Refine lib -> xautodl

xautodl/models/cell_infers/cells.py

@@ -6,7 +6,7 @@ import torch
 import torch.nn as nn
 from copy import deepcopy
 
-from models.cell_operations import OPS
+from xautodl.models.cell_operations import OPS
 
 
 # Cell for NAS-Bench-201

xautodl/models/cell_infers/nasnet_cifar.py

@@ -4,6 +4,7 @@
 import torch
 import torch.nn as nn
 from copy import deepcopy
+
 from .cells import NASNetInferCell as InferCell, AuxiliaryHeadCIFAR
 
 

xautodl/models/xcore.py

@@ -9,11 +9,11 @@ import torch
 __all__ = ["get_model"]
 
 
-from xlayers.super_core import SuperSequential
-from xlayers.super_core import SuperLinear
-from xlayers.super_core import SuperDropout
-from xlayers.super_core import super_name2norm
-from xlayers.super_core import super_name2activation
+from xautodl.xlayers.super_core import SuperSequential
+from xautodl.xlayers.super_core import SuperLinear
+from xautodl.xlayers.super_core import SuperDropout
+from xautodl.xlayers.super_core import super_name2norm
+from xautodl.xlayers.super_core import super_name2activation
 
 
 def get_model(config: Dict[Text, Any], **kwargs):

xautodl/procedures/advanced_main.py

@@ -7,8 +7,7 @@ import os, sys, time, torch
 from typing import Optional, Text, Callable
 
 # modules in AutoDL
-from log_utils import AverageMeter
-from log_utils import time_string
+from xautodl.log_utils import AverageMeter, time_string
 from .eval_funcs import obtain_accuracy
 
 

xautodl/procedures/basic_main.py

@@ -4,8 +4,7 @@
 import os, sys, time, torch
 
 # modules in AutoDL
-from log_utils import AverageMeter
-from log_utils import time_string
+from xautodl.log_utils import AverageMeter, time_string
 from .eval_funcs import obtain_accuracy
 
 

xautodl/procedures/eval_funcs.py

@@ -15,6 +15,6 @@ def obtain_accuracy(output, target, topk=(1,)):
 
     res = []
     for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+        correct_k = correct[:k].contiguous().view(-1).float().sum(0, keepdim=True)
         res.append(correct_k.mul_(100.0 / batch_size))
     return res

xautodl/procedures/funcs_nasbench.py

@@ -4,7 +4,7 @@
 import os, time, copy, torch, pathlib
 
 # modules in AutoDL
-import xautodl.datasets
+from xautodl import datasets
 from xautodl.config_utils import load_config
 from xautodl.procedures import prepare_seed, get_optim_scheduler
 from xautodl.log_utils import AverageMeter, time_string, convert_secs2time

xautodl/procedures/q_exps.py

@@ -8,7 +8,6 @@ import pprint
 import logging
 from copy import deepcopy
 
-from log_utils import pickle_load
 import qlib
 from qlib.utils import init_instance_by_config
 from qlib.workflow import R

xautodl/procedures/search_main.py

@@ -2,8 +2,9 @@
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
 ##################################################
 import os, sys, time, torch
-from log_utils import AverageMeter, time_string
-from models import change_key
+
+from xautodl.log_utils import AverageMeter, time_string
+from xautodl.models import change_key
 
 from .eval_funcs import obtain_accuracy
 

xautodl/procedures/search_main_v2.py

@@ -4,8 +4,8 @@
 import os, sys, time, torch
 
 # modules in AutoDL
-from log_utils import AverageMeter, time_string
-from models import change_key
+from xautodl.log_utils import AverageMeter, time_string
+from xautodl.models import change_key
 from .eval_funcs import obtain_accuracy
 
 

xautodl/procedures/simple_KD_main.py

@@ -5,7 +5,7 @@ import os, sys, time, torch
 import torch.nn.functional as F
 
 # modules in AutoDL
-from log_utils import AverageMeter, time_string
+from xautodl.log_utils import AverageMeter, time_string
 from .eval_funcs import obtain_accuracy
 
 

xautodl/procedures/starts.py

@@ -16,7 +16,7 @@ def prepare_seed(rand_seed):
 
 def prepare_logger(xargs):
     args = copy.deepcopy(xargs)
-    from log_utils import Logger
+    from xautodl.log_utils import Logger
 
     logger = Logger(args.save_dir, args.rand_seed)
     logger.log("Main Function with logger : {:}".format(logger))