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