Merge branch 'dev'
This commit is contained in:
commit
0dc8aba668
1 changed files with 187 additions and 3 deletions
190
model.py
190
model.py
|
|
@ -1,12 +1,13 @@
|
|||
"""
|
||||
这个文件是模型的定义文件，请不要擅自修改，如有疑问微信群里反馈
|
||||
单独运行本文件将会输出模型结构
|
||||
目前的话是一个36层的模型，模型总量应该是在10M左右 如果到时候还是欠拟合的话再考虑去做更深的结构
|
||||
author : yukun-hh
|
||||
date : 2026-4-10
|
||||
|
||||
"""
|
||||
#神经网络模型库
|
||||
import torch
|
||||
from modelscope.msdatasets.dataset_cls.custom_datasets.audio.kws_nearfield_processor import padding
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
|
|
@ -22,7 +23,7 @@ class Resblock(nn.Module):
|
|||
"""
|
||||
super().__init__()
|
||||
self.conv1 = nn.Conv2d(input_channels,output_channels,kernel_size=3,padding=1,stride=strides)
|
||||
self.conv2 = nn.Conv2d(output_channels,output_channels,kernel_size=3,padding=1,stride=strides)
|
||||
self.conv2 = nn.Conv2d(output_channels,output_channels,kernel_size=3,padding=1,stride=1)
|
||||
if use_1x1conv:
|
||||
self.conv3 = nn.Conv2d(input_channels, output_channels,kernel_size=1, stride=strides)
|
||||
else:
|
||||
|
|
@ -38,6 +39,189 @@ class Resblock(nn.Module):
|
|||
return F.relu(Y)
|
||||
|
||||
class Net():
|
||||
def
|
||||
"""
|
||||
模型的主要结构就在这里了，到时也好该和调用
|
||||
现在必须实现的方法：
|
||||
目前还是以图片缩放到256＊256构建残差块
|
||||
"""
|
||||
net = nn.Sequential()
|
||||
def resnet_block(self,input_channels, num_channels, num_residuals,
|
||||
first_block=False):
|
||||
"""
|
||||
:param input_channels: 输入维度
|
||||
:param num_channels: 输出维度
|
||||
:param num_residuals: 单个残差层的残差块数
|
||||
:param first_block: 第一块不用下采样 特殊控制
|
||||
:return: list[nn.Module]
|
||||
"""
|
||||
blk = []
|
||||
|
||||
for i in range(num_residuals):
|
||||
if i == 0 and not first_block:
|
||||
blk.append(Resblock(input_channels, num_channels,
|
||||
use_1x1conv=True, strides=2))
|
||||
else:
|
||||
blk.append(Resblock(num_channels, num_channels))
|
||||
return blk
|
||||
def __init__(self):
|
||||
b1 = nn.Sequential( nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
|
||||
nn.BatchNorm2d(64), nn.ReLU(),
|
||||
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
||||
)
|
||||
"""
|
||||
7×7 卷积层，输出通道 64，步长 2，填充 3
|
||||
(3×256×256)->(64×128×128)
|
||||
批归一化 relu层
|
||||
最大池化
|
||||
(64×128×128)->(64×64×64)
|
||||
"""
|
||||
b2 = nn.Sequential(*self.resnet_block(64, 64, num_residuals=3, first_block=True))
|
||||
b3 = nn.Sequential(*self.resnet_block(64, 128, num_residuals=4))
|
||||
b4 = nn.Sequential(*self.resnet_block(128, 256, num_residuals=6))
|
||||
b5 = nn.Sequential(*self.resnet_block(256, 512, num_residuals=3))
|
||||
self.net = nn.Sequential(b1, b2, b3, b4, b5,nn.AdaptiveAvgPool2d((1,1)),nn.Flatten(), nn.Linear(512, 4))
|
||||
def get_network(self):
|
||||
return self.net
|
||||
|
||||
"""
|
||||
Sequential(
|
||||
(0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))
|
||||
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(2): ReLU()
|
||||
(3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
|
||||
)
|
||||
Sequential output shape: torch.Size([1, 64, 64, 64])
|
||||
Sequential(
|
||||
(0): Resblock(
|
||||
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
(1): Resblock(
|
||||
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
(2): Resblock(
|
||||
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
)
|
||||
Sequential output shape: torch.Size([1, 64, 64, 64])
|
||||
Sequential(
|
||||
(0): Resblock(
|
||||
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
|
||||
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv3): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2))
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
(1): Resblock(
|
||||
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
(2): Resblock(
|
||||
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
(3): Resblock(
|
||||
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
)
|
||||
Sequential output shape: torch.Size([1, 128, 32, 32])
|
||||
Sequential(
|
||||
(0): Resblock(
|
||||
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
|
||||
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2))
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
(1): Resblock(
|
||||
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
(2): Resblock(
|
||||
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
(3): Resblock(
|
||||
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
(4): Resblock(
|
||||
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
(5): Resblock(
|
||||
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
)
|
||||
Sequential output shape: torch.Size([1, 256, 16, 16])
|
||||
Sequential(
|
||||
(0): Resblock(
|
||||
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
|
||||
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2))
|
||||
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
(1): Resblock(
|
||||
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
(2): Resblock(
|
||||
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
||||
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
||||
)
|
||||
)
|
||||
Sequential output shape: torch.Size([1, 512, 8, 8])
|
||||
AdaptiveAvgPool2d(output_size=(1, 1))
|
||||
AdaptiveAvgPool2d output shape: torch.Size([1, 512, 1, 1])
|
||||
Flatten(start_dim=1, end_dim=-1)
|
||||
Flatten output shape: torch.Size([1, 512])
|
||||
Linear(in_features=512, out_features=4, bias=True)
|
||||
Linear output shape: torch.Size([1, 4])
|
||||
"""
|
||||
|
||||
if __name__ == '__main__':
|
||||
Net_new = Net()
|
||||
X = torch.rand(size=(1, 3, 256, 256))
|
||||
for layer in Net_new.get_network():
|
||||
print(layer)
|
||||
X = layer(X)
|
||||
print(layer.__class__.__name__, 'output shape:\t', X.shape)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue