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import mmcv
import pytest
import torch
from mmseg.models.utils import InvertedResidual, InvertedResidualV3
def test_inv_residual():
with pytest.raises(AssertionError):
# test stride assertion.
InvertedResidual(32, 32, 3, 4)
# test default config with res connection.
# set expand_ratio = 4, stride = 1 and inp=oup.
inv_module = InvertedResidual(32, 32, 1, 4)
assert inv_module.use_res_connect
assert inv_module.conv[0].kernel_size == (1, 1)
assert inv_module.conv[0].padding == 0
assert inv_module.conv[1].kernel_size == (3, 3)
assert inv_module.conv[1].padding == 1
assert inv_module.conv[0].with_norm
assert inv_module.conv[1].with_norm
x = torch.rand(1, 32, 64, 64)
output = inv_module(x)
assert output.shape == (1, 32, 64, 64)
# test inv_residual module without res connection.
# set expand_ratio = 4, stride = 2.
inv_module = InvertedResidual(32, 32, 2, 4)
assert not inv_module.use_res_connect
assert inv_module.conv[0].kernel_size == (1, 1)
x = torch.rand(1, 32, 64, 64)
output = inv_module(x)
assert output.shape == (1, 32, 32, 32)
# test expand_ratio == 1
inv_module = InvertedResidual(32, 32, 1, 1)
assert inv_module.conv[0].kernel_size == (3, 3)
x = torch.rand(1, 32, 64, 64)
output = inv_module(x)
assert output.shape == (1, 32, 64, 64)
# test with checkpoint forward
inv_module = InvertedResidual(32, 32, 1, 1, with_cp=True)
assert inv_module.with_cp
x = torch.rand(1, 32, 64, 64, requires_grad=True)
output = inv_module(x)
assert output.shape == (1, 32, 64, 64)
def test_inv_residualv3():
with pytest.raises(AssertionError):
# test stride assertion.
InvertedResidualV3(32, 32, 16, stride=3)
with pytest.raises(AssertionError):
# test assertion.
InvertedResidualV3(32, 32, 16, with_expand_conv=False)
# test with se_cfg=None, with_expand_conv=False
inv_module = InvertedResidualV3(32, 32, 32, with_expand_conv=False)
assert inv_module.with_res_shortcut is True
assert inv_module.with_se is False
assert inv_module.with_expand_conv is False
assert not hasattr(inv_module, 'expand_conv')
assert isinstance(inv_module.depthwise_conv.conv, torch.nn.Conv2d)
assert inv_module.depthwise_conv.conv.kernel_size == (3, 3)
assert inv_module.depthwise_conv.conv.stride == (1, 1)
assert inv_module.depthwise_conv.conv.padding == (1, 1)
assert isinstance(inv_module.depthwise_conv.bn, torch.nn.BatchNorm2d)
assert isinstance(inv_module.depthwise_conv.activate, torch.nn.ReLU)
assert inv_module.linear_conv.conv.kernel_size == (1, 1)
assert inv_module.linear_conv.conv.stride == (1, 1)
assert inv_module.linear_conv.conv.padding == (0, 0)
assert isinstance(inv_module.linear_conv.bn, torch.nn.BatchNorm2d)
x = torch.rand(1, 32, 64, 64)
output = inv_module(x)
assert output.shape == (1, 32, 64, 64)
# test with se_cfg and with_expand_conv
se_cfg = dict(
channels=16,
ratio=4,
act_cfg=(dict(type='ReLU'),
dict(type='HSigmoid', bias=3.0, divisor=6.0)))
act_cfg = dict(type='HSwish')
inv_module = InvertedResidualV3(
32, 40, 16, 3, 2, se_cfg=se_cfg, act_cfg=act_cfg)
assert inv_module.with_res_shortcut is False
assert inv_module.with_se is True
assert inv_module.with_expand_conv is True
assert inv_module.expand_conv.conv.kernel_size == (1, 1)
assert inv_module.expand_conv.conv.stride == (1, 1)
assert inv_module.expand_conv.conv.padding == (0, 0)
assert isinstance(inv_module.expand_conv.activate, mmcv.cnn.HSwish)
assert isinstance(inv_module.depthwise_conv.conv,
mmcv.cnn.bricks.Conv2dAdaptivePadding)
assert inv_module.depthwise_conv.conv.kernel_size == (3, 3)
assert inv_module.depthwise_conv.conv.stride == (2, 2)
assert inv_module.depthwise_conv.conv.padding == (0, 0)
assert isinstance(inv_module.depthwise_conv.bn, torch.nn.BatchNorm2d)
assert isinstance(inv_module.depthwise_conv.activate, mmcv.cnn.HSwish)
assert inv_module.linear_conv.conv.kernel_size == (1, 1)
assert inv_module.linear_conv.conv.stride == (1, 1)
assert inv_module.linear_conv.conv.padding == (0, 0)
assert isinstance(inv_module.linear_conv.bn, torch.nn.BatchNorm2d)
x = torch.rand(1, 32, 64, 64)
output = inv_module(x)
assert output.shape == (1, 40, 32, 32)
# test with checkpoint forward
inv_module = InvertedResidualV3(
32, 40, 16, 3, 2, se_cfg=se_cfg, act_cfg=act_cfg, with_cp=True)
assert inv_module.with_cp
x = torch.randn(2, 32, 64, 64, requires_grad=True)
output = inv_module(x)
assert output.shape == (2, 40, 32, 32)
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