C3D是使用三维卷积进行视频动作识别的开荒者，论文链接：Learning Spatiotemporal Features with 3D Convolutional Networks。
本例程对MMAction的C3D_UCF101模型进行了移植，在相同的预处理流程下可以做到精度对齐。
- 支持BM1688/CV186X(SoC)、BM1684X(x86 PCIe、SoC)、BM1684(x86 PCIe、SoC、arm PCIe)
- 支持FP32、FP16(BM1688/BM1684X)、INT8模型编译和推理
- 支持基于BMCV和OpenCV预处理的C++推理
- 支持基于OpenCV预处理的Python推理
- 支持单batch和多batch模型推理
- 支持视频文件夹测试
建议使用TPU-MLIR编译BModel，Pytorch模型在编译前要导出成onnx模型。
本例程在scripts目录下提供了所有相关的模型和数据集的下载脚本download.sh，您也可以自己准备模型和数据集，并参考4. 模型转换进行模型转换。
如果您有自己训练的Pytorch C3D模型，您可以参考tools/c3d_transform.py，自行修改源模型路径和模型网络的层名，确保能够加载您的参数，以成功转换torchscript和onnx模型。同时，您需要准备用于测试的数据集，如果量化模型，还要准备用于量化的数据集。
# 安装unzip，若已安装请跳过，非ubuntu系统视情况使用yum或其他方式安装
sudo apt install unzip
chmod -R +x scripts/
./scripts/download.sh执行后，模型保存在models，数据集在datasets
下载的模型包括：
./models
├── BM1684
│ ├── c3d_fp32_1b.bmodel # 使用TPU-MLIR编译，用于BM1684的FP32 BModel，batch_size=1
│ ├── c3d_fp32_4b.bmodel # 使用TPU-MLIR编译，用于BM1684的FP32 BModel，batch_size=4
│ ├── c3d_int8_1b.bmodel # 使用TPU-MLIR编译，用于BM1684的INT8 BModel，batch_size=1
│ └── c3d_int8_4b.bmodel # 使用TPU-MLIR编译，用于BM1684的INT8 BModel，batch_size=4
├── BM1684X
│ ├── c3d_fp32_1b.bmodel # 使用TPU-MLIR编译，用于BM1684X的FP32 BModel，batch_size=1
│ ├── c3d_fp32_4b.bmodel # 使用TPU-MLIR编译，用于BM1684X的FP32 BModel，batch_size=4
│ ├── c3d_fp16_1b.bmodel # 使用TPU-MLIR编译，用于BM1684X的FP16 BModel，batch_size=1
│ ├── c3d_fp16_4b.bmodel # 使用TPU-MLIR编译，用于BM1684X的FP16 BModel，batch_size=4
│ ├── c3d_int8_1b.bmodel # 使用TPU-MLIR编译，用于BM1684X的INT8 BModel，batch_size=1
│ └── c3d_int8_4b.bmodel # 使用TPU-MLIR编译，用于BM1684X的INT8 BModel，batch_size=4
├── BM1688
│ ├── c3d_fp32_1b.bmodel # 使用TPU-MLIR编译，用于BM1688的FP32 BModel，batch_size=1，num_core=1
│ ├── c3d_fp32_4b.bmodel # 使用TPU-MLIR编译，用于BM1688的FP32 BModel，batch_size=4，num_core=1
│ ├── c3d_fp16_1b.bmodel # 使用TPU-MLIR编译，用于BM1688的FP16 BModel，batch_size=1，num_core=1
│ ├── c3d_fp16_4b.bmodel # 使用TPU-MLIR编译，用于BM1688的FP16 BModel，batch_size=4，num_core=1
│ ├── c3d_int8_1b.bmodel # 使用TPU-MLIR编译，用于BM1688的INT8 BModel，batch_size=1，num_core=1
│ ├── c3d_int8_4b.bmodel # 使用TPU-MLIR编译，用于BM1688的INT8 BModel，batch_size=4，num_core=1
│ ├── c3d_fp32_1b_2core.bmodel # 使用TPU-MLIR编译，用于BM1688的FP32 BModel，batch_size=1，num_core=2
│ ├── c3d_fp32_4b_2core.bmodel # 使用TPU-MLIR编译，用于BM1688的FP32 BModel，batch_size=4，num_core=2
│ ├── c3d_fp16_1b_2core.bmodel # 使用TPU-MLIR编译，用于BM1688的FP16 BModel，batch_size=1，num_core=2
