reqs added

app.py

@@ -4,5 +4,10 @@ import numpy as np
 import requests
 from PIL import Image
 import torch 
+from inference import predict, random_sample
 
-    
+
+def main(): 
+    gr.Interface(fn=predict,
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Image(type="pil"),).launch()

util/skeletonize.py

@@ -1,486 +0,0 @@
-"""
-Courtesy of Martin Mentan: 
-
-Works Cited
-Menten, Martin J., et al. ‘A Skeletonization Algorithm for Gradient-Based Optimization’. 
-Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023.
-
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class Skeletonize(torch.nn.Module):
-    """
-    Class based on PyTorch's Module class to skeletonize two- or three-dimensional input images
-    while being fully compatible with PyTorch's autograd automatic differention engine as proposed in [1].
-
-    Attributes:
-        propabilistic: a Boolean that indicates whether the input image should be binarized using
-                       the reparametrization trick and straight-through estimator.
-                       It should always be set to True if non-binary inputs are being provided.
-        beta: scale of added logistic noise during the reparametrization trick. If too small, there will not be any learning via
-              gradient-based optimization; if too large, the learning is very slow.
-        tau: Boltzmann temperature for reparametrization trick.
-        simple_point_detection: decides whether simple points should be identified using Boolean characterization of their 26-neighborhood (Boolean) [2]
-                                or by checking whether the Euler characteristic changes under their deletion (EulerCharacteristic) [3].
-        num_iter: number of iterations that each include one end-point check, eight checks for simple points and eight subsequent deletions.
-                  The number of iterations should be tuned to the type of input image.
-
-    [1] Martin J. Menten et al. A skeletonization algorithm for gradient-based optimization.
-        Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023.
-    [2] Gilles Bertrand. A boolean characterization of three- dimensional simple points.
-        Pattern recognition letters, 17(2):115-124, 1996.
-    [3] Steven Lobregt et al. Three-dimensional skeletonization:principle and algorithm.
-        IEEE Transactions on pattern analysis and machine intelligence, 2(1):75-77, 1980.
-    """
-
-    def __init__(self, probabilistic=True, beta=0.33, tau=1.0, simple_point_detection='Boolean', num_iter=5):
-
-        super(Skeletonize, self).__init__()
-        
-        self.probabilistic = probabilistic
-        self.tau = tau
-        self.beta = beta
-
-        self.num_iter = num_iter
-        self.endpoint_check = self._single_neighbor_check
-        if simple_point_detection == 'Boolean':
-            self.simple_check = self._boolean_simple_check
-        elif simple_point_detection == 'EulerCharacteristic':
-            self.simple_check = self._euler_characteristic_simple_check
-        else:
-            raise Exception()
-
-
-    def forward(self, img):
-
-        img = self._prepare_input(img)
-
-        if self.probabilistic:
-            img = self._stochastic_discretization(img)
-
-        for current_iter in range(self.num_iter):
-
-            # At each iteration create a new map of the end-points
-            is_endpoint = self.endpoint_check(img)
-
-            # Sub-iterate through eight different subfields
-            x_offsets = [0, 1, 0, 1, 0, 1, 0, 1]
-            y_offsets = [0, 0, 1, 1, 0, 0, 1, 1]
-            z_offsets = [0, 0, 0, 0, 1, 1, 1, 1]
-
-            for x_offset, y_offset, z_offset in zip(x_offsets, y_offsets, z_offsets):
-
-                # At each sub-iteration detect all simple points and delete all simple points that are not end-points
-                is_simple = self.simple_check(img[:, :, x_offset:, y_offset:, z_offset:])
-                deletion_candidates = is_simple * (1 - is_endpoint[:, :, x_offset::2, y_offset::2, z_offset::2])
-                img[:, :, x_offset::2, y_offset::2, z_offset::2] = torch.min(img[:, :, x_offset::2, y_offset::2, z_offset::2].clone(), 1 - deletion_candidates)
-
-        img = self._prepare_output(img)
-
-        return img
-
-
-
-    def _prepare_input(self, img):
-        """
-        Function to check that the input image is compatible with the subsequent calculations.
-        Only two- and three-dimensional images with values between 0 and 1 are supported.
-        If the input image is two-dimensional then it is converted into a three-dimensional one for further processing.
-        """
-
-        if img.dim() == 5:
-            self.expanded_dims = False
-        elif img.dim() == 4:
-            self.expanded_dims = True
-            img = img.unsqueeze(2)
-        else:
-            raise Exception("Only two-or three-dimensional images (tensor dimensionality of 4 or 5) are supported as input.")
-
-        if img.shape[2] == 2 or img.shape[3] == 2 or img.shape[4] == 2 or img.shape[3] == 1 or img.shape[4] == 1:
-            raise Exception()
-
-        if img.min() < 0.0 or img.max() > 1.0:
-            raise Exception("Image values must lie between 0 and 1.")
-
-        img = F.pad(img, (1, 1, 1, 1, 1, 1), value=0)
-        
-        return img
-
-
-    def _stochastic_discretization(self, img):
-        """
-        Function to binarize the image so that it can be processed by our skeletonization method.
-        In order to remain compatible with backpropagation we utilize the reparameterization trick and a straight-through estimator.
-        """
-
-        alpha = (img + 1e-8) / (1.0 - img + 1e-8)
-
-        uniform_noise = torch.rand_like(img)
-        uniform_noise = torch.empty_like(img).uniform_(1e-8, 1 - 1e-8)
-        logistic_noise = (torch.log(uniform_noise) - torch.log(1 - uniform_noise))
-
-        img = torch.sigmoid((torch.log(alpha) + logistic_noise * self.beta) / self.tau)
-        img = (img.detach() > 0.5).float() - img.detach() + img
-
-        return img
-
-
-    def _single_neighbor_check(self, img):
-        """
-        Function that characterizes points as endpoints if they have a single neighbor or no neighbor at all.
-        """
-
-        img = F.pad(img, (1, 1, 1, 1, 1, 1))
-
-        # Check that number of ones in twentysix-neighborhood is exactly 0 or 1
-        K = torch.tensor([[[1.0, 1.0, 1.0],
-                           [1.0, 1.0, 1.0],
-                           [1.0, 1.0, 1.0]],
-                          [[1.0, 1.0, 1.0],
-                           [1.0, 0.0, 1.0],
-                           [1.0, 1.0, 1.0]],
-                          [[1.0, 1.0, 1.0],
-                           [1.0, 1.0, 1.0],
-                           [1.0, 1.0, 1.0]]], device=img.device).view(1, 1, 3, 3, 3)
-
-        num_twentysix_neighbors = F.conv3d(img, K)
-        condition1 = F.hardtanh(-(num_twentysix_neighbors - 2), min_val=0, max_val=1) # 1 or fewer neigbors
-        
-        return condition1
-
-
-    def _boolean_simple_check(self, img):
-        """
-        Function that identifies simple points using Boolean conditions introduced by Bertrand et al. [1].
-        Each Boolean conditions can be assessed via convolutions with a limited number of pre-defined kernels.
-        It total, four conditions are checked. If any one is fulfilled, the point is deemed simple.
-
-        [1] Gilles Bertrand. A boolean characterization of three- dimensional simple points.
-            Pattern recognition letters, 17(2):115-124, 1996.
-        """
-
-        img = F.pad(img, (1, 1, 1, 1, 1, 1), value=0)
-
-        # Condition 1: number of zeros in the six-neighborhood is exactly 1
-        K_N6 = torch.tensor([[[0.0, 0.0, 0.0],
-                              [0.0, 1.0, 0.0],
-                              [0.0, 0.0, 0.0]],
-                             [[0.0, 1.0, 0.0],
-                              [1.0, 0.0, 1.0],
-                              [0.0, 1.0, 0.0]],
-                             [[0.0, 0.0, 0.0],
-                              [0.0, 1.0, 0.0],
-                              [0.0, 0.0, 0.0]]], device=img.device).view(1, 1, 3, 3, 3)
-
-        num_six_neighbors = F.conv3d(1 - img, K_N6, stride=2)
-
-        subcondition1a = F.hardtanh(num_six_neighbors, min_val=0, max_val=1) # 1 or more neighbors
-        subcondition1b = F.hardtanh(-(num_six_neighbors - 2), min_val=0, max_val=1) # 1 or fewer neighbors
-        
-        condition1 = subcondition1a * subcondition1b
-
-
-        # Condition 2: number of ones in twentysix-neighborhood is exactly 1
-        K_N26 = torch.tensor([[[1.0, 1.0, 1.0],
-                               [1.0, 1.0, 1.0],
-                               [1.0, 1.0, 1.0]],
-                              [[1.0, 1.0, 1.0],
-                               [1.0, 0.0, 1.0],
-                               [1.0, 1.0, 1.0]],
-                              [[1.0, 1.0, 1.0],
-                               [1.0, 1.0, 1.0],
-                               [1.0, 1.0, 1.0]]], device=img.device).view(1, 1, 3, 3, 3)
-
-        num_twentysix_neighbors = F.conv3d(img, K_N26, stride=2)
-
-        subcondition2a = F.hardtanh(num_twentysix_neighbors, min_val=0, max_val=1) # 1 or more neighbors
-        subcondition2b = F.hardtanh(-(num_twentysix_neighbors - 2), min_val=0, max_val=1) # 1 or fewer neigbors
-        
-        condition2 =  subcondition2a * subcondition2b
-
-
-        # Condition 3: Number of ones in eighteen-neigborhood exactly 1...
