quantification-MobileNetV5-V4 / scripts /generate_scientific_graphs.py

Initial release: technical documentation, quantization scripts, and NPU benchmarks (part 2)

2248c41 verified 2 days ago

6.79 kB

import os
import shutil
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

plt.style.use('seaborn-v0_8-whitegrid' if 'seaborn-v0_8-whitegrid' in plt.style.available else 'default')

BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
RESULTS_DIR = os.environ.get("LITHO_RESULTS_DIR", os.path.join(BASE_DIR, "results"))

CSV_PATH = os.path.join(RESULTS_DIR, "MobileNetV5", "machine_1", "machine_1_scientific_analysis.csv")
if not os.path.exists(CSV_PATH):
    CSV_PATH = os.path.join(RESULTS_DIR, "consolidated", "csv", "lithotheque_scientific_analysis.csv")

if not os.path.exists(CSV_PATH):
    raise FileNotFoundError(f"Cannot find scientific analysis dataset: {CSV_PATH}")

df = pd.read_csv(CSV_PATH)
print(f"[INFO] Loaded dataset from: {CSV_PATH}")
print(df.groupby(["Format", "Backend"]).agg({"Latency_ms": ["count", "mean", "std", "min", "max"]}))

# ==============================================================================
# Graph 1: Comparative Latency Across Backends and Quantizations (Error Bar Chart)
# ==============================================================================
fig, ax = plt.subplots(figsize=(10, 6), dpi=300)

summary = df.groupby(["Format", "Backend"])["Latency_ms"].agg(["mean", "std"]).reset_index()
order = [
    ("INT8", "Tensor_Cores_INT8"),
    ("INT8", "CPU_x64"),
    ("W4A8", "Tensor_Cores_INT4"),
    ("W4A8", "CPU_x64")
]
summary["order"] = summary.apply(lambda r: order.index((r["Format"], r["Backend"])) if (r["Format"], r["Backend"]) in order else 99, axis=1)
summary = summary.sort_values("order")

labels = [
    "INT8\n(Machine 1 GPU Tensor Cores)",
    "INT8\n(Machine 1 CPU Intel Core i3)",
    "W4A8\n(Machine 1 GPU Tensor Cores)",
    "W4A8\n(Machine 1 CPU Intel Core i3)"
]
means = summary["mean"].values
stds = summary["std"].values
colors = ["#10b981", "#ef4444", "#3b82f6", "#f59e0b"]

bars = ax.bar(labels, means, yerr=stds, capsize=8, color=colors, edgecolor="black", alpha=0.88, width=0.55)

for bar, mean in zip(bars, means):
    yval = bar.get_height()
    ax.text(bar.get_x() + bar.get_width() / 2.0, yval + 80, f"{mean:.1f} ms", ha='center', va='bottom', fontsize=11, fontweight='bold')

ax.set_ylabel("Inference Latency (ms) - Lower is Better", fontsize=12, fontweight='bold')
ax.set_title("MobileNetV5 (512x512) - Inference Latency Across Backends\nMachine 1: Workstation RTX 5060 vs Intel Core i3-12100F", fontsize=13, fontweight='bold', pad=15)
ax.set_ylim(0, max(means) * 1.22)
ax.grid(axis='y', linestyle='--', alpha=0.6)

plt.tight_layout()
chart1_dir = os.path.join(RESULTS_DIR, "MobileNetV5", "machine_1")
os.makedirs(chart1_dir, exist_ok=True)
chart1_path = os.path.join(chart1_dir, "latency_comparison_machine_1.png")
fig.savefig(chart1_path)
plt.close(fig)
print(f"[OK] Generated Graph 1: {chart1_path}")

# ==============================================================================
# Graph 2: Empirical Latency Stability Across Iterations (Boxplot Distribution)
# ==============================================================================
fig, ax = plt.subplots(figsize=(10, 6), dpi=300)

data_groups = []
labels_box = []
for fmt, bk, lbl in [
    ("INT8", "Tensor_Cores_INT8", "INT8 Tensor Cores\n(Machine 1 GPU)"),
    ("W4A8", "Tensor_Cores_INT4", "W4A8 Tensor Cores\n(Machine 1 GPU)"),
    ("INT8", "CPU_x64", "INT8 CPU\n(Machine 1 CPU)"),
    ("W4A8", "CPU_x64", "W4A8 CPU\n(Machine 1 CPU)")
]:
    sub = df[(df["Format"] == fmt) & (df["Backend"] == bk)]["Latency_ms"].values
    data_groups.append(sub)
    labels_box.append(lbl)