│ ├── c3d_fp16_4b_2core.bmodel # 使用TPU-MLIR编译，用于BM1688的FP16 BModel，batch_size=4，num_core=2
│ ├── c3d_int8_1b_2core.bmodel # 使用TPU-MLIR编译，用于BM1688的INT8 BModel，batch_size=1，num_core=2
│ └── c3d_int8_4b_2core.bmodel # 使用TPU-MLIR编译，用于BM1688的INT8 BModel，batch_size=4，num_core=2
├── CV186X
│ ├── c3d_fp32_1b.bmodel # 使用TPU-MLIR编译，用于CV186X的FP32 BModel，batch_size=1
│ ├── c3d_fp32_4b.bmodel # 使用TPU-MLIR编译，用于CV186X的FP32 BModel，batch_size=4
│ ├── c3d_fp16_1b.bmodel # 使用TPU-MLIR编译，用于CV186X的FP16 BModel，batch_size=1
│ ├── c3d_fp16_4b.bmodel # 使用TPU-MLIR编译，用于CV186X的FP16 BModel，batch_size=4
│ ├── c3d_int8_1b.bmodel # 使用TPU-MLIR编译，用于CV186X的INT8 BModel，batch_size=1
│ └── c3d_int8_4b.bmodel # 使用TPU-MLIR编译，用于CV186X的INT8 BModel，batch_size=4
│── torch
│ └── c3d_ucf101.pt # trace后的torchscript模型
└── onnx
└── c3d_ucf101.onnx # 导出的onnx动态模型
下载的数据包括：
./datasets/UCF_test_01 #UCF101的一个测试子集。
导出的模型需要编译成BModel才能在SOPHON TPU上运行，如果使用下载好的BModel可跳过本节。建议使用TPU-MLIR编译BModel。
模型编译前需要安装TPU-MLIR，具体可参考TPU-MLIR环境搭建。安装好后需在TPU-MLIR环境中进入例程目录。使用TPU-MLIR将onnx模型编译为BModel，具体方法可参考《TPU-MLIR快速入门手册》的“3. 编译ONNX模型”(请从算能官网相应版本的SDK中获取)。
- 生成FP32 BModel
本例程在scripts目录下提供了TPU-MLIR编译FP32 BModel的脚本，请注意修改gen_fp32bmodel_mlir.sh中的onnx模型路径、生成模型目录和输入大小shapes等参数，并在执行时指定BModel运行的目标平台（支持BM1684/BM1684X/BM1688/CV186X），如：
./scripts/gen_fp32bmodel_mlir.sh bm1684 #bm1684x/bm1688/cv186x执行上述命令会在models/BM1684等文件夹下生成c3d_fp32_1b.bmodel等文件，即转换好的FP32 BModel。
- 生成FP16 BModel
本例程在scripts目录下提供了TPU-MLIR编译FP16 BModel的脚本，请注意修改gen_fp16bmodel_mlir.sh中的onnx模型路径、生成模型目录和输入大小shapes等参数，并在执行时指定BModel运行的目标平台（支持BM1684X/BM1688/CV186X），如：
./scripts/gen_fp16bmodel_mlir.sh bm1684x #bm1688/cv186x执行上述命令会在models/BM1684X/等文件夹下生成c3d_fp16_1b.bmodel等文件，即转换好的FP16 BModel。
- 生成INT8 BModel
本例程在scripts目录下提供了量化INT8 BModel的脚本，请注意修改gen_int8bmodel_mlir.sh中的onnx模型路径、生成模型目录和输入大小shapes等参数，在执行时输入BModel的目标平台（支持BM1684/BM1684X/BM1688/CV186X），如：
./scripts/gen_int8bmodel_mlir.sh bm1684 #bm1684x/bm1688/cv186x上述脚本会在models/BM1684等文件夹下生成c3d_int8_1b.bmodel等文件，即转换好的INT8 BModel。
如果您不使用本例程的数据集，本例程在tools目录下提供了准备npy数据的python脚本，用户可以根据脚本自己准备npy格式量化数据集。
cd tools
python3 c3d_npy.py --input_path ../datasets/UCF_test_01 #for tpu-mlir执行后，会在datasets目录下产生cali_set_npy文件夹，可以作为量化模型使用的数据集。
首先，参考C++例程或Python例程推理要测试的数据集，生成预测的json文件。
然后，使用tools目录下的eval_ucf.py脚本，将测试生成的json文件与测试集标签json文件进行对比，计算出准确率信息，命令如下：
