Spaces:
Runtime error
Runtime error
File size: 49,818 Bytes
db5855f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "rQc-wXjqrEuR"
},
"source": [
"# Quantization of Image Classification Models\n",
"\n",
"This tutorial demonstrates how to apply `INT8` quantization to Image Classification model using [NNCF](https://github.com/openvinotoolkit/nncf). It uses the MobileNet V2 model, trained on Cifar10 dataset. The code is designed to be extendable to custom models and datasets. The tutorial uses OpenVINO backend for performing model quantization in NNCF, if you interested how to apply quantization on PyTorch model, please check this [tutorial](../pytorch-post-training-quantization-nncf/pytorch-post-training-quantization-nncf.ipynb).\n",
"\n",
"This tutorial consists of the following steps:\n",
"\n",
"- Prepare the model for quantization.\n",
"- Define a data loading functionality.\n",
"- Perform quantization.\n",
"- Compare accuracy of the original and quantized models.\n",
"- Compare performance of the original and quantized models.\n",
"- Compare results on one picture.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"#### Table of contents:\n",
"\n",
"- [Prepare the Model](#Prepare-the-Model)\n",
"- [Prepare Dataset](#Prepare-Dataset)\n",
"- [Perform Quantization](#Perform-Quantization)\n",
" - [Create Dataset for Validation](#Create-Dataset-for-Validation)\n",
"- [Run nncf.quantize for Getting an Optimized Model](#Run-nncf.quantize-for-Getting-an-Optimized-Model)\n",
"- [Serialize an OpenVINO IR model](#Serialize-an-OpenVINO-IR-model)\n",
"- [Compare Accuracy of the Original and Quantized Models](#Compare-Accuracy-of-the-Original-and-Quantized-Models)\n",
" - [Select inference device](#Select-inference-device)\n",
"- [Compare Performance of the Original and Quantized Models](#Compare-Performance-of-the-Original-and-Quantized-Models)\n",
"- [Compare results on four pictures](#Compare-results-on-four-pictures)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import platform\n",
"\n",
"# Install required packages\n",
"%pip install -q \"openvino>=2023.1.0\" \"nncf>=2.6.0\" torch torchvision tqdm --extra-index-url https://download.pytorch.org/whl/cpu\n",
"\n",
"if platform.system() != \"Windows\":\n",
" %pip install -q \"matplotlib>=3.4\"\n",
"else:\n",
" %pip install -q \"matplotlib>=3.4,<3.7\""
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"# Set the data and model directories\n",
"DATA_DIR = Path(\"data\")\n",
"MODEL_DIR = Path(\"model\")\n",
"model_repo = \"pytorch-cifar-models\"\n",
"\n",
"DATA_DIR.mkdir(exist_ok=True)\n",
"MODEL_DIR.mkdir(exist_ok=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "D-frbVLKrkmv"
},
"source": [
"## Prepare the Model\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"Model preparation stage has the following steps:\n",
"\n",
"- Download a PyTorch model\n",
"- Convert model to OpenVINO Intermediate Representation format (IR) using model conversion Python API\n",
"- Serialize converted model on disk\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import sys\n",
"\n",
"if not Path(model_repo).exists():\n",
" !git clone https://github.com/chenyaofo/pytorch-cifar-models.git\n",
"\n",
"sys.path.append(model_repo)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from pytorch_cifar_models import cifar10_mobilenetv2_x1_0\n",
"\n",
