diff --git a/source/yolov9-edgetpu-dbro/LICENSE.md b/source/yolov9-edgetpu-dbro/LICENSE.md new file mode 100644 index 0000000000000000000000000000000000000000..bc03afd74c184cd36f6e49e3f89b68f7b43b2ef4 --- /dev/null +++ b/source/yolov9-edgetpu-dbro/LICENSE.md @@ -0,0 +1,21 @@ +The MIT License + +Copyright (c) 2025 Dan Brown + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/source/yolov9-edgetpu-dbro/README.md b/source/yolov9-edgetpu-dbro/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9b531ee1e5254360dc8183dddbf116ac8f61c71a --- /dev/null +++ b/source/yolov9-edgetpu-dbro/README.md @@ -0,0 +1,183 @@ +# frigate-detector-edgetpu-yolo9 + +***DO NOT USE THE CODE IN THIS REPO!*** + +***Official [Frigate](https://github.com/blakeblackshear/frigate) version 0.17 added support to run YOLO v9 models on Google Coral device.*** The [pull request to add YOLO v9 support to the main Frigate repository](https://github.com/blakeblackshear/frigate/pull/21124) was accepted on December 2 2025. Since then, there is no need to use this code. + +This repository provides a custom detector plugin for [Frigate](https://github.com/blakeblackshear/frigate), specifically designed to enable the Google Coral Edge TPU to run a **YOLOv9** model to detect objects visible in camera feeds. The plugin handles the necessary post-processing for YOLO model outputs, making them compatible with Frigate's detection pipeline without modifying Frigate's core source code. YOLOv9 model weights can be downloaded here as well, see the release notes and installation instructions. + +![abbeyroad-detections](https://github.com/user-attachments/assets/a4dfbe6f-2ed3-43f1-9697-6ea119e463fe) + +## Why YOLOv9 and this Plugin? + +Frigate is an open-source NVR system that supports various detector acceleration hardware, including the Google Coral Edge TPU. While the default SSD MobileDet model is an effective choice in many systems, running a YOLOv9 model may improve detection accuracy and reduce false positives. + +YOLOv9 can run efficiently on the EdgeTPU, and requires a small amount of post-processing by the CPU. This detection method is both high-speed and low-energy, handling approximately 100 detections per second and using only a few Watts of power, while leaving the CPU available for other tasks. Another consideration is minimizing heat produced by the CPU, which can challenge my system's small fan. + +There are other YOLO models with incompatible software license terms (eg Ultralytics is licensed with the AGPL). This approach is meant to be used to start with the GPL-licensed YOLOv9 model, which IS compatible with Frigate (also MIT-licensed). For more information, see [this discussion in Frigate's issues](https://github.com/blakeblackshear/frigate/discussions/15630#discussioncomment-11639733). + +## Features + +* **Improved Accuracy:** Fewer false positive detections than the default SSD MobileDet object detection model. +* **YOLO Compatibility:** Processes output from YOLO models exported for Edge TPU. +* **Google Coral Edge TPU Support:** Optimized for efficient inference on Coral devices. 10ms inference time at 320x320 image size (vs 7ms for the MobileDet model) +* **Simple Integration:** Adds as a Frigate plugin via a Docker volume mount, no core Frigate code changes needed. + +## Example System Performance + +* CPU is Intel 3rd Generation i7, 8GB RAM, circa 2012 (the built in GPU is not supported by OpenVINO) +* Google Coral mini-PCIe card +* 10ms detection speed when running a load test averaging 40 detections per second for images having size 320x320 pixels +* zero skipped frames +* Detector CPU usage varies between 15% and 20% + +Regarding CPU usage, all YOLO models (pre YOLO v10) use the CPU during post-processing to decode and de-duplicate the raw results. + +## Model Input Size, Detection Sensitivity, and Run Time + +There are currently two versions of YOLO v9 model weights available for download that can be used with this plugin: + +* [YOLO v9 "small" 320x320 input size](https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best_320_int8_edgetpu.tflite) **recommended** + +This version takes 10ms to run detections on my old system, and should run a bit faster on newer systems. This translates to a capacity of 100 detections per second. Note that there can be multiple detections run for a single frame from the video feed, if there are multiple separate areas where motion gets detected. According to the EdgeTPU compiler, all 334 operations that are part of the model run on the Google Coral TPU. + +* [YOLO v9 "small" 512x512 input size](https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.5/yolov9-s-relu6-tpumax_512_int8_edgetpu.tflite) + +This model runs in about 21ms. It can detect smaller objects relative to the full frame size when compared with the 320x320 size version of the model. It might be preferable in situations where it is important to detect small or faraway objects within a large region of motion. All but 2 of the 334 operations in the model run on the Google Coral TPU, and the 2 that do not run at the end of the model's sequence which means the slowdown is minimal. The main reason for the slower operation is the larger tensor sizes. + +Other versions of YOLO may run with this plugin, but they are not supported or offered for download here. + +## Performance Benchmarks + +| Device | Model | Size | mAP 50% | Detection Time | +|---|---|---|---|---| +| Coral | [YOLO v9 s](https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best_320_int8_edgetpu.tflite) (**recommended**) | 320x320 | 40.6% | 10ms | +| Coral | [YOLO v9 s](https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.5/yolov9-s-relu6-tpumax_512_int8_edgetpu.tflite) | 512x512 | 44.3% | 21ms | +| Coral | [SSD/MobileNet](https://github.com/google-coral/test_data/raw/release-frogfish/ssdlite_mobiledet_coco_qat_postprocess_edgetpu.tflite) (Frigate's default for Coral) | 320x320 | 25.6% | 8ms | +| OpenVINO (CPU) | YOLO v9 s ReLU6 ONNX | 320x320 | 48.5% | 133ms | +| OpenVINO (CPU) | YOLO v9 s SiLU ONNX | 320x320 | 52.1% | 155ms | + +The Coral performance benchmarks above were measured using the models available for download here, running on Coral TPU hardware. The images used for validation were [COCO validation images](https://www.google.com/url?sa=E&q=http%3A%2F%2Fimages.cocodataset.org%2Fzips%2Fval2017.zip) and [labels](https://huggingface.co/datasets/merve/coco/blob/main/annotations/instances_train2017.json). The threshold for minimum score was 0.4, and the NMS threshold was 0.4. mAP50 is the industry-standard accuracy score that considers a detection "correct" only if the predicted bounding box overlaps the real object by at least 50%. For comparison, the accuracy metrics for two ONNX OpenVINO models is shown, which indicate a 3.6 percentage point reduction in accuracy when using the ReLU6 activation function instead of SiLU, and a further 8 percentage point reduction when using the INT8 quantized version of the model. (The detection times for CPU inference with OpenVINO are likely to be much slower than iGPU or GPU performance.) + +## Prerequisites + +Before you begin, ensure you have: + +* **A running Frigate installation:** This plugin has been tested with Frigate **v0.15 and v0.16**. +* **Google Coral Edge TPU:** Properly configured and accessible by your Frigate Docker container. Should work with any variation of Google Coral hardware (USB, M2, mPCIe) + +## Installation + +Follow these steps to integrate the replacement `edgetpu_tfl.py` plugin into your Frigate setup. + +### 1. Download the Model Weights, Plugin File and Class Label File + +Create a directory on your host system to store the plugin file. For example, you might create `/opt/frigate-plugins/`. + +```bash +sudo mkdir -p /opt/frigate-plugins +cd /opt/frigate-plugins +# download weights +sudo wget https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best_320_int8_edgetpu.tflite +# download plugin +sudo wget https://raw.githubusercontent.com/dbro/frigate-detector-edgetpu-yolo9/main/edgetpu_tfl.py +# download labels +sudo wget https://raw.githubusercontent.com/dbro/frigate-detector-edgetpu-yolo9/main/labels-coco17.txt +``` + +### 2. Update docker-compose.yml + +You need to add a volume mount to your Frigate service in your docker-compose.yml file. This mounts the plugin file into Frigate's detector plugins directory. + +Locate your Frigate service definition and add the following two lines in the volumes: section. Adjust /opt/frigate-plugins/edgetpu_tfl.py if you stored the file elsewhere. + +The second line to add will make your YOLO model accessible. + +```yaml +# ... other services ... +frigate: + # ... other frigate configurations ... + volumes: + # ... existing volumes ... + - /opt/frigate-plugins/edgetpu_tfl.py:/opt/frigate/frigate/detectors/plugins/edgetpu_tfl.py:ro + - /opt/frigate-plugins/labels-coco17.txt:/opt/frigate/models/labels-coco17.txt:ro + - /opt/frigate-plugins/yolov9-s-relu6-best_320_int8_edgetpu.tflite:/opt/frigate/models/yolov9-s-relu6-best_320_int8_edgetpu.tflite:ro + # ... rest of frigate service ... +``` + +After modifying docker-compose.yml, restart your Frigate container: + +```bash +docker-compose down +docker-compose up -d +``` + +### 3. Configure Frigate's config.yml + +Now, you need to tell Frigate to use this new detector plugin and your YOLO model. + +In your config.yml, in the model: section, add the model_type parameter as shown below and update the model path to point to your YOLO model file that you mounted in the previous step. + +```yaml +model: + model_type: yolo-generic + labelmap_path: /opt/frigate/models/labels-coco17.txt + path: /opt/frigate/models/yolov9-s-relu6-best_320_int8_edgetpu.tflite + # Optionally specify the model dimensions (these are the same as Frigate's default 320x320) + width: 320 + height: 320 +detectors: + coral: + type: edgetpu + # ... other detector settings ... +``` + +### 4. Restart Frigate and Check Performance + +Save the Frigate configuration and rexstart Frigate. + +* Are there any error messages in Frigate logs during startup? +* What is the inference speed reported in Frigate's System Metrics page? +* What is the level of Detector CPU Usage? +* Are there any skipped frames? +* How many detection errors do you observe? Look for both false positives and false negatives. + +## How it Works With Frigate + +This modified version of edgetpu_tfl.py replaces the standard version of the plugin and acts as an intermediary. When Frigate requests a detection: + +1. Frigate's standard Edge TPU handler passes the image frame to the plugin. +2. The plugin loads your specified YOLO model. +3. It performs inference on the Edge TPU. +4. It then post-processes the raw output from the YOLO model to transform it into the format expected by Frigate (e.g., converting bounding box coordinates, handling confidence scores). +5. The processed detections are returned to Frigate. + +Note that Frigate does not automatically send the entire image from the camera to the detector. Instead, it looks for areas in the image that changed from previous frames, and sends only these regions with motion to the detector. There can be more than one region sent per frame from the camera. For example, on a still day, a region might be tightly cropped around a cat walking across a lawn. On a windy autumn day with many leaves or rain creating motion in the overall image, the region of motion could be the entire image or there could be multiple regions sent to the detector. Frigate next resizes the region to match the detection model's input size, then sends it to the detector. + +To understand how Frigate uses motion to trigger object detection processing, see [this explanation in the Frigate FAQ](https://docs.frigate.video/configuration/masks/#further-clarification) and a live view of motion regions is in Frigate's "Debug" view in the Settings page.. + +## Lessons Learned Getting YOLO v9 to Run on Google Coral + +It is possible to convert the pre-trained weights for YOLO from PyTorch format to ONNX and then to TFLite and then compile for EdgeTPU, which runs on Google Coral. However, simple conversions of the models available for download perform poorly on the Coral, because it uses operations that are not appropriate for the 8-bit limitation of Google Coral. This leads to a total loss of information and zero detections. The following changes to the YOLO model structure enable the information to be preserved accurately through the sequence of operations run on Google Coral. The goal is to let the Coral perform all the convolution operations, and then send the data for post-processing on the CPU. + +* **ReLU6 activation** should be used instead of SiLU activation. It is smaller and faster than SiLU, and quantizes better. +* **Send Logit scores** to the CPU for post-processing to transform them into probabilities. The Coral cannot do the sigmoid() transformation. +* **Decode boxes and run Non-Maximum Supression (NMS) on the CPU** because the Coral cannot run these operations efficiently. +* **Separate tensors for each quantization scale** to preserve precision. +* **Data size limits are tricky.** For example, using a non-standard order of dimensions (BNC) in the boxes tensor enables it to run entirely on the TPU, but with the standard order (BCN) some operations must either run on the CPU or be done repeatedly on smaller chunks. Coral has a limited amount of on-chip RAM for caching the intermediate results of the operations, and if it needs more it will use the (slower) system RAM. +* **Post-processing takes time** Careful post-processing reduced the overall detection time by about 2ms. + +Applying those lessons lets the Google Coral run all of the convolutiuon operations on its TPU for a YOLO v9 "s" model with size 320x320 pixels. The resulting model uses 385kB of off-chip RAM, which slows down the process. + +## Contributing + +This project is open-source. If you find issues or have improvements, feel free to open an issue or submit a pull request. + +## References + +* [https://github.com/blakeblackshear/frigate](https://github.com/blakeblackshear/frigate) +* [https://github.com/WongKinYiu/yolov9/tree/main](https://github.com/WongKinYiu/yolov9/tree/main) +* [https://engineering.cloudflight.io/quantized-yolo-for-edge-solutions](https://engineering.cloudflight.io/quantized-yolo-for-edge-solutions) + +## License +This project is licensed under the MIT License. See the LICENSE file for details. diff --git a/source/yolov9-edgetpu-dbro/SOURCE.md b/source/yolov9-edgetpu-dbro/SOURCE.md new file mode 100644 index 0000000000000000000000000000000000000000..3018cb9ee98c049e834426c3aa9f9e824b616aca --- /dev/null +++ b/source/yolov9-edgetpu-dbro/SOURCE.md @@ -0,0 +1,32 @@ +# YOLOv9-s for the Coral Edge TPU — corresponding source + +The two `.tflite` files under `objectDetection/yolov9/tflite/` are mirrored +UNCHANGED from Dan Brown's releases of +https://github.com/dbro/frigate-detector-edgetpu-yolo9 (repository commit +d8ddfdaee7af76bf3847274604a5ecff0d8c1cc4, mirrored 2026-10-22): + +| File | Release | SHA-256 | +| --- | --- | --- | +| yolov9-s-relu6-best_320_int8_edgetpu.tflite | v1.0 | 49a38e77439b475d0d259a6d9fe7ea32fcaa58de3748499dd120aceebad9c591 | +| yolov9-s-relu6-tpumax_512_int8_edgetpu.tflite | v1.5 | f634f60259101556825fd69662f96282e74364b507f83e6382cc34d5d0e6170e | + +Corresponding source, in this directory: + +- `yolov9-s-relu6-best.pt` — the fine-tuned PyTorch checkpoint (release v1.0, + SHA-256 0005b3e03044acb007dbc1d6be2d1040b64ca98e5d2c30aaf5678f0935c6ebf9). +- `notebooks/Fine_tune_YOLOv9_s_for_EdgeTPU_use_ReLU6_and_17_COCO_classes.ipynb` + — the fine-tuning recipe (ReLU6 activations, 17 COCO classes). +- `notebooks/Convert_YOLO_v9_ReLU6_PyTorch_format_to_EdgeTPU_for_Google_Coral.ipynb` + — the PyTorch → int8 TFLite → edgetpu_compiler conversion. +- `labels-coco17.txt`, `README.md`, `LICENSE.md` — as published. + +Licences: + +- Weights: derived from WongKinYiu/yolov9 `yolov9-s-converted.pt`, + GPL-3.0 (https://github.com/WongKinYiu/yolov9). The training code is that + repository. +- Notebooks and plugin code: MIT, © 2025 Dan Brown (`LICENSE.md`). +- Training data: COCO 2017, filtered to 17 classes. + +CamStack does not modify these files; it decodes their output with its own +`yolo-split-dfl` postprocessor. diff --git a/source/yolov9-edgetpu-dbro/labels-coco17.txt b/source/yolov9-edgetpu-dbro/labels-coco17.txt new file mode 100644 index 0000000000000000000000000000000000000000..f05d3d5e8d2653a325cc2c4d1cdb2017c7fd394b --- /dev/null +++ b/source/yolov9-edgetpu-dbro/labels-coco17.txt @@ -0,0 +1,17 @@ +person +bicycle +car +motorcycle +airplane +bus +train +truck +boat +bird +cat +dog +horse +sheep +cow +elephant +bear diff --git a/source/yolov9-edgetpu-dbro/notebooks/Convert_YOLO_v9_ReLU6_PyTorch_format_to_EdgeTPU_for_Google_Coral.ipynb b/source/yolov9-edgetpu-dbro/notebooks/Convert_YOLO_v9_ReLU6_PyTorch_format_to_EdgeTPU_for_Google_Coral.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9be079334db31975b955d5338f57bd955c0a711b --- /dev/null +++ b/source/yolov9-edgetpu-dbro/notebooks/Convert_YOLO_v9_ReLU6_PyTorch_format_to_EdgeTPU_for_Google_Coral.ipynb @@ -0,0 +1,2619 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "header" + }, + "source": [ + "# YOLO v9 Detection Model For Google Coral and Frigate\n", + "\n", + "This notebook generates a YOLO v9 model ready to run on a Google Coral TPU for detecting objects in Frigate.\n", + "\n", + "**Key Features**\n", + "- **Accurate:** (in my own use) YOLO v9 has **fewer false positive** detections than the default model provided by Frigate for Google Coral.\n", + "- **Fast:** achieves **11ms inference latency** because the Google Coral TPU runs most of the YOLO v9 model operations.\n", + "- **Free to use and distribute:** The starting point is the YOLO v9 pre-trained model and weights from [WongKinYiu](https://github.com/WongKinYiu/yolov9) which is GPL licensed and that enables this repo to be **MIT licensed** (same license as Frigate).\n", + "\n", + "**How Does It Work?**\n", + "- **Fixes Quantization Issue:** Building a model for Google Coral requires special handling to address two challenges: (1) limited data precision (INT8), and (2) quantization happens across the full tensor. If quantization goes wrong, then all of the scores have zero values and no objects get detected.\n", + "- **REQUIRES PATCHED DETECTOR PLUGIN: [See this github repository](https://github.com/dbro/frigate-detector-edgetpu-yolo9)** This Colab workbook corresponds to the v1.5 release in Github.\n", + "\n", + "**Caveats**\n", + "- **Some operations happen outside the TPU:** To produce data ready for use by Frigate, the model uses some CPU instead of TPU to decode the box coordinates into XYWH format, as well as the NMS de-duplication. There is a [new patched version of Frigate's edgetpu detector plugin](https://github.com/dbro/frigate-detector-edgetpu-yolo9) and it post-processes the data in a way that is similar to Frigate's OpenVINO detector.\n", + "\n", + "**References**\n", + "- [WongKinYiu's implementation of the YOLO v9 paper](https://github.com/WongKinYiu/yolov9)\n", + "- [onnx2tf transformation library](https://github.com/PINTO0309/onnx2tf)\n", + " - [especially helpful was this discussion about loss of precision in the YOLOX model when doing integer quantization](https://github.com/PINTO0309/onnx2tf/issues/269#issuecomment-1483090981)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "install_packages", + "outputId": "b7fe8013-27b7-482c-bd58-0124bfdc78d6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Installing packages...\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m887.9/887.9 MB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m594.3/594.3 MB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.2/10.2 MB\u001b[0m \u001b[31m126.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.0/88.0 MB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m954.8/954.8 kB\u001b[0m \u001b[31m55.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m193.1/193.1 MB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m56.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.6/63.6 MB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m267.5/267.5 MB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m288.2/288.2 MB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m322.4/322.4 MB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.3/39.3 MB\u001b[0m \u001b[31m20.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.0/90.0 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m155.6/155.6 MB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m87.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.2/18.2 MB\u001b[0m \u001b[31m76.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.8/166.8 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "torchaudio 2.9.0+cu126 requires torch==2.9.0, but you have torch 2.8.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.2/91.2 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m300.5/300.5 MB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.9/57.9 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m152.0/152.0 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.7/468.7 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.8/107.8 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h✓ Package installation complete!\n" + ] + } + ], + "source": [ + "# ============================================================================\n", + "# STEP 1: Install Required Packages\n", + "# ============================================================================\n", + "print(\"Installing packages...\")\n", + "\n", + "# Core packages\n", + "!pip install -q \"torch<=2.8.0\" torchvision \"onnx<=1.19.1\" \"onnxslim>=0.1.67\"\n", + "\n", + "# TensorFlow and related packages\n", + "!pip install -q \"tensorflow>=2.0.0,<=2.19.0\" \"tf_keras<=2.19.0\" \"ai-edge-litert>=1.2.0\" \"onnxruntime-gpu\"\n", + "\n", + "# onnx2tf and dependencies\n", + "!pip install -q \"sng4onnx>=1.0.1\"\n", + "!pip install -q \"onnx_graphsurgeon>=0.3.26\" --extra-index-url https://pypi.ngc.nvidia.com\n", + "!pip install -q \"onnx2tf>=1.26.3\" \"protobuf>=5\" \"mct-quantizers>=1.6.0\"\n", + "!pip install -q sne4onnx\n", + "print(\"✓ Package installation complete!