Merge pull request #16 from juglab/merger

Merger

Author: tibuch

README.md

@@ -6,7 +6,7 @@ Build Python package:
 `python setup.py bdist_wheel`
 
 Build singularity recipe:
-`neurodocker generate singularity -b nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04 -p apt --copy /home/tibuch/Gitrepos/FourierImageTransformer/dist/fourier_image_transformers-0.1.19-py3-none-any.whl /fourier_image_transformers-0.1.19-py3-none-any.whl --miniconda create_env=fit conda_install='python=3.7 astra-toolbox pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch -c astra-toolbox/label/dev' pip_install='/fourier_image_transformers-0.1.19-py3-none-any.whl' activate=true --entrypoint "/neurodocker/startup.sh python" > v0.1.19.Singularity`
+`neurodocker generate singularity -b nvidia/cuda:10.2-cudnn7-devel-ubuntu18.04 -p apt --copy /home/tibuch/Gitrepos/FourierImageTransformer/dist/fourier_image_transformers-0.1.20-py3-none-any.whl /fourier_image_transformers-0.1.20-py3-none-any.whl --miniconda create_env=fit conda_install='python=3.7 astra-toolbox pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch -c astra-toolbox/label/dev' pip_install='/fourier_image_transformers-0.1.20-py3-none-any.whl' activate=true --entrypoint "/neurodocker/startup.sh python" > v0.1.20.Singularity`
 
 Build singularity container:
-`sudo singularity build fit_v0.1.19.simg v0.1.19.Singularity`
+`sudo singularity build fit_v0.1.20.simg v0.1.20.Singularity`

fit/datamodules/super_res/SRecDataModule.py

@@ -16,7 +16,7 @@
 
 
 class MNISTSResFourierTargetDataModule(LightningDataModule):
-    IMG_SHAPE = 28
+    IMG_SHAPE = 27
 
     def __init__(self, root_dir, batch_size):
         """
@@ -38,8 +38,9 @@ def setup(self, stage: Optional[str] = None):
         mnist_train_val = MNIST(self.root_dir, train=True, download=True).data.type(torch.float32)
         np.random.seed(1612)
         perm = np.random.permutation(mnist_train_val.shape[0])
-        mnist_train = mnist_train_val[perm[:55000]]
-        mnist_val = mnist_train_val[perm[55000:]]
+        mnist_train = mnist_train_val[perm[:55000], 1:, 1:]
+        mnist_val = mnist_train_val[perm[55000:], 1:, 1:]
+        mnist_test = mnist_test[:, 1:, 1:]
 
         assert mnist_train.shape[1] == MNISTSResFourierTargetDataModule.IMG_SHAPE
         assert mnist_train.shape[2] == MNISTSResFourierTargetDataModule.IMG_SHAPE
@@ -77,7 +78,7 @@ def test_dataloader(self, *args, **kwargs) -> Union[DataLoader, List[DataLoader]
         return DataLoader(
             SResFourierCoefficientDataset(self.gt_ds, mag_min=self.mag_min, mag_max=self.mag_max, part='test',
                                           img_shape=MNISTSResFourierTargetDataModule.IMG_SHAPE),
-            batch_size=1)
+            batch_size=self.batch_size)
 
 
 class CelebASResFourierTargetDataModule(LightningDataModule):
@@ -134,4 +135,4 @@ def test_dataloader(self, *args, **kwargs) -> Union[DataLoader, List[DataLoader]
         return DataLoader(
             SResFourierCoefficientDataset(self.gt_ds, mag_min=self.mag_min, mag_max=self.mag_max, part='test',
                                           img_shape=self.gt_shape),
-            batch_size=1)
+            batch_size=self.batch_size)

fit/datamodules/super_res/SResFCDataset.py

@@ -35,7 +35,7 @@ def __getitem__(self, item):
 
         img_mag = 2 * (img_mag - self.mag_min) / (self.mag_max - self.mag_min) - 1
 
-        img_phi = 2 * img_phi / (2 * np.pi) - 1
+        img_phi = img_phi / np.pi
 
         img_fft = torch.stack([img_mag.flatten(), img_phi.flatten()], dim=-1)
         return img_fft, (self.mag_min.unsqueeze(-1), self.mag_max.unsqueeze(-1))

fit/datamodules/tomo_rec/TRecDataModule.py

@@ -208,7 +208,100 @@ def setup(self, stage: Optional[str] = None):
 
