如何部署 YOLO11n-pose
在本教程中，我们介绍如何使用 ESP-PPQ 对预训练的 YOLO11n-pose 模型进行量化，并使用 ESP-DL 部署量化后的 YOLO11n-pose 模型。
准备工作
模型量化
预训练模型
你可以从 Ultralytics release 下载预训练的 yolo11n-pose 模型。
目前ESP-PPQ支持 ONNX、PyTorch、TensorFlow 模型。在量化过程中，PyTorch 和 TensorFlow 会先转化为 ONNX 模型，因此将与训练的 yolo11n-pose 转化成ONNX模型。
具体来说，参考脚本： export_onnx.py 将预训练的 yolo11n-pose 模型转换为 ONNX 模型。
在该脚本中，我们重载了 Pose 类的 forward 方法，具有以下优势：
更快的推理速度。 与原始的 yolo11n-pose 模型相比, 将推理过程中 Pose 里与解码边界框相关的操作移至后处理中完成, 从而显著减少了推理延迟。一方面，
Conv，Transpose，Slice，Split和Concat操作在推理过程中运行是非常耗时的。另一方面，在后处理阶段，模型推理的输出首先进行置信度筛选，然后再解码边界框，这大大减少了计算量，从而加快了整体推理速度。更低的量化误差。 ESP-PPQ中的
Concat和Add操作采用了联合量化。为了减少量化误差，由于 box 和 score 的范围差异较大，它们通过不同的分支输出，而不是拼接在一起。类似地，由于Add和Sub的输入的范围差异较大，相关计算被移到了后处理中进行，避免被量化。
校准数据集
校准数据集需要和模型输入格式一致，同时尽可能覆盖模型输入的所有可能情况，以便更好地量化模型。本示例中，我们使用的校准集为 calib_yolo11n-pose 。
8bit 默认配置量化
量化设置
target="esp32p4"
num_of_bits=8
batch_size=32
quant_setting = QuantizationSettingFactory.espdl_setting() # default setting
量化结果
Layer | NOISE:SIGNAL POWER RATIO
/model.22/m.0/cv2/conv/Conv: | ████████████████████ | 29.305%
/model.23/cv3.2/cv3.2.0/cv3.2.0.1/conv/Conv: | ██████████████████ | 26.959%
/model.23/cv4.1/cv4.1.0/conv/Conv: | ██████████████████ | 26.555%
/model.23/cv3.2/cv3.2.1/cv3.2.1.0/conv/Conv: | █████████████████ | 25.611%
/model.20/conv/Conv: | █████████████████ | 24.738%
/model.23/cv3.2/cv3.2.0/cv3.2.0.0/conv/Conv: | ████████████████ | 24.122%
/model.23/cv4.1/cv4.1.1/conv/Conv: | ███████████████ | 22.512%
/model.19/m.0/cv2/conv/Conv: | ███████████████ | 22.397%
/model.23/cv2.0/cv2.0.1/conv/Conv: | ███████████████ | 22.174%
/model.23/cv4.0/cv4.0.0/conv/Conv: | ███████████████ | 21.621%
/model.23/cv2.1/cv2.1.1/conv/Conv: | ███████████████ | 21.489%
/model.23/cv4.0/cv4.0.1/conv/Conv: | ███████████████ | 21.445%
/model.23/cv3.1/cv3.1.0/cv3.1.0.1/conv/Conv: | ██████████████ | 20.528%
/model.23/cv3.1/cv3.1.1/cv3.1.1.0/conv/Conv: | ██████████████ | 20.083%
/model.23/cv3.1/cv3.1.0/cv3.1.0.0/conv/Conv: | ██████████████ | 20.066%
/model.13/m.0/cv2/conv/Conv: | ██████████████ | 20.042%
/model.22/m.0/cv3/conv/Conv: | █████████████ | 19.737%
/model.10/m/m.0/ffn/ffn.1/conv/Conv: | █████████████ | 19.585%
/model.23/cv3.1/cv3.1.1/cv3.1.1.1/conv/Conv: | █████████████ | 19.392%
/model.23/cv3.0/cv3.0.1/cv3.0.1.0/conv/Conv: | █████████████ | 18.773%
/model.23/cv3.2/cv3.2.1/cv3.2.1.1/conv/Conv: | █████████████ | 18.688%
/model.22/cv1/conv/Conv: | █████████████ | 18.579%
/model.19/cv2/conv/Conv: | █████████████ | 18.494%
/model.22/m.0/m/m.1/cv2/conv/Conv: | ████████████ | 17.576%