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "import_libs", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "74415c9f-8669-4cd5-8228-28286c2201ff" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "python version: 3.12.12 (main, Oct 10 2025, 08:52:57) [GCC 11.4.0]\n", + "PyTorch version: 2.8.0+cu128\n", + "ONNX version: 1.19.1\n", + "onnx2tf version: 1.28.5\n", + "TensorFlow version: 2.19.0\n" + ] + } + ], + "source": [ + "# ============================================================================\n", + "# STEP 3: Import Libraries and Check Versions\n", + "# ============================================================================\n", + "import sys\n", + "from pathlib import Path\n", + "import subprocess\n", + "print(f\"python version: {sys.version}\")\n", + "import torch\n", + "print(f\"PyTorch version: {torch.__version__}\")\n", + "import torch.nn as nn\n", + "import numpy as np\n", + "import onnx\n", + "print(f\"ONNX version: {onnx.__version__}\")\n", + "import onnxslim\n", + "import onnx2tf\n", + "print(f\"onnx2tf version: {onnx2tf.__version__}\")\n", + "import tensorflow as tf\n", + "print(f\"TensorFlow version: {tf.__version__}\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "download_model", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3cb2de3a-493c-4a26-d266-2396e0b3d981" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Downloading YOLO v9-s (GPL) from: https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best.pt\n", + "✓ Model downloaded: yolov9-s-relu6-best.pt\n" + ] + } + ], + "source": [ + "# ============================================================================\n", + "# STEP 4: DOWNLOAD YOLO v9-s Model Weights\n", + "# ============================================================================\n", + "\n", + "IMGSZ = (320,320) # Limited to maximum 320x320 to fit on Google Coral device\n", + "BATCH_SIZE = 1\n", + "COCO_CLASSES_TO_KEEP = { # original COCO index, and COCO name\n", + " '0' : 'person',\n", + " '1' : 'bicycle',\n", + " '2' : 'car',\n", + " '3' : 'motorcycle',\n", + " '4' : 'airplane',\n", + " '5' : 'bus',\n", + " '6' : 'train',\n", + " '7' : 'truck',\n", + " '8' : 'boat',\n", + " '14' : 'bird', # 15 in Frigate's standard labelmap.txt\n", + " '15' : 'cat', # 16\n", + " '16' : 'dog', # 17\n", + " '17' : 'horse', # 18\n", + " '18' : 'sheep', # 19\n", + " '19' : 'cow', # 20\n", + " '20' : 'elephant', # 21\n", + " '21' : 'bear', # 22\n", + "}\n", + "NUM_CLASSES = len(COCO_CLASSES_TO_KEEP) # 17 classes out of 80 in standard COCO dataset\n", + "\n", + "# Upload the model file created by fine-tuning the YOLO model to use ReLU and 17 COCO classes\n", + "# See here https://colab.research.google.com/drive/1RAZqCHuDWFdFxqFpL78OVfZQvnGoM6rH\n", + "MODEL_PATH = \"yolov9-s-relu6-best.pt\"\n", + "MODEL_URL = \"https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best.pt\"\n", + "# (not recommended) Use the pre-trained model (has bad SiLU activation, and all 80 COCO classes)\n", + "#MODEL_PATH = \"yolov9-s-converted.pt\"\n", + "#MODEL_URL = \"https://github.com/WongKinYiu/yolov9/releases/download/v0.1/\" + MODEL_PATH\n", + "print(f\"\\nDownloading YOLO v9-s (GPL) from: {MODEL_URL}\")\n", + "!wget -q {MODEL_URL} -O {MODEL_PATH}\n", + "print(f\"✓ Model downloaded: {MODEL_PATH}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "Cwi7WFvfRXfY", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6c3b9f40-d068-475d-a057-01a07df51f5e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning the original YOLOv9 repository for model definitions...\n", + "✓ Repository cloned.\n" + ] + } + ], + "source": [ + "# ===========================================================================\n", + "# STEP 4.5: Clone Original YOLOv9 Repo to Load Model Architecture\n", + "# ===========================================================================\n", + "# The .pt file was saved with a dependency on the original\n", + "# repository's code structure. We must clone the repo so that torch.load()\n", + "# can find the necessary model class definitions (e.g., in the 'models' module).\n", + "print(\"Cloning the original YOLOv9 repository for model definitions...\")\n", + "!git clone -q https://github.com/WongKinYiu/yolov9.git\n", + "print(\"✓ Repository cloned.\")" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Add new head DualDDetectEdgeTPU" + ], + "metadata": { + "id": "3f5Gmzq-2kTU" + } + }, + { + "cell_type": "code", + "source": [ + "########################################\n", + "# Add new node to yolo model definition\n", + "########################################\n", + "\n", + "print(\"=\"*70)\n", + "print(\"DualDDetectEdgeTPU - Single Combined Output\")\n", + "print(\"=\"*70)\n", + "# This version combines normalized boxes [0,1] and scores [0,1]\n", + "# into a single output tensor for simpler export.\n", + "\n", + "import os\n", + "\n", + "LOGIT_CLAMP_MAX = 4.0\n", + "# a value of 4.0 here means the values for class scores will be limited\n", + "# to the range of -4.0 to +4.0, corresponding to the range [1.8%, 98.2%] probability\n", + "# and the 8-bit quantization will have 255 steps that span (no wider than)\n", + "# these extremes. This is especially important if the model outputs\n", + "# concatenate class scores with other metrics (eg box coordinates or class ids)\n", + "\n", + "dualddetect_edgetpu_code = f'''\n", + "class DualDDetectEdgeTPU(nn.Module):\n", + " \"\"\"\n", + " EdgeTPU-optimized DualDDetect that outputs three tensors with:\n", + " - DFL distribution for calculating box coordinates\n", + " - Logit value class scores clamped to [-4, 4], or about 2% to 98% probability\n", + " Each tensor has its own quantization scale, calculated and fixed by the\n", + " post-training quantization step which uses a representative dataset\n", + " \"\"\"\n", + "\n", + " dynamic = False\n", + " export = False\n", + " shape = None\n", + " anchors = torch.empty(0)\n", + " strides = torch.empty(0)\n", + "\n", + " def __init__(self, nc=80, ch=(), inplace=True):\n", + " super().__init__()\n", + " self.nc = nc\n", + " self.nl = len(ch) // 2\n", + " self.reg_max = 16 # used in validation\n", + " self.no = nc + self.reg_max * 4\n", + " self.inplace = inplace\n", + " self.stride = torch.zeros(self.nl)\n", + "\n", + " c2 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4)\n", + " c3 = max((ch[0], min((self.nc * 2, 128))))\n", + " c4 = make_divisible(max((ch[self.nl] // 4, self.reg_max * 4, 16)), 4)\n", + " c5 = max((ch[self.nl], min((self.nc * 2, 128))))\n", + "\n", + " # Branch 1 (auxiliary)\n", + " self.cv2 = nn.ModuleList(\n", + " nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4),\n", + " nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4))\n", + " for x in ch[:self.nl]\n", + " )\n", + " self.cv3 = nn.ModuleList(\n", + " nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1))\n", + " for x in ch[:self.nl]\n", + " )\n", + "\n", + " # Branch 2 (main)\n", + " self.cv4 = nn.ModuleList(\n", + " nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3, g=4),\n", + " nn.Conv2d(c4, 4 * self.reg_max, 1, groups=4))\n", + " for x in ch[self.nl:]\n", + " )\n", + " self.cv5 = nn.ModuleList(\n", + " nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1))\n", + " for x in ch[self.nl:]\n", + " )\n", + "\n", + " self.dfl = DFL(self.reg_max) # used in validation\n", + " self.dfl2 = DFL(self.reg_max) # used in validation\n", + "\n", + " def forward(self, x):\n", + "\n", + " if self.export:\n", + " # Inference path\n", + "\n", + " box_dfl_logits = torch.cat([\n", + " self.cv4[i](x[self.nl+i]).permute(0, 2, 3, 1).flatten(1, 2)\n", + " for i in range(self.nl)\n", + " ], dim=1) # Shape: (bs, 2100, 4, 16)\n", + "\n", + " # class logits have clamped values between 2% and 98% probability\n", + " class_logits = torch.cat([\n", + " torch.clamp(self.cv5[i](x[self.nl+i]), -1 * {LOGIT_CLAMP_MAX}, {LOGIT_CLAMP_MAX}).permute(0, 2, 3, 1).flatten(1, 2)\n", + " for i in range(self.nl)\n", + " ], dim=1) # Shape: (bs, 2100, 17)\n", + "\n", + " return {{\n", + " \"boxes_dfl_logit\": box_dfl_logits,\n", + " \"class_scores_logit\": class_logits,\n", + " }}\n", + "\n", + " # Either training or validation path\n", + " shape = x[0].shape # BCHW\n", + " d1 = []\n", + " d2 = []\n", + "\n", + " for i in range(self.nl):\n", + " d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))\n", + " d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))\n", + "\n", + " if self.training:\n", + " return [d1, d2]\n", + "\n", + " else:\n", + " # Validation path\n", + " # Set up anchors if needed\n", + " if self.dynamic or self.shape != shape:\n", + " self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))\n", + " self.shape = shape\n", + "\n", + " # Decode boxes\n", + " box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)\n", + " dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides\n", + "\n", + " box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)\n", + " dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides\n", + "\n", + " # Apply sigmoid to classes\n", + " cls_sigmoid = cls.sigmoid()\n", + " cls2_sigmoid = cls2.sigmoid()\n", + " # Validation needs PIXEL coordinates\n", + " y_aux = torch.cat((dbox, cls_sigmoid), 1)\n", + " y_main = torch.cat((dbox2, cls2_sigmoid), 1) # NO normalization!\n", + " return ([y_aux, y_main], [d1, d2])\n", + "\n", + " def bias_init(self):\n", + " \"\"\"Initialize biases\"\"\"\n", + " m = self\n", + " for a, b, s in zip(m.cv2, m.cv3, m.stride):\n", + " a[-1].bias.data[:] = 1.0\n", + " b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)\n", + " for a, b, s in zip(m.cv4, m.cv5, m.stride):\n", + " a[-1].bias.data[:] = 1.0\n", + " b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)\n", + "\n", + "'''\n", + "\n", + "print(\"=\"*70)\n", + "print(\"Adding DualDDetectEdgeTPU to models/yolo.py\")\n", + "print(\"=\"*70)\n", + "\n", + "os.chdir('/content/yolov9')\n", + "\n", + "# Read the file\n", + "with open('models/yolo.py', 'r') as f:\n", + " lines = f.readlines()\n", + "\n", + "# Check if code already present\n", + "code_already_present = False\n", + "for i, line in enumerate(lines):\n", + " if 'class DualDDetectEdgeTPU(nn.Module):' in line:\n", + " code_already_present = True\n", + " print(f\"✅ Code already present at line {i+1}\")\n", + " break\n", + "\n", + "# Find where to insert (after DualDDetect class)\n", + "insert_idx = -1\n", + "for i, line in enumerate(lines):\n", + " if 'class TripleDetect(nn.Module):' in line:\n", + " insert_idx = i\n", + " print(f\"✅ Found insertion point at line {i+1} (before TripleDetect)\")\n", + " break\n", + "\n", + "if insert_idx == -1:\n", + " print(\"❌ Could not find TripleDetect class\")\n", + " print(\"Looking for alternative insertion point...\")\n", + " for i, line in enumerate(lines):\n", + " if 'class DualDDetect(nn.Module):' in line:\n", + " # Find the end of DualDDetect\n", + " for j in range(i+1, len(lines)):\n", + " if lines[j].strip().startswith('class ') and j > i + 10:\n", + " insert_idx = j\n", + " print(f\"✅ Found insertion point at line {j+1}\")\n", + " break\n", + " break\n", + "\n", + "if code_already_present:\n", + " print(\"✅ Code already present\")\n", + "\n", + "if insert_idx == -1:\n", + " print(\"❌ Error: Could not find insertion point!\")\n", + "else:\n", + " # Insert the new class\n", + " lines.insert(insert_idx, '\\n\\n' + dualddetect_edgetpu_code + '\\n\\n')\n", + "\n", + " # Write back\n", + " with open('models/yolo.py', 'w') as f:\n", + " f.writelines(lines)\n", + "\n", + " print(\"✅ Successfully added DualDDetectEdgeTPU class\")\n", + "\n", + " # Now update parse_model to recognize the new class\n", + " print(\"\\n\" + \"=\"*70)\n", + " print(\"Updating parse_model to recognize DualDDetectEdgeTPU\")\n", + " print(\"=\"*70)\n", + "\n", + " # Read again\n", + " with open('models/yolo.py', 'r') as f:\n", + " content = f.read()\n", + "\n", + " # Update all the isinstance checks\n", + " updates = [\n", + " ('elif m in {Detect, DualDetect, TripleDetect, DDetect, DualDDetect, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic}:',\n", + " 'elif m in {Detect, DualDetect, TripleDetect, DDetect, DualDDetect, DualDDetectEdgeTPU, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic}:'),\n", + "\n", + " ('if isinstance(m, (Detect, DualDetect, TripleDetect, DDetect, DualDDetect, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic)):',\n", + " 'if isinstance(m, (Detect, DualDetect, TripleDetect, DDetect, DualDDetect, DualDDetectEdgeTPU, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic)):'),\n", + "\n", + " ('if isinstance(m, (DualDetect, TripleDetect, DualDDetect, TripleDDetect, DualDSegment)):',\n", + " 'if isinstance(m, (DualDetect, TripleDetect, DualDDetect, DualDDetectEdgeTPU, TripleDDetect, DualDSegment)):'),\n", + " ]\n", + "\n", + " for old, new in updates:\n", + " if old in content:\n", + " content = content.replace(old, new)\n", + " print(f\"✅ Updated: {old[:50]}...\")\n", + " else:\n", + " print(f\"WARNING: did not find {old[:50]} !\")\n", + "\n", + " # Write back\n", + " with open('models/yolo.py', 'w') as f:\n", + " f.write(content)\n", + "\n", + " print(\"\\n✅ All updates completed successfully!\")\n", + "\n", + "os.chdir('/content')\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AU97FAXSxyT4", + "outputId": "2d580a3c-ae42-4f23-dbe0-b22af95a7fc3" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "DualDDetectEdgeTPU - Single Combined Output\n", + "======================================================================\n", + "======================================================================\n", + "Adding DualDDetectEdgeTPU to models/yolo.py\n", + "======================================================================\n", + "✅ Found insertion point at line 259 (before TripleDetect)\n", + "✅ Successfully added DualDDetectEdgeTPU class\n", + "\n", + "======================================================================\n", + "Updating parse_model to recognize DualDDetectEdgeTPU\n", + "======================================================================\n", + "✅ Updated: elif m in {Detect, DualDetect, TripleDetect, DDete...\n", + "✅ Updated: if isinstance(m, (Detect, DualDetect, TripleDetect...\n", + "✅ Updated: if isinstance(m, (DualDetect, TripleDetect, DualDD...\n", + "\n", + "✅ All updates completed successfully!\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(\"=\"*70)\n", + "print(\"Creating ReLU6 Model Configuration with DualDDetectEdgeTPU\")\n", + "print(\"=\"*70)\n", + "\n", + "from pathlib import Path\n", + "\n", + "os.chdir('/content/yolov9')\n", + "\n", + "# Read the original config\n", + "config_path = Path('models/detect/yolov9-s.yaml')\n", + "with open(config_path, 'r') as f:\n", + " lines = f.readlines()\n", + "\n", + "relu6_config_path = Path('models/detect/yolov9-s-relu6.yaml')\n", + "new_lines = []\n", + "\n", + "for line in lines:\n", + " if line.strip() == '#activation: nn.ReLU()':\n", + " new_lines.append('activation: nn.ReLU6()\\n')\n", + " print(\"✓ Set activation: nn.ReLU6()\")\n", + " elif line.strip().startswith('#activation:'):\n", + " # Keep other activation lines commented\n", + " new_lines.append(line)\n", + " elif 'DualDDetect' in line and 'DualDDetectEdgeTPU' not in line:\n", + " # Replace DualDDetect with DualDDetectEdgeTPU\n", + " new_line = line.replace('DualDDetect', 'DualDDetectEdgeTPU')\n", + " new_lines.append(new_line)\n", + " print(f\"✓ Changed detection head: DualDDetect → DualDDetectEdgeTPU\")\n", + " elif line.strip() == 'nc: 80 # number of classes':\n", + " new_lines.append(f'nc: {NUM_CLASSES} # number of classes\\n')\n", + " print(f\"✓ Changed number of classes to {NUM_CLASSES}\")\n", + " else:\n", + " new_lines.append(line)\n", + "\n", + "# Save config\n", + "with open(relu6_config_path, 'w') as f:\n", + " f.writelines(new_lines)\n", + "print(f\"✓ Created: {relu6_config_path}\")\n", + "\n", + "# Verify the changes\n", + "print(\"\\nVerifying configuration:\")\n", + "with open(relu6_config_path, 'r') as f:\n", + " content = f.read()\n", + " for line in content.split('\\n'):\n", + " if 'activation:' in line and not line.strip().startswith('#'):\n", + " print(f\" Activation: {line.strip()}\")\n", + " if 'DualDDetect' in line:\n", + " print(f\" Detection head: {line.strip()}\")\n", + "\n", + "\n", + "# Create PReLU + DualDDetectTwoOutputs version\n", + "models_common_path = Path('models/common.py')\n", + "with open(models_common_path, 'r') as f:\n", + " lines = f.readlines()\n", + "new_lines = []\n", + "indent1 = ' '\n", + "indent2 = ' '\n", + "for line in lines:\n", + " if line.strip() == 'self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()':\n", + " # TODO: this does not seem to indent properly. why not?\n", + " new_lines.append(f'{indent1}self.act = nn.ReLU6() # PATCHED for EdgeTPU')\n", + " print(\"✓ Set activation in init(): nn.ReLU6()\")\n", + " elif line.strip().startswith('default_act = nn.SiLU()'):\n", + " # Keep other activation lines commented\n", + " new_lines.append(f'{indent2}default_act = nn.ReLU6()')\n", + " print(\"✓ Set activation: nn.ReLU6()\")\n", + " else:\n", + " new_lines.append(line)\n", + "\n", + "# Save config\n", + "with open(models_common_path, 'w') as f:\n", + " f.writelines(new_lines)\n", + "print(f\"✓ Patched: {models_common_path}\")\n", + "\n", + "print(\"\\n✅ Configuration ready for EdgeTPU quantization:\")\n", + "print(\" - ReLU6 activation\")\n", + "print(\" - DualDDetectEdgeTPU (separate box/class tensors)\")\n", + "print(\" - Both prevent quantization value crushing\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-0RZ9HJlRGQ5", + "outputId": "6caf1822-1bdb-4e67-cf81-39f17f0da0b9" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "Creating ReLU6 Model Configuration with DualDDetectEdgeTPU\n", + "======================================================================\n", + "✓ Changed number of classes to 17\n", + "✓ Set activation: nn.ReLU6()\n", + "✓ Changed detection head: DualDDetect → DualDDetectEdgeTPU\n", + "✓ Created: models/detect/yolov9-s-relu6.yaml\n", + "\n", + "Verifying configuration:\n", + " Activation: activation: nn.ReLU6()\n", + " Detection head: [[28, 25, 22, 15, 18, 21], 1, DualDDetectEdgeTPU, [nc]], # Detect(P3, P4, P5)\n", + "✓ Set activation: nn.ReLU6()\n", + "✓ Set activation in init(): nn.ReLU6()\n", + "✓ Set activation: nn.ReLU6()\n", + "✓ Set activation in init(): nn.ReLU6()\n", + "✓ Set activation: nn.ReLU6()\n", + "✓ Set activation in init(): nn.ReLU6()\n", + "✓ Patched: models/common.py\n", + "\n", + "✅ Configuration ready for EdgeTPU quantization:\n", + " - ReLU6 activation\n", + " - DualDDetectEdgeTPU (separate box/class tensors)\n", + " - Both prevent quantization value crushing\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# ============================================================================\n", + "# STEP 5: Build model from corrected YAML and load trained weights\n", + "# ============================================================================\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"STEP 5: Building model from YAML and loading weights...\")\n", + "print(\"=\"*80)\n", + "\n", + "import os\n", + "os.chdir('/content')\n", + "\n", + "# Add the cloned repository to the Python path\n", + "import sys\n", + "sys.path.append('./yolov9')\n", + "from models.yolo import Model # Import the main Model class\n", + "\n", + "# --- DEFINE YOUR PATHS AND PARAMETERS HERE ---\n", + "CFG_PATH = 'yolov9/models/detect/yolov9-s-relu6.yaml'\n", + "#MODEL_PATH = 'yolov9-s-hardswish-epoch11.pt' # already defined\n", + "NC = NUM_CLASSES\n", + "CH = 3 # Input channels (3 for RGB images)\n", + "\n", + "# 1. Build a new, empty model structure from your corrected YAML file.\n", + "print(f\"Building new model structure from: {CFG_PATH}\")\n", + "model = Model(CFG_PATH, ch=CH, nc=NC)\n", + "\n", + "# 2. Load the checkpoint containing your trained weights.\n", + "print(f\"Loading trained weights from: {MODEL_PATH}\")\n", + "checkpoint = torch.load(MODEL_PATH, map_location='cpu', weights_only=False)\n", + "\n", + "# 3. Load the weights' state_dict into the new model structure.\n", + "# This transfers the knowledge from your trained model into the corrected architecture.\n", + "state_dict = checkpoint['model'].float().state_dict()\n", + "model.load_state_dict(state_dict, strict=True)\n", + "# Note: strict=True ensures all weights match. If you get errors, you might\n", + "# have a mismatch, but for an activation swap, it should work perfectly.\n", + "\n", + "# 4. Fuse layers and set to evaluation mode, which is required for export.\n", + "print(\"Fusing model...\")\n", + "model.fuse()\n", + "model.eval()\n", + "\n", + "# 5. Prepare the model and a dummy input for the ONNX export.\n", + "device = torch.device('cpu')\n", + "model = model.to(device)\n", + "dummy_input = torch.zeros(BATCH_SIZE, 3, *IMGSZ).to(device)\n", + "\n", + "print(f\"✓ Model loaded with corrected architecture and is ready for export.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xa_U0cm1RzPe", + "outputId": "55993874-6576-478c-8dce-889d4dcda88d" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "================================================================================\n", + "STEP 5: Building model from YAML and loading weights...