Dev

README.md

@@ -47,10 +47,10 @@ Run the following command to install the latest released version of PyMIC:
 ```bash
 pip install PYMIC
 ```
-To install a specific version of PYMIC such as 0.3.0, run:
+To install a specific version of PYMIC such as 0.3.1, run:
 
 ```bash
-pip install PYMIC==0.3.0
+pip install PYMIC==0.3.1
 ```
 Alternatively, you can download the source code for the latest version. Run the following command to compile and install:
 

pymic/loss/cls/basic.py

@@ -65,10 +65,7 @@ def forward(self, loss_input_dict):
         labels  = loss_input_dict['ground_truth'][:, None] # reshape to N, 1
         softmax = nn.Softmax(dim = 1)
         predict = softmax(predict)
-        num_class  = list(predict.size())[1]
-        data_type = 'float' if(predict.dtype is torch.float32) else 'double'
-        soft_y = get_soft_label(labels, num_class, data_type)
-        loss = self.l1_loss(predict, soft_y)
+        loss = self.l1_loss(predict, labels)
         return loss
 
 class MSELoss(AbstractClassificationLoss):
@@ -84,10 +81,7 @@ def forward(self, loss_input_dict):
         labels  = loss_input_dict['ground_truth'][:, None] # reshape to N, 1
         softmax = nn.Softmax(dim = 1)
         predict = softmax(predict)
-        num_class  = list(predict.size())[1]
-        data_type = 'float' if(predict.dtype is torch.float32) else 'double'
-        soft_y = get_soft_label(labels, num_class, data_type)
-        loss = self.mse_loss(predict, soft_y)
+        loss = self.mse_loss(predict, labels)
         return loss
 
 class NLLLoss(AbstractClassificationLoss):

pymic/loss/seg/ce.py

@@ -18,7 +18,6 @@ class CrossEntropyLoss(AbstractSegLoss):
     """
     def __init__(self, params = None):
         super(CrossEntropyLoss, self).__init__(params)
-
 
     def forward(self, loss_input_dict):
         predict = loss_input_dict['prediction']

pymic/loss/seg/ssl.py

@@ -6,8 +6,9 @@
 import torch.nn as nn
 import numpy as np 
 from pymic.loss.seg.util import reshape_tensor_to_2D
+from pymic.loss.seg.abstract import AbstractSegLoss
 
-class EntropyLoss(nn.Module):
+class EntropyLoss(AbstractSegLoss):
     """
     Entropy Minimization for segmentation tasks.
     The parameters should be written in the `params` dictionary, and it has the
@@ -43,7 +44,7 @@ def forward(self, loss_input_dict):
         avg_ent = torch.mean(entropy)
         return avg_ent
 
-class TotalVariationLoss(nn.Module):
+class TotalVariationLoss(AbstractSegLoss):
     """
     Total Variation Loss for segmentation tasks.
     The parameters should be written in the `params` dictionary, and it has the

pymic/net/cls/torch_pretrained_net.py

@@ -75,7 +75,7 @@ def __init__(self, params):
     def get_parameters_to_update(self):
         if(self.update_mode == "all"):
             return self.net.parameters()
-        elif(self.update_layers == "last"):
+        elif(self.update_mode == "last"):
             params = self.net.fc.parameters()
             if(self.in_chns !=3):
                 # combining the two iterables into a single one 
@@ -119,7 +119,7 @@ def get_parameters_to_update(self):
             params = self.net.classifier[-1].parameters()
             if(self.in_chns !=3):
                 params = itertools.chain()
-                for pram in [self.net.classifier[-1].parameters(), self.net.net.features[0].parameters()]:
+                for pram in [self.net.classifier[-1].parameters(), self.net.features[0].parameters()]:
                     params = itertools.chain(params, pram)
             return  params
         else:
@@ -138,7 +138,7 @@ class MobileNetV2(BuiltInNet):
         as well as the first layer when `input_chns` is not 3.
     """
     def __init__(self, params):
-        super(MobileNetV2, self).__init__()
+        super(MobileNetV2, self).__init__(params)
         self.net = models.mobilenet_v2(pretrained = self.pretrain)
 
         # replace the last layer 
@@ -157,7 +157,7 @@ def get_parameters_to_update(self):
             params = self.net.classifier[-1].parameters()
             if(self.in_chns !=3):
                 params = itertools.chain()
-                for pram in [self.net.classifier[-1].parameters(), self.net.net.features[0][0].parameters()]:
+                for pram in [self.net.classifier[-1].parameters(), self.net.features[0][0].parameters()]:
                     params = itertools.chain(params, pram)
             return  params
         else:

pymic/net/net_dict_seg.py

@@ -4,7 +4,6 @@
 
 * UNet2D :mod:`pymic.net.net2d.unet2d.UNet2D`
 * UNet2D_DualBranch :mod:`pymic.net.net2d.unet2d_dual_branch.UNet2D_DualBranch`
-* UNet2D_URPC :mod:`pymic.net.net2d.unet2d_urpc.UNet2D_URPC`
 * UNet2D_CCT  :mod:`pymic.net.net2d.unet2d_cct.UNet2D_CCT`
 * UNet2D_ScSE  :mod:`pymic.net.net2d.unet2d_scse.UNet2D_ScSE`
 * AttentionUNet2D  :mod:`pymic.net.net2d.unet2d_attention.AttentionUNet2D`
@@ -17,7 +16,6 @@
 from __future__ import print_function, division
 from pymic.net.net2d.unet2d import UNet2D
 from pymic.net.net2d.unet2d_dual_branch import UNet2D_DualBranch
-from pymic.net.net2d.unet2d_urpc import UNet2D_URPC
 from pymic.net.net2d.unet2d_cct import UNet2D_CCT
 from pymic.net.net2d.cople_net import COPLENet
 from pymic.net.net2d.unet2d_attention import AttentionUNet2D
@@ -30,7 +28,6 @@
 SegNetDict = {
 	'UNet2D': UNet2D,
 	'UNet2D_DualBranch': UNet2D_DualBranch,
-	'UNet2D_URPC': UNet2D_URPC,
 	'UNet2D_CCT': UNet2D_CCT,
 	'COPLENet': COPLENet,
 	'AttentionUNet2D': AttentionUNet2D,