# 请根据实际情况修改程序路径和json文件路径
python3 tools/eval_ucf.py --gt_path datasets/ground_truth.json --result_json cpp/c3d_bmcv/results/c3d_fp32_1b.bmodel_bmcv_cpp.json根据本例程提供的数据集，测试结果如下：
| 测试平台 | 测试程序 | 测试模型 | ACC |
|---|---|---|---|
| SE5-16 | c3d_opencv.py | c3d_fp32_1b.bmodel | 0.715 |
| SE5-16 | c3d_opencv.py | c3d_fp32_4b.bmodel | 0.715 |
| SE5-16 | c3d_opencv.py | c3d_int8_1b.bmodel | 0.712 |
| SE5-16 | c3d_opencv.py | c3d_int8_4b.bmodel | 0.712 |
| SE5-16 | c3d_opencv.soc | c3d_fp32_1b.bmodel | 0.715 |
| SE5-16 | c3d_opencv.soc | c3d_fp32_4b.bmodel | 0.715 |
| SE5-16 | c3d_opencv.soc | c3d_int8_1b.bmodel | 0.712 |
| SE5-16 | c3d_opencv.soc | c3d_int8_4b.bmodel | 0.712 |
| SE5-16 | c3d_bmcv.soc | c3d_fp32_1b.bmodel | 0.715 |
| SE5-16 | c3d_bmcv.soc | c3d_fp32_4b.bmodel | 0.715 |
| SE5-16 | c3d_bmcv.soc | c3d_int8_1b.bmodel | 0.710 |
| SE5-16 | c3d_bmcv.soc | c3d_int8_4b.bmodel | 0.710 |
| SE7-32 | c3d_opencv.py | c3d_fp32_1b.bmodel | 0.715 |
| SE7-32 | c3d_opencv.py | c3d_fp32_4b.bmodel | 0.715 |
| SE7-32 | c3d_opencv.py | c3d_fp16_1b.bmodel | 0.715 |
| SE7-32 | c3d_opencv.py | c3d_fp16_4b.bmodel | 0.715 |
| SE7-32 | c3d_opencv.py | c3d_int8_1b.bmodel | 0.715 |
| SE7-32 | c3d_opencv.py | c3d_int8_4b.bmodel | 0.715 |
| SE7-32 | c3d_opencv.soc | c3d_fp32_1b.bmodel | 0.715 |
| SE7-32 | c3d_opencv.soc | c3d_fp32_4b.bmodel | 0.715 |
| SE7-32 | c3d_opencv.soc | c3d_fp16_1b.bmodel | 0.715 |
| SE7-32 | c3d_opencv.soc | c3d_fp16_4b.bmodel | 0.715 |
| SE7-32 | c3d_opencv.soc | c3d_int8_1b.bmodel | 0.715 |
| SE7-32 | c3d_opencv.soc | c3d_int8_4b.bmodel | 0.715 |
| SE7-32 | c3d_bmcv.soc | c3d_fp32_1b.bmodel | 0.715 |
| SE7-32 | c3d_bmcv.soc | c3d_fp32_4b.bmodel | 0.715 |
| SE7-32 | c3d_bmcv.soc | c3d_fp16_1b.bmodel | 0.715 |
| SE7-32 | c3d_bmcv.soc | c3d_fp16_4b.bmodel | 0.715 |
| SE7-32 | c3d_bmcv.soc | c3d_int8_1b.bmodel | 0.712 |
| SE7-32 | c3d_bmcv.soc | c3d_int8_4b.bmodel | 0.712 |
| SE9-16 | c3d_opencv.py | c3d_fp32_1b.bmodel | 0.715 |
| SE9-16 | c3d_opencv.py | c3d_fp32_4b.bmodel | 0.715 |
| SE9-16 | c3d_opencv.py | c3d_fp16_1b.bmodel | 0.715 |
| SE9-16 | c3d_opencv.py | c3d_fp16_4b.bmodel | 0.715 |
| SE9-16 | c3d_opencv.py | c3d_int8_1b.bmodel | 0.712 |
| SE9-16 | c3d_opencv.py | c3d_int8_4b.bmodel | 0.712 |
| SE9-16 | c3d_opencv.soc | c3d_fp32_1b.bmodel | 0.715 |
| SE9-16 | c3d_opencv.soc | c3d_fp32_4b.bmodel | 0.715 |
