TNN currently support main-stream CNN networks：
- Classical CNN: Vgg AlexNet GoogleNet(v1,v2,v3)
- Practical CNN: ResNet DenseNet SENet
- Light-weight CNN: SqueezeNet MobileNet(v1,v2,v3) ShuffleNet(v1,v2) MNasNet
- Detection: Mtcnn-v2
- Detection: Vgg-ssd SqueezeNet-ssd MobileNetv2-SSDLite ...
- Detection: Yolo-v2 MobileNet-YOLOV3 ...
- Segmentation: FCN PSPNet
| model name | onnx2tnn | cpu | armv7 | armv8 | opencl | metal |
|---|---|---|---|---|---|---|
| AlexNet | yes | yes | ||||
| DenseNet(121) | yes | yes | ||||
| FCN | Yes | yes | ||||
| GoogleNet-v1 | yes | yes | ||||
| GoogleNet-v2 | yes | yes | ||||
| GoogleNet-v3(inception) | yes | yes | ||||
| MnasNet | yes | yes | ||||
| MobileNet-v1-ssd(caffe) | yes | yes | - | - | - | - |
| MobileNet-v1-ssd(pytorch) | yes | yes | ||||
| MobileNet-v2-SSDLite | yes | yes | ||||
| MobileNet-yolov3 | ? | ? | ||||
| MobileNet-v1 | yes | yes | ||||
| MobileNet-v2 | yes | yes | ||||
| MobileNet-v3(small,large) | yes | yes | ||||
| Mtcnn-v2 | yes | yes | ||||
| PSPNet | yes | yes | ||||
| ResNet50 | yes | yes | ||||
| SENet(154) | yes | yes | ||||
| ShuffleNet-v1 | yes | yes | ||||
| ShuffleNet-v2 | yes | yes | ||||
| SqueezeNet-ssd | No | - | - | - | - | - |
| SqueezeNet-v1 | yes | yes | ||||
| UNet | yes | yes | ||||
| Vgg-ssd | yes | yes | ||||
| Vgg16 | yes | yes | ||||
| Yolo-v3-tiny | yes | yes | ||||
| Yolo-v2 | ? | ? | ||||
| Yolo-v2-tiny | yes | yes | ||||
| Yolo-v3 | yes | yes |
- Regarding the upsample calculation of upsample, when the parameter mode == "bilinear" or mode == "linear", the onnx model exported by pytorch has some issues, and the calculation results of pytorch and onnx are not aligned. This is a bug of onnx itself, which deserves special attention. But don't worry about this problem. After converting the converted ONNX model to TNN, we ensure that the calculation results of TNN and Pytorch are aligned. In our testing, FCN and PSPNet have such problems.
- The "?" In the above table means unknown. Since the corresponding type of the model cannot be found, the compatibility of the model cannot be tested now.
| TNN Operators | ONNX Operators | Naive | armv7 | armv8 | opencl | metal | npu |
|---|---|---|---|---|---|---|---|
| Abs | Abs | yes | yes | yes | yes | yes | yes |
| Acos | Acos | yes | yes | yes | yes | ||
| Add | Add | yes | yes | yes | yes | yes | yes |
| Asin | Asin | yes | yes | yes | yes | ||
| Atan | Atan | yes | yes | yes | |||
| BatchNormCxx | BatchNormalization | yes | yes | yes | yes | yes | |
| Cast | Cast | ||||||
| Ceil | Ceil | yes | yes | ||||
| Clip | Clip | yes | yes | yes | yes | yes | |
| Concat | Concat | yes | yes | yes | yes | yes | yes |
| Const | Constant | ||||||
| Convolution | Conv | yes | yes | yes | yes | yes | yes |
| Convolution(depthwise) | Conv | yes | yes | yes | yes | yes | yes |
| Convolution(group) | Conv | yes | yes | yes | yes | yes | yes |
| Cos | Cos | yes | yes | yes | yes | ||
| Deconvolution | ConvTranspose | yes | yes | yes | yes | yes | |
| Deconvolution(depthwise) | ConvTranspose | yes | yes | yes | yes | yes | |
| Deconvolution(group) | ConvTranspose | yes | yes | yes | yes | yes | |
| DepthToSpace | DepthToSpace | ||||||
| DetectionOutput | DectectionOutput(custom operator) | yes | |||||
| Div | Div | yes | yes | yes | yes | yes | yes |
| Dropout | Dropout | ||||||
| Elu | Elu | yes | yes | yes | yes | yes | yes |
| Exp | Exp | yes | yes | yes | yes | yes | yes |
| Flatten | Flatten | ||||||
| Flatten | Shape+Gather+Constant+Unsqueeze+Concat+Reshape | ||||||
| Floor | Floor | yes | yes | yes | |||
| Gather | Gather | ||||||
| HardSigmoid | HardSigmoid | yes | yes | yes | yes | yes | |