\n", + "================================================================================\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n", + " from n params module arguments \n", + "\u001b[34m\u001b[1mactivation:\u001b[0m nn.ReLU6()\n", + " 0 -1 1 928 models.common.Conv [3, 32, 3, 2] \n", + " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", + " 2 -1 1 31104 models.common.ELAN1 [64, 64, 64, 32] \n", + " 3 -1 1 73984 models.common.AConv [64, 128] \n", + " 4 -1 1 258432 models.common.RepNCSPELAN4 [128, 128, 128, 64, 3] \n", + " 5 -1 1 221568 models.common.AConv [128, 192] \n", + " 6 -1 1 579648 models.common.RepNCSPELAN4 [192, 192, 192, 96, 3] \n", + " 7 -1 1 442880 models.common.AConv [192, 256] \n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Building new model structure from: yolov9/models/detect/yolov9-s-relu6.yaml\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + " 8 -1 1 1028864 models.common.RepNCSPELAN4 [256, 256, 256, 128, 3] \n", + " 9 -1 1 164608 models.common.SPPELAN [256, 256, 128] \n", + " 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 11 [-1, 6] 1 0 models.common.Concat [1] \n", + " 12 -1 1 628800 models.common.RepNCSPELAN4 [448, 192, 192, 96, 3] \n", + " 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 14 [-1, 4] 1 0 models.common.Concat [1] \n", + " 15 -1 1 283008 models.common.RepNCSPELAN4 [320, 128, 128, 64, 3] \n", + " 16 -1 1 110784 models.common.AConv [128, 96] \n", + " 17 [-1, 12] 1 0 models.common.Concat [1] \n", + " 18 -1 1 598080 models.common.RepNCSPELAN4 [288, 192, 192, 96, 3] \n", + " 19 -1 1 221440 models.common.AConv [192, 128] \n", + " 20 [-1, 9] 1 0 models.common.Concat [1] \n", + " 21 -1 1 1061632 models.common.RepNCSPELAN4 [384, 256, 256, 128, 3] \n", + " 22 8 1 164608 models.common.SPPELAN [256, 256, 128] \n", + " 23 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 24 [-1, 6] 1 0 models.common.Concat [1] \n", + " 25 -1 1 628800 models.common.RepNCSPELAN4 [448, 192, 192, 96, 3] \n", + " 26 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 27 [-1, 4] 1 0 models.common.Concat [1] \n", + " 28 -1 1 283008 models.common.RepNCSPELAN4 [320, 128, 128, 64, 3] \n", + " 29[28, 25, 22, 15, 18, 21] 1 2955014 models.yolo.DualDDetectEdgeTPU [17, [128, 192, 256, 128, 192, 256]]\n", + "yolov9-s-relu6 summary: 1454 layers, 9755750 parameters, 9755718 gradients\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loading trained weights from: yolov9-s-relu6-best.pt\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Fusing layers... \n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Fusing model...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "yolov9-s-relu6 summary: 893 layers, 9610406 parameters, 8501958 gradients\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "✓ Model loaded with corrected architecture and is ready for export.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# ============================================================================\n", + "# STEP 8: Export to ONNX\n", + "# ============================================================================\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"STEP 8: Exporting to ONNX...\")\n", + "print(\"=\"*80)\n", + "\n", + "ONNX_PATH = Path(MODEL_PATH).with_suffix('.onnx')\n", + "\n", + "def best_onnx_opset():\n", + " version = \".\".join(torch.__version__.split(\".\")[:2])\n", + " print(f\"Looking up opset for torch version {version}\")\n", + " opset_map = {\n", + " \"1.8\": 12, \"1.9\": 12, \"1.10\": 13, \"1.11\": 14, \"1.12\": 15,\n", + " \"1.13\": 17, \"2.0\": 17, \"2.1\": 17, \"2.2\": 17, \"2.3\": 17,\n", + " \"2.4\": 20, \"2.5\": 20,\n", + " }\n", + " opset = opset_map.get(version, 14)\n", + " return min(opset, onnx.defs.onnx_opset_version())\n", + "\n", + "opset = best_onnx_opset()\n", + "print(f\"Using ONNX opset: {opset}\")\n", + "\n", + "detection_head = model.model[-1]\n", + "detection_head.export = True # Set export mode on the detection head\n", + "\n", + "# ATTENTION! this should match what is in the DualDDetectEdgeTPU class above !\n", + "outputnames = [\"boxes_dfl_logit\",\n", + " \"class_scores_logit\",\n", + " ]\n", + "\n", + "OUTPUT_COUNT = len(outputnames)\n", + "print(f\"Model has {OUTPUT_COUNT} outputs\")\n", + "print(f\"Model final node has shape {model.model[-1].shape}\")\n", + "\n", + "#print(model) # can indicate which activation function is being used\n", + "\n", + "torch.onnx.export(\n", + " model,\n", + " dummy_input,\n", + " str(ONNX_PATH),\n", + " opset_version=opset,\n", + " input_names=['images'],\n", + " output_names=outputnames,\n", + " dynamic_axes=None, # Static shapes for EdgeTPU\n", + ")\n", + "\n", + "print(f\"✓ ONNX export successful: {ONNX_PATH}\")\n", + "\n", + "print(\"\\nSimplifying ONNX model...\")\n", + "model_onnx = onnx.load(str(ONNX_PATH))\n", + "try:\n", + " model_onnx = onnxslim.slim(model_onnx)\n", + " onnx.save(model_onnx, str(ONNX_PATH))\n", + " print(\"✓ ONNX simplification successful\")\n", + "except Exception as e:\n", + " print(f\"⚠ ONNX simplification failed: {e}\")\n", + "\n", + "# Verify ONNX model\n", + "import onnxruntime as ort\n", + "ort_session = ort.InferenceSession(str(ONNX_PATH))\n", + "\n", + "print(\"\\n🔍 Verifying ONNX export:\")\n", + "print(f\" Outputs: {[o.name for o in ort_session.get_outputs()]}\")\n", + "for outp in ort_session.get_outputs():\n", + " print(f\" {outp.name}: {outp.shape}\")\n", + "\n", + "test_output = ort_session.run(None, {'images': dummy_input.numpy()})\n", + "for i in range(len(test_output)):\n", + " print(f\" Output {i}: {test_output[i].shape}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aInSYps2Sb11", + "outputId": "5d60f742-7163-41c4-93bc-3a3b8251af0e" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "================================================================================\n", + "STEP 8: Exporting to ONNX...\n", + "================================================================================\n", + "Looking up opset for torch version 2.8\n", + "Using ONNX opset: 14\n", + "Model has 2 outputs\n", + "Model final node has shape None\n", + "✓ ONNX export successful: yolov9-s-relu6-best.onnx\n", + "\n", + "Simplifying ONNX model...\n", + "✓ ONNX simplification successful\n", + "\n", + "🔍 Verifying ONNX export:\n", + " Outputs: ['boxes_dfl_logit', 'class_scores_logit']\n", + " boxes_dfl_logit: [1, 2100, 64]\n", + " class_scores_logit: [1, 2100, 17]\n", + " Output 0: (1, 2100, 64)\n", + " Output 1: (1, 2100, 17)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Functions to parse model output" + ], + "metadata": { + "id": "fnzdpkXm2bvw" + } + }, + { + "cell_type": "code", + "source": [ + "####################\n", + "# HELPER : Decode DFL boxes\n", + "####################\n", + "\n", + "import torch\n", + "from typing import Tuple\n", + "\n", + "def decode_dfl_boxes(boxes_dfl):\n", + " if isinstance(boxes_dfl, np.ndarray):\n", + " boxes_dfl = torch.from_numpy(boxes_dfl)\n", + " strides = (8, 16, 32) # YOLO standard\n", + " print(f\"boxes_dfl.shape = {boxes_dfl.shape}\") # (2100, 64)\n", + " if boxes_dfl.shape[0] > boxes_dfl.shape[1]: # If N > C, we need to transpose\n", + " print(f\"transposing boxes_dfl\")\n", + " boxes_dfl = boxes_dfl.transpose(1, 0)\n", + " print(f\"boxes_dfl.shape = {boxes_dfl.shape}\") # (64, 2100)\n", + " device = boxes_dfl.device\n", + " dfl_channels, total_proposals = boxes_dfl.shape\n", + "\n", + " assert dfl_channels % 4 == 0\n", + " reg_max = dfl_channels // 4\n", + "\n", + " # 1. Generate anchor points and strides for all 2100 proposals\n", + " def generate_anchors_and_strides_for_dfl(\n", + " image_size: Tuple[int, int],\n", + " strides: Tuple[int, ...],\n", + " device: torch.device\n", + " ) -> Tuple[torch.Tensor, torch.Tensor]:\n", + " all_anchors = []\n", + " all_strides = []\n", + " for stride in strides:\n", + " feat_h, feat_w = image_size[0] // stride, image_size[1] // stride\n", + "\n", + " grid_y, grid_x = torch.meshgrid(\n", + " torch.arange(feat_h, device=device, dtype=torch.float32),\n", + " torch.arange(feat_w, device=device, dtype=torch.float32),\n", + " indexing='ij'\n", + " )\n", + "\n", + " grid_coords = torch.stack((grid_x.flatten(), grid_y.flatten()), dim=1)\n", + " anchor_points = (grid_coords + 0.5)\n", + "\n", + " all_anchors.append(anchor_points)\n", + " all_strides.append(torch.full((feat_h * feat_w, 1), stride, device=device, dtype=torch.float32))\n", + "\n", + " return torch.cat(all_anchors, dim=0), torch.cat(all_strides, dim=0)\n", + "\n", + " anchors, anchor_strides = generate_anchors_and_strides_for_dfl(IMGSZ, strides, device)\n", + "\n", + " # 2. Decode DFL to distances (ltrb)\n", + " project = torch.arange(reg_max, dtype=torch.float32, device=device)\n", + " # Reshape: [64, 2100] -> [4, 16, 2100]\n", + " dfl_distributions = boxes_dfl.view(4, reg_max, total_proposals)\n", + " dfl_probs = dfl_distributions.softmax(dim=1) # Softmax over the 16 channels\n", + " # Weighted sum: [4, 16, 2100] * [16] -> [4, 2100]\n", + " distances = torch.einsum('crp,r->cp', dfl_probs, project)\n", + " ##distances = boxes_raw\n", + "\n", + " # 3. Convert distances to bounding boxes (xyxy)\n", + " # Permute to [2100, 4] for easier processing\n", + " distances_permuted = distances.permute(1, 0)\n", + "\n", + " # Calculate box corners in pixel coordinates\n", + " # anchors are in grid units, so multiply by stride first\n", + " # distances are also in grid units, so multiply by stride\n", + " x1y1 = (anchors - distances_permuted[..., [0, 1]]) * anchor_strides\n", + " x2y2 = (anchors + distances_permuted[..., [2, 3]]) * anchor_strides\n", + "\n", + " boxes_decoded = torch.cat((x1y1, x2y2), dim=-1)\n", + " return boxes_decoded" + ], + "metadata": { + "id": "nX9-1Uk3Vxb2" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# ONNX Test Abbey Road" + ], + "metadata": { + "id": "zXhQ77Iq25Ts" + } + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "w34LBqmNBry9", + "outputId": "c57dbe32-5844-4daa-9873-d97798f23e8c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "================================================================================\n", + "STEP 8.5: Verifying ONNX output with Abbey Road image...\n", + "================================================================================\n", + "Original image size: (1200, 1200)\n", + "onnx_outputs[0].shape = (1, 2100, 64) , min = -5.343 , max = 13.476\n", + "onnx_outputs[1].shape = (1, 2100, 17) , min = -4.000 , max = 1.791\n", + "boxes_dfl.shape = torch.Size([2100, 64])\n", + "transposing boxes_dfl\n", + "boxes_dfl.shape = torch.Size([64, 2100])\n", + "boxes_xyxy_decoded[0:5]\n", + "tensor([[ -4.69742, -7.77360, 59.98768, 145.25444],\n", + " [ -5.44631, -1.39374, 169.42268, 169.55489],\n", + " [ -3.89445, -1.08935, 236.13510, 125.72262],\n", + " [ 7.15157, -0.46535, 269.48795, 137.64694],\n", + " [115.95653, 2.01208, 226.12323, 45.62277]])\n", + "class_ids_argmax[0:5]\n", + "[0 0 0 0 0]\n", + "scores_max[0:5]\n", + "[ 0.017986 0.017986 0.017986 0.017986 0.017986]\n", + "\n", + "✅ Score Precision Check:\n", + " unique_boxes_xyxy_decoded: 8396\n", + " unique_class_ids_argmax: 2\n", + " unique_scores_max: 95\n", + "CPU: Found 63 detections above 0.25 confidence.\n", + "CPU Found 8 detections after NMS.\n", + "CPU box 1931 class person score 0.85711 x1 562.4 y1 628.9 x2 823.1 y2 1051.3\n", + "CPU box 1902 class person score 0.84238 x1 71.0 y1 615.8 x2 328.4 y2 1053.4\n", + "CPU box 1887 class person score 0.80396 x1 344.0 y1 612.5 x2 557.8 y2 1034.5\n", + "CPU box 810 class car score 0.74826 x1 216.4 y1 592.7 x2 382.4 y2 731.0\n", + "CPU box 1936 class person score 0.65993 x1 828.9 y1 619.2 x2 1087.4 y2 1067.1\n", + "CPU box 867 class car score 0.50832 x1 737.5 y1 569.6 x2 865.4 y2 664.0\n", + "CPU box 776 class car score 0.39580 x1 466.5 y1 572.2 x2 522.7 y2 620.2\n", + "CPU box 784 class car score 0.36845 x1 699.3 y1 573.4 x2 759.4 y2 618.1\n", + "\n", + "✓ Detections saved to 'detections_onnx.jpg'\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/jpeg": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {} + } + ], + "source": [ + "# ============================================================================\n", + "# STEP 8.5: Verification with ONNX Model on Abbey Road Image\n", + "# ============================================================================\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"STEP 8.5: Verifying ONNX output with Abbey Road image...\")\n", + "print(\"=\"*80)\n", + "\n", + "import onnxruntime as ort\n", + "ort_session = ort.InferenceSession(str(ONNX_PATH))\n", + "\n", + "import numpy as np\n", + "from PIL import Image, ImageDraw, ImageFont\n", + "from IPython.display import Image as IPyImage\n", + "\n", + "# --- 1. Load Image and Run Inference ---\n", + "#test_image_path = \"Beatles_-_Abbey_Road.jpg\"\n", + "#if not Path(test_image_path).exists():\n", + "# !wget -q https://upload.wikimedia.org/wikipedia/en/4/42/Beatles_-_Abbey_Road.jpg\n", + "test_image_path = \"81sBKBIcwvL._SL1200_.jpg\"\n", + "if not Path(test_image_path).exists():\n", + " !wget -q https://m.media-amazon.com/images/I/81sBKBIcwvL._SL1200_.jpg\n", + "\n", + "img = Image.open(test_image_path).convert(\"RGB\")\n", + "original_size = img.size\n", + "print(f\"Original image size: {original_size}\")\n", + "\n", + "# Resize to model input size\n", + "img_resized = img.resize(IMGSZ)\n", + "img_array_hwc = np.array(img_resized).astype(np.float32)\n", + "img_scale_factor = original_size[0] / IMGSZ[0]\n", + "\n", + "# Convert to NCHW format for ONNX (CHW with batch dimension)\n", + "img_array_nchw = np.transpose(img_array_hwc, (2, 0, 1))[np.newaxis, :] # [1, 3, H, W]\n", + "img_normalized = img_array_nchw / 255.0 # Normalize to [0, 1]\n", + "\n", + "# Run ONNX inference\n", + "onnx_outputs = ort_session.run(None, {'images': img_normalized})\n", + "\n", + "for i,o in enumerate(onnx_outputs):\n", + " print(f\"onnx_outputs[{i}].shape = {o.shape} , min = {o.min():.3f} , max = {o.max():.3f}\")\n", + "\n", + "boxes_xyxy_decoded = decode_dfl_boxes(onnx_outputs[0][0,:,:]) * img_scale_factor\n", + "#class_ids = onnx_outputs[1][0,:,:].squeeze()\n", + "#scores = 1 / (1+(np.exp(-1 * onnx_outputs[1][0,:,:].squeeze()))) # sigmoid\n", + "class_scores = 1 / (1+(np.exp(-1 * onnx_outputs[1][0,:,:].squeeze()))) # sigmoid\n", + "#boxes_xyxy = onnx_outputs[4][0,:,:] * img_scale_factor\n", + "class_ids_argmax = class_scores.argmax(axis=1)\n", + "scores_max = class_scores.max(axis=1)\n", + "\n", + "# quantization investigation\n", + "#sum_logits = onnx_outputs[5][0,:,:].squeeze()\n", + "#inv_sum = onnx_outputs[6][0,:,:].squeeze()\n", + "#weights = onnx_outputs[7][0,:,:].squeeze()\n", + "#hbox = onnx_outputs[5][0,:,:].squeeze()\n", + "#distances = onnx_outputs[6][0,:,:].squeeze()\n", + "\n", + "# --- 2. Process the Decoded Output ---\n", + "#print(f\"boxes_xyxy (from TPU)[0:5]\\n{boxes_xyxy[:5,:]}\")\n", + "print(f\"boxes_xyxy_decoded[0:5]\\n{boxes_xyxy_decoded[:5,:]}\")\n", + "#print(f\"class_ids[0:5] (from TPU)\\n{class_ids[:5]}\") # calculated by TPU\n", + "print(f\"class_ids_argmax[0:5]\\n{class_ids_argmax[:5]}\")\n", + "#print(f\"scores[0:5] (from TPU)\\n{scores[:5]}\") # calculated by TPU\n", + "print(f\"scores_max[0:5]\\n{scores_max[:5]}\")\n", + "\n", + "#print(\"BOX[12] COORDINATES\")\n", + "#print(f\"boxes_xyxy (from TPU)[12]\\n{boxes_xyxy[12,:]}\")\n", + "#print(f\"boxes_xyxy_decoded[12]\\n{boxes_xyxy_decoded[12,:]}\")\n", + "\n", + "\n", + "#unique_boxes_xyxy = np.unique(boxes_xyxy)\n", + "unique_boxes_xyxy_decoded = np.unique(boxes_xyxy_decoded)\n", + "#unique_class_ids = np.unique(class_ids)\n", + "unique_class_ids_argmax = np.unique(class_ids_argmax)\n", + "#unique_scores = np.unique(scores)\n", + "unique_scores_max = np.unique(scores_max)\n", + "print(f\"\\n✅ Score Precision Check:\")\n", + "#print(f\" unique_boxes_xyxy: {len(unique_boxes_xyxy)}\")\n", + "print(f\" unique_boxes_xyxy_decoded: {len(unique_boxes_xyxy_decoded)}\")\n", + "#print(f\" unique_class_ids: {len(unique_class_ids)}\")\n", + "print(f\" unique_class_ids_argmax: {len(unique_class_ids_argmax)}\")\n", + "#print(f\" unique_scores: {len(unique_scores)}\")\n", + "print(f\" unique_scores_max: {len(unique_scores_max)}\")\n", + "\n", + "#print(f\" sum_logits : min={sum_logits.min():.3f} , max={sum_logits.max():.3f} , unique vals: {len(np.unique(sum_logits))}\")\n", + "#print(f\" inv_sum : min={inv_sum.min():.3f} , max={inv_sum.max():.3f} , unique vals: {len(np.unique(inv_sum))}\")\n", + "#print(f\" weights : min={weights.min():.3f} , max={weights.max():.3f} , unique vals: {len(np.unique(weights))}\")\n", + "#print(f\" hbox : min={hbox.min():.3f} , max={hbox.max():.3f} , unique vals: {len(np.unique(hbox))}\")\n", + "#print(f\" distances : min={distances.min():.3f} , max={distances.max():.3f} , unique vals: {len(np.unique(distances))}\")\n", + "\n", + "\n", + "conf_threshold = 0.25\n", + "#filtered_indices_tpu = scores > conf_threshold\n", + "#print(f\"\\nTPU: Found {np.sum(filtered_indices_tpu)} detections above {conf_threshold} confidence.\")\n", + "if isinstance(scores_max, torch.Tensor):\n", + " # .cpu() is important if the tensor is on the GPU\n", + " scores_max = scores_max.cpu().numpy()\n", + "if isinstance(boxes_xyxy_decoded, torch.Tensor):\n", + " boxes_xyxy_decoded = boxes_xyxy_decoded.cpu().numpy()\n", + "filtered_indices_cpu = (scores_max > conf_threshold) & \\\n", + " (boxes_xyxy_decoded[:,0] < boxes_xyxy_decoded[:,2]) & \\\n", + " (boxes_xyxy_decoded[:,1] < boxes_xyxy_decoded[:,3])\n", + "print(f\"CPU: Found {filtered_indices_cpu.sum()} detections above {conf_threshold} confidence.\")\n", + "\n", + "#if np.sum(filtered_indices_tpu) > 0:\n", + "if np.sum(filtered_indices_cpu) > 0:\n", + " #map_to_original_indices_tpu = np.where(filtered_indices_tpu)[0]\n", + " map_to_original_indices_cpu = np.where(filtered_indices_cpu)[0]\n", + " #filtered_boxes_xyxy = boxes_xyxy[filtered_indices_tpu]\n", + " #filtered_scores = scores[filtered_indices_tpu]\n", + " #filtered_class_ids = class_ids[filtered_indices_tpu]\n", + " filtered_boxes_xyxy_cpu = boxes_xyxy_decoded[filtered_indices_cpu]\n", + " filtered_scores_cpu = scores_max[filtered_indices_cpu]\n", + " filtered_class_ids_cpu = class_ids_argmax[filtered_indices_cpu]\n", + " filtered_class_scores_cpu = class_scores[filtered_indices_cpu]\n", + "\n", + " # --- 3. NMS & Drawing ---\n", + " def nms(boxes, scores, iou_threshold):\n", + " order = scores.argsort()[::-1]; keep = []\n", + " while order.size > 0:\n", + " i = order[0]; keep.append(i)\n", + " xx1 = np.maximum(boxes[i, 0], boxes[order[1:], 0]); yy1 = np.maximum(boxes[i, 1], boxes[order[1:], 1])\n", + " xx2 = np.minimum(boxes[i, 2], boxes[order[1:], 2]); yy2 = np.minimum(boxes[i, 3], boxes[order[1:], 3])\n", + " w = np.maximum(0.0, xx2 - xx1); h = np.maximum(0.0, yy2 - yy1); inter = w * h\n", + " ovr = inter / ((boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1]) + (boxes[order[1:], 2] - boxes[order[1:], 0]) * (boxes[order[1:], 3] - boxes[order[1:], 1]) - inter + 1e-6)\n", + " inds = np.where(ovr <= iou_threshold)[0]; order = order[inds + 1]\n", + " return keep\n", + "\n", + " #nms_indices_tpu = nms(filtered_boxes_xyxy, filtered_scores, iou_threshold=0.45)\n", + " #print(f\"\\nTPU Found {len(nms_indices_tpu)} detections after NMS.\")\n", + " nms_indices_cpu = nms(filtered_boxes_xyxy_cpu, filtered_scores_cpu, iou_threshold=0.45)\n", + " print(f\"CPU Found {len(nms_indices_cpu)} detections after NMS.\")\n", + "\n", + " coco_classes = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']\n", + " draw = ImageDraw.Draw(img)\n", + " #for nms_rank, i in enumerate(nms_indices_tpu):\n", + " if False:\n", + " original_index = map_to_original_indices_tpu[i]\n", + " box = filtered_boxes_xyxy[i]\n", + " label = f\"{coco_classes[int(filtered_class_ids[i])]}: {filtered_scores[i]:.2f}\"\n", + " #label = f\"{filtered_class_ids[i]:.1f}: {filtered_scores[i]:.2f}\"\n", + " draw.rectangle(box.tolist(), outline=\"red\", width=3)\n", + " text_bbox = draw.textbbox((box[0], box[1] - 12), label)\n", + " draw.rectangle(text_bbox, fill=\"red\")\n", + " draw.text((box[0], box[1] - 12), label, fill=\"white\")\n", + " print(f\"TPU box {original_index} class {filtered_class_ids[i]:.2f}:{coco_classes[int(filtered_class_ids[i])]} score {filtered_scores[i]:.5f} x1 {box[0]:.1f} y1 {box[1]:.1f} x2 {box[2]:.1f} y2 {box[3]:.1f}\")\n", + " for nms_rank, i in enumerate(nms_indices_cpu):\n", + " original_index = map_to_original_indices_cpu[i]\n", + " box = filtered_boxes_xyxy_cpu[i]\n", + " label = f\"{coco_classes[int(filtered_class_ids_cpu[i])]}: {filtered_scores_cpu[i]:.2f}\"\n", + " #label = f\"{filtered_class_ids_cpu[i]}: {filtered_scores_cpu[i]:.2f}\"\n", + " draw.rectangle(box.tolist(), outline=\"blue\", width=3)\n", + " text_bbox = draw.textbbox((box[0], box[1] - 12), label)\n", + " draw.rectangle(text_bbox, fill=\"blue\")\n", + " draw.text((box[0], box[1] - 12), label, fill=\"white\")\n", + " print(f\"CPU box {original_index} class {coco_classes[int(filtered_class_ids_cpu[i])]} score {filtered_scores_cpu[i]:.5f} x1 {box[0]:.1f} y1 {box[1]:.1f} x2 {box[2]:.1f} y2 {box[3]:.1f}\")\n", + " #class_scores_to_print = [f\"{float(x):.2f}\" for x in filtered_class_scores_cpu[i]]\n", + " #print(f\" box {i} class_scores: {class_scores_to_print}\")\n", + "\n", + " # --- 4. Save and Display ---\n", + " output_image_path = \"detections_onnx.jpg\"\n", + " img.save(output_image_path)\n", + " print(f\"\\n✓ Detections saved to '{output_image_path}'\")\n", + " display(IPyImage(filename=output_image_path))\n", + "else:\n", + " print(\"⚠️ No detections found above confidence threshold.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wQ2X3dAKiW3z", + "outputId": "bb488de0-1542-4e7f-fade-09c6fc53d3ac" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "================================================================================\n", + "STEP 10: Preparing calibration data...\n", + "================================================================================\n", + "\n", + "Loading COCO128 dataset for calibration...\n", + "Found 128 images in COCO128\n", + "Loading 128 COCO images...\n", + " Processed 10/128 images\n", + " Processed 20/128 images\n", + " Processed 30/128 images\n", + " Processed 40/128 images\n", + " Processed 50/128 images\n", + " Processed 60/128 images\n", + " Processed 70/128 images\n", + " Processed 80/128 images\n", + " Processed 90/128 images\n", + " Processed 100/128 images\n", + " Processed 110/128 images\n", + " Processed 120/128 images\n", + "Successfully loaded 128 COCO images\n", + "Generating 50 random noise images...