Refine lib -> xautodl

.github/workflows/super_model_test.yml

@@ -35,3 +35,8 @@ jobs:
           python -m pip install torch torchvision
           python -m pytest ./tests/test_super_*.py
         shell: bash
+
+      - name: Test TAS (NeurIPS 2019)
+        run: |
+          python -m pytest ./tests/test_tas.py
+        shell: bash

exps/KD-main.py

@@ -25,7 +25,7 @@ def main(args):
     torch.backends.cudnn.enabled = True
     torch.backends.cudnn.benchmark = True
     # torch.backends.cudnn.deterministic = True
-    torch.set_num_threads(args.workers)
+    # torch.set_num_threads(args.workers)
 
     prepare_seed(args.rand_seed)
     logger = prepare_logger(args)

exps/NATS-Bench/main-sss.py

@@ -470,7 +470,7 @@ if __name__ == "__main__":
     assert torch.cuda.is_available(), "CUDA is not available."
     torch.backends.cudnn.enabled = True
     torch.backends.cudnn.deterministic = True
-    torch.set_num_threads(args.workers)
+    # torch.set_num_threads(args.workers)
 
     main(
         save_dir,

exps/NATS-Bench/main-tss.py

@@ -340,7 +340,7 @@ def train_single_model(
     torch.backends.cudnn.enabled = True
     torch.backends.cudnn.deterministic = True
     # torch.backends.cudnn.benchmark = True
-    torch.set_num_threads(workers)
+    # torch.set_num_threads(workers)
 
     save_dir = (
         Path(save_dir)
@@ -675,7 +675,7 @@ if __name__ == "__main__":
         assert torch.cuda.is_available(), "CUDA is not available."
         torch.backends.cudnn.enabled = True
         torch.backends.cudnn.deterministic = True
-        torch.set_num_threads(args.workers if args.workers > 0 else 1)
+        # torch.set_num_threads(args.workers if args.workers > 0 else 1)
 
         main(
             save_dir,

exps/NATS-algos/reinforce.py

@@ -132,7 +132,7 @@ def select_action(policy):
 
 
 def main(xargs, api):
-    torch.set_num_threads(4)
+    # torch.set_num_threads(4)
     prepare_seed(xargs.rand_seed)
     logger = prepare_logger(args)
 

exps/NATS-algos/search-size.py

@@ -204,7 +204,7 @@ def main(xargs):
     torch.backends.cudnn.enabled = True
     torch.backends.cudnn.benchmark = False
     torch.backends.cudnn.deterministic = True
-    torch.set_num_threads(xargs.workers)
+    # torch.set_num_threads(xargs.workers)
     prepare_seed(xargs.rand_seed)
     logger = prepare_logger(args)
 

exps/basic-main.py

@@ -8,17 +8,14 @@ ImageFile.LOAD_TRUNCATED_IMAGES = True
 from copy import deepcopy
 from pathlib import Path
 
-lib_dir = (Path(__file__).parent / ".." / "lib").resolve()
+from xautodl.datasets import get_datasets
-if str(lib_dir) not in sys.path:
+from xautodl.config_utils import load_config, obtain_basic_args as obtain_args
-    sys.path.insert(0, str(lib_dir))
+from xautodl.procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
-from config_utils import load_config, obtain_basic_args as obtain_args
+from xautodl.procedures import get_optim_scheduler, get_procedures
-from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
+from xautodl.models import obtain_model
-from procedures import get_optim_scheduler, get_procedures
+from xautodl.nas_infer_model import obtain_nas_infer_model
-from datasets import get_datasets
+from xautodl.utils import get_model_infos
-from models import obtain_model
+from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
-from nas_infer_model import obtain_nas_infer_model
-from utils import get_model_infos
-from log_utils import AverageMeter, time_string, convert_secs2time
 
 
 def main(args):
@@ -26,7 +23,7 @@ def main(args):
     torch.backends.cudnn.enabled = True
     torch.backends.cudnn.benchmark = True
     # torch.backends.cudnn.deterministic = True
-    torch.set_num_threads(args.workers)
+    # torch.set_num_threads(args.workers)
 
     prepare_seed(args.rand_seed)
     logger = prepare_logger(args)

exps/search-shape.py

@@ -10,21 +10,17 @@ import numpy as np
 from copy import deepcopy
 from pathlib import Path
 
