config

Author: zhengxiawu

config.py

@@ -4,6 +4,7 @@
 import models.darts.genotypes as gt
 from functools import partial
 import torch
+import time
 
 
 def get_parser(name):
@@ -61,20 +62,23 @@ def build_parser(self):
         parser.add_argument('--layers', type=int, default=20, help='# of layers')
         parser.add_argument('--seed', type=int, default=2, help='random seed')
         parser.add_argument('--workers', type=int, default=4, help='# of workers')
-        parser.add_argument('--aux_weight', type=float, default=0.4, help='auxiliary loss weight')
+        parser.add_argument('--aux_weight', type=float, default=0, help='auxiliary loss weight')
         parser.add_argument('--cutout_length', type=int, default=16, help='cutout length')
-        parser.add_argument('--drop_path_prob', type=float, default=0.2, help='drop path prob')
+        # parser.add_argument('--drop_path_prob', type=float, default=0.2, help='drop path prob')
+        parser.add_argument('--drop_path_prob', type=float, default=0, help='drop path prob')
 
         parser.add_argument('--genotype', default='', help='Cell genotype')
+        parser.add_argument('--deterministic', type=bool, default=True, help='momentum')
 
         return parser
 
     def __init__(self):
         parser = self.build_parser()
         args = parser.parse_args()
         super().__init__(**vars(args))
-
-        self.path = os.path.join('expreiments', self.model_method + '_' + self.model_name)
+        time_str = time.asctime(time.localtime()).replace(' ', '_')
+        self.path = os.path.join('/userhome/project/pytorch_image_classification/expreiments', self.model_method + '_'
+                                 + self.model_name + '_' + time_str)
         if len(self.genotype) > 1:
             self.genotype = gt.from_str(self.genotype)
         else:

models/darts/augment_cnn.py

@@ -1,7 +1,7 @@
 """ CNN for network augmentation """
 import torch.nn as nn
-from models.darts.augment_cells import AugmentCell
-from models.darts import ops
+from .augment_cells import AugmentCell
+from . import ops
 
 
 class AuxiliaryHead(nn.Module):

models/darts/genotypes.py

@@ -6,7 +6,7 @@
 from collections import namedtuple
 import torch
 import torch.nn as nn
-from models.darts import ops
+from . import ops
 
 
 Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat')

models/darts/ops.py

@@ -1,7 +1,7 @@
 """ Operations """
 import torch
 import torch.nn as nn
-import models.darts.genotypes as gt
+from . import genotypes as gt
 
 
 OPS = {

models/get_model.py

@@ -123,7 +123,7 @@
               'DARTS_V1': DARTS_V1,
               'DARTS_V2': DARTS_V2}
 
-Manual_model_dict = {'resnet18': resnet18.ResNet18()}
+Manual_model_dict = {'Resnet18': resnet18.ResNet18()}
 
 
 def get_model(method, name):
@@ -134,6 +134,6 @@ def get_model(method, name):
     else:
         raise NotImplementedError
     if name in model_dict:
-        return model_dict[method]
+        return model_dict[name]
     else:
         raise NotImplementedError

train.py

@@ -19,7 +19,7 @@
 writer = SummaryWriter(log_dir=os.path.join(config.path, "tb"))
 writer.add_text('config', config.as_markdown(), 0)
 
-logger = utils.get_logger(os.path.join(config.path, "{}.log".format(config.name)))
+logger = utils.get_logger(os.path.join(config.path, "logger.log"))
 config.print_params(logger.info)
 
 
@@ -34,7 +34,12 @@ def main():
     torch.manual_seed(config.seed)
     torch.cuda.manual_seed_all(config.seed)
 
-    torch.backends.cudnn.benchmark = True
+    if config.deterministic:
+        torch.backends.cudnn.benchmark = False
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.enabled = True
+    else:
+        torch.backends.cudnn.benchmark = True
 
     # get data with meta info
     input_size, input_channels, n_classes, train_data, valid_data = get_data.get_data(
@@ -77,8 +82,9 @@ def main():
     # training loop
     for epoch in range(config.epochs):
         lr_scheduler.step()
-        drop_prob = config.drop_path_prob * epoch / config.epochs
-        model.module.drop_path_prob(drop_prob)
+        if config.drop_path_prob > 0:
+            drop_prob = config.drop_path_prob * epoch / config.epochs
+            model.module.drop_path_prob(drop_prob)
 
         # training
         train(train_loader, model, optimizer, criterion, epoch)

utils.py

@@ -73,7 +73,7 @@ def netParams(model):
             p *= parameter.size(j)
         total_paramters += p
 
-    return total_paramters
+    return total_paramters / 1024. /1024.
 
 
 class AverageMeter(object):