Add new file

skeleton/layers/resnet.py

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+import torch as t
+import torch.nn as nn
+
+from skeleton.layers.ResidualBlock import ResidualBlock
+
+class ResNet(nn.Module):
+    def __init__(self, triples):
+        super(ResNet, self).__init__()
+        modules = []
+        for i, triple in enumerate(triples):
+            block = self.block(*triple)
+            modules.append(block)
+
+        self.sb = self.starting_block(40, 32)
+
+        self.net = nn.Sequential(*modules)
+
+    def starting_block(self, in_channels, out_channels):
+        return nn.Sequential(
+            nn.Conv1d(in_channels, out_channels, kernel_size=7, stride=2, padding=3),
+            nn.BatchNorm1d(out_channels), 
+            nn.ReLU(),
+            nn.MaxPool1d(kernel_size=3, stride=2, padding=1))
+
+    def block(self, in_channels, num_residuals, out_channels):
+        blk = []
+        for i in range(num_residuals):
+            if i == 0:
+                blk.append(ResidualBlock(in_channels, out_channels))
+            else:
+                blk.append(ResidualBlock(in_channels*2, out_channels))
+        return nn.Sequential(*blk)
+
+    def forward(self, x):
+        x = self.sb(x)
+        x = self.net(x)
+        return x