init
This commit is contained in:
esenke
642
finetune/mmseg/models/backbones/hrnet.py
Normal file
642
finetune/mmseg/models/backbones/hrnet.py
Normal file
@@ -0,0 +1,642 @@
|
||||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
import warnings
|
||||
|
||||
import torch.nn as nn
|
||||
from mmcv.cnn import build_conv_layer, build_norm_layer
|
||||
from mmengine.model import BaseModule, ModuleList, Sequential
|
||||
from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm
|
||||
|
||||
from mmseg.registry import MODELS
|
||||
from ..utils import Upsample, resize
|
||||
from .resnet import BasicBlock, Bottleneck
|
||||
|
||||
|
||||
class HRModule(BaseModule):
|
||||
"""High-Resolution Module for HRNet.
|
||||
|
||||
In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange
|
||||
is in this module.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
num_branches,
|
||||
blocks,
|
||||
num_blocks,
|
||||
in_channels,
|
||||
num_channels,
|
||||
multiscale_output=True,
|
||||
with_cp=False,
|
||||
conv_cfg=None,
|
||||
norm_cfg=dict(type='BN', requires_grad=True),
|
||||
block_init_cfg=None,
|
||||
init_cfg=None):
|
||||
super().__init__(init_cfg)
|
||||
self.block_init_cfg = block_init_cfg
|
||||
self._check_branches(num_branches, num_blocks, in_channels,
|
||||
num_channels)
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.num_branches = num_branches
|
||||
|
||||
self.multiscale_output = multiscale_output
|
||||
self.norm_cfg = norm_cfg
|
||||
self.conv_cfg = conv_cfg
|
||||
self.with_cp = with_cp
|
||||
self.branches = self._make_branches(num_branches, blocks, num_blocks,
|
||||
num_channels)
|
||||
self.fuse_layers = self._make_fuse_layers()
|
||||
self.relu = nn.ReLU(inplace=False)
|
||||
|
||||
def _check_branches(self, num_branches, num_blocks, in_channels,
|
||||
num_channels):
|
||||
"""Check branches configuration."""
|
||||
if num_branches != len(num_blocks):
|
||||
error_msg = f'NUM_BRANCHES({num_branches}) <> NUM_BLOCKS(' \
|
||||
f'{len(num_blocks)})'
|
||||
raise ValueError(error_msg)
|
||||
|
||||
if num_branches != len(num_channels):
|
||||
error_msg = f'NUM_BRANCHES({num_branches}) <> NUM_CHANNELS(' \
|
||||
f'{len(num_channels)})'
|
||||
raise ValueError(error_msg)
|
||||
|
||||
if num_branches != len(in_channels):
|
||||
error_msg = f'NUM_BRANCHES({num_branches}) <> NUM_INCHANNELS(' \
|
||||
f'{len(in_channels)})'
|
||||
raise ValueError(error_msg)
|
||||
|
||||
def _make_one_branch(self,
|
||||
branch_index,
|
||||
block,
|
||||
num_blocks,
|
||||
num_channels,
|
||||
stride=1):
|
||||
"""Build one branch."""
|
||||
downsample = None
|
||||
if stride != 1 or \
|
||||
self.in_channels[branch_index] != \
|
||||
num_channels[branch_index] * block.expansion:
|
||||
downsample = nn.Sequential(
|
||||
build_conv_layer(
|
||||
self.conv_cfg,
|
||||
self.in_channels[branch_index],
|
||||
num_channels[branch_index] * block.expansion,
|
||||
kernel_size=1,
|
||||
stride=stride,
|
||||
bias=False),
|
||||
build_norm_layer(self.norm_cfg, num_channels[branch_index] *
|
||||
block.expansion)[1])
|
||||
|
||||
layers = []
|
||||
layers.append(
|
||||
block(
|
||||
self.in_channels[branch_index],
|
||||
num_channels[branch_index],
|
||||
stride,
|
||||
downsample=downsample,
|
||||
with_cp=self.with_cp,
|
||||
norm_cfg=self.norm_cfg,
|
||||
conv_cfg=self.conv_cfg,
|
||||
init_cfg=self.block_init_cfg))
|
||||
self.in_channels[branch_index] = \
|
||||
num_channels[branch_index] * block.expansion
|
||||
for i in range(1, num_blocks[branch_index]):
|
||||
layers.append(
|
||||
block(
|
||||
self.in_channels[branch_index],
|
||||
num_channels[branch_index],
|
||||
with_cp=self.with_cp,
|
||||
norm_cfg=self.norm_cfg,
|
||||
conv_cfg=self.conv_cfg,
|
||||
init_cfg=self.block_init_cfg))
|
||||
|
||||
return Sequential(*layers)
|
||||
|
||||
def _make_branches(self, num_branches, block, num_blocks, num_channels):
|
||||
"""Build multiple branch."""