-        K_N18 = torch.tensor([[[0.0, 1.0, 0.0],
-                               [1.0, 1.0, 1.0],
-                               [0.0, 1.0, 0.0]],
-                              [[1.0, 1.0, 1.0],
-                               [1.0, 0.0, 1.0],
-                               [1.0, 1.0, 1.0]],
-                              [[0.0, 1.0, 0.0],
-                               [1.0, 1.0, 1.0],
-                               [0.0, 1.0, 0.0]]], device=img.device).view(1, 1, 3, 3, 3)
-
-        num_eighteen_neighbors = F.conv3d(img, K_N18, stride=2)
-
-        subcondition3a = F.hardtanh(num_eighteen_neighbors, min_val=0, max_val=1) # 1 or more neighbors
-        subcondition3b = F.hardtanh(-(num_eighteen_neighbors - 2), min_val=0, max_val=1) # 1 or fewer neigbors
-
-        # ... and cell configration B26 does not exist
-        K_B26 =  torch.tensor([[[1.0, -1.0, 0.0],
-                                [-1.0, -1.0, 0.0],
-                                [0.0, 0.0, 0.0]],
-                               [[-1.0, -1.0, 0.0],
-                                [-1.0, 0.0, 0.0],
-                                [0.0, 0.0, 0.0]],
-                               [[0.0, 0.0, 0.0],
-                                [0.0, 0.0, 0.0],
-                                [0.0, 0.0, 0.0]]], device=img.device).view(1, 1, 3, 3, 3)
-
-        B26_1_present = F.relu(F.conv3d(2.0 * img - 1.0, K_B26, stride=2) - 6)
-        B26_2_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[2]), stride=2) - 6)
-        B26_3_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[3]), stride=2) - 6)
-        B26_4_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[4]), stride=2) - 6)
-        B26_5_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[2, 3]), stride=2) - 6)
-        B26_6_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[2, 4]), stride=2) - 6)
-        B26_7_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[3, 4]), stride=2) - 6)
-        B26_8_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[2, 3, 4]), stride=2) - 6)
-        num_B26_cells = B26_1_present + B26_2_present + B26_3_present + B26_4_present + B26_5_present + B26_6_present + B26_7_present + B26_8_present
-
-        subcondition3c = F.hardtanh(-(num_B26_cells - 1), min_val=0, max_val=1)
-
-        condition3 = subcondition3a * subcondition3b * subcondition3c
-
-
-        # Condition 4: cell configuration A6 does not exist...
-        K_A6 = torch.tensor([[[0.0, 1.0, 0.0],
-                              [1.0, -1.0, 1.0],
-                              [0.0, 1.0, 0.0]],
-                             [[0.0, 0.0, 0.0],
-                              [0.0, 0.0, 0.0],
-                              [0.0, 0.0, 0.0]],
-                             [[0.0, 0.0, 0.0],
-                              [0.0, 0.0, 0.0],
-                              [0.0, 0.0, 0.0]]], device=img.device).view(1, 1, 3, 3, 3)
-
-        A6_1_present = F.relu(F.conv3d(2.0 * img - 1.0, K_A6, stride=2) - 4)
-        A6_2_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(K_A6, dims=[2, 3]), stride=2) - 4)
-        A6_3_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(K_A6, dims=[2, 4]), stride=2) - 4)
-        A6_4_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_A6, dims=[2]), stride=2) - 4)
-        A6_5_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.flip(K_A6, dims=[2]), dims=[2, 3]), stride=2) - 4)
-        A6_6_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.flip(K_A6, dims=[2]), dims=[2, 4]), stride=2) - 4)
-        num_A6_cells = A6_1_present + A6_2_present + A6_3_present + A6_4_present + A6_5_present + A6_6_present
-
-        subcondition4a = F.hardtanh(-(num_A6_cells - 1), min_val=0, max_val=1)
-
-        # ... and cell configuration B26 does not exist...
