update tensor.min to torch.min(tensor) in pythonic chamfer distance to fix #27

Author: ThibaultGROUEIX

inference/run_AE_AtlasNet.py

@@ -53,7 +53,7 @@ def distChamfer(a,b):
     rx = xx[:, diag_ind, diag_ind].unsqueeze(1).expand_as(xx)
     ry = yy[:, diag_ind, diag_ind].unsqueeze(1).expand_as(yy)
     P = (rx.transpose(2,1) + ry - 2*zz)
-    return P.min(1)[0], P.min(2)[0]
+    return torch.min(P, 1)[0], torch.min(P, 2)[0], torch.min(P, 1)[1], torch.min(P, 2)[1]
 # ========================================================== #
 
 blue = lambda x:'\033[94m' + x + '\033[0m'

inference/run_AE_AtlasNet_sphere.py

@@ -53,7 +53,7 @@ def distChamfer(a,b):
     rx = xx[:, diag_ind, diag_ind].unsqueeze(1).expand_as(xx)
     ry = yy[:, diag_ind, diag_ind].unsqueeze(1).expand_as(yy)
     P = (rx.transpose(2,1) + ry - 2*zz)
-    return P.min(1)[0], P.min(2)[0]
+    return torch.min(P, 1)[0], torch.min(P, 2)[0], torch.min(P, 1)[1], torch.min(P, 2)[1]
 # ========================================================== #
 
 blue = lambda x:'\033[94m' + x + '\033[0m'

inference/run_AE_Baseline.py

@@ -51,7 +51,7 @@ def distChamfer(a,b):
     rx = xx[:, diag_ind, diag_ind].unsqueeze(1).expand_as(xx)
     ry = yy[:, diag_ind, diag_ind].unsqueeze(1).expand_as(yy)
     P = (rx.transpose(2,1) + ry - 2*zz)
-    return P.min(1)[0], P.min(2)[0]
+    return torch.min(P, 1)[0], torch.min(P, 2)[0], torch.min(P, 1)[1], torch.min(P, 2)[1]
 # ========================================================== #
 
 blue = lambda x:'\033[94m' + x + '\033[0m'

inference/run_SVR_AtlasNet.py

@@ -53,7 +53,7 @@ def distChamfer(a,b):
     rx = xx[:, diag_ind, diag_ind].unsqueeze(1).expand_as(xx)
     ry = yy[:, diag_ind, diag_ind].unsqueeze(1).expand_as(yy)
     P = (rx.transpose(2,1) + ry - 2*zz)
-    return P.min(1)[0], P.min(2)[0]
+    return torch.min(P, 1)[0], torch.min(P, 2)[0], torch.min(P, 1)[1], torch.min(P, 2)[1]
 # ========================================================== #
 
 blue = lambda x:'\033[94m' + x + '\033[0m'

inference/run_SVR_AtlasNet_sphere.py

@@ -53,7 +53,7 @@ def distChamfer(a,b):
     rx = xx[:, diag_ind, diag_ind].unsqueeze(1).expand_as(xx)
     ry = yy[:, diag_ind, diag_ind].unsqueeze(1).expand_as(yy)
     P = (rx.transpose(2,1) + ry - 2*zz)
-    return P.min(1)[0], P.min(2)[0]
+    return torch.min(P, 1)[0], torch.min(P, 2)[0], torch.min(P, 1)[1], torch.min(P, 2)[1]
 # ========================================================== #
 
 blue = lambda x:'\033[94m' + x + '\033[0m'

inference/run_SVR_Baseline.py

@@ -50,7 +50,7 @@ def distChamfer(a,b):
     rx = xx[:, diag_ind, diag_ind].unsqueeze(1).expand_as(xx)
     ry = yy[:, diag_ind, diag_ind].unsqueeze(1).expand_as(yy)
     P = (rx.transpose(2,1) + ry - 2*zz)
-    return P.min(1)[0], P.min(2)[0]
+    return torch.min(P, 1)[0], torch.min(P, 2)[0], torch.min(P, 1)[1], torch.min(P, 2)[1]
 # ========================================================== #
 
 blue = lambda x:'\033[94m' + x + '\033[0m'

training/train_AE_AtlasNet.py

@@ -62,7 +62,7 @@ def distChamfer(a,b):
         rx = xx[:, diag_ind, diag_ind].unsqueeze(1).expand_as(xx)
         ry = yy[:, diag_ind, diag_ind].unsqueeze(1).expand_as(yy)
         P = (rx.transpose(2,1) + ry - 2*zz)
-        return P.min(1)[0], P.min(2)[0]
+        return torch.min(P, 1)[0], torch.min(P, 2)[0], torch.min(P, 1)[1], torch.min(P, 2)[1]
 # ========================================================== #
 
 # =============DEFINE stuff for logs ======================================== #

training/train_AE_AtlasNet_sphere.py

@@ -63,7 +63,7 @@ def distChamfer(a,b):
         rx = xx[:, diag_ind, diag_ind].unsqueeze(1).expand_as(xx)
         ry = yy[:, diag_ind, diag_ind].unsqueeze(1).expand_as(yy)
         P = (rx.transpose(2,1) + ry - 2*zz)
-        return P.min(1)[0], P.min(2)[0]
+        return torch.min(P, 1)[0], torch.min(P, 2)[0], torch.min(P, 1)[1], torch.min(P, 2)[1]
 # ========================================================== #
 
 

training/train_AE_Baseline.py

@@ -63,7 +63,7 @@ def distChamfer(a,b):
         rx = xx[:, diag_ind, diag_ind].unsqueeze(1).expand_as(xx)
         ry = yy[:, diag_ind, diag_ind].unsqueeze(1).expand_as(yy)
         P = (rx.transpose(2,1) + ry - 2*zz)
-        return P.min(1)[0], P.min(2)[0]
+        return torch.min(P, 1)[0], torch.min(P, 2)[0], torch.min(P, 1)[1], torch.min(P, 2)[1]
 # ========================================================== #
 
 # =============DEFINE stuff for logs ======================================== #

training/train_SVR_AtlasNet.py

@@ -65,7 +65,7 @@ def distChamfer(a,b):
         rx = xx[:, diag_ind, diag_ind].unsqueeze(1).expand_as(xx)
         ry = yy[:, diag_ind, diag_ind].unsqueeze(1).expand_as(yy)
         P = (rx.transpose(2,1) + ry - 2*zz)
-        return P.min(1)[0], P.min(2)[0]
+        return torch.min(P, 1)[0], torch.min(P, 2)[0], torch.min(P, 1)[1], torch.min(P, 2)[1]
 # ========================================================== #
 
 # =============DEFINE stuff for logs ======================================== #