Pytorch version 0.4.0, python version 2.7 and cuda-8.0, cudnn-8.0-v6
it also happens with Pytorch version 0.4.1.
import torch.nn as nn
import torch
import numpy as np


extract_local_feature = nn.Conv2d(
    in_channels=256,
    out_channels=256,
    kernel_size=15,
    padding=7,
    bias=True
).cuda()


input = torch.Tensor(np.random.rand(1,256, 112, 112)).cuda()
import ipdb;

ipdb.set_trace()
out = extract_local_feature(input)

ipdb.set_trace()

the line out = extract_local_feature(input) gives me 3381MiB / 12196MiB memory usage.
This issues only happen with batch_size = 1
Could someone help me fix this?