try:
    bplot = ax.boxplot(data_groups, patch_artist=True, tick_labels=labels_box, medianprops=dict(color='black', linewidth=2))
except TypeError:
    bplot = ax.boxplot(data_groups, patch_artist=True, labels=labels_box, medianprops=dict(color='black', linewidth=2))
for patch, color in zip(bplot['boxes'], colors):
    patch.set_facecolor(color)
    patch.set_alpha(0.8)

ax.set_ylabel("Inference Latency (ms)", fontsize=12, fontweight='bold')
ax.set_title("Empirical Latency Stability Across Iterations (Variance & Outliers)\nMobileNetV5 Rock Classification Pipeline", fontsize=13, fontweight='bold', pad=15)
ax.grid(axis='y', linestyle='--', alpha=0.6)

plt.tight_layout()
chart2_dir = os.path.join(RESULTS_DIR, "consolidated", "images")
os.makedirs(chart2_dir, exist_ok=True)
chart2_path = os.path.join(chart2_dir, "latency_stability_distribution.png")
fig.savefig(chart2_path)
plt.close(fig)
print(f"[OK] Generated Graph 2: {chart2_path}")

# ==============================================================================
# Graph 3: Pareto Frontier (Inference Latency vs Top-1 Accuracy)
# ==============================================================================
fig, ax = plt.subplots(figsize=(9, 6), dpi=300)

pts = [
    {"label": "INT8 (Machine 1 GPU)", "lat": df[(df["Format"] == "INT8") & (df["Backend"] == "Tensor_Cores_INT8")]["Latency_ms"].mean(), "acc": 83.33, "color": "#10b981", "size": 180},
    {"label": "W4A8 (Machine 1 GPU)", "lat": df[(df["Format"] == "W4A8") & (df["Backend"] == "Tensor_Cores_INT4")]["Latency_ms"].mean(), "acc": 80.33, "color": "#3b82f6", "size": 180},
    {"label": "INT8 (Machine 1 CPU)", "lat": df[(df["Format"] == "INT8") & (df["Backend"] == "CPU_x64")]["Latency_ms"].mean(), "acc": 83.33, "color": "#ef4444", "size": 140},
    {"label": "W4A8 (Machine 1 CPU)", "lat": df[(df["Format"] == "W4A8") & (df["Backend"] == "CPU_x64")]["Latency_ms"].mean(), "acc": 80.33, "color": "#f59e0b", "size": 140},
    {"label": "Machine 2 CPU INT8 (Oryon ARM64)", "lat": 5695.84, "acc": 83.33, "color": "#8b5cf6", "size": 140},
    {"label": "Machine 2 CPU W4A8 (Oryon ARM64)", "lat": 7119.80, "acc": 80.33, "color": "#ec4899", "size": 140}
]

for p in pts:
    ax.scatter(p["lat"], p["acc"], color=p["color"], s=p["size"], edgecolors="black", zorder=4)
    ax.annotate(p["label"], (p["lat"], p["acc"]), textcoords="offset points", xytext=(10, 8 if "INT8" in p["label"] else -16),
                fontsize=9.5, fontweight='bold', bbox=dict(boxstyle="round,pad=0.3", fc="white", ec=p["color"], alpha=0.9))

ax.set_xlabel("Inference Latency (ms) - Log/Linear Scale", fontsize=12, fontweight='bold')
ax.set_ylabel("Top-1 Accuracy (%)", fontsize=12, fontweight='bold')
ax.set_title("Pareto Efficiency: Geological Accuracy vs Inference Latency\nComparison Across Machine 1 & Machine 2 Environments", fontsize=13, fontweight='bold', pad=15)
ax.set_ylim(78.5, 85.5)
ax.set_xlim(-200, 7800)
ax.grid(True, linestyle='--', alpha=0.5)

plt.tight_layout()
chart3_path = os.path.join(chart2_dir, "accuracy_vs_latency_pareto.png")
fig.savefig(chart3_path)
plt.close(fig)
print(f"[OK] Generated Graph 3: {chart3_path}")
print("[SUCCESS] All scientific figures successfully generated in English.")