"model = cifar10_mobilenetv2_x1_0(pretrained=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OpenVINO supports PyTorch models via conversion to OpenVINO Intermediate Representation format using model conversion Python API. `ov.convert_model` accept PyTorch model instance and convert it into `openvino.runtime.Model` representation of model in OpenVINO. Optionally, you may specify `example_input` which serves as a helper for model tracing and `input_shape` for converting the model with static shape. The converted model is ready to be loaded on a device for inference and can be saved on a disk for next usage via the `save_model` function. More details about model conversion Python API can be found on this [page](https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "f7i6dWUmhloy",
"tags": []
},
"outputs": [],
"source": [
"import openvino as ov\n",
"\n",
"model.eval()\n",
"\n",
"ov_model = ov.convert_model(model, input=[1, 3, 32, 32])\n",
"\n",
"ov.save_model(ov_model, MODEL_DIR / \"mobilenet_v2.xml\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ynLvh8rNc2wv"
},
"source": [
"## Prepare Dataset\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"We will use [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset from [torchvision](https://pytorch.org/vision/stable/generated/torchvision.datasets.CIFAR10.html). Preprocessing for model obtained from training [config](https://github.com/chenyaofo/image-classification-codebase/blob/master/conf/cifar10.conf)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Files already downloaded and verified\n"
]
}
],
"source": [
"import torch\n",
"from torchvision import transforms\n",
"from torchvision.datasets import CIFAR10\n",
"\n",
"transform = transforms.Compose(\n",
" [\n",
" transforms.ToTensor(),\n",
" transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261)),\n",
" ]\n",
")\n",
"dataset = CIFAR10(root=DATA_DIR, train=False, transform=transform, download=True)\n",
"val_loader = torch.utils.data.DataLoader(\n",
" dataset,\n",
" batch_size=1,\n",
" shuffle=False,\n",
" num_workers=0,\n",
" pin_memory=True,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Perform Quantization\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"[NNCF](https://github.com/openvinotoolkit/nncf) provides a suite of advanced algorithms for Neural Networks inference optimization in OpenVINO with minimal accuracy drop.\n",
"We will use 8-bit quantization in post-training mode (without the fine-tuning pipeline) to optimize MobileNetV2.\n",
"The optimization process contains the following steps:\n",
"\n",
"1. Create a Dataset for quantization.\n",
"2. Run `nncf.quantize` for getting an optimized model.\n",
"3. Serialize an OpenVINO IR model, using the `openvino.save_model` function.\n",
"\n",
"\n",
"### Create Dataset for Validation\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"NNCF is compatible with `torch.utils.data.DataLoader` interface. For performing quantization it should be passed into `nncf.Dataset` object with transformation function, which prepares input data to fit into model during quantization, in our case, to pick input tensor from pair (input tensor and label) and convert PyTorch tensor to numpy. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "DErQofk8tO6c",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino\n"
]
}
],
"source": [
"import nncf\n",
"\n",
"\n",
"def transform_fn(data_item):\n",
" image_tensor = data_item[0]\n",
" return image_tensor.numpy()\n",
"\n",
"\n",
"quantization_dataset = nncf.Dataset(val_loader, transform_fn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run nncf.quantize for Getting an Optimized Model\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"`nncf.quantize` function accepts model and prepared quantization dataset for performing basic quantization. Optionally, additional parameters like `subset_size`, `preset`, `ignored_scope` can be provided to improve quantization result if applicable. More details about supported parameters can be found on this [page](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/quantizing-models-post-training/basic-quantization-flow.html#tune-quantization-parameters)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"tags": [],