| HardSwish | Add + Clip + Div + Mul | yes | yes | yes | yes | yes | |
| HardSwish | Add + Clip + Mul + Div | yes | yes | yes | yes | yes | |
| HardSwish | HardSigmoid + Mul | yes | yes | yes | yes | yes | |
| InnerProduct | Gemm | yes | yes | yes | yes | yes | yes |
| InstBatchNormCxx | InstanceNormalization | yes | yes | yes | yes | yes | yes |
| LRN | LRN | yes | |||||
| Log | Log | yes | yes | yes | yes | ||
| LogSigmoid | Sigmoid + Log | yes | yes | yes | yes | yes | |
| MatMul | Matmul | ||||||
| Max | Max | yes | yes | yes | yes | yes | yes |
| Min | Min | yes | yes | yes | yes | yes | yes |
| Mul | Mul | yes | yes | yes | yes | yes | yes |
| Neg | Neg | yes | yes | yes | yes | ||
| Normalize | ReduceL2+Clip+Shape+Expand+Div | yes | yes | yes | yes | yes | |
| Normalize | Reduce + Clip + Expand + Div | yes | yes | yes | yes | yes | |
| Normalize | Mul(square)+Reduce+Max+Sqrt+Mul | yes | yes | yes | yes | yes | |
| PRelu | LeakyRelu / PRelu | yes | yes | yes | yes | yes | |
| Pad | Pad | yes | yes | yes | yes | yes | |
| Permute | Transpose | yes | yes | yes | yes | yes | |
| Pooling (Avg) | AveragePool | yes | yes | yes | yes | yes | yes |
| Pooling (GlobalAverage) | GlobalAveragePool | yes | yes | yes | yes | yes | yes |
| Pooling (GlobalMax) | GlobalMaxPool | yes | yes | yes | yes | yes | yes |
| Pooling (Max) | MaxPool | yes | yes | yes | yes | yes | yes |
| Power | Pow | yes | yes | yes | yes | yes | |
| PriorBox | PriorBox(custom operator) | yes | yes | ||||
| Reciprocal | Reciprocal | yes | yes | yes | |||
| ReduceL1 | ReduceL1 | yes | |||||
| ReduceL2 | ReduceL2 | yes | yes | ||||
| ReduceLogSum | ReduceLogSum | yes | yes | ||||
| ReduceLogSumExp | ReduceLogSumExp | yes | yes | ||||
| ReduceMax | ReduceMax | yes | yes | yes | |||
| ReduceMean | ReduceMean | yes | yes | yes | yes | yes | |
| ReduceMin | ReduceMin | yes | yes | ||||
| ReduceProd | ReduceProd | yes | yes | ||||
| ReduceSum | ReduceSum | yes | yes | yes | yes | yes | |
| ReduceSumSquare | ReduceSumSquare | yes | yes | ||||
| Relu | Relu | yes | yes | yes | yes | yes | yes |
| Relu6 | Clip | yes | yes | yes | yes | yes | yes |
| Reorg | DepthToSpace | yes | yes | ||||
| Repeat | Tile | ||||||
| Reshape | Reshape | yes | yes | yes | yes | yes | yes |
| Selu | Selu | yes | yes | yes | yes | ||
| ShuffleChannel | Reshape + Transpose + Reshape | yes | yes | yes | yes | yes | yes |
| Sigmoid | Sigmoid | yes | yes | yes | yes | yes | yes |
| Sign | Sign | yes | yes | yes | yes | yes | |
| SignedMul | Sub+Sign+Add+Div+Gather+Slice+Mul | ||||||
| Sin | Sin | yes | yes | yes | yes | ||
| Slice(StrideSlice) | Slice | yes | yes | yes | yes | yes | yes |
| Softmax | Softmax | yes | yes | yes | yes | yes | yes |
| Softplus | Softplus | yes | yes | yes | yes | yes | yes |
| Split | Split | yes | yes | ||||
| Sqrt | Sqrt | yes | yes | yes | yes | yes | |
| Squeeze | Squeeze | ||||||
| Sub | Sub | yes | yes | yes | yes | yes | yes |
| Sum | |||||||
| Tan | Tan | yes | yes | yes | yes | ||
| Tanh | Tanh | yes | yes | yes | yes | yes | yes |
| Unsqueeze | Unsqueeze | no | |||||
| Upsample | Upsample / Resize | yes | yes | yes | yes | yes | yes |
- In the above table, the TNN HardSwish operator maps to "Add + Clip + Div + Mul" in ONNX, which means that the four operators of ONNX are combined into the HardSwsh operator in TNN. The "+" symbol in the table represents the combination of operators. This applies to other similar operators.
- In the table above, the PRelu operator of TNN maps to "LeakyRelu / PRelu" in ONNX, which means that the RRelu operator in TNN supports both PRelu and LeakyRelu operators in ONNX.
| device | support |
|---|---|
| ARMv7 | Yes |
| ARMv8 | Yes |
| OpenCL | Yes |
| Metal | Yes |