\n", + "\n", + "✓ Created calibration data: coco128_calibration_images.npy\n", + " Shape: (178, 320, 320, 3) (NHWC)\n", + " Dtype: float32\n", + "\n", + "🔍 Calibration data verification:\n", + " Shape: (178, 320, 320, 3)\n", + " Range: [0.0, 255.0]\n", + " Mean: 115.8\n", + " First image stats - min: 0.0, max: 255.0\n", + " Last image stats - min: 0.0, max: 254.0\n" + ] + } + ], + "source": [ + "# ============================================================================\n", + "# STEP 10: Prepare Calibration Data (COCO128)\n", + "# ============================================================================\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"STEP 10: Preparing calibration data...\")\n", + "print(\"=\"*80)\n", + "\n", + "# Download and extract COCO128 if needed\n", + "if not Path(\"coco128.zip\").exists():\n", + " !wget -q https://ultralytics.com/assets/coco128.zip\n", + " !unzip -q coco128.zip\n", + "\n", + "print(\"\\nLoading COCO128 dataset for calibration...\")\n", + "\n", + "# Simple image loader to replace YOLODataset\n", + "import cv2\n", + "from pathlib import Path\n", + "#import numpy as np\n", + "from glob import glob\n", + "\n", + "# TODO: ensure that the min/max value for each tensor is represented\n", + "# box coordinates is probably OK\n", + "# class max scores may benefit from a 100% score\n", + "# class argmax depends on having class 0 and class NC-1 present !!\n", + "# those are 0:person (OK) and 16:bear (note that bear is class 21 in the original COCO label index)\n", + "# there is one photo of a bear in the coco128 data set:\n", + "# 000000000502.jpg so make sure this is included here. It is #92 in sorted order\n", + "\n", + "# the coco128 labels have these counts of each category kept here:\n", + "coco_128_class_counts = \"\"\"\n", + " 261 0\n", + " 6 1\n", + " 48 2\n", + " 6 3\n", + " 6 4\n", + " 7 5\n", + " 4 6\n", + " 12 7\n", + " 6 8\n", + " 16 14\n", + " 4 15\n", + " 9 16\n", + " 2 17\n", + " 0 18 <--- ZERO sheep\n", + " 0 19 <--- ZERO cow\n", + " 17 20 <--- elephant\n", + " 1 21 <--- bear\n", + "\"\"\"\n", + "\n", + "def load_and_preprocess_image(img_path, target_size):\n", + " \"\"\"\n", + " Load and preprocess an image for calibration.\n", + "\n", + " Args:\n", + " img_path: Path to the image file\n", + " target_size: Tuple of (height, width) for resizing\n", + "\n", + " Returns:\n", + " Preprocessed image in HWC format (numpy array)\n", + " \"\"\"\n", + " # Read image\n", + " img = cv2.imread(str(img_path))\n", + " if img is None:\n", + " return None\n", + "\n", + " # Convert BGR to RGB\n", + " img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", + "\n", + " # Resize with letterbox (maintain aspect ratio)\n", + " h, w = img.shape[:2]\n", + " target_h, target_w = target_size\n", + "\n", + " # Calculate scale to fit image within target size\n", + " scale = min(target_w / w, target_h / h)\n", + " new_w, new_h = int(w * scale), int(h * scale)\n", + "\n", + " # Resize image\n", + " img_resized = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_LINEAR)\n", + "\n", + " # Create letterbox (padding)\n", + " canvas = np.full((target_h, target_w, 3), 114, dtype=np.uint8)\n", + "\n", + " # Center the resized image on canvas\n", + " top = (target_h - new_h) // 2\n", + " left = (target_w - new_w) // 2\n", + " canvas[top:top+new_h, left:left+new_w, :] = img_resized\n", + "\n", + " return canvas.astype(np.float32)\n", + "\n", + "# Get all image files from COCO128\n", + "image_dir = Path('coco128/images/train2017')\n", + "image_files = sorted(glob(str(image_dir / '*.jpg')) + glob(str(image_dir / '*.png')))\n", + "\n", + "if not image_files:\n", + " raise RuntimeError(f\"No images found in {image_dir}\")\n", + "\n", + "print(f\"Found {len(image_files)} images in COCO128\")\n", + "\n", + "calibration_images = []\n", + "\n", + "# Load and preprocess **ALL** 128 COCO images\n", + "num_coco_images = min(200, len(image_files))\n", + "print(f\"Loading {num_coco_images} COCO images...\")\n", + "\n", + "for i in range(num_coco_images):\n", + " img = load_and_preprocess_image(image_files[i], (IMGSZ[0], IMGSZ[1]))\n", + " if img is not None:\n", + " calibration_images.append(img)\n", + "\n", + " if (i + 1) % 10 == 0:\n", + " print(f\" Processed {i + 1}/{num_coco_images} images\")\n", + "\n", + "print(f\"Successfully loaded {len(calibration_images)} COCO images\")\n", + "\n", + "# Add random noise images\n", + "print(\"Generating 50 random noise images...\")\n", + "for i in range(50):\n", + " # Generate random image with correct shape (no batch dimension)\n", + " data = np.random.rand(IMGSZ[0], IMGSZ[1], 3).astype(np.float32) * 255\n", + " calibration_images.append(data)\n", + "\n", + "# Convert to arrays\n", + "calibration_images_uint8 = np.array(calibration_images).astype(np.uint8)\n", + "calibration_images_float32 = calibration_images_uint8.astype(np.float32)\n", + "\n", + "# Save calibration data\n", + "CALIB_NPY = Path(\"coco128_calibration_images.npy\")\n", + "np.save(str(CALIB_NPY), calibration_images_float32)\n", + "\n", + "# Prepare calibration data for onnx2tf\n", + "np_data = [[\"images\", str(CALIB_NPY), [[[[0, 0, 0]]]], [[[[255, 255, 255]]]]]]\n", + "\n", + "print(f\"\\n✓ Created calibration data: {CALIB_NPY}\")\n", + "print(f\" Shape: {calibration_images_float32.shape} (NHWC)\")\n", + "print(f\" Dtype: {calibration_images_float32.dtype}\")\n", + "\n", + "# Calibration data verification\n", + "print(f\"\\n🔍 Calibration data verification:\")\n", + "print(f\" Shape: {calibration_images_float32.shape}\")\n", + "print(f\" Range: [{calibration_images_float32.min():.1f}, {calibration_images_float32.max():.1f}]\")\n", + "print(f\" Mean: {calibration_images_float32.mean():.1f}\")\n", + "print(f\" First image stats - min: {calibration_images_float32[0].min():.1f}, max: {calibration_images_float32[0].max():.1f}\")\n", + "print(f\" Last image stats - min: {calibration_images_float32[-1].min():.1f}, max: {calibration_images_float32[-1].max():.1f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "neN7iTFCE_WS", + "outputId": "243c105b-6b96-4309-d272-b106b3ceea30" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "================================================================================\n", + "STEPS 11 & 12: Converting and Quantizing with TFLiteConverter\n", + "================================================================================\n", + "Step 1: Converting ONNX to TensorFlow SavedModel...\n", + "Saved artifact at 'yolov9-s-relu6-best_savedmodel'. The following endpoints are available:\n", + "\n", + "* Endpoint 'serving_default'\n", + " inputs_0 (POSITIONAL_ONLY): TensorSpec(shape=(1, 320, 320, 3), dtype=tf.float32, name='images')\n", + "Output Type:\n", + " List[TensorSpec(shape=(1, 2100, 64), dtype=tf.float32, name=None), TensorSpec(shape=(1, 2100, 17), dtype=tf.float32, name=None)]\n", + "Captures:\n", + " 134412922613648: TensorSpec(shape=(4, 2), dtype=tf.int32, name=None)\n", + " 134412922614608: TensorSpec(shape=(3, 3, 3, 32), dtype=tf.float32, name=None)\n", + " 134412922612880: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412922610576: TensorSpec(shape=(4, 2), dtype=tf.int32, name=None)\n", + " 134412922609232: TensorSpec(shape=(3, 3, 32, 64), dtype=tf.float32, name=None)\n", + " 134412922606544: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412922607120: TensorSpec(shape=(1, 1, 64, 64), dtype=tf.float32, name=None)\n", + " 134412922603664: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412923762640: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412923763408: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412922602896: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412922602128: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412923754960: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412923758032: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412923761872: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412923762448: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412923758224: TensorSpec(shape=(1, 1, 128, 64), dtype=tf.float32, name=None)\n", + " 134412923758800: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134419982424912: TensorSpec(shape=(4, 2), dtype=tf.int32, name=None)\n", + " 134419982425680: TensorSpec(shape=(3, 3, 64, 128), dtype=tf.float32, name=None)\n", + " 134419982424720: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134419982426256: TensorSpec(shape=(1, 1, 128, 128), dtype=tf.float32, name=None)\n", + " 134419982425296: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134419982426832: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134419982426640: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134419982428944: TensorSpec(shape=(1, 1, 64, 32), dtype=tf.float32, name=None)\n", + " 134419982429136: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134419982428368: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134419982429520: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134419982427984: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134419982429328: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134419982430480: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134419982429904: 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134412880821712: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412880821904: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412880821328: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412868616848: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412868616656: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412868617232: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412868617424: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412868617040: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412868617616: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412868618000: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412868618192: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412880820368: TensorSpec(shape=(1, 1, 128, 64), dtype=tf.float32, name=None)\n", + " 134412868617808: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412880819792: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412868618768: TensorSpec(shape=(1, 1, 128, 128), dtype=tf.float32, name=None)\n", + " 134412868618960: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412868618384: TensorSpec(shape=(3, 3, 128, 128), dtype=tf.float32, name=None)\n", + " 134412868618576: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412868619152: TensorSpec(shape=(1, 1, 128, 64), dtype=tf.float32, name=None)\n", + " 134412868619344: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412868620112: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412868620304: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412868619728: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412868620496: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 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134412868622224: TensorSpec(shape=(3, 3, 128, 128), dtype=tf.float32, name=None)\n", + " 134412868622416: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412880820560: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412880820752: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412868623184: TensorSpec(shape=(1, 1, 512, 256), dtype=tf.float32, name=None)\n", + " 134412868623376: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n", + " 134412868622992: TensorSpec(shape=(1, 1, 256, 128), dtype=tf.float32, name=None)\n", + " 134412868623568: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412868623952: TensorSpec(shape=(1, 1, 512, 256), dtype=tf.float32, name=None)\n", + " 134412868624144: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n", + " 134412868624528: TensorSpec(shape=(1, 1, 448, 192), dtype=tf.float32, name=None)\n", + " 134412868624336: TensorSpec(shape=(192,), dtype=tf.float32, name=None)\n", + " 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TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412868628752: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412868628944: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412868625104: TensorSpec(shape=(1, 1, 96, 48), dtype=tf.float32, name=None)\n", + " 134412868628560: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412868623760: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412868629520: TensorSpec(shape=(1, 1, 96, 96), dtype=tf.float32, name=None)\n", + " 134412868629712: TensorSpec(shape=(96,), dtype=tf.float32, name=None)\n", + " 134412868629136: TensorSpec(shape=(3, 3, 96, 96), dtype=tf.float32, name=None)\n", + " 134412868629328: TensorSpec(shape=(96,), dtype=tf.float32, name=None)\n", + " 134412868629904: TensorSpec(shape=(1, 1, 96, 48), dtype=tf.float32, name=None)\n", + " 134412868630096: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412868630864: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412868631056: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412868630480: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412868631248: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412868631440: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412868631824: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412868632016: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412868631632: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412868632208: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412868632400: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412877971536: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412868630672: TensorSpec(shape=(1, 1, 96, 48), dtype=tf.float32, name=None)\n", + " 134412877971728: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412868630288: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412877972304: TensorSpec(shape=(1, 1, 96, 96), dtype=tf.float32, name=None)\n", + " 134412877972496: TensorSpec(shape=(96,), dtype=tf.float32, name=None)\n", + " 134412877971920: TensorSpec(shape=(3, 3, 96, 96), dtype=tf.float32, name=None)\n", + " 134412877972112: TensorSpec(shape=(96,), dtype=tf.float32, name=None)\n", + " 134412868625296: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412868625488: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412877972880: TensorSpec(shape=(1, 1, 384, 192), dtype=tf.float32, name=None)\n", + " 134412877973072: TensorSpec(shape=(192,), dtype=tf.float32, name=None)\n", + " 134412877973456: TensorSpec(shape=(1, 1, 320, 128), dtype=tf.float32, name=None)\n", + " 134412877972688: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412877973840: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412877973648: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412877975952: TensorSpec(shape=(1, 1, 64, 32), dtype=tf.float32, name=None)\n", + " 134412877976144: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877975376: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412877976528: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877974992: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412877976336: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877976720: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412877976912: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877977104: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412877975568: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877977296: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412877977680: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877977872: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412877974032: TensorSpec(shape=(1, 1, 64, 32), dtype=tf.float32, name=None)\n", + " 134412877977488: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877973264: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877978448: TensorSpec(shape=(1, 1, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877978640: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877978064: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877978256: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877978832: TensorSpec(shape=(1, 1, 64, 32), dtype=tf.float32, name=None)\n", + " 134412877979024: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877979792: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412877979984: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877979408: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412877980176: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877980368: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412877980752: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877980944: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412877980560: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877981136: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412877981520: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877981712: TensorSpec(shape=(3, 3, 32, 32), dtype=tf.float32, name=None)\n", + " 134412877979600: TensorSpec(shape=(1, 1, 64, 32), dtype=tf.float32, name=None)\n", + " 134412877981328: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877979216: TensorSpec(shape=(32,), dtype=tf.float32, name=None)\n", + " 134412877982288: TensorSpec(shape=(1, 1, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877982480: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877981904: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877982096: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877974224: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412877974416: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412877982864: TensorSpec(shape=(1, 1, 256, 128), dtype=tf.float32, name=None)\n", + " 134412877983056: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412877983632: TensorSpec(shape=(4, 2), dtype=tf.int32, name=None)\n", + " 134412877984208: TensorSpec(shape=(3, 3, 128, 96), dtype=tf.float32, name=None)\n", + " 134412877984400: TensorSpec(shape=(96,), dtype=tf.float32, name=None)\n", + " 134412877986128: TensorSpec(shape=(1, 1, 288, 192), dtype=tf.float32, name=None)\n", + " 134412877985744: TensorSpec(shape=(192,), dtype=tf.float32, name=None)\n", + " 134412869911568: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412869910800: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412869911376: TensorSpec(shape=(1, 1, 96, 48), dtype=tf.float32, name=None)\n", + " 134412869912144: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869910608: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412869913104: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869913296: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412869914064: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869913488: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412869913680: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869914448: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412869912720: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869913872: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412869914832: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869915024: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412869914256: TensorSpec(shape=(1, 1, 96, 48), dtype=tf.float32, name=None)\n", + " 134412869914640: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869912528: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869915600: TensorSpec(shape=(1, 1, 96, 96), dtype=tf.float32, name=None)\n", + " 134412869915792: TensorSpec(shape=(96,), dtype=tf.float32, name=None)\n", + " 134412869915216: TensorSpec(shape=(3, 3, 96, 96), dtype=tf.float32, name=None)\n", + " 134412869915408: TensorSpec(shape=(96,), dtype=tf.float32, name=None)\n", + " 134412869915984: TensorSpec(shape=(1, 1, 96, 48), dtype=tf.float32, name=None)\n", + " 134412869916176: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869916944: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412869917136: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869916560: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412869917328: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869917520: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412869917904: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869918096: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412869917712: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869918288: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412869918672: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869918864: TensorSpec(shape=(3, 3, 48, 48), dtype=tf.float32, name=None)\n", + " 134412869916752: TensorSpec(shape=(1, 1, 96, 48), dtype=tf.float32, name=None)\n", + " 134412869918480: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869916368: TensorSpec(shape=(48,), dtype=tf.float32, name=None)\n", + " 134412869919440: TensorSpec(shape=(1, 1, 96, 96), dtype=tf.float32, name=None)\n", + " 134412869919632: TensorSpec(shape=(96,), dtype=tf.float32, name=None)\n", + " 134412869919056: TensorSpec(shape=(3, 3, 96, 96), dtype=tf.float32, name=None)\n", + " 134412869919248: TensorSpec(shape=(96,), dtype=tf.float32, name=None)\n", + " 134412869911952: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412869910992: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412869920016: TensorSpec(shape=(1, 1, 384, 192), dtype=tf.float32, name=None)\n", + " 134412869920208: TensorSpec(shape=(192,), dtype=tf.float32, name=None)\n", + " 134412869920976: TensorSpec(shape=(4, 2), dtype=tf.int32, name=None)\n", + " 134412869921360: TensorSpec(shape=(3, 3, 192, 128), dtype=tf.float32, name=None)\n", + " 134412869921552: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412869923280: TensorSpec(shape=(1, 1, 384, 256), dtype=tf.float32, name=None)\n", + " 134412869922896: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n", + " 134412869926736: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412869925584: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412869925200: TensorSpec(shape=(1, 1, 128, 64), dtype=tf.float32, name=None)\n", + " 134412869926160: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412869926544: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877268560: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877267792: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877268752: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877268944: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877268176: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877268368: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877267216: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877267984: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877269328: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877269520: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412869925392: TensorSpec(shape=(1, 1, 128, 64), dtype=tf.float32, name=None)\n", + " 134412877269136: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412869925776: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877270096: TensorSpec(shape=(1, 1, 128, 128), dtype=tf.float32, name=None)\n", + " 134412877270288: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412877269712: TensorSpec(shape=(3, 3, 128, 128), dtype=tf.float32, name=None)\n", + " 134412877269904: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412877270480: TensorSpec(shape=(1, 1, 128, 64), dtype=tf.float32, name=None)\n", + " 134412877270672: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877271440: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877271632: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877271056: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877271824: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877272016: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877272400: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877272592: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877272208: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877272784: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877273168: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877273360: TensorSpec(shape=(3, 3, 64, 64), dtype=tf.float32, name=None)\n", + " 134412877271248: TensorSpec(shape=(1, 1, 128, 64), dtype=tf.float32, name=None)\n", + " 134412877272976: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877270864: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877273936: TensorSpec(shape=(1, 1, 128, 128), dtype=tf.float32, name=None)\n", + " 134412877274128: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412877273552: TensorSpec(shape=(3, 3, 128, 128), dtype=tf.float32, name=None)\n", + " 134412877273744: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412869926352: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412869925008: TensorSpec(shape=(4,), dtype=tf.int64, name=None)\n", + " 134412877274512: TensorSpec(shape=(1, 1, 512, 256), dtype=tf.float32, name=None)\n", + " 134412877274704: TensorSpec(shape=(256,), dtype=tf.float32, name=None)\n", + " 134412877274320: TensorSpec(shape=(3, 3, 256, 64), dtype=tf.float32, name=None)\n", + " 134412869920400: TensorSpec(shape=(3, 3, 192, 64), dtype=tf.float32, name=None)\n", + " 134412877983248: TensorSpec(shape=(3, 3, 128, 64), dtype=tf.float32, name=None)\n", + " 134412877274896: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412869919824: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877983440: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877275472: TensorSpec(shape=(3, 3, 256, 128), dtype=tf.float32, name=None)\n", + " 134412869920592: TensorSpec(shape=(3, 3, 192, 128), dtype=tf.float32, name=None)\n", + " 134412877983824: TensorSpec(shape=(3, 3, 128, 128), dtype=tf.float32, name=None)\n", + " 134412877275088: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412869920784: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412877982672: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412877276048: TensorSpec(shape=(3, 3, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877276240: TensorSpec(shape=(3, 3, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877275280: TensorSpec(shape=(3, 3, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877276432: TensorSpec(shape=(3, 3, 16, 16), dtype=tf.float32, name=None)\n", + " 134412869922128: TensorSpec(shape=(3, 3, 16, 16), dtype=tf.float32, name=None)\n", + " 134412869922320: TensorSpec(shape=(3, 3, 16, 16), dtype=tf.float32, name=None)\n", + " 134412869921168: TensorSpec(shape=(3, 3, 16, 16), dtype=tf.float32, name=None)\n", + " 134412869922512: TensorSpec(shape=(3, 3, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877984976: TensorSpec(shape=(3, 3, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877985168: TensorSpec(shape=(3, 3, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877984016: TensorSpec(shape=(3, 3, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877985360: TensorSpec(shape=(3, 3, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877275664: TensorSpec(shape=(3, 3, 128, 128), dtype=tf.float32, name=None)\n", + " 134412869921936: TensorSpec(shape=(3, 3, 128, 128), dtype=tf.float32, name=None)\n", + " 134412877984784: TensorSpec(shape=(3, 3, 128, 128), dtype=tf.float32, name=None)\n", + " 134412877275856: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412869921744: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877984592: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877276624: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412869922704: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412877985552: TensorSpec(shape=(128,), dtype=tf.float32, name=None)\n", + " 134412877277008: TensorSpec(shape=(1, 1, 128, 17), dtype=tf.float32, name=None)\n", + " 134412869923664: TensorSpec(shape=(1, 1, 128, 17), dtype=tf.float32, name=None)\n", + " 134412877986512: TensorSpec(shape=(1, 1, 128, 17), dtype=tf.float32, name=None)\n", + " 134412877277392: TensorSpec(shape=(1, 1, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877277584: TensorSpec(shape=(1, 1, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877276816: TensorSpec(shape=(1, 1, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877277776: TensorSpec(shape=(1, 1, 16, 16), dtype=tf.float32, name=None)\n", + " 134412869923856: TensorSpec(shape=(1, 1, 16, 16), dtype=tf.float32, name=None)\n", + " 134412869924048: TensorSpec(shape=(1, 1, 16, 16), dtype=tf.float32, name=None)\n", + " 134412869923472: TensorSpec(shape=(1, 1, 16, 16), dtype=tf.float32, name=None)\n", + " 134412869924240: TensorSpec(shape=(1, 1, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877986704: TensorSpec(shape=(1, 1, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877986896: TensorSpec(shape=(1, 1, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877986320: TensorSpec(shape=(1, 1, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877987088: TensorSpec(shape=(1, 1, 16, 16), dtype=tf.float32, name=None)\n", + " 134412877277968: TensorSpec(shape=(17,), dtype=tf.float32, name=None)\n", + " 134412869924432: TensorSpec(shape=(17,), dtype=tf.float32, name=None)\n", + " 134412877987280: TensorSpec(shape=(17,), dtype=tf.float32, name=None)\n", + " 134412877278160: TensorSpec(shape=(), dtype=tf.float32, name=None)\n", + " 134412877278544: TensorSpec(shape=(), dtype=tf.float32, name=None)\n", + " 134412869924816: TensorSpec(shape=(), dtype=tf.float32, name=None)\n", + " 134412869924624: TensorSpec(shape=(), dtype=tf.float32, name=None)\n", + " 134412877987664: TensorSpec(shape=(), dtype=tf.float32, name=None)\n", + " 134412877987472: TensorSpec(shape=(), dtype=tf.float32, name=None)\n", + " 134412877277200: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412869923088: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + " 134412877985936: TensorSpec(shape=(64,), dtype=tf.float32, name=None)\n", + "✓ SavedModel created at: yolov9-s-relu6-best_savedmodel\n", + "\n", + "Step 2: Quantizing with official TFLiteConverter...\n", + " Quantizing model (this may take 1-2 minutes)...\n", + "\n", + "✓ INT8 TFLite model created: yolov9-s-relu6-best_320_int8.tflite\n", + " Size: 7.23 MB\n" + ] + } + ], + "source": [ + "# =======================================================================================\n", + "# STEPS 11 & 12: Convert to TFLite using official TFLiteConverter\n", + "# =======================================================================================\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"STEPS 11 & 12: Converting and Quantizing with TFLiteConverter\")\n", + "print(\"=\"*80)\n", + "\n", + "# Step 1: Create SavedModel using onnx2tf (without quantization)\n", + "SAVEDMODEL_FOLDER = Path(MODEL_PATH).stem + \"_savedmodel\"\n", + "if Path(SAVEDMODEL_FOLDER).exists():\n", + " import shutil\n", + " shutil.rmtree(SAVEDMODEL_FOLDER)\n", + "\n", + "print(\"Step 1: Converting ONNX to TensorFlow SavedModel...\")\n", + "min_range_val = np.array([[[[0, 0, 0]]]], dtype=np.float32)\n", + "max_range_val = np.array([[[[255, 255, 255]]]], dtype=np.float32)\n", + "np_data = [\n", + " [\"images\", str(CALIB_NPY), min_range_val, max_range_val]\n", + "]\n", + "\n", + "onnx2tf.convert(\n", + " input_onnx_file_path=str(ONNX_PATH),\n", + " output_folder_path=SAVEDMODEL_FOLDER,\n", + " output_integer_quantized_tflite=False, # Don't quantize yet\n", + " custom_input_op_name_np_data_path=np_data,\n", + " not_use_onnxsim=True,\n", + " verbosity='error',\n", + " output_signaturedefs=True,\n", + " disable_group_convolution=True,\n", + " batch_size=1,\n", + " copy_onnx_input_output_names_to_tflite=True, # does not work -?\n", + " # see -coion notes here https://github.com/PINTO0309/onnx2tf\n", + " # or do later (yuck) https://github.com/PINTO0309/onnx2tf?tab=readme-ov-file#5-rewriting-of-tflite-inputoutput-op-names-and-signature_defs\n", + ")\n", + "\n", + "savedmodel_path = Path(SAVEDMODEL_FOLDER)\n", + "print(f\"✓ SavedModel created at: {savedmodel_path}\")\n", + "\n", + "if True:\n", + " # Step 2: Quantize using official TFLiteConverter\n", + " # This is faster than onnx2tf. both seem to succeed.\n", + " print(\"\\nStep 2: Quantizing with official TFLiteConverter...\")\n", + "\n", + " # Load calibration data\n", + " calib_images = np.load(str(CALIB_NPY))\n", + "\n", + " def representative_dataset():\n", + " for i in range(len(calib_images)):\n", + " img = calib_images[i:i+1] / 255.0\n", + " yield [img.astype(np.float32)]\n", + "\n", + " # Convert with official TFLiteConverter\n", + " converter = tf.lite.TFLiteConverter.from_saved_model(str(savedmodel_path))\n", + " converter.optimizations = [tf.lite.Optimize.DEFAULT]\n", + " converter.representative_dataset = representative_dataset\n", + " converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n", + " converter.inference_input_type = tf.uint8\n", + " converter.inference_output_type = tf.int8\n", + "\n", + " print(\" Quantizing model (this may take 1-2 minutes)...\")\n", + " tflite_model = converter.convert()\n", + "\n", + " # Save quantized model\n", + " int8_tflite = Path(f\"{Path(MODEL_PATH).stem}_{IMGSZ[0]}_int8.tflite\")\n", + " with open(int8_tflite, 'wb') as f:\n", + " f.write(tflite_model)\n", + "else:\n", + " int8_tflite = Path(f\"/content/yolov9-s-relu6-best_savedmodel/yolov9-s-relu6-best_full_integer_quant.tflite\")\n", + "\n", + "\n", + "size_mb = int8_tflite.stat().st_size / 1024 / 1024\n", + "print(f\"\\n✓ INT8 TFLite model created: {int8_tflite}\")\n", + "print(f\" Size: {size_mb:.2f} MB\")\n", + "\n", + "# Cleanup SavedModel folder to save space (optional)\n", + "# shutil.rmtree(SAVEDMODEL_FOLDER)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "verify_quant", + "outputId": "869e9207-2202-49af-f6e6-ba488108a4d2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "================================================================================\n", + "STEP 13: Verifying quantization...\n", + "================================================================================\n", + "tflite model file yolov9-s-relu6-best_320_int8.tflite\n", + "{'serving_default': {'inputs': ['images'], 'outputs': ['output_0', 'output_1']}}\n", + "\n", + "📥 Input:\n", + " Index: 0\n", + " Name: serving_default_images:0\n", + " Type: \n", + " Shape: [ 1 320 320 3]\n", + " Quantization: scale=0.003922, zero_point=0\n", + "\n", + "📤 Output 0:\n", + " Index: 787\n", + " Name: PartitionedCall:1\n", + " Type: \n", + " Shape: [ 1 2100 17]\n", + " Quantization: scale=0.028768, zero_point=11\n", + "\n", + "📤 Output 1:\n", + " Index: 780\n", + " Name: PartitionedCall:0\n", + " Type: \n", + " Shape: [ 1 2100 64]\n", + " Quantization: scale=0.080557, zero_point=-46\n", + "\n", + "🔍 Tensor Analysis:\n", + " Total tensors: 788\n", + " INT8/UINT8 tensors: 567\n", + " Non-INT8 tensors: 221\n", + "\n", + "❌ WARNING: Found 221 non-INT8 tensors! (might be OK)\n", + " 1. arith.constant: , shape=[4 2]\n", + " 2. arith.constant1: , shape=[4]\n", + " 3. arith.constant2: , shape=[4]\n", + " 4. arith.constant3: , shape=[4]\n", + " 5. arith.constant4: , shape=[4]\n" + ] + } + ], + "source": [ + "# ============================================================================\n", + "# STEP 13: Verify Quantization\n", + "# ============================================================================\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"STEP 13: Verifying quantization...\")\n", + "print(\"=\"*80)\n", + "\n", + "# temporary\n", + "#int8_tflite = savedmodel_path / \"yolov9-s-relu6-best_full_integer_quant.tflite\"\n", + "#int8_tflite = Path(f\"{Path(MODEL_PATH).stem}_{IMGSZ[0]}_int8.tflite\")\n", + "\n", + "print(f\"tflite model file {int8_tflite}\")\n", + "interpreter = tf.lite.Interpreter(model_path=str(int8_tflite)) # onnx2tf version\n", + "#sigrunner = interpreter.get_signature_runner()\n", + "#print(\"Signature Inputs:\", sigrunner.get_input_details().keys())\n", + "#print(\"Signature Outputs:\", sigrunner.get_output_details().keys())\n", + "\n", + "interpreter.allocate_tensors()\n", + "\n", + "signatures = interpreter.get_signature_list()\n", + "print(signatures)\n", + "\n", + "input_details = interpreter.get_input_details()\n", + "output_details = interpreter.get_output_details()\n", + "\n", + "print(f\"\\n📥 Input:\")\n", + "print(f\" Index: {input_details[0]['index']}\")\n", + "print(f\" Name: {input_details[0]['name']}\")\n", + "print(f\" Type: {input_details[0]['dtype']}\")\n", + "print(f\" Shape: {input_details[0]['shape']}\")\n", + "print(f\" Quantization: scale={input_details[0]['quantization'][0]:.6f}, \"\n", + " f\"zero_point={input_details[0]['quantization'][1]}\")\n", + "\n", + "for n in range(len(output_details)):\n", + " print(f\"\\n📤 Output {n}:\")\n", + " print(f\" Index: {output_details[n]['index']}\")\n", + " print(f\" Name: {output_details[n]['name']}\")\n", + " print(f\" Type: {output_details[n]['dtype']}\")\n", + " print(f\" Shape: {output_details[n]['shape']}\")\n", + " print(f\" Quantization: scale={output_details[n]['quantization'][0]:.6f}, \"\n", + " f\"zero_point={output_details[n]['quantization'][1]}\")\n", + "\n", + "tensor_details = interpreter.get_tensor_details()\n", + "non_int8 = [t for t in tensor_details if t['dtype'] not in [np.int8, np.uint8]]\n", + "\n", + "print(f\"\\n🔍 Tensor Analysis:\")\n", + "print(f\" Total tensors: {len(tensor_details)}\")\n", + "print(f\" INT8/UINT8 tensors: {len(tensor_details) - len(non_int8)}\")\n", + "print(f\" Non-INT8 tensors: {len(non_int8)}\")\n", + "\n", + "if non_int8:\n", + " print(f\"\\n❌ WARNING: Found {len(non_int8)} non-INT8 tensors! (might be OK)\")\n", + " for i, t in enumerate(non_int8[:5]):\n", + " print(f\" {i+1}. {t['name']}: {t['dtype']}, shape={t['shape']}\")\n", + "else:\n", + " print(f\"\\n✅ SUCCESS: All tensors are INT8/UINT8!\")\n", + " print(\" Model is quantized for EdgeTPU\")" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# INT8 Test Abbey Road" + ], + "metadata": { + "id": "jZVUTSGS2NL8" + } + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "v6yJO0CTWwYh", + "outputId": "24529475-5485-4fc9-adf9-04c11bd995d2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "================================================================================\n", + "STEP 14.5: Verification with real image and XYWH Box and PROB Score Outputs...\n", + "================================================================================\n", + "Resizing to (320, 320)\n", + "tflite_outputs[0].shape = (1, 2100, 17) , min = -3.999 , max = 1.784\n", + "tflite_outputs[1].shape = (1, 2100, 64) , min = -5.397 , max = 13.453\n", + "boxes_dfl.shape = torch.Size([2100, 64])\n", + "transposing boxes_dfl\n", + "boxes_dfl.shape = torch.Size([64, 2100])\n", + "boxes_xyxy_decoded[0:5]\n", + "tensor([[ 0.00000, 0.00000, 81.51700, 142.48100],\n", + " [ 0.00000, 0.00000, 172.76675, 152.71349],\n", + " [ 0.00000, 0.00000, 237.64046, 110.86037],\n", + " [ 6.23018, 0.00000, 275.77612, 120.63554],\n", + " [112.97264, 1.64132, 231.60574, 51.69906]])\n", + "class_ids_argmax[0:5]\n", + "[0 0 0 0 0]\n", + "\n", + "✅ Score Precision Check:\n", + " unique_boxes_xyxy_decoded: 7618\n", + " unique_class_ids_argmax: 2\n", + " unique_scores_max: 71\n", + "CPU: Found 63 detections above 0.25 confidence.\n", + "CPU Found 9 detections after NMS.\n", + "CPU box 1931 class person score 0.85614 x1 561.5 y1 629.9 x2 824.3 y2 1051.4\n", + "CPU box 1902 class person score 0.84138 x1 71.3 y1 615.6 x2 327.6 y2 1053.5\n", + "CPU box 1887 class person score 0.80371 x1 345.5 y1 610.6 x2 559.2 y2 1034.5\n", + "CPU box 810 class car score 0.74898 x1 216.5 y1 592.5 x2 382.6 y2 731.7\n", + "CPU box 1936 class person score 0.71516 x1 825.1 y1 619.4 x2 1087.1 y2 1068.2\n", + "CPU box 867 class car score 0.50000 x1 738.8 y1 568.3 x2 866.2 y2 663.8\n", + "CPU box 776 class car score 0.39376 x1 466.9 y1 571.7 x2 522.9 y2 620.4\n", + "CPU box 784 class car score 0.36666 x1 700.2 y1 573.5 x2 759.2 y2 618.4\n", + "CPU box 830 class person score 0.28484 x1 900.1 y1 564.2 x2 933.8 y2 660.6\n", + "\n", + "✓ Detections saved to 'detections_separate_outputs.jpg'\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/jpeg": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {} + } + ], + "source": [ + "# ============================================================================\n", + "# STEP 14.5: Verification with real image and XYWH Box and PROB Score Outputs\n", + "# ============================================================================\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"STEP 14.5: Verification with real image and XYWH Box and PROB Score Outputs...\")\n", + "print(\"=\"*80)\n", + "\n", + "import numpy as np\n", + "from PIL import Image, ImageDraw, ImageFont\n", + "from IPython.display import Image as IPyImage\n", + "\n", + "# --- 1. Load Image and Run Inference ---\n", + "#test_image_path = \"Beatles_-_Abbey_Road.jpg\"\n", + "#if not Path(test_image_path).exists():\n", + "# !wget -q https://upload.wikimedia.org/wikipedia/en/4/42/Beatles_-_Abbey_Road.jpg\n", + "test_image_path = \"81sBKBIcwvL._SL1200_.jpg\"\n", + "#test_image_path = \"coco128/images/train2017/000000000502.jpg\" # bear for testing class index range coverage\n", + "# warning: the bear picture is 640x427, not a square\n", + "#if not Path(test_image_path).exists():\n", + "# !wget -q https://m.media-amazon.com/images/I/81sBKBIcwvL._SL1200_.jpg\n", + "\n", + "img = Image.open(test_image_path).convert(\"RGB\")\n", + "original_size = img.size\n", + "print(f\"Resizing to {repr(IMGSZ)}\")\n", + "img_scale_factor = original_size[0] / IMGSZ[0]\n", + "\n", + "img_resized = img.resize(IMGSZ)\n", + "img_array_nhwc = np.array(img_resized).astype(np.float32)\n", + "img_batch = np.expand_dims(img_array_nhwc, axis=0)\n", + "\n", + "input_details = interpreter.get_input_details()\n", + "output_details = interpreter.get_output_details()\n", + "input_scale, input_zero_point = input_details[0]['quantization']\n", + "input_data_int8 = (img_batch / 255.0 / input_scale + input_zero_point).astype(np.uint8)\n", + "#input_data_int8 = (img_batch / 255.0 / input_scale + input_zero_point).astype(np.int8)\n", + "interpreter.set_tensor(input_details[0]['index'], input_data_int8)\n", + "interpreter.invoke()\n", + "output_details = interpreter.get_output_details()\n", + "\n", + "tflite_outputs = []\n", + "for detail in output_details:\n", + " int8_data = interpreter.get_tensor(detail['index'])\n", + " shape = tuple([int(x) for x in detail['shape']])\n", + " dtype = detail['dtype']\n", + " name = detail['name']\n", + " #print(f\"DEBUG anchor_count:{anchor_count} found output with shape {shape}\")\n", + " #print(f\"DEBUG tensor output found with shape {detail['shape']} and dtype {detail['dtype']}\")\n", + " scale, zero_point = detail['quantization']\n", + " if scale < 0.00000001:\n", + " #print(f\"DEBUG not scaling output\")\n", + " scaled_output = int8_data.astype(np.float32)\n", + " else:\n", + " #print(f\"DEBUG scaling output with zeropoint {zero_point} and scale {scale}\")\n", + " scaled_output = (int8_data.astype(np.float32) - zero_point) * scale\n", + " tflite_outputs.append(scaled_output)\n", + "\n", + "for i,o in enumerate(tflite_outputs):\n", + " print(f\"tflite_outputs[{i}].shape = {o.shape} , min = {o.min():.3f} , max = {o.max():.3f}\")\n", + "\n", + "# 1,0,2 for accessing original order of 3-tensor output\n", + "# 3,1,4,2,0 is the id for each respective call when using 5 tensor output\n", + "# 5,1,6,4,0,3,7,2 is the id for each when using 8 tensor output for quantization investigation\n", + "# 4,1,5,3,0,2,6 when 7 tensor output\n", + "\n", + "dfl_weights = tflite_outputs[1][0,:,:]\n", + "boxes_xyxy_decoded = decode_dfl_boxes(dfl_weights) * img_scale_factor\n", + "boxes_xyxy_decoded = boxes_xyxy_decoded.clamp(0,IMGSZ[0] * img_scale_factor)\n", + "#class_ids = tflite_outputs[1][0,:,:].squeeze()\n", + "#scores = 1 / (1+(np.exp(-1 * tflite_outputs[0][0,:,:].squeeze()))) # sigmoid\n", + "class_scores = 1 / (1+(np.exp(-1 * tflite_outputs[0][0,:,:].squeeze()))) # sigmoid\n", + "#boxes_xyxy = tflite_outputs[0][0,:,:] * img_scale_factor\n", + "class_ids_argmax = class_scores.argmax(axis=1)\n", + "scores_max = class_scores.max(axis=1)\n", + "\n", + "# quantization investigation\n", + "#sum_logits = tflite_outputs[3][0,:,:].squeeze()\n", + "#inv_sum = tflite_outputs[7][0,:,:].squeeze()\n", + "#weights = tflite_outputs[2][0,:,:].squeeze()\n", + "#hbox = tflite_outputs[2][0,:,:].squeeze()\n", + "#distances = tflite_outputs[6][0,:,:].squeeze()\n", + "\n", + "# --- 2. Process the Decoded Output ---\n", + "#print(f\"boxes_xyxy (from TPU)[0:5]\\n{boxes_xyxy[:5,:]}\")\n", + "print(f\"boxes_xyxy_decoded[0:5]\\n{boxes_xyxy_decoded[:5,:]}\")\n", + "#print(f\"class_ids[0:5] (from TPU)\\n{class_ids[:5]}\") # calculated by TPU\n", + "print(f\"class_ids_argmax[0:5]\\n{class_ids_argmax[:5]}\")\n", + "#print(f\"scores[0:5] (from TPU)\\n{scores[:5]}\") # calculated by TPU\n", + "#print(f\"scores_max[0:5]\\n{scores_max[:5]}\")\n", + "\n", + "#if len(boxes_xyxy) > 12:\n", + "# print(\"BOX[12] COORDINATES\")\n", + "# print(f\"boxes_xyxy (from TPU)[12]\\n{boxes_xyxy[12,:]}\")\n", + "# print(f\"boxes_xyxy_decoded[12]\\n{boxes_xyxy_decoded[12,:]}\")\n", + "\n", + "#unique_boxes_xyxy = np.unique(boxes_xyxy)\n", + "unique_boxes_xyxy_decoded = np.unique(boxes_xyxy_decoded)\n", + "#unique_class_ids = np.unique(class_ids)\n", + "unique_class_ids_argmax = np.unique(class_ids_argmax)\n", + "#unique_scores = np.unique(scores)\n", + "unique_scores_max = np.unique(scores_max)\n", + "print(f\"\\n✅ Score Precision Check:\")\n", + "#print(f\" unique_boxes_xyxy: {len(unique_boxes_xyxy)}\")\n", + "print(f\" unique_boxes_xyxy_decoded: {len(unique_boxes_xyxy_decoded)}\")\n", + "#print(f\" unique_class_ids: {len(unique_class_ids)}\")\n", + "print(f\" unique_class_ids_argmax: {len(unique_class_ids_argmax)}\")\n", + "#print(f\" unique_scores: {len(unique_scores)}\")\n", + "print(f\" unique_scores_max: {len(unique_scores_max)}\")\n", + "\n", + "#print(f\" sum_logits : min={sum_logits.min():.3f} , max={sum_logits.max():.3f} , unique vals: {len(np.unique(sum_logits))}\")\n", + "#print(f\" inv_sum : min={inv_sum.min():.3f} , max={inv_sum.max():.3f} , unique vals: {len(np.unique(inv_sum))}\")\n", + "#print(f\" weights : min={weights.min():.3f} , max={weights.max():.3f} , unique vals: {len(np.unique(weights))}\")\n", + "#print(f\" hbox : min={hbox.min():.3f} , max={hbox.max():.3f} , unique vals: {len(np.unique(hbox))}\")\n", + "\n", + "\n", + "conf_threshold = 0.25\n", + "#filtered_indices_tpu = scores > conf_threshold\n", + "#print(f\"\\nTPU: Found {np.sum(filtered_indices_tpu)} detections above {conf_threshold} confidence.