-        K_B26 =  torch.tensor([[[1.0, -1.0, 0.0],
-                                [-1.0, -1.0, 0.0],
-                                [0.0, 0.0, 0.0]],
-                               [[-1.0, -1.0, 0.0],
-                                [-1.0, 0.0, 0.0],
-                                [0.0, 0.0, 0.0]],
-                               [[0.0, 0.0, 0.0],
-                                [0.0, 0.0, 0.0],
-                                [0.0, 0.0, 0.0]]], device=img.device).view(1, 1, 3, 3, 3)
-
-        B26_1_present = F.relu(F.conv3d(2.0 * img - 1.0, K_B26, stride=2) - 6)
-        B26_2_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[2]), stride=2) - 6)
-        B26_3_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[3]), stride=2) - 6)
-        B26_4_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[4]), stride=2) - 6)
-        B26_5_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[2, 3]), stride=2) - 6)
-        B26_6_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[2, 4]), stride=2) - 6)
-        B26_7_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[3, 4]), stride=2) - 6)
-        B26_8_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_B26, dims=[2, 3, 4]), stride=2) - 6)
-        num_B26_cells = B26_1_present + B26_2_present + B26_3_present + B26_4_present + B26_5_present + B26_6_present + B26_7_present + B26_8_present
-
-        subcondition4b = F.hardtanh(-(num_B26_cells - 1), min_val=0, max_val=1)
-        
-        # ... and cell configuration B18 does not exist...
-        K_B18 = torch.tensor([[[0.0, 1.0, 0.0],
-                               [-1.0, -1.0, -1.0],
-                               [0.0, 0.0, 0.0]],
-                              [[-1.0, -1.0, -1.0],
-                               [-1.0, 0.0, -1.0],
-                               [0.0, 0.0, 0.0]],
-                              [[0.0, 0.0, 0.0],
-                               [0.0, 0.0, 0.0],
-                               [0.0, 0.0, 0.0]]], device=img.device).view(1, 1, 3, 3, 3)
-
-        B18_1_present = F.relu(F.conv3d(2.0 * img - 1.0, K_B18, stride=2) - 8)
-        B18_2_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(K_B18, dims=[2, 4]), stride=2) - 8)
-        B18_3_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(K_B18, dims=[2, 4], k=2), stride=2) - 8)
-        B18_4_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(K_B18, dims=[2, 4], k=3), stride=2) - 8)
-        B18_5_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(K_B18, dims=[3, 4]), stride=2) - 8)
-        B18_6_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.rot90(K_B18, dims=[3, 4]), dims=[2, 4]), stride=2) - 8)
-        B18_7_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.rot90(K_B18, dims=[3, 4]), dims=[2, 4], k=2), stride=2) - 8)
-        B18_8_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.rot90(K_B18, dims=[3, 4]), dims=[2, 4], k=3), stride=2) - 8)
-        B18_9_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(K_B18, dims=[3, 4], k=2), stride=2) - 8)
-        B18_10_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.rot90(K_B18, dims=[3, 4], k=2), dims=[2, 4]), stride=2) - 8)
-        B18_11_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.rot90(K_B18, dims=[3, 4], k=2), dims=[2, 4], k=2), stride=2) - 8)
-        B18_12_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.rot90(K_B18, dims=[3, 4], k=2), dims=[2, 4], k=3), stride=2) - 8)
-        num_B18_cells = B18_1_present + B18_2_present + B18_3_present + B18_4_present + B18_5_present + B18_6_present + B18_7_present + B18_8_present + B18_9_present + B18_10_present + B18_11_present + B18_12_present
-
-        subcondition4c = F.hardtanh(-(num_B18_cells - 1), min_val=0, max_val=1)
-
-        # ... and the number of zeros in the six-neighborhood minus the number of A18 cell configurations plus the number of A26 cell configurations is exactly one
-        K_N6 = torch.tensor([[[0.0, 0.0, 0.0],
-                              [0.0, 1.0, 0.0],
-                              [0.0, 0.0, 0.0]],
-                             [[0.0, 1.0, 0.0],
-                              [1.0, 0.0, 1.0],
-                              [0.0, 1.0, 0.0]],
-                             [[0.0, 0.0, 0.0],
-                              [0.0, 1.0, 0.0],
-                              [0.0, 0.0, 0.0]]], device=img.device).view(1, 1, 3, 3, 3)
-
