From: Francois Fleuret <francois@fleuret.org>
Date: Sat, 3 Apr 2021 10:55:50 +0000 (+0200)
Subject: Simplified calls to superclass constructors.
X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=pytorch.git;a=commitdiff_plain;h=114939c1db199607c9c1f6df0d23e5c23b2915f2

Simplified calls to superclass constructors.
---

diff --git a/attentiontoy1d.py b/attentiontoy1d.py
index d389f0c..b463340 100755
--- a/attentiontoy1d.py
+++ b/attentiontoy1d.py
@@ -181,7 +181,7 @@ def save_sequence_images(filename, sequences, tr = None, bx = None):
 
 class AttentionLayer(nn.Module):
     def __init__(self, in_channels, out_channels, key_channels):
-        super(AttentionLayer, self).__init__()
+        super().__init__()
         self.conv_Q = nn.Conv1d(in_channels, key_channels, kernel_size = 1, bias = False)
         self.conv_K = nn.Conv1d(in_channels, key_channels, kernel_size = 1, bias = False)
         self.conv_V = nn.Conv1d(in_channels, out_channels, kernel_size = 1, bias = False)
diff --git a/causal-autoregression.py b/causal-autoregression.py
index c2f6161..7754265 100755
--- a/causal-autoregression.py
+++ b/causal-autoregression.py
@@ -85,7 +85,7 @@ else:
 
 class NetToy1d(nn.Module):
     def __init__(self, nb_classes, ks = 2, nc = 32):
-        super(NetToy1d, self).__init__()
+        super().__init__()
         self.pad = (ks - 1, 0)
         self.conv0 = nn.Conv1d(1, nc, kernel_size = 1)
         self.conv1 = nn.Conv1d(nc, nc, kernel_size = ks)
@@ -105,7 +105,7 @@ class NetToy1d(nn.Module):
 
 class NetToy1dWithDilation(nn.Module):
     def __init__(self, nb_classes, ks = 2, nc = 32):
-        super(NetToy1dWithDilation, self).__init__()
+        super().__init__()
         self.conv0 = nn.Conv1d(1, nc, kernel_size = 1)
         self.pad1 = ((ks-1) * 2, 0)
         self.conv1 = nn.Conv1d(nc, nc, kernel_size = ks, dilation = 2)
@@ -130,7 +130,7 @@ class NetToy1dWithDilation(nn.Module):
 
 class PixelCNN(nn.Module):
     def __init__(self, nb_classes, in_channels = 1, ks = 5):
-        super(PixelCNN, self).__init__()
+        super().__init__()
 
         self.hpad = (ks//2, ks//2, ks//2, 0)
         self.vpad = (ks//2,     0,     0, 0)
diff --git a/hallu.py b/hallu.py
index b738b52..de25188 100755
--- a/hallu.py
+++ b/hallu.py
@@ -14,7 +14,7 @@ from torch.nn import functional as F
 
 class MultiScaleEdgeEnergy(torch.nn.Module):
     def __init__(self):
-        super(MultiScaleEdgeEnergy, self).__init__()
+        super().__init__()
         k = torch.exp(- torch.tensor([[-2., -1., 0., 1., 2.]])**2 / 2)
         k = (k.t() @ k).view(1, 1, 5, 5)
         self.gaussian_5x5 = torch.nn.Parameter(k / k.sum()).requires_grad_(False)
diff --git a/lazy_linear.py b/lazy_linear.py
index 2fa6a29..97530ef 100755
--- a/lazy_linear.py
+++ b/lazy_linear.py
@@ -12,7 +12,7 @@ from torch import nn, Tensor
 class LazyLinear(nn.Module):
 
     def __init__(self, out_dim, bias = True):
-        super(LazyLinear, self).__init__()
+        super().__init__()
         self.out_dim = out_dim
         self.bias = bias
         self.core = None
@@ -30,7 +30,7 @@ class LazyLinear(nn.Module):
 
     def named_parameters(self, memo=None, prefix=''):
         assert self.core is not None, 'Parameters not yet defined'
-        return super(LazyLinear, self).named_parameters(memo, prefix)
+        return super().named_parameters(memo, prefix)
 
 ######################################################################
 
diff --git a/mi_estimator.py b/mi_estimator.py
index 68fd51f..47381ef 100755
--- a/mi_estimator.py
+++ b/mi_estimator.py
@@ -226,7 +226,7 @@ def create_sequences_pairs(train = False):
 
 class NetForImagePair(nn.Module):
     def __init__(self):
-        super(NetForImagePair, self).__init__()
+        super().__init__()
         self.features_a = nn.Sequential(
             nn.Conv2d(1, 16, kernel_size = 5),
             nn.MaxPool2d(3), nn.ReLU(),
@@ -257,7 +257,7 @@ class NetForImagePair(nn.Module):
 
 class NetForImageValuesPair(nn.Module):
     def __init__(self):
-        super(NetForImageValuesPair, self).__init__()
+        super().__init__()
         self.features_a = nn.Sequential(
             nn.Conv2d(1, 16, kernel_size = 5),
             nn.MaxPool2d(3), nn.ReLU(),
@@ -306,7 +306,7 @@ class NetForSequencePair(nn.Module):
         )
 
     def __init__(self):
-        super(NetForSequencePair, self).__init__()
+        super().__init__()
 
         self.nc = 32
         self.nh = 256
diff --git a/miniflow.py b/miniflow.py
index b5c8cb4..eb2d4c7 100755
--- a/miniflow.py
+++ b/miniflow.py
@@ -44,7 +44,7 @@ def LogProba(x, ldj):
 # START_MODEL
 class PiecewiseLinear(nn.Module):
     def __init__(self, nb, xmin, xmax):
-        super(PiecewiseLinear, self).__init__()
+        super().__init__()
         self.xmin = xmin
         self.xmax = xmax
         self.nb = nb