rscd/models/decoderheads/BIThead.py

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)