-        num_six_neighbors = F.conv3d(1-img, K_N6, stride=2)
-
-        K_A18 = torch.tensor([[[0.0, -1.0, 0.0],
-                               [0.0, -1.0, 0.0],
-                               [0.0, 0.0, 0.0]],
-                              [[0.0, -1.0, 0.0],
-                               [0.0, 0.0, 0.0],
-                               [0.0, 0.0, 0.0]],
-                              [[0.0, 0.0, 0.0],
-                               [0.0, 0.0, 0.0],
-                               [0.0, 0.0, 0.0]]], device=img.device).view(1, 1, 3, 3, 3)
-
-        A18_1_present = F.relu(F.conv3d(2.0 * img - 1.0, K_A18, stride=2) - 2)
-        A18_2_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(K_A18, dims=[2, 4]), stride=2) - 2)
-        A18_3_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(K_A18, dims=[2, 4], k=2), stride=2) - 2)
-        A18_4_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(K_A18, dims=[2, 4], k=3), stride=2) - 2)
-        A18_5_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(K_A18, dims=[3, 4]), stride=2) - 2)
-        A18_6_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.rot90(K_A18, dims=[3, 4]), dims=[2, 4]), stride=2) - 2)
-        A18_7_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.rot90(K_A18, dims=[3, 4]), dims=[2, 4], k=2), stride=2) - 2)
-        A18_8_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.rot90(K_A18, dims=[3, 4]), dims=[2, 4], k=3), stride=2) - 2)
-        A18_9_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(K_A18, dims=[3, 4], k=2), stride=2) - 2)
-        A18_10_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.rot90(K_A18, dims=[3, 4], k=2), dims=[2, 4]), stride=2) - 2)
-        A18_11_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.rot90(K_A18, dims=[3, 4], k=2), dims=[2, 4], k=2), stride=2) - 2)
-        A18_12_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.rot90(torch.rot90(K_A18, dims=[3, 4], k=2), dims=[2, 4], k=3), stride=2) - 2)
-        num_A18_cells = A18_1_present + A18_2_present + A18_3_present + A18_4_present + A18_5_present + A18_6_present + A18_7_present + A18_8_present + A18_9_present + A18_10_present + A18_11_present + A18_12_present
-
-        K_A26 = torch.tensor([[[-1.0, -1.0, 0.0],
-                               [-1.0, -1.0, 0.0],
-                               [0.0, 0.0, 0.0]],
-                              [[-1.0, -1.0, 0.0],
-                               [-1.0, 0.0, 0.0],
-                               [0.0, 0.0, 0.0]],
-                              [[0.0, 0.0, 0.0],
-                               [0.0, 0.0, 0.0],
-                               [0.0, 0.0, 0.0]]], device=img.device).view(1, 1, 3, 3, 3)
-
-        A26_1_present = F.relu(F.conv3d(2.0 * img - 1.0, K_A26, stride=2) - 6)
-        A26_2_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_A26, dims=[2]), stride=2) - 6)
-        A26_3_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_A26, dims=[3]), stride=2) - 6)
-        A26_4_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_A26, dims=[4]), stride=2) - 6)
-        A26_5_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_A26, dims=[2, 3]), stride=2) - 6)
-        A26_6_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_A26, dims=[2, 4]), stride=2) - 6)
-        A26_7_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_A26, dims=[3, 4]), stride=2) - 6)
-        A26_8_present = F.relu(F.conv3d(2.0 * img - 1.0, torch.flip(K_A26, dims=[2, 3, 4]), stride=2) - 6)
-        num_A26_cells = A26_1_present + A26_2_present + A26_3_present + A26_4_present + A26_5_present + A26_6_present + A26_7_present + A26_8_present
-
-        subcondition4d = F.hardtanh(num_six_neighbors - num_A18_cells + num_A26_cells, min_val=0, max_val=1) # 1 or more configurations
-        subcondition4e = F.hardtanh(-(num_six_neighbors - num_A18_cells + num_A26_cells - 2), min_val=0, max_val=1) # 1 or fewer configurations
-
-        condition4 = subcondition4a * subcondition4b * subcondition4c * subcondition4d * subcondition4e
-
-        # If any of the four conditions is fulfilled the point is simple
-        combined = torch.cat([condition1, condition2, condition3, condition4], dim=1)
-        is_simple = torch.amax(combined, dim=1, keepdim=True)
-        
-        return is_simple
-
-
-    # Specifically designed to be used with the eight-subfield iterative scheme from above.
