67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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@project:
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@File : temple_part
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@Author : qiqq
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@create_time : 2023/5/22 22:38
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"""
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import torch
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from torch import nn
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import torch.nn.functional as F
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import numpy as np
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import math
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# class upmodel(nn.Module):
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#
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# def __init__(self,
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# in_channels,
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# out_channels,
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# in_feat_output_strides=(4, 8, 16, 32),
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# out_feat_output_stride=4,
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# ):
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# super(upmodel, self).__init__()
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# self.blocks = nn.ModuleList()
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# for in_feat_os in in_feat_output_strides:
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# num_upsample = int(math.log2(int(in_feat_os))) - int(math.log2(int(out_feat_output_stride)))
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#
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# num_layers = num_upsample if num_upsample != 0 else 1
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#
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# self.blocks.append(nn.Sequential(*[
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# nn.Sequential(
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# nn.Conv2d(in_channels if idx == 0 else out_channels, out_channels, 3, 1, 1, bias=False),
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# nn.BatchNorm2d(out_channels),
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# nn.ReLU(inplace=True),
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# nn.UpsamplingBilinear2d(scale_factor=2) if num_upsample != 0 else nn.Identity(),
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# )
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# for idx in range(num_layers)]))
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#
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# def forward(self, feat_list: list):
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# inner_feat_list = []
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# for idx, block in enumerate(self.blocks):
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# decoder_feat = block(feat_list[idx])
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# inner_feat_list.append(decoder_feat)
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#
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# out_feat = sum(inner_feat_list) / 4. #待定
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# return out_feat
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#
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#
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# impo1=torch.rand(1,64,128,128)
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# impo2=torch.rand(1,256,64,64)
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# impo1=torch.rand(1,512,32,32)
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# impo1=torch.rand(1,768,16,16)
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#
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# lis=[torch.rand(1,64,128,128),
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# torch.rand(1,256,64,64),
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# torch.rand(1,512,32,32),
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# torch.rand(1,768,16,16)
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# ]
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#
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# model=upmodel()
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