J4、ResNet与DenseNet结合探索

🍨 本文为🔗365天深度学习训练营中的学习记录博客

🍦 参考文章：365天深度学习训练营-第J4周：ResNet与DenseNet结合探索

🍖 原作者：K同学啊|接辅导、项目定制

学习要求¶

任务类型：自主探索任务难度：偏难任务描述：

根据J1~J3周的内容自由探索ResNet与DenseNet结合的可能性
是否可以根据两种的特性构建一个新的模型框架？
用之前的任一图像识别任务验证改进后模型的效果

ResNet模型的核心是通过建立前面层与后面层之间的“短路连接”（shortcuts，skip connection），进而训练出更深的CNN网络。

DenseNet模型的基本思路与ResNet一致，但是它建立的是前面所有层与后面层的密集连接（dense connection），它的名称也是由此而来。DenseNet的另一大特色是通过特征在channel上的连接来实现特征重用（feature reuse）。

可是说DenseNet是ResNet的扩展，但是使用中很容易炸显存，简单看一下二者的区别

一、设计网络¶

想自己设计一个的，课题太多实在是没法逐渐完善，搜了一下，发现已经有人对这二者进行了整合，叫做DPN，与ResNet和DensNet的对比图如下：

DPN的理念是使用ResNet和DensNet的优势：

ResNet特征重用（因为前面特征被sum到了后面层上面）
DensNet容易发现新特征（将前面特征均进行了concat组合）

Dual Path Architecture（DPA）以ResNet为主要框架，保证了特征的低冗余度，并在其基础上添加了一个非常小的DenseNet分支，用于生成新的特征。DPA的结构可以使用

二、实现DPN¶

In [8]:

# PyTorch搭建DPN
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
 
__all__ = ['DPN', 'dpn92', 'dpn98', 'dpn131', 'dpn107', 'dpns']
 
 
def dpn92(num_classes=1000):
    return DPN(num_init_features=64, k_R=96, G=32, k_sec=(3,4,20,3), inc_sec=(16,32,24,128), num_classes=num_classes)
 
 
def dpn98(num_classes=1000):
    return DPN(num_init_features=96, k_R=160, G=40, k_sec=(3,6,20,3), inc_sec=(16,32,32,128), num_classes=num_classes)
 
 
def dpn131(num_classes=1000):
    return DPN(num_init_features=128, k_R=160, G=40, k_sec=(4,8,28,3), inc_sec=(16,32,32,128), num_classes=num_classes)
 
 
def dpn107(num_classes=1000):
    return DPN(num_init_features=128, k_R=200, G=50, k_sec=(4,8,20,3), inc_sec=(20,64,64,128), num_classes=num_classes)
 
 
dpns = {
    'dpn92': dpn92,
    'dpn98': dpn98,
    'dpn107': dpn107,
    'dpn131': dpn131,
}
 
 
class DualPathBlock(nn.Module):
    def __init__(self, in_chs, num_1x1_a, num_3x3_b, num_1x1_c, inc, G, _type='normal'):
        super(DualPathBlock, self).__init__()
        self.num_1x1_c = num_1x1_c
 
        if _type == 'proj':
            key_stride = 1
            self.has_proj = True
        if _type == 'down':
            key_stride = 2
            self.has_proj = True
        if _type == 'normal':
            key_stride = 1
            self.has_proj = False
 
        if self.has_proj:
            self.c1x1_w = self.BN_ReLU_Conv(in_chs=in_chs, out_chs=num_1x1_c+2*inc, kernel_size=1, stride=key_stride)
 
        self.layers = nn.Sequential(OrderedDict([
            ('c1x1_a', self.BN_ReLU_Conv(in_chs=in_chs, out_chs=num_1x1_a, kernel_size=1, stride=1)),
            ('c3x3_b', self.BN_ReLU_Conv(in_chs=num_1x1_a, out_chs=num_3x3_b, kernel_size=3, stride=key_stride, padding=1, groups=G)),
            ('c1x1_c', self.BN_ReLU_Conv(in_chs=num_3x3_b, out_chs=num_1x1_c+inc, kernel_size=1, stride=1)),
        ]))
 
    def BN_ReLU_Conv(self, in_chs, out_chs, kernel_size, stride, padding=0, groups=1):
        return nn.Sequential(OrderedDict([
            ('norm', nn.BatchNorm2d(in_chs)),
            ('relu', nn.ReLU(inplace=True)),
            ('conv', nn.Conv2d(in_chs, out_chs, kernel_size, stride, padding, groups=groups, bias=False)),
        ]))
 
    def forward(self, x):
        data_in = torch.cat(x, dim=1) if isinstance(x, list) else x
        if self.has_proj:
            data_o = self.c1x1_w(data_in)
            data_o1 = data_o[:,:self.num_1x1_c,:,:]
            data_o2 = data_o[:,self.num_1x1_c:,:,:]
        else:
            data_o1 = x[0]
            data_o2 = x[1]
 
        out = self.layers(data_in)
 
        summ = data_o1 + out[:,:self.num_1x1_c,:,:]
        dense = torch.cat([data_o2, out[:,self.num_1x1_c:,:,:]], dim=1)
        return [summ, dense]
 
 
class DPN(nn.Module):
 
    def __init__(self, num_init_features=64, k_R=96, G=32,
                 k_sec=(3, 4, 20, 3), inc_sec=(16,32,24,128), num_classes=1000):
 
        super(DPN, self).__init__()
        blocks = OrderedDict()
 