|
||||
branches = []
|
||||
|
||||
for i in range(num_branches):
|
||||
branches.append(
|
||||
self._make_one_branch(i, block, num_blocks, num_channels))
|
||||
|
||||
return ModuleList(branches)
|
||||
|
||||
def _make_fuse_layers(self):
|
||||
"""Build fuse layer."""
|
||||
if self.num_branches == 1:
|
||||
return None
|
||||
|
||||
num_branches = self.num_branches
|
||||
in_channels = self.in_channels
|
||||
fuse_layers = []
|
||||
num_out_branches = num_branches if self.multiscale_output else 1
|
||||
for i in range(num_out_branches):
|
||||
fuse_layer = []
|
||||
for j in range(num_branches):
|
||||
if j > i:
|
||||
fuse_layer.append(
|
||||
nn.Sequential(
|
||||
build_conv_layer(
|
||||
self.conv_cfg,
|
||||
in_channels[j],
|
||||
in_channels[i],
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
bias=False),
|
||||
build_norm_layer(self.norm_cfg, in_channels[i])[1],
|
||||
# we set align_corners=False for HRNet
|
||||
Upsample(
|
||||
scale_factor=2**(j - i),
|
||||
mode='bilinear',
|
||||
align_corners=False)))
|
||||
elif j == i:
|
||||
fuse_layer.append(None)
|
||||
else:
|
||||
conv_downsamples = []
|
||||
for k in range(i - j):
|
||||
if k == i - j - 1:
|
||||
conv_downsamples.append(
|
||||
nn.Sequential(
|
||||
build_conv_layer(
|
||||
self.conv_cfg,
|
||||
in_channels[j],
|
||||
in_channels[i],
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
bias=False),
|
||||
build_norm_layer(self.norm_cfg,
|
||||
in_channels[i])[1]))
|
||||
else:
|
||||
conv_downsamples.append(
|
||||
nn.Sequential(
|
||||
build_conv_layer(
|
||||
self.conv_cfg,
|
||||
in_channels[j],
|
||||
in_channels[j],
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
bias=False),
|
||||
build_norm_layer(self.norm_cfg,
|
||||
in_channels[j])[1],
|
||||
nn.ReLU(inplace=False)))
|
||||
fuse_layer.append(nn.Sequential(*conv_downsamples))
|
||||
fuse_layers.append(nn.ModuleList(fuse_layer))
|
||||
|
||||
return nn.ModuleList(fuse_layers)
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
if self.num_branches == 1:
|
||||
return [self.branches[0](x[0])]
|
||||
|
||||
for i in range(self.num_branches):
|
||||
x[i] = self.branches[i](x[i])
|
||||
|
||||
x_fuse = []
|
||||
for i in range(len(self.fuse_layers)):
|
||||
y = 0
|
||||
for j in range(self.num_branches):
|
||||
if i == j:
|
||||
y += x[j]
|
||||
elif j > i:
|
||||
y = y + resize(
|
||||
self.fuse_layers[i][j](x[j]),
|
||||
size=x[i].shape[2:],
|
||||
mode='bilinear',
|
||||
align_corners=False)
|
||||
else:
|
||||
y += self.fuse_layers[i][j](x[j])
|
||||
x_fuse.append(self.relu(y))
|
||||
return x_fuse
|
||||
|
||||
|
||||
@MODELS.register_module()
|
||||
class HRNet(BaseModule):
|
||||
"""HRNet backbone.
|
||||
|
||||
This backbone is the implementation of `High-Resolution Representations
|
||||
for Labeling Pixels and Regions <https://arxiv.org/abs/1904.04514>`_.
|
||||
|
||||
Args:
|
||||
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.
|
||||
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. Default: False.
|
||||
with_cp (bool): Use checkpoint or not. Using checkpoint will save some
|
||||
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. 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.
|
||||
|
||||
Example:
|
||||
>>> from mmseg.models import HRNet
|
||||
>>> import torch
|
||||
>>> extra = dict(
|
||||
>>> stage1=dict(
|
||||
>>> num_modules=1,
|
||||
>>> num_branches=1,
|
||||
>>> block='BOTTLENECK',
|
||||
>>> num_blocks=(4, ),
|
||||
>>> num_channels=(64, )),
|
||||
>>> stage2=dict(
|
||||
>>> num_modules=1,
|
||||
>>> num_branches=2,
|
||||
>>> block='BASIC',
|
||||
>>> num_blocks=(4, 4),
|
||||
>>> num_channels=(32, 64)),
|
||||
>>> stage3=dict(
|
||||
>>> num_modules=4,
|
||||
>>> 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)))
|
||||
>>> self = HRNet(extra, in_channels=1)
|
||||
>>> self.eval()
|
||||
>>> inputs = torch.rand(1, 1, 32, 32)
|
||||
>>> level_outputs = self.forward(inputs)
|
||||
>>> for level_out in level_outputs:
|
||||
... print(tuple(level_out.shape))
|
||||
(1, 32, 8, 8)
|
||||
(1, 64, 4, 4)
|
||||
(1, 128, 2, 2)
|
||||
(1, 256, 1, 1)
|
||||
"""
|
||||
|
||||
blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck}
|
||||
|
||||
def __init__(self,
|
||||
extra,
|
||||
in_channels=3,
|
||||
conv_cfg=None,
|
||||
norm_cfg=dict(type='BN', requires_grad=True),
|
||||
norm_eval=False,
|
||||
with_cp=False,
|
||||
frozen_stages=-1,
|
||||
zero_init_residual=False,
|
||||
multiscale_output=True,
|
||||
pretrained=None,
|
||||
init_cfg=None):
|
||||
super().__init__(init_cfg)
|
||||
|
||||
self.pretrained = pretrained
|
||||
self.zero_init_residual = zero_init_residual
|
||||
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 deprecated, '
|
||||
'please use "init_cfg" instead')
|
||||
self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
|
||||
elif pretrained is None:
|
||||
if init_cfg is None:
|
||||
self.init_cfg = [
|
||||
dict(type='Kaiming', layer='Conv2d'),
|
||||
dict(
|
||||
type='Constant',
|
||||
val=1,
|
||||
layer=['_BatchNorm', 'GroupNorm'])
|
||||
]
|
||||
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
|
||||
self.norm_eval = norm_eval
|
||||
self.with_cp = with_cp
|
||||
self.frozen_stages = frozen_stages
|
||||
|
||||
# stem net
|
||||
self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
|
||||
self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)
|
||||
|
||||
self.conv1 = build_conv_layer(
|
||||
self.conv_cfg,
|
||||
in_channels,
|
||||
64,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
bias=False)
|
||||
|
||||
self.add_module(self.norm1_name, norm1)
|
||||
self.conv2 = build_conv_layer(
|
||||
self.conv_cfg,
|
||||
64,
|
||||
64,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
bias=False)
|
||||
|
||||
self.add_module(self.norm2_name, norm2)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
|
||||
# stage 1
|
||||
self.stage1_cfg = self.extra['stage1']
|
||||
num_channels = self.stage1_cfg['num_channels'][0]
|
||||
block_type = self.stage1_cfg['block']
|
||||
num_blocks = self.stage1_cfg['num_blocks'][0]
|
||||
|
||||
block = self.blocks_dict[block_type]
|
||||
stage1_out_channels = num_channels * block.expansion
|
||||
self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
|
||||
|
||||
# stage 2
|
||||
self.stage2_cfg = self.extra['stage2']
|
||||
num_channels = self.stage2_cfg['num_channels']
|
||||
block_type = self.stage2_cfg['block']
|
||||
|
||||
block = self.blocks_dict[block_type]
|
||||
num_channels = [channel * block.expansion for channel in num_channels]
|
||||
self.transition1 = self._make_transition_layer([stage1_out_channels],
|
||||
num_channels)
|
||||
self.stage2, pre_stage_channels = self._make_stage(
|
||||
self.stage2_cfg, num_channels)
|
||||
|
||||
# stage 3
|
||||
self.stage3_cfg = self.extra['stage3']
|
||||
num_channels = self.stage3_cfg['num_channels']
|
||||
block_type = self.stage3_cfg['block']
|
||||
|
||||
block = self.blocks_dict[block_type]
|
||||
num_channels = [channel * block.expansion for channel in num_channels]
|
||||
self.transition2 = self._make_transition_layer(pre_stage_channels,
|
||||
num_channels)
|
||||
self.stage3, pre_stage_channels = self._make_stage(
|
||||
self.stage3_cfg, num_channels)
|
||||
|
||||
# stage 4
|
||||
self.stage4_cfg = self.extra['stage4']
|
||||
num_channels = self.stage4_cfg['num_channels']
|
||||
block_type = self.stage4_cfg['block']
|
||||
|
||||
block = self.blocks_dict[block_type]
|
||||
num_channels = [channel * block.expansion for channel in num_channels]
|
||||
self.transition3 = self._make_transition_layer(pre_stage_channels,
|
||||
num_channels)
|
||||
self.stage4, pre_stage_channels = self._make_stage(
|
||||
self.stage4_cfg, num_channels, multiscale_output=multiscale_output)
|
||||
|
||||
self._freeze_stages()
|
||||
|
||||
@property
|
||||
def norm1(self):
|
||||
"""nn.Module: the normalization layer named "norm1" """
|
||||
return getattr(self, self.norm1_name)
|
||||
|
||||
@property
|
||||
def norm2(self):
|
||||
"""nn.Module: the normalization layer named "norm2" """
|
||||
return getattr(self, self.norm2_name)
|
||||
|
||||
def _make_transition_layer(self, num_channels_pre_layer,
|
||||
num_channels_cur_layer):
|
||||
"""Make transition layer."""
