File size: 6,790 Bytes
2248c41 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | 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.")
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