update losses, metrics

bioMONAI/_modidx.py

@@ -54,15 +54,14 @@
                                  'bioMONAI.losses.DiceLoss.__init__': ('losses.html#diceloss.__init__', 'bioMONAI/losses.py'),
                                  'bioMONAI.losses.DiceLoss.forward': ('losses.html#diceloss.forward', 'bioMONAI/losses.py'),
                                  'bioMONAI.losses.FRCLoss': ('losses.html#frcloss', 'bioMONAI/losses.py'),
-                                 'bioMONAI.losses.FRCM': ('losses.html#frcm', 'bioMONAI/losses.py'),
-                                 'bioMONAI.losses.SSIM': ('losses.html#ssim', 'bioMONAI/losses.py'),
                                  'bioMONAI.losses.get_fourier_ring_correlations': ( 'losses.html#get_fourier_ring_correlations',
                                                                                     'bioMONAI/losses.py'),
                                  'bioMONAI.losses.get_radial_masks': ('losses.html#get_radial_masks', 'bioMONAI/losses.py'),
                                  'bioMONAI.losses.radial_mask': ('losses.html#radial_mask', 'bioMONAI/losses.py'),
                                  'bioMONAI.losses.seventh_fourier_ring_correlation': ( 'losses.html#seventh_fourier_ring_correlation',
                                                                                        'bioMONAI/losses.py')},
-            'bioMONAI.metrics': {'bioMONAI.metrics.foo': ('metrics.html#foo', 'bioMONAI/metrics.py')},
+            'bioMONAI.metrics': { 'bioMONAI.metrics.FRCM': ('metrics.html#frcm', 'bioMONAI/metrics.py'),
+                                  'bioMONAI.metrics.SSIM': ('metrics.html#ssim', 'bioMONAI/metrics.py')},
             'bioMONAI.nets': { 'bioMONAI.nets.DnCNN': ('nets.html#dncnn', 'bioMONAI/nets.py'),
                                'bioMONAI.nets.DnCNN.__init__': ('nets.html#dncnn.__init__', 'bioMONAI/nets.py'),
                                'bioMONAI.nets.DnCNN.forward': ('nets.html#dncnn.forward', 'bioMONAI/nets.py'),

bioMONAI/core.py

@@ -18,7 +18,7 @@
 from torch import from_numpy as torch_from_numpy
 
 # %% ../nbs/00_core.ipynb 6
-from fastai.vision.all import BypassNewMeta, DisplayedTransform
+from fastai.vision.all import BypassNewMeta, DisplayedTransform, store_attr
 from fastai.data.all import delegates, hasattrs, Path, List, L, typedispatch
 
 # %% ../nbs/00_core.ipynb 8

bioMONAI/losses.py

@@ -1,14 +1,22 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/03_losses.ipynb.
 
 # %% auto 0
-__all__ = ['SSIMMetric', 'CombinedLoss', 'DiceLoss', 'radial_mask', 'get_radial_masks', 'get_fourier_ring_correlations',
-           'FRCLoss', 'seventh_fourier_ring_correlation', 'SSIM', 'FRCM']
+__all__ = ['CombinedLoss', 'DiceLoss', 'radial_mask', 'get_radial_masks', 'get_fourier_ring_correlations', 'FRCLoss',
+           'seventh_fourier_ring_correlation']
 
 # %% ../nbs/03_losses.ipynb 4
-from fastai.vision.all import *
+from .core import store_attr
 
 # %% ../nbs/03_losses.ipynb 5
+import numpy as np
+
 from monai.losses import SSIMLoss
+import torch.nn as nn
+from torch import abs, sqrt, div, sigmoid, complex64, where, isinf, zeros_like, real, isnan
+from torch.fft import fftshift
+from torch.fft import fft2
+
+from .core import torch_from_numpy
 
 # %% ../nbs/03_losses.ipynb 8
 class CombinedLoss:
@@ -58,7 +66,7 @@ def __init__(self, smooth=1):
     def forward(self, inputs, targets):
 
         # Make sure the inputs are probabilities
-        inputs = torch.sigmoid(inputs)
+        inputs = sigmoid(inputs)
 
         # Flatten tensors
         inputs = inputs.view(-1)
@@ -175,23 +183,23 @@ def get_fourier_ring_correlations(image1, image2):
     freq_nyq = len(spatial_frequency)
 
     # Transform tensor to complex
-    image1 = image1.to(torch.complex64)
-    image2 = image2.to(torch.complex64)
+    image1 = image1.to(complex64)
+    image2 = image2.to(complex64)
 
     # Transofrm array dimensions to (freq_nyq, width. height)
     image1 = image1.unsqueeze(0).repeat(freq_nyq, 1, 1)
     image2 = image2.unsqueeze(0).repeat(freq_nyq, 1, 1)
 
     # Convert spatial frequency and radial masks to torch.tensor
-    spatial_frequency = torch.from_numpy(spatial_frequency)
-    radial_masks = torch.from_numpy(radial_masks)
+    spatial_frequency = torch_from_numpy(spatial_frequency)
+    radial_masks = torch_from_numpy(radial_masks)
 
