add autodl

AutoDL-Projects/xautodl/nas_infer_model/__init__.py

@@ -0,0 +1,51 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+# I write this package to make AutoDL-Projects to be compatible with the old GDAS projects.
+# Ideally, this package will be merged into lib/models/cell_infers in future.
+# Currently, this package is used to reproduce the results in GDAS (Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019).
+##################################################
+
+import os, torch
+
+
+def obtain_nas_infer_model(config, extra_model_path=None):
+
+    if config.arch == "dxys":
+        from .DXYs import CifarNet, ImageNet, Networks
+        from .DXYs import build_genotype_from_dict
+
+        if config.genotype is None:
+            if extra_model_path is not None and not os.path.isfile(extra_model_path):
+                raise ValueError(
+                    "When genotype in confiig is None, extra_model_path must be set as a path instead of {:}".format(
+                        extra_model_path
+                    )
+                )
+            xdata = torch.load(extra_model_path)
+            current_epoch = xdata["epoch"]
+            genotype_dict = xdata["genotypes"][current_epoch - 1]
+            genotype = build_genotype_from_dict(genotype_dict)
+        else:
+            genotype = Networks[config.genotype]
+        if config.dataset == "cifar":
+            return CifarNet(
+                config.ichannel,
+                config.layers,
+                config.stem_multi,
+                config.auxiliary,
+                genotype,
+                config.class_num,
+            )
+        elif config.dataset == "imagenet":
+            return ImageNet(
+                config.ichannel,
+                config.layers,
+                config.auxiliary,
+                genotype,
+                config.class_num,
+            )
+        else:
+            raise ValueError("invalid dataset : {:}".format(config.dataset))
+    else:
+        raise ValueError("invalid nas arch type : {:}".format(config.arch))