Net_BiT_DASeg.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
import numpy as np
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg
from lavaz_loss import lovasz_hinge2
import math


class DWConv(nn.Module):
    def __init__(self, dim=768):
        super(DWConv, self).__init__()
        self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)

    def forward(self, x, H, W):
        B, N, C = x.shape
        x = x.transpose(1, 2).view(B, C, H, W)
        x = self.dwconv(x)
        x = x.flatten(2).transpose(1, 2)

        return x


class Mlp(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.dwconv = DWConv(hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x, H, W):
        x = self.fc1(x)
        x = self.dwconv(x, H, W)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."

        self.dim = dim
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.q = nn.Linear(dim, dim, bias=qkv_bias)
        self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        self.sr_ratio = sr_ratio
        if sr_ratio > 1:
            self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
            self.norm = nn.LayerNorm(dim)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x, H, W):
        B, N, C = x.shape
        q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)

        if self.sr_ratio > 1:
            x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
            x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
            x_ = self.norm(x_)
            kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        else:
            kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        k, v = kv[0], kv[1]

        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)

        return x


class Block(nn.Module):

    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim,
            num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
            attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x, H, W):
        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
        x = x + self.drop_path(self.mlp(self.norm2(x), H, W))

        return x


class OverlapPatchEmbed(nn.Module):
    """ Image to Patch Embedding
    """

    def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)

        self.img_size = img_size
        self.patch_size = patch_size
        self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
        self.num_patches = self.H * self.W
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,
                              padding=(patch_size[0] // 2, patch_size[1] // 2))
        self.norm = nn.LayerNorm(embed_dim)

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def forward(self, x):
        x = self.proj(x)
        _, _, H, W = x.shape
        x = x.flatten(2).transpose(1, 2)
        x = self.norm(x)

        return x, H, W


class MixVisionTransformer(nn.Module):
    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
                 num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
                 attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
                 depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
        super().__init__()
        self.num_classes = num_classes
        self.depths = depths
        self.embed_dims = embed_dims

        # patch_embed
        self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_chans=in_chans,
                                              embed_dim=embed_dims[0])
        self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0],
                                              embed_dim=embed_dims[1])
        self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1],
                                              embed_dim=embed_dims[2])
        self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_chans=embed_dims[2],
                                              embed_dim=embed_dims[3])

        # transformer encoder
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
        cur = 0
        self.block1 = nn.ModuleList([Block(
            dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[0])
            for i in range(depths[0])])
        self.norm1 = norm_layer(embed_dims[0])

        cur += depths[0]
        self.block2 = nn.ModuleList([Block(
            dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[1])
            for i in range(depths[1])])
        self.norm2 = norm_layer(embed_dims[1])

        cur += depths[1]
        self.block3 = nn.ModuleList([Block(
            dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[2])
            for i in range(depths[2])])
        self.norm3 = norm_layer(embed_dims[2])

        cur += depths[2]
        self.block4 = nn.ModuleList([Block(
            dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
            drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
            sr_ratio=sr_ratios[3])
            for i in range(depths[3])])
        self.norm4 = norm_layer(embed_dims[3])

        # classification head
        # self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()

        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            fan_out //= m.groups
            m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
            if m.bias is not None:
                m.bias.data.zero_()

    def init_weights(self, pretrained=None):
        pass

    # if isinstance(pretrained, str):
    # 	logger = get_root_logger()
    # 	load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)

    def reset_drop_path(self, drop_path_rate):
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
        cur = 0
        for i in range(self.depths[0]):
            self.block1[i].drop_path.drop_prob = dpr[cur + i]

        cur += self.depths[0]
        for i in range(self.depths[1]):
            self.block2[i].drop_path.drop_prob = dpr[cur + i]

        cur += self.depths[1]
        for i in range(self.depths[2]):
            self.block3[i].drop_path.drop_prob = dpr[cur + i]

        cur += self.depths[2]
        for i in range(self.depths[3]):
            self.block4[i].drop_path.drop_prob = dpr[cur + i]

    def freeze_patch_emb(self):
        self.patch_embed1.requires_grad = False

    @torch.jit.ignore
    def no_weight_decay(self):
        return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'}  # has pos_embed may be better

    def get_classifier(self):
        return self.head

    def reset_classifier(self, num_classes, global_pool=''):
        self.num_classes = num_classes
        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()

    def forward_features(self, x):
        B = x.shape[0]
        outs = []

        # stage 1
        x, H, W = self.patch_embed1(x)
        for i, blk in enumerate(self.block1):
            x = blk(x, H, W)
        x = self.norm1(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
        outs.append(x)

