Merge branch 'dev'

.gitignore

@@ -4,6 +4,7 @@ dist/*
 *egg*/*
 *stop*
 files.txt
+pymic/test/runs/*
 
 # Created by https://www.toptal.com/developers/gitignore/api/python,jupyternotebooks
 # Edit at https://www.toptal.com/developers/gitignore?templates=python,jupyternotebooks

docs/source/api.rst

@@ -9,8 +9,5 @@ API
    pymic.loss
    pymic.net
    pymic.net_run
-   pymic.net_run_nll
-   pymic.net_run_ssl
-   pymic.net_run_wsl
    pymic.transform
    pymic.util

docs/source/pymic.net.net2d.rst

@@ -52,6 +52,14 @@ pymic.net.net2d.unet2d\_dual\_branch module
    :undoc-members:
    :show-inheritance:
 
+pymic.net.net2d.unet2d\_mcnet module
+-------------------------------------------
+
+.. automodule:: pymic.net.net2d.unet2d_mcnet
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 pymic.net.net2d.unet2d\_nest module
 -----------------------------------
 

docs/source/pymic.net_run.semi_sup.rst

@@ -28,6 +28,14 @@ pymic.net\_run.semi\_sup.ssl\_cps module
    :undoc-members:
    :show-inheritance:
 
+pymic.net\_run.semi\_sup.ssl\_mcnet module
+----------------------------------------
+
+.. automodule:: pymic.net_run.semi_sup.ssl_mcnet
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 pymic.net\_run.semi\_sup.ssl\_em module
 ---------------------------------------
 

pymic/io/h5_dataset.py

@@ -8,8 +8,9 @@
 import pandas as pd
 from torch.utils.data import Dataset
 from torch.utils.data.sampler import Sampler
+from pymic import TaskType
 
-class H5DataSet(Dataset):
+class H5DataSet_backup(Dataset):
     """
     Dataset for loading images stored in h5 format. It generates 
     4D tensors with dimention order [C, D, H, W] for 3D images, and 
@@ -39,7 +40,9 @@ def __getitem__(self, idx):
         if self.transform:
             sample = self.transform(sample)
         return sample
-
+
+
+
 class TwoStreamBatchSampler(Sampler):
     """Iterate two sets of indices
 

pymic/io/image_read_write.py

@@ -23,11 +23,9 @@ def load_nifty_volume_as_4d_array(filename):
     spacing    = img_obj.GetSpacing()
     direction  = img_obj.GetDirection()
     shape = data_array.shape
-    if(len(shape) == 4):
-        assert(shape[3] == 1) 
-    elif(len(shape) == 3):
+    if(len(shape) == 3):
         data_array = np.expand_dims(data_array, axis = 0)
-    else:
+    elif(len(shape) > 4 or len(shape) < 3):
         raise ValueError("unsupported image dim: {0:}".format(len(shape)))
     output = {}
     output['data_array'] = data_array
@@ -81,25 +79,32 @@ def load_image_as_nd_array(image_name):
          image_name.endswith(".tif") or image_name.endswith(".png")):
         image_dict = load_rgb_image_as_3d_array(image_name)
     else:
-        raise ValueError("unsupported image format")
+        raise ValueError("unsupported image format: {0:}".format(image_name))
     return image_dict
 
-def save_array_as_nifty_volume(data, image_name, reference_name = None):
+def save_array_as_nifty_volume(data, image_name, reference_name = None, spacing = [1.0,1.0,1.0]):
     """
     Save a numpy array as nifty image
 
     :param data:  (numpy.ndarray) A numpy array with shape [Depth, Height, Width].
     :param image_name:  (str) The ouput file name.
     :param reference_name:  (str) File name of the reference image of which 
         meta information is used.
+    :param spacing: (list or tuple) the spacing of a volume data when `reference_name` is not provided.  
     """
     img = sitk.GetImageFromArray(data)
     if(reference_name is not None):
         img_ref = sitk.ReadImage(reference_name)
         #img.CopyInformation(img_ref)
         img.SetSpacing(img_ref.GetSpacing())
         img.SetOrigin(img_ref.GetOrigin())
-        img.SetDirection(img_ref.GetDirection())
+        direction0 = img_ref.GetDirection()
+        direction1 = img.GetDirection()
+        if(len(direction0) == len(direction1)):
+            img.SetDirection(direction0)
+    else:
+        nifty_spacing = spacing[1:] + spacing[:1]
+        img.SetSpacing(nifty_spacing)
     sitk.WriteImage(img, image_name)
 
 def save_array_as_rgb_image(data, image_name):
@@ -118,21 +123,22 @@ def save_array_as_rgb_image(data, image_name):
     img = Image.fromarray(data)
     img.save(image_name)
 