-    def _euler_characteristic_simple_check(self, img):
-        """
-        Function that identifies simple points by assessing whether the Euler characteristic changes when deleting it [1].
-        In order to calculate the Euler characteristic, the amount of vertices, edges, faces and octants are counted using convolutions with pre-defined kernels.
-        The function is meant to be used in combination with the subfield-based iterative scheme employed in the forward function.
-
-        [1] Steven Lobregt et al. Three-dimensional skeletonization:principle and algorithm.
-            IEEE Transactions on pattern analysis and machine intelligence, 2(1):75-77, 1980.
-        """
-
-        img = F.pad(img, (1, 1, 1, 1, 1, 1), value=0)
-
-        # Create masked version of the image where the center of 26-neighborhoods is changed to zero
-        mask = torch.ones_like(img)
-        mask[:, :, 1::2, 1::2, 1::2] = 0
-        masked_img = img.clone() * mask
-
-        # Count vertices
-        vertices = F.relu(-(2.0 * img - 1.0))
-        num_vertices = F.avg_pool3d(vertices, (3, 3, 3), stride=2) * 27
-
-        masked_vertices = F.relu(-(2.0 * masked_img - 1.0))
-        num_masked_vertices = F.avg_pool3d(masked_vertices, (3, 3, 3), stride=2) * 27
-    
-        # Count edges
-        K_ud_edge = torch.tensor([0.5, 0.5], device=img.device).view(1, 1, 2, 1, 1)
-        K_ns_edge = torch.tensor([0.5, 0.5], device=img.device).view(1, 1, 1, 2, 1)
-        K_we_edge = torch.tensor([0.5, 0.5], device=img.device).view(1, 1, 1, 1, 2)
-
-        ud_edges = F.relu(F.conv3d(-(2.0 * img - 1.0), K_ud_edge))
-        num_ud_edges = F.avg_pool3d(ud_edges, (2, 3, 3), stride=2) * 18
-        ns_edges = F.relu(F.conv3d(-(2.0 * img - 1.0), K_ns_edge))
-        num_ns_edges = F.avg_pool3d(ns_edges, (3, 2, 3), stride=2) * 18
-        we_edges = F.relu(F.conv3d(-(2.0 * img - 1.0), K_we_edge))
-        num_we_edges = F.avg_pool3d(we_edges, (3, 3, 2), stride=2) * 18
-        num_edges = num_ud_edges + num_ns_edges + num_we_edges
-
-        masked_ud_edges = F.relu(F.conv3d(-(2.0 * masked_img - 1.0), K_ud_edge))
-        num_masked_ud_edges = F.avg_pool3d(masked_ud_edges, (2, 3, 3), stride=2) * 18
-        masked_ns_edges = F.relu(F.conv3d(-(2.0 * masked_img - 1.0), K_ns_edge))
-        num_masked_ns_edges = F.avg_pool3d(masked_ns_edges, (3, 2, 3), stride=2) * 18
-        masked_we_edges = F.relu(F.conv3d(-(2.0 * masked_img - 1.0), K_we_edge))
-        num_masked_we_edges = F.avg_pool3d(masked_we_edges, (3, 3, 2), stride=2) * 18
-        num_masked_edges = num_masked_ud_edges + num_masked_ns_edges + num_masked_we_edges
-
-        # Count faces
-        K_ud_face = torch.tensor([[0.25, 0.25], [0.25, 0.25]], device=img.device).view(1, 1, 1, 2, 2)
-        K_ns_face = torch.tensor([[0.25, 0.25], [0.25, 0.25]], device=img.device).view(1, 1, 2, 1, 2)
