我就废话不多说了，直接上代码吧！

import torch
import torch.nn as nn
import torch.nn.functional as F

class CatBnAct(nn.Module):
 def __init__(self,in_chs,activation_fn=nn.ReLU(inplace=True)):
  super(CatBnAct,self).__init__()
  self.bn = nn.BatchNorm2d(in_chs,eps=0.001)
  self.act = activation_fn

 def forward(self,x):
  x = torch.cat(x,dim=1) if isinstance(x,tuple) else x
  return self.act(self.bn(x))

class BnActConv2d(nn.Module):
 def __init__(self,s,out_chs,kernel_size,stride,padding=0,groups=1,activation_fn=nn.ReLU(inplace=True)):
  super(BnActConv2d,eps=0.001)
  self.act = activation_fn
  self.conv = nn.Conv2d(in_chs,padding,groups=groups,bias=False)

 def forward(self,x):
  return self.conv(self.act(self.bn(x)))

class InputBlock(nn.Module):
 def __init__(self,num_init_features,kernel_size=7,padding=3,activation_fn=nn.ReLU(inplace=True)):
  super(InputBlock,self).__init__()
  self.conv = nn.Conv2d(
   3,kernel_size=kernel_size,stride=2,padding=padding,bias=False)
  self.bn = nn.BatchNorm2d(num_init_features,eps=0.001)
  self.act = activation_fn
  self.pool = nn.MaxPool2d(kernel_size=3,padding=1)

 def forward(self,x):
  x = self.conv(x)
  x = self.bn(x)
  x = self.act(x)
  x = self.pool(x)
  return x

class DualPathBlock(nn.Module):
 def __init__(
   self,num_1x1_a,num_3x3_b,num_1x1_c,inc,groups,block_type='normal',b=False):
  super(DualPathBlock,self).__init__()
  self.num_1x1_c = num_1x1_c
  self.inc = inc
  self.b = b
  if block_type is 'proj':
   self.key_stride = 1
   self.has_proj = True
  elif block_type is 'down':
   self.key_stride = 2
   self.has_proj = True
  else:
   assert block_type is 'normal'
   self.key_stride = 1
   self.has_proj = False

  if self.has_proj:
   # Using different member names here to allow easier parameter key matching for conversion
   if self.key_stride == 2:
    self.c1x1_w_s2 = BnActConv2d(
     in_chs=in_chs,out_chs=num_1x1_c + 2 * inc,kernel_size=1,stride=2)
   else:
    self.c1x1_w_s1 = BnActConv2d(
     in_chs=in_chs,stride=1)
  self.c1x1_a = BnActConv2d(in_chs=in_chs,out_chs=num_1x1_a,stride=1)
  self.c3x3_b = BnActConv2d(
   in_chs=num_1x1_a,out_chs=num_3x3_b,kernel_size=3,stride=self.key_stride,padding=1,groups=groups)
  if b:
   self.c1x1_c = CatBnAct(in_chs=num_3x3_b)
   self.c1x1_c1 = nn.Conv2d(num_3x3_b,bias=False)
   self.c1x1_c2 = nn.Conv2d(num_3x3_b,bias=False)
  else:
   self.c1x1_c = BnActConv2d(in_chs=num_3x3_b,out_chs=num_1x1_c + inc,stride=1)

 def forward(self,x):
  x_in = torch.cat(x,tuple) else x
  if self.has_proj:
   if self.key_stride == 2:
    x_s = self.c1x1_w_s2(x_in)
   else:
    x_s = self.c1x1_w_s1(x_in)
   x_s1 = x_s[:,:self.num_1x1_c,:,:]
   x_s2 = x_s[:,self.num_1x1_c:,:]
  else:
   x_s1 = x[0]
   x_s2 = x[1]
  x_in = self.c1x1_a(x_in)
  x_in = self.c3x3_b(x_in)
  if self.b:
   x_in = self.c1x1_c(x_in)
   out1 = self.c1x1_c1(x_in)
   out2 = self.c1x1_c2(x_in)
  else:
   x_in = self.c1x1_c(x_in)
   out1 = x_in[:,:]
   out2 = x_in[:,:]
  resid = x_s1 + out1
  dense = torch.cat([x_s2,out2],dim=1)
  return resid,dense

class dpn(nn.Module):
 def __init__(self,small=False,num_init_features=64,k_r=96,groups=32,b=False,k_sec=(3,4,20,3),inc_sec=(16,32,24,128),num_classes=1000,test_time_pool=False):
  super(dpn,self).__init__()
  self.test_time_pool = test_time_pool
  self.b = b
  bw_factor = 1 if small else 4

  blocks = OrderedDict()

  # conv1
  if small:
   blocks['conv1_1'] = InputBlock(num_init_features,padding=1)
  else:
   blocks['conv1_1'] = InputBlock(num_init_features,padding=3)