-def save_nd_array_as_image(data, image_name, reference_name = None):
+def save_nd_array_as_image(data, image_name, reference_name = None, spacing = [1.0,1.0,1.0]):
     """
     Save a 3D or 2D numpy array as medical image or RGB image
     
     :param data:  (numpy.ndarray) A numpy array with shape [3, H, W] or
         [H, W, 3] or [H, W]. 
     :param reference_name: (str) File name of the reference image of which 
         meta information is used.
+    :param spacing: (list or tuple) the spacing of a volume data when `reference_name` is not provided.  
     """
     data_dim = len(data.shape)
     assert(data_dim == 2 or data_dim == 3)
     if (image_name.endswith(".nii.gz") or image_name.endswith(".nii") or
         image_name.endswith(".mha")):
         assert(data_dim == 3)
-        save_array_as_nifty_volume(data, image_name, reference_name)
+        save_array_as_nifty_volume(data, image_name, reference_name, spacing)
 
     elif(image_name.endswith(".jpg") or image_name.endswith(".jpeg") or
          image_name.endswith(".tif") or image_name.endswith(".png")):

pymic/io/nifty_dataset.py

@@ -3,11 +3,9 @@
 
 import logging
 import os
-import torch
 import pandas as pd
 import numpy as np
-from torch.utils.data import Dataset, DataLoader
-from torchvision import transforms, utils
+from torch.utils.data import Dataset
 from pymic import TaskType
 from pymic.io.image_read_write import load_image_as_nd_array
 
@@ -38,7 +36,8 @@ def __init__(self, root_dir, csv_file, modal_num = 1,
         if('label' not in csv_keys):
             logging.warning("`label` section is not found in the csv file {0:}".format(
                 csv_file) + "\n -- This is only allowed for self-supervised learning" + 
-                "\n -- when `SelfSuperviseLabel` is used in the transform.")
+                "\n -- when `SelfSuperviseLabel` is used in the transform, or when" + 
+                "\n -- loading the unlabeled data for preprocessing.")
             self.with_label = False
         self.image_weight_idx = None
         self.pixel_weight_idx = None
@@ -52,15 +51,15 @@ def __len__(self):
 
     def __getlabel__(self, idx):
         csv_keys = list(self.csv_items.keys())        
-        label_idx = csv_keys.index('label')
-        label_name = "{0:}/{1:}".format(self.root_dir, 
-            self.csv_items.iloc[idx, label_idx])
-        label = load_image_as_nd_array(label_name)['data_array']
+        label_idx  = csv_keys.index('label')
+        label_name = self.csv_items.iloc[idx, label_idx]
+        label_name_full = "{0:}/{1:}".format(self.root_dir, label_name)
+        label = load_image_as_nd_array(label_name_full)['data_array']
         if(self.task ==  TaskType.SEGMENTATION):
             label = np.asarray(label, np.int32)
         elif(self.task == TaskType.RECONSTRUCTION):
             label = np.asarray(label, np.float32)
-        return label
+        return label, label_name
 
     def __get_pixel_weight__(self, idx):
         weight_name = "{0:}/{1:}".format(self.root_dir, 
@@ -69,6 +68,25 @@ def __get_pixel_weight__(self, idx):
         weight = np.asarray(weight, np.float32)
         return weight        
 