/model.17/conv/Conv: | ████████████ | 17.224%
/model.19/cv1/conv/Conv: | ████████████ | 17.140%
/model.22/cv2/conv/Conv: | ███████████ | 16.785%
/model.23/cv4.2/cv4.2.1/conv/Conv: | ███████████ | 16.375%
/model.23/cv4.2/cv4.2.0/conv/Conv: | ███████████ | 16.167%
/model.23/cv2.1/cv2.1.0/conv/Conv: | ███████████ | 15.655%
/model.23/cv3.0/cv3.0.1/cv3.0.1.1/conv/Conv: | ███████████ | 15.504%
/model.23/cv2.2/cv2.2.0/conv/Conv: | ███████████ | 15.431%
/model.10/m/m.0/attn/proj/conv/Conv: | ██████████ | 15.251%
/model.23/cv3.0/cv3.0.0/cv3.0.0.1/conv/Conv: | ██████████ | 15.171%
/model.22/m.0/m/m.0/cv2/conv/Conv: | ██████████ | 15.006%
/model.19/m.0/cv1/conv/Conv: | ██████████ | 14.692%
/model.23/cv2.2/cv2.2.1/conv/Conv: | ██████████ | 14.548%
/model.22/m.0/m/m.0/cv1/conv/Conv: | █████████ | 13.065%
/model.16/m.0/cv2/conv/Conv: | █████████ | 12.980%
/model.22/m.0/m/m.1/cv1/conv/Conv: | █████████ | 12.921%
/model.10/m/m.0/attn/pe/conv/Conv: | █████████ | 12.745%
/model.23/cv4.1/cv4.1.2/Conv: | ████████ | 12.498%
/model.13/cv2/conv/Conv: | ████████ | 11.932%
/model.23/cv4.2/cv4.2.2/Conv: | ████████ | 11.797%
/model.13/m.0/cv1/conv/Conv: | ████████ | 11.777%
/model.16/cv2/conv/Conv: | ███████ | 10.892%
/model.13/cv1/conv/Conv: | ███████ | 10.760%
/model.23/cv2.0/cv2.0.0/conv/Conv: | ███████ | 10.352%
/model.23/cv4.0/cv4.0.2/Conv: | ███████ | 10.325%
/model.22/m.0/cv1/conv/Conv: | ███████ | 10.257%
/model.8/m.0/cv2/conv/Conv: | ███████ | 9.687%
/model.10/m/m.0/ffn/ffn.0/conv/Conv: | ██████ | 8.997%
/model.10/cv1/conv/Conv: | ██████ | 8.787%
/model.16/m.0/cv1/conv/Conv: | ██████ | 8.629%
/model.10/m/m.0/attn/qkv/conv/Conv: | ██████ | 8.600%
/model.8/m.0/cv3/conv/Conv: | ██████ | 8.328%
/model.10/m/m.0/attn/MatMul_1: | ██████ | 8.293%
/model.16/cv1/conv/Conv: | █████ | 7.947%
/model.10/cv2/conv/Conv: | █████ | 7.824%
/model.8/cv2/conv/Conv: | █████ | 7.696%
/model.23/cv3.0/cv3.0.0/cv3.0.0.0/conv/Conv: | █████ | 7.615%
/model.8/m.0/m/m.1/cv2/conv/Conv: | █████ | 7.145%
/model.8/m.0/m/m.0/cv2/conv/Conv: | █████ | 7.033%
/model.10/m/m.0/attn/MatMul: | █████ | 6.707%
/model.8/m.0/m/m.1/cv1/conv/Conv: | ████ | 6.376%
/model.23/cv2.1/cv2.1.2/Conv: | ████ | 6.321%
/model.8/cv1/conv/Conv: | ████ | 6.296%
/model.6/m.0/cv2/conv/Conv: | ████ | 5.605%
/model.23/cv3.2/cv3.2.2/Conv: | ████ | 5.599%
/model.6/m.0/m/m.0/cv2/conv/Conv: | ████ | 5.559%
/model.23/cv2.0/cv2.0.2/Conv: | ████ | 5.262%
/model.23/cv2.2/cv2.2.2/Conv: | ████ | 5.207%
/model.6/m.0/cv3/conv/Conv: | ███ | 4.840%
/model.8/m.0/cv1/conv/Conv: | ███ | 4.667%
