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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/backbones/plain.py
+27 -6
View File
@@ -1,7 +1,28 @@
"""The plain backbone.
The plain backbone only contains the BasicConv2d, FocalConv2d and MaxPool2d and LeakyReLU layers.
"""
import torch.nn as nn
from ..modules import BasicConv2d, FocalConv2d
class Plain(nn.Module):
"""
The Plain backbone class.
An implicit LeakyRelu appended to each layer except maxPooling.
The kernel size, stride and padding of the first convolution layer are 5, 1, 2, the ones of other layers are 3, 1, 1.
Typical usage:
- BC-64: Basic conv2d with output channel 64. The input channel is the output channel of previous layer.
- M: nn.MaxPool2d(kernel_size=2, stride=2)].
- FC-128-1: Focal conv2d with output channel 64 and halving 1(divided to 2^1=2 parts).
Use it in your configuration file.
"""
def __init__(self, layers_cfg, in_channels=1):
super(Plain, self).__init__()
@@ -13,9 +34,11 @@ class Plain(nn.Module):
def forward(self, seqs):
out = self.feature(seqs)
return out
# torchvision/models/vgg.py
def make_layers(self):
"""
Reference: torchvision/models/vgg.py
"""
def get_layer(cfg, in_c, kernel_size, stride, padding):
cfg = cfg.split('-')
typ = cfg[0]
@@ -27,7 +50,8 @@ class Plain(nn.Module):
return BasicConv2d(in_c, out_c, kernel_size=kernel_size, stride=stride, padding=padding)
return FocalConv2d(in_c, out_c, kernel_size=kernel_size, stride=stride, padding=padding, halving=int(cfg[2]))
Layers = [get_layer(self.layers_cfg[0], self.in_channels, 5, 1, 2), nn.LeakyReLU(inplace=True)]
Layers = [get_layer(self.layers_cfg[0], self.in_channels,
5, 1, 2), nn.LeakyReLU(inplace=True)]
in_c = int(self.layers_cfg[0].split('-')[1])
for cfg in self.layers_cfg[1:]:
if cfg == 'M':
@@ -37,6 +61,3 @@ class Plain(nn.Module):
Layers += [conv2d, nn.LeakyReLU(inplace=True)]
in_c = int(cfg.split('-')[1])
return nn.Sequential(*Layers)