From bf09641ffcaacc5f7c8a0ffa4bb202c5e21fca8d Mon Sep 17 00:00:00 2001
From: Tamino Huxohl <thuxohl@techfak.uni-bielefeld.de>
Date: Thu, 5 Jan 2023 10:35:56 +0100
Subject: [PATCH] update cgan random search
---
mu_map/training/random_search.py | 20 +++++++++-----------
1 file changed, 9 insertions(+), 11 deletions(-)
diff --git a/mu_map/training/random_search.py b/mu_map/training/random_search.py
index 5f87aba..fcba379 100644
--- a/mu_map/training/random_search.py
+++ b/mu_map/training/random_search.py
@@ -237,11 +237,13 @@ class RandomSearchCGAN(RandomSearch):
self.dataset_dir = "data/second"
self.iterations = iterations
- self.dir = "cgan_random_search"
- validate_and_make_directory(self.dir)
self.device = torch.device("cuda")
+ self.n_slices = 32
self.params = {}
+ self.dir = "cgan_random_search"
+ validate_and_make_directory(self.dir)
+
self.dir_train = os.path.join(self.dir, "train_data")
self.logger = (
logger
@@ -258,28 +260,24 @@ class RandomSearchCGAN(RandomSearch):
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["scatter_correction"] = ChoiceSampler([True, False])
self.param_sampler["scatter_correction"] = ChoiceSampler([False])
self.param_sampler["shuffle"] = ChoiceSampler([False, True])
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([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)
- # self.param_sampler["lr"] = ChoiceSampler([0.001])
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])
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_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):
@@ -305,17 +303,16 @@ class RandomSearchCGAN(RandomSearch):
return nmae_min
def eval_run(self):
- return random.randint(0, 200)
self.logger.debug("Perform evaluation ...")
torch.set_grad_enabled(False)
weights_file = os.path.join(self.training.snapshot_dir, "val_min_generator.pth")
self.logger.debug(f"Load weights from {weights_file}")
- model = self.training.params_g.model.eval()
+ model = self.training.generator.model.eval()
model.load_state_dict(torch.load(weights_file, map_location=self.device))
transform_normalization = SequenceTransform(
- [self.params["normalization"], PadCropTranform(dim=3, size=32)]
+ [self.params["normalization"], PadCropTranform(dim=3, size=self.n_slices)]
)
dataset = MuMapDataset(
self.dataset_dir,
@@ -373,6 +370,7 @@ class RandomSearchCGAN(RandomSearch):
self.dataset_dir,
patches_per_image=self.params["patch_number"],
patch_size=self.params["patch_size"],
+ patch_size_z=self.n_slices,
patch_offset=self.params["patch_offset"],
shuffle=self.params["shuffle"],
transform_normalization=transform_normalization,
@@ -382,7 +380,7 @@ class RandomSearchCGAN(RandomSearch):
self.logger.debug(f"Init discriminator ....")
discriminator = Discriminator(
- in_channels=2, input_size=self.params["patch_size"]
+ in_channels=2, input_size=(self.n_slices, self.params["patch_size"], self.params["patch_size"])
)
discriminator = discriminator.to(self.device)
optimizer = torch.optim.Adam(
--
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