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- import torch
- import torch.nn as nn
- import torch.nn.functional as functional
-
- from models._util import try_index
- from .bn import ABN
-
-
- class DeeplabV3(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels=256,
- dilations=(12, 24, 36),
- norm_act=ABN,
- pooling_size=None):
- super(DeeplabV3, self).__init__()
- self.pooling_size = pooling_size
-
- self.map_convs = nn.ModuleList([
- nn.Conv2d(in_channels, hidden_channels, 1, bias=False),
- nn.Conv2d(in_channels, hidden_channels, 3, bias=False, dilation=dilations[0], padding=dilations[0]),
- nn.Conv2d(in_channels, hidden_channels, 3, bias=False, dilation=dilations[1], padding=dilations[1]),
- nn.Conv2d(in_channels, hidden_channels, 3, bias=False, dilation=dilations[2], padding=dilations[2])
- ])
- self.map_bn = norm_act(hidden_channels * 4)
-
- self.global_pooling_conv = nn.Conv2d(in_channels, hidden_channels, 1, bias=False)
- self.global_pooling_bn = norm_act(hidden_channels)
-
- self.red_conv = nn.Conv2d(hidden_channels * 4, out_channels, 1, bias=False)
- self.pool_red_conv = nn.Conv2d(hidden_channels, out_channels, 1, bias=False)
- self.red_bn = norm_act(out_channels)
-
- self.reset_parameters(self.map_bn.activation, self.map_bn.slope)
-
- def reset_parameters(self, activation, slope):
- gain = nn.init.calculate_gain(activation, slope)
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- nn.init.xavier_normal_(m.weight.data, gain)
- if hasattr(m, "bias") and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, ABN):
- if hasattr(m, "weight") and m.weight is not None:
- nn.init.constant_(m.weight, 1)
- if hasattr(m, "bias") and m.bias is not None:
- nn.init.constant_(m.bias, 0)
-
- def forward(self, x):
- # Map convolutions
- out = torch.cat([m(x) for m in self.map_convs], dim=1)
- out = self.map_bn(out)
- out = self.red_conv(out)
-
- # Global pooling
- pool = self._global_pooling(x)
- pool = self.global_pooling_conv(pool)
- pool = self.global_pooling_bn(pool)
- pool = self.pool_red_conv(pool)
- if self.training or self.pooling_size is None:
- pool = pool.repeat(1, 1, x.size(2), x.size(3))
-
- out += pool
- out = self.red_bn(out)
- return out
-
- def _global_pooling(self, x):
- if self.training or self.pooling_size is None:
- pool = x.view(x.size(0), x.size(1), -1).mean(dim=-1)
- pool = pool.view(x.size(0), x.size(1), 1, 1)
- else:
- pooling_size = (min(try_index(self.pooling_size, 0), x.shape[2]),
- min(try_index(self.pooling_size, 1), x.shape[3]))
- padding = (
- (pooling_size[1] - 1) // 2,
- (pooling_size[1] - 1) // 2 if pooling_size[1] % 2 == 1 else (pooling_size[1] - 1) // 2 + 1,
- (pooling_size[0] - 1) // 2,
- (pooling_size[0] - 1) // 2 if pooling_size[0] % 2 == 1 else (pooling_size[0] - 1) // 2 + 1
- )
-
- pool = functional.avg_pool2d(x, pooling_size, stride=1)
- pool = functional.pad(pool, pad=padding, mode="replicate")
- return pool
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