wip
This commit is contained in:
Jan Kowalczyk
@@ -23,6 +23,8 @@ dependencies = [
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"scipy>=1.16.0",
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"seaborn>=0.13.2",
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"six>=1.17.0",
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"tabulate>=0.9.0",
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"thop>=0.1.1.post2209072238",
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"torch-receptive-field",
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"torchscan>=0.1.1",
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"visualtorch>=0.2.4",
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101
Deep-SAD-PyTorch/src/network_statistics.py
Normal file
101
Deep-SAD-PyTorch/src/network_statistics.py
Normal file
@@ -0,0 +1,101 @@
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import torch
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from thop import profile
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from networks.subter_LeNet import SubTer_LeNet, SubTer_LeNet_Autoencoder
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from networks.subter_LeNet_rf import SubTer_Efficient_AE, SubTer_EfficientEncoder
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# Configuration
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LATENT_DIMS = [32, 64, 128, 256, 512, 768, 1024]
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BATCH_SIZE = 1
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INPUT_SHAPE = (BATCH_SIZE, 1, 32, 2048)
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def count_parameters(model, input_shape):
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"""Count MACs and parameters for a model."""
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model.eval()
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with torch.no_grad():
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input_tensor = torch.randn(input_shape)
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macs, params = profile(model, inputs=(input_tensor,))
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return {"MACs": macs, "Parameters": params}
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def format_number(num: float) -> str:
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"""Format large numbers with K, M, B, T suffixes."""
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for unit in ["", "K", "M", "B", "T"]:
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if abs(num) < 1000.0 or unit == "T":
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return f"{num:3.2f}{unit}"
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num /= 1000.0
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def main():
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# Collect results per latent dimension
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results = {} # dim -> dict of 8 values
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for dim in LATENT_DIMS:
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# Instantiate models for this latent dim
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lenet_enc = SubTer_LeNet(rep_dim=dim)
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eff_enc = SubTer_EfficientEncoder(rep_dim=dim)
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lenet_ae = SubTer_LeNet_Autoencoder(rep_dim=dim)
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eff_ae = SubTer_Efficient_AE(rep_dim=dim)
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# Profile each
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lenet_enc_stats = count_parameters(lenet_enc, INPUT_SHAPE)
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eff_enc_stats = count_parameters(eff_enc, INPUT_SHAPE)
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lenet_ae_stats = count_parameters(lenet_ae, INPUT_SHAPE)
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eff_ae_stats = count_parameters(eff_ae, INPUT_SHAPE)
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results[dim] = {
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"lenet_enc_params": format_number(lenet_enc_stats["Parameters"]),
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"lenet_enc_macs": format_number(lenet_enc_stats["MACs"]),
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"eff_enc_params": format_number(eff_enc_stats["Parameters"]),
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"eff_enc_macs": format_number(eff_enc_stats["MACs"]),
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"lenet_ae_params": format_number(lenet_ae_stats["Parameters"]),
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"lenet_ae_macs": format_number(lenet_ae_stats["MACs"]),
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"eff_ae_params": format_number(eff_ae_stats["Parameters"]),
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"eff_ae_macs": format_number(eff_ae_stats["MACs"]),
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}
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# Build LaTeX table with tabularx
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header = (
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"\\begin{table}[!ht]\n"
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"\\centering\n"
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"\\renewcommand{\\arraystretch}{1.15}\n"
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"\\begin{tabularx}{\\linewidth}{lXXXXXXXX}\n"
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"\\hline\n"
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" & \\multicolumn{4}{c}{\\textbf{Encoders}} & "
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"\\multicolumn{4}{c}{\\textbf{Autoencoders}} \\\\\n"
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"\\cline{2-9}\n"
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"\\textbf{Latent $z$} & "
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"\\textbf{LeNet Params} & \\textbf{LeNet MACs} & "
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"\\textbf{Eff. Params} & \\textbf{Eff. MACs} & "
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"\\textbf{LeNet Params} & \\textbf{LeNet MACs} & "
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"\\textbf{Eff. Params} & \\textbf{Eff. MACs} \\\\\n"
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"\\hline\n"
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)
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rows = []
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for dim in LATENT_DIMS:
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r = results[dim]
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row = (
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f"{dim} & "
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f"{r['lenet_enc_params']} & {r['lenet_enc_macs']} & "
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f"{r['eff_enc_params']} & {r['eff_enc_macs']} & "
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f"{r['lenet_ae_params']} & {r['lenet_ae_macs']} & "
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f"{r['eff_ae_params']} & {r['eff_ae_macs']} \\\\"
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)
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rows.append(row)
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footer = (
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"\\hline\n"
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"\\end{tabularx}\n"
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"\\caption{Parameter and MAC counts for SubTer variants across latent dimensionalities.}\n"
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"\\label{tab:subter_counts}\n"
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"\\end{table}\n"
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)
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latex_table = header + "\n".join(rows) + "\n" + footer
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print(latex_table)
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if __name__ == "__main__":
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main()
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155
Deep-SAD-PyTorch/src/network_statistics.tex
Normal file
155
Deep-SAD-PyTorch/src/network_statistics.tex
Normal file
@@ -0,0 +1,155 @@
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.ConvTranspose2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_upsample() for <class 'torch.nn.modules.upsampling.Upsample'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.ConvTranspose2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_upsample() for <class 'torch.nn.modules.upsampling.Upsample'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.ConvTranspose2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_upsample() for <class 'torch.nn.modules.upsampling.Upsample'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.ConvTranspose2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_upsample() for <class 'torch.nn.modules.upsampling.Upsample'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.ConvTranspose2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
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||||
[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
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||||
[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
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[INFO] Register count_upsample() for <class 'torch.nn.modules.upsampling.Upsample'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
|
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
|
||||
[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
|
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[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
|
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[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
|
||||
[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
|
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[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
|
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[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
|
||||
[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
|
||||
[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
|
||||
[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
|
||||
[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.ConvTranspose2d'>.
