Merge pull request #186 from automl/predictor_evaluator_fix

naslib/defaults/predictor_evaluator.py

@@ -10,9 +10,12 @@
 from sklearn import metrics
 import math
 
+from naslib.predictors.zerocost import ZeroCost
 from naslib.search_spaces.core.query_metrics import Metric
 from naslib.utils import generate_kfold, cross_validation
 
+from naslib import utils
+
 logger = logging.getLogger(__name__)
 
 
@@ -47,6 +50,9 @@ def __init__(self, predictor, config=None):
         self.num_arches_to_mutate = 5
         self.max_mutation_rate = 3
 
+        # For ZeroCost proxies
+        self.dataloader = None
+
     def adapt_search_space(
         self, search_space, load_labeled, scope=None, dataset_api=None
     ):
@@ -70,6 +76,9 @@ def adapt_search_space(
                 "This search space is not yet implemented in PredictorEvaluator."
             )
 
+        if isinstance(self.predictor, ZeroCost):
+            self.dataloader, _, _, _, _ = utils.get_train_val_loaders(self.config)
+
     def get_full_arch_info(self, arch):
         """
         Given an arch, return the accuracy, train_time,
@@ -139,10 +148,8 @@ def load_dataset(self, load_labeled=False, data_size=10, arch_hash_map={}):
                 arch.load_labeled_architecture(dataset_api=self.dataset_api)
 
             arch_hash = arch.get_hash()
-            if False: # removing this for consistency, for now
-                continue
-            else:
-                arch_hash_map[arch_hash] = True
+
+            arch_hash_map[arch_hash] = True
 
             accuracy, train_time, info_dict = self.get_full_arch_info(arch)
             xdata.append(arch)
@@ -295,7 +302,11 @@ def single_evaluate(self, train_data, test_data, fidelity):
         hyperparams = self.predictor.get_hyperparams()
 
         fit_time_end = time.time()
-        test_pred = self.predictor.query(xtest, test_info)
+        if isinstance(self.predictor, ZeroCost):
+            [g.parse() for g in xtest] # parse the graphs because they will be used
+            test_pred = self.predictor.query_batch(xtest, self.dataloader)
+        else:
+            test_pred = self.predictor.query(xtest, test_info)
         query_time_end = time.time()
 
         # If the predictor is an ensemble, take the mean

naslib/predictors/zerocost.py

@@ -4,6 +4,7 @@
 based on https://github.com/mohsaied/zero-cost-nas
 """
 import torch
+import numpy as np
 import logging
 import math
 
@@ -24,12 +25,21 @@ def __init__(self, method_type="jacov"):
         self.num_imgs_or_batches = 1
         self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-    def query(self, graph, dataloader=None, info=None):
+    def query_batch(self, graphs, dataloader):
+        scores = []
+
+        for graph in graphs:
+            score = self.query(graph, dataloader)
+            scores.append(score)
+
+        return np.array(scores)
+
+    def query(self, graph, dataloader):
         loss_fn = graph.get_loss_fn()
 
         n_classes = graph.num_classes
         score = predictive.find_measures(
-                net_orig=graph,
+                net_orig=graph.to(self.device),
                 dataloader=dataloader,
                 dataload_info=(self.dataload, self.num_imgs_or_batches, n_classes),
                 device=self.device,