"test_replace": {
"quant_ov_model = nncf.quantize(ov_model, quantization_dataset)": "quant_ov_model = nncf.quantize(ov_model, quantization_dataset, subset_size=10)"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Statistics collection: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 300/300 [00:06<00:00, 44.58it/s]\n",
"Biases correction: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 36/36 [00:01<00:00, 24.92it/s]\n"
]
}
],
"source": [
"quant_ov_model = nncf.quantize(ov_model, quantization_dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Serialize an OpenVINO IR model\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"Similar to `ov.convert_model`, quantized model is `ov.Model` object which ready to be loaded into device and can be serialized on disk using `ov.save_model`."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"ov.save_model(quant_ov_model, MODEL_DIR / \"quantized_mobilenet_v2.xml\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare Accuracy of the Original and Quantized Models\n",
"[back to top ⬆️](#Table-of-contents:)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from tqdm.notebook import tqdm\n",
"import numpy as np\n",
"\n",
"\n",
"def test_accuracy(ov_model, data_loader):\n",
" correct = 0\n",
" total = 0\n",
" for batch_imgs, batch_labels in tqdm(data_loader):\n",
" result = ov_model(batch_imgs)[0]\n",
" top_label = np.argmax(result)\n",
" correct += top_label == batch_labels.numpy()\n",
" total += 1\n",
" return correct / total"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Select inference device\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"select device from dropdown list for running inference using OpenVINO"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"jupyter": {
"source_hidden": true
},
"tags": [
"hide-input"
]
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ff9e95c4d1a9464f980a05ebca58fcbc",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Dropdown(description='Device:', index=2, options=('CPU', 'GPU', 'AUTO'), value='AUTO')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import ipywidgets as widgets\n",
"\n",
"core = ov.Core()\n",
"device = widgets.Dropdown(\n",
" options=core.available_devices + [\"AUTO\"],\n",
" value=\"AUTO\",\n",
" description=\"Device:\",\n",
" disabled=False,\n",
")\n",
"\n",
"device"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6f318f01f707487495bf8464a8884098",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/10000 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b821f21f3d2345e3b598a02735be4173",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/10000 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"core = ov.Core()\n",
"compiled_model = core.compile_model(ov_model, device.value)\n",
"optimized_compiled_model = core.compile_model(quant_ov_model, device.value)\n",
"\n",
"orig_accuracy = test_accuracy(compiled_model, val_loader)\n",
"optimized_accuracy = test_accuracy(optimized_compiled_model, val_loader)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of the original model: 93.61%\n",
"Accuracy of the optimized model: 93.51%\n"
]
}
],
"source": [
"print(f\"Accuracy of the original model: {orig_accuracy[0] * 100 :.2f}%\")\n",