  # conv2
  bw = 64 * bw_factor
  inc = inc_sec[0]
  r = (k_r * bw) // (64 * bw_factor)
  blocks['conv2_1'] = DualPathBlock(num_init_features,r,bw,'proj',b)
  in_chs = bw + 3 * inc
  for i in range(2,k_sec[0] + 1):
   blocks['conv2_' + str(i)] = DualPathBlock(in_chs,'normal',b)
   in_chs += inc

  # conv3
  bw = 128 * bw_factor
  inc = inc_sec[1]
  r = (k_r * bw) // (64 * bw_factor)
  blocks['conv3_1'] = DualPathBlock(in_chs,'down',k_sec[1] + 1):
   blocks['conv3_' + str(i)] = DualPathBlock(in_chs,b)
   in_chs += inc

  # conv4
  bw = 256 * bw_factor
  inc = inc_sec[2]
  r = (k_r * bw) // (64 * bw_factor)
  blocks['conv4_1'] = DualPathBlock(in_chs,k_sec[2] + 1):
   blocks['conv4_' + str(i)] = DualPathBlock(in_chs,b)
   in_chs += inc

  # conv5
  bw = 512 * bw_factor
  inc = inc_sec[3]
  r = (k_r * bw) // (64 * bw_factor)
  blocks['conv5_1'] = DualPathBlock(in_chs,k_sec[3] + 1):
   blocks['conv5_' + str(i)] = DualPathBlock(in_chs,b)
   in_chs += inc
  blocks['conv5_bn_ac'] = CatBnAct(in_chs)

  self.features = nn.Sequential(blocks)

  # Using 1x1 conv for the FC layer to allow the extra pooling scheme
  self.last_linear = nn.Conv2d(in_chs,num_classes,bias=True)

 def logits(self,features):
  if not self.training and self.test_time_pool:
   x = F.avg_pool2d(features,stride=1)
   out = self.last_linear(x)
   # The extra test time pool should be pooling an img_size//32 - 6 size patch
   out = adaptive_avgmax_pool2d(out,pool_type='avgmax')
  else:
   x = adaptive_avgmax_pool2d(features,pool_type='avg')
   out = self.last_linear(x)
  return out.view(out.size(0),-1)

 def forward(self,input):
  x = self.features(input)
  x = self.logits(x)
  return x

""" PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
 * 'avg' - Average pooling
 * 'max' - Max pooling
 * 'avgmax' - Sum of average and max pooling re-scaled by 0.5
 * 'avgmaxc' - Concatenation of average and max pooling along feature dim,doubles feature dim

Both a functional and a nn.Module version of the pooling is provided.

"""

def pooling_factor(pool_type='avg'):
 return 2 if pool_type == 'avgmaxc' else 1

def adaptive_avgmax_pool2d(x,pool_type='avg',count_include_pad=False):
 """Selectable global pooling function with dynamic input kernel size
 """
 if pool_type == 'avgmaxc':
  x = torch.cat([
   F.avg_pool2d(
    x,kernel_size=(x.size(2),x.size(3)),count_include_pad=count_include_pad),F.max_pool2d(x,padding=padding)
  ],dim=1)
 elif pool_type == 'avgmax':
  x_avg = F.avg_pool2d(
    x,count_include_pad=count_include_pad)
  x_max = F.max_pool2d(x,padding=padding)
  x = 0.5 * (x_avg + x_max)
 elif pool_type == 'max':
  x = F.max_pool2d(x,padding=padding)
 else:
  if pool_type != 'avg':
   print('Invalid pool type %s specified. Defaulting to average pooling.' % pool_type)
  x = F.avg_pool2d(
   x,count_include_pad=count_include_pad)
 return x

class AdaptiveAvgMaxPool2d(torch.nn.Module):
 """Selectable global pooling layer with dynamic input kernel size
 """
 def __init__(self,output_size=1,pool_type='avg'):
  super(AdaptiveAvgMaxPool2d,self).__init__()
  self.output_size = output_size
  self.pool_type = pool_type
  if pool_type == 'avgmaxc' or pool_type == 'avgmax':
   self.pool = nn.ModuleList([nn.AdaptiveAvgPool2d(output_size),nn.AdaptiveMaxPool2d(output_size)])
  elif pool_type == 'max':
   self.pool = nn.AdaptiveMaxPool2d(output_size)
  else:
   if pool_type != 'avg':
    print('Invalid pool type %s specified. Defaulting to average pooling.' % pool_type)
   self.pool = nn.AdaptiveAvgPool2d(output_size)

 def forward(self,x):
  if self.pool_type == 'avgmaxc':
   x = torch.cat([p(x) for p in self.pool],dim=1)
  elif self.pool_type == 'avgmax':
   x = 0.5 * torch.sum(torch.stack([p(x) for p in self.pool]),0).squeeze(dim=0)
  else:
   x = self.pool(x)
  return x

 def factor(self):
  return pooling_factor(self.pool_type)

 def __repr__(self):
  return self.__class__.__name__ + ' (' \
    + 'output_size=' + str(self.output_size) \
    + ',pool_type=' + self.pool_type + ')'

以上这篇dpn网络的pytorch实现方式就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持我们。