/model.6/cv1/conv/Conv: | ███ | 4.523%
/model.9/cv1/conv/Conv: | ███ | 4.038%
/model.9/cv2/conv/Conv: | ██ | 3.733%
/model.7/conv/Conv: | ██ | 3.605%
/model.6/cv2/conv/Conv: | ██ | 3.478%
/model.3/conv/Conv: | ██ | 3.352%
/model.2/cv2/conv/Conv: | ██ | 3.230%
/model.8/m.0/m/m.0/cv1/conv/Conv: | ██ | 3.136%
/model.4/cv1/conv/Conv: | ██ | 2.913%
/model.6/m.0/m/m.1/cv1/conv/Conv: | ██ | 2.830%
/model.6/m.0/m/m.1/cv2/conv/Conv: | ██ | 2.692%
/model.6/m.0/m/m.0/cv1/conv/Conv: | ██ | 2.557%
/model.6/m.0/cv1/conv/Conv: | ██ | 2.475%
/model.5/conv/Conv: | ██ | 2.413%
/model.2/cv1/conv/Conv: | █ | 2.267%
/model.4/cv2/conv/Conv: | █ | 2.135%
/model.2/m.0/cv2/conv/Conv: | █ | 2.104%
/model.4/m.0/cv1/conv/Conv: | █ | 1.910%
/model.1/conv/Conv: | █ | 1.708%
/model.2/m.0/cv1/conv/Conv: | █ | 1.658%
/model.23/cv3.1/cv3.1.2/Conv: | █ | 1.455%
/model.4/m.0/cv2/conv/Conv: | █ | 1.056%
/model.23/cv3.0/cv3.0.2/Conv: | | 0.364%
/model.0/conv/Conv: | | 0.087%
Analysing Layerwise quantization error:: 100%|██████████| 98/98 [16:09<00:00, 9.89s/it]
Layer | NOISE:SIGNAL POWER RATIO
/model.0/conv/Conv: | ████████████████████ | 1.017%
/model.9/cv2/conv/Conv: | ██████████ | 0.493%
/model.8/cv1/conv/Conv: | ████████ | 0.410%
/model.2/cv2/conv/Conv: | ██████ | 0.287%
/model.1/conv/Conv: | ████ | 0.228%
/model.2/cv1/conv/Conv: | ███ | 0.163%
/model.16/cv2/conv/Conv: | ███ | 0.130%
/model.4/cv2/conv/Conv: | ██ | 0.096%
/model.3/conv/Conv: | █ | 0.070%
/model.4/cv1/conv/Conv: | █ | 0.068%
/model.10/cv1/conv/Conv: | █ | 0.049%
/model.2/m.0/cv2/conv/Conv: | █ | 0.047%
/model.2/m.0/cv1/conv/Conv: | █ | 0.043%
/model.4/m.0/cv2/conv/Conv: | █ | 0.041%
/model.13/cv2/conv/Conv: | █ | 0.037%
/model.16/cv1/conv/Conv: | █ | 0.030%
/model.22/cv2/conv/Conv: | █ | 0.027%
/model.8/cv2/conv/Conv: | █ | 0.027%
/model.13/cv1/conv/Conv: | | 0.025%
/model.5/conv/Conv: | | 0.025%
/model.19/m.0/cv2/conv/Conv: | | 0.025%
/model.6/cv2/conv/Conv: | | 0.024%
/model.4/m.0/cv1/conv/Conv: | | 0.022%
/model.6/cv1/conv/Conv: | | 0.021%
/model.19/cv1/conv/Conv: | | 0.020%
/model.23/cv2.1/cv2.1.1/conv/Conv: | | 0.018%
/model.23/cv4.1/cv4.1.0/conv/Conv: | | 0.017%
/model.9/cv1/conv/Conv: | | 0.015%
/model.23/cv4.2/cv4.2.1/conv/Conv: | | 0.014%
/model.10/m/m.0/attn/qkv/conv/Conv: | | 0.014%
/model.19/cv2/conv/Conv: | | 0.014%
/model.16/m.0/cv2/conv/Conv: | | 0.014%
/model.23/cv4.2/cv4.2.0/conv/Conv: | | 0.014%
/model.6/m.0/m/m.0/cv1/conv/Conv: | | 0.013%
/model.22/m.0/cv3/conv/Conv: | | 0.013%
/model.23/cv3.2/cv3.2.0/cv3.2.0.1/conv/Conv: | | 0.013%