     # Transform tensor to complex
-    radial_masks = radial_masks.to(torch.complex64)
+    radial_masks = radial_masks.to(complex64)
 
     # Compute fourier transform
-    fft_image1 = torch.fft.fftshift(torch.fft.fft2(image1))
-    fft_image2 = torch.fft.fftshift(torch.fft.fft2(image2))
+    fft_image1 = fftshift(fft2(image1))
+    fft_image2 = fftshift(fft2(image2))
 
     # Get elements only in the ring
     t1 = fft_image1 * radial_masks
@@ -201,19 +209,19 @@ def get_fourier_ring_correlations(image1, image2):
     t2_conj = t2.conj()
 
     # Numerator
-    numerador = torch.abs(torch.real((t1 * t2_conj).sum(dim=(1,2))))
+    numerador = abs(real((t1 * t2_conj).sum(dim=(1,2))))
 
     # Denominator    
-    denominador_1 = ((torch.abs(t1) * torch.abs(t1)).sum(dim=(1,2)))
-    denominador_2 = ((torch.abs(t2) * torch.abs(t2)).sum(dim=(1,2)))     
-    denominador = torch.sqrt(denominador_1 * denominador_2)
+    denominador_1 = ((abs(t1) * abs(t1)).sum(dim=(1,2)))
+    denominador_2 = ((abs(t2) * abs(t2)).sum(dim=(1,2)))     
+    denominador = sqrt(denominador_1 * denominador_2)
 
     # Fourier shell correlation
-    FRC = torch.div(numerador, denominador)
+    FRC = div(numerador, denominador)
 
     # Remove possible inf and NaN.
-    FRC = torch.where(torch.isinf(FRC), torch.zeros_like(FRC), FRC)  # inf
-    FRC = torch.where(torch.isnan(FRC), torch.zeros_like(FRC), FRC)  # NaN
+    FRC = where(isinf(FRC), zeros_like(FRC), FRC)  # inf
+    FRC = where(isnan(FRC), zeros_like(FRC), FRC)  # NaN
 
     return FRC , spatial_frequency
 
@@ -271,37 +279,3 @@ def exponential_func(x, a, b, c):
     cutoff_frequency = (exponential_func((1/7), *params))
 
     return cutoff_frequency
-
-# %% ../nbs/03_losses.ipynb 22
-def SSIM(x, y, spatial_dims=2):
-    return 1 - SSIMLoss(spatial_dims)(x,y)
-
-SSIMMetric = AvgMetric(SSIM)
-
-# %% ../nbs/03_losses.ipynb 23
-def FRCM(image1, image2):
-
-
-    """
-    Compute the area under the Fourier Ring Correlation (FRC) curve between two images.
-
-    #### Args:
-        - image1 (torch.Tensor): The first input image.
-        - image2 (torch.Tensor): The second input image.
-
-    #### Returns:
-        - float: The area under the FRC curve.
-    """
-
-    # Calculate the Fourier Ring Correlation and spatial frequency
-    FRC, spatial_frequency = get_fourier_ring_correlations(image1, image2)
-
-    # Convert to numpy
-    FRC = FRC.numpy()
-    spatial_frequency = spatial_frequency.numpy()
-
-    # Compute the area under the curve using trapezoidal integration
-    area = np.trapz(FRC, spatial_frequency)
-
-    return area
-

bioMONAI/metrics.py

@@ -1,7 +1,43 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/06_metrics.ipynb.
 
 # %% auto 0
-__all__ = ['foo']
+__all__ = ['SSIMMetric', 'SSIM', 'FRCM']
 
 # %% ../nbs/06_metrics.ipynb 3
-def foo(): pass
+from numpy import trapz
+from fastai.vision.all import AvgMetric
+from .losses import SSIMLoss, get_fourier_ring_correlations
+
+# %% ../nbs/06_metrics.ipynb 4
+def SSIM(x, y, spatial_dims=2):
+    return 1 - SSIMLoss(spatial_dims)(x,y)
+
+SSIMMetric = AvgMetric(SSIM)
+
+# %% ../nbs/06_metrics.ipynb 5
+def FRCM(image1, image2):
+
+
+    """
+    Compute the area under the Fourier Ring Correlation (FRC) curve between two images.
+
+    #### Args:
+        - image1 (torch.Tensor): The first input image.
+        - image2 (torch.Tensor): The second input image.
+
+    #### Returns:
+        - float: The area under the FRC curve.
+    """
+
+    # Calculate the Fourier Ring Correlation and spatial frequency
+    FRC, spatial_frequency = get_fourier_ring_correlations(image1, image2)
+
+    # Convert to numpy
+    FRC = FRC.numpy()
+    spatial_frequency = spatial_frequency.numpy()
+
+    # Compute the area under the curve using trapezoidal integration
+    area = trapz(FRC, spatial_frequency)
+
+    return area
+

nbs/00_core.ipynb

@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,12 +65,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "#| export\n",
-    "from fastai.vision.all import BypassNewMeta, DisplayedTransform\n",
+    "from fastai.vision.all import BypassNewMeta, DisplayedTransform, store_attr\n",
     "from fastai.data.all import delegates, hasattrs, Path, List, L, typedispatch"
    ]
   },
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [