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"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "70cab2b9",
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"outputs": [],
"source": [
"from huggingface_hub import snapshot_download\n",
"import os\n",
"\n",
"repo_id = \"realAABeigi/tra-base-1\"\n",
"\n",
"print(f\"[INFO] Downloading all files from {repo_id} to root...\")\n",
"\n",
"try:\n",
" # This will download all files from the repo and place them in the current directory\n",
" snapshot_download(\n",
" repo_id=repo_id,\n",
" local_dir=\"./\",\n",
" local_dir_use_symlinks=False\n",
" )\n",
" print(\"[SUCCESS] All files downloaded to root directory.\")\n",
"except Exception as e:\n",
" print(f\"[ERROR] Failed to download: {e}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9aeb2c2a",
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"outputs": [],
"source": [
"!pip install onnxruntime opencv-python-headless scipy matplotlib psutil --quiet\n",
"\n",
"import os\n",
"import cv2\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.ndimage import maximum_filter\n",
"import gc\n",
"import tracemalloc\n",
"import time\n",
"import psutil\n",
"import onnxruntime as ort\n",
"\n",
"THRESHOLD = 0.4\n",
"IMG_SIZE = 192\n",
"GRID_SIZE = 48\n",
"ONNX_PATH = \"tiny_heatmap_car.onnx\"\n",
"DATA_PATH = \"tiny_heatmap_car.onnx.data\"\n",
"TEST_DIR = \"/content/Test\"\n",
"\n",
"def get_process_memory():\n",
" process = psutil.Process(os.getpid())\n",
" return process.memory_info().rss\n",
"\n",
"try:\n",
" model_size = os.path.getsize(ONNX_PATH)\n",
" if os.path.exists(DATA_PATH):\n",
" model_size += os.path.getsize(DATA_PATH)\n",
"\n",
" session = ort.InferenceSession(ONNX_PATH, providers=['CPUExecutionProvider'])\n",
" input_name = session.get_inputs()[0].name\n",
"\n",
" def preprocess(img_path):\n",
" orig_img = cv2.imread(img_path)\n",
" if orig_img is None: return None, None\n",
" img_rgb = cv2.cvtColor(orig_img, cv2.COLOR_BGR2RGB)\n",
" img_resized = cv2.resize(img_rgb, (IMG_SIZE, IMG_SIZE))\n",
" img_data = img_resized.astype(np.float32) / 255.0\n",
" mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)\n",
" std = np.array([0.229, 0.224, 0.225], dtype=np.float32)\n",
" img_data = (img_data - mean) / std\n",
" img_data = np.transpose(img_data, (2, 0, 1))\n",
" img_data = np.expand_dims(img_data, axis=0)\n",
" return img_data, img_rgb\n",
"\n",
" def run_onnx_cpu_inference(img_path):\n",
" gc.collect()\n",
" tracemalloc.start()\n",
"\n",
" mem_before = get_process_memory()\n",
" start_time = time.time()\n",
"\n",
" img_data, img_rgb = preprocess(img_path)\n",
" if img_data is None: return\n",
"\n",
" outputs = session.run(None, {input_name: img_data})\n",
"\n",
" mem_after = get_process_memory()\n",
" inference_time = (time.time() - start_time) * 1000\n",
"\n",
" current, peak = tracemalloc.get_traced_memory()\n",
" tracemalloc.stop()\n",
"\n",
" heatmap = outputs[0].squeeze()\n",
" data_max = maximum_filter(heatmap, size=3)\n",
" maxima = (heatmap == data_max) & (heatmap > THRESHOLD)\n",
" y_coords, x_coords = np.where(maxima)\n",
"\n",
" system_delta = mem_after - mem_before\n",
" total_footprint_kb = (model_size + peak + abs(system_delta)) / 1024\n",
"\n",
" print(f\"\\n--- Image: {os.path.basename(img_path)} ---\")\n",
" print(f\"Latency: {inference_time:.2f}ms\")\n",
" print(f\"System Memory Change: {system_delta/1024:.2f} KB\")\n",
" print(f\"Total RAM Footprint (Est): {total_footprint_kb:.2f} KB\")\n",
"\n",
" plt.figure(figsize=(10, 4))\n",
" plt.subplot(1, 2, 1)\n",
" display_img = cv2.resize(img_rgb, (384, 384))\n",
" for y, x in zip(y_coords, x_coords):\n",
" cx, cy = int(x * (384/GRID_SIZE)), int(y * (384/GRID_SIZE))\n",
" cv2.circle(display_img, (cx, cy), 6, (255, 0, 0), -1)\n",
" plt.imshow(display_img)\n",
" plt.title(f\"Cars: {len(y_coords)} | Time: {inference_time:.1f}ms\")\n",
" plt.axis('off')\n",
"\n",
" plt.subplot(1, 2, 2)\n",
" plt.imshow(heatmap, cmap='jet')\n",
" plt.title(f\"Total RAM: {total_footprint_kb:.1f} KB\")\n",
" plt.axis('off')\n",
" plt.show()\n",
"\n",
" if os.path.exists(TEST_DIR):\n",
" image_files = [f for f in os.listdir(TEST_DIR) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]\n",
" for img_file in image_files:\n",
" run_onnx_cpu_inference(os.path.join(TEST_DIR, img_file))\n",
" else:\n",
" print(\"Folder Test not found.\")\n",
"\n",
"except Exception as e:\n",
" print(f\"[ERROR]: {e}\")"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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