\")\n", + "if isinstance(scores_max, torch.Tensor):\n", + " # .cpu() is important if the tensor is on the GPU\n", + " scores_max = scores_max.cpu().numpy()\n", + "if isinstance(boxes_xyxy_decoded, torch.Tensor):\n", + " boxes_xyxy_decoded = boxes_xyxy_decoded.cpu().numpy()\n", + "filtered_indices_cpu = (scores_max > conf_threshold) & \\\n", + " (boxes_xyxy_decoded[:,0] < boxes_xyxy_decoded[:,2]) & \\\n", + " (boxes_xyxy_decoded[:,1] < boxes_xyxy_decoded[:,3])\n", + "print(f\"CPU: Found {filtered_indices_cpu.sum()} detections above {conf_threshold} confidence.\")\n", + "\n", + "if np.sum(filtered_indices_cpu) > 0:\n", + " #map_to_original_indices_tpu = np.where(filtered_indices_tpu)[0]\n", + " map_to_original_indices_cpu = np.where(filtered_indices_cpu)[0]\n", + " #filtered_boxes_xyxy = boxes_xyxy[filtered_indices_tpu]\n", + " #filtered_scores = scores[filtered_indices_tpu]\n", + " #filtered_class_ids = class_ids[filtered_indices_tpu]\n", + " filtered_boxes_xyxy_cpu = boxes_xyxy_decoded[filtered_indices_cpu]\n", + " filtered_scores_cpu = scores_max[filtered_indices_cpu]\n", + " filtered_class_ids_cpu = class_ids_argmax[filtered_indices_cpu]\n", + " filtered_class_scores_cpu = class_scores[filtered_indices_cpu]\n", + "\n", + " # --- 3. NMS & Drawing ---\n", + " def nms(boxes, scores, iou_threshold):\n", + " order = scores.argsort()[::-1]; keep = []\n", + " while order.size > 0:\n", + " i = order[0]; keep.append(i)\n", + " xx1 = np.maximum(boxes[i, 0], boxes[order[1:], 0]); yy1 = np.maximum(boxes[i, 1], boxes[order[1:], 1])\n", + " xx2 = np.minimum(boxes[i, 2], boxes[order[1:], 2]); yy2 = np.minimum(boxes[i, 3], boxes[order[1:], 3])\n", + " w = np.maximum(0.0, xx2 - xx1); h = np.maximum(0.0, yy2 - yy1); inter = w * h\n", + " ovr = inter / ((boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1]) + (boxes[order[1:], 2] - boxes[order[1:], 0]) * (boxes[order[1:], 3] - boxes[order[1:], 1]) - inter + 1e-6)\n", + " inds = np.where(ovr <= iou_threshold)[0]; order = order[inds + 1]\n", + " return keep\n", + "\n", + " #nms_indices_tpu = nms(filtered_boxes_xyxy, filtered_scores, iou_threshold=0.45)\n", + " #print(f\"\\nTPU Found {len(nms_indices_tpu)} detections after NMS.\")\n", + " nms_indices_cpu = nms(filtered_boxes_xyxy_cpu, filtered_scores_cpu, iou_threshold=0.45)\n", + " #nms_indices_cpu.append(20) # temporary for investigation of errant bicycle detection\n", + " print(f\"CPU Found {len(nms_indices_cpu)} detections after NMS.\")\n", + "\n", + " coco_classes = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']\n", + " draw = ImageDraw.Draw(img)\n", + " #for nms_rank, i in enumerate(nms_indices_tpu):\n", + " if False:\n", + " original_index = map_to_original_indices_tpu[i]\n", + " box = filtered_boxes_xyxy[i]\n", + " if (box[2] <= box[0]) or (box[3] <= box[1]):\n", + " print(f\"TPU box {original_index} class {filtered_class_ids[i]:.2f}:{coco_classes[int(filtered_class_ids[i])]} score {filtered_scores[i]:.5f} INVALID COORDINATES! x1 {box[0]:.1f} y1 {box[1]:.1f} x2 {box[2]:.1f} y2 {box[3]:.1f}\")\n", + " #continue\n", + " label = f\"{coco_classes[int(filtered_class_ids[i])]}: {filtered_scores[i]:.2f}\"\n", + " #label = f\"{filtered_class_ids[i]:.1f}: {filtered_scores[i]:.2f}\"\n", + " draw.rectangle(box.tolist(), outline=\"red\", width=3)\n", + " text_bbox = draw.textbbox((box[0], box[1] - 12), label)\n", + " draw.rectangle(text_bbox, fill=\"red\")\n", + " draw.text((box[0], box[1] - 12), label, fill=\"white\")\n", + " print(f\"TPU box {original_index} class {filtered_class_ids[i]:.2f}:{coco_classes[int(filtered_class_ids[i])]} score {filtered_scores[i]:.5f} x1 {box[0]:.1f} y1 {box[1]:.1f} x2 {box[2]:.1f} y2 {box[3]:.1f}\")\n", + " for nms_rank, i in enumerate(nms_indices_cpu):\n", + " original_index = map_to_original_indices_cpu[i]\n", + " box = filtered_boxes_xyxy_cpu[i]\n", + " label = f\"{coco_classes[int(filtered_class_ids_cpu[i])]}: {filtered_scores_cpu[i]:.2f}\"\n", + " #label = f\"{filtered_class_ids_cpu[i]}: {filtered_scores_cpu[i]:.2f}\"\n", + " draw.rectangle(box.tolist(), outline=\"blue\", width=3)\n", + " text_bbox = draw.textbbox((box[0], box[1] - 12), label)\n", + " draw.rectangle(text_bbox, fill=\"blue\")\n", + " draw.text((box[0], box[1] - 12), label, fill=\"white\")\n", + " print(f\"CPU box {original_index} class {coco_classes[int(filtered_class_ids_cpu[i])]} score {filtered_scores_cpu[i]:.5f} x1 {box[0]:.1f} y1 {box[1]:.1f} x2 {box[2]:.1f} y2 {box[3]:.1f}\")\n", + " #class_scores_to_print = [f\"{float(x):.2f}\" for x in filtered_class_scores_cpu[i]]\n", + " #print(f\" box {i} class_scores: {class_scores_to_print}\")\n", + "\n", + " #for i, box in enumerate(decoded_boxes_xyxy):\n", + " # print(f\"box {i} class {valid_class_ids[i]} score {valid_scores[i]:.5f} {'NMS ' if i in nms_indices else ''} x1 {box[0]:.1f} y1 {box[1]:.1f} x2 {box[2]:.1f} y2 {box[3]:.1f}\")\n", + "\n", + " #print(\"\\nLOOKING FOR BOXES WITH LARGE DIFFERENCES TPU vs CPU\\n\")\n", + " #for i in range(min(5,boxes_xyxy.shape[0])):\n", + " if False:\n", + " tpubox = boxes_xyxy[i]\n", + " cpubox = boxes_xyxy_decoded[i]\n", + " diffs = [abs(tpubox[j] - cpubox[j]) for j in range(4)]\n", + " stridefactor = 8 if i < 1600 else (16 if i < 2000 else 32)\n", + " max_diff = max(diffs)\n", + " max_diff_normalized = max_diff / stridefactor\n", + " if max_diff_normalized > 10:\n", + " print(f\"box {i} : diff_norm {max_diff_normalized:.1f} : diff_pixels {max_diff:.1f} : TPU {tpubox} , CPU {cpubox}\")\n", + "\n", + "\n", + " # --- 4. Save and Display ---\n", + " output_image_path = \"detections_separate_outputs.jpg\"\n", + " img.save(output_image_path)\n", + " print(f\"\\n✓ Detections saved to '{output_image_path}'\")\n", + " display(IPyImage(filename=output_image_path))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "install_edgetpu", + "outputId": "1b7d3b9a-6962-491b-b8e4-33016c8a26ba" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Installing Edge TPU Compiler...\n", + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 1022 100 1022 0 0 8183 0 --:--:-- --:--:-- --:--:-- 8176\n", + "Warning: apt-key is deprecated. Manage keyring files in trusted.gpg.d instead (see apt-key(8)).\n", + "OK\n", + "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main\n", + "Hit:1 http://archive.ubuntu.com/ubuntu jammy InRelease\n", + "Get:2 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\n", + "Get:3 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n", + "Hit:4 https://cli.github.com/packages stable InRelease\n", + "Get:5 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB]\n", + "Get:6 https://packages.cloud.google.com/apt coral-edgetpu-stable InRelease [1,423 B]\n", + "Get:7 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease [3,632 B]\n", + "Get:8 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease [1,581 B]\n", + "Get:9 https://r2u.stat.illinois.edu/ubuntu jammy InRelease [6,555 B]\n", + "Get:10 http://security.ubuntu.com/ubuntu jammy-security/restricted amd64 Packages [6,066 kB]\n", + "Get:11 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease [18.1 kB]\n", + "Hit:12 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n", + "Get:13 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [3,563 kB]\n", + "Hit:14 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n", + "Get:15 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1,286 kB]\n", + "Get:16 http://archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 Packages [6,281 kB]\n", + "Get:17 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1,598 kB]\n", + "Get:18 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [3,901 kB]\n", + "Get:19 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ Packages [83.6 kB]\n", + "Get:20 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 Packages [2,160 kB]\n", + "Get:21 https://packages.cloud.google.com/apt coral-edgetpu-stable/main all Packages [1,865 B]\n", + "Get:22 https://packages.cloud.google.com/apt coral-edgetpu-stable/main amd64 Packages [6,888 B]\n", + "Get:23 https://r2u.stat.illinois.edu/ubuntu jammy/main amd64 Packages [2,841 kB]\n", + "Get:24 https://r2u.stat.illinois.edu/ubuntu jammy/main all Packages [9,504 kB]\n", + "Get:25 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy/main amd64 Packages [38.8 kB]\n", + "Fetched 37.7 MB in 4s (10.0 MB/s)\n", + "Reading package lists...\n", + "W: https://packages.cloud.google.com/apt/dists/coral-edgetpu-stable/InRelease: Key is stored in legacy trusted.gpg keyring (/etc/apt/trusted.gpg), see the DEPRECATION section in apt-key(8) for details.\n", + "W: Skipping acquire of configured file 'main/source/Sources' as repository 'https://r2u.stat.illinois.edu/ubuntu jammy InRelease' does not seem to provide it (sources.list entry misspelt?)\n", + "Reading package lists...\n", + "Building dependency tree...\n", + "Reading state information...\n", + "The following NEW packages will be installed:\n", + " edgetpu-compiler\n", + "0 upgraded, 1 newly installed, 0 to remove and 62 not upgraded.\n", + "Need to get 7,913 kB of archives.\n", + "After this operation, 31.2 MB of additional disk space will be used.\n", + "Get:1 https://packages.cloud.google.com/apt coral-edgetpu-stable/main amd64 edgetpu-compiler amd64 16.0 [7,913 kB]\n", + "Fetched 7,913 kB in 0s (17.0 MB/s)\n", + "debconf: unable to initialize frontend: Dialog\n", + "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 1.)\n", + "debconf: falling back to frontend: Readline\n", + "debconf: unable to initialize frontend: Readline\n", + "debconf: (This frontend requires a controlling tty.)\n", + "debconf: falling back to frontend: Teletype\n", + "dpkg-preconfigure: unable to re-open stdin: \n", + "Selecting previously unselected package edgetpu-compiler.\n", + "(Reading database ... 121713 files and directories currently installed.)\n", + "Preparing to unpack .../edgetpu-compiler_16.0_amd64.deb ...\n", + "Unpacking edgetpu-compiler (16.0) ...\n", + "Setting up edgetpu-compiler (16.0) ...\n", + "Processing triggers for libc-bin (2.35-0ubuntu3.8) ...\n", + "/sbin/ldconfig.real: /usr/local/lib/libhwloc.so.15 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero_v2.so.0 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtcm_debug.so.1 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtcm.so.1 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_opencl.so.0 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libumf.so.1 is not a symbolic link\n", + "\n", + "✓ EdgeTPU Compiler installed!\n" + ] + } + ], + "source": [ + "# ============================================================================\n", + "# STEP 15pre: Install EdgeTPU Compiler (Linux only)\n", + "# ============================================================================\n", + "print(\"\\nInstalling Edge TPU Compiler...\")\n", + "!curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -\n", + "!echo \"deb https://packages.cloud.google.com/apt coral-edgetpu-stable main\" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list\n", + "!sudo apt-get update -q\n", + "!sudo apt-get install -q -y edgetpu-compiler\n", + "\n", + "print(\"✓ EdgeTPU Compiler installed!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Y9ExtYbVlv0x", + "outputId": "c6ce8ac2-f512-4b62-c848-68675ffea4e7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "================================================================================\n", + "STEP 15: Compile model for EdgeTPU...\n", + "================================================================================\n", + "Running: edgetpu_compiler yolov9-s-relu6-best_320_int8.tflite -o . --show_operations --search_delegate --delegate_search_step 30 --timeout_sec 180\n", + "Edge TPU Compiler version 16.0.384591198\n", + "Searching for valid delegate with step 30\n", + "Try to compile segment with 333 ops\n", + "Started a compilation timeout timer of 180 seconds.\n", + "\n", + "Model compiled successfully in 5966 ms.\n", + "\n", + "Input model: yolov9-s-relu6-best_320_int8.tflite\n", + "Input size: 7.23MiB\n", + "Output model: ./yolov9-s-relu6-best_320_int8_edgetpu.tflite\n", + "Output size: 7.80MiB\n", + "On-chip memory used for caching model parameters: 7.23MiB\n", + "On-chip memory remaining for caching model parameters: 768.00B\n", + "Off-chip memory used for streaming uncached model parameters: 385.38KiB\n", + "Number of Edge TPU subgraphs: 1\n", + "Total number of operations: 333\n", + "Operation log: ./yolov9-s-relu6-best_320_int8_edgetpu.log\n", + "\n", + "Operator Count Status\n", + "\n", + "ADD 48 Mapped to Edge TPU\n", + "MAX_POOL_2D 3 Mapped to Edge TPU\n", + "MAXIMUM 3 Mapped to Edge TPU\n", + "QUANTIZE 1 Mapped to Edge TPU\n", + "CONV_2D 203 Mapped to Edge TPU\n", + "RESIZE_NEAREST_NEIGHBOR 2 Mapped to Edge TPU\n", + "CONCATENATION 35 Mapped to Edge TPU\n", + "SPLIT 6 Mapped to Edge TPU\n", + "AVERAGE_POOL_2D 5 Mapped to Edge TPU\n", + "STRIDED_SLICE 16 Mapped to Edge TPU\n", + "RESHAPE 6 Mapped to Edge TPU\n", + "PAD 2 Mapped to Edge TPU\n", + "MINIMUM 3 Mapped to Edge TPU\n", + "Compilation child process completed within timeout period.\n", + "Compilation succeeded! \n", + "\n" + ] + } + ], + "source": [ + "# ============================================================================\n", + "# STEP 15: Compile model for EdgeTPU\n", + "# ============================================================================\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"STEP 15: Compile model for EdgeTPU...\")\n", + "print(\"=\"*80)\n", + "\n", + "EDGETPU_OUTPUT = int8_tflite.parent / f\"{Path(MODEL_PATH).stem}_edgetpu.tflite\"\n", + "\n", + "# Run Edge TPU compiler\n", + "cmd = [\n", + " \"edgetpu_compiler\",\n", + " str(int8_tflite),\n", + " \"-o\", str(int8_tflite.parent),\n", + " \"--show_operations\",\n", + " \"--search_delegate\",\n", + " \"--delegate_search_step\", \"30\",\n", + " \"--timeout_sec\", \"180\",\n", + " #\"-a\"\n", + "]\n", + "\n", + "print(f\"Running: {' '.join(cmd)}\")\n", + "result = subprocess.run(cmd, capture_output=True, text=True)\n", + "\n", + "print(result.stdout)\n", + "if result.returncode != 0:\n", + " print(\"ERROR:\", result.stderr)\n", + " raise RuntimeError(\"Edge TPU compilation failed\")\n", + "\n", + "# Find the compiled EdgeTPU model\n", + "#edgetpu_files = list(int8_tflite.parent.glob(\"*_edgetpu.tflite\"))\n", + "#if not edgetpu_files:\n", + "# raise ValueError(\"EdgeTPU compiled file not found\")\n", + "#\n", + "#final_edgetpu_model = edgetpu_files[0]\n", + "#print(f\"\\n✓ Edge TPU model created: {final_edgetpu_model}\")\n", + "#print(f\" Size: {final_edgetpu_model.stat().st_size / 1024:.1f} KB\")" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/source/yolov9-edgetpu-dbro/notebooks/Fine_tune_YOLOv9_s_for_EdgeTPU_use_ReLU6_and_17_COCO_classes.ipynb b/source/yolov9-edgetpu-dbro/notebooks/Fine_tune_YOLOv9_s_for_EdgeTPU_use_ReLU6_and_17_COCO_classes.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..52f1387ba872284dcbcff5f01830f195baa019d6 --- /dev/null +++ b/source/yolov9-edgetpu-dbro/notebooks/Fine_tune_YOLOv9_s_for_EdgeTPU_use_ReLU6_and_17_COCO_classes.ipynb @@ -0,0 +1,1843 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "header" + }, + "source": [ + "# YOLOv9-s ReLU6 Training for EdgeTPU\n", + "\n", + "This notebook fine-tunes YOLOv9-s with ReLU6 activations for better INT8 quantization." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gpu_check" + }, + "source": [ + "## Step 1: Check GPU and Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "setup", + "outputId": "f6d8f7fc-20d8-425e-bb9d-d4e69f343e3c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "SETUP: Checking GPU\n", + "======================================================================\n", + "✓ GPU detected: Tesla T4\n", + " Memory: 15.83 GB\n" + ] + } + ], + "source": [ + "import torch\n", + "import os\n", + "from pathlib import Path\n", + "\n", + "print(\"=\"*70)\n", + "print(\"SETUP: Checking GPU\")\n", + "print(\"=\"*70)\n", + "\n", + "if torch.cuda.is_available():\n", + " print(f\"✓ GPU detected: {torch.cuda.get_device_name(0)}\")\n", + " print(f\" Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB\")\n", + "else:\n", + " print(\"⚠️ NO GPU DETECTED!\")\n", + " print(\" Go to: Runtime > Change runtime type > Hardware accelerator > GPU\")\n", + " print(\" Then restart this notebook\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "clone_repo" + }, + "source": [ + "## Step 2: Clone YOLOv9 Repository" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "clone", + "outputId": "fe1a758b-51e8-4707-8623-ca6e16fd3b01" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "Cloning YOLOv9 Repository\n", + "======================================================================\n", + "Cloning into 'yolov9'...\n", + "remote: Enumerating objects: 781, done.\u001b[K\n", + "remote: Total 781 (delta 0), reused 0 (delta 0), pack-reused 781 (from 1)\u001b[K\n", + "Receiving objects: 100% (781/781), 3.27 MiB | 4.74 MiB/s, done.\n", + "Resolving deltas: 100% (330/330), done.\n", + "✓ Repository cloned\n", + "✓ Working directory: /content/yolov9\n", + "\n", + "Installing requirements...\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m44.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h✓ Requirements installed\n" + ] + } + ], + "source": [ + "print(\"=\"*70)\n", + "print(\"Cloning YOLOv9 Repository\")\n", + "print(\"=\"*70)\n", + "\n", + "if not Path('yolov9').exists():\n", + " !git clone https://github.com/WongKinYiu/yolov9.git\n", + " print(\"✓ Repository cloned\")\n", + "else:\n", + " print(\"✓ Repository already exists\")\n", + "\n", + "# Navigate to repo\n", + "os.chdir('/content/yolov9')\n", + "print(f\"✓ Working directory: {os.getcwd()}\")\n", + "\n", + "# Install requirements\n", + "print(\"\\nInstalling requirements...