From 0994425e5836be3033bcc12005e48246e105ee4b Mon Sep 17 00:00:00 2001
From: "s.islam" <s.islam@fz-juelich.de>
Date: Fri, 29 Apr 2022 14:20:00 +0200
Subject: [PATCH] Using 2 channels(NTransmittance & Retardation) for more
regional information.
---
code/data.py | 43 +++++++++++++++++++++++++++++++++----------
code/model.py | 6 +++---
2 files changed, 36 insertions(+), 13 deletions(-)
diff --git a/code/data.py b/code/data.py
index 4b98b44..868f218 100644
--- a/code/data.py
+++ b/code/data.py
@@ -121,6 +121,7 @@ class TestDataModule(pl.LightningDataModule):
# TODO: Load the PLI and Cytp train data here as lists of numpy arrays: List[np.ndarray]
# Load the pyramid/00 per file
+ '''
#For single channel
#For JSC Training.
pli_path = '/p/fastdata/pli/Private/oberstrass1/datasets/vervet1818/vervet1818-stained/data/aligned/pli/NTransmittance'
@@ -147,14 +148,26 @@ class TestDataModule(pl.LightningDataModule):
pli_train_file = pli_train_file - 0.5
pli_train_file = np.clip(pli_train_file, 0, 1)
self.pli_train.append(pli_train_file)
-
+ '''
+
+ # For 2/3 Channel.
+
+ #Path for JSC.
+
+ pli_NTransmittance_path = '/p/fastdata/pli/Private/oberstrass1/datasets/vervet1818/vervet1818-stained/data/aligned/pli/NTransmittance'
+ pli_Retardation_path = '/p/fastdata/pli/Private/oberstrass1/datasets/vervet1818/vervet1818-stained/data/aligned/pli/Retardation'
+ pli_Direction_path = '/p/fastdata/pli/Private/oberstrass1/datasets/vervet1818/vervet1818-stained/data/aligned/pli/Direction'
+ cyto_path = '/p/fastdata/pli/Private/oberstrass1/datasets/vervet1818/vervet1818-stained/data/aligned/stained'
+
+
+ #Path for local machine.
- # For 3 Channel.
'''
pli_NTransmittance_path = '/media/tushar/A2246889246861F1/Master Thesis MAIA/example-data/pli/NTransmittance'
pli_Retardation_path = '/media/tushar/A2246889246861F1/Master Thesis MAIA/example-data/pli/Retardation'
pli_Direction_path = '/media/tushar/A2246889246861F1/Master Thesis MAIA/example-data/pli/Direction'
cyto_path = '/media/tushar/A2246889246861F1/Master Thesis MAIA/example-data/stained'
+ '''
pli_NTransmittance_files_list = [file for file in os.listdir(pli_NTransmittance_path) if
file.endswith(('.h5', '.hdf', '.h4', '.hdf4', '.he2', '.hdf5', '.he5'))]
@@ -186,19 +199,23 @@ class TestDataModule(pl.LightningDataModule):
pli_train_file = np.asarray(pli_train_file).astype(np.float32)
pli_train_file = pli_train_file - 0.5
pli_Retardation_train.append(pli_train_file)
+
+ #For Stacking Direction data. As it is not required, don't touch it!