|
||||
num_branches_cur = len(num_channels_cur_layer)
|
||||
num_branches_pre = len(num_channels_pre_layer)
|
||||
|
||||
transition_layers = []
|
||||
for i in range(num_branches_cur):
|
||||
if i < num_branches_pre:
|
||||
if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
|
||||
transition_layers.append(
|
||||
nn.Sequential(
|
||||
build_conv_layer(
|
||||
self.conv_cfg,
|
||||
num_channels_pre_layer[i],
|
||||
num_channels_cur_layer[i],
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1,
|
||||
bias=False),
|
||||
build_norm_layer(self.norm_cfg,
|
||||
num_channels_cur_layer[i])[1],
|
||||
nn.ReLU(inplace=True)))
|
||||
else:
|
||||
transition_layers.append(None)
|
||||
else:
|
||||
conv_downsamples = []
|
||||
for j in range(i + 1 - num_branches_pre):
|
||||
in_channels = num_channels_pre_layer[-1]
|
||||
out_channels = num_channels_cur_layer[i] \
|
||||
if j == i - num_branches_pre else in_channels
|
||||
conv_downsamples.append(
|
||||
nn.Sequential(
|
||||
build_conv_layer(
|
||||
self.conv_cfg,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=2,
|
||||
padding=1,
|
||||
bias=False),
|
||||
build_norm_layer(self.norm_cfg, out_channels)[1],
|
||||
nn.ReLU(inplace=True)))
|
||||
transition_layers.append(nn.Sequential(*conv_downsamples))
|
||||
|
||||
return nn.ModuleList(transition_layers)
|
||||
|
||||
def _make_layer(self, block, inplanes, planes, blocks, stride=1):
|
||||
"""Make each layer."""
|
||||
downsample = None
|
||||
if stride != 1 or inplanes != planes * block.expansion:
|
||||
downsample = nn.Sequential(
|
||||
build_conv_layer(
|
||||
self.conv_cfg,
|
||||
inplanes,
|
||||
planes * block.expansion,
|
||||
kernel_size=1,
|
||||
stride=stride,
|
||||
bias=False),
|
||||
build_norm_layer(self.norm_cfg, planes * block.expansion)[1])
|
||||
|
||||
layers = []
|
||||
block_init_cfg = None
|
||||
if self.pretrained is None and not hasattr(
|
||||
self, 'init_cfg') and self.zero_init_residual:
|
||||
if block is BasicBlock:
|
||||
block_init_cfg = dict(
|
||||
type='Constant', val=0, override=dict(name='norm2'))
|
||||
elif block is Bottleneck:
|
||||
block_init_cfg = dict(
|
||||
type='Constant', val=0, override=dict(name='norm3'))
|
||||
|
||||
layers.append(
|
||||
block(
|
||||
inplanes,
|
||||
planes,
|
||||
stride,
|
||||
downsample=downsample,
|
||||
with_cp=self.with_cp,
|
||||
norm_cfg=self.norm_cfg,
|
||||
conv_cfg=self.conv_cfg,
|
||||
init_cfg=block_init_cfg))
|
||||
inplanes = planes * block.expansion
|
||||
for i in range(1, blocks):
|
||||
layers.append(
|
||||
block(
|
||||
inplanes,
|
||||
planes,
|
||||
with_cp=self.with_cp,
|
||||
norm_cfg=self.norm_cfg,
|
||||
conv_cfg=self.conv_cfg,
|
||||
init_cfg=block_init_cfg))
|
||||
|
||||
return Sequential(*layers)
|
||||
|
||||
def _make_stage(self, layer_config, in_channels, multiscale_output=True):
|
||||
"""Make each stage."""
