From e4b298cf06c3874c02b6e15a970a2b0c63e6fddf Mon Sep 17 00:00:00 2001 From: Jan Kowalczyk Date: Thu, 11 Sep 2025 14:50:16 +0200 Subject: [PATCH] wip --- Deep-SAD-PyTorch/pyproject.toml | 2 + Deep-SAD-PyTorch/src/network_statistics.py | 101 +++++++++++++ Deep-SAD-PyTorch/src/network_statistics.tex | 155 ++++++++++++++++++++ Deep-SAD-PyTorch/uv.lock | 24 +++ thesis/Main.pdf | Bin 5744814 -> 5745132 bytes thesis/Main.tex | 106 +++++++++---- 6 files changed, 362 insertions(+), 26 deletions(-) create mode 100644 Deep-SAD-PyTorch/src/network_statistics.py create mode 100644 Deep-SAD-PyTorch/src/network_statistics.tex diff --git a/Deep-SAD-PyTorch/pyproject.toml b/Deep-SAD-PyTorch/pyproject.toml index 508c8f9..4d91e40 100644 --- a/Deep-SAD-PyTorch/pyproject.toml +++ b/Deep-SAD-PyTorch/pyproject.toml @@ -23,6 +23,8 @@ dependencies = [ "scipy>=1.16.0", "seaborn>=0.13.2", "six>=1.17.0", + "tabulate>=0.9.0", + "thop>=0.1.1.post2209072238", "torch-receptive-field", "torchscan>=0.1.1", "visualtorch>=0.2.4", diff --git a/Deep-SAD-PyTorch/src/network_statistics.py b/Deep-SAD-PyTorch/src/network_statistics.py new file mode 100644 index 0000000..ebb0840 --- /dev/null +++ b/Deep-SAD-PyTorch/src/network_statistics.py @@ -0,0 +1,101 @@ +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() diff --git a/Deep-SAD-PyTorch/src/network_statistics.tex b/Deep-SAD-PyTorch/src/network_statistics.tex new file mode 100644 index 0000000..c9186d5 --- /dev/null +++ b/Deep-SAD-PyTorch/src/network_statistics.tex @@ -0,0 +1,155 @@ +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register count_upsample() for . +[INFO] Register zero_ops() for . +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register count_upsample() for . +[INFO] Register zero_ops() for . +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register count_upsample() for . +[INFO] Register zero_ops() for . +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register count_upsample() for . +[INFO] Register zero_ops() for . +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register count_upsample() for . +[INFO] Register zero_ops() for . +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register count_upsample() for . +[INFO] Register zero_ops() for . +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register zero_ops() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register count_linear() for . +[INFO] Register count_convNd() for . +[INFO] Register count_convNd() for . +[INFO] Register count_normalization() for . +[INFO] Register zero_ops() for . +[INFO] Register count_linear() for . +[INFO] Register count_upsample() for . +[INFO] Register zero_ops() for . +\begin{table}[!ht] +\centering +\renewcommand{\arraystretch}{1.15} +\begin{tabularx}{\linewidth}{lXXXXXXXX} +\hline + & \multicolumn{4}{c}{\textbf{Encoders}} & \multicolumn{4}{c}{\textbf{Autoencoders}} \\ +\cline{2-9} +\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} \\ +\hline +32 & 525.29K & 27.92M & 263.80K & 29.82M & 1.05M & 54.95M & 532.35K & 168.49M \\ +64 & 1.05M & 28.44M & 525.94K & 30.08M & 2.10M & 56.00M & 1.06M & 169.02M \\ +128 & 2.10M & 29.49M & 1.05M & 30.61M & 4.20M & 58.10M & 2.11M & 170.07M \\ +256 & 4.20M & 31.59M & 2.10M & 31.65M & 8.39M & 62.29M & 4.20M & 172.16M \\ +512 & 8.39M & 35.78M & 4.20M & 33.75M & 16.78M & 70.68M & 8.40M & 176.36M \\ +768 & 12.58M & 39.98M & 6.29M & 35.85M & 25.17M & 79.07M & 12.59M & 180.55M \\ +1024 & 16.78M & 44.17M & 8.39M & 37.95M & 33.56M & 87.46M & 16.79M & 184.75M \\ +\hline +\end{tabularx} +\caption{Parameter and MAC counts for SubTer variants across latent dimensionalities.} +\label{tab:subter_counts} +\end{table} + diff --git a/Deep-SAD-PyTorch/uv.lock b/Deep-SAD-PyTorch/uv.lock index ad8ca75..72fc5b5 100644 --- a/Deep-SAD-PyTorch/uv.lock +++ b/Deep-SAD-PyTorch/uv.lock @@ -141,6 +141,8 @@ dependencies = [ { name = "scipy" }, { name = "seaborn" }, { name = "six" }, + { name = "tabulate" 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To quantify this, we compared the number of trainable parameters and the number of multiply–accumulate operations (MACs) for different latent space sizes used in our experiments. +%Even though both encoders were designed for the same input dimensionality of $1\times 2048 \times 32$, their computational requirements differ significantly. To quantify this, we compared the number of trainable parameters and the number of multiply–accumulate