-lib_dir = (Path(__file__).parent / ".." / "lib").resolve()
+from xautodl.config_utils import (
-print("lib_dir : {:}".format(lib_dir))
-if str(lib_dir) not in sys.path:
-    sys.path.insert(0, str(lib_dir))
-from config_utils import (
     load_config,
     configure2str,
     obtain_search_single_args as obtain_args,
 )
-from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
+from xautodl.procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint
-from procedures import get_optim_scheduler, get_procedures
+from xautodl.procedures import get_optim_scheduler, get_procedures
-from datasets import get_datasets, SearchDataset
+from xautodl.datasets import get_datasets, SearchDataset
-from models import obtain_search_model, obtain_model, change_key
+from xautodl.models import obtain_search_model, obtain_model, change_key
-from utils import get_model_infos
+from xautodl.utils import get_model_infos
-from log_utils import AverageMeter, time_string, convert_secs2time
+from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
 
 
 def main(args):
@@ -32,7 +28,7 @@ def main(args):
     torch.backends.cudnn.enabled = True
     torch.backends.cudnn.benchmark = True
     # torch.backends.cudnn.deterministic = True
-    torch.set_num_threads(args.workers)
+    # torch.set_num_threads(args.workers)
 
     prepare_seed(args.rand_seed)
     logger = prepare_logger(args)

tests/test_loader.py

@@ -0,0 +1,29 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.03 #
+#####################################################
+# pytest tests/test_loader.py -s                    #
+#####################################################
+import unittest
+import tempfile
+import torch
+
+from xautodl.datasets import get_datasets
+
+
+def test_simple():
+    xdir = tempfile.mkdtemp()
+    train_data, valid_data, xshape, class_num = get_datasets("cifar10", xdir, -1)
+    print(train_data)
+    print(valid_data)
+
+    xloader = torch.utils.data.DataLoader(
+        train_data, batch_size=256, shuffle=True, num_workers=4, pin_memory=True
+    )
+    print(xloader)
+    print(next(iter(xloader)))
+
+    for i, data in enumerate(xloader):
+        print(i)
+
+
+test_simple()

tests/test_tas.py

@@ -0,0 +1,23 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import torch
+import torch.nn as nn
+
+from xautodl.models.shape_searchs.SoftSelect import ChannelWiseInter
+
+
+class TestTASFunc(unittest.TestCase):
+    """Test the TAS function."""
+
+    def test_channel_interplation(self):
+        tensors = torch.rand((16, 128, 7, 7))
+
+        for oc in range(200, 210):
+            out_v1 = ChannelWiseInter(tensors, oc, "v1")
+            out_v2 = ChannelWiseInter(tensors, oc, "v2")
+            assert (out_v1 == out_v2).any().item() == 1
+        for oc in range(48, 160):
+            out_v1 = ChannelWiseInter(tensors, oc, "v1")
+            out_v2 = ChannelWiseInter(tensors, oc, "v2")
+            assert (out_v1 == out_v2).any().item() == 1

xautodl/models/shape_searchs/generic_size_tiny_cell_model.py

@@ -9,9 +9,9 @@ from typing import List, Text, Any
 import random, torch
 import torch.nn as nn
 
-from models.cell_operations import ResNetBasicblock
+from ..cell_operations import ResNetBasicblock
-from models.cell_infers.cells import InferCell
+from ..cell_infers.cells import InferCell
-from models.shape_searchs.SoftSelect import select2withP, ChannelWiseInter
+from .shape_searchs.SoftSelect import select2withP, ChannelWiseInter
 
 
 class GenericNAS301Model(nn.Module):

xautodl/models/shape_searchs/test.py

@@ -1,20 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##################################################
-import torch
-import torch.nn as nn
-from SoftSelect import ChannelWiseInter
-
-
-if __name__ == "__main__":
-
-    tensors = torch.rand((16, 128, 7, 7))
-
-    for oc in range(200, 210):
-        out_v1 = ChannelWiseInter(tensors, oc, "v1")
-        out_v2 = ChannelWiseInter(tensors, oc, "v2")
-        assert (out_v1 == out_v2).any().item() == 1
-    for oc in range(48, 160):
-        out_v1 = ChannelWiseInter(tensors, oc, "v1")
-        out_v2 = ChannelWiseInter(tensors, oc, "v2")
-        assert (out_v1 == out_v2).any().item() == 1