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from model import common
# from Train.model import common
import torch
import torch.nn as nn
import torch.nn.functional as F
def make_model(args, parent=False):
return SMGARN(args)
class SnowMaskBlock(nn.Module):
def __init__(self, embed_dim):
super(SnowMaskBlock, self).__init__()
self.smblock = common.MaskBlock(embed_dim)
self.conv3 = common.default_conv(embed_dim, embed_dim, 3)
self.norm = nn.LayerNorm(embed_dim)
def forward(self, x):
B, H, W = x.shape[0], x.shape[2], x.shape[3]
shortcut = x
x = self.smblock(x)
x = self.norm(x.flatten(2).transpose(-1, -2))
x = self.conv3(x.transpose(-1, -2).view(B, -1, H, W))
return x + shortcut
class Mask_Net(nn.Module):
def __init__(self, n_colors, embed_dim, conv):
super(Mask_Net, self).__init__()
h = []
h.append(conv(n_colors, embed_dim, 3))
h.append(conv(embed_dim, embed_dim, 3))
self.head = nn.Sequential(*h)
self.g_mp1 = SnowMaskBlock(embed_dim)
self.conv_out1 = common.default_conv(embed_dim, embed_dim, 3)
self.conv_out2 = common.default_conv(embed_dim, 3, 3)
def forward(self, x):
x = self.head(x)
out_1 = self.g_mp1(x)
out_1 = self.conv_out1(out_1)
out = self.conv_out2(out_1)
return out, out_1
class ReconstructNet(nn.Module):
def __init__(self, n_colors, dim, depth):
super(ReconstructNet, self).__init__()
self.fusion = common.FusionBlock(n_colors, dim)
block = []
for i in range(depth):
block.append(common.MARB(dim))
self.recon = nn.Sequential(*block)
t = []
t.append(common.default_conv(dim, dim, 3))
t.append(nn.ReLU(True))
t.append(common.default_conv(dim, n_colors, 3))
self.tail = nn.Sequential(*t)
def forward(self, x, mask):
x = self.fusion(x, mask)
out = self.recon(x) + x
out = self.tail(out)
return out
class SMGARN(nn.Module):
def __init__(self, args, conv=common.default_conv):
super(SMGARN, self).__init__()
print("SMGARN")
n_colors = 3
dim = 112
ReconNet_num = 3
self.Stage_I = Mask_Net(n_colors=n_colors, embed_dim=dim, conv=conv)
self.Stage_II = ReconstructNet(n_colors, dim, ReconNet_num)
def forward(self, x):
mask, mask_feature = self.Stage_I(x)
x = self.Stage_II(x, mask_feature)
return x, mask |