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1.0.0 official release (#18)

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* fix bug in fix_BN

* gaitgl OUMVLP support.

* update ./doc/3.advance_usage.md Cross-Dataset Evalution & Data Agumentation

* update config

* update docs.3

* update docs.3

* add loss doc and gather input decorator

* refine the create model doc

* support rearrange directory of unzipped OUMVLP

* fix some bugs in loss_aggregator.py

* refine docs and little fix

* add oumvlp pretreatment description

* pretreatment dataset fix oumvlp description

* add gaitgl oumvlp result

* assert gaitgl input size

* add pipeline

* update the readme.

* update pipeline and readme

* Corrigendum.

* add logo and remove path

* update new logo

* Update README.md

* modify logo size

Co-authored-by: 12131100 <12131100@mail.sustech.edu.cn>
Co-authored-by: noahshen98 <77523610+noahshen98@users.noreply.github.com>
Co-authored-by: Noah <595311942@qq.com>
This commit is contained in:
Junhao Liang
2021-12-08 20:05:28 +08:00
committed by GitHub
parent 6e71c7ac34
commit bb6cd5149a
39 changed files with 11401 additions and 230 deletions
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lib/modeling/models/gaitgl.py
+66 -24
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@@ -63,8 +63,8 @@ class GeMHPP(nn.Module):
class GaitGL(BaseModel):
"""
Title: Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation
ICCV2021: https://openaccess.thecvf.com/content/ICCV2021/papers/Lin_Gait_Recognition_via_Effective_Global-Local_Feature_Representation_and_Local_Temporal_ICCV_2021_paper.pdf
GaitGL: Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation
Arxiv : https://arxiv.org/pdf/2011.01461.pdf
"""
def __init__(self, *args, **kargs):
@@ -73,31 +73,71 @@ class GaitGL(BaseModel):
def build_network(self, model_cfg):
in_c = model_cfg['channels']
class_num = model_cfg['class_num']
dataset_name = self.cfgs['data_cfg']['dataset_name']
# For CASIA-B
self.conv3d = nn.Sequential(
BasicConv3d(1, in_c[0], kernel_size=(3, 3, 3),
stride=(1, 1, 1), padding=(1, 1, 1)),
nn.LeakyReLU(inplace=True)
)
self.LTA = nn.Sequential(
BasicConv3d(in_c[0], in_c[0], kernel_size=(
3, 1, 1), stride=(3, 1, 1), padding=(0, 0, 0)),
nn.LeakyReLU(inplace=True)
)
if dataset_name == 'OUMVLP':
# For OUMVLP
self.conv3d = nn.Sequential(
BasicConv3d(1, in_c[0], kernel_size=(3, 3, 3),
stride=(1, 1, 1), padding=(1, 1, 1)),
nn.LeakyReLU(inplace=True),
BasicConv3d(in_c[0], in_c[0], kernel_size=(3, 3, 3),
stride=(1, 1, 1), padding=(1, 1, 1)),
nn.LeakyReLU(inplace=True),
)
self.LTA = nn.Sequential(
BasicConv3d(in_c[0], in_c[0], kernel_size=(
3, 1, 1), stride=(3, 1, 1), padding=(0, 0, 0)),
nn.LeakyReLU(inplace=True)
)
self.GLConvA0 = GLConv(in_c[0], in_c[1], halving=3, fm_sign=False, kernel_size=(
3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
self.MaxPool0 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
self.GLConvA0 = nn.Sequential(
GLConv(in_c[0], in_c[1], halving=1, fm_sign=False, kernel_size=(
3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1)),
GLConv(in_c[1], in_c[1], halving=1, fm_sign=False, kernel_size=(
3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1)),
)
self.MaxPool0 = nn.MaxPool3d(
kernel_size=(1, 2, 2), stride=(1, 2, 2))
self.GLConvA1 = GLConv(in_c[1], in_c[2], halving=3, fm_sign=False, kernel_size=(
3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
self.GLConvB2 = GLConv(in_c[2], in_c[2], halving=3, fm_sign=True, kernel_size=(
3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
self.GLConvA1 = nn.Sequential(
GLConv(in_c[1], in_c[2], halving=1, fm_sign=False, kernel_size=(
3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1)),
GLConv(in_c[2], in_c[2], halving=1, fm_sign=False, kernel_size=(
3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1)),
)
self.GLConvB2 = nn.Sequential(
GLConv(in_c[2], in_c[3], halving=1, fm_sign=False, kernel_size=(
3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1)),
GLConv(in_c[3], in_c[3], halving=1, fm_sign=True, kernel_size=(
3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1)),
)
else:
# For CASIA-B or other unstated datasets.
self.conv3d = nn.Sequential(
BasicConv3d(1, in_c[0], kernel_size=(3, 3, 3),
stride=(1, 1, 1), padding=(1, 1, 1)),
nn.LeakyReLU(inplace=True)
)
self.LTA = nn.Sequential(
BasicConv3d(in_c[0], in_c[0], kernel_size=(
3, 1, 1), stride=(3, 1, 1), padding=(0, 0, 0)),
nn.LeakyReLU(inplace=True)
)
self.Head0 = SeparateFCs(64, in_c[2], in_c[2])
self.Bn = nn.BatchNorm1d(in_c[2])
self.Head1 = SeparateFCs(64, in_c[2], class_num)
self.GLConvA0 = GLConv(in_c[0], in_c[1], halving=3, fm_sign=False, kernel_size=(
3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
self.MaxPool0 = nn.MaxPool3d(
kernel_size=(1, 2, 2), stride=(1, 2, 2))
self.GLConvA1 = GLConv(in_c[1], in_c[2], halving=3, fm_sign=False, kernel_size=(
3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
self.GLConvB2 = GLConv(in_c[2], in_c[2], halving=3, fm_sign=True, kernel_size=(
3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
self.Head0 = SeparateFCs(64, in_c[-1], in_c[-1])
self.Bn = nn.BatchNorm1d(in_c[-1])
self.Head1 = SeparateFCs(64, in_c[-1], class_num)
self.TP = PackSequenceWrapper(torch.max)
self.HPP = GeMHPP()
@@ -105,7 +145,9 @@ class GaitGL(BaseModel):
def forward(self, inputs):
ipts, labs, _, _, seqL = inputs
seqL = None if not self.training else seqL
if not self.training and len(labs) != 1:
raise ValueError(
'The input size of each GPU must be 1 in testing mode, but got {}!'.format(len(labs)))
sils = ipts[0].unsqueeze(1)
del ipts
n, _, s, h, w = sils.size()