test a few different parameters for random search

mu_map/training/random_search.py

@@ -139,29 +139,30 @@ class RandomSearchCGAN(RandomSearch):
 
         # dataset params
         self.param_sampler["patch_size"] = ChoiceSampler([32])
-        self.param_sampler["patch_offset"] = IntIntervalSampler(0, 32)
-        self.param_sampler["patch_number"] = IntIntervalSampler(50, 200)
+        self.param_sampler["patch_offset"] = ChoiceSampler([0])
+        self.param_sampler["patch_number"] = ChoiceSampler([100])
         self.param_sampler["scatter_correction"] = ChoiceSampler([True, False])
-        self.param_sampler["shuffle"] = ChoiceSampler([True, False])
+        self.param_sampler["shuffle"] = ChoiceSampler([False])
         self.param_sampler["normalization"] = ChoiceSampler(
             [MeanNormTransform(), MaxNormTransform(), GaussianNormTransform()]
         )
-        self.param_sampler["pad_crop"] = ChoiceSampler(
-            [None, PadCropTranform(dim=3, size=32)]
-        )
+        self.param_sampler["pad_crop"] = ChoiceSampler([PadCropTranform(dim=3, size=32)])
 
         # training params
         self.param_sampler["epochs"] = ChoiceSampler([100])
         self.param_sampler["batch_size"] = ChoiceSampler([64])
-        self.param_sampler["lr"] = FloatIntervalSampler(0.01, 0.0001)
-        self.param_sampler["lr_decay"] = ChoiceSampler([True, False])
+        # self.param_sampler["lr"] = FloatIntervalSampler(0.01, 0.0001)
+        self.param_sampler["lr"] = ChoiceSampler([0.001])
+        self.param_sampler["lr_decay"] = ChoiceSampler([False])
         self.param_sampler["lr_decay_epoch"] = ChoiceSampler([1])
         self.param_sampler["lr_decay_factor"] = ChoiceSampler([0.99])
         self.param_sampler["criterion_dist"] = ChoiceSampler(
             [WeightedLoss.from_str("L1"), WeightedLoss.from_str("L2+GDL")]
         )
-        self.param_sampler["weight_criterion_dist"] = FloatIntervalSampler(1.0, 100.0)
-        self.param_sampler["weight_criterion_adv"] = FloatIntervalSampler(1.0, 100.0)
+        # self.param_sampler["weight_criterion_dist"] = FloatIntervalSampler(1.0, 100.0)
+        self.param_sampler["weight_criterion_dist"] = ChoiceSampler([1.0, 20.0, 100.0])
+        # self.param_sampler["weight_criterion_adv"] = FloatIntervalSampler(1.0, 100.0)
+        self.param_sampler["weight_criterion_adv"] = ChoiceSampler([1.0, 20.0, 100.0])
 
     def run(self):
         nmae_min = sys.maxsize