| SE9-16 | c3d_opencv.soc | c3d_fp16_1b.bmodel | 0.715 |
| SE9-16 | c3d_opencv.soc | c3d_fp16_4b.bmodel | 0.715 |
| SE9-16 | c3d_opencv.soc | c3d_int8_1b.bmodel | 0.712 |
| SE9-16 | c3d_opencv.soc | c3d_int8_4b.bmodel | 0.712 |
| SE9-16 | c3d_bmcv.soc | c3d_fp32_1b.bmodel | 0.715 |
| SE9-16 | c3d_bmcv.soc | c3d_fp32_4b.bmodel | 0.715 |
| SE9-16 | c3d_bmcv.soc | c3d_fp16_1b.bmodel | 0.715 |
| SE9-16 | c3d_bmcv.soc | c3d_fp16_4b.bmodel | 0.715 |
| SE9-16 | c3d_bmcv.soc | c3d_int8_1b.bmodel | 0.715 |
| SE9-16 | c3d_bmcv.soc | c3d_int8_4b.bmodel | 0.715 |
| SE9-16 | c3d_opencv.py | c3d_fp32_1b_2core.bmodel | 0.715 |
| SE9-16 | c3d_opencv.py | c3d_fp32_4b_2core.bmodel | 0.715 |
| SE9-16 | c3d_opencv.py | c3d_fp16_1b_2core.bmodel | 0.715 |
| SE9-16 | c3d_opencv.py | c3d_fp16_4b_2core.bmodel | 0.715 |
| SE9-16 | c3d_opencv.py | c3d_int8_1b_2core.bmodel | 0.712 |
| SE9-16 | c3d_opencv.py | c3d_int8_4b_2core.bmodel | 0.712 |
| SE9-16 | c3d_opencv.soc | c3d_fp32_1b_2core.bmodel | 0.715 |
| SE9-16 | c3d_opencv.soc | c3d_fp32_4b_2core.bmodel | 0.715 |
| SE9-16 | c3d_opencv.soc | c3d_fp16_1b_2core.bmodel | 0.715 |
| SE9-16 | c3d_opencv.soc | c3d_fp16_4b_2core.bmodel | 0.715 |
| SE9-16 | c3d_opencv.soc | c3d_int8_1b_2core.bmodel | 0.712 |
| SE9-16 | c3d_opencv.soc | c3d_int8_4b_2core.bmodel | 0.712 |
| SE9-16 | c3d_bmcv.soc | c3d_fp32_1b_2core.bmodel | 0.715 |
| SE9-16 | c3d_bmcv.soc | c3d_fp32_4b_2core.bmodel | 0.715 |
| SE9-16 | c3d_bmcv.soc | c3d_fp16_1b_2core.bmodel | 0.715 |
| SE9-16 | c3d_bmcv.soc | c3d_fp16_4b_2core.bmodel | 0.715 |
| SE9-16 | c3d_bmcv.soc | c3d_int8_1b_2core.bmodel | 0.715 |
| SE9-16 | c3d_bmcv.soc | c3d_int8_4b_2core.bmodel | 0.715 |
| SE9-8 | c3d_opencv.py | c3d_fp32_1b.bmodel | 0.715 |
| SE9-8 | c3d_opencv.py | c3d_fp32_4b.bmodel | 0.715 |
| SE9-8 | c3d_opencv.py | c3d_fp16_1b.bmodel | 0.715 |
| SE9-8 | c3d_opencv.py | c3d_fp16_4b.bmodel | 0.715 |
| SE9-8 | c3d_opencv.py | c3d_int8_1b.bmodel | 0.712 |
| SE9-8 | c3d_opencv.py | c3d_int8_4b.bmodel | 0.712 |
| SE9-8 | c3d_opencv.soc | c3d_fp32_1b.bmodel | 0.715 |
| SE9-8 | c3d_opencv.soc | c3d_fp32_4b.bmodel | 0.715 |
| SE9-8 | c3d_opencv.soc | c3d_fp16_1b.bmodel | 0.715 |
| SE9-8 | c3d_opencv.soc | c3d_fp16_4b.bmodel | 0.715 |
| SE9-8 | c3d_opencv.soc | c3d_int8_1b.bmodel | 0.712 |
