61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: OSAG.py
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# Created Date: Tuesday April 28th 2022
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# Author: Chen Xuanhong
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Sunday, 23rd April 2023 3:08:49 pm
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# Modified By: Chen Xuanhong
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# Copyright (c) 2020 Shanghai Jiao Tong University
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#############################################################
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import torch.nn as nn
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from .esa import ESA
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from .OSA import OSA_Block
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class OSAG(nn.Module):
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def __init__(
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self,
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channel_num=64,
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bias=True,
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block_num=4,
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ffn_bias=False,
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window_size=0,
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pe=False,
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):
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super(OSAG, self).__init__()
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# print("window_size: %d" % (window_size))
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# print("with_pe", pe)
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# print("ffn_bias: %d" % (ffn_bias))
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# block_script_name = kwargs.get("block_script_name", "OSA")
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# block_class_name = kwargs.get("block_class_name", "OSA_Block")
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# script_name = "." + block_script_name
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# package = __import__(script_name, fromlist=True)
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block_class = OSA_Block # getattr(package, block_class_name)
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group_list = []
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for _ in range(block_num):
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temp_res = block_class(
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channel_num,
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bias,
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ffn_bias=ffn_bias,
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window_size=window_size,
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with_pe=pe,
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)
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group_list.append(temp_res)
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group_list.append(nn.Conv2d(channel_num, channel_num, 1, 1, 0, bias=bias))
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self.residual_layer = nn.Sequential(*group_list)
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esa_channel = max(channel_num // 4, 16)
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self.esa = ESA(esa_channel, channel_num)
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def forward(self, x):
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out = self.residual_layer(x)
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out = out + x
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return self.esa(out)
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