Nn.functional.interpolate is really slow

It seems nn.functional.interpolate is working on CPU instead of GPU.
The CPU occupancy is 100%, however the GPU occupancy is merely 11%.
Here are the parts my model forward codes, I wrote the nn.functional.interpolate into it.
Can the nn.functional.interpolate function be ran by cuda?

    def forward(self, x):
        x = nn.functional.interpolate(x, scale_factor=self.scale, mode='bicubic',align_corners=True)
        return x

:face_with_head_bandage::face_with_head_bandage::face_with_head_bandage:

:face_with_raised_eyebrow::face_with_raised_eyebrow:Did somebody also encounter this problem?

I cannot reproduce this issue.
Are you sure F.interpolate is using the CPUs?
This code snippet utilizes the GPU 100% and a single CPU code, which seems to be fine:

import torch
import torch.nn as nn
import torch.nn.functional as F

import time


class MyModel(nn.Module):
    def __init__(self, scale_factor=2):
        super(MyModel, self).__init__()
        self.scale_factor = scale_factor
        
    def forward(self, x):
        x = F.interpolate(x, scale_factor=self.scale_factor, mode='bicubic',align_corners=True)
        return x


model = MyModel()

b, c, h, w = 64, 3, 128, 128
x = torch.randn(b, c, h, w, device='cuda')

nb_iters = 100000

torch.cuda.synchronize()
t0 = time.perf_counter()

for _ in range(nb_iters):
    output = model(x)
    
torch.cuda.synchronize()
t1 = time.perf_counter()

print('Took {}s, {}s per iter'.format((t1 - t0), (t1 - t0)/nb_iters))

Could you try this code?

:face_with_head_bandage:Dear ptrblck. It was found that high CPU occupancy was caused by slow dataloader during training data preparing process instead of nn.functional.interpolate.
:sob:It was all my fault.
Thank you for your help sincerely!:smile_cat::zipper_mouth_face::zipper_mouth_face:

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