| SE9-8 | c3d_opencv.soc | c3d_int8_4b.bmodel | 0.712 |
| SE9-8 | c3d_bmcv.soc | c3d_fp32_1b.bmodel | 0.715 |
| SE9-8 | c3d_bmcv.soc | c3d_fp32_4b.bmodel | 0.715 |
| SE9-8 | c3d_bmcv.soc | c3d_fp16_1b.bmodel | 0.715 |
| SE9-8 | c3d_bmcv.soc | c3d_fp16_4b.bmodel | 0.715 |
| SE9-8 | c3d_bmcv.soc | c3d_int8_1b.bmodel | 0.715 |
| SE9-8 | c3d_bmcv.soc | c3d_int8_4b.bmodel | 0.715 |
测试说明：
- 由于sdk版本之间可能存在差异，实际运行结果与本表有<0.01的精度误差是正常的；
- 在搭载了相同TPU和SOPHONSDK的PCIe或SoC平台上，相同程序的精度一致，SE5系列对应BM1684，SE7系列对应BM1684X，SE9系列中SE9-16对应BM1688，SE9-8对应CV186X；
使用bmrt_test测试模型的理论性能：
# 请根据实际情况修改要测试的bmodel路径和devid参数
bmrt_test --bmodel models/BM1684/c3d_fp32_1b.bmodel测试结果中的calculate time就是模型推理的时间，多batch size模型应当除以相应的batch size才是理论推理时间。
测试各个模型的理论推理时间，结果如下：
| 测试模型 | calculate time(ms) |
|---|---|
| BM1684/c3d_fp32_1b.bmodel | 62.37 |
| BM1684/c3d_fp32_4b.bmodel | 50.10 |
| BM1684/c3d_int8_1b.bmodel | 28.25 |
| BM1684/c3d_int8_4b.bmodel | 7.39 |
| BM1684X/c3d_fp32_1b.bmodel | 79.05 |
| BM1684X/c3d_fp32_4b.bmodel | 73.64 |
| BM1684X/c3d_fp16_1b.bmodel | 9.50 |
| BM1684X/c3d_fp16_4b.bmodel | 7.11 |
| BM1684X/c3d_int8_1b.bmodel | 5.57 |
| BM1684X/c3d_int8_4b.bmodel | 4.41 |
| BM1688/c3d_fp32_1b.bmodel | 405.18 |
| BM1688/c3d_fp32_4b.bmodel | 387.96 |
| BM1688/c3d_fp16_1b.bmodel | 69.44 |
| BM1688/c3d_fp16_4b.bmodel | 62.81 |
| BM1688/c3d_int8_1b.bmodel | 25.90 |
| BM1688/c3d_int8_4b.bmodel | 22.57 |
| BM1688/c3d_fp32_1b_2core.bmodel | 404.29 |
| BM1688/c3d_fp32_4b_2core.bmodel | 389.69 |
| BM1688/c3d_fp16_1b_2core.bmodel | 55.28 |
| BM1688/c3d_fp16_4b_2core.bmodel | 48.73 |
| BM1688/c3d_int8_1b_2core.bmodel | 22.47 |
| BM1688/c3d_int8_4b_2core.bmodel | 19.40 |
| CV186X/c3d_fp32_1b.bmodel | 417.85 |
| CV186X/c3d_fp32_4b.bmodel | 394.11 |
| CV186X/c3d_fp16_1b.bmodel | 76.09 |
| CV186X/c3d_fp16_4b.bmodel | 65.99 |
| CV186X/c3d_int8_1b.bmodel | 32.57 |
| CV186X/c3d_int8_4b.bmodel | 27.78 |
测试说明：
- 性能测试结果具有一定的波动性；
calculate time已折算为每个视频平均推理时间；- SoC和PCIe的测试结果基本一致。
参考C++例程或Python例程运行程序，并查看统计的视频解码时间、预处理时间、推理时间、后处理时间。C++和Python例程打印的时间已经折算为单张图片的处理时间。
在不同的测试平台上，使用不同的例程、模型测试datasets/UCF_test_01，性能测试结果如下：
| 测试平台 | 测试程序 | 测试模型 | decode_time | preprocess_time | inference_time | postprocess_time |
|---|---|---|---|---|---|---|
| SE5-16 | c3d_opencv.py | c3d_fp32_1b.bmodel | 66.43 | 30.22 | 68.69 | 0.09 |