"print(f\"Accuracy of the optimized model: {optimized_accuracy[0] * 100 :.2f}%\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vQACMfAUo52V"
},
"source": [
"## Compare Performance of the Original and Quantized Models\n",
"[back to top ⬆️](#Table-of-contents:)\n",
"\n",
"Finally, measure the inference performance of the `FP32` and `INT8` models, using [Benchmark Tool](https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-tool.html) - an inference performance measurement tool in OpenVINO.\n",
"\n",
"> **NOTE**: For more accurate performance, it is recommended to run benchmark_app in a terminal/command prompt after closing other applications. Run `benchmark_app -m model.xml -d CPU` to benchmark async inference on CPU for one minute. Change CPU to GPU to benchmark on GPU. Run `benchmark_app --help` to see an overview of all command-line options.\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "pC0gnO0c9-tI"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Step 1/11] Parsing and validating input arguments\n",
"[ INFO ] Parsing input parameters\n",
"[Step 2/11] Loading OpenVINO Runtime\n",
"[ INFO ] OpenVINO:\n",
"[ INFO ] Build ................................. 2023.0.0-10926-b4452d56304-releases/2023/0\n",
"[ INFO ] \n",
"[ INFO ] Device info:\n",
"[ INFO ] AUTO\n",
"[ INFO ] Build ................................. 2023.0.0-10926-b4452d56304-releases/2023/0\n",
"[ INFO ] \n",
"[ INFO ] \n",
"[Step 3/11] Setting device configuration\n",
"[ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.\n",
"[Step 4/11] Reading model files\n",
"[ INFO ] Loading model files\n",
"[ INFO ] Read model took 8.60 ms\n",
"[ INFO ] Original model I/O parameters:\n",
"[ INFO ] Model inputs:\n",
"[ INFO ] x , 1 , x.1 (node: Parameter_2) : f32 / [...] / [1,3,32,32]\n",
"[ INFO ] Model outputs:\n",
"[ INFO ] 223 (node: aten::linear_928) : f32 / [...] / [1,10]\n",
"[Step 5/11] Resizing model to match image sizes and given batch\n",
"[ INFO ] Model batch size: 1\n",
"[Step 6/11] Configuring input of the model\n",
"[ INFO ] Model inputs:\n",
"[ INFO ] x , 1 , x.1 (node: Parameter_2) : u8 / [N,C,H,W] / [1,3,32,32]\n",
"[ INFO ] Model outputs:\n",
"[ INFO ] 223 (node: aten::linear_928) : f32 / [...] / [1,10]\n",
"[Step 7/11] Loading the model to the device\n",
"[ INFO ] Compile model took 312.76 ms\n",
"[Step 8/11] Querying optimal runtime parameters\n",
"[ INFO ] Model:\n",
"[ INFO ] PERFORMANCE_HINT: PerformanceMode.THROUGHPUT\n",
"[ INFO ] NETWORK_NAME: Model0\n",
"[ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 18\n",
"[ INFO ] MODEL_PRIORITY: Priority.MEDIUM\n",
"[ INFO ] MULTI_DEVICE_PRIORITIES: CPU\n",
"[ INFO ] CPU:\n",
"[ INFO ] CPU_BIND_THREAD: YES\n",
"[ INFO ] CPU_THREADS_NUM: 0\n",
"[ INFO ] CPU_THROUGHPUT_STREAMS: 18\n",
"[ INFO ] DEVICE_ID: \n",
"[ INFO ] DUMP_EXEC_GRAPH_AS_DOT: \n",
"[ INFO ] DYN_BATCH_ENABLED: NO\n",
"[ INFO ] DYN_BATCH_LIMIT: 0\n",
"[ INFO ] ENFORCE_BF16: NO\n",
"[ INFO ] EXCLUSIVE_ASYNC_REQUESTS: NO\n",
"[ INFO ] NETWORK_NAME: Model0\n",
"[ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 18\n",
"[ INFO ] PERFORMANCE_HINT: THROUGHPUT\n",
"[ INFO ] PERFORMANCE_HINT_NUM_REQUESTS: 0\n",
"[ INFO ] PERF_COUNT: NO\n",
"[ INFO ] EXECUTION_DEVICES: ['CPU']\n",
"[Step 9/11] Creating infer requests and preparing input tensors\n",
"[ WARNING ] No input files were given for input '1'!. This input will be filled with random values!\n",
"[ INFO ] Fill input '1' with random values \n",
"[Step 10/11] Measuring performance (Start inference asynchronously, 18 inference requests, limits: 15000 ms duration)\n",
"[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).\n",
"[ INFO ] First inference took 2.29 ms\n",
