我就废话不多说了，直接上代码吧！
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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服务器之家。
原文链接：https://blog.csdn.net/weixin_40123108/article/details/89682449