/model.23/cv4.0/cv4.0.0/conv/Conv: | | 0.013%
/model.23/cv3.1/cv3.1.1/cv3.1.1.1/conv/Conv: | | 0.013%
/model.22/m.0/m/m.1/cv1/conv/Conv: | | 0.012%
/model.6/m.0/cv3/conv/Conv: | | 0.012%
/model.10/m/m.0/attn/pe/conv/Conv: | | 0.012%
/model.23/cv4.1/cv4.1.1/conv/Conv: | | 0.011%
/model.8/m.0/m/m.1/cv1/conv/Conv: | | 0.011%
/model.13/m.0/cv1/conv/Conv: | | 0.011%
/model.22/m.0/m/m.0/cv1/conv/Conv: | | 0.011%
/model.6/m.0/m/m.1/cv1/conv/Conv: | | 0.011%
/model.23/cv3.2/cv3.2.1/cv3.2.1.1/conv/Conv: | | 0.011%
/model.8/m.0/cv3/conv/Conv: | | 0.010%
/model.7/conv/Conv: | | 0.010%
/model.17/conv/Conv: | | 0.009%
/model.8/m.0/m/m.0/cv1/conv/Conv: | | 0.009%
/model.13/m.0/cv2/conv/Conv: | | 0.009%
/model.10/m/m.0/attn/MatMul: | | 0.009%
/model.19/m.0/cv1/conv/Conv: | | 0.008%
/model.16/m.0/cv1/conv/Conv: | | 0.008%
/model.23/cv2.2/cv2.2.1/conv/Conv: | | 0.008%
/model.8/m.0/m/m.1/cv2/conv/Conv: | | 0.008%
/model.8/m.0/cv1/conv/Conv: | | 0.008%
/model.10/cv2/conv/Conv: | | 0.007%
/model.23/cv2.0/cv2.0.2/Conv: | | 0.007%
/model.22/m.0/cv1/conv/Conv: | | 0.007%
/model.6/m.0/cv1/conv/Conv: | | 0.007%
/model.23/cv2.0/cv2.0.0/conv/Conv: | | 0.006%
/model.23/cv2.1/cv2.1.0/conv/Conv: | | 0.006%
/model.22/m.0/m/m.1/cv2/conv/Conv: | | 0.006%
/model.23/cv3.2/cv3.2.1/cv3.2.1.0/conv/Conv: | | 0.005%
/model.8/m.0/m/m.0/cv2/conv/Conv: | | 0.005%
/model.23/cv2.1/cv2.1.2/Conv: | | 0.005%
/model.23/cv3.2/cv3.2.0/cv3.2.0.0/conv/Conv: | | 0.005%
/model.23/cv2.2/cv2.2.2/Conv: | | 0.005%
/model.22/cv1/conv/Conv: | | 0.004%
/model.10/m/m.0/attn/proj/conv/Conv: | | 0.004%
/model.23/cv4.2/cv4.2.2/Conv: | | 0.004%
/model.23/cv4.1/cv4.1.2/Conv: | | 0.004%
/model.22/m.0/m/m.0/cv2/conv/Conv: | | 0.004%
/model.23/cv2.2/cv2.2.0/conv/Conv: | | 0.003%
/model.6/m.0/m/m.1/cv2/conv/Conv: | | 0.003%
/model.23/cv4.0/cv4.0.1/conv/Conv: | | 0.003%
/model.6/m.0/m/m.0/cv2/conv/Conv: | | 0.003%
/model.10/m/m.0/attn/MatMul_1: | | 0.002%
/model.23/cv4.0/cv4.0.2/Conv: | | 0.002%
/model.10/m/m.0/ffn/ffn.1/conv/Conv: | | 0.002%
/model.20/conv/Conv: | | 0.002%
/model.23/cv2.0/cv2.0.1/conv/Conv: | | 0.002%
/model.10/m/m.0/ffn/ffn.0/conv/Conv: | | 0.001%
/model.23/cv3.1/cv3.1.0/cv3.1.0.1/conv/Conv: | | 0.001%
/model.23/cv3.1/cv3.1.1/cv3.1.1.0/conv/Conv: | | 0.001%
/model.23/cv3.2/cv3.2.2/Conv: | | 0.001%
/model.23/cv3.0/cv3.0.1/cv3.0.1.0/conv/Conv: | | 0.001%
/model.23/cv3.1/cv3.1.0/cv3.1.0.0/conv/Conv: | | 0.001%
/model.23/cv3.1/cv3.1.2/Conv: | | 0.000%
/model.23/cv3.0/cv3.0.2/Conv: | | 0.000%
/model.23/cv3.0/cv3.0.0/cv3.0.0.1/conv/Conv: | | 0.000%