        # stage 2
        x, H, W = self.patch_embed2(x)
        for i, blk in enumerate(self.block2):
            x = blk(x, H, W)
        x = self.norm2(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
        outs.append(x)

        # stage 3
        x, H, W = self.patch_embed3(x)
        for i, blk in enumerate(self.block3):
            x = blk(x, H, W)
        x = self.norm3(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
        outs.append(x)

        # stage 4
        x, H, W = self.patch_embed4(x)
        for i, blk in enumerate(self.block4):
            x = blk(x, H, W)
        x = self.norm4(x)
        x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
        outs.append(x)

        return outs

    def forward(self, x):
        x = self.forward_features(x)
        # x = self.head(x)

        return x

    
class _ASPPModule(nn.Module):
    def __init__(self, inplanes, planes, kernel_size, padding, dilation, groups=1):
        super().__init__()
        self.depth = nn.Conv2d(inplanes, inplanes, kernel_size=kernel_size,
                                     stride=1, padding=padding, dilation=dilation, bias=False, groups=groups)
        self.point = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU()

        self._init_weight()

    def forward(self, x):
        x = self.depth(x)
        x = self.point(x)
        x = self.bn(x)

        return self.relu(x)

    def _init_weight(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                torch.nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()


class ASPP(nn.Module):
    def __init__(self, inplanes=512, mid_c=256, dilations=[6, 12, 18, 24], out_c=None):
        super().__init__()
        self.aspps = [_ASPPModule(inplanes, mid_c, 1, padding=0, dilation=1)] + \
            [_ASPPModule(inplanes, mid_c, 3, padding=d, dilation=d,groups=4) for d in dilations]
        self.aspps = nn.ModuleList(self.aspps)
        self.global_pool = nn.Sequential(nn.AdaptiveMaxPool2d((1, 1)),
                        nn.Conv2d(inplanes, mid_c, 1, stride=1, bias=False),
                        nn.BatchNorm2d(mid_c), nn.ReLU())
        out_c = out_c if out_c is not None else mid_c
        self.out_conv = nn.Sequential(nn.Conv2d(2304, out_c, 3, 1, 1, bias=False),
                                    nn.BatchNorm2d(out_c), nn.ReLU(inplace=True))
        self.conv1 = nn.Conv2d(mid_c*(2+len(dilations)), out_c, 1, bias=False)
        self._init_weight()

    def forward(self, x):
        x0 = x
        xs = [aspp(x) for aspp in self.aspps]
        x0 = F.interpolate(x0, size=xs[0].size()[2:], mode='bilinear', align_corners=True)
        x = torch.cat([x0] + xs, dim=1)
        return self.out_conv(x)

    def _init_weight(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                torch.nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
                

class mit_b0(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b0, self).__init__(
            patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b1(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b1, self).__init__(
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b2(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b2, self).__init__(
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b3(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b3, self).__init__(
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b4(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b4, self).__init__(
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


class mit_b5(MixVisionTransformer):
    def __init__(self, **kwargs):
        super(mit_b5, self).__init__(
            patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
            qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
            drop_rate=0.0, drop_path_rate=0.1)


pretrain_dir = '/root/.cache/torch/hub/checkpoints'
if 1:
    cfg = dict(
        mit_b0=dict(
            checkpoint=pretrain_dir + '/mit_b0.pth',
            builder=mit_b0,
        ),
        mit_b1=dict(
            checkpoint=pretrain_dir + '/mit_b1.pth',
            builder=mit_b1,
        ),
        mit_b2=dict(
            checkpoint=pretrain_dir + '/segformer_b2_backbone_imagenet-3c162bb8.pth',
            builder=mit_b2,
        ),
        mit_b3=dict(
            checkpoint=pretrain_dir + '/mit_b3.pth',
            builder=mit_b3,
        ),

    )


class MixUpSample(nn.Module):
    def __init__(self, scale_factor=2):
        super().__init__()
        self.mixing = nn.Parameter(torch.tensor(0.5))
        self.scale_factor = scale_factor

    def forward(self, x):
        x = self.mixing * F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear', align_corners=False) \
            + (1 - self.mixing) * F.interpolate(x, scale_factor=self.scale_factor, mode='nearest')
        return x


class SegformerDecoder(nn.Module):
    def __init__(
            self,
            encoder_dim=[64, 128, 320, 512],
            decoder_dim=256,
    ):
        super().__init__()
        self.mixing = nn.Parameter(torch.FloatTensor([0.5, 0.5, 0.5, 0.5]))
        self.mlp = nn.ModuleList([
            nn.Sequential(
                nn.Conv2d(dim, decoder_dim, 1, padding=0, bias=False),  # follow mmseg to use conv-bn-relu
                MixUpSample(2 ** i) if i != 0 else nn.Identity(),
            ) for i, dim in enumerate(encoder_dim)])