         self.gt_ds = get_projection_dataset(
             GroundTruthDataset(gt_train, gt_val, gt_test),
-            num_angles=self.num_angles, im_shape=450, impl='astra_cpu', inner_circle=self.inner_circle)
+            num_angles=self.num_angles, im_shape=self.gt_shape + (self.gt_shape // 2 - 7), impl='astra_cpu',
+            inner_circle=self.inner_circle)
+
+        tmp_fcds = TRecFourierCoefficientDataset(self.gt_ds, mag_min=None, mag_max=None, part='train',
+                                                 img_shape=self.gt_shape)
+        self.mag_min = tmp_fcds.mag_min
+        self.mag_max = tmp_fcds.mag_max
+
+    def train_dataloader(self, *args, **kwargs) -> DataLoader:
+        return DataLoader(
+            TRecFourierCoefficientDataset(self.gt_ds, mag_min=self.mag_min, mag_max=self.mag_max, part='train',
+                                          img_shape=self.gt_shape),
+            batch_size=self.batch_size, num_workers=1)
+
+    def val_dataloader(self, *args, **kwargs) -> Union[DataLoader, List[DataLoader]]:
+        return DataLoader(
+            TRecFourierCoefficientDataset(self.gt_ds, mag_min=self.mag_min, mag_max=self.mag_max, part='validation',
+                                          img_shape=self.gt_shape),
+            batch_size=self.batch_size, num_workers=1)
+
+    def test_dataloader(self, *args, **kwargs) -> Union[DataLoader, List[DataLoader]]:
+        return DataLoader(
+            TRecFourierCoefficientDataset(self.gt_ds, mag_min=self.mag_min, mag_max=self.mag_max, part='test',
+                                          img_shape=self.gt_shape),
+            batch_size=1)
+
+
+class CropLoDoPaBFourierTargetDataModule(LightningDataModule):
+    IMG_SHAPE = 361
+
+    def __init__(self, batch_size, gt_shape=361, num_angles=15):
+        """
+        :param root_dir:
+        :param batch_size:
+        :param num_angles:
+        """
+        super().__init__()
+        self.batch_size = batch_size
+        self.gt_shape = gt_shape
+        self.num_angles = num_angles
+        self.inner_circle = True
+        self.gt_ds = None
+        self.mean = None
+        self.std = None
+
+    def setup(self, stage: Optional[str] = None):
+        lodopab = dival.get_standard_dataset('lodopab', impl='astra_cpu')
+        assert self.gt_shape <= self.IMG_SHAPE, 'GT is larger than original images.'
+        if self.gt_shape < self.IMG_SHAPE:
+            crop_off = (362 - self.gt_shape) // 2
+            gt_train = np.array([lodopab.get_sample(i, part='train', out=(False, True))[1][crop_off:-(crop_off + 1),
+                                 crop_off:-(crop_off + 1)] for i in
+                                 range(4000)])
+            gt_val = np.array([lodopab.get_sample(i, part='validation', out=(False, True))[1][crop_off:-(crop_off + 1),
+                               crop_off:-(crop_off + 1)] for i in
+                               range(400)])
+            gt_test = np.array([lodopab.get_sample(i, part='test', out=(False, True))[1][crop_off:-(crop_off + 1),
+                                crop_off:-(crop_off + 1)] for i in
+                                range(3553)])
+        else:
+            gt_train = np.array(
+                [lodopab.get_sample(i, part='train', out=(False, True))[1][1:, 1:] for i in range(4000)])
+            gt_val = np.array(
+                [lodopab.get_sample(i, part='validation', out=(False, True))[1][1:, 1:] for i in range(400)])
+            gt_test = np.array(
+                [lodopab.get_sample(i, part='test', out=(False, True))[1][1:, 1:] for i in range(3553)])
+
+        gt_train = torch.from_numpy(gt_train)
+        gt_val = torch.from_numpy(gt_val)
+        gt_test = torch.from_numpy(gt_test)
+
+        assert gt_train.shape[1] == self.gt_shape
+        assert gt_train.shape[2] == self.gt_shape
+        x, y = torch.meshgrid(torch.arange(-self.gt_shape // 2 + 1,
+                                           self.gt_shape // 2 + 1),
+                              torch.arange(-self.gt_shape // 2 + 1,
+                                           self.gt_shape // 2 + 1))
+
+        self.mean = gt_train.mean()
+        self.std = gt_train.std()
+
+        gt_train = normalize(gt_train, self.mean, self.std)
+        gt_val = normalize(gt_val, self.mean, self.std)
+        gt_test = normalize(gt_test, self.mean, self.std)
+
+        circle = torch.sqrt(x ** 2. + y ** 2.) <= self.gt_shape // 2
+        gt_train *= circle
+        gt_val *= circle
+        gt_test *= circle
+
+        self.gt_ds = get_projection_dataset(
+            GroundTruthDataset(gt_train, gt_val, gt_test),
+            num_angles=self.num_angles, im_shape=self.gt_shape + (self.gt_shape // 2 - 7), impl='astra_cpu',
+            inner_circle=self.inner_circle)
 
         tmp_fcds = TRecFourierCoefficientDataset(self.gt_ds, mag_min=None, mag_max=None, part='train',
                                                  img_shape=self.gt_shape)

fit/datamodules/tomo_rec/TRecFCDataset.py

@@ -43,8 +43,8 @@ def __getitem__(self, item):
         sino_mag = 2 * (sino_mag - self.mag_min) / (self.mag_max - self.mag_min) - 1
         img_mag = 2 * (img_mag - self.mag_min) / (self.mag_max - self.mag_min) - 1
 
-        sino_phi = 2 * sino_phi / (2 * np.pi) - 1
-        img_phi = 2 * img_phi / (2 * np.pi) - 1
+        sino_phi = sino_phi / np.pi
+        img_phi = img_phi / np.pi
 
         sino_fft = torch.stack([sino_mag.flatten(), sino_phi.flatten()], dim=-1)
         img_fft = torch.stack([img_mag.flatten(), img_phi.flatten()], dim=-1)