Spaces:
Running
on
L40S
Running
on
L40S
File size: 3,853 Bytes
d7e58f0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
import torch.nn as nn
from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
class Loss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, input, target):
input = input.view(-1)
target = target.view(-1)
return torch.mean(input - target)
class TestPSAMask:
@pytest.mark.parametrize('device', [
pytest.param(
'cuda',
marks=pytest.mark.skipif(
not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
pytest.param(
'mlu',
marks=pytest.mark.skipif(
not IS_MLU_AVAILABLE, reason='requires MLU support'))
])
def test_psa_mask_collect(self, device):
from mmcv.ops import PSAMask
test_loss = Loss()
input = np.fromfile(
'tests/data/for_psa_mask/psa_input.bin', dtype=np.float32)
output_collect = np.fromfile(
'tests/data/for_psa_mask/psa_output_collect.bin', dtype=np.float32)
input = input.reshape((4, 16, 8, 8))
output_collect = output_collect.reshape((4, 64, 8, 8))
label = torch.ones((4, 64, 8, 8))
input = torch.FloatTensor(input)
input.requires_grad = True
psamask_collect = PSAMask('collect', (4, 4))
# test collect cpu
test_output = psamask_collect(input)
loss = test_loss(test_output, label)
loss.backward()
test_output = test_output.detach().numpy()
assert np.allclose(test_output, output_collect)
assert test_output.shape == output_collect.shape
psamask_collect.to(device)
input = input.to(device)
label = label.to(device)
# test collect on device
test_output = psamask_collect(input)
loss = test_loss(test_output, label)
loss.backward()
test_output = test_output.detach().cpu().numpy()
assert np.allclose(test_output, output_collect)
assert test_output.shape == output_collect.shape
@pytest.mark.parametrize('device', [
pytest.param(
'cuda',
marks=pytest.mark.skipif(
not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
pytest.param(
'mlu',
marks=pytest.mark.skipif(
not IS_MLU_AVAILABLE, reason='requires MLU support'))
])
def test_psa_mask_distribute(self, device):
from mmcv.ops import PSAMask
test_loss = Loss()
input = np.fromfile(
'tests/data/for_psa_mask/psa_input.bin', dtype=np.float32)
output_distribute = np.fromfile(
'tests/data/for_psa_mask/psa_output_distribute.bin',
dtype=np.float32)
input = input.reshape((4, 16, 8, 8))
output_distribute = output_distribute.reshape((4, 64, 8, 8))
label = torch.ones((4, 64, 8, 8))
input = torch.FloatTensor(input)
input.requires_grad = True
psamask_distribute = PSAMask('distribute', (4, 4))
# test distribute cpu
test_output = psamask_distribute(input)
loss = test_loss(test_output, label)
loss.backward()
test_output = test_output.detach().numpy()
assert np.allclose(test_output, output_distribute)
assert test_output.shape == output_distribute.shape
psamask_distribute.to(device)
input = input.to(device)
label = label.to(device)
# test distribute on device
test_output = psamask_distribute(input)
loss = test_loss(test_output, label)
loss.backward()
test_output = test_output.detach().cpu().numpy()
assert np.allclose(test_output, output_distribute)
assert test_output.shape == output_distribute.shape
|