+    # def __getitem__(self, idx):
+    #     sample_name = self.csv_items.iloc[idx, 0]
+    #     h5f = h5py.File(self.root_dir + '/' +  sample_name, 'r')
+    #     image = np.asarray(h5f['image'][:], np.float32)
+
+    #     # this a temporaory process, will be delieted later
+    #     if(len(image.shape) == 3 and image.shape[0] > 1):
+    #         image = np.expand_dims(image, 0)
+    #     sample = {'image': image, 'names':sample_name}
+
+    #     if('label' in h5f):
+    #         label = np.asarray(h5f['label'][:], np.uint8)
+    #         if(len(label.shape) == 3 and label.shape[0] > 1):
+    #             label = np.expand_dims(label, 0)
+    #         sample['label'] = label
+    #     if self.transform:
+    #         sample = self.transform(sample)
+    #     return sample
+
     def __getitem__(self, idx):
         names_list, image_list = [], []
         for i in range (self.modal_num):
@@ -80,12 +98,14 @@ def __getitem__(self, idx):
             image_list.append(image_data)
         image = np.concatenate(image_list, axis = 0)
         image = np.asarray(image, np.float32)    
-        sample = {'image': image, 'names' : names_list[0], 
+
+        sample = {'image': image, 'names' : names_list, 
                  'origin':image_dict['origin'],
                  'spacing': image_dict['spacing'],
                  'direction':image_dict['direction']}
         if (self.with_label):   
-            sample['label'] = self.__getlabel__(idx) 
+            sample['label'], label_name = self.__getlabel__(idx) 
+            sample['names'].append(label_name)
             assert(image.shape[1:] == sample['label'].shape[1:])
         if (self.image_weight_idx is not None):
             sample['image_weight'] = self.csv_items.iloc[idx, self.image_weight_idx]

pymic/loss/seg/abstract.py

@@ -16,9 +16,20 @@ class AbstractSegLoss(nn.Module):
     def __init__(self, params = None):
         super(AbstractSegLoss, self).__init__()
         if(params is None):
-            self.softmax = True
+            self.acti_func = 'softmax'
         else:
-            self.softmax = params.get('loss_softmax', True)
+            self.acti_func = params.get('loss_acti_func', 'softmax')
+
+    def get_activated_prediction(self, p, acti_func = 'softmax'):
+        if(acti_func == "softmax"):
+            p = nn.Softmax(dim = 1)(p)
+        elif(acti_func == "tanh"):
+            p = nn.Tanh()(p)
+        elif(acti_func == "sigmoid"):
+            p = nn.Sigmoid()(p)
+        else:
+            raise ValueError("activation for output is not supported: {0:}".format(acti_func))
+        return p 
 
     def forward(self, loss_input_dict):
         """

pymic/loss/seg/ce.py

@@ -13,8 +13,10 @@ class CrossEntropyLoss(AbstractSegLoss):
     The parameters should be written in the `params` dictionary, and it has the
     following fields:
 
-    :param `loss_softmax`: (optional, bool) 
-        Apply softmax to the prediction of network or not. Default is True.
+    :param `loss_acti_func`: (optional, string) 
+        Apply an activation function to the prediction of network or not, for example,
+        'softmax' for image segmentation tasks, 'tanh' for reconstruction tasks, and None
+        means no activation is used. 
     """
     def __init__(self, params = None):
         super(CrossEntropyLoss, self).__init__(params)
@@ -27,8 +29,9 @@ def forward(self, loss_input_dict):
 
         if(isinstance(predict, (list, tuple))):
             predict = predict[0]
-        if(self.softmax):
-            predict = nn.Softmax(dim = 1)(predict)
+        if(self.acti_func is not None):
+            predict = self.get_activated_prediction(predict, self.acti_func)
+
         predict = reshape_tensor_to_2D(predict)
         soft_y  = reshape_tensor_to_2D(soft_y)
 
