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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from functools import partial |
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import math |
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from .helpers import load_pretrained |
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from .layers import DropPath, to_2tuple, trunc_normal_ |
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from ..builder import HEADS |
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from .decode_head import BaseDecodeHead |
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from ..backbones.vit import Block |
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from mmcv.cnn import build_norm_layer |
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@HEADS.register_module() |
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class VIT_MLA_AUXIHead(BaseDecodeHead): |
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""" Vision Transformer with support for patch or hybrid CNN input stage |
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""" |
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def __init__(self, img_size=768, **kwargs): |
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super(VIT_MLA_AUXIHead, self).__init__(**kwargs) |
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self.img_size = img_size |
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if self.in_channels==1024: |
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self.aux_0 = nn.Conv2d(self.in_channels, 256, kernel_size=1, bias=False) |
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self.aux_1 = nn.Conv2d(256, self.num_classes, kernel_size=1, bias=False) |
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elif self.in_channels==256: |
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self.aux = nn.Conv2d(self.in_channels, self.num_classes, kernel_size=1, bias=False) |
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def to_2D(self, x): |
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n, hw, c = x.shape |
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h=w = int(math.sqrt(hw)) |
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x = x.transpose(1,2).reshape(n, c, h, w) |
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return x |
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def forward(self, x): |
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x = self._transform_inputs(x) |
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if x.dim()==3: |
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x = x[:,1:] |
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x = self.to_2D(x) |
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if self.in_channels==1024: |
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x = self.aux_0(x) |
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x = self.aux_1(x) |
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elif self.in_channels==256: |
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x = self.aux(x) |
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x = F.interpolate(x, size=self.img_size, mode='bilinear', align_corners=self.align_corners) |
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return x |
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