neurobench & memtorch

1 files changed, 49 insertions, 0 deletions

diff --git a/neurobench_testing/examples/gsc/ANN.py b/neurobench_testing/examples/gsc/ANN.py
new file mode 100644
index 0000000..7607833
--- /dev/null
+++ b/neurobench_testing/examples/gsc/ANN.py
@@ -0,0 +1,49 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torchaudio
+import sys
+
+from tqdm import tqdm
+
+class M5(nn.Module):
+    def __init__(self, n_input=1, n_output=35, stride=16, n_channel=32):
+        super().__init__()
+        self.conv1 = nn.Conv1d(n_input, n_channel, kernel_size=80, stride=stride)
+        self.bn1 = nn.BatchNorm1d(n_channel)
+        self.pool1 = nn.MaxPool1d(4)
+        self.conv2 = nn.Conv1d(n_channel, n_channel, kernel_size=3)
+        self.bn2 = nn.BatchNorm1d(n_channel)
+        self.pool2 = nn.MaxPool1d(4)
+        self.conv3 = nn.Conv1d(n_channel, 2 * n_channel, kernel_size=3)
+        self.bn3 = nn.BatchNorm1d(2 * n_channel)
+        self.pool3 = nn.MaxPool1d(4)
+        self.conv4 = nn.Conv1d(2 * n_channel, 2 * n_channel, kernel_size=3)
+        self.bn4 = nn.BatchNorm1d(2 * n_channel)
+        self.pool4 = nn.MaxPool1d(4)
+        self.fc1 = nn.Linear(2 * n_channel, n_output)
+
+        # these need to be different ReLU objects so that they can be individually hooked
+        self.act1 = nn.ReLU()
+        self.act2 = nn.ReLU()
+        self.act3 = nn.ReLU()
+        self.act4 = nn.ReLU()
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.act1(self.bn1(x))
+        x = self.pool1(x)
+        x = self.conv2(x)
+        x = self.act2(self.bn2(x))
+        x = self.pool2(x)
+        x = self.conv3(x)
+        x = self.act3(self.bn3(x))
+        x = self.pool3(x)
+        x = self.conv4(x)
+        x = self.act4(self.bn4(x))
+        x = self.pool4(x)
+        x = F.avg_pool1d(x, x.shape[-1])
+        x = x.permute(0, 2, 1)
+        x = self.fc1(x)
+        return F.log_softmax(x, dim=2)
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