnet
 CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/NAS-Bench-102/train-a-net.sh '|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|skip_connect~1|skip_connect~2|' 16 5
 ```
 
+
 ## To Reproduce 10 Baseline NAS Algorithms in NAS-Bench-102
 
 We have tried our best to implement each method. However, still, some algorithms might obtain non-optimal results since their hyper-parameters might not fit our NAS-Bench-102.
diff --git a/lib/nas_102_api/api.py b/lib/nas_102_api/api.py
index 07de2f7..4e0053c 100644
--- a/lib/nas_102_api/api.py
+++ b/lib/nas_102_api/api.py
@@ -1,6 +1,8 @@
 ##################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##################################################
+#################################################################################
+# NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search #
+#################################################################################
 import os, sys, copy, random, torch, numpy as np
 from collections import OrderedDict, defaultdict
 
@@ -12,19 +14,21 @@ def print_information(information, extra_info=None, show=False):
     return 'loss = {:.3f}, top1 = {:.2f}%'.format(loss, acc)
 
   for ida, dataset in enumerate(dataset_names):
-    flop, param, latency = information.get_comput_costs(dataset)
-    str1 = '{:14s} FLOP={:6.2f} M, Params={:.3f} MB, latency={:} ms.'.format(dataset, flop, param, '{:.2f}'.format(latency*1000) if latency > 0 else None)
-    train_loss, train_acc = information.get_metrics(dataset, 'train')
+    #flop, param, latency = information.get_comput_costs(dataset)
+    metric = information.get_comput_costs(dataset)
+    flop, param, latency = metric['flops'], metric['params'], metric['latency']
+    str1 = '{:14s} FLOP={:6.2f} M, Params={:.3f} MB, latency={:} ms.'.format(dataset, flop, param, '{:.2f}'.format(latency*1000) if latency is not None and latency > 0 else None)
+    train_info = information.get_metrics(dataset, 'train')
     if dataset == 'cifar10-valid':
-      valid_loss, valid_acc = information.get_metrics(dataset, 'x-valid')
-      str2 = '{:14s} train : [{:}], valid : [{:}]'.format(dataset, metric2str(train_loss, train_acc), metric2str(valid_loss, valid_acc))
+      valid_info = information.get_metrics(dataset, 'x-valid')
+      str2 = '{:14s} train : [{:}], valid : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(valid_info['loss'], valid_info['accuracy']))
     elif dataset == 'cifar10':
-      test__loss, test__acc = information.get_metrics(dataset, 'ori-test')
-      str2 = '{:14s} train : [{:}], test  : [{:}]'.format(dataset, metric2str(train_loss, train_acc), metric2str(test__loss, test__acc))
+      test__info = information.get_metrics(dataset, 'ori-test')
+      str2 = '{:14s} train : [{:}], test  : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(test__info['loss'], test__info['accuracy']))
     else:
-      valid_loss, valid_acc = information.get_metrics(dataset, 'x-valid')
-      test__loss, test__acc = information.get_metrics(dataset, 'x-test')
-      str2 = '{:14s} train : [{:}], valid : [{:}], test : [{:}]'.format(dataset, metric2str(train_loss, train_acc), metric2str(valid_loss, valid_acc), metric2str(test__loss, test__acc))
+      valid_info = information.get_metrics(dataset, 'x-valid')
+      test__info = information.get_metrics(dataset, 'x-test')
+      str2 = '{:14s} train : [{:}], valid : [{:}], test : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(valid_info['loss'], valid_info['accuracy']), metric2str(test__info['loss'], test__info['accuracy']))
     strings += [str1, str2]
   if show: print('\n'.join(strings))
   return strings
@@ -34,19 +38,21 @@ class NASBench102API(object):
 
   def __init__(self, file_path_or_dict, verbose=True):
     if isinstance(file_path_or_dict, str):
-      if verbose: print('try to create NAS-Bench-102 api from {:}'.format(file_path_or_dict))
+      if verbose: print('try to create the NAS-Bench-102 api from {:}'.format(file_path_or_dict))
       assert os.path.isfile(file_path_or_dict), 'invalid path : {:}'.format(file_path_or_dict)
       file_path_or_dict = torch.load(file_path_or_dict)
     else:
       file_path_or_dict = copy.deepcopy( file_path_or_dict )
     assert isinstance(file_path_or_dict, dict), 'It should be a dict instead of {:}'.format(type(file_path_or_dict))
-    import pdb; pdb.set_trace() # we will update this api soon
     keys = ('meta_archs', 'arch2infos', 'evaluated_indexes')
     for key in keys: assert key in file_path_or_dict, 'Can not find key[{:}] in the dict'.format(key)
     self.meta_archs = copy.deepcopy( file_path_or_dict['meta_archs'] )
-    self.arch2infos = OrderedDict()
+    self.arch2infos_less = OrderedDict()
+    self.arch2infos_full = OrderedDict()
     for xkey in sorted(list(file_path_or_dict['arch2infos'].keys())):
-      self.arch2infos[xkey] = ArchResults.create_from_state_dict( file_path_or_dict['arch2infos'][xkey] )
+      all_info = file_path_or_dict['arch2infos'][xkey]
+      self.arch2infos_less[xkey] = ArchResults.create_from_state_dict( all_info['less'] )
+      self.arch2infos_full[xkey] = ArchResults.create_from_state_dict( all_info['full'] )
     self.evaluated_indexes = sorted(list(file_path_or_dict['evaluated_indexes']))