/model.6/m.0/cv2/conv/Conv: | | 0.000%
/model.23/cv3.0/cv3.0.1/cv3.0.1.1/conv/Conv: | | 0.000%
/model.23/cv3.0/cv3.0.0/cv3.0.0.0/conv/Conv: | | 0.000%
/model.8/m.0/cv2/conv/Conv: | | 0.000%
/model.22/m.0/cv2/conv/Conv: | | 0.000%
量化误差分析
在相同输入下，量化后的模型在 COCO 上的 Pose mAP50:95 仅为 43.1%，低于浮点模型（50.0%），存在一定的精度损失。
量化感知训练
为了进一步提高量化模型的精度，可以采用量化感知训练。本示例基于8-bit量化方式进行量化感知训练。
量化设置
量化结果
Layer | NOISE:SIGNAL POWER RATIO
/model.22/m.0/cv2/conv/Conv: | ████████████████████ | 27.739%
/model.23/cv3.2/cv3.2.0/cv3.2.0.1/conv/Conv: | ███████████████████ | 26.872%
/model.23/cv4.1/cv4.1.0/conv/Conv: | ███████████████████ | 26.229%
/model.23/cv2.1/cv2.1.1/conv/Conv: | ██████████████████ | 25.300%
/model.23/cv3.2/cv3.2.1/cv3.2.1.0/conv/Conv: | ██████████████████ | 24.625%
/model.23/cv2.0/cv2.0.1/conv/Conv: | █████████████████ | 23.751%
/model.20/conv/Conv: | █████████████████ | 23.320%
/model.23/cv3.2/cv3.2.0/cv3.2.0.0/conv/Conv: | █████████████████ | 22.901%
/model.23/cv4.1/cv4.1.1/conv/Conv: | ████████████████ | 22.516%
/model.10/m/m.0/ffn/ffn.1/conv/Conv: | ████████████████ | 22.035%
/model.19/m.0/cv2/conv/Conv: | ████████████████ | 21.569%
/model.23/cv4.0/cv4.0.0/conv/Conv: | ███████████████ | 21.199%
/model.23/cv3.1/cv3.1.0/cv3.1.0.1/conv/Conv: | ███████████████ | 20.785%
/model.23/cv3.1/cv3.1.1/cv3.1.1.0/conv/Conv: | ███████████████ | 20.597%
/model.23/cv3.1/cv3.1.1/cv3.1.1.1/conv/Conv: | ███████████████ | 20.329%
/model.23/cv4.0/cv4.0.1/conv/Conv: | ███████████████ | 20.179%
/model.23/cv3.1/cv3.1.0/cv3.1.0.0/conv/Conv: | ██████████████ | 19.983%
/model.22/m.0/cv3/conv/Conv: | ██████████████ | 19.919%
/model.13/m.0/cv2/conv/Conv: | ██████████████ | 19.424%
/model.23/cv3.0/cv3.0.1/cv3.0.1.0/conv/Conv: | ██████████████ | 18.893%
/model.19/cv2/conv/Conv: | █████████████ | 18.055%
/model.23/cv3.2/cv3.2.1/cv3.2.1.1/conv/Conv: | █████████████ | 17.915%
/model.22/m.0/m/m.1/cv2/conv/Conv: | █████████████ | 17.796%
/model.22/cv1/conv/Conv: | █████████████ | 17.777%
/model.23/cv4.2/cv4.2.1/conv/Conv: | █████████████ | 17.573%
/model.19/cv1/conv/Conv: | ████████████ | 17.116%
/model.17/conv/Conv: | ████████████ | 16.869%
/model.22/cv2/conv/Conv: | ████████████ | 16.750%
/model.23/cv2.2/cv2.2.1/conv/Conv: | ████████████ | 16.540%
/model.10/m/m.0/attn/proj/conv/Conv: | ████████████ | 16.491%
/model.23/cv2.2/cv2.2.0/conv/Conv: | ████████████ | 16.421%
/model.23/cv2.1/cv2.1.0/conv/Conv: | ████████████ | 16.205%
/model.23/cv4.2/cv4.2.0/conv/Conv: | ████████████ | 16.116%
