import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange, repeat from timm.models.layers import DropPath, to_2tuple, trunc_normal_ import timm class ConvBNReLU(nn.Sequential): def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, norm_layer=nn.BatchNorm2d, bias=False): super(ConvBNReLU, self).__init__( nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias, dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2), norm_layer(out_channels), nn.ReLU6() ) class ConvBN(nn.Sequential): def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, norm_layer=nn.BatchNorm2d, bias=False): super(ConvBN, self).__init__( nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias, dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2), norm_layer(out_channels) ) class Conv(nn.Sequential): def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, bias=False): super(Conv, self).__init__( nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias, dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2) ) class SeparableConvBNReLU(nn.Sequential): def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, norm_layer=nn.BatchNorm2d): super(SeparableConvBNReLU, self).__init__( nn.Conv2d(in_channels, in_channels, kernel_size, stride=stride, dilation=dilation, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2, groups=in_channels, bias=False), norm_layer(out_channels), nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), nn.ReLU6() ) class SeparableConvBN(nn.Sequential): def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, norm_layer=nn.BatchNorm2d): super(SeparableConvBN, self).__init__( nn.Conv2d(in_channels, in_channels, kernel_size, stride=stride, dilation=dilation, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2, groups=in_channels, bias=False), norm_layer(out_channels), nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) ) class SeparableConv(nn.Sequential): def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1): super(SeparableConv, self).__init__( nn.Conv2d(in_channels, in_channels, kernel_size, stride=stride, dilation=dilation, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2, groups=in_channels, bias=False), nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) ) class Mlp(nn.Module): def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU6, drop=0.): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Conv2d(in_features, hidden_features, 1, 1, 0, bias=True) self.act = act_layer() self.fc2 = nn.Conv2d(hidden_features, out_features, 1, 1, 0, bias=True) self.drop = nn.Dropout(drop, inplace=True) def forward(self, x): x = self.fc1(x) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class GlobalLocalAttention(nn.Module): def __init__(self, dim=256, num_heads=16, qkv_bias=False, window_size=8, relative_pos_embedding=True ): super().__init__() self.num_heads = num_heads head_dim = dim // self.num_heads self.scale = head_dim ** -0.5 self.ws = window_size self.qkv = Conv(dim, 3 * dim, kernel_size=1, bias=qkv_bias) self.local1 = ConvBN(dim, dim, kernel_size=3) self.local2 = ConvBN(dim, dim, kernel_size=1) self.proj = SeparableConvBN(dim, dim, kernel_size=window_size) self.attn_x = nn.AvgPool2d(kernel_size=(window_size, 1), stride=1, padding=(window_size // 2 - 1, 0)) self.attn_y = nn.AvgPool2d(kernel_size=(1, window_size), stride=1, padding=(0, window_size // 2 - 1)) self.relative_pos_embedding = relative_pos_embedding if self.relative_pos_embedding: # define a parameter table of relative position bias self.relative_position_bias_table = nn.Parameter( torch.zeros((2 * window_size - 1) * (2 * window_size - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH # get pair-wise relative position index for each token inside the window coords_h = torch.arange(self.ws) coords_w = torch.arange(self.ws) coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 relative_coords[:, :, 0] += self.ws - 1 # shift to start from 0 relative_coords[:, :, 1] += self.ws - 1 relative_coords[:, :, 0] *= 2 * self.ws - 1 relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww self.register_buffer("relative_position_index", relative_position_index) trunc_normal_(self.relative_position_bias_table, std=.02) def pad(self, x, ps): _, _, H, W = x.size() if W % ps != 0: # x = F.pad(x, (0, ps - W % ps), mode='reflect') x = F.pad(x, (0, ps - W % ps,0,0), mode='reflect') if H % ps != 0: x = F.pad(x, (0, 0, 0, ps - H % ps), mode='reflect') return x def pad_out(self, x): x = F.pad(x, pad=(0, 1, 0, 1), mode='reflect') return x def forward(self, x): B, C, H, W = x.shape local = self.local2(x) + self.local1(x) x = self.pad(x, self.ws) B, C, Hp, Wp = x.shape qkv = self.qkv(x) q, k, v = rearrange(qkv, 'b (qkv h d) (hh ws1) (ww ws2) -> qkv (b hh ww) h (ws1 ws2) d', h=self.num_heads, d=C // self.num_heads, hh=Hp // self.ws, ww=Wp // self.ws, qkv=3, ws1=self.ws, ws2=self.ws) dots = (q @ k.transpose(-2, -1)) * self.scale if self.relative_pos_embedding: relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( self.ws * self.ws, self.ws * self.ws, -1) # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww dots += relative_position_bias.unsqueeze(0) attn = dots.softmax(dim=-1) attn = attn @ v attn = rearrange(attn, '(b hh ww) h (ws1 ws2) d -> b (h d) (hh ws1) (ww ws2)', h=self.num_heads, d=C // self.num_heads, hh=Hp // self.ws, ww=Wp // self.ws, ws1=self.ws, ws2=self.ws) attn = attn[:, :, :H, :W] out = self.attn_x(F.pad(attn, pad=(0, 0, 0, 1), mode='reflect')) + \ self.attn_y(F.pad(attn, pad=(0, 1, 0, 0), mode='reflect')) out = out + local out = self.pad_out(out) out = self.proj(out) # print(out.size()) out = out[:, :, :H, :W] return out class Block(nn.Module): def __init__(self, dim=256, num_heads=16, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.ReLU6, norm_layer=nn.BatchNorm2d, window_size=8): super().__init__() self.norm1 = norm_layer(dim) self.attn = GlobalLocalAttention(dim, num_heads=num_heads, qkv_bias=qkv_bias, window_size=window_size) self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, out_features=dim, act_layer=act_layer, drop=drop) self.norm2 = norm_layer(dim) def forward(self, x): x = x + self.drop_path(self.attn(self.norm1(x))) x = x + self.drop_path(self.mlp(self.norm2(x))) return x class WF(nn.Module): def __init__(self, in_channels=128, decode_channels=128, eps=1e-8): super(WF, self).__init__() self.pre_conv = Conv(in_channels, decode_channels, kernel_size=1) self.weights = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) self.eps = eps self.post_conv = ConvBNReLU(decode_channels, decode_channels, kernel_size=3) def forward(self, x, res): # x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False) x = F.interpolate(x, size=res.shape[2:], mode='bilinear', align_corners=False) weights = nn.ReLU()(self.weights) fuse_weights = weights / (torch.sum(weights, dim=0) + self.eps) x = fuse_weