#!/usr/bin/env python3 # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Step 8: Generate exactly 5 benchmark charts as PNG images for the report. Usage: python3 generate-benchmark-charts.py Expected JSON format (benchmark_data.json written by nv-import-vision-model-report skill pre-flight): { "model_name": "yolo26_nano", "engine": "models/yolo26_nano/benchmarks/engines/yolo26n_dynamic_b256.engine", "max_bs": 256, "trtexec": { "bs1": {"qps": 220.5, "gpu_mean_ms": 4.53}, "bsmax": {"qps": 39.2, "gpu_mean_ms": 103.7, "p99_ms": 105.1, "imgs_per_sec": 10035.2} }, "peak_gpu_streams": 334, "deepstream": { "run1": {"streams": 334, "total_fps": 7850.0, "fps_per_stream": 23.5}, "run2": {"streams": 238, "total_fps": 7378.4, "fps_per_stream": 31.0} } } Outputs (fixed names — do not rename): chart_trtexec_bs1_vs_bsmax.png — grouped bar: QPS BS=1 vs BS=MAX_BS chart_trtexec_throughput.png — bar: imgs/sec at MAX_BS + PEAK_GPU_STREAMS annotation chart_ds_streams_vs_fps.png — line: stream count vs fps/stream, 30fps threshold chart_trt_vs_ds.png — grouped bar: trtexec vs DS Run1 vs DS Run2 total imgs/s chart_efficiency.png — bar: DS Run1 and Run2 pipeline efficiency % """ import sys import json import os import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt plt.rcParams.update({ 'figure.facecolor': 'white', 'axes.facecolor': '#FAFAFA', 'axes.grid': True, 'grid.alpha': 0.3, 'font.size': 11, 'axes.titlesize': 13, 'axes.titleweight': 'bold', }) COLORS = { 'blue': '#2196F3', 'green': '#4CAF50', 'orange': '#FF9800', 'pink': '#E91E63', 'purple': '#9C27B0', 'teal': '#00BCD4', 'red': '#FF5722', } def two_line_title(model_name, subtitle): """Two-line title: model name (line 1) + subtitle (line 2).""" return f'{model_name}\n{subtitle}' def chart_trtexec_bs1_vs_bsmax(data, output_dir): """Grouped bar chart: QPS at BS=1 vs BS=MAX_BS side by side.""" max_bs = data['max_bs'] qps_bs1 = data['trtexec']['bs1']['qps'] qps_bsmax = data['trtexec']['bsmax']['qps'] labels = ['BS=1', f'BS={max_bs}'] values = [qps_bs1, qps_bsmax] colors = [COLORS['blue'], COLORS['green']] fig, ax = plt.subplots(figsize=(10, 6)) bars = ax.bar(labels, values, color=colors, width=0.5, edgecolor='white', linewidth=1.5) for bar, val in zip(bars, values): ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + max(values) * 0.01, f'{val:.1f}', ha='center', va='bottom', fontweight='bold', fontsize=13) ax.set_ylabel('QPS (queries/sec)', fontsize=13) ax.set_ylim(0, max(values) * 1.18) ax.grid(axis='y', alpha=0.3) ax.set_title(two_line_title(data['model_name'], f'trtexec QPS: BS=1 vs BS={max_bs}')) plt.tight_layout() out = os.path.join(output_dir, 'chart_trtexec_bs1_vs_bsmax.png') fig.savefig(out, dpi=150) plt.close(fig) print(f' chart_trtexec_bs1_vs_bsmax.png') def chart_trtexec_throughput(data, output_dir): """Single bar: GPU-only imgs/sec at MAX_BS with PEAK_GPU_STREAMS annotation.""" max_bs = data['max_bs'] imgs_per_sec = data['trtexec']['bsmax']['imgs_per_sec'] peak_streams = data['peak_gpu_streams'] realtime_imgs = peak_streams * 30 # the throughput that satisfies peak_streams at 30fps fig, ax = plt.subplots(figsize=(10, 6)) bar = ax.bar([f'BS={max_bs}'], [imgs_per_sec], color=COLORS['blue'], width=0.4, edgecolor='white', linewidth=1.5) ax.text(bar[0].get_x() + bar[0].get_width() / 2, imgs_per_sec + imgs_per_sec * 0.01, f'{imgs_per_sec:.0f}', ha='center', va='bottom', fontweight='bold', fontsize=13) # Annotation line at PEAK_GPU_STREAMS × 30fps threshold ax.axhline(y=realtime_imgs, color=COLORS['red'], linestyle='--', linewidth=2, label=f'PEAK_GPU_STREAMS={peak_streams} × 30fps = {realtime_imgs:.0f} imgs/s') ax.text(0.98, realtime_imgs + imgs_per_sec * 0.01, f'PEAK={peak_streams} streams', ha='right', va='bottom', color=COLORS['red'], fontsize=10, fontweight='bold', transform=ax.get_yaxis_transform()) ax.set_ylabel('Images / sec', fontsize=13) ax.set_ylim(0, imgs_per_sec * 1.25) ax.grid(axis='y', alpha=0.3) ax.legend(loc='upper left', fontsize=10) ax.set_title(two_line_title(data['model_name'], f'GPU Throughput at BS={max_bs} (PEAK_GPU_STREAMS={peak_streams})')) plt.tight_layout() out = os.path.join(output_dir, 'chart_trtexec_throughput.png') fig.savefig(out, dpi=150) plt.close(fig) print(f' chart_trtexec_throughput.png') def chart_ds_streams_vs_fps(data, output_dir): """Line chart: X=stream count, Y=fps/stream. Red dashed line at 30fps.""" run1 = data['deepstream']['run1'] run2 = data['deepstream']['run2'] stream_counts = [run1['streams'], run2['streams']] fps_vals = [run1['fps_per_stream'], run2['fps_per_stream']] # Sort by stream count ascending pairs = sorted(zip(stream_counts, fps_vals)) stream_counts = [p[0] for p in pairs] fps_vals = [p[1] for p in pairs] fig, ax = plt.subplots(figsize=(10, 6)) ax.plot(stream_counts, fps_vals, color=COLORS['blue'], linewidth=2.5, marker='o', markersize=10, zorder=4) for sc, fp in zip(stream_counts, fps_vals): ax.text(sc, fp + max(fps_vals) * 0.025, f'{fp:.1f} fps', ha='center', va='bottom', fontweight='bold', fontsize=12) ax.axhline(y=30, color=COLORS['red'], linestyle='--', linewidth=2, label='30 fps/stream real-time threshold') ax.set_xlabel('Stream Count', fontsize=13) ax.set_ylabel('FPS / Stream', fontsize=13) lower = -max(fps_vals) * 0.15 ax.set_ylim(lower, max(fps_vals) * 1.3) ax.set_xticks(stream_counts) ax.grid(axis='y', alpha=0.3) ax.legend(loc='upper right', fontsize=10) # Label each point run_labels = {run1['streams']: 'Run 1\n(PEAK_GPU_STREAMS)', run2['streams']: 'Run 2\n(RT_STREAMS)'} for sc in stream_counts: ax.annotate(run_labels.get(sc, ''), xy=(sc, 0), xytext=(sc, -max(fps_vals) * 0.12), ha='center', fontsize=9, color='#555555') ax.set_title(two_line_title(data['model_name'], 'DeepStream: FPS/Stream vs Stream Count')) plt.tight_layout() out = os.path.join(output_dir, 'chart_ds_streams_vs_fps.png') fig.savefig(out, dpi=150) plt.close(fig) print(f' chart_ds_streams_vs_fps.png') def chart_trt_vs_ds(data, output_dir): """Grouped bars: trtexec total imgs/s | DS Run 1 total imgs/s | DS Run 2 total imgs/s.""" max_bs = data['max_bs'] trt_imgs = data['trtexec']['bsmax']['imgs_per_sec'] ds1_imgs = data['deepstream']['run1']['total_fps'] ds2_imgs = data['deepstream']['run2']['total_fps'] n1 = data['deepstream']['run1']['streams'] n2 = data['deepstream']['run2']['streams'] labels = [f'trtexec\nBS={max_bs}', f'DS Run 1\n({n1} streams)', f'DS Run 2\n({n2} streams)'] values = [trt_imgs, ds1_imgs, ds2_imgs] colors = [COLORS['pink'], COLORS['blue'], COLORS['green']] fig, ax = plt.subplots(figsize=(10, 6)) bars = ax.bar(labels, values, color=colors, width=0.5, edgecolor='white', linewidth=1.5) for bar, val in zip(bars, values): ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + max(values) * 0.01, f'{val:.0f}', ha='center', va='bottom', fontweight='bold', fontsize=13) ax.set_ylabel('Total Images / sec', fontsize=13) ax.set_ylim(0, max(values) * 1.18) ax.grid(axis='y', alpha=0.3) ax.set_title(two_line_title(data['model_name'], 'trtexec vs DeepStream: Total Throughput')) plt.tight_layout() out = os.path.join(output_dir, 'chart_trt_vs_ds.png') fig.savefig(out, dpi=150) plt.close(fig) print(f' chart_trt_vs_ds.png') def chart_efficiency(data, output_dir): """Bar chart: DS Run 1 and Run 2 pipeline efficiency %, dashed line at 100%.""" trt_imgs = data['trtexec']['bsmax']['imgs_per_sec'] ds1_imgs = data['deepstream']['run1']['total_fps'] ds2_imgs = data['deepstream']['run2']['total_fps'] n1 = data['deepstream']['run1']['streams'] n2 = data['deepstream']['run2']['streams'] if trt_imgs <= 0: print("ERROR: trtexec imgs_per_sec is zero or negative — cannot compute efficiency", file=sys.stderr) sys.exit(1) eff1 = round(ds1_imgs / trt_imgs * 100, 1) eff2 = round(ds2_imgs / trt_imgs * 100, 1) labels = [f'DS Run 1\n({n1} streams)', f'DS Run 2\n({n2} streams)'] values = [eff1, eff2] colors = [COLORS['purple'], COLORS['teal']] fig, ax = plt.subplots(figsize=(10, 6)) bars = ax.bar(labels, values, color=colors, width=0.4, edgecolor='white', linewidth=1.5) ax.axhline(y=100, color='#333333', linestyle='--', linewidth=1.5, alpha=0.6, label='100% efficiency') for bar, val in zip(bars, values): ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.5, f'{val}%', ha='center', va='bottom', fontweight='bold', fontsize=13) ax.set_ylabel('DS Efficiency (%)', fontsize=13) ax.set_ylim(0, max(values) * 1.2) ax.grid(axis='y', alpha=0.3) ax.legend(loc='upper right', fontsize=10) ax.set_title(two_line_title(data['model_name'], 'DeepStream Pipeline Efficiency vs trtexec')) plt.tight_layout() out = os.path.join(output_dir, 'chart_efficiency.png') fig.savefig(out, dpi=150) plt.close(fig) print(f' chart_efficiency.png') def main(): if len(sys.argv) != 3: print(f"Usage: {sys.argv[0]} ") sys.exit(1) output_dir = sys.argv[1] json_file = sys.argv[2] os.makedirs(output_dir, exist_ok=True) with open(json_file) as f: data = json.load(f) model = data.get('model_name', 'unknown') print(f"Generating 5 charts for {model} -> {output_dir}/") chart_trtexec_bs1_vs_bsmax(data, output_dir) chart_trtexec_throughput(data, output_dir) chart_ds_streams_vs_fps(data, output_dir) chart_trt_vs_ds(data, output_dir) chart_efficiency(data, output_dir) print("Done — 5 charts written.") if __name__ == "__main__": main()