From aa5b7986f94673874d9c66359ebbb223ded00767 Mon Sep 17 00:00:00 2001
From: Marcin Chrzanowski <m@m-chrzan.xyz>
Date: Sat, 1 May 2021 18:15:17 +0200
Subject: Add missing file

---
 src/net.py | 57 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 57 insertions(+)
 create mode 100644 src/net.py

diff --git a/src/net.py b/src/net.py
new file mode 100644
index 0000000..af4e984
--- /dev/null
+++ b/src/net.py
@@ -0,0 +1,57 @@
+import torch.nn as nn
+
+class Net(nn.Module):
+    def __init__(self, convolutions, linears, outputs, finalizer, batch_norm=False, dropout=False):
+        super(Net, self).__init__()
+
+        self.finalizer = finalizer
+
+        self.convolutions = self.make_convolutions(convolutions, batch_norm,
+                dropout)
+        self.linears = self.make_linears(linears, batch_norm, dropout)
+        self.final_linear = nn.Linear(
+                linears[-1].linear_args()['out_features'], outputs)
+
+    def forward(self, x):
+        x = x.unsqueeze(1)
+        x = self.convolutions(x)
+        x = x.view(-1, self.num_flat_features(x))
+        x = self.linears(x)
+        x = self.final_linear(x)
+        x = self.finalizer(x)
+        return x
+
+    def num_flat_features(self, x):
+        size = x.size()[1:]  # all dimensions except the batch dimension
+        num_features = 1
+        for s in size:
+            num_features *= s
+        return num_features
+
+    def make_convolutions(self, convolutions, batch_norm, dropout):
+        layers = []
+        for convolution in convolutions:
+            conv_args = convolution.conv_args()
+            layers.append(nn.Conv2d(**conv_args))
+            if batch_norm:
+                layers.append(nn.BatchNorm2d(conv_args['out_channels']))
+            layers.append(nn.ReLU())
+            if dropout:
+                layers.append(nn.Dropout2d(dropout))
+            if convolution.max_pool:
+                layers.append(nn.MaxPool2d(2))
+
+        return nn.Sequential(*layers)
+
+    def make_linears(self, linears, batch_norm, dropout):
+        layers = []
+        for linear in linears:
+            linear_args = linear.linear_args()
+            layers.append(nn.Linear(**linear_args))
+            if batch_norm:
+                layers.append(nn.BatchNorm1d(linear_args['out_features']))
+            layers.append(nn.ReLU())
+            if dropout:
+                layers.append(nn.Dropout(dropout))
+
+        return nn.Sequential(*layers)
-- 
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