| SE5-16 | c3d_opencv.py | c3d_fp32_4b.bmodel | 67.00 | 37.55 | 56.48 | 0.03 |
| SE5-16 | c3d_opencv.py | c3d_int8_1b.bmodel | 66.39 | 30.39 | 34.65 | 0.09 |
| SE5-16 | c3d_opencv.py | c3d_int8_4b.bmodel | 67.18 | 37.69 | 13.71 | 0.03 |
| SE5-16 | c3d_opencv.soc | c3d_fp32_1b.bmodel | 71.78 | 26.17 | 62.32 | 0.01 |
| SE5-16 | c3d_opencv.soc | c3d_fp32_4b.bmodel | 71.94 | 25.91 | 50.09 | 0.00 |
| SE5-16 | c3d_opencv.soc | c3d_int8_1b.bmodel | 71.73 | 26.07 | 28.22 | 0.01 |
| SE5-16 | c3d_opencv.soc | c3d_int8_4b.bmodel | 71.67 | 25.80 | 7.39 | 0.00 |
| SE5-16 | c3d_bmcv.soc | c3d_fp32_1b.bmodel | 75.55 | 6.74 | 62.29 | 0.01 |
| SE5-16 | c3d_bmcv.soc | c3d_fp32_4b.bmodel | 74.63 | 6.62 | 50.08 | 0.00 |
| SE5-16 | c3d_bmcv.soc | c3d_int8_1b.bmodel | 74.72 | 6.72 | 28.21 | 0.01 |
| SE5-16 | c3d_bmcv.soc | c3d_int8_4b.bmodel | 74.97 | 6.57 | 7.38 | 0.00 |
| SE7-32 | c3d_opencv.py | c3d_fp32_1b.bmodel | 65.80 | 30.95 | 86.39 | 0.09 |
| SE7-32 | c3d_opencv.py | c3d_fp32_4b.bmodel | 67.21 | 38.68 | 80.74 | 0.03 |
| SE7-32 | c3d_opencv.py | c3d_fp16_1b.bmodel | 66.06 | 31.05 | 16.78 | 0.09 |
| SE7-32 | c3d_opencv.py | c3d_fp16_4b.bmodel | 67.21 | 38.67 | 14.18 | 0.03 |
| SE7-32 | c3d_opencv.py | c3d_int8_1b.bmodel | 65.83 | 30.88 | 12.88 | 0.09 |
| SE7-32 | c3d_opencv.py | c3d_int8_4b.bmodel | 67.07 | 38.60 | 11.53 | 0.03 |
| SE7-32 | c3d_opencv.soc | c3d_fp32_1b.bmodel | 71.89 | 26.43 | 79.06 | 0.01 |
| SE7-32 | c3d_opencv.soc | c3d_fp32_4b.bmodel | 72.32 | 26.08 | 73.65 | 0.00 |
| SE7-32 | c3d_opencv.soc | c3d_fp16_1b.bmodel | 71.86 | 26.39 | 9.48 | 0.01 |
| SE7-32 | c3d_opencv.soc | c3d_fp16_4b.bmodel | 72.34 | 26.15 | 7.11 | 0.00 |
| SE7-32 | c3d_opencv.soc | c3d_int8_1b.bmodel | 72.16 | 26.40 | 5.57 | 0.01 |
| SE7-32 | c3d_opencv.soc | c3d_int8_4b.bmodel | 72.36 | 26.14 | 4.40 | 0.01 |
| SE7-32 | c3d_bmcv.soc | c3d_fp32_1b.bmodel | 74.45 | 3.64 | 79.03 | 0.01 |
| SE7-32 | c3d_bmcv.soc | c3d_fp32_4b.bmodel | 75.02 | 3.48 | 73.63 | 0.00 |
| SE7-32 | c3d_bmcv.soc | c3d_fp16_1b.bmodel | 74.71 | 3.60 | 9.46 | 0.01 |
| SE7-32 | c3d_bmcv.soc | c3d_fp16_4b.bmodel | 74.66 | 3.49 | 7.10 | 0.00 |
| SE7-32 | c3d_bmcv.soc | c3d_int8_1b.bmodel | 74.84 | 3.62 | 5.52 | 0.01 |
| SE7-32 | c3d_bmcv.soc | c3d_int8_4b.bmodel | 75.16 | 3.49 | 4.41 | 0.00 |
| SE9-16 | c3d_opencv.py | c3d_fp32_1b.bmodel | 96.75 | 42.28 | 414.48 | 0.15 |