-        K_we_face = torch.tensor([[0.25, 0.25], [0.25, 0.25]], device=img.device).view(1, 1, 2, 2, 1)
-        
-        ud_faces = F.relu(F.conv3d(-(2.0 * img - 1.0), K_ud_face) - 0.5) * 2
-        num_ud_faces = F.avg_pool3d(ud_faces, (3, 2, 2), stride=2) * 12
-        ns_faces = F.relu(F.conv3d(-(2.0 * img - 1.0), K_ns_face) - 0.5) * 2
-        num_ns_faces = F.avg_pool3d(ns_faces, (2, 3, 2), stride=2) * 12
-        we_faces = F.relu(F.conv3d(-(2.0 * img - 1.0), K_we_face) - 0.5) * 2
-        num_we_faces = F.avg_pool3d(we_faces, (2, 2, 3), stride=2) * 12
-        num_faces = num_ud_faces + num_ns_faces + num_we_faces
-        
-        masked_ud_faces = F.relu(F.conv3d(-(2.0 * masked_img - 1.0), K_ud_face) - 0.5) * 2
-        num_masked_ud_faces = F.avg_pool3d(masked_ud_faces, (3, 2, 2), stride=2) * 12
-        masked_ns_faces = F.relu(F.conv3d(-(2.0 * masked_img - 1.0), K_ns_face) - 0.5) * 2
-        num_masked_ns_faces = F.avg_pool3d(masked_ns_faces, (2, 3, 2), stride=2) * 12
-        masked_we_faces = F.relu(F.conv3d(-(2.0 * masked_img - 1.0), K_we_face) - 0.5) * 2
-        num_masked_we_faces = F.avg_pool3d(masked_we_faces, (2, 2, 3), stride=2) * 12
-        num_masked_faces = num_masked_ud_faces + num_masked_ns_faces + num_masked_we_faces
-
-        # Count octants
-        K_octants = torch.tensor([[[0.125, 0.125], [0.125, 0.125]], [[0.125, 0.125], [0.125, 0.125]]], device=img.device).view(1, 1, 2, 2, 2)
-
-        octants = F.relu(F.conv3d(-(2.0 * img - 1.0), K_octants) - 0.75) * 4
-        num_octants = F.avg_pool3d(octants, (2, 2, 2), stride=2) * 8
-
-        masked_octants = F.relu(F.conv3d(-(2.0 * masked_img - 1.0), K_octants) - 0.75) * 4
-        num_masked_octants = F.avg_pool3d(masked_octants, (2, 2, 2), stride=2) * 8
-
-        # Combined number of vertices, edges, faces and octants to calculate the euler characteristic
-        euler_characteristic = num_vertices - num_edges + num_faces - num_octants
-        masked_euler_characteristic = num_masked_vertices - num_masked_edges + num_masked_faces - num_masked_octants
-
-        # If the Euler characteristic is unchanged after switching a point from 1 to 0 this indicates that the point is simple
-        euler_change = F.hardtanh(torch.abs(masked_euler_characteristic - euler_characteristic), min_val=0, max_val=1)
-        is_simple = 1 - euler_change
-        is_simple = (is_simple.detach() > 0.5).float() - is_simple.detach() + is_simple
-
-        return is_simple
-
-
-    def _prepare_output(self, img):
-        """
-        Function that removes the padding and dimensions added by _prepare_input function.
-        """
-
-        img = img[:, :, 1:-1, 1:-1, 1:-1]
-
-        if self.expanded_dims:
-            img = torch.squeeze(img, dim=2)
-        
-        return img

util/tools.py

@@ -1,143 +0,0 @@
-'''
-Author: Jintao Li
-Date: 2022-05-30 16:42:14
-LastEditors: Jintao Li
-LastEditTime: 2022-07-11 23:05:53
-2022 by CIG.