        # conv1
        blocks['conv1'] = nn.Sequential(
            nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(num_init_features),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
        )
 
        # conv2
        bw = 256
        inc = inc_sec[0]
        R = int((k_R*bw)/256)
        blocks['conv2_1'] = DualPathBlock(num_init_features, R, R, bw, inc, G, 'proj')
        in_chs = bw + 3 * inc
        for i in range(2, k_sec[0]+1):
            blocks['conv2_{}'.format(i)] = DualPathBlock(in_chs, R, R, bw, inc, G, 'normal')
            in_chs += inc
 
        # conv3
        bw = 512
        inc = inc_sec[1]
        R = int((k_R*bw)/256)
        blocks['conv3_1'] = DualPathBlock(in_chs, R, R, bw, inc, G, 'down')
        in_chs = bw + 3 * inc
        for i in range(2, k_sec[1]+1):
            blocks['conv3_{}'.format(i)] = DualPathBlock(in_chs, R, R, bw, inc, G, 'normal')
            in_chs += inc
 
        # conv4
        bw = 1024
        inc = inc_sec[2]
        R = int((k_R*bw)/256)
        blocks['conv4_1'] = DualPathBlock(in_chs, R, R, bw, inc, G, 'down')
        in_chs = bw + 3 * inc
        for i in range(2, k_sec[2]+1):
            blocks['conv4_{}'.format(i)] = DualPathBlock(in_chs, R, R, bw, inc, G, 'normal')
            in_chs += inc
 
        # conv5
        bw = 2048
        inc = inc_sec[3]
        R = int((k_R*bw)/256)
        blocks['conv5_1'] = DualPathBlock(in_chs, R, R, bw, inc, G, 'down')
        in_chs = bw + 3 * inc
        for i in range(2, k_sec[3]+1):
            blocks['conv5_{}'.format(i)] = DualPathBlock(in_chs, R, R, bw, inc, G, 'normal')
            in_chs += inc
 
        self.features = nn.Sequential(blocks)
        self.classifier = nn.Linear(in_chs, num_classes)
 
 
    def forward(self, x):
        features = torch.cat(self.features(x), dim=1)
        out = F.avg_pool2d(features, kernel_size=7).view(features.size(0), -1)
        out = self.classifier(out)
        return out

In [9]:

device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))

Using cuda device

In [10]:

model = DPN().to(device)
model

Out[10]:

DPN(
  (features): Sequential(
    (conv1): Sequential(
      (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
      (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    )
    (conv2_1): DualPathBlock(
      (c1x1_w): Sequential(
        (norm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv): Conv2d(64, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(64, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(96, 272, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv2_2): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(304, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(304, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(96, 272, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv2_3): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(320, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(96, 272, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv3_1): DualPathBlock(
      (c1x1_w): Sequential(
        (norm): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv): Conv2d(336, 576, kernel_size=(1, 1), stride=(2, 2), bias=False)
      )
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(336, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(192, 544, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv3_2): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(608, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(192, 544, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv3_3): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(640, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(192, 544, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv3_4): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(192, 544, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_1): DualPathBlock(
      (c1x1_w): Sequential(
        (norm): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv): Conv2d(704, 1072, kernel_size=(1, 1), stride=(2, 2), bias=False)
      )
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(704, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_2): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1096, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_3): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1120, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_4): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1144, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_5): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1168, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1168, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_6): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1192, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_7): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1216, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_8): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1240, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_9): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1264, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1264, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_10): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1288, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_11): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1312, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_12): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1336, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1336, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_13): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1360, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1360, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_14): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_15): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1408, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_16): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1432, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1432, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_17): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1456, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1456, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_18): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1480, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_19): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1504, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv4_20): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1528, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1528, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(384, 1048, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv5_1): DualPathBlock(
      (c1x1_w): Sequential(
        (norm): BatchNorm2d(1552, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv): Conv2d(1552, 2304, kernel_size=(1, 1), stride=(2, 2), bias=False)
      )
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(1552, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1552, 768, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(768, 768, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(768, 2176, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv5_2): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(2432, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(2432, 768, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(768, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(768, 2176, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
    (conv5_3): DualPathBlock(
      (layers): Sequential(
        (c1x1_a): Sequential(
          (norm): BatchNorm2d(2560, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(2560, 768, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
        (c3x3_b): Sequential(
          (norm): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(768, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        )
        (c1x1_c): Sequential(
          (norm): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(768, 2176, kernel_size=(1, 1), stride=(1, 1), bias=False)
        )
      )
    )
  )
  (classifier): Linear(in_features=2688, out_features=1000, bias=True)
)

三、训练结果¶

In [ ]:

四、总结¶

DPN的结构并没有太复杂，通过Inception式的结构合并两种或者网络本质上是一种模型集成的方式

posted @ 2023-03-10 17:03 CASTWJ 阅读(79) 评论(0) 收藏举报

刷新页面返回顶部

cauwj