operations (MACs) for different latent space sizes used in our experiments. +To compare the computational efficiency of the two architectures we show the number of trainable parameters and the number of multiply–accumulate operations (MACs) for different latent space sizes used in our experiments in table~\ref{tab:params_lenet_vs_efficient}. Even though the efficient architecture employs more layers and channels which allows the network to learn to recognize more types of patterns when compared to the LeNet-inspired one, the encoders' MACs are quite similar. The more complex decoder design of the efficient network appears to contribute a lot more MACs, which leads to longer pretraining times which we report in section~\ref{sec:setup_experiments_environment}. -\begin{table}[h] +\begin{table}[!ht] \centering + \renewcommand{\arraystretch}{1.15} + \begin{tabularx}{\linewidth}{crrrrrrrr} + \hline + & \multicolumn{4}{c}{\textbf{Encoders}} & \multicolumn{4}{c}{\textbf{Autoencoders}} \\ + \cline{2-9} + & \multicolumn{2}{c}{\textbf{LeNet}} & \multicolumn{2}{c}{\textbf{Efficient}} & \multicolumn{2}{c}{\textbf{LeNet}} & \multicolumn{2}{c}{\textbf{Efficient}} \\ + \cline{2-9} + \textbf{Latent $z$} & \textbf{Params} & \textbf{MACs} & \textbf{Params} & \textbf{MACs} & \textbf{Params} & \textbf{MACs} & \textbf{Params} & \textbf{MACs} \\ + \hline + 32 & 0.53M & 27.92M & 0.26M & 29.82M & 1.05M & 54.95M & 0.53M & 168.49M \\ + 64 & 1.05M & 28.44M & 0.53M & 30.08M & 2.10M & 56.00M & 1.06M & 169.02M \\ + 128 & 2.10M & 29.49M & 1.05M & 30.61M & 4.20M & 58.10M & 2.11M & 170.07M \\ + 256 & 4.20M & 31.59M & 2.10M & 31.65M & 8.39M & 62.29M & 4.20M & 172.16M \\ + 512 & 8.39M & 35.78M & 4.20M & 33.75M & 16.78M & 70.68M & 8.40M & 176.36M \\ + 768 & 12.58M & 39.98M & 6.29M & 35.85M & 25.17M & 79.07M & 12.59M & 180.55M \\ + 1024 & 16.78M & 44.17M & 8.39M & 37.95M & 33.56M & 87.46M & 16.79M & 184.75M \\ + \hline + \end{tabularx} \caption{Comparison of parameter count and MACs for SubTer\_LeNet and SubTer\_Efficient encoders across different latent space sizes.} - \begin{tabular}{c|cc|cc} - \toprule - \multirow{2}{*}{Latent dim} & \multicolumn{2}{c|}{SubTer\_LeNet} & \multicolumn{2}{c}{SubTer\_Efficient} \\ - & Params & MACs & Params & MACs \\ - \midrule - 32 & 8.40M & 17.41G & 1.17M & 2.54G \\ - 64 & 16.38M & 17.41G & 1.22M & 2.54G \\ - 128 & 32.35M & 17.41G & 1.33M & 2.54G \\ - 256 & 64.30M & 17.41G & 1.55M & 2.54G \\ - 512 & 128.19M & 17.41G & 1.99M & 2.54G \\ - 768 & 192.07M & 17.41G & 2.43M & 2.54G \\ - 1024 & 255.96M & 17.41G & 2.87M & 2.54G \\ - \bottomrule - \end{tabular} - \label{tab:lenet_vs_efficient} + \label{tab:params_lenet_vs_efficient} \end{table} -\todo[inline]{rework table and calculate with actual scripts and network archs in deepsad codebase} + + +%\todo[inline]{rework table and calculate with actual scripts and network archs in deepsad codebase} As can be seen, the efficient encoder requires an order of magnitude fewer parameters and significantly fewer operations while maintaining a comparable representational capacity. The key reason is the use of depth–wise separable convolutions, aggressive pooling along the densely sampled horizontal axis, and a channel squeezing strategy before the fully connected layer. Interestingly, the Efficient network also processes more intermediate channels (up to 32 compared to only 8 in the LeNet variant), which increases its ability to capture a richer set of patterns despite the reduced computational cost. This combination of efficiency and representational power makes the Efficient encoder a more suitable backbone for our anomaly detection task. @@ -1211,7 +1218,7 @@ During training, the algorithm balances two competing objectives: capturing as m We adapted the baseline implementations to our data loader and input format \todo[inline]{briefly describe file layout / preprocessing}, and added support for multiple evaluation targets per frame (two labels per data point), reporting both results per experiment. For OCSVM, the dimensionality reduction step is \emph{always} performed with the corresponding DeepSAD encoder and its autoencoder pretraining weights that match the evaluated setting (i.e., same latent size and backbone). Both baselines, like DeepSAD, output continuous anomaly scores. This allows us to evaluate them directly without committing to a fixed threshold. -\section{Experiment Overview \& Computational Environment} +\newsection{setup_experiments_environment}{Experiment Overview \& Computational Environment} \threadtodo {\textit{"What should the reader know after reading this section?"