Sure. I have been trying to profile for a large number of filters of Convolution on CPUs and found that process gets killed on few Machines with 16, 32 G of RAM.
ExpEpoch = 30

input_dim = 224
batch_size = 1
kernel = 3
stride = 1
padding = 1
OutputLayers = [3, 6, 12, 16, 18, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 144, 160, 176, 192, 208, 224, 240, 256, 288, 320, 384, 432, 480, 512, 576, 640, 672, 720, 768, 896, 960]
twopair = []
input_channels = 3
for i in OutputLayers:
    for j in OutputLayers:
        twopair.append([i,j])

name =  "backupconv_" + platform.node() + "two.csv"
bfile = open(name,'a')
layer = 2

for i in range(len(twopair)):
    model = nn.Sequential(nn.Conv2d(input_channels, twopair[i][0], kernel, stride, padding, bias=False),
    nn.Conv2d(twopair[i][0], twopair[i][1], kernel, stride, padding, bias=False))
    time = []
    for j in range(ExpEpoch):
        x = torch.randn([batch_size, input_channels, input_dim, input_dim])
        with torch.autograd.profiler.profile() as prof:
            y = model(x)
        time.append(prof.self_cpu_time_total)
    mean_time = statistics.mean(time)
    vari_time = statistics.stdev(time)
    point = [layer, input_dim, kernel, stride, padding, input_channels, twopair[i][0], twopair[i][1],  mean_time, vari_time]
    for itr in point:
        writestring = writestring + str(itr) + ','
    writestring += '\n'
    bfile.write(writestring)