"[Step 11/11] Dumping statistics report\n",
"[ INFO ] Execution Devices:['CPU']\n",
"[ INFO ] Count: 117540 iterations\n",
"[ INFO ] Duration: 15005.85 ms\n",
"[ INFO ] Latency:\n",
"[ INFO ] Median: 1.99 ms\n",
"[ INFO ] Average: 2.11 ms\n",
"[ INFO ] Min: 1.25 ms\n",
"[ INFO ] Max: 118.00 ms\n",
"[ INFO ] Throughput: 7832.95 FPS\n"
]
}
],
"source": [
"# Inference FP16 model (OpenVINO IR)\n",
"!benchmark_app -m \"model/mobilenet_v2.xml\" -d $device.value -api async -t 15"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "4VR3-joFu9hH"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Step 1/11] Parsing and validating input arguments\n",
"[ INFO ] Parsing input parameters\n",
"[Step 2/11] Loading OpenVINO Runtime\n",
"[ INFO ] OpenVINO:\n",
"[ INFO ] Build ................................. 2023.0.0-10926-b4452d56304-releases/2023/0\n",
"[ INFO ] \n",
"[ INFO ] Device info:\n",
"[ INFO ] AUTO\n",
"[ INFO ] Build ................................. 2023.0.0-10926-b4452d56304-releases/2023/0\n",
"[ INFO ] \n",
"[ INFO ] \n",
"[Step 3/11] Setting device configuration\n",
"[ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.\n",
"[Step 4/11] Reading model files\n",
"[ INFO ] Loading model files\n",
"[ INFO ] Read model took 16.74 ms\n",
"[ INFO ] Original model I/O parameters:\n",
"[ INFO ] Model inputs:\n",
"[ INFO ] x , x.1 , 1 (node: Parameter_2) : f32 / [...] / [1,3,32,32]\n",
"[ INFO ] Model outputs:\n",
"[ INFO ] 223 (node: aten::linear_928) : f32 / [...] / [1,10]\n",
"[Step 5/11] Resizing model to match image sizes and given batch\n",
"[ INFO ] Model batch size: 1\n",
"[Step 6/11] Configuring input of the model\n",
"[ INFO ] Model inputs:\n",
"[ INFO ] x , x.1 , 1 (node: Parameter_2) : u8 / [N,C,H,W] / [1,3,32,32]\n",
"[ INFO ] Model outputs:\n",
"[ INFO ] 223 (node: aten::linear_928) : f32 / [...] / [1,10]\n",
"[Step 7/11] Loading the model to the device\n",
"[ INFO ] Compile model took 392.77 ms\n",
"[Step 8/11] Querying optimal runtime parameters\n",
"[ INFO ] Model:\n",
"[ INFO ] PERFORMANCE_HINT: PerformanceMode.THROUGHPUT\n",
"[ INFO ] NETWORK_NAME: Model0\n",
"[ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 18\n",
"[ INFO ] MODEL_PRIORITY: Priority.MEDIUM\n",
"[ INFO ] MULTI_DEVICE_PRIORITIES: CPU\n",
"[ INFO ] CPU:\n",
"[ INFO ] CPU_BIND_THREAD: YES\n",
"[ INFO ] CPU_THREADS_NUM: 0\n",
"[ INFO ] CPU_THROUGHPUT_STREAMS: 18\n",
"[ INFO ] DEVICE_ID: \n",
"[ INFO ] DUMP_EXEC_GRAPH_AS_DOT: \n",
"[ INFO ] DYN_BATCH_ENABLED: NO\n",
"[ INFO ] DYN_BATCH_LIMIT: 0\n",
"[ INFO ] ENFORCE_BF16: NO\n",
"[ INFO ] EXCLUSIVE_ASYNC_REQUESTS: NO\n",
"[ INFO ] NETWORK_NAME: Model0\n",
"[ INFO ] OPTIMAL_NUMBER_OF_INFER_REQUESTS: 18\n",
"[ INFO ] PERFORMANCE_HINT: THROUGHPUT\n",
"[ INFO ] PERFORMANCE_HINT_NUM_REQUESTS: 0\n",
"[ INFO ] PERF_COUNT: NO\n",
"[ INFO ] EXECUTION_DEVICES: ['CPU']\n",
"[Step 9/11] Creating infer requests and preparing input tensors\n",
"[ WARNING ] No input files were given for input '1'!. This input will be filled with random values!\n",
"[ INFO ] Fill input '1' with random values \n",
"[Step 10/11] Measuring performance (Start inference asynchronously, 18 inference requests, limits: 15000 ms duration)\n",
"[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).\n",
"[ INFO ] First inference took 2.20 ms\n",
"[Step 11/11] Dumping statistics report\n",
"[ INFO ] Execution Devices:['CPU']\n",
"[ INFO ] Count: 210528 iterations\n",
"[ INFO ] Duration: 15001.67 ms\n",
"[ INFO ] Latency:\n",
"[ INFO ] Median: 1.04 ms\n",
"[ INFO ] Average: 1.10 ms\n",
"[ INFO ] Min: 0.71 ms\n",
"[ INFO ] Max: 79.19 ms\n",
"[ INFO ] Throughput: 14033.63 FPS\n"
]
}
