From b7fad94bfb06a03ff3daecb88f66d2944ca896ac Mon Sep 17 00:00:00 2001
From: Tamino Huxohl <thuxohl@techfak.uni-bielefeld.de>
Date: Fri, 6 Jan 2023 12:05:35 +0100
Subject: [PATCH] change parameters in random search
---
mu_map/training/random_search.py | 48 ++++++++++++++++++++++----------
1 file changed, 33 insertions(+), 15 deletions(-)
diff --git a/mu_map/training/random_search.py b/mu_map/training/random_search.py
index fcba379..0b08c60 100644
--- a/mu_map/training/random_search.py
+++ b/mu_map/training/random_search.py
@@ -21,9 +21,9 @@ from mu_map.dataset.normalization import (
)
from mu_map.dataset.transform import PadCropTranform, Transform, SequenceTransform
from mu_map.eval.measures import nmae, mse
-from mu_map.models.discriminator import Discriminator
+from mu_map.models.discriminator import Discriminator, PatchDiscriminator
from mu_map.models.unet import UNet
-from mu_map.training.cgan2 import cGANTraining, DiscriminatorParams, GeneratorParams
+from mu_map.training.cgan import cGANTraining, DiscriminatorParams, GeneratorParams
from mu_map.training.loss import WeightedLoss
from mu_map.logging import get_logger
@@ -216,7 +216,7 @@ class RandomSearch:
def validate_and_make_directory(_dir: str):
"""
- Uility method to validate that a directory exists and is empty.
+ Utility method to validate that a directory exists and is empty.
If is does not exist, it is created.
"""
if not os.path.exists(_dir):
@@ -237,7 +237,7 @@ class RandomSearchCGAN(RandomSearch):
self.dataset_dir = "data/second"
self.iterations = iterations
- self.device = torch.device("cuda")
+ self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
self.n_slices = 32
self.params = {}
@@ -256,10 +256,10 @@ class RandomSearchCGAN(RandomSearch):
)
self.training: cGANTraining = None
- # dataset params
+ # dataset parameters
self.param_sampler["patch_size"] = ChoiceSampler([32, 64])
self.param_sampler["patch_offset"] = ChoiceSampler([0])
- self.param_sampler["patch_number"] = IntIntervalSampler(min_val=50, max_val=200)
+ self.param_sampler["patch_number"] = IntIntervalSampler(min_val=50, max_val=100)
self.param_sampler["scatter_correction"] = ChoiceSampler([False])
self.param_sampler["shuffle"] = ChoiceSampler([False, True])
self.param_sampler["normalization"] = ChoiceSampler(
@@ -267,10 +267,24 @@ class RandomSearchCGAN(RandomSearch):
)
self.param_sampler["pad_crop"] = ChoiceSampler([None, PadCropTranform(dim=3, size=self.n_slices)])
- # training params
- self.param_sampler["epochs"] = IntIntervalSampler(min_val=50, max_val=200)
- self.param_sampler["batch_size"] = ChoiceSampler([64])
- self.param_sampler["lr"] = FloatIntervalSampler(0.01, 0.0001)
+ # model parameters
+ self.param_sampler["discriminator_type"] = ChoiceSampler(["class", "patch"])
+ def discriminator_conv_features(discriminator_type: str, **kwargs):
+ if discriminator_type == "class":
+ return [[32, 64, 128], [64, 128, 256], [32, 64, 128, 256]]
+ else:
+ return [[32, 64, 128, 256], [64, 128, 256, 512]]
+ self.param_sampler["discriminator_conv_features"] = DependentChoiceSampler(discriminator_conv_features)
+ self.param_sampler["generator_features"] = ChoiceSampler([
+ [128, 256, 512],
+ [64, 128, 256, 512],
+ [32, 64, 128, 256, 512],
+ ])
+
+ # training parameters
+ self.param_sampler["epochs"] = ChoiceSampler([50, 60, 70, 80, 90])
+ self.param_sampler["batch_size"] = ChoiceSampler([32, 64])
+ self.param_sampler["lr"] = FloatIntervalSampler(0.1, 0.0001)
self.param_sampler["lr_decay"] = ChoiceSampler([False, True])
self.param_sampler["lr_decay_epoch"] = ChoiceSampler([1])
self.param_sampler["lr_decay_factor"] = ChoiceSampler([0.99])
@@ -379,10 +393,15 @@ class RandomSearchCGAN(RandomSearch):
)
self.logger.debug(f"Init discriminator ....")
- discriminator = Discriminator(
- in_channels=2, input_size=(self.n_slices, self.params["patch_size"], self.params["patch_size"])
- )
+ input_size = (2, self.n_slices, self.params["patch_size"], self.params["patch_size"])
+ if self.params["discriminator_type"] == "class":
+ discriminator = Discriminator(
+ in_channels=2, input_size=input_size, conv_features=self.params["discriminator_conv_features"],
+ )
+ else:
+ discriminator = PatchDiscriminator(in_channels=2, features=self.params["discriminator_conv_features"])
discriminator = discriminator.to(self.device)
+
optimizer = torch.optim.Adam(
discriminator.parameters(), lr=self.params["lr"], betas=(0.5, 0.999)
)
@@ -400,8 +419,7 @@ class RandomSearchCGAN(RandomSearch):
)
self.logger.debug(f"Init generator ....")
- features = [64, 128, 256, 512]
- generator = UNet(in_channels=1, features=features)
+ generator = UNet(in_channels=1, features=self.params["generator_features"])
generator = generator.to(self.device)
optimizer = torch.optim.Adam(
generator.parameters(), lr=self.params["lr"], betas=(0.5, 0.999)
--
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