\")\n", + "!pip install -q -r requirements.txt\n", + "print(\"✓ Requirements installed\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "config_relu" + }, + "source": [ + "## Step 5: Create ReLU6 Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WHzMYpBm9Ide" + }, + "outputs": [], + "source": [ + "# needed for model definition yaml\n", + "# keep only the classes that are relevant for object detection in surveillance\n", + "CLASSES_TO_KEEP = [\n", + " 0, # person\n", + " 1, # bicycle\n", + " 2, # car\n", + " 3, # motorcycle\n", + " 4, # airplane\n", + " 5, # bus\n", + " 6, # train\n", + " 7, # truck\n", + " 8, # boat\n", + " 14, # bird\n", + " 15, # cat\n", + " 16, # dog\n", + " 17, # horse\n", + " 18, # sheep\n", + " 19, # cow\n", + " 20, # elephant\n", + " 21, # bear\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "create_relu_config", + "outputId": "08af4f61-a8a2-4b19-dd88-88fe36a8cd6c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "Creating ReLU6 Model Configuration with DualDDetectEdgeTPU\n", + "======================================================================\n", + "✓ Changed number of classes to 17\n", + "✓ Set activation: nn.ReLU6()\n", + "✓ Changed detection head: DualDDetect → DualDDetectEdgeTPU\n", + "✓ Created: models/detect/yolov9-s-relu6.yaml\n", + "\n", + "Verifying configuration:\n", + " Activation: activation: nn.ReLU6()\n", + " Detection head: [[28, 25, 22, 15, 18, 21], 1, DualDDetectEdgeTPU, [nc]], # Detect(P3, P4, P5)\n", + "\n", + "✅ Configuration ready for EdgeTPU quantization:\n", + " - ReLU6 activation (bounded [0,6] for all layers)\n", + " - DualDDetectTwoOutputs (separate box/class tensors)\n", + " - Both prevent quantization value crushing\n" + ] + } + ], + "source": [ + "print(\"=\"*70)\n", + "print(\"Creating ReLU6 Model Configuration with DualDDetectEdgeTPU\")\n", + "print(\"=\"*70)\n", + "\n", + "from pathlib import Path\n", + "\n", + "# Read the original config\n", + "config_path = Path('models/detect/yolov9-s.yaml')\n", + "with open(config_path, 'r') as f:\n", + " lines = f.readlines()\n", + "\n", + "# Create ReLU6 + DualDDetectTwoOutputs version\n", + "relu6_config_path = Path('models/detect/yolov9-s-relu6.yaml')\n", + "new_lines = []\n", + "\n", + "for line in lines:\n", + " if line.strip() == '#activation: nn.ReLU()':\n", + " # Uncomment and change to ReLU6\n", + " new_lines.append('activation: nn.ReLU6()\\n')\n", + " print(\"✓ Set activation: nn.ReLU6()\")\n", + " elif line.strip().startswith('#activation:'):\n", + " # Keep other activation lines commented\n", + " new_lines.append(line)\n", + " elif 'DualDDetect' in line and 'DualDDetectEdgeTPU' not in line:\n", + " # Replace DualDDetect with DualDDetectEdgeTPU\n", + " new_line = line.replace('DualDDetect', 'DualDDetectEdgeTPU')\n", + " new_lines.append(new_line)\n", + " print(f\"✓ Changed detection head: DualDDetect → DualDDetectEdgeTPU\")\n", + " elif line.strip() == 'nc: 80 # number of classes':\n", + " new_lines.append(f'nc: {len(CLASSES_TO_KEEP)} # number of classes\\n')\n", + " print(f\"✓ Changed number of classes to {len(CLASSES_TO_KEEP)}\")\n", + " else:\n", + " new_lines.append(line)\n", + "\n", + "# Save config\n", + "with open(relu6_config_path, 'w') as f:\n", + " f.writelines(new_lines)\n", + "print(f\"✓ Created: {relu6_config_path}\")\n", + "\n", + "# Verify the changes\n", + "print(\"\\nVerifying configuration:\")\n", + "with open(relu6_config_path, 'r') as f:\n", + " content = f.read()\n", + " for line in content.split('\\n'):\n", + " if 'activation:' in line and not line.strip().startswith('#'):\n", + " print(f\" Activation: {line.strip()}\")\n", + " if 'DualDDetect' in line:\n", + " print(f\" Detection head: {line.strip()}\")\n", + "\n", + "print(\"\\n✅ Configuration ready for EdgeTPU quantization:\")\n", + "print(\" - ReLU6 activation (bounded [0,6] for all layers)\")\n", + "print(\" - DualDDetectTwoOutputs (separate box/class tensors)\")\n", + "print(\" - Both prevent quantization value crushing\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HWTEDxIvdRJJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "eb81b7dc-9969-47a6-d393-ad43cc44eba2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Updated 1 path from the index\n" + ] + } + ], + "source": [ + "# reset / clean up for use in re-runs\n", + "\n", + "if False:\n", + " os.chdir('/content/yolov9')\n", + " !rm -rf runs/train/yolov9-s-relu6\n", + "\n", + "if False:\n", + " !git checkout models/yolo.py" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gxFHEtKf6q9i", + "outputId": "f6412b0e-565e-4a61-cf76-c335c8e686bc" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "DualDDetectEdgeTPU - Two Tensors Output\n", + "======================================================================\n", + "======================================================================\n", + "Adding DualDDetectEdgeTPU to models/yolo.py\n", + "======================================================================\n", + "✅ Found insertion point at line 259 (before TripleDetect)\n", + "✅ Successfully added DualDDetectEdgeTPU class\n", + "\n", + "======================================================================\n", + "Updating parse_model to recognize DualDDetectEdgeTPU\n", + "======================================================================\n", + "✅ Updated: elif m in {Detect, DualDetect, TripleDetect, DDete...\n", + "✅ Updated: if isinstance(m, (Detect, DualDetect, TripleDetect...\n", + "✅ Updated: if isinstance(m, (DualDetect, TripleDetect, DualDD...\n", + "\n", + "✅ All updates completed successfully!\n" + ] + } + ], + "source": [ + "print(\"=\"*70)\n", + "print(\"DualDDetectEdgeTPU - Two Tensors Output\")\n", + "print(\"=\"*70)\n", + "# This version keeps the tensors separate to allow different ranges of values\n", + "# that the EdgeTPU can quantize individually.\n", + "\n", + "import os\n", + "os.chdir('/content/yolov9')\n", + "\n", + "dualddetect_edgetpu_code = '''\n", + "class DualDDetectEdgeTPU(nn.Module):\n", + " \"\"\"\n", + " EdgeTPU-optimized DualDDetect that outputs two tensores with:\n", + " - DFL distribution for calculating box coordinates\n", + " - Logit value class scores clamped to [-4, 4], or about 2% to 98% probability\n", + "\n", + " Both ranges are compatible for INT8 quantization.\n", + " \"\"\"\n", + "\n", + " dynamic = False\n", + " export = False\n", + " shape = None\n", + " anchors = torch.empty(0)\n", + " strides = torch.empty(0)\n", + "\n", + " def __init__(self, nc=80, ch=(), inplace=True):\n", + " super().__init__()\n", + " self.nc = nc\n", + " self.nl = len(ch) // 2\n", + " self.reg_max = 16\n", + " self.no = nc + self.reg_max * 4\n", + " self.inplace = inplace\n", + " self.stride = torch.zeros(self.nl)\n", + "\n", + " c2 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4)\n", + " c3 = max((ch[0], min((self.nc * 2, 128))))\n", + " c4 = make_divisible(max((ch[self.nl] // 4, self.reg_max * 4, 16)), 4)\n", + " c5 = max((ch[self.nl], min((self.nc * 2, 128))))\n", + "\n", + " # Branch 1 (auxiliary)\n", + " self.cv2 = nn.ModuleList(\n", + " nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4),\n", + " nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4))\n", + " for x in ch[:self.nl]\n", + " )\n", + " self.cv3 = nn.ModuleList(\n", + " nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1))\n", + " for x in ch[:self.nl]\n", + " )\n", + "\n", + " # Branch 2 (main)\n", + " self.cv4 = nn.ModuleList(\n", + " nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3, g=4),\n", + " nn.Conv2d(c4, 4 * self.reg_max, 1, groups=4))\n", + " for x in ch[self.nl:]\n", + " )\n", + " self.cv5 = nn.ModuleList(\n", + " nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1))\n", + " for x in ch[self.nl:]\n", + " )\n", + "\n", + " self.dfl = DFL(self.reg_max)\n", + " self.dfl2 = DFL(self.reg_max)\n", + "\n", + " def forward(self, x):\n", + " shape = x[0].shape # BCHW\n", + " d1 = []\n", + " d2 = []\n", + "\n", + " for i in range(self.nl):\n", + " d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1))\n", + " d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1))\n", + "\n", + " if self.training:\n", + " return [d1, d2]\n", + "\n", + " if self.export:\n", + " # Inference path: keep boxes and classes separate on edgetpu\n", + " # Output in non-standard B(H*W)C format instead of BC(H*W)\n", + " # to avoid limits of EdgeTPU concatenation sizes\n", + " # AND to facilitate post-processing on CPU\n", + "\n", + " boxes_raw = torch.cat([\n", + " self.cv4[i](x[self.nl+i]).permute(0, 2, 3, 1).flatten(1, 2)\n", + " for i in range(self.nl)\n", + " ], dim=1)\n", + "\n", + " classes_raw = torch.cat([\n", + " torch.clamp(self.cv5[i](x[self.nl+i]), -4.0, 4.0).permute(0, 2, 3, 1).flatten(1, 2)\n", + " for i in range(self.nl)\n", + " ], dim=1)\n", + "\n", + " return boxes_raw, classes_raw\n", + " else:\n", + " # Validation path\n", + " # Set up anchors if needed\n", + " if self.dynamic or self.shape != shape:\n", + " self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5))\n", + " self.shape = shape\n", + "\n", + " # Decode boxes\n", + " box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1)\n", + " dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides\n", + "\n", + " box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1)\n", + " dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides\n", + "\n", + " # Apply sigmoid to classes\n", + " cls_sigmoid = cls.sigmoid()\n", + " cls2_sigmoid = cls2.sigmoid()\n", + " # Validation needs PIXEL coordinates\n", + " y_aux = torch.cat((dbox, cls_sigmoid), 1)\n", + " y_main = torch.cat((dbox2, cls2_sigmoid), 1) # NO normalization!\n", + " return ([y_aux, y_main], [d1, d2])\n", + "\n", + " def bias_init(self):\n", + " \"\"\"Initialize biases\"\"\"\n", + " m = self\n", + " for a, b, s in zip(m.cv2, m.cv3, m.stride):\n", + " a[-1].bias.data[:] = 1.0\n", + " b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)\n", + " for a, b, s in zip(m.cv4, m.cv5, m.stride):\n", + " a[-1].bias.data[:] = 1.0\n", + " b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2)\n", + "\n", + "'''\n", + "\n", + "print(\"=\"*70)\n", + "print(\"Adding DualDDetectEdgeTPU to models/yolo.py\")\n", + "print(\"=\"*70)\n", + "\n", + "# Read the file\n", + "with open('models/yolo.py', 'r') as f:\n", + " lines = f.readlines()\n", + "\n", + "# Check if code already present\n", + "code_already_present = False\n", + "for i, line in enumerate(lines):\n", + " if 'class DualDDetectEdgeTPU(nn.Module):' in line:\n", + " code_already_present = True\n", + " print(f\"✅ Code already present at line {i+1}\")\n", + " break\n", + "\n", + "# Find where to insert (after DualDDetect class)\n", + "insert_idx = -1\n", + "for i, line in enumerate(lines):\n", + " if 'class TripleDetect(nn.Module):' in line:\n", + " insert_idx = i\n", + " print(f\"✅ Found insertion point at line {i+1} (before TripleDetect)\")\n", + " break\n", + "\n", + "if insert_idx == -1:\n", + " print(\"❌ Could not find TripleDetect class\")\n", + " print(\"Looking for alternative insertion point...\")\n", + " for i, line in enumerate(lines):\n", + " if 'class DualDDetect(nn.Module):' in line:\n", + " # Find the end of DualDDetect\n", + " for j in range(i+1, len(lines)):\n", + " if lines[j].strip().startswith('class ') and j > i + 10:\n", + " insert_idx = j\n", + " print(f\"✅ Found insertion point at line {j+1}\")\n", + " break\n", + " break\n", + "\n", + "if insert_idx == -1:\n", + " print(\"❌ Error: Could not find insertion point!\")\n", + "elif code_already_present:\n", + " print(\"✅ Code already present\")\n", + "else:\n", + " # Insert the new class\n", + " lines.insert(insert_idx, '\\n\\n' + dualddetect_edgetpu_code + '\\n\\n')\n", + "\n", + " # Write back\n", + " with open('models/yolo.py', 'w') as f:\n", + " f.writelines(lines)\n", + "\n", + " print(\"✅ Successfully added DualDDetectEdgeTPU class\")\n", + "\n", + " # Now update parse_model to recognize the new class\n", + " print(\"\\n\" + \"=\"*70)\n", + " print(\"Updating parse_model to recognize DualDDetectEdgeTPU\")\n", + " print(\"=\"*70)\n", + "\n", + " # Read again\n", + " with open('models/yolo.py', 'r') as f:\n", + " content = f.read()\n", + "\n", + " # Update all the isinstance checks\n", + " updates = [\n", + " ('elif m in {Detect, DualDetect, TripleDetect, DDetect, DualDDetect, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic}:',\n", + " 'elif m in {Detect, DualDetect, TripleDetect, DDetect, DualDDetect, DualDDetectEdgeTPU, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic}:'),\n", + "\n", + " ('if isinstance(m, (Detect, DualDetect, TripleDetect, DDetect, DualDDetect, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic)):',\n", + " 'if isinstance(m, (Detect, DualDetect, TripleDetect, DDetect, DualDDetect, DualDDetectEdgeTPU, TripleDDetect, Segment, DSegment, DualDSegment, Panoptic)):'),\n", + "\n", + " ('if isinstance(m, (DualDetect, TripleDetect, DualDDetect, TripleDDetect, DualDSegment)):',\n", + " 'if isinstance(m, (DualDetect, TripleDetect, DualDDetect, DualDDetectEdgeTPU, TripleDDetect, DualDSegment)):'),\n", + " ]\n", + "\n", + " for old, new in updates:\n", + " if old in content:\n", + " content = content.replace(old, new)\n", + " print(f\"✅ Updated: {old[:50]}...\")\n", + "\n", + " # Write back\n", + " with open('models/yolo.py', 'w') as f:\n", + " f.write(content)\n", + "\n", + " print(\"\\n✅ All updates completed successfully!\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "download_weights" + }, + "source": [ + "## Step 4: Download Pretrained Weights" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "weights_download", + "outputId": "7eabb677-901f-4b6c-88a0-335c837baa0c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "Downloading YOLOv9-t Pretrained Weights (based on SiLU activation and 80 classes)\n", + "======================================================================\n", + "Downloading pretrained weights...\n", + "yolov9-s-converted. 100%[===================>] 14.35M 76.0MB/s in 0.2s \n", + "✓ Weights downloaded\n", + " File size: 15.04 MB\n" + ] + } + ], + "source": [ + "print(\"=\"*70)\n", + "print(\"Downloading YOLOv9-s Pretrained Weights (based on SiLU activation and 80 classes)\")\n", + "print(\"=\"*70)\n", + "\n", + "weights_file = Path('yolov9-s-converted.pt')\n", + "\n", + "if not weights_file.exists():\n", + " print(\"Downloading pretrained weights...\")\n", + " !wget -q --show-progress https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-s-converted.pt\n", + " print(\"✓ Weights downloaded\")\n", + "else:\n", + " print(\"✓ Weights already exist\")\n", + "\n", + "print(f\" File size: {weights_file.stat().st_size / 1e6:.2f} MB\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "download_data" + }, + "source": [ + "## Step 3: Download COCO Dataset\n", + "\n", + "**This takes 10-15 minutes and downloads ~20GB**\n", + "\n", + "Only needs to be done once - Colab will cache it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "coco_download", + "outputId": "a251dfd3-a4b2-4854-da26-5dd934517014" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "Downloading COCO Dataset (YOLO Format)\n", + "======================================================================\n", + "\n", + "Downloading COCO dataset in YOLO format...\n", + "This includes both images and labels in the correct structure\n", + "coco2017labels.zip 100%[===================>] 46.39M 53.7MB/s in 0.9s \n", + "train2017.zip 100%[===================>] 18.01G 43.9MB/s in 7m 21s \n", + "val2017.zip 100%[===================>] 777.80M 45.3MB/s in 18s \n", + "\n", + "✓ COCO dataset downloaded with correct structure!\n", + "\n", + "Structure:\n", + " coco/\n", + " images/\n", + " train2017/\n", + " val2017/\n", + " labels/\n", + " train2017/\n", + " val2017/\n", + "\n", + "✓ Train: 118,287 images, 117,266 labels\n" + ] + } + ], + "source": [ + "# ============================================================================\n", + "# Download Pre-Converted COCO Dataset with YOLO Format Labels\n", + "# ============================================================================\n", + "print(\"=\"*70)\n", + "print(\"Downloading COCO Dataset (YOLO Format)\")\n", + "print(\"=\"*70)\n", + "\n", + "# Remove the old incorrect structure\n", + "import shutil\n", + "\n", + "if False:\n", + " if Path('coco').exists():\n", + " shutil.rmtree('coco')\n", + " print(\"✓ Removed old COCO directory\")\n", + "\n", + "if not Path('coco').exists():\n", + "\n", + " # Download COCO in YOLO format (images + labels already structured correctly)\n", + " print(\"\\nDownloading COCO dataset in YOLO format...\")\n", + " print(\"This includes both images and labels in the correct structure\")\n", + "\n", + " TEST_SET = False # can reduce data size with this\n", + "\n", + " !mkdir -p coco/images\n", + " if not Path('coco2017labels.zip').exists():\n", + " !wget -q --show-progress https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip\n", + " !unzip -q coco2017labels.zip # -d coco\n", + " if TEST_SET:\n", + " if not Path('coco128.zip').exists():\n", + " !wget -q --show-progress https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip\n", + " !rm -rf coco/labels/train2017\n", + " !rm -rf coco/images/train2017\n", + " !unzip -q coco128.zip\n", + " !mv coco128/images/* coco/images/\n", + " !mv coco128/labels/* coco/labels/\n", + " !rm -rf coco128\n", + " !rm coco/train2017.txt\n", + " !ls coco/images/train2017/*.jpg | sed 's|^coco|.|' > coco/train2017.txt\n", + " else:\n", + " if not Path('train2017.zip').exists():\n", + " !wget -q --show-progress http://images.cocodataset.org/zips/train2017.zip\n", + " !unzip -q train2017.zip -d coco/images\n", + " if not Path('val2017.zip').exists():\n", + " !wget -q --show-progress http://images.cocodataset.org/zips/val2017.zip\n", + " !unzip -q -n val2017.zip -d coco/images\n", + "\n", + " # Cleanup\n", + " #!rm coco2017labels.zip train2017.zip val2017.zip\n", + "\n", + " print(\"\\n✓ COCO dataset downloaded with correct structure!\")\n", + " print(\"\\nStructure:\")\n", + " print(\" coco/\")\n", + " print(\" images/\")\n", + " print(\" train2017/\")\n", + " print(\" val2017/\")\n", + " print(\" labels/\")\n", + " print(\" train2017/\")\n", + " print(\" val2017/\")\n", + "\n", + "# Verify\n", + "train_imgs = len(list(Path('coco/images/train2017').glob('*.jpg')))\n", + "train_lbls = len(list(Path('coco/labels/train2017').glob('*.txt')))\n", + "print(f\"\\n✓ Train: {train_imgs:,} images, {train_lbls:,} labels\")" + ] + }, + { + "cell_type": "code", + "source": [ + "# cleanup commands in case of re-try\n", + "\n", + "#!rm -rf coco\n", + "#!unzip -q coco2017labels.zip # -d coco\n", + "#!unzip -q val2017.zip -d coco/images\n", + "#!unzip -q -n val2017.zip -d coco/images" + ], + "metadata": { + "id": "i0THhtR6fv1l" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XM6zmAjCDtJT", + "outputId": "1f6d1dbc-3ac4-4b2e-b30b-14ec36587cea" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "Filtering COCO Dataset to Your Classes\n", + "======================================================================\n", + "\n", + "Keeping 17 classes:\n", + "\n", + "Filtering labels...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + " train2017: 100%|██████████| 117266/117266 [00:51<00:00, 2295.91it/s]\n", + " val2017: 100%|██████████| 4952/4952 [00:01<00:00, 2624.20it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "✓ Filtering complete:\n", + "\n", + "Training set:\n", + " Images: 118,287 → 86,145 (72.8%)\n", + " Objects: 849,947 → 404,191 (47.6%)\n", + "\n", + "Validation set:\n", + " Images: 5,000 → 3,645 (72.9%)\n", + " Objects: 36,335 → 16,998 (46.8%)\n", + "\n", + "✓ Labels replaced (original backed up to labels_full)\n", + "✓ Cache cleared\n" + ] + } + ], + "source": [ + "# ============================================================================\n", + "# Filter COCO Dataset to 17 Specific Classes\n", + "# ============================================================================\n", + "from pathlib import Path\n", + "from tqdm import tqdm\n", + "import shutil\n", + "\n", + "print(\"=\"*70)\n", + "print(\"Filtering COCO Dataset to Your Classes\")\n", + "print(\"=\"*70)\n", + "print(f\"\\nKeeping {len(CLASSES_TO_KEEP)} classes:\")\n", + "\n", + "# Map old COCO IDs to new sequential IDs\n", + "old_to_new = {old_id: new_id for new_id, old_id in enumerate(CLASSES_TO_KEEP)}\n", + "\n", + "def filter_labels(split='train2017'):\n", + " \"\"\"Filter labels to only include specified classes\"\"\"\n", + " label_dir = Path(f'coco/labels/{split}')\n", + " filtered_dir = Path(f'coco/labels_filtered/{split}')\n", + " filtered_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " kept_images = 0\n", + " total_objects_before = 0\n", + " total_objects_after = 0\n", + "\n", + " for label_file in tqdm(list(label_dir.glob('*.txt')), desc=f\" {split}\"):\n", + " with open(label_file, 'r') as f:\n", + " lines = f.readlines()\n", + "\n", + " total_objects_before += len(lines)\n", + "\n", + " # Filter and remap class IDs\n", + " filtered_lines = []\n", + " for line in lines:\n", + " parts = line.strip().split()\n", + " if len(parts) >= 5:\n", + " class_id = int(parts[0])\n", + " if class_id in old_to_new:\n", + " # Remap to new sequential ID\n", + " new_class_id = old_to_new[class_id]\n", + " filtered_lines.append(f\"{new_class_id} {' '.join(parts[1:])}\\n\")\n", + "\n", + " # Only keep label files for images that have at least one relevant object\n", + " if filtered_lines:\n", + " output_file = filtered_dir / label_file.name\n", + " with open(output_file, 'w') as f:\n", + " f.writelines(filtered_lines)\n", + " kept_images += 1\n", + " total_objects_after += len(filtered_lines)\n", + "\n", + " return kept_images, total_objects_before, total_objects_after\n", + "\n", + "# Filter both train and val\n", + "print(\"\\nFiltering labels...