/model.23/cv3.0/cv3.0.1/cv3.0.1.1/conv/Conv: | ███████████ | 15.400%
/model.22/m.0/m/m.0/cv2/conv/Conv: | ███████████ | 15.251%
/model.23/cv3.0/cv3.0.0/cv3.0.0.1/conv/Conv: | ███████████ | 14.851%
/model.10/m/m.0/attn/pe/conv/Conv: | ███████████ | 14.659%
/model.19/m.0/cv1/conv/Conv: | ██████████ | 14.289%
/model.22/m.0/m/m.1/cv1/conv/Conv: | █████████ | 13.038%
/model.16/m.0/cv2/conv/Conv: | █████████ | 12.941%
/model.22/m.0/m/m.0/cv1/conv/Conv: | █████████ | 12.791%
/model.23/cv4.2/cv4.2.2/Conv: | █████████ | 12.508%
/model.23/cv4.1/cv4.1.2/Conv: | █████████ | 12.226%
/model.13/cv1/conv/Conv: | ████████ | 11.821%
/model.13/cv2/conv/Conv: | ████████ | 11.612%
/model.13/m.0/cv1/conv/Conv: | ████████ | 11.515%
/model.10/m/m.0/attn/MatMul_1: | ████████ | 11.303%
/model.16/cv2/conv/Conv: | ████████ | 11.028%
/model.10/m/m.0/attn/qkv/conv/Conv: | ████████ | 10.951%
/model.10/cv1/conv/Conv: | ████████ | 10.755%
/model.23/cv2.0/cv2.0.0/conv/Conv: | ████████ | 10.684%
/model.22/m.0/cv1/conv/Conv: | ███████ | 10.164%
/model.10/m/m.0/ffn/ffn.0/conv/Conv: | ███████ | 9.968%
/model.16/m.0/cv1/conv/Conv: | ███████ | 9.656%
/model.23/cv4.0/cv4.0.2/Conv: | ███████ | 9.566%
/model.8/m.0/cv2/conv/Conv: | ███████ | 9.521%
/model.10/cv2/conv/Conv: | ██████ | 8.068%
/model.16/cv1/conv/Conv: | ██████ | 7.989%
/model.23/cv2.1/cv2.1.2/Conv: | ██████ | 7.969%
/model.8/m.0/cv3/conv/Conv: | ██████ | 7.725%
/model.23/cv3.0/cv3.0.0/cv3.0.0.0/conv/Conv: | █████ | 7.570%
/model.8/m.0/m/m.0/cv2/conv/Conv: | █████ | 7.339%
/model.8/m.0/m/m.1/cv2/conv/Conv: | █████ | 7.283%
/model.8/cv2/conv/Conv: | █████ | 7.092%
/model.10/m/m.0/attn/MatMul: | █████ | 6.654%
/model.8/cv1/conv/Conv: | █████ | 6.492%
/model.8/m.0/m/m.1/cv1/conv/Conv: | █████ | 6.451%
/model.23/cv2.0/cv2.0.2/Conv: | ████ | 5.990%
/model.23/cv2.2/cv2.2.2/Conv: | ████ | 5.902%
/model.6/m.0/m/m.0/cv2/conv/Conv: | ████ | 5.898%
/model.6/m.0/cv2/conv/Conv: | ████ | 5.881%
/model.6/m.0/cv3/conv/Conv: | ████ | 5.402%
/model.8/m.0/cv1/conv/Conv: | ████ | 5.210%
/model.23/cv3.2/cv3.2.2/Conv: | ████ | 5.126%
/model.6/cv1/conv/Conv: | ████ | 4.983%
/model.9/cv2/conv/Conv: | ███ | 4.616%
/model.9/cv1/conv/Conv: | ███ | 3.934%
/model.7/conv/Conv: | ███ | 3.906%
/model.3/conv/Conv: | ███ | 3.654%
/model.6/cv2/conv/Conv: | ██ | 3.429%
/model.8/m.0/m/m.0/cv1/conv/Conv: | ██ | 3.319%
/model.2/cv2/conv/Conv: | ██ | 3.220%
/model.6/m.0/m/m.1/cv1/conv/Conv: | ██ | 3.191%
/model.6/m.0/m/m.0/cv1/conv/Conv: | ██ | 3.157%
/model.4/cv1/conv/Conv: | ██ | 2.893%
/model.6/m.0/m/m.1/cv2/conv/Conv: | ██ | 2.792%
