From e932dc6c75b73e3a3f25221ac78cbdf9d0dd8862 Mon Sep 17 00:00:00 2001
From: shahules786 <shahules786@gmail.com>
Date: Thu, 3 Nov 2022 11:37:58 +0530
Subject: [PATCH] batchnorm

---
 enhancer/models/complexnn/norm.py | 72 +++++++++++++++++++++++++++++++
 1 file changed, 72 insertions(+)
 create mode 100644 enhancer/models/complexnn/norm.py

diff --git a/enhancer/models/complexnn/norm.py b/enhancer/models/complexnn/norm.py
new file mode 100644
index 0000000..eec2130
--- /dev/null
+++ b/enhancer/models/complexnn/norm.py
@@ -0,0 +1,72 @@
+import torch
+from torch import nn
+
+
+class ComplexBatchNorm(nn.Module):
+    def __init__(
+        self,
+        num_features: int,
+        eps: float = 1e-5,
+        momentum: bool = True,
+        affine: bool = True,
+        track_running_stats: bool = True,
+    ):
+        self.num_features = num_features // 2
+        self.affine = affine
+        self.momentum = momentum
+        self.track_running_stats = track_running_stats
+
+        if self.affine:
+            values = torch.Tensor(self.num_features)
+            self.Wrr = nn.parameter.Parameter(values)
+            self.Wri = nn.parameter.Parameter(values)
+            self.Wii = nn.parameter.Parameter(values)
+            self.Br = nn.parameter.Parameter(values)
+            self.Bi = nn.parameter.Parameter(values)
+        else:
+            self.register_parameter("Wrr", None)
+            self.register_parameter("Wri", None)
+            self.register_parameter("Wii", None)
+            self.register_parameter("Br", None)
+            self.register_parameter("Bi", None)
+
+        if self.track_running_stats:
+            values = torch.Tensor(self.num_features)
+            self.register_buffer("Mean_real", values)
+            self.register_buffer("Mean_imag", values)
+            self.register_buffer("Var_rr", values)
+            self.register_buffer("Var_ri", values)
+            self.register_buffer("Var_ii", values)
+            self.register_buffer(
+                "num_batches_tracked", torch.tensor(0, dtype=torch.long)
+            )
+        else:
+            self.register_parameter("Mean_real", None)
+            self.register_parameter("Mean_imag", None)
+            self.register_parameter("Var_rr", None)
+            self.register_parameter("Var_ri", None)
+            self.register_parameter("Var_ii", None)
+            self.register_parameter("num_batches_tracked", None)
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        if self.affine:
+            self.Wrr.data.fill_(1)
+            self.Wii.data.fill(1)
+            self.Wri.data.uniform_(-0.9, 0.9)
+            self.Br.data.fill_(0)
+            self.Bi.data.fill_(0)
+        self.reset_running_stats()
+
+    def reset_running_stats(self):
+        if self.track_running_stats:
+            self.Mean_real.zero_()
+            self.Mean_imag.zero_()
+            self.Var_rr.fill_(1)
+            self.Var_ri.zero_()
+            self.Var_ii.fill_(1)
+            self.num_batches_tracked.zero_()
+
+    def forward(self, input):
+        pass