| SE9-16 | c3d_opencv.py | c3d_fp32_4b.bmodel | 98.52 | 52.92 | 397.24 | 0.05 |
| SE9-16 | c3d_opencv.py | c3d_fp16_1b.bmodel | 92.39 | 42.43 | 78.57 | 0.13 |
| SE9-16 | c3d_opencv.py | c3d_fp16_4b.bmodel | 95.02 | 52.87 | 72.27 | 0.05 |
| SE9-16 | c3d_opencv.py | c3d_int8_1b.bmodel | 93.03 | 42.33 | 34.95 | 0.13 |
| SE9-16 | c3d_opencv.py | c3d_int8_4b.bmodel | 94.30 | 53.22 | 32.14 | 0.05 |
| SE9-16 | c3d_opencv.soc | c3d_fp32_1b.bmodel | 122.06 | 30.70 | 405.25 | 0.02 |
| SE9-16 | c3d_opencv.soc | c3d_fp32_4b.bmodel | 122.44 | 30.67 | 388.04 | 0.01 |
| SE9-16 | c3d_opencv.soc | c3d_fp16_1b.bmodel | 122.15 | 30.77 | 69.35 | 0.02 |
| SE9-16 | c3d_opencv.soc | c3d_fp16_4b.bmodel | 121.63 | 30.65 | 62.87 | 0.01 |
| SE9-16 | c3d_opencv.soc | c3d_int8_1b.bmodel | 122.96 | 30.66 | 25.71 | 0.02 |
| SE9-16 | c3d_opencv.soc | c3d_int8_4b.bmodel | 122.21 | 30.44 | 22.66 | 0.01 |
| SE9-16 | c3d_bmcv.soc | c3d_fp32_1b.bmodel | 128.66 | 7.74 | 405.21 | 0.02 |
| SE9-16 | c3d_bmcv.soc | c3d_fp32_4b.bmodel | 127.97 | 7.67 | 388.03 | 0.01 |
| SE9-16 | c3d_bmcv.soc | c3d_fp16_1b.bmodel | 128.49 | 7.75 | 69.32 | 0.02 |
| SE9-16 | c3d_bmcv.soc | c3d_fp16_4b.bmodel | 128.36 | 7.58 | 62.86 | 0.01 |
| SE9-16 | c3d_bmcv.soc | c3d_int8_1b.bmodel | 128.36 | 7.73 | 25.67 | 0.02 |
| SE9-16 | c3d_bmcv.soc | c3d_int8_4b.bmodel | 129.14 | 7.62 | 22.64 | 0.01 |
| SE9-16 | c3d_opencv.py | c3d_fp32_1b_2core.bmodel | 93.07 | 42.35 | 413.64 | 0.14 |
| SE9-16 | c3d_opencv.py | c3d_fp32_4b_2core.bmodel | 94.06 | 52.93 | 397.59 | 0.05 |
| SE9-16 | c3d_opencv.py | c3d_fp16_1b_2core.bmodel | 92.29 | 42.29 | 64.51 | 0.13 |
| SE9-16 | c3d_opencv.py | c3d_fp16_4b_2core.bmodel | 93.87 | 53.11 | 58.81 | 0.05 |
| SE9-16 | c3d_opencv.py | c3d_int8_1b_2core.bmodel | 92.49 | 42.50 | 31.77 | 0.13 |
| SE9-16 | c3d_opencv.py | c3d_int8_4b_2core.bmodel | 93.98 | 52.95 | 28.65 | 0.05 |
| SE9-16 | c3d_opencv.soc | c3d_fp32_1b_2core.bmodel | 122.02 | 30.92 | 404.39 | 0.02 |
| SE9-16 | c3d_opencv.soc | c3d_fp32_4b_2core.bmodel | 122.63 | 30.58 | 388.57 | 0.01 |
| SE9-16 | c3d_opencv.soc | c3d_fp16_1b_2core.bmodel | 122.53 | 30.66 | 55.30 | 0.02 |
| SE9-16 | c3d_opencv.soc | c3d_fp16_4b_2core.bmodel | 122.32 | 30.46 | 48.80 | 0.01 |
| SE9-16 | c3d_opencv.soc | c3d_int8_1b_2core.bmodel | 122.52 | 30.69 | 22.54 | 0.02 |
