Search space zoo example fix (#2801)

examples/nas/search_space_zoo/darts_example.py

@@ -14,7 +14,7 @@
 from utils import accuracy
 
 from nni.nas.pytorch.search_space_zoo import DartsCell
-from darts_search_space import DartsStackedCells
+from darts_stack_cells import DartsStackedCells
 
 logger = logging.getLogger('nni')
 

examples/nas/search_space_zoo/darts_stack_cells.py

@@ -2,7 +2,7 @@
 # Licensed under the MIT license.
 
 import torch.nn as nn
-import ops
+from nni.nas.pytorch.search_space_zoo.darts_ops import DropPath
 
 
 class DartsStackedCells(nn.Module):
@@ -79,5 +79,5 @@ def forward(self, x):
 
     def drop_path_prob(self, p):
         for module in self.modules():
-            if isinstance(module, ops.DropPath):
+            if isinstance(module, DropPath):
                 module.p = p

examples/nas/search_space_zoo/enas_macro_example.py

@@ -58,7 +58,6 @@ def forward(self, x):
     parser = ArgumentParser("enas")
     parser.add_argument("--batch-size", default=128, type=int)
     parser.add_argument("--log-frequency", default=10, type=int)
-    # parser.add_argument("--search-for", choices=["macro", "micro"], default="macro")
     parser.add_argument("--epochs", default=None, type=int, help="Number of epochs (default: macro 310, micro 150)")
     parser.add_argument("--visualization", default=False, action="store_true")
     args = parser.parse_args()
@@ -71,7 +70,6 @@ def forward(self, x):
     criterion = nn.CrossEntropyLoss()
     optimizer = torch.optim.SGD(model.parameters(), 0.05, momentum=0.9, weight_decay=1.0E-4)
     lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=0.001)
-
     trainer = enas.EnasTrainer(model,
                                loss=criterion,
                                metrics=accuracy,

examples/nas/search_space_zoo/enas_micro_example.py

@@ -62,7 +62,7 @@ def __init__(self, num_layers=2, num_nodes=5, out_channels=24, in_channels=3, nu
             reduction = False
             if layer_id in pool_layers:
                 c_cur, reduction = c_p * 2, True
-            self.layers.append(ENASMicroLayer(self.layers, num_nodes, c_pp, c_p, c_cur, reduction))
+            self.layers.append(ENASMicroLayer(num_nodes, c_pp, c_p, c_cur, reduction))
             if reduction:
                 c_pp = c_p = c_cur
             c_pp, c_p = c_p, c_cur
@@ -98,7 +98,6 @@ def forward(self, x):
     parser = ArgumentParser("enas")
     parser.add_argument("--batch-size", default=128, type=int)
     parser.add_argument("--log-frequency", default=10, type=int)
-    # parser.add_argument("--search-for", choices=["macro", "micro"], default="macro")
     parser.add_argument("--epochs", default=None, type=int, help="Number of epochs (default: macro 310, micro 150)")
     parser.add_argument("--visualization", default=False, action="store_true")
     args = parser.parse_args()

src/sdk/pynni/nni/nas/pytorch/search_space_zoo/enas_cell.py

@@ -79,7 +79,6 @@ class ENASMicroLayer(nn.Module):
     """
     def __init__(self, num_nodes, in_channels_pp, in_channels_p, out_channels, reduction):
         super().__init__()
-        print(in_channels_pp, in_channels_p, out_channels, reduction)
         self.reduction = reduction
         if self.reduction:
             self.reduce0 = FactorizedReduce(in_channels_pp, out_channels, affine=False)
@@ -160,7 +159,7 @@ def __init__(self, key, prev_labels, in_filters, out_filters):
             PoolBranch('avg', in_filters, out_filters, 3, 1, 1),
             PoolBranch('max', in_filters, out_filters, 3, 1, 1)
         ])
-        if prev_labels > 0:
+        if prev_labels:
             self.skipconnect = mutables.InputChoice(choose_from=prev_labels, n_chosen=None)
         else:
             self.skipconnect = None