	import os
	import shutil
	import pandas as pd
	import numpy as np
	import matplotlib.pyplot as plt

	plt.style.use('seaborn-v0_8-whitegrid' if 'seaborn-v0_8-whitegrid' in plt.style.available else 'default')

	BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
	RESULTS_DIR = os.environ.get("LITHO_RESULTS_DIR", os.path.join(BASE_DIR, "results"))

	CSV_PATH = os.path.join(RESULTS_DIR, "MobileNetV5", "machine_1", "machine_1_scientific_analysis.csv")
	if not os.path.exists(CSV_PATH):
	CSV_PATH = os.path.join(RESULTS_DIR, "consolidated", "csv", "lithotheque_scientific_analysis.csv")

	if not os.path.exists(CSV_PATH):
	raise FileNotFoundError(f"Cannot find scientific analysis dataset: {CSV_PATH}")

	df = pd.read_csv(CSV_PATH)
	print(f"[INFO] Loaded dataset from: {CSV_PATH}")
	print(df.groupby(["Format", "Backend"]).agg({"Latency_ms": ["count", "mean", "std", "min", "max"]}))

	# ==============================================================================
	# Graph 1: Comparative Latency Across Backends and Quantizations (Error Bar Chart)
	# ==============================================================================
	fig, ax = plt.subplots(figsize=(10, 6), dpi=300)

	summary = df.groupby(["Format", "Backend"])["Latency_ms"].agg(["mean", "std"]).reset_index()
	order = [
	("INT8", "Tensor_Cores_INT8"),
	("INT8", "CPU_x64"),
	("W4A8", "Tensor_Cores_INT4"),
	("W4A8", "CPU_x64")
	]
	summary["order"] = summary.apply(lambda r: order.index((r["Format"], r["Backend"])) if (r["Format"], r["Backend"]) in order else 99, axis=1)
	summary = summary.sort_values("order")

	labels = [
	"INT8\n(Machine 1 GPU Tensor Cores)",
	"INT8\n(Machine 1 CPU Intel Core i3)",
	"W4A8\n(Machine 1 GPU Tensor Cores)",
	"W4A8\n(Machine 1 CPU Intel Core i3)"
	]
	means = summary["mean"].values
	stds = summary["std"].values
	colors = ["#10b981", "#ef4444", "#3b82f6", "#f59e0b"]

	bars = ax.bar(labels, means, yerr=stds, capsize=8, color=colors, edgecolor="black", alpha=0.88, width=0.55)

	for bar, mean in zip(bars, means):
	yval = bar.get_height()
	ax.text(bar.get_x() + bar.get_width() / 2.0, yval + 80, f"{mean:.1f} ms", ha='center', va='bottom', fontsize=11, fontweight='bold')

	ax.set_ylabel("Inference Latency (ms) - Lower is Better", fontsize=12, fontweight='bold')
	ax.set_title("MobileNetV5 (512x512) - Inference Latency Across Backends\nMachine 1: Workstation RTX 5060 vs Intel Core i3-12100F", fontsize=13, fontweight='bold', pad=15)
	ax.set_ylim(0, max(means) * 1.22)
	ax.grid(axis='y', linestyle='--', alpha=0.6)

	plt.tight_layout()
	chart1_dir = os.path.join(RESULTS_DIR, "MobileNetV5", "machine_1")
	os.makedirs(chart1_dir, exist_ok=True)
	chart1_path = os.path.join(chart1_dir, "latency_comparison_machine_1.png")
	fig.savefig(chart1_path)
	plt.close(fig)
	print(f"[OK] Generated Graph 1: {chart1_path}")

	# ==============================================================================
	# Graph 2: Empirical Latency Stability Across Iterations (Boxplot Distribution)
	# ==============================================================================
	fig, ax = plt.subplots(figsize=(10, 6), dpi=300)

	data_groups = []
	labels_box = []
	for fmt, bk, lbl in [
	("INT8", "Tensor_Cores_INT8", "INT8 Tensor Cores\n(Machine 1 GPU)"),
	("W4A8", "Tensor_Cores_INT4", "W4A8 Tensor Cores\n(Machine 1 GPU)"),
	("INT8", "CPU_x64", "INT8 CPU\n(Machine 1 CPU)"),
	("W4A8", "CPU_x64", "W4A8 CPU\n(Machine 1 CPU)")
	]:
	sub = df[(df["Format"] == fmt) & (df["Backend"] == bk)]["Latency_ms"].values
	data_groups.append(sub)
	labels_box.append(lbl)