/model.6/m.0/cv1/conv/Conv: | ██ | 2.761%
/model.5/conv/Conv: | ██ | 2.629%
/model.4/cv2/conv/Conv: | ██ | 2.298%
/model.2/cv1/conv/Conv: | █ | 2.107%
/model.2/m.0/cv2/conv/Conv: | █ | 2.095%
/model.4/m.0/cv1/conv/Conv: | █ | 2.069%
/model.23/cv3.1/cv3.1.2/Conv: | █ | 1.744%
/model.1/conv/Conv: | █ | 1.631%
/model.2/m.0/cv1/conv/Conv: | █ | 1.583%
/model.4/m.0/cv2/conv/Conv: | █ | 1.126%
/model.23/cv3.0/cv3.0.2/Conv: | | 0.535%
/model.0/conv/Conv: | | 0.067%
Analysing Layerwise quantization error:: 100%|██████████| 98/98 [10:49<00:00, 6.63s/it]
Layer | NOISE:SIGNAL POWER RATIO
/model.9/cv2/conv/Conv: | ████████████████████ | 2.976%
/model.2/cv2/conv/Conv: | ███████████ | 1.610%
/model.3/conv/Conv: | ██████ | 0.854%
/model.2/cv1/conv/Conv: | ████ | 0.543%
/model.1/conv/Conv: | ███ | 0.487%
/model.8/cv1/conv/Conv: | ███ | 0.414%
/model.4/cv2/conv/Conv: | ███ | 0.397%
/model.0/conv/Conv: | ██ | 0.364%
/model.6/m.0/cv3/conv/Conv: | ██ | 0.230%
/model.5/conv/Conv: | █ | 0.181%
/model.2/m.0/cv2/conv/Conv: | █ | 0.144%
/model.13/cv2/conv/Conv: | █ | 0.140%
/model.2/m.0/cv1/conv/Conv: | █ | 0.138%
/model.4/cv1/conv/Conv: | █ | 0.129%
/model.16/cv2/conv/Conv: | █ | 0.122%
/model.23/cv4.2/cv4.2.0/conv/Conv: | █ | 0.120%
/model.4/m.0/cv1/conv/Conv: | █ | 0.107%
/model.23/cv4.1/cv4.1.0/conv/Conv: | █ | 0.096%
/model.19/cv2/conv/Conv: | █ | 0.078%
/model.23/cv2.2/cv2.2.2/Conv: | █ | 0.076%
/model.4/m.0/cv2/conv/Conv: | | 0.071%
/model.8/m.0/m/m.1/cv1/conv/Conv: | | 0.071%
/model.6/cv2/conv/Conv: | | 0.067%
/model.6/cv1/conv/Conv: | | 0.066%
/model.17/conv/Conv: | | 0.060%
/model.23/cv4.2/cv4.2.1/conv/Conv: | | 0.057%
/model.22/m.0/m/m.1/cv1/conv/Conv: | | 0.056%
/model.16/cv1/conv/Conv: | | 0.051%
/model.10/cv1/conv/Conv: | | 0.050%
/model.23/cv4.2/cv4.2.2/Conv: | | 0.046%
/model.22/cv2/conv/Conv: | | 0.044%
/model.7/conv/Conv: | | 0.043%
/model.10/m/m.0/attn/pe/conv/Conv: | | 0.043%
/model.10/cv2/conv/Conv: | | 0.037%
/model.19/cv1/conv/Conv: | | 0.037%
/model.8/cv2/conv/Conv: | | 0.036%
/model.13/cv1/conv/Conv: | | 0.036%
/model.6/m.0/m/m.1/cv1/conv/Conv: | | 0.033%
/model.22/m.0/cv3/conv/Conv: | | 0.031%
/model.19/m.0/cv1/conv/Conv: | | 0.027%
/model.23/cv3.2/cv3.2.0/cv3.2.0.1/conv/Conv: | | 0.026%
/model.8/m.0/cv1/conv/Conv: | | 0.025%
/model.19/m.0/cv2/conv/Conv: | | 0.025%
/model.8/m.0/cv3/conv/Conv: | | 0.024%
/model.10/m/m.0/attn/qkv/conv/Conv: | | 0.023%
/model.8/m.0/m/m.0/cv1/conv/Conv: | | 0.023%
/model.22/m.0/cv1/conv/Conv: | | 0.021%
/model.6/m.0/m/m.0/cv1/conv/Conv: | | 0.021%