\")\n", + "train_kept, train_before, train_after = filter_labels('train2017')\n", + "val_kept, val_before, val_after = filter_labels('val2017')\n", + "\n", + "print(f\"\\n✓ Filtering complete:\")\n", + "print(f\"\\nTraining set:\")\n", + "print(f\" Images: 118,287 → {train_kept:,} ({train_kept/118287*100:.1f}%)\")\n", + "print(f\" Objects: {train_before:,} → {train_after:,} ({train_after/train_before*100:.1f}%)\")\n", + "print(f\"\\nValidation set:\")\n", + "print(f\" Images: 5,000 → {val_kept:,} ({val_kept/5000*100:.1f}%)\")\n", + "print(f\" Objects: {val_before:,} → {val_after:,} ({val_after/val_before*100:.1f}%)\")\n", + "\n", + "# Backup and replace\n", + "if Path('coco/labels_full').exists():\n", + " shutil.rmtree('coco/labels_full')\n", + "shutil.move('coco/labels', 'coco/labels_full')\n", + "shutil.move('coco/labels_filtered', 'coco/labels')\n", + "print(f\"\\n✓ Labels replaced (original backed up to labels_full)\")\n", + "\n", + "# Delete old cache\n", + "for cache in Path('coco/labels').rglob('*.cache'):\n", + " cache.unlink()\n", + "print(f\"✓ Cache cleared\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vN6KCyT3FYHL", + "outputId": "75e46a4e-70df-42d0-bfde-c1647d1a892a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "Subsampling to 10% of Filtered Dataset\n", + "======================================================================\n", + "\n", + "✓ Subsampling complete:\n", + " Training: 86,145 → 8,614 images\n", + " Validation: 3,645 → 364 images\n" + ] + } + ], + "source": [ + "# ============================================================================\n", + "# Subsample Filtered Dataset\n", + "# ============================================================================\n", + "import random\n", + "from pathlib import Path\n", + "import shutil\n", + "\n", + "SUBSAMPLE_PERCENT = 10 # Use 20% of data\n", + "\n", + "print(\"=\"*70)\n", + "print(f\"Subsampling to {SUBSAMPLE_PERCENT}% of Filtered Dataset\")\n", + "print(\"=\"*70)\n", + "\n", + "SUBSAMPLE_SUFFIX = \"_sub\"\n", + "\n", + "def subsample_dataset(split='train2017', percent=15):\n", + " label_dir = Path(f'coco/labels/{split}')\n", + " img_dir = Path(f'coco/images/{split}')\n", + "\n", + " # Get all labels\n", + " all_labels = list(label_dir.glob('*.txt'))\n", + "\n", + " # Random subsample\n", + " random.seed(42) # Reproducible\n", + " n_keep = int(len(all_labels) * percent / 100)\n", + " selected = random.sample(all_labels, n_keep)\n", + "\n", + " # Create subsample directories\n", + " sub_label_dir = Path(f'coco/labels/{split}{SUBSAMPLE_SUFFIX}')\n", + " sub_img_dir = Path(f'coco/images/{split}{SUBSAMPLE_SUFFIX}')\n", + " sub_label_dir.mkdir(parents=True, exist_ok=True)\n", + " sub_img_dir.mkdir(parents=True, exist_ok=True)\n", + "\n", + " # Copy selected files\n", + " for label_file in selected:\n", + " # Copy label\n", + " shutil.copy(label_file, sub_label_dir / label_file.name)\n", + "\n", + " # Copy corresponding image\n", + " img_file = img_dir / f\"{label_file.stem}.jpg\"\n", + " if img_file.exists():\n", + " shutil.copy(img_file, sub_img_dir / img_file.name)\n", + "\n", + " return n_keep, len(all_labels)\n", + "\n", + "\n", + "if SUBSAMPLE_PERCENT < 99.999:\n", + " # Subsample both splits\n", + " train_kept, train_total = subsample_dataset('train2017', SUBSAMPLE_PERCENT)\n", + " val_kept, val_total = subsample_dataset('val2017', SUBSAMPLE_PERCENT)\n", + "\n", + " print(f\"\\n✓ Subsampling complete:\")\n", + " print(f\" Training: {train_total:,} → {train_kept:,} images\")\n", + " print(f\" Validation: {val_total:,} → {val_kept:,} images\")\n", + "else:\n", + " SUBSAMPLE_SUFFIX = \"\"\n", + " print(\"✓ No subsampling required\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "setup_data_config" + }, + "source": [ + "## Step 6: Setup COCO Data Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "data_config", + "outputId": "2357a671-9401-4a02-a184-ab3400dcba11" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "Configuring Dataset Paths\n", + "======================================================================\n", + "✓ COCO configuration created\n" + ] + } + ], + "source": [ + "print(\"=\"*70)\n", + "print(\"Configuring Dataset Paths\")\n", + "print(\"=\"*70)\n", + "\n", + "# COCO data config (if not already correct)\n", + "coco_yaml_content = f\"\"\"\n", + "# COCO 2017 dataset\n", + "\n", + "path: {os.getcwd()}/coco\n", + "train: images/train2017{SUBSAMPLE_SUFFIX}\n", + "val: images/val2017{SUBSAMPLE_SUFFIX}\n", + "\n", + "nc: {len(CLASSES_TO_KEEP)}\n", + "\n", + "# Classes\n", + "names:\n", + " 0: person\n", + " 1: bicycle\n", + " 2: car\n", + " 3: motorcycle\n", + " 4: airplane\n", + " 5: bus\n", + " 6: train\n", + " 7: truck\n", + " 8: boat\n", + " 9: bird\n", + " 10: cat\n", + " 11: dog\n", + " 12: horse\n", + " 13: sheep\n", + " 14: cow\n", + " 15: elephant\n", + " 16: bear\n", + "\"\"\"\n", + "\n", + "with open('data/coco.yaml', 'w') as f:\n", + " f.write(coco_yaml_content)\n", + "\n", + "print(\"✓ COCO configuration created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "training_config" + }, + "source": [ + "## Step 7: Configure Training Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_TgLuSYyQ25D" + }, + "outputs": [], + "source": [ + "#!rm -rf /content/yolov9/runs/train/yolov9-s-edgetpu" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "config_training", + "outputId": "082c7115-69da-4947-f2e8-0ea429054d38" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "Training Configuration\n", + "======================================================================\n", + "\n", + "📋 Training Configuration:\n", + " Model: YOLOv9-s with ReLU6 activation\n", + " Starting weights: yolov9-s-converted.pt\n", + " Batch size: 24\n", + " Image size: 640x640\n", + " Epochs: 10\n", + " Checkpoint frequency: every 3 epochs\n", + "\n", + "💡 Colab Free Tier Tips:\n", + " • Training will take ~3-5 hours\n", + " • Session may disconnect - rerun next cell to resume\n", + " • Don't close the browser tab\n", + " • Checkpoints saved automatically\n" + ] + } + ], + "source": [ + "print(\"=\"*70)\n", + "print(\"Training Configuration\")\n", + "print(\"=\"*70)\n", + "\n", + "# Training parameters\n", + "BATCH_SIZE = 24 # Adjust if out of memory (try 8 or 12 or 16)\n", + "EPOCHS = 20 # Fine-tuning epochs\n", + "IMAGE_SIZE = 640 # Match pre-training size\n", + "CHECKPOINT_FREQ = 3 # Save every N epochs\n", + "\n", + "print(f\"\\n📋 Training Configuration:\")\n", + "print(f\" Model: YOLOv9-s with ReLU6 activation\")\n", + "print(f\" Starting weights: {weights_file}\")\n", + "print(f\" Batch size: {BATCH_SIZE}\")\n", + "print(f\" Image size: {IMAGE_SIZE}x{IMAGE_SIZE}\")\n", + "print(f\" Epochs: {EPOCHS}\")\n", + "print(f\" Checkpoint frequency: every {CHECKPOINT_FREQ} epochs\")\n", + "\n", + "print(f\"\\n💡 Colab Free Tier Tips:\")\n", + "print(f\" • Training will take ~3-5 hours\")\n", + "print(f\" • Session may disconnect - rerun next cell to resume\")\n", + "print(f\" • Don't close the browser tab\")\n", + "print(f\" • Checkpoints saved automatically\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "start_training" + }, + "source": [ + "## Step 8: Start Training\n", + "\n", + "Leave browser tab open while this runs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NCFldBD8yGNL", + "outputId": "92008548-f5e2-4615-a96e-b9e838587b33" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Patching train_dual.py for PyTorch 2.8 compatibility...\n", + "✓ train_dual.py patched successfully\n" + ] + } + ], + "source": [ + "# ============================================================================\n", + "# FIX: Patch train_dual.py to allow loading pretrained weights\n", + "# ============================================================================\n", + "print(\"Patching train_dual.py for PyTorch 2.8 compatibility...\")\n", + "\n", + "# Read the file\n", + "with open('train_dual.py', 'r') as f:\n", + " content = f.read()\n", + "\n", + "# Find and replace the torch.load line\n", + "old_line = \"ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak\"\n", + "new_line = \"ckpt = torch.load(weights, map_location='cpu', weights_only=False) # load checkpoint to CPU to avoid CUDA memory leak\"\n", + "\n", + "if old_line in content:\n", + " content = content.replace(old_line, new_line)\n", + "\n", + " # Write back\n", + " with open('train_dual.py', 'w') as f:\n", + " f.write(content)\n", + "\n", + " print(\"✓ train_dual.py patched successfully\")\n", + "else:\n", + " print(\"⚠️ Line not found - manual edit needed\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "train", + "outputId": "8196025a-f194-4b88-94ce-8bfdf72e13d1", + "collapsed": true + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "STARTING TRAINING\n", + "======================================================================\n", + "You can minimize browser but don't close the tab\n", + "\n", + "======================================================================\n", + "\n", + "2025-11-10 12:09:02.588121: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1762776542.847725 5421 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1762776542.920591 5421 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "W0000 00:00:1762776543.458684 5421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1762776543.458722 5421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1762776543.458725 5421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "W0000 00:00:1762776543.458731 5421 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n", + "2025-11-10 12:09:03.508261: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: (1) Create a W&B account\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: (2) Use an existing W&B account\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: (3) Don't visualize my results\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Enter your choice: (30 second timeout) 3\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: You chose \"Don't visualize my results\"\n", + "\u001b[34m\u001b[1mtrain_dual: \u001b[0mweights=yolov9-s-converted.pt, cfg=models/detect/yolov9-s-relu6.yaml, data=data/coco.yaml, hyp=data/hyps/hyp.scratch-high.yaml, epochs=10, batch_size=24, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=0, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=4, project=runs/train, name=yolov9-s-relu6, exist_ok=True, quad=False, cos_lr=False, flat_cos_lr=False, fixed_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=3, seed=0, local_rank=-1, min_items=0, close_mosaic=0, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n", + "YOLO 🚀 v0.1-104-g5b1ea9a Python-3.12.12 torch-2.8.0+cu126 CUDA:0 (Tesla T4, 15095MiB)\n", + "\n", + "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, cls_pw=1.0, obj=0.7, obj_pw=1.0, dfl=1.5, iou_t=0.2, anchor_t=5.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.3\n", + "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLO 🚀 in ClearML\n", + "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLO 🚀 runs in Comet\n", + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n", + "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n", + "100% 755k/755k [00:00<00:00, 42.4MB/s]\n", + "\n", + " from n params module arguments \n", + "\u001b[34m\u001b[1mactivation:\u001b[0m nn.ReLU6()\n", + " 0 -1 1 928 models.common.Conv [3, 32, 3, 2] \n", + " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", + " 2 -1 1 31104 models.common.ELAN1 [64, 64, 64, 32] \n", + " 3 -1 1 73984 models.common.AConv [64, 128] \n", + " 4 -1 1 258432 models.common.RepNCSPELAN4 [128, 128, 128, 64, 3] \n", + " 5 -1 1 221568 models.common.AConv [128, 192] \n", + " 6 -1 1 579648 models.common.RepNCSPELAN4 [192, 192, 192, 96, 3] \n", + " 7 -1 1 442880 models.common.AConv [192, 256] \n", + " 8 -1 1 1028864 models.common.RepNCSPELAN4 [256, 256, 256, 128, 3] \n", + " 9 -1 1 164608 models.common.SPPELAN [256, 256, 128] \n", + " 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 11 [-1, 6] 1 0 models.common.Concat [1] \n", + " 12 -1 1 628800 models.common.RepNCSPELAN4 [448, 192, 192, 96, 3] \n", + " 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 14 [-1, 4] 1 0 models.common.Concat [1] \n", + " 15 -1 1 283008 models.common.RepNCSPELAN4 [320, 128, 128, 64, 3] \n", + " 16 -1 1 110784 models.common.AConv [128, 96] \n", + " 17 [-1, 12] 1 0 models.common.Concat [1] \n", + " 18 -1 1 598080 models.common.RepNCSPELAN4 [288, 192, 192, 96, 3] \n", + " 19 -1 1 221440 models.common.AConv [192, 128] \n", + " 20 [-1, 9] 1 0 models.common.Concat [1] \n", + " 21 -1 1 1061632 models.common.RepNCSPELAN4 [384, 256, 256, 128, 3] \n", + " 22 8 1 164608 models.common.SPPELAN [256, 256, 128] \n", + " 23 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 24 [-1, 6] 1 0 models.common.Concat [1] \n", + " 25 -1 1 628800 models.common.RepNCSPELAN4 [448, 192, 192, 96, 3] \n", + " 26 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 27 [-1, 4] 1 0 models.common.Concat [1] \n", + " 28 -1 1 283008 models.common.RepNCSPELAN4 [320, 128, 128, 64, 3] \n", + " 29[28, 25, 22, 15, 18, 21] 1 2955014 models.yolo.DualDDetectEdgeTPU [17, [128, 192, 256, 128, 192, 256]]\n", + "yolov9-s-relu6 summary: 1219 layers, 9755750 parameters, 9755718 gradients, 39.7 GFLOPs\n", + "\n", + "Transferred 1254/1772 items from yolov9-s-converted.pt\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 291 weight(decay=0.0), 305 weight(decay=0.0005625000000000001), 303 bias\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n", + "size\n", + " Field required [type=missing, input_value={'scale': (0.8, 1.0), 'ra...: None, 'strict': False}, input_type=dict]\n", + " For further information visit https://errors.pydantic.dev/2.11/v/missing\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/yolov9/coco/labels/train2017_sub... 8614 images, 0 backgrounds, 0 corrupt: 100% 8614/8614 [00:01<00:00, 8128.97it/s]\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/yolov9/coco/labels/train2017_sub.cache\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (7.0GB ram): 100% 8614/8614 [00:06<00:00, 1362.93it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/yolov9/coco/labels/val2017_sub... 364 images, 0 backgrounds, 0 corrupt: 100% 364/364 [00:00<00:00, 2436.87it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/yolov9/coco/labels/val2017_sub.cache\n", + "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.3GB ram): 100% 364/364 [00:00<00:00, 1244.03it/s]\n", + "Plotting labels to runs/train/yolov9-s-relu6/labels.jpg... \n", + "/content/yolov9/train_dual.py:255: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", + " scaler = torch.cuda.amp.GradScaler(enabled=amp)\n", + "Image sizes 640 train, 640 val\n", + "Using 4 dataloader workers\n", + "Logging results to \u001b[1mruns/train/yolov9-s-relu6\u001b[0m\n", + "Starting training for 10 epochs...\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + " 0% 0/359 [00:00\n", + " main(opt)\n", + " File \"/content/yolov9/train_dual.py\", line 538, in main\n", + " train(opt.hyp, opt, device, callbacks)\n", + " File \"/content/yolov9/train_dual.py\", line 413, in train\n", + " strip_optimizer(f) # strip optimizers\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"/content/yolov9/utils/general.py\", line 999, in strip_optimizer\n", + " x = torch.load(f, map_location=torch.device('cpu'))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/usr/local/lib/python3.12/dist-packages/torch/serialization.py\", line 1529, in load\n", + " raise pickle.UnpicklingError(_get_wo_message(str(e))) from None\n", + "_pickle.UnpicklingError: Weights only load failed. This file can still be loaded, to do so you have two options, \u001b[1mdo those steps only if you trust the source of the checkpoint\u001b[0m. \n", + "\t(1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.\n", + "\t(2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.\n", + "\tWeightsUnpickler error: Unsupported global: GLOBAL numpy._core.multiarray._reconstruct was not an allowed global by default. Please use `torch.serialization.add_safe_globals([numpy._core.multiarray._reconstruct])` or the `torch.serialization.safe_globals([numpy._core.multiarray._reconstruct])` context manager to allowlist this global if you trust this class/function.\n", + "\n", + "Check the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.\n", + "\n", + "======================================================================\n", + "✓ TRAINING COMPLETE!\n", + "======================================================================\n" + ] + } + ], + "source": [ + "print(\"=\"*70)\n", + "print(\"STARTING TRAINING\")\n", + "print(\"=\"*70)\n", + "print(\"You can minimize browser but don't close the tab\\n\")\n", + "print(\"=\"*70 + \"\\n\")\n", + "\n", + "\n", + "import shutil\n", + "from pathlib import Path\n", + "import threading\n", + "import time\n", + "\n", + "# Run training\n", + "!python train_dual.py \\\n", + " --workers 4 \\\n", + " --device 0 \\\n", + " --batch-size {BATCH_SIZE} \\\n", + " --data data/coco.yaml \\\n", + " --img {IMAGE_SIZE} \\\n", + " --cfg models/detect/yolov9-s-relu6.yaml \\\n", + " --weights {weights_file} \\\n", + " --name yolov9-s-relu6 \\\n", + " --hyp data/hyps/hyp.scratch-high.yaml \\\n", + " --epochs {EPOCHS} \\\n", + " --save-period {CHECKPOINT_FREQ} \\\n", + " --cache \\\n", + " --exist-ok\n", + "\n", + "print(\"\\n\" + \"=\"*70)\n", + "print(\"✓ TRAINING COMPLETE!\")\n", + "print(\"=\"*70)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "download" + }, + "source": [ + "## Step 9: Download Trained Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "download_model", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 403 + }, + "outputId": "0830fe0a-99a8-4917-b020-f3db19f1834e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "======================================================================\n", + "Download Trained Model\n", + "======================================================================\n", + "✓ Model ready: /content/yolov9-s-relu6-best.pt\n", + " File size: 79.88 MB\n", + "\n", + "Downloading to your computer...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_78f70657-8b62-4119-bff7-460ff6495d73\", \"yolov9-s-relu6-best.pt\", 79878777)" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "✓ Download complete!\n", + "✓ Model ready: /content/yolov9-s-relu6-last.pt\n", + " File size: 79.88 MB\n", + "\n", + "Downloading to your computer...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_d27f3ab9-2747-4643-83f6-90341961ff7e\", \"yolov9-s-relu6-last.pt\", 79878777)" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "✓ Download complete!\n", + "✓ Epoch summary ready: /content/epoch-results.csv\n", + " File size: 0.00 MB\n", + "\n", + "Downloading to your computer...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_2d70d0be-02c0-412c-8449-42ab400ba56f\", \"epoch-results.csv\", 1470)" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "✓ Download complete!\n" + ] + } + ], + "source": [ + "from google.colab import files\n", + "import shutil\n", + "\n", + "print(\"=\"*70)\n", + "print(\"Download Trained Model\")\n", + "print(\"=\"*70)\n", + "\n", + "best_weights = Path('runs/train/yolov9-s-relu6/weights/best.pt')\n", + "last_weights = Path('runs/train/yolov9-s-relu6/weights/last.pt')\n", + "epoch_summary = Path('runs/train/yolov9-s-relu6/results.csv')\n", + "\n", + "if best_weights.exists():\n", + " output_path = '/content/yolov9-s-relu6-best.pt'\n", + " shutil.copy(best_weights, output_path)\n", + " print(f\"✓ Model ready: {output_path}\")\n", + " print(f\" File size: {Path(output_path).stat().st_size / 1e6:.2f} MB\")\n", + " print(\"\\nDownloading to your computer...\")\n", + " files.download(output_path)\n", + " print(\"\\n✓ Download complete!\")\n", + "if last_weights.exists():\n", + " output_path = '/content/yolov9-s-relu6-last.pt'\n", + " shutil.copy(last_weights, output_path)\n", + " print(f\"✓ Model ready: {output_path}\")\n", + " print(f\" File size: {Path(output_path).stat().st_size / 1e6:.2f} MB\")\n", + " print(\"\\nDownloading to your computer...\")\n", + " files.download(output_path)\n", + " print(\"\\n✓ Download complete!\")\n", + "if epoch_summary.exists():\n", + " output_path = '/content/epoch-results.csv'\n", + " shutil.copy(epoch_summary, output_path)\n", + " print(f\"✓ Epoch summary ready: {output_path}\")\n", + " print(f\" File size: {Path(output_path).stat().st_size / 1e6:.2f} MB\")\n", + " print(\"\\nDownloading to your computer...\")\n", + " files.download(output_path)\n", + " print(\"\\n✓ Download complete!\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "validate" + }, + "source": [ + "## Step 10: (Optional) Validate Trained Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "validation" + }, + "outputs": [], + "source": [ + "print(\"=\"*70)\n", + "print(\"Validating Trained Model\")\n", + "print(\"=\"*70)\n", + "\n", + "best_weights = Path('runs/train/yolov9-s-relu6/weights/best.pt')\n", + "\n", + "if best_weights.exists():\n", + " print(f\"✓ Best weights found: {best_weights}\\n\")\n", + "\n", + " # Run validation\n", + " print(\"Running validation on COCO val set...\\n\")\n", + " !python val_dual.py \\\n", + " --data data/coco.yaml \\\n", + " --img {IMAGE_SIZE} \\\n", + " --batch 32 \\\n", + " --conf 0.001 \\\n", + " --iou 0.7 \\\n", + " --device 0 \\\n", + " --weights {best_weights} \\\n", + " --task val\n", + "\n", + " print(\"\\n✓ Validation complete!\")\n", + "else:\n", + " print(\"⚠️ Best weights not found. Training may have failed.\")\n", + " print(\" Check training output above for errors.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "plot_results" + }, + "source": [ + "## Step 11: (Optional) Plot Training Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "plot" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "print(\"=\"*70)\n", + "print(\"Training Results\")\n", + "print(\"=\"*70)\n", + "\n", + "results_file = Path('runs/train/yolov9-s-relu6/results.txt')\n", + "\n", + "if results_file.exists():\n", + " # Read results\n", + " results = pd.read_csv(results_file, sep=r'\\s+', header=0)\n", + "\n", + " # Plot\n", + " fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n", + " fig.suptitle('YOLOv9-s ReLU Training Results', fontsize=16, fontweight='bold')\n", + "\n", + " # Box loss\n", + " axes[0, 0].plot(results['epoch'], results['train/box_loss'], label='Train', linewidth=2)\n", + " axes[0, 0].plot(results['epoch'], results['val/box_loss'], label='Val', linewidth=2)\n", + " axes[0, 0].set_xlabel('Epoch')\n", + " axes[0, 0].set_ylabel('Loss')\n", + " axes[0, 0].set_title('Box Loss')\n", + " axes[0, 0].legend()\n", + " axes[0, 0].grid(True, alpha=0.3)\n", + "\n", + " # Object loss\n", + " axes[0, 1].plot(results['epoch'], results['train/obj_loss'], label='Train', linewidth=2)\n", + " axes[0, 1].plot(results['epoch'], results['val/obj_loss'], label='Val', linewidth=2)\n", + " axes[0, 1].set_xlabel('Epoch')\n", + " axes[0, 1].set_ylabel('Loss')\n", + " axes[0, 1].set_title('Object Loss')\n", + " axes[0, 1].legend()\n", + " axes[0, 1].grid(True, alpha=0.3)\n", + "\n", + " # Class loss\n", + " axes[1, 0].plot(results['epoch'], results['train/cls_loss'], label='Train', linewidth=2)\n", + " axes[1, 0].plot(results['epoch'], results['val/cls_loss'], label='Val', linewidth=2)\n", + " axes[1, 0].set_xlabel('Epoch')\n", + " axes[1, 0].set_ylabel('Loss')\n", + " axes[1, 0].set_title('Class Loss')\n", + " axes[1, 0].legend()\n", + " axes[1, 0].grid(True, alpha=0.3)\n", + "\n", + " # mAP metrics\n", + " axes[1, 1].plot(results['epoch'], results['metrics/mAP50(B)'], label='mAP@0.5', linewidth=2)\n", + " axes[1, 1].plot(results['epoch'], results['metrics/mAP50-95(B)'], label='mAP@0.5:0.95', linewidth=2)\n", + " axes[1, 1].set_xlabel('Epoch')\n", + " axes[1, 1].set_ylabel('mAP')\n", + " axes[1, 1].set_title('Mean Average Precision')\n", + " axes[1, 1].legend()\n", + " axes[1, 1].grid(True, alpha=0.3)\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig('training_results.png', dpi=150, bbox_inches='tight')\n", + " plt.show()\n", + "\n", + " # Show final metrics\n", + " last_row = results.iloc[-1]\n", + " print(f\"\\n📊 Final Metrics (Epoch {int(last_row['epoch'])}):\")\n", + " print(f\" mAP@0.5: {last_row['metrics/mAP50(B)']:.4f}\")\n", + " print(f\" mAP@0.5:0.95: {last_row['metrics/mAP50-95(B)']:.4f}\")\n", + "else:\n", + " print(\"⚠️ Results file not found\")" + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4", + "machine_shape": "hm" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/source/yolov9-edgetpu-dbro/yolov9-s-relu6-best.pt b/source/yolov9-edgetpu-dbro/yolov9-s-relu6-best.pt new file mode 100644 index 0000000000000000000000000000000000000000..6f3ac35858922fdfd620db07cbd0aceb78002f3f --- /dev/null +++ b/source/yolov9-edgetpu-dbro/yolov9-s-relu6-best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0005b3e03044acb007dbc1d6be2d1040b64ca98e5d2c30aaf5678f0935c6ebf9 +size 79878777