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