Inconsistent inference results between PyTorch and converted TensorRT model with Interpolate operator

[hongliyu0716]

Description:

I'm experiencing a discrepancy between the inference results of PyTorch model and the TensorRT model obtained by converting it using the torch2trt tool.

Reproduce

This issue can be reproduced by the following script:

import torch
from torch.nn import Module
from torch2trt import torch2trt


para_0 = torch.randn([1, 2, 4, 4], dtype=torch.float32).cuda()
class interpolate(Module):
    def forward(self, *args):
        return torch.nn.functional.interpolate(args[0], size=(4, 6),scale_factor=None,mode='bicubic',align_corners=True,)
model = interpolate().float().eval().cuda()
model_trt = torch2trt(model, [para_0])

output = model(para_0)
output_trt = model_trt(para_0)
print(torch.max(torch.abs(output - output_trt)))

Environment

• torch: 2.1.1
• torch2trt: 0.4.0
• tensorrt: 8.6.1