step 1:find your installed onnx_tf ops ( /site-packages/onnx_tf/handlers/backend/ ) step 2:copygrid_sample.pyto onnx_tf ops (/site-packages/onnx_tf/handlers/backend/grid_sample.py) grid_sample.pycode: from onnx_
Tensors and Dynamic neural networks in Python with strong GPU acceleration - ONNX Opset 16 GridSample Does Not Support 5D Volumetric Input Tensor · pytorch/pytorch@cb2bce9
```python # 输入图像:[1, 3, 256, 256] input_image = torch.randn(1, 3, 256, 256) # 生成平移网格(向右移动10%宽度) grid_x = torch.linspace(-1, 1, 256) + 0.1 grid_y = torch.linspace(-1, 1, 256) grid = torch.stack(torch.meshgrid(grid_x, grid_y), -1).unsqueeze(0) # ...
我试图通过ONNX (opset 11)将torch.nn.functional.grid_sample的模型从Pytorch (1.9)转换为使用INT8量化的INT8 (7)。有了它,转换到TensorRT (有和没有INT8量化)是成功的。没有INT8量化的Pytorch和TRT模型提供的结果接近相同的结果(MSE为e-10阶)。在我的示例中,绝对采样网格中的X坐标(在转换为grid_sample<...
问如何将grid_sample模型转换为INT8量化的TensorRT模型？EN当前CNN模型基本都是 float32，将其转换为 INT...
import torch from torch.nn import functional as F inp = torch.ones(1, 1, 4, 4) inp = torch.randint(1, 10, (1, 1, 4, 4)).float()# 目的是得到一个 长宽为20的tensorout_h = 20 out_w = 20# grid的生成方式等价于用mesh_gridnew_h = torch.linspace(-1, 1, out_h).view(-1,...
pytorch 如何为TensorRT推断优化grid_sample的自定义双线性采样替代方案？以下代码可用于将图像从Pytorch转换...
🚀 The feature, motivation and pitch #92209 I need to use the gridsample operator in my code. I can use onnx in Pytorch1.12.0 to do this successfully, but there are errors in infer, because 5D input is not supported, so I checked the update log for Pytorch 2.0, found that as of...
而TensorRT-LLM是在TensorRT基础上针对大模型进一步优化的加速推理库，它号称可以增加4倍的推理速度。
pull / linux-focal-py3.8-clang10-onnx / test (default, 2, 2, linux.2xlarge) (gh) ##[error]The operation was canceled. pull / linux-focal-rocm6.1-py3.8 / build (gh) ##[error]The operation was canceled. pull / linux-jammy-cuda11.8-cudnn8-py3.8-clang12 / build (gh) ##[error...