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