dpn网络的pytorch实现方式

Python  /  管理员 发布于 5年前   338

我就废话不多说了，直接上代码吧！

import torchimport torch.nn as nnimport torch.nn.functional as Fclass 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, self).__init__()  self.bn = nn.BatchNorm2d(in_chs, eps=0.001)  self.act = activation_fn  self.conv = nn.Conv2d(in_chs, out_chs, kernel_size, stride, 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, num_init_features, 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, stride=2, padding=1) def forward(self, x):  x = self.conv(x)  x = self.bn(x)  x = self.act(x)  x = self.pool(x)  return xclass DualPathBlock(nn.Module): def __init__(   self, in_chs, 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, out_chs=num_1x1_c + 2 * inc, kernel_size=1, stride=1)  self.c1x1_a = BnActConv2d(in_chs=in_chs, out_chs=num_1x1_a, kernel_size=1, 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, num_1x1_c, kernel_size=1, bias=False)   self.c1x1_c2 = nn.Conv2d(num_3x3_b, inc, kernel_size=1, bias=False)  else:   self.c1x1_c = BnActConv2d(in_chs=num_3x3_b, out_chs=num_1x1_c + inc, kernel_size=1, stride=1) def forward(self, x):  x_in = torch.cat(x, dim=1) if isinstance(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[:, :self.num_1x1_c, :, :]   out2 = x_in[:, self.num_1x1_c:, :, :]  resid = x_s1 + out1  dense = torch.cat([x_s2, out2], dim=1)  return resid, denseclass 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, kernel_size=3, padding=1)  else:   blocks['conv1_1'] = InputBlock(num_init_features, kernel_size=7, 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, r, bw, inc, groups, 'proj', b)  in_chs = bw + 3 * inc  for i in range(2, k_sec[0] + 1):   blocks['conv2_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, '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, r, r, bw, inc, groups, 'down', b)  in_chs = bw + 3 * inc  for i in range(2, k_sec[1] + 1):   blocks['conv3_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', 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, r, r, bw, inc, groups, 'down', b)  in_chs = bw + 3 * inc  for i in range(2, k_sec[2] + 1):   blocks['conv4_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', 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, r, r, bw, inc, groups, 'down', b)  in_chs = bw + 3 * inc  for i in range(2, k_sec[3] + 1):   blocks['conv5_' + str(i)] = DualPathBlock(in_chs, r, r, bw, inc, groups, 'normal', 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, kernel_size=1, bias=True) def logits(self, features):  if not self.training and self.test_time_pool:   x = F.avg_pool2d(features, kernel_size=7, 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 poolingAdaptive 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 dimBoth 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 1def adaptive_avgmax_pool2d(x, pool_type='avg', padding=0, 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)), padding=padding, count_include_pad=count_include_pad),   F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)  ], dim=1) elif pool_type == 'avgmax':  x_avg = F.avg_pool2d(    x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad)  x_max = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)  x = 0.5 * (x_avg + x_max) elif pool_type == 'max':  x = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding) else:  if pool_type != 'avg':   print('Invalid pool type %s specified. Defaulting to average pooling.' % pool_type)  x = F.avg_pool2d(   x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad) return xclass 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。