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import torch
import torchvision.models as models
import time
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from qiskit import QuantumCircuit
from torch import nn
# === 1. ResNet50 Inference Benchmark ===
def benchmark_resnet50():
model = models.resnet50(pretrained=False).eval()
input_data = torch.randn(64, 3, 224, 224)
start = time.time()
with torch.no_grad():
_ = model(input_data)
end = time.time()
return end - start
# === 2. GAN Benchmark ===
class SimpleGAN(nn.Module):
def __init__(self):
super(SimpleGAN, self).__init__()
self.fc = nn.Sequential(
nn.Linear(100, 256),
nn.ReLU(),
nn.Linear(256, 784),
nn.Tanh()
)
def forward(self, z):
return self.fc(z)
def benchmark_gan():
model = SimpleGAN()
z = torch.randn((1, 100))
start = time.time()
_ = model(z)
end = time.time()
return end - start
# === 3. QAOA Simulation (Mock) ===
def benchmark_qaoa(qubits=1000):
start = time.time()
qc = QuantumCircuit(qubits)
for i in range(qubits):
qc.h(i)
qc.barrier()
end = time.time()
return end - start
# === 4. Save Results ===
def run_benchmarks():
results = {
"ResNet50 (s)": benchmark_resnet50(),
"GAN Gen (s)": benchmark_gan(),
"QAOA 1000q (s)": benchmark_qaoa()
}
df = pd.DataFrame([results])
df.to_csv("benchmark_results.csv", index=False)
# Plot
plt.figure(figsize=(10, 6))
plt.bar(results.keys(), results.values(), color='purple')
plt.ylabel("Temps (s)")
plt.title("Benchmarks MONSTERDOG Multi-Noeuds")
plt.savefig("benchmark_plot.png")
plt.close()
if __name__ == "__main__":
run_benchmarks() |