+ '''
for i in range(0, 4):
pli_train_file = h5py.File(os.path.join(pli_Direction_path, pli_Direction_files_list[i]), 'r')
pli_train_file = pli_train_file['pyramid/00']
pli_train_file = np.asarray(pli_train_file).astype(np.float32)
pli_train_file = (pli_train_file/255) - 0.5
pli_Direction_train.append(pli_train_file)
+ '''
self.pli_train = []
self.cyto_train = []
for i in range(0,4):
- self.pli_train.append(np.stack((pli_NTransmittance_train[i], pli_Retardation_train[i], pli_Direction_train[i]), axis=-1))
- '''
+ self.pli_train.append(np.stack((pli_NTransmittance_train[i], pli_Retardation_train[i]), axis=-1))
+
for i in range(0,4):
cyto_train_file = h5py.File(os.path.join(cyto_path, cyto_files_list[i]), 'r')
@@ -207,6 +224,7 @@ class TestDataModule(pl.LightningDataModule):
cyto_train_file = (cyto_train_file/255) - 0.5
self.cyto_train.append(cyto_train_file)
+
else:
print(f"Train data for rank {rank}/{size} already prepared")
@@ -220,6 +238,7 @@ class TestDataModule(pl.LightningDataModule):
self.cyto_val = []
+ '''
#Single Channel
pli_val_file = h5py.File(os.path.join(pli_path, pli_files_list[4]), 'r')
pli_val_file = pli_val_file['pyramid/00']
@@ -227,9 +246,10 @@ class TestDataModule(pl.LightningDataModule):
pli_val_file = np.clip(pli_val_file, 0, 1)
pli_val_file = pli_val_file - 0.5
self.pli_val.append(pli_val_file)
-
- #3 Channels
'''
+
+ #2/3 Channels
+
pli_NTransmittance_val = []
pli_Retardation_val = []
pli_Direction_val = []
@@ -247,15 +267,18 @@ class TestDataModule(pl.LightningDataModule):
pli_Retardation_val_file = pli_Retardation_val_file - 0.5
pli_Retardation_val.append(pli_Retardation_val_file)
+ #For Stacking Direction data. As it is not required, don't touch it!
+ '''
pli_Direction_val_file = h5py.File(os.path.join(pli_Direction_path, pli_Direction_files_list[4]), 'r')
pli_Direction_val_file = pli_Direction_val_file['pyramid/00']
pli_Direction_val_file = np.asarray(pli_Direction_val_file).astype(np.float32)
pli_Direction_val_file = (pli_Direction_val_file/255) - 0.5
pli_Direction_val.append(pli_Direction_val_file)
-
- self.pli_val = np.stack((pli_NTransmittance_val, pli_Retardation_val, pli_Direction_val), axis=-1)
'''
+ self.pli_val = np.stack((pli_NTransmittance_val, pli_Retardation_val), axis=-1)
+
+
cyto_val_file = h5py.File(os.path.join(cyto_path, cyto_files_list[4]), 'r')
@@ -291,7 +314,7 @@ class TestDataModule(pl.LightningDataModule):
# Define the datasets for training and validation
self.train_sampler = TestSampler(
- self.pli_train,
+ list(self.pli_train),
self.cyto_train,
self._train_transforms,
self.crop_size,
@@ -299,7 +322,7 @@ class TestDataModule(pl.LightningDataModule):
)
self.val_sampler = TestSampler(
- self.pli_val,
+ list(self.pli_val),
self.cyto_val,
self._test_transforms,
self.crop_size,
diff --git a/code/model.py b/code/model.py
index 4ebd957..22a408f 100644
--- a/code/model.py
+++ b/code/model.py
@@ -30,9 +30,9 @@ class TestModule(pl.LightningModule):
# Define the model
self.model = smp.Unet(
- encoder_name="resnet34", # Also consider using smaller or larger encoders
+ encoder_name="resnet18", # Also consider using smaller or larger encoders
encoder_weights= "imagenet", # Do the pretrained weights help? Try with or without
- in_channels=1, # We use 1 chanel transmittance as input
+ in_channels=2, # We use 1 chanel transmittance as input
classes=1, # classes == output channels. We use one output channel for cyto data
)
self.loss_f = RMILoss(with_logits=True) #torch.nn.MSELoss()
@@ -57,7 +57,7 @@ class TestModule(pl.LightningModule):
batch['cyto_image'] = batch['cyto_image'] + 0.5
cyto_imag_generated = cyto_imag_generated +0.5
if batch_idx == 0:
- grid = make_grid([batch['pli_image'][0], batch['cyto_image'][0], cyto_imag_generated[0]])
+ grid = make_grid([batch['pli_image'][0, :1], batch['pli_image'][:1, 0], batch['cyto_image'][0], cyto_imag_generated[0]])
self.logger.experiment.add_image('Grid_images', grid, self.current_epoch, dataformats="CHW")
def configure_optimizers(self):
--
GitLab