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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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