Fix bugs in TAS: missing ReLU in the end of each searching block

exps/NAS-Bench-201/xshape-file.py

@@ -0,0 +1,59 @@
+###############################################################
+# NAS-Bench-201, ICLR 2020 (https://arxiv.org/abs/2001.00326) #
+###############################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.01           #
+###############################################################
+# Usage: python exps/NAS-Bench-201/xshape-file.py --mode check
+###############################################################
+import os, sys, time, torch, argparse
+from typing import List, Text, Dict, Any
+from tqdm import tqdm
+from collections import defaultdict
+from copy    import deepcopy
+from pathlib import Path
+
+lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
+if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
+from config_utils import dict2config, load_config
+from procedures   import bench_evaluate_for_seed
+from procedures   import get_machine_info
+from datasets     import get_datasets
+from log_utils    import Logger, AverageMeter, time_string, convert_secs2time
+
+
+def obtain_valid_ckp(save_dir: Text, total: int):
+  possible_seeds = [777, 888]
+  seed2ckps = defaultdict(list)
+  miss2ckps = defaultdict(list)
+  for i in range(total):
+    for seed in possible_seeds:
+      path = os.path.join(save_dir, 'arch-{:06d}-seed-{:04d}.pth'.format(i, seed))
+      if os.path.exists(path):
+        seed2ckps[seed].append(i)
+      else:
+        miss2ckps[seed].append(i)
+    """
+    ckps = [x for x in save_dir.glob('arch-{:06d}-seed-*.pth'.format(i))]
+    for ckp in ckps:
+      seed = ckp.name.split('-seed-')[-1].split('.pth')[0]
+      seed2ckps[int(seed)].append(i)
+    """
+  for seed, xlist in seed2ckps.items():
+    print('[{:}] [seed={:}] has {:}/{:}'.format(save_dir, seed, len(xlist), total))
+  return dict(seed2ckps), dict(miss2ckps)
+    
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser(description='NAS-Bench-X', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument('--mode',        type=str, required=True, choices=['check', 'copy'], help='The script mode.')
+  parser.add_argument('--save_dir',    type=str, default='output/NAS-BENCH-202', help='Folder to save checkpoints and log.')
+  parser.add_argument('--check_N',     type=int, default=32768,  help='For safety.')
+  # use for train the model
+  args = parser.parse_args()
+  possible_configs = ['01', '12', '90']
+  if args.mode == 'check':
+    for config in possible_configs:
+      cur_save_dir = '{:}/raw-data-{:}'.format(args.save_dir, config)
+      seed2ckps, miss2ckps = obtain_valid_ckp(cur_save_dir, args.check_N)
+      torch.save(dict(seed2ckps=seed2ckps, miss2ckps=miss2ckps), '{:}/meta-{:}.pth'.format(args.save_dir, config))
+  

exps/NAS-Bench-201/xshapes.py

@@ -165,7 +165,7 @@ def filter_indexes(xlist, mode, save_dir, seeds):
         if not temp_path.exists():
           all_indexes.append(index)
           break
-  print('{:} [FILTER-INDEXES] : there are {:} architectures in total'.format(time_string(), len(all_indexes)))
+  print('{:} [FILTER-INDEXES] : there are {:}/{:} architectures in total'.format(time_string(), len(all_indexes), len(xlist)))
 
   SLURM_PROCID, SLURM_NTASKS = 'SLURM_PROCID', 'SLURM_NTASKS'
   if SLURM_PROCID in os.environ and  SLURM_NTASKS in os.environ:  # run on the slurm

lib/models/cell_operations.py

@@ -15,8 +15,8 @@ OPS = {
   'nor_conv_1x1' : lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (1,1), (stride,stride), (0,0), (1,1), affine, track_running_stats),
   'dua_sepc_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(C_in, C_out, (3,3), (stride,stride), (1,1), (1,1), affine, track_running_stats),
   'dua_sepc_5x5' : lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(C_in, C_out, (5,5), (stride,stride), (2,2), (1,1), affine, track_running_stats),
-  'dil_sepc_3x3' : lambda C_in, C_out, stride, affine, track_running_stats:     SepConv(C_in, C_out, (3,3), (stride,stride), (2,2), (2,2), affine, track_running_stats),
+  'dil_sepc_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: SepConv(C_in, C_out, (3,3), (stride,stride), (2,2), (2,2), affine, track_running_stats),
-  'dil_sepc_5x5' : lambda C_in, C_out, stride, affine, track_running_stats:     SepConv(C_in, C_out, (5,5), (stride,stride), (4,4), (2,2), affine, track_running_stats),
+  'dil_sepc_5x5' : lambda C_in, C_out, stride, affine, track_running_stats: SepConv(C_in, C_out, (5,5), (stride,stride), (4,4), (2,2), affine, track_running_stats),
   'skip_connect' : lambda C_in, C_out, stride, affine, track_running_stats: Identity() if stride == 1 and C_in == C_out else FactorizedReduce(C_in, C_out, stride, affine, track_running_stats),
 }
 

