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import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.functional as F
from einops import rearrange
from rscd.models.decoderheads.help_func import Transformer, TransformerDecoder, TwoLayerConv2d
def init_weights(net, init_type='normal', init_gain=0.02):
"""Initialize network weights.
Parameters:
net (network) -- network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
work better for some applications. Feel free to try yourself.
"""
def init_func(m): # define the initialization function
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
init.normal_(m.weight.data, 0.0, init_gain)
elif init_type == 'xavier':
init.xavier_normal_(m.weight.data, gain=init_gain)
elif init_type == 'kaiming':
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
init.orthogonal_(m.weight.data, gain=init_gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
init.normal_(m.weight.data, 1.0, init_gain)
init.constant_(m.bias.data, 0.0)
print('initialize network with %s' % init_type)
net.apply(init_func) # apply the initialization function <init_func>
class Diff_map(torch.nn.Module):
def __init__(self, input_nc, output_nc,
output_sigmoid=False, if_upsample_2x=True):
"""
In the constructor we instantiate two nn.Linear modules and assign them as
member variables.
"""
super(Diff_map, self).__init__()
self.upsamplex2 = nn.Upsample(scale_factor=2)
self.upsamplex4 = nn.Upsample(scale_factor=4, mode='bilinear')
self.classifier = TwoLayerConv2d(in_channels=input_nc, out_channels=output_nc)
self.if_upsample_2x = if_upsample_2x
self.output_sigmoid = output_sigmoid
self.sigmoid = nn.Sigmoid()
def forward(self, x12):
x = torch.abs(x12[0] - x12[1])
if not self.if_upsample_2x:
x = self.upsamplex2(x)
x = self.upsamplex4(x)
x = self.classifier(x)
if self.output_sigmoid:
x = self.sigmoid(x)
return x
class BASE_Transformer(Diff_map):
"""
Resnet of 8 downsampling + BIT + bitemporal feature Differencing + a small CNN
"""
def __init__(self, input_nc, output_nc, with_pos,
token_len=4, token_trans=True,
enc_depth=1, dec_depth=1,
dim_head=64, decoder_dim_head=64,
tokenizer=True, if_upsample_2x=True,
pool_mode='max', pool_size=2,
decoder_softmax=True, with_decoder_pos=None,
with_decoder=True):
super(BASE_Transformer, self).__init__(input_nc, output_nc,
if_upsample_2x=if_upsample_2x,
)
self.token_len = token_len
self.conv_a = nn.Conv2d(32, self.token_len, kernel_size=1,
padding=0, bias=False)
self.tokenizer = tokenizer
if not self.tokenizer:
# if not use tokenzier，then downsample the feature map into a certain size
self.pooling_size = pool_size
self.pool_mode = pool_mode
self.token_len = self.pooling_size * self.pooling_size
self.token_trans = token_trans
self.with_decoder = with_decoder
dim = 32
mlp_dim = 2*dim
self.with_pos = with_pos
if with_pos == 'learned':
self.pos_embedding = nn.Parameter(torch.randn(1, self.token_len*2, 32))
decoder_pos_size = 256//4
self.with_decoder_pos = with_decoder_pos
if self.with_decoder_pos == 'learned':
self.pos_embedding_decoder =nn.Parameter(torch.randn(1, 32,
decoder_pos_size,
decoder_pos_size))
self.enc_depth = enc_depth
self.dec_depth = dec_depth
self.dim_head = dim_head
self.decoder_dim_head = decoder_dim_head
self.transformer = Transformer(dim=dim, depth=self.enc_depth, heads=8,
dim_head=self.dim_head,
mlp_dim=mlp_dim, dropout=0)
self.transformer_decoder = TransformerDecoder(dim=dim, depth=self.dec_depth,
heads=8, dim_head=self.decoder_dim_head, mlp_dim=mlp_dim, dropout=0,
softmax=decoder_softmax)
def _forward_semantic_tokens(self, x):
b, c, h, w = x.shape
spatial_attention = self.conv_a(x)
