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Deep-SAD-PyTorch/src/network_statistics.py

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+import torch
+from thop import profile
+
+from networks.subter_LeNet import SubTer_LeNet, SubTer_LeNet_Autoencoder
+from networks.subter_LeNet_rf import SubTer_Efficient_AE, SubTer_EfficientEncoder
+
+# Configuration
+LATENT_DIMS = [32, 64, 128, 256, 512, 768, 1024]
+BATCH_SIZE = 1
+INPUT_SHAPE = (BATCH_SIZE, 1, 32, 2048)
+
+
+def count_parameters(model, input_shape):
+    """Count MACs and parameters for a model."""
+    model.eval()
+    with torch.no_grad():
+        input_tensor = torch.randn(input_shape)
+        macs, params = profile(model, inputs=(input_tensor,))
+    return {"MACs": macs, "Parameters": params}
+
+
+def format_number(num: float) -> str:
+    """Format large numbers with K, M, B, T suffixes."""
+    for unit in ["", "K", "M", "B", "T"]:
+        if abs(num) < 1000.0 or unit == "T":
+            return f"{num:3.2f}{unit}"
+        num /= 1000.0
+
+
+def main():
+    # Collect results per latent dimension
+    results = {}  # dim -> dict of 8 values
+    for dim in LATENT_DIMS:
+        # Instantiate models for this latent dim
+        lenet_enc = SubTer_LeNet(rep_dim=dim)
+        eff_enc = SubTer_EfficientEncoder(rep_dim=dim)
+        lenet_ae = SubTer_LeNet_Autoencoder(rep_dim=dim)
+        eff_ae = SubTer_Efficient_AE(rep_dim=dim)
+
+        # Profile each
+        lenet_enc_stats = count_parameters(lenet_enc, INPUT_SHAPE)
+        eff_enc_stats = count_parameters(eff_enc, INPUT_SHAPE)
+        lenet_ae_stats = count_parameters(lenet_ae, INPUT_SHAPE)
+        eff_ae_stats = count_parameters(eff_ae, INPUT_SHAPE)
+
+        results[dim] = {
+            "lenet_enc_params": format_number(lenet_enc_stats["Parameters"]),
+            "lenet_enc_macs": format_number(lenet_enc_stats["MACs"]),
+            "eff_enc_params": format_number(eff_enc_stats["Parameters"]),
+            "eff_enc_macs": format_number(eff_enc_stats["MACs"]),
+            "lenet_ae_params": format_number(lenet_ae_stats["Parameters"]),
+            "lenet_ae_macs": format_number(lenet_ae_stats["MACs"]),
+            "eff_ae_params": format_number(eff_ae_stats["Parameters"]),
+            "eff_ae_macs": format_number(eff_ae_stats["MACs"]),
+        }
+
+    # Build LaTeX table with tabularx
+    header = (
+        "\\begin{table}[!ht]\n"
+        "\\centering\n"
+        "\\renewcommand{\\arraystretch}{1.15}\n"
+        "\\begin{tabularx}{\\linewidth}{lXXXXXXXX}\n"
+        "\\hline\n"
+        " & \\multicolumn{4}{c}{\\textbf{Encoders}} & "
+        "\\multicolumn{4}{c}{\\textbf{Autoencoders}} \\\\\n"
+        "\\cline{2-9}\n"
+        "\\textbf{Latent $z$} & "
+        "\\textbf{LeNet Params} & \\textbf{LeNet MACs} & "
+        "\\textbf{Eff. Params} & \\textbf{Eff. MACs} & "
+        "\\textbf{LeNet Params} & \\textbf{LeNet MACs} & "
+        "\\textbf{Eff. Params} & \\textbf{Eff. MACs} \\\\\n"
+        "\\hline\n"
+    )
+
+    rows = []
+    for dim in LATENT_DIMS:
+        r = results[dim]
+        row = (
+            f"{dim} & "
+            f"{r['lenet_enc_params']} & {r['lenet_enc_macs']} & "
+            f"{r['eff_enc_params']} & {r['eff_enc_macs']} & "
+            f"{r['lenet_ae_params']} & {r['lenet_ae_macs']} & "
+            f"{r['eff_ae_params']} & {r['eff_ae_macs']} \\\\"
+        )
+        rows.append(row)
+
+    footer = (
+        "\\hline\n"
+        "\\end{tabularx}\n"
+        "\\caption{Parameter and MAC counts for SubTer variants across latent dimensionalities.}\n"
+        "\\label{tab:subter_counts}\n"
+        "\\end{table}\n"
+    )
+
+    latex_table = header + "\n".join(rows) + "\n" + footer
+
+    print(latex_table)
+
+
+if __name__ == "__main__":
+    main()