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from time import perf_counter
import numpy as np
import matplotlib.pyplot as plt
from optimum.amd.ryzenai.models import YoloV8ImageProcessor
def measure_latency(model, image):
processor = YoloV8ImageProcessor()
pixel_values = processor(image, return_tensors="np").pixel_values
latencies = []
# warm up
for _ in range(10):
_ = model(pixel_values)
# Timed run
for _ in range(10):
start_time = perf_counter()
_ = model(pixel_values)
latency = perf_counter() - start_time
latencies.append(latency)
# Compute run statistics
time_avg_ms = 1000 * np.mean(latencies)
time_std_ms = 1000 * np.std(latencies)
return time_avg_ms, time_std_ms
def plot_latency(cpu_latency, ryzen_latency):
"""
Creates a bar plot comparing CPU and Ryzen latencies.
Parameters:
cpu_latency (float): Average latency for the CPU.
ryzen_latency (float): Average latency for the Ryzen.
"""
# Labels for the bars
labels = ['RyzenAI', 'CPU']
# Latency values
latency_values = [ryzen_latency, cpu_latency]
# Colors for the bars
colors = ['blue', 'green']
# Create a bar plot
x = np.arange(len(labels)) # the label locations
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
bars = ax.bar(x, latency_values, width, label='Latency', color=colors)
# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_xlabel('Processor')
ax.set_ylabel('Average Latency (ms)')
ax.set_title('Average Latency by Processor')
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.legend()
# Attach a text label above each bar in *bars*, displaying its height.
for bar in bars:
height = bar.get_height()
ax.annotate(f'{height:.2f}',
xy=(bar.get_x() + bar.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom')
fig.tight_layout()
plt.show() |