|
||||
num_modules = layer_config['num_modules']
|
||||
num_branches = layer_config['num_branches']
|
||||
num_blocks = layer_config['num_blocks']
|
||||
num_channels = layer_config['num_channels']
|
||||
block = self.blocks_dict[layer_config['block']]
|
||||
|
||||
hr_modules = []
|
||||
block_init_cfg = None
|
||||
if self.pretrained is None and not hasattr(
|
||||
self, 'init_cfg') and self.zero_init_residual:
|
||||
if block is BasicBlock:
|
||||
block_init_cfg = dict(
|
||||
type='Constant', val=0, override=dict(name='norm2'))
|
||||
elif block is Bottleneck:
|
||||
block_init_cfg = dict(
|
||||
type='Constant', val=0, override=dict(name='norm3'))
|
||||
|
||||
for i in range(num_modules):
|
||||
# multi_scale_output is only used for the last module
|
||||
if not multiscale_output and i == num_modules - 1:
|
||||
reset_multiscale_output = False
|
||||
else:
|
||||
reset_multiscale_output = True
|
||||
|
||||
hr_modules.append(
|
||||
HRModule(
|
||||
num_branches,
|
||||
block,
|
||||
num_blocks,
|
||||
in_channels,
|
||||
num_channels,
|
||||
reset_multiscale_output,
|
||||
with_cp=self.with_cp,
|
||||
norm_cfg=self.norm_cfg,
|
||||
conv_cfg=self.conv_cfg,
|
||||
block_init_cfg=block_init_cfg))
|
||||
|
||||
return Sequential(*hr_modules), in_channels
|
||||
|
||||
def _freeze_stages(self):
|
||||
"""Freeze stages param and norm stats."""
|
||||
if self.frozen_stages >= 0:
|
||||
|
||||
self.norm1.eval()
|
||||
self.norm2.eval()
|
||||
for m in [self.conv1, self.norm1, self.conv2, self.norm2]:
|
||||
for param in m.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
for i in range(1, self.frozen_stages + 1):
|
||||
if i == 1:
|
||||
m = getattr(self, f'layer{i}')
|
||||
t = getattr(self, f'transition{i}')
|
||||
elif i == 4:
|
||||
m = getattr(self, f'stage{i}')
|
||||
else:
|
||||
m = getattr(self, f'stage{i}')
|
||||
t = getattr(self, f'transition{i}')
|
||||
m.eval()
|
||||
for param in m.parameters():
|
||||
param.requires_grad = False
|
||||
t.eval()
|
||||
for param in t.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.norm1(x)
|
||||
x = self.relu(x)
|
||||
x = self.conv2(x)
|
||||
x = self.norm2(x)
|
||||
x = self.relu(x)
|
||||
x = self.layer1(x)
|
||||
|
||||
x_list = []
|
||||
for i in range(self.stage2_cfg['num_branches']):
|
||||
if self.transition1[i] is not None:
|
||||
x_list.append(self.transition1[i](x))
|
||||
else:
|
||||
x_list.append(x)
|
||||
y_list = self.stage2(x_list)
|
||||
|
||||
x_list = []
|
||||
for i in range(self.stage3_cfg['num_branches']):
|
||||
if self.transition2[i] is not None:
|
||||
x_list.append(self.transition2[i](y_list[-1]))
|
||||
else:
|
||||
x_list.append(y_list[i])
|
||||
y_list = self.stage3(x_list)
|
||||
|
||||
x_list = []
|
||||
for i in range(self.stage4_cfg['num_branches']):
|
||||
if self.transition3[i] is not None:
|
||||
x_list.append(self.transition3[i](y_list[-1]))
|
||||
else:
|
||||
x_list.append(y_list[i])
|
||||
y_list = self.stage4(x_list)
|
||||
|
||||
return y_list
|
||||
|
||||
def train(self, mode=True):
|
||||
"""Convert the model into training mode will keeping the normalization
|
||||
layer freezed."""
|
||||
super().train(mode)
|
||||
self._freeze_stages()
|
||||
if mode and self.norm_eval:
|
||||
for m in self.modules():
|
||||
# trick: eval have effect on BatchNorm only
|
||||
if isinstance(m, _BatchNorm):
|
||||
m.eval()
|
||||
Reference in New Issue
Block a user