lib/models/shape_searchs/SearchCifarResNet.py

@@ -172,7 +172,7 @@ class ResNetBasicblock(nn.Module):
     else:
       residual, expected_flop_c = inputs, 0
     out = additive_func(residual, out_b)
-    return out, expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
+    return nn.functional.relu(out, inplace=True), expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
 
   def basic_forward(self, inputs):
     basicblock = self.conv_a(inputs)
@@ -244,8 +244,7 @@ class ResNetBottleneck(nn.Module):
     else:
       residual, expected_flop_c = inputs, 0
     out = additive_func(residual, out_1x4)
-    return out, expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
+    return nn.functional.relu(out, inplace=True), expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
-
 
 
 class SearchShapeCifarResNet(nn.Module):

lib/models/shape_searchs/SearchCifarResNet_width.py

@@ -156,7 +156,7 @@ class ResNetBasicblock(nn.Module):
     else:
       residual, expected_flop_c = inputs, 0
     out = additive_func(residual, out_b)
-    return out, expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
+    return nn.functional.relu(out, inplace=True), expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
 
   def basic_forward(self, inputs):
     basicblock = self.conv_a(inputs)
@@ -228,8 +228,7 @@ class ResNetBottleneck(nn.Module):
     else:
       residual, expected_flop_c = inputs, 0
     out = additive_func(residual, out_1x4)
-    return out, expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
+    return nn.functional.relu(out, inplace=True), expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
-
 
 
 class SearchWidthCifarResNet(nn.Module):

lib/models/shape_searchs/SearchImagenetResNet.py

@@ -171,7 +171,7 @@ class ResNetBasicblock(nn.Module):
     else:
       residual, expected_flop_c = inputs, 0
     out = additive_func(residual, out_b)
-    return out, expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
+    return nn.functional.relu(out, inplace=True), expected_inC_b, sum([expected_flop_a, expected_flop_b, expected_flop_c])
 
   def basic_forward(self, inputs):
     basicblock = self.conv_a(inputs)
@@ -243,8 +243,7 @@ class ResNetBottleneck(nn.Module):
     else:
       residual, expected_flop_c = inputs, 0
     out = additive_func(residual, out_1x4)
-    return out, expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
+    return nn.functional.relu(out, inplace=True), expected_inC_1x4, sum([expected_flop_1x1, expected_flop_3x3, expected_flop_1x4, expected_flop_c])
-
 
 
 class SearchShapeImagenetResNet(nn.Module):

lib/models/shape_searchs/SearchSimResNet_width.py

@@ -153,7 +153,7 @@ class SimBlock(nn.Module):
     else:
       residual, expected_flop_c = inputs, 0
     out = additive_func(residual, out)
-    return out, expected_next_inC, sum([expected_flop, expected_flop_c])
+    return nn.functional.relu(out, inplace=True), expected_next_inC, sum([expected_flop, expected_flop_c])
 
   def basic_forward(self, inputs):
     basicblock = self.conv(inputs)

scripts-search/X-X/train-shapes-01.sh

@@ -0,0 +1,36 @@
+#!/bin/bash
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.01 #
+#####################################################
+# bash ./scripts-search/X-X/train-shapes-01.sh 0 4
+echo script name: $0
+echo $# arguments
+if [ "$#" -ne 2 ] ;then
+  echo "Input illegal number of parameters " $#
+  echo "Need 2 parameters for hyper-parameters-opt-file, and seeds"
+  exit 1
+fi
+if [ "$TORCH_HOME" = "" ]; then
+  echo "Must set TORCH_HOME envoriment variable for data dir saving"
+  exit 1
+else
+  echo "TORCH_HOME : $TORCH_HOME"
+fi
+
+srange=00000-32767
+opt=01
+all_seeds=777
+cpus=4
+
+save_dir=./output/NAS-BENCH-202/
+
+SLURM_PROCID=$1 SLURM_NTASKS=$2 OMP_NUM_THREADS=${cpus} python exps/NAS-Bench-201/xshapes.py \
+	--mode new --srange ${srange} --hyper ${opt} --save_dir ${save_dir} \
+	--datasets cifar10 cifar10 cifar100 ImageNet16-120 \
+	--splits   1       0       0        0 \
+	--xpaths $TORCH_HOME/cifar.python \
+		 $TORCH_HOME/cifar.python \
+		 $TORCH_HOME/cifar.python \
+		 $TORCH_HOME/cifar.python/ImageNet16 \
+	--workers ${cpus} \
+	--seeds ${all_seeds}

scripts-search/algos/README.md

@@ -0,0 +1,3 @@
+# 10 NAS algorithms in NAS-Bench-201
+
+Each script in this folder corresponds to one NAS algorithm, you can simple run it by one command.