],
"source": [
"# Inference INT8 model (OpenVINO IR)\n",
"!benchmark_app -m \"model/quantized_mobilenet_v2.xml\" -d $device.value -api async -t 15"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare results on four pictures\n",
"[back to top ⬆️](#Table-of-contents:)\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Define all possible labels from the CIFAR10 dataset\n",
"labels_names = [\n",
" \"airplane\",\n",
" \"automobile\",\n",
" \"bird\",\n",
" \"cat\",\n",
" \"deer\",\n",
" \"dog\",\n",
" \"frog\",\n",
" \"horse\",\n",
" \"ship\",\n",
" \"truck\",\n",
"]\n",
"all_pictures = []\n",
"all_labels = []\n",
"\n",
"# Get all pictures and their labels.\n",
"for i, batch in enumerate(val_loader):\n",
" all_pictures.append(batch[0].numpy())\n",
" all_labels.append(batch[1].item())"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"def plot_pictures(indexes: list, all_pictures=all_pictures, all_labels=all_labels):\n",
" \"\"\"Plot 4 pictures.\n",
" :param indexes: a list of indexes of pictures to be displayed.\n",
" :param all_batches: batches with pictures.\n",
" \"\"\"\n",
" images, labels = [], []\n",
" num_pics = len(indexes)\n",
" assert num_pics == 4, f\"No enough indexes for pictures to be displayed, got {num_pics}\"\n",
" for idx in indexes:\n",
" assert idx < 10000, \"Cannot get such index, there are only 10000\"\n",
" pic = np.rollaxis(all_pictures[idx].squeeze(), 0, 3)\n",
" images.append(pic)\n",
"\n",
" labels.append(labels_names[all_labels[idx]])\n",
"\n",
" f, axarr = plt.subplots(1, 4)\n",
" axarr[0].imshow(images[0])\n",
" axarr[0].set_title(labels[0])\n",
"\n",
" axarr[1].imshow(images[1])\n",
" axarr[1].set_title(labels[1])\n",
"\n",
" axarr[2].imshow(images[2])\n",
" axarr[2].set_title(labels[2])\n",
"\n",
" axarr[3].imshow(images[3])\n",
" axarr[3].set_title(labels[3])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def infer_on_pictures(model, indexes: list, all_pictures=all_pictures):\n",
" \"\"\"Inference model on a few pictures.\n",
" :param net: model on which do inference\n",
" :param indexes: list of indexes\n",
" \"\"\"\n",
" output_key = model.output(0)\n",
" predicted_labels = []\n",
" for idx in indexes:\n",
" assert idx < 10000, \"Cannot get such index, there are only 10000\"\n",
" result = model(all_pictures[idx])[output_key]\n",
" result = labels_names[np.argmax(result[0])]\n",
" predicted_labels.append(result)\n",
" return predicted_labels"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
"Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Labels for picture from float model : ['frog', 'dog', 'ship', 'horse'].\n",
"Labels for picture from quantized model : ['frog', 'dog', 'ship', 'horse'].\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"indexes_to_infer = [7, 12, 15, 20] # To plot, specify 4 indexes.\n",
"\n",
"plot_pictures(indexes_to_infer)\n",
"\n",
"results_float = infer_on_pictures(compiled_model, indexes_to_infer)\n",
"results_quanized = infer_on_pictures(optimized_compiled_model, indexes_to_infer)\n",
"\n",
"print(f\"Labels for picture from float model : {results_float}.\")\n",
"print(f\"Labels for picture from quantized model : {results_quanized}.\")"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "tutorial_tiny.ipynb",
"private_outputs": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
},
"openvino_notebooks": {
"imageUrl": "",
"tags": {
"categories": [
"Optimize",
"API Overview"
],
"libraries": [],
"other": [],
"tasks": [
"Image Classification"
]
}
},
"vscode": {
"interpreter": {
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
}
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|