|
||||
[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
|
||||
[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
|
||||
[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
|
||||
[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
|
||||
[INFO] Register count_upsample() for <class 'torch.nn.modules.upsampling.Upsample'>.
|
||||
[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.
|
||||
[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
|
||||
[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
|
||||
[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
|
||||
[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
|
||||
[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
|
||||
[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
|
||||
[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
|
||||
[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
|
||||
[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
|
||||
[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
|
||||
[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
|
||||
[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
|
||||
[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.ConvTranspose2d'>.
|
||||
[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
|
||||
[INFO] Register count_normalization() for <class 'torch.nn.modules.batchnorm.BatchNorm2d'>.
|
||||
[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
|
||||
[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
|
||||
[INFO] Register count_upsample() for <class 'torch.nn.modules.upsampling.Upsample'>.
|
||||
[INFO] Register zero_ops() for <class 'torch.nn.modules.container.Sequential'>.
|
||||
\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}
|
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\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} \\
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\hline
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32 & 525.29K & 27.92M & 263.80K & 29.82M & 1.05M & 54.95M & 532.35K & 168.49M \\
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64 & 1.05M & 28.44M & 525.94K & 30.08M & 2.10M & 56.00M & 1.06M & 169.02M \\
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128 & 2.10M & 29.49M & 1.05M & 30.61M & 4.20M & 58.10M & 2.11M & 170.07M \\
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256 & 4.20M & 31.59M & 2.10M & 31.65M & 8.39M & 62.29M & 4.20M & 172.16M \\
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||||
512 & 8.39M & 35.78M & 4.20M & 33.75M & 16.78M & 70.68M & 8.40M & 176.36M \\
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||||
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 \\
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||||
\hline
|
||||
\end{tabularx}
|
||||
\caption{Parameter and MAC counts for SubTer variants across latent dimensionalities.}
|
||||
\label{tab:subter_counts}
|
||||
\end{table}
|
||||
|
||||
24
Deep-SAD-PyTorch/uv.lock
generated
24
Deep-SAD-PyTorch/uv.lock
generated
@@ -141,6 +141,8 @@ dependencies = [
|
||||
{ name = "scipy" },
|
||||
{ name = "seaborn" },
|
||||
{ name = "six" },
|
||||
{ name = "tabulate" },
|
||||
{ name = "thop" },
|
||||
{ name = "torch-receptive-field" },
|
||||
{ name = "torchscan" },
|
||||
{ name = "visualtorch" },
|
||||
@@ -166,6 +168,8 @@ requires-dist = [
|
||||
{ name = "scipy", specifier = ">=1.16.0" },
|
||||
{ name = "seaborn", specifier = ">=0.13.2" },
|
||||
{ name = "six", specifier = ">=1.17.0" },
|
||||
{ name = "tabulate", specifier = ">=0.9.0" },
|
||||
{ name = "thop", specifier = ">=0.1.1.post2209072238" },
|
||||
{ name = "torch-receptive-field", git = "https://github.com/Fangyh09/pytorch-receptive-field.git" },
|
||||
{ name = "torchscan", specifier = ">=0.1.1" },
|
||||
{ name = "visualtorch", specifier = ">=0.2.4" },
|
||||
@@ -882,6 +886,26 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tabulate"
|
||||
version = "0.9.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ec/fe/802052aecb21e3797b8f7902564ab6ea0d60ff8ca23952079064155d1ae1/tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c", size = 81090, upload-time = "2022-10-06T17:21:48.54Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f", size = 35252, upload-time = "2022-10-06T17:21:44.262Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "thop"
|
||||
version = "0.1.1.post2209072238"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "torch" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/bb/0f/72beeab4ff5221dc47127c80f8834b4bcd0cb36f6ba91c0b1d04a1233403/thop-0.1.1.post2209072238-py3-none-any.whl", hash = "sha256:01473c225231927d2ad718351f78ebf7cffe6af3bed464c4f1ba1ef0f7cdda27", size = 15443, upload-time = "2022-09-07T14:38:37.211Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "threadpoolctl"
|
||||
version = "3.6.0"
|
||||
|
||||
Reference in New Issue
Block a user