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Update torch.mdx
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@tp-nan
tp-nan authored Jul 16, 2024
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6 changes: 3 additions & 3 deletions docs/backend-reference/torch.mdx
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Expand Up @@ -128,7 +128,7 @@ For onnx models, there are the following additional parameters:

| Parameter | Description | Note |
|-------------------------|-----------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| min/max | Minimum/maximum input of the model | In form, it can be `1`, `1x3x224x224`, or `1,1` (for multi-input networks). Sorted in lexicographic order;<br />When `instance_num > 1`, it can be multiple configurations separated by `;`. |
| min/max | Minimum/maximum input of the model | In form, it can be `1`, `1x3x224x224`, or `1,1` (for multi-input networks). <br />When `instance_num > 1`, it can be multiple configurations separated by `;`. |
| precision | Model precision | [fp32,fp16,int8,best]. The default value for versions <=0.3.1b1 is fp16, and for versions >0.3.1b1 it is [fp16(SM>6.1), fp32(SM<=6.1)]; If the precision is not supported, it will automatically degrade to a supported precision. |
| precision::fp32 | Set the precision of some layers to fp32 (overrides the precision setting) | Layer name(s) (can provide only part of the name), separated by commas. |
| precision::fp16 | Set the precision of some layers to fp16 (overrides the precision setting) | Layer name(s) (can provide only part of the name), separated by commas. |
Expand All @@ -155,8 +155,8 @@ See [example](https://github.com/torchpipe/torchpipe/tree/main/examples/int8).

| | Description | Note |
|--------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------|
| TASK_DATA_KEY | When the network has a single input and output, the type is at::Tensor/torch.Tensor. When the network has multiple inputs and outputs, the type is vector/List. | Sorted in lexicographic order |
| TASK_RESULT_KEY (Output) | The output type is the same as the input type, and the postprocessor can customize the output. | Sorted in lexicographic order |
| TASK_DATA_KEY | When the network has a single input and output, the type is at::Tensor/torch.Tensor. When the network has multiple inputs and outputs, the type is vector/List. | Sorted in lexicographic order for trt<=9 |
| TASK_RESULT_KEY (Output) | The output type is the same as the input type, and the postprocessor can customize the output. | Sorted in lexicographic order for trt<=9 |


### min()/max()
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