@@ -74,8 +77,8 @@ def forward(self, loss_input_dict):
 
         if(isinstance(predict, (list, tuple))):
             predict = predict[0]
-        if(self.softmax):
-            predict = nn.Softmax(dim = 1)(predict)
+        if(self.acti_func is not None):
+            predict = self.get_activated_prediction(predict, self.acti_func)
         predict = reshape_tensor_to_2D(predict)
         soft_y  = reshape_tensor_to_2D(soft_y)
         gce     = (1.0 - torch.pow(predict, self.q)) / self.q * soft_y

pymic/loss/seg/dice.py

@@ -25,8 +25,8 @@ def forward(self, loss_input_dict):
 
         if(isinstance(predict, (list, tuple))):
             predict = predict[0]
-        if(self.softmax):
-            predict = nn.Softmax(dim = 1)(predict)
+        if(self.acti_func is not None):
+            predict = self.get_activated_prediction(predict, self.acti_func)
         predict = reshape_tensor_to_2D(predict)
         soft_y  = reshape_tensor_to_2D(soft_y) 
         if(pix_w is not None):
@@ -52,8 +52,8 @@ def forward(self, loss_input_dict):
 
         if(isinstance(predict, (list, tuple))):
             predict = predict[0]
-        if(self.softmax):
-            predict = nn.Softmax(dim = 1)(predict)
+        if(self.acti_func is not None):
+            predict = self.get_activated_prediction(predict, self.acti_func)
         predict = 1.0 - predict[:, :1, :, :, :]
         soft_y  = 1.0 -  soft_y[:, :1, :, :, :]
         predict = reshape_tensor_to_2D(predict)
@@ -76,8 +76,8 @@ def forward(self, loss_input_dict):
 
         if(isinstance(predict, (list, tuple))):
             predict = predict[0]
-        if(self.softmax):
-            predict = nn.Softmax(dim = 1)(predict)
+        if(self.acti_func is not None):
+            predict = self.get_activated_prediction(predict, self.acti_func)
         predict = reshape_tensor_to_2D(predict)
         soft_y  = reshape_tensor_to_2D(soft_y) 
         num_class  = list(predict.size())[1]
@@ -115,8 +115,8 @@ def forward(self, loss_input_dict):
 
         if(isinstance(predict, (list, tuple))):
             predict = predict[0]
-        if(self.softmax):
-            predict = nn.Softmax(dim = 1)(predict)
+        if(self.acti_func is not None):
+            predict = self.get_activated_prediction(predict, self.acti_func)
         predict = reshape_tensor_to_2D(predict)
         soft_y  = reshape_tensor_to_2D(soft_y) 
 
@@ -149,8 +149,8 @@ def forward(self, loss_input_dict):
 
         if(isinstance(predict, (list, tuple))):
             predict = predict[0]
-        if(self.softmax):
-            predict = nn.Softmax(dim = 1)(predict)
+        if(self.acti_func is not None):
+            predict = self.get_activated_prediction(predict, self.acti_func)
         predict = reshape_tensor_to_2D(predict)
         soft_y  = reshape_tensor_to_2D(soft_y) 
 

pymic/loss/seg/exp_log.py

@@ -32,8 +32,8 @@ def forward(self, loss_input_dict):
 
         if(isinstance(predict, (list, tuple))):
             predict = predict[0]
-        if(self.softmax):
-            predict = nn.Softmax(dim = 1)(predict)
+        if(self.acti_func is not None):
+            predict = self.get_activated_prediction(predict, self.acti_func)
         predict = reshape_tensor_to_2D(predict)
         soft_y  = reshape_tensor_to_2D(soft_y)
 

pymic/loss/seg/mse.py

@@ -19,8 +19,8 @@ def forward(self, loss_input_dict):
 
         if(isinstance(predict, (list, tuple))):
             predict = predict[0]
-        if(self.softmax):
-            predict = nn.Softmax(dim = 1)(predict)
+        if(self.acti_func is not None):
+            predict = self.get_activated_prediction(predict, self.acti_func)
         mse  = torch.square(predict - soft_y)
         mse  = torch.mean(mse) 
         return mse 
@@ -44,8 +44,8 @@ def forward(self, loss_input_dict):
 
         if(isinstance(predict, (list, tuple))):
             predict = predict[0]
-        if(self.softmax):
-            predict = nn.Softmax(dim = 1)(predict)
+        if(self.acti_func is not None):
+            predict = self.get_activated_prediction(predict, self.acti_func)
         mae = torch.abs(predict - soft_y)
         if(weight is None):
             mae = torch.mean(mae)