        self.aspp = ASPP(1024, 256, out_c=256, dilations=[1, 6, 12, 18])

    def forward(self, feature):
        out = []
        for i, f in enumerate(feature):
            f = self.mlp[i](f)
            out.append(f)

        x = self.aspp(torch.cat(out, dim=1))
        return x, out


class RGB(nn.Module):
    IMAGE_RGB_MEAN = [0.485, 0.456, 0.406]  # [0.5, 0.5, 0.5]
    IMAGE_RGB_STD = [0.229, 0.224, 0.225]  # [0.5, 0.5, 0.5]

    def __init__(self, ):
        super(RGB, self).__init__()
        self.register_buffer('mean', torch.zeros(1, 3, 1, 1))
        self.register_buffer('std', torch.ones(1, 3, 1, 1))
        self.mean.data = torch.FloatTensor(self.IMAGE_RGB_MEAN).view(self.mean.shape).cuda()
        self.std.data = torch.FloatTensor(self.IMAGE_RGB_STD).view(self.std.shape).cuda()

    def forward(self, x):
        x = (x - self.mean) / self.std
        return x


class Net5(nn.Module):
    def load_pretrain(self, ):
        checkpoint = cfg[self.arch]['checkpoint']
        print('load %s' % checkpoint)
        checkpoint = torch.load(checkpoint, map_location=lambda storage, loc: storage)  # True
        print(self.encoder.load_state_dict(checkpoint, strict=False))  # True

    def __init__(self, ):
        super(Net5, self).__init__()
        self.output_type = ['inference', 'loss']
        self.rgb = RGB()
        self.dropout = nn.Dropout(0.1)

        self.arch = 'mit_b2'
        self.encoder = cfg[self.arch]['builder']()
        encoder_dim = self.encoder.embed_dims
        # [64, 128, 320, 512]

        self.decoder = SegformerDecoder(
            encoder_dim=encoder_dim,
            decoder_dim=256,
        )
        self.logit = nn.Sequential(
            nn.Conv2d(256, 1, kernel_size=1, padding=0),
        )
        self.aux = nn.ModuleList([
            nn.Conv2d(encoder_dim[i], 1, kernel_size=1, padding=0) for i in range(4)
        ])

    def forward(self, batch):

        x = batch['image']
        x = self.rgb(x)

        B, C, H, W = x.shape
        encoder = self.encoder(x)
        # print([f.shape for f in encoder])

        last, decoder = self.decoder(encoder)
        last = self.dropout(last)
        logit = self.logit(last)
        logit = F.interpolate(logit, size=None, scale_factor=4, mode='bilinear', align_corners=False)
        # print(logit.shape)

        output = {}
        if 'loss' in self.output_type:
            if 0:
                edge_weight = 6.
                clsy = batch['cls'].cpu().numpy()
                batch['edge'][batch['edge'] == 0] = 1.
                batch['edge'][batch['edge'] == 1] = edge_weight
                if 2 in clsy:
                    index = np.argwhere(clsy != 2)
                    batch['edge'][index, :, :, :] = 1
                    output['bce_loss'] = F.binary_cross_entropy_with_logits(logit, batch['mask'], reduction='none')
                    output['bce_loss'] *= batch['edge']
                else:
                    output['bce_loss'] = F.binary_cross_entropy_with_logits(logit, batch['mask'], reduction='none')
            if 1:
                output['bce_loss'] = F.binary_cross_entropy_with_logits(logit, batch['mask'])
#                 output['bce_loss'] += lovasz_hinge2(logit.view(-1, 768, 768), batch['mask'].view(-1, 768, 768))
            for i in range(4):
                output['aux%d_loss' % i] = criterion_aux_loss(self.aux[i](encoder[i]), batch['mask'])
        if 'inference' in self.output_type:
            output['probability'] = torch.sigmoid(logit)

        return output


def criterion_aux_loss(logit, mask):
    mask = F.interpolate(mask, size=logit.shape[-2:], mode='nearest')
    loss = F.binary_cross_entropy_with_logits(logit, mask)
    return loss


if __name__ == '__main__':
    model = Net5()
    model.load_pretrain()
    input = {}
    input['image'] = torch.rand(2, 3, 768, 768)
    input['mask'] = torch.ones((2, 1, 768, 768))
    output = model(input)

    total = sum([param.nelement() for param in model.parameters()])
    print("Number of parameters: %.2fM" % (total / 1e6))