-'''
-
-import os, shutil
-import yaml, argparse
-from sklearn.metrics import confusion_matrix
-import numpy as np
-import torch
-
-
-def accuracy(output, target):
-    '''
-    output: [N, num_classes, ...], torch.float
-    target: [N, ...], torch.int
-    '''
-    output = output.argmax(dim=1).flatten().detach().cpu().numpy()
-    target = target.flatten().detach().cpu().numpy()
-    return pixel_acc(output, target), _miou(output, target)
-
-
-def pixel_acc(output, target):
-    r"""
-    计算像素准确率 (Pixel Accuracy, PA)
-    $$ PA = \frac{\sum_{i=0}^k p_{ii}}
-    {\sum_{i=0}^k \sum_{j=0}^k p_{ij}} $$ and
-    $n_class = k+1$ 
-    Parameters:
-    -----------
-        shape: [N, ], (use flatten() function)
-    return:
-    ----------
-        - PA
-    """
-    assert output.shape == target.shape, "shapes must be same"
-    cm = confusion_matrix(target, output)
-    return np.diag(cm).sum() / cm.sum()
-
-
-def _miou(output, target):
-    r"""
-    计算均值交并比 MIoU (Mean Intersection over Union)
-    $$ MIoU = \frac{1}{k+1} \sum_{i=0}^k \frac{p_{ii}}
-    {\sum_{j=0}^k p_{ij} + \sum_{j=0}^k p_{ji} - p_{ii}} $$
-    Parameters:
-        output, target: [N, ]
-    return:
-        MIoU
-    """
-    assert output.shape == target.shape, "shapes must be same"
-    cm = confusion_matrix(target, output)
-    intersection = np.diag(cm)
-    union = np.sum(cm, 1) + np.sum(cm, 0) - np.diag(cm)
-    iou = intersection / union
-    miou = np.nanmean(iou)
-
-    return miou
-
-
-def yaml_config_hook(config_file: str) -> argparse.Namespace:
-    """ 
-    加载yaml文件里面的参数配置, 并生成argparse形式的参数集合
-    """
-    with open(config_file) as f:
-        cfg = yaml.safe_load(f)
-        for d in cfg.get("defaults", []):
-            config_dir, cf = d.popitem()
-            cf = os.path.join(os.path.dirname(config_file), config_dir,
-                              cf + ".yaml")
-            with open(cf) as f:
-                l = yaml.safe_load(f)
-                cfg.update(l)
-
-    if "defaults" in cfg.keys():
-        del cfg["defaults"]
-
-    parser = argparse.ArgumentParser()
-    for k, v in cfg.items():
-        parser.add_argument(f"--{k}", default=v, type=type(v))
-    args = parser.parse_args()
-
-    return args
-
-
-def backup_code(work_dir, back_dir, exceptions=[], include=[]):
-    r"""
-    备份本次运行的代码到指定目录下, 并排除某些文件和目录
-
-    Args:
-        work_dir: 工作目录, i.e. 需要备份的代码
-        back_dir: 目标目录.备份代码放置的目录
-        exception (list): 被排除的目录和以指定后缀结尾的文件, 默认的有
-                ["__pycache__", ".pyc", ".dat", "backup", ".vscode"]
-        include (list): 某些必须被备份的文件,该文件可能在exception里面
-    """
-    _exp = [
-        "*__pycache__*", "*.pyc", "*.dat", "backup", ".vscode", "*.log",
-        "*log*"
-    ]
-    exceptions = exceptions + _exp
-
-    # if not os.path.exists(back_dir):
-    os.makedirs(back_dir, exist_ok=True)
-
-    shutil.copytree(work_dir,
-                    back_dir + 'code/',
-                    ignore=shutil.ignore_patterns(*exceptions),
-                    dirs_exist_ok=True)
-
-    for f in include:
-        shutil.copyfile(os.path.join(work_dir, f),
-                        os.path.join(back_dir + 'code', f))
-
-
-def list_files(path, full=False):
-    r"""
-    递归列出目录下所有的文件，包括子目录下的文件
-    """
-    out = []
-    for f in os.listdir(path):
-        fname = os.path.join(path, f)
-        if os.path.isdir(fname):
-            fname = list_files(fname)
-            out += [os.path.join(f, i) for i in fname]
-        else:
-            out.append(f)
-    if full:
-        out = [os.path.join(path, i) for i in out]
-    return out
-
-
-if __name__ == "__main__":
-    output = torch.randn(4, 2, 6, 6)
-    target = torch.randn(4, 2, 6, 6)
-    # output = output.cuda()
-    # target = target.cuda()
-    target = target.argmax(1)
-
-    accuracy(output, target)