mirrors
/
mmsegmentation
mirror of https://github.com/open-mmlab/mmsegmentation.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

[Fix] The interface multiscale_output is defined but not used (#830) 

* Add interface multiscale_output

* Add space between args and their types

* Fix default value
pull/790/merge
Guangchen Lin 2021-08-27 05:53:55 -05:00 committed by GitHub
parent 4ca42a3ee4
commit aa438f5c95
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 129 additions and 23 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

52
mmseg/models/backbones/hrnet.py
View File

@ -218,26 +218,41 @@ class HRModule(BaseModule):
class HRNet(BaseModule):
"""HRNet backbone.
High-Resolution Representations for Labeling Pixels and Regions
arXiv: https://arxiv.org/abs/1904.04514
`High-Resolution Representations for Labeling Pixels and Regions
arXiv: <https://arxiv.org/abs/1904.04514>`_.
Args:
extra (dict): detailed configuration for each stage of HRNet.
extra (dict): Detailed configuration for each stage of HRNet.
There must be 4 stages, the configuration for each stage must have
5 keys:
- num_modules (int): The number of HRModule in this stage.
- num_branches (int): The number of branches in the HRModule.
- block (str): The type of convolution block.
- num_blocks (tuple): The number of blocks in each branch.
The length must be equal to num_branches.
- num_channels (tuple): The number of channels in each branch.
The length must be equal to num_branches.
in_channels (int): Number of input image channels. Normally 3.
conv_cfg (dict): dictionary to construct and config conv layer.
norm_cfg (dict): dictionary to construct and config norm layer.
conv_cfg (dict): Dictionary to construct and config conv layer.
Default: None.
norm_cfg (dict): Dictionary to construct and config norm layer.
Use `BN` by default.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only.
and its variants only. Default: False.
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
memory while slowing down the training speed.
memory while slowing down the training speed. Default: False.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters. Default: -1.
zero_init_residual (bool): whether to use zero init for last norm layer
in resblocks to let them behave as identity.
pretrained (str, optional): model pretrained path. Default: None
zero_init_residual (bool): Whether to use zero init for last norm layer
in resblocks to let them behave as identity. Default: False.
multiscale_output (bool): Whether to output multi-level features
produced by multiple branches. If False, only the first level
feature will be output. Default: True.
pretrained (str, optional): Model pretrained path. Default: None.
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None
Default: None.
Example:
>>> from mmseg.models import HRNet
@ -290,6 +305,7 @@ class HRNet(BaseModule):
with_cp=False,
frozen_stages=-1,
zero_init_residual=False,
multiscale_output=True,
pretrained=None,
init_cfg=None):
super(HRNet, self).__init__(init_cfg)
@ -299,7 +315,7 @@ class HRNet(BaseModule):
assert not (init_cfg and pretrained), \
'init_cfg and pretrained cannot be setting at the same time'
if isinstance(pretrained, str):
warnings.warn('DeprecationWarning: pretrained is a deprecated, '
warnings.warn('DeprecationWarning: pretrained is deprecated, '
'please use "init_cfg" instead')
self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
elif pretrained is None:
@ -314,6 +330,16 @@ class HRNet(BaseModule):
else:
raise TypeError('pretrained must be a str or None')
# Assert configurations of 4 stages are in extra
assert 'stage1' in extra and 'stage2' in extra \
and 'stage3' in extra and 'stage4' in extra
# Assert whether the length of `num_blocks` and `num_channels` are
# equal to `num_branches`
for i in range(4):
cfg = extra[f'stage{i + 1}']
assert len(cfg['num_blocks']) == cfg['num_branches'] and \
len(cfg['num_channels']) == cfg['num_branches']
self.extra = extra
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
@ -391,7 +417,7 @@ class HRNet(BaseModule):
self.transition3 = self._make_transition_layer(pre_stage_channels,
num_channels)
self.stage4, pre_stage_channels = self._make_stage(
self.stage4_cfg, num_channels)
self.stage4_cfg, num_channels, multiscale_output=multiscale_output)
self._freeze_stages()

100
tests/test_models/test_backbones/test_hrnet.py
View File

@ -1,12 +1,59 @@
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmseg.models.backbones import HRNet
from mmseg.models.backbones.hrnet import HRModule, HRNet
from mmseg.models.backbones.resnet import BasicBlock, Bottleneck
@pytest.mark.parametrize('block', [BasicBlock, Bottleneck])
def test_hrmodule(block):
# Test multiscale forward
num_channles = (32, 64)
in_channels = [c * block.expansion for c in num_channles]
hrmodule = HRModule(
num_branches=2,
blocks=block,
in_channels=in_channels,
num_blocks=(4, 4),
num_channels=num_channles,
)
feats = [
torch.randn(1, in_channels[0], 64, 64),
torch.randn(1, in_channels[1], 32, 32)
]
feats = hrmodule(feats)
assert len(feats) == 2
assert feats[0].shape == torch.Size([1, in_channels[0], 64, 64])
assert feats[1].shape == torch.Size([1, in_channels[1], 32, 32])
# Test single scale forward
num_channles = (32, 64)
in_channels = [c * block.expansion for c in num_channles]
hrmodule = HRModule(
num_branches=2,
blocks=block,
in_channels=in_channels,
num_blocks=(4, 4),
num_channels=num_channles,
multiscale_output=False,
)
feats = [
torch.randn(1, in_channels[0], 64, 64),
torch.randn(1, in_channels[1], 32, 32)
]
feats = hrmodule(feats)
assert len(feats) == 1
assert feats[0].shape == torch.Size([1, in_channels[0], 64, 64])
def test_hrnet_backbone():
# Test HRNET with two stage frozen
# only have 3 stages
extra = dict(
stage1=dict(
num_modules=1,
@ -25,13 +72,46 @@ def test_hrnet_backbone():
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256)))
num_channels=(32, 64, 128)))
with pytest.raises(AssertionError):
# HRNet now only support 4 stages
HRNet(extra=extra)
extra['stage4'] = dict(
num_modules=3,
num_branches=3, # should be 4
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))
with pytest.raises(AssertionError):
# len(num_blocks) should equal num_branches
HRNet(extra=extra)
extra['stage4']['num_branches'] = 4
# Test hrnetv2p_w32
model = HRNet(extra=extra)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 256, 256)
feats = model(imgs)
assert len(feats) == 4
assert feats[0].shape == torch.Size([1, 32, 64, 64])
assert feats[3].shape == torch.Size([1, 256, 8, 8])
# Test single scale output
model = HRNet(extra=extra, multiscale_output=False)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 256, 256)
feats = model(imgs)
assert len(feats) == 1
assert feats[0].shape == torch.Size([1, 32, 64, 64])
# Test HRNET with two stage frozen
frozen_stages = 2
model = HRNet(extra, frozen_stages=frozen_stages)
model.init_weights()