| SE9-16 | c3d_opencv.soc | c3d_int8_4b_2core.bmodel | 122.31 | 30.63 | 19.48 | 0.01 |
| SE9-16 | c3d_bmcv.soc | c3d_fp32_1b_2core.bmodel | 127.54 | 7.87 | 404.36 | 0.02 |
| SE9-16 | c3d_bmcv.soc | c3d_fp32_4b_2core.bmodel | 128.10 | 7.66 | 388.56 | 0.01 |
| SE9-16 | c3d_bmcv.soc | c3d_fp16_1b_2core.bmodel | 131.29 | 7.90 | 55.29 | 0.02 |
| SE9-16 | c3d_bmcv.soc | c3d_fp16_4b_2core.bmodel | 130.37 | 7.57 | 48.79 | 0.01 |
| SE9-16 | c3d_bmcv.soc | c3d_int8_1b_2core.bmodel | 128.57 | 7.83 | 22.49 | 0.02 |
| SE9-16 | c3d_bmcv.soc | c3d_int8_4b_2core.bmodel | 127.93 | 7.50 | 19.46 | 0.01 |
| SE9-8 | c3d_opencv.py | c3d_fp32_1b.bmodel | 91.67 | 41.95 | 427.43 | 0.13 |
| SE9-8 | c3d_opencv.py | c3d_fp32_4b.bmodel | 93.11 | 50.04 | 403.67 | 0.05 |
| SE9-8 | c3d_opencv.py | c3d_fp16_1b.bmodel | 91.76 | 42.03 | 85.14 | 0.13 |
| SE9-8 | c3d_opencv.py | c3d_fp16_4b.bmodel | 88.07 | 49.75 | 75.09 | 0.05 |
| SE9-8 | c3d_opencv.py | c3d_int8_1b.bmodel | 86.82 | 42.07 | 41.89 | 0.13 |
| SE9-8 | c3d_opencv.py | c3d_int8_4b.bmodel | 87.39 | 49.91 | 37.04 | 0.04 |
| SE9-8 | c3d_opencv.soc | c3d_fp32_1b.bmodel | 120.19 | 33.83 | 418.01 | 0.02 |
| SE9-8 | c3d_opencv.soc | c3d_fp32_4b.bmodel | 119.16 | 33.51 | 394.04 | 0.01 |
| SE9-8 | c3d_opencv.soc | c3d_fp16_1b.bmodel | 119.90 | 33.60 | 75.82 | 0.02 |
| SE9-8 | c3d_opencv.soc | c3d_fp16_4b.bmodel | 118.36 | 33.43 | 66.01 | 0.01 |
| SE9-8 | c3d_opencv.soc | c3d_int8_1b.bmodel | 119.01 | 33.79 | 32.55 | 0.02 |
| SE9-8 | c3d_opencv.soc | c3d_int8_4b.bmodel | 118.10 | 33.40 | 27.83 | 0.01 |
| SE9-8 | c3d_bmcv.soc | c3d_fp32_1b.bmodel | 130.30 | 9.05 | 418.00 | 0.02 |
| SE9-8 | c3d_bmcv.soc | c3d_fp32_4b.bmodel | 130.70 | 8.84 | 394.03 | 0.01 |
| SE9-8 | c3d_bmcv.soc | c3d_fp16_1b.bmodel | 131.08 | 9.00 | 75.79 | 0.02 |
| SE9-8 | c3d_bmcv.soc | c3d_fp16_4b.bmodel | 128.82 | 8.75 | 66.03 | 0.01 |
| SE9-8 | c3d_bmcv.soc | c3d_int8_1b.bmodel | 130.24 | 8.95 | 32.53 | 0.02 |
| SE9-8 | c3d_bmcv.soc | c3d_int8_4b.bmodel | 128.00 | 8.81 | 27.79 | 0.01 |
测试说明：
- 时间单位均为毫秒(ms)，统计的时间均为平均每张图片处理的时间；
- 性能测试结果具有一定的波动性，建议多次测试取平均值；
- SE5-16/SE7-32的主控处理器均为8核[email protected]，SE9-16的主控处理器为8核[email protected]，SE9-8为6核[email protected]，PCIe上的性能由于处理器的不同可能存在较大差异；
- 图片分辨率对解码时间影响较大，推理结果对后处理时间影响较大，不同的测试图片可能存在较大差异，不同的阈值对后处理时间影响较大。
- C3D的后处理只有argmax，耗时很短，可以忽略。
请参考FAQ查看一些常见的问题与解答。