Junhao Liang
bb6cd5149a
* 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>
64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
"""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__()
|
|
self.layers_cfg = layers_cfg
|
|
self.in_channels = in_channels
|
|
|
|
self.feature = self.make_layers()
|
|
|
|
def forward(self, seqs):
|
|
out = self.feature(seqs)
|
|
return out
|
|
|
|
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]
|
|
if typ not in ['BC', 'FC']:
|
|
raise AssertionError
|
|
out_c = int(cfg[1])
|
|
|
|
if typ == 'BC':
|
|
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)]
|
|
in_c = int(self.layers_cfg[0].split('-')[1])
|
|
for cfg in self.layers_cfg[1:]:
|
|
if cfg == 'M':
|
|
Layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
|
|
else:
|
|
conv2d = get_layer(cfg, in_c, 3, 1, 1)
|
|
Layers += [conv2d, nn.LeakyReLU(inplace=True)]
|
|
in_c = int(cfg.split('-')[1])
|
|
return nn.Sequential(*Layers)
|