}} @@ -1239,27 +1246,74 @@ Furthermore, we investigated the effect of semi-supervised labeling. DeepSAD can All models were pre-trained for 50~epochs and then trained for 150~epochs with the same learning rate of $1\cdot 10^{-5}$ and evaluated with 5-fold cross-validation. Table~\ref{tab:exp_grid} summarizes the full experiment matrix. +% \begin{table}[h] +% \centering +% \caption{Experiment grid of all DeepSAD trainings. Each latent space size was tested for both network architectures and three levels of semi-supervised labeling.} +% \begin{tabular}{c|c|c} +% \toprule +% \textbf{Latent sizes} & \textbf{Architectures} & \textbf{Labeling regimes (normal, anomalous)} \\ +% \midrule +% $32, 64, 128, 256, 512, 768, 1024$ & LeNet-inspired, Efficient & (0,0), (50,10), (500,100) \\ +% \bottomrule +% \end{tabular} +% \label{tab:exp_grid} +% \end{table} + +% \begin{table}[h] +% \centering +% \caption{Experiment grid of all DeepSAD trainings. Each latent space size was tested for both network architectures and three levels of semi-supervised labeling.} +% \renewcommand{\arraystretch}{1.2} +% \begin{tabularx}{\textwidth}{cXX} +% \hline +% \textbf{Latent sizes} & \textbf{Architectures} & \textbf{Labeling regimes (normal, anomalous)} \\ +% \hline +% \begin{tabular}{@{}c@{}} +% 32 \\ 64 \\ 128 \\ 256 \\ 512 \\ 768 \\ 1024 +% \end{tabular} +% & +% \begin{tabular}{@{}c@{}} +% LeNet-inspired \\ Efficient +% \end{tabular} +% & +% \begin{tabular}{@{}c@{}} +% (0,0) \\ (50,10) \\ (500,100) +% \end{tabular} \\ +% \hline +% \end{tabularx} +% \label{tab:exp_grid} +% \end{table} + \begin{table}[h] \centering - \caption{Experiment grid of all DeepSAD trainings. Each latent space size was tested for both network architectures and three levels of semi-supervised labeling.} - \begin{tabular}{c|c|c} + \caption{Parameter space for the DeepSAD grid search. Each latent size is tested for both architectures and all labeling regimes.} + \renewcommand{\arraystretch}{1.15} + \begin{tabularx}{\linewidth}{lYYY} \toprule - \textbf{Latent sizes} & \textbf{Architectures} & \textbf{Labeling regimes (normal, anomalous)} \\ + & \textbf{Latent sizes} & \textbf{Architectures} & \textbf{Labeling regimes (normal, anomalous)} \\ \midrule - $32, 64, 128, 256, 512, 768, 1024$ & LeNet-inspired, Efficient & (0,0), (50,10), (500,100) \\ + \textbf{Levels} & + \makecell[c]{32 \\64\\128\\256\\512\\768\\1024} & + \makecell[c]{LeNet-inspired \\[-2pt]\rule{0.65\linewidth}{0.4pt}\\[-2pt]Efficient} & + \makecell[c]{(0,0) \\(50,10)\\(500,100)} \\ + \addlinespace[2pt] + \textbf{Count} & 7 & 2 & 3 \\ + \midrule + \multicolumn{2}{c}{\textbf{Total combinations}} & \(\,7 \times 2 \times 3 = \mathbf{42}\,\) & \\ \bottomrule - \end{tabular} + \end{tabularx} \label{tab:exp_grid} \end{table} + + \threadtodo {give overview about hardware setup and how long things take to train} {we know what we trained but not how long that takes} {table of hardware and of how long different trainings took} {experiment setup understood $\rightarrow$ what were the experiments' results} -Having outlined the full grid of experiments in Table~\ref{tab:exp_grid}, we next describe the computational environment in which they were conducted. The hardware and software stack used throughout all experiments is summarized in Table~\ref{tab:system_setup}. +These experiments were run on a computational environment for which we summarize the hardware and software stack in table~\ref{tab:system_setup}. \begin{table}[p] \centering @@ -1302,7 +1356,7 @@ Having outlined the full grid of experiments in Table~\ref{tab:exp_grid}, we nex \end{tabularx} \end{table} -Pretraining runtimes for the autoencoders are reported in Table~\ref{tab:ae_pretrain_runtimes}. These values are averaged across folds and labeling regimes, since the pretraining step itself does not make use of labels. \todo[inline]{why is efficient taking longer with less params and MACs?} +Pretraining runtimes for the autoencoders are reported in Table~\ref{tab:ae_pretrain_runtimes}. These values are averaged across folds and labeling regimes, since the pretraining step itself does not make use of labels. %\todo[inline]{why is efficient taking longer with less params and MACs?} \begin{table} \centering