/model.23/cv2.0/cv2.0.0/conv/Conv: | | 0.020%
/model.6/m.0/cv1/conv/Conv: | | 0.020%
/model.23/cv4.0/cv4.0.0/conv/Conv: | | 0.019%
/model.9/cv1/conv/Conv: | | 0.018%
/model.23/cv4.1/cv4.1.2/Conv: | | 0.018%
/model.23/cv2.1/cv2.1.1/conv/Conv: | | 0.018%
/model.13/m.0/cv1/conv/Conv: | | 0.016%
/model.23/cv2.1/cv2.1.0/conv/Conv: | | 0.016%
/model.23/cv4.1/cv4.1.1/conv/Conv: | | 0.016%
/model.16/m.0/cv2/conv/Conv: | | 0.015%
/model.10/m/m.0/attn/proj/conv/Conv: | | 0.013%
/model.23/cv3.1/cv3.1.1/cv3.1.1.1/conv/Conv: | | 0.013%
/model.8/m.0/m/m.0/cv2/conv/Conv: | | 0.013%
/model.16/m.0/cv1/conv/Conv: | | 0.012%
/model.23/cv2.2/cv2.2.0/conv/Conv: | | 0.011%
/model.20/conv/Conv: | | 0.011%
/model.22/m.0/m/m.0/cv1/conv/Conv: | | 0.011%
/model.23/cv3.2/cv3.2.1/cv3.2.1.1/conv/Conv: | | 0.011%
/model.8/m.0/m/m.1/cv2/conv/Conv: | | 0.010%
/model.23/cv2.0/cv2.0.2/Conv: | | 0.009%
/model.10/m/m.0/attn/MatMul: | | 0.009%
/model.22/cv1/conv/Conv: | | 0.009%
/model.13/m.0/cv2/conv/Conv: | | 0.008%
/model.23/cv2.2/cv2.2.1/conv/Conv: | | 0.008%
/model.23/cv2.1/cv2.1.2/Conv: | | 0.007%
/model.23/cv3.2/cv3.2.1/cv3.2.1.0/conv/Conv: | | 0.007%
/model.22/m.0/m/m.1/cv2/conv/Conv: | | 0.007%
/model.6/m.0/m/m.0/cv2/conv/Conv: | | 0.006%
/model.22/m.0/m/m.0/cv2/conv/Conv: | | 0.006%
/model.23/cv4.0/cv4.0.1/conv/Conv: | | 0.005%
/model.23/cv3.2/cv3.2.0/cv3.2.0.0/conv/Conv: | | 0.005%
/model.23/cv4.0/cv4.0.2/Conv: | | 0.004%
/model.6/m.0/m/m.1/cv2/conv/Conv: | | 0.004%
/model.23/cv3.0/cv3.0.0/cv3.0.0.1/conv/Conv: | | 0.004%
/model.10/m/m.0/ffn/ffn.1/conv/Conv: | | 0.003%
/model.23/cv3.2/cv3.2.2/Conv: | | 0.003%
/model.10/m/m.0/attn/MatMul_1: | | 0.002%
/model.10/m/m.0/ffn/ffn.0/conv/Conv: | | 0.002%
/model.23/cv3.1/cv3.1.0/cv3.1.0.1/conv/Conv: | | 0.002%
/model.23/cv2.0/cv2.0.1/conv/Conv: | | 0.002%
/model.23/cv3.1/cv3.1.1/cv3.1.1.0/conv/Conv: | | 0.001%
/model.23/cv3.0/cv3.0.2/Conv: | | 0.001%
/model.23/cv3.1/cv3.1.2/Conv: | | 0.001%
/model.23/cv3.0/cv3.0.1/cv3.0.1.0/conv/Conv: | | 0.001%
/model.23/cv3.1/cv3.1.0/cv3.1.0.0/conv/Conv: | | 0.001%
/model.23/cv3.0/cv3.0.0/cv3.0.0.0/conv/Conv: | | 0.000%
/model.6/m.0/cv2/conv/Conv: | | 0.000%
/model.23/cv3.0/cv3.0.1/cv3.0.1.1/conv/Conv: | | 0.000%
/model.8/m.0/cv2/conv/Conv: | | 0.000%
/model.22/m.0/cv2/conv/Conv: | | 0.000%
量化误差分析
在对8-bit量化应用量化感知训练后，在相同输入下，量化后的模型在 COCO 上的 Pose mAP50:95 提升至44.9%；同时输出层的累计误差大幅减少。相比8-bit后量化方式，量化感知训练后的8-bit量化模型可以在相同的推理速度下达到最高的量化精度。
模型部署
目标检测基类
前处理
ImagePreprocessor 类中封装了常用的图像前处理流程，包括 color conversion, crop, resize, normalization, quantize。