spatial_attention = spatial_attention.view([b, self.token_len, -1]).contiguous()
spatial_attention = torch.softmax(spatial_attention, dim=-1)
x = x.view([b, c, -1]).contiguous()
tokens = torch.einsum('bln,bcn->blc', spatial_attention, x)
return tokens
def _forward_reshape_tokens(self, x):
# b,c,h,w = x.shape
if self.pool_mode == 'max':
x = F.adaptive_max_pool2d(x, [self.pooling_size, self.pooling_size])
elif self.pool_mode == 'ave':
x = F.adaptive_avg_pool2d(x, [self.pooling_size, self.pooling_size])
else:
x = x
tokens = rearrange(x, 'b c h w -> b (h w) c')
return tokens
def _forward_transformer(self, x):
if self.with_pos:
x += self.pos_embedding
x = self.transformer(x)
return x
def _forward_transformer_decoder(self, x, m):
b, c, h, w = x.shape
if self.with_decoder_pos == 'fix':
x = x + self.pos_embedding_decoder
elif self.with_decoder_pos == 'learned':
x = x + self.pos_embedding_decoder
x = rearrange(x, 'b c h w -> b (h w) c')
x = self.transformer_decoder(x, m)
x = rearrange(x, 'b (h w) c -> b c h w', h=h)
return x
def _forward_simple_decoder(self, x, m):
b, c, h, w = x.shape
b, l, c = m.shape
m = m.expand([h,w,b,l,c])
m = rearrange(m, 'h w b l c -> l b c h w')
m = m.sum(0)
x = x + m
return x
def forward(self, x12):
x1, x2 = x12[0], x12[1]
# forward tokenzier
if self.tokenizer:
token1 = self._forward_semantic_tokens(x1)
token2 = self._forward_semantic_tokens(x2)
else:
token1 = self._forward_reshape_tokens(x1)
token2 = self._forward_reshape_tokens(x2)
# forward transformer encoder
if self.token_trans:
self.tokens_ = torch.cat([token1, token2], dim=1)
self.tokens = self._forward_transformer(self.tokens_)
token1, token2 = self.tokens.chunk(2, dim=1)
# forward transformer decoder
if self.with_decoder:
x1 = self._forward_transformer_decoder(x1, token1)
x2 = self._forward_transformer_decoder(x2, token2)
else:
x1 = self._forward_simple_decoder(x1, token1)
x2 = self._forward_simple_decoder(x2, token2)
# feature differencing
x = torch.abs(x1 - x2)
if not self.if_upsample_2x:
x = self.upsamplex2(x)
x = self.upsamplex4(x)
# forward small cnn
x = self.classifier(x)
if self.output_sigmoid:
x = self.sigmoid(x)
return x
def base_resnet18(cfg):
net = Diff_map(input_nc=cfg.input_nc,
output_nc=cfg.output_nc,
output_sigmoid=cfg.output_sigmoid)
init_weights(net, cfg.init_type, init_gain=cfg.init_gain)
return net
def base_transformer_pos_s4(cfg):
net = BASE_Transformer(input_nc=cfg.input_nc,
output_nc=cfg.output_nc,
token_len=cfg.token_len,
with_pos=cfg.with_pos)
init_weights(net, cfg.init_type, init_gain=cfg.init_gain)
return net
def base_transformer_pos_s4_dd8(cfg):
net = BASE_Transformer(input_nc=cfg.input_nc,
output_nc=cfg.output_nc,
token_len=cfg.token_len,
with_pos=cfg.with_pos,
enc_depth=cfg.enc_depth,
dec_depth=cfg.dec_depth)
init_weights(net, cfg.init_type, init_gain=cfg.init_gain)
return net
def base_transformer_pos_s4_dd8_dedim8(cfg):
net = BASE_Transformer(input_nc=cfg.input_nc,
output_nc=cfg.output_nc,
token_len=cfg.token_len,
with_pos=cfg.with_pos,
enc_depth=cfg.enc_depth,
dec_depth=cfg.dec_depth,
dim_head=cfg.dim_head,
decoder_dim_head=cfg.decoder_dim_head)
init_weights(net, cfg.init_type, init_gain=cfg.init_gain)
return net
if __name__ == "__main__":
x1 = torch.randn(4,32,128,128)
x2 = torch.randn(4,32,128,128)
cfg = dict(
type = 'base_transformer_pos_s4_dd8_dedim8',
input_nc=32,
output_nc=2,
token_len=4,
with_pos='learned',
enc_depth=1,
dec_depth=8,
dim_head=8,
decoder_dim_head=8,
init_type='normal',
init_gain=0.02,
)
from munch import DefaultMunch
cfg = DefaultMunch.fromDict(cfg)
model = base_transformer_pos_s4_dd8_dedim8(cfg)
outs = model([x1, x2])
print('BIT_head', outs)
print(outs.shape) |