	try:
	bplot = ax.boxplot(data_groups, patch_artist=True, tick_labels=labels_box, medianprops=dict(color='black', linewidth=2))
	except TypeError:
	bplot = ax.boxplot(data_groups, patch_artist=True, labels=labels_box, medianprops=dict(color='black', linewidth=2))
	for patch, color in zip(bplot['boxes'], colors):
	patch.set_facecolor(color)
	patch.set_alpha(0.8)

	ax.set_ylabel("Inference Latency (ms)", fontsize=12, fontweight='bold')
	ax.set_title("Empirical Latency Stability Across Iterations (Variance & Outliers)\nMobileNetV5 Rock Classification Pipeline", fontsize=13, fontweight='bold', pad=15)
	ax.grid(axis='y', linestyle='--', alpha=0.6)

	plt.tight_layout()
	chart2_dir = os.path.join(RESULTS_DIR, "consolidated", "images")
	os.makedirs(chart2_dir, exist_ok=True)
	chart2_path = os.path.join(chart2_dir, "latency_stability_distribution.png")
	fig.savefig(chart2_path)
	plt.close(fig)
	print(f"[OK] Generated Graph 2: {chart2_path}")

	# ==============================================================================
	# Graph 3: Pareto Frontier (Inference Latency vs Top-1 Accuracy)
	# ==============================================================================
	fig, ax = plt.subplots(figsize=(9, 6), dpi=300)

	pts = [
	{"label": "INT8 (Machine 1 GPU)", "lat": df[(df["Format"] == "INT8") & (df["Backend"] == "Tensor_Cores_INT8")]["Latency_ms"].mean(), "acc": 83.33, "color": "#10b981", "size": 180},
	{"label": "W4A8 (Machine 1 GPU)", "lat": df[(df["Format"] == "W4A8") & (df["Backend"] == "Tensor_Cores_INT4")]["Latency_ms"].mean(), "acc": 80.33, "color": "#3b82f6", "size": 180},
	{"label": "INT8 (Machine 1 CPU)", "lat": df[(df["Format"] == "INT8") & (df["Backend"] == "CPU_x64")]["Latency_ms"].mean(), "acc": 83.33, "color": "#ef4444", "size": 140},
	{"label": "W4A8 (Machine 1 CPU)", "lat": df[(df["Format"] == "W4A8") & (df["Backend"] == "CPU_x64")]["Latency_ms"].mean(), "acc": 80.33, "color": "#f59e0b", "size": 140},
	{"label": "Machine 2 CPU INT8 (Oryon ARM64)", "lat": 5695.84, "acc": 83.33, "color": "#8b5cf6", "size": 140},
	{"label": "Machine 2 CPU W4A8 (Oryon ARM64)", "lat": 7119.80, "acc": 80.33, "color": "#ec4899", "size": 140}
	]

	for p in pts:
	ax.scatter(p["lat"], p["acc"], color=p["color"], s=p["size"], edgecolors="black", zorder=4)
	ax.annotate(p["label"], (p["lat"], p["acc"]), textcoords="offset points", xytext=(10, 8 if "INT8" in p["label"] else -16),
	fontsize=9.5, fontweight='bold', bbox=dict(boxstyle="round,pad=0.3", fc="white", ec=p["color"], alpha=0.9))

	ax.set_xlabel("Inference Latency (ms) - Log/Linear Scale", fontsize=12, fontweight='bold')
	ax.set_ylabel("Top-1 Accuracy (%)", fontsize=12, fontweight='bold')
	ax.set_title("Pareto Efficiency: Geological Accuracy vs Inference Latency\nComparison Across Machine 1 & Machine 2 Environments", fontsize=13, fontweight='bold', pad=15)
	ax.set_ylim(78.5, 85.5)
	ax.set_xlim(-200, 7800)
	ax.grid(True, linestyle='--', alpha=0.5)

	plt.tight_layout()
	chart3_path = os.path.join(chart2_dir, "accuracy_vs_latency_pareto.png")
	fig.savefig(chart3_path)
	plt.close(fig)
	print(f"[OK] Generated Graph 3: {chart3_path}")
	print("[SUCCESS] All scientific figures successfully generated in English.")