Shufflenet_v2_x1_0 on TesorRT7.0, F32 and FP16 Inference results are quite different

Description

Environment

TensorRT Version: 7.0
GPU Type: Tesla T4
Nvidia Driver Version: 440.33.01
CUDA Version: 10.2
CUDNN Version: 7.6.5.32
Operating System + Version: ubuntu18.04 + 4.15.0-101-generic
Python Version (if applicable): 3.7.3
PyTorch Version (if applicable): 1.2.0

1、 Generate ONNX
import torch
import torchvision.models as models
model = models.shufflenet_v2_x1_0(pretrained=True).cuda()
model.eval()
dummy_input = torch.randn(1,3,224,224).cuda()
input_names = ["input"]
output_names = ["output"]
torch.onnx.export(model, dummy_input, "shufflenet_v2_x1_0.onnx", verbose=False, opset_version=9,input_names=input_names, output_names=output_names)
2、pytorch onnx to tensorrt
    fp32
    trtexec --onnx=shufflenet_v2_x1_0.onnx --saveEngine=32.trt
    fp16
   trtexec --onnx=shufflenet_v2_x1_0.onnx --fp16 --saveEngine=16.trt
3、interfence
   trtexec  --loadEngine=32.trt  --exportOutput=result_32.trt
   trtexec  --loadEngine=16.trt  --exportOutput=result_16.trt
4、compare resutl
    It's not a difference of decimal places
1 Like

Issue is fixed and should be available in next TRT release.
Request you to please stay tuned for TRT release announcement.

Thanks

Thanks for your reply. When will the next version be released?

I don’t have info regarding the exact release dates.
Please stay tuned for TRT announcement.

Thanks

thank you