     self.archstr2index = {}
     for idx, arch in enumerate(self.meta_archs):
@@ -73,35 +79,46 @@ class NASBench102API(object):
     else: arch_index = -1
     return arch_index
   
-  def query_by_arch(self, arch):
-    arch_index = self.query_index_by_arch(arch)
-    if arch_index == -1: return None
-    if arch_index in self.arch2infos:
-      strings = print_information(self.arch2infos[ arch_index ], 'arch-index={:}'.format(arch_index))
+  def query_by_arch(self, arch, use_12epochs_result=False):
+    if isinstance(arch, int):
+      arch_index = arch
+    else:
+      arch_index = self.query_index_by_arch(arch)
+    if arch_index == -1: return None # the following two lines are used to support few training epochs
+    if use_12epochs_result: arch2infos = self.arch2infos_less
+    else                  : arch2infos = self.arch2infos_full
+    if arch_index in arch2infos:
+      strings = print_information(arch2infos[ arch_index ], 'arch-index={:}'.format(arch_index))
       return '\n'.join(strings)
     else:
       print ('Find this arch-index : {:}, but this arch is not evaluated.'.format(arch_index))
       return None
 
-  def query_by_index(self, arch_index, dataname):
-    assert arch_index in self.arch2infos, 'arch_index [{:}] does not in arch2info'.format(arch_index)
-    archInfo = copy.deepcopy( self.arch2infos[ arch_index ] )
+  def query_by_index(self, arch_index, dataname, use_12epochs_result=False):
+    if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
+    else                  : basestr, arch2infos = '200epochs', self.arch2infos_full
+    assert arch_index in arch2infos, 'arch_index [{:}] does not in arch2info with {:}'.format(arch_index, basestr)
+    archInfo = copy.deepcopy( arch2infos[ arch_index ] )
     assert dataname in archInfo.get_dataset_names(), 'invalid dataset-name : {:}'.format(dataname)
     info = archInfo.query(dataname)
     return info
 
-  def query_meta_info_by_index(self, arch_index):
-    assert arch_index in self.arch2infos, 'arch_index [{:}] does not in arch2info'.format(arch_index)
-    archInfo = copy.deepcopy( self.arch2infos[ arch_index ] )
+  def query_meta_info_by_index(self, arch_index, use_12epochs_result=False):
+    if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
+    else                  : basestr, arch2infos = '200epochs', self.arch2infos_full
+    assert arch_index in arch2infos, 'arch_index [{:}] does not in arch2info with {:}'.format(arch_index, basestr)
+    archInfo = copy.deepcopy( arch2infos[ arch_index ] )
     return archInfo
 
-  def find_best(self, dataset, metric_on_set, FLOP_max=None, Param_max=None):
+  def find_best(self, dataset, metric_on_set, FLOP_max=None, Param_max=None, use_12epochs_result=False):
+    if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
+    else                  : basestr, arch2infos = '200epochs', self.arch2infos_full
     best_index, highest_accuracy = -1, None
     for i, idx in enumerate(self.evaluated_indexes):
-      flop, param, latency = self.arch2infos[idx].get_comput_costs(dataset)
+      flop, param, latency = arch2infos[idx].get_comput_costs(dataset)
       if FLOP_max  is not None and flop  > FLOP_max : continue
       if Param_max is not None and param > Param_max: continue
-      loss, accuracy = self.arch2infos[idx].get_metrics(dataset, metric_on_set)
+      loss, accuracy = arch2infos[idx].get_metrics(dataset, metric_on_set)
       if best_index == -1:
         best_index, highest_accuracy = idx, accuracy
       elif highest_accuracy < accuracy:
@@ -113,21 +130,29 @@ class NASBench102API(object):
     return copy.deepcopy(self.meta_archs[index])
 
   def show(self, index=-1):
-    if index == -1: # show all architectures
+    if index < 0: # show all architectures
       print(self)
       for i, idx in enumerate(self.evaluated_indexes):
         print('\n' + '-' * 10 + ' The ({:5d}/{:5d}) {:06d}-th architecture! '.format(i, len(self.evaluated_indexes), idx) + '-'*10)
         print('arch : {:}'.format(self.meta_archs[idx]))
-        strings = print_information(self.arch2infos[idx])
-        print('>' * 20)
+        strings = print_information(self.arch2infos_full[idx])
+        print('>' * 40 + ' 200 epochs ' + '>' * 40)
         print('\n'.join(strings))
-        print('<' * 20)
+        strings = print_information(self.arch2infos_less[idx])
+        print('>' * 40 + '  12 epochs ' + '>' * 40)
+        print('\n'.join(strings))
+        print('<' * 40 + '------------' + '<' * 40)
     else:
       if 0 <= index < len(self.meta_archs):
         if index not in self.evaluated_indexes: print('The {:}-th architecture has not been evaluated or not saved.'.format(index))
         else:
-          strings = print_information(self.arch2infos[index])
+          strings = print_information(self.arch2infos_full[index])
+          print('>' * 40 + ' 200 epochs ' + '>' * 40)
           print('\n'.join(strings))
+          strings = print_information(self.arch2infos_less[index])
+          print('>' * 40 + '  12 epochs ' + '>' * 40)
+          print('\n'.join(strings))
+          print('<' * 40 + '------------' + '<' * 40)
       else:
         print('This index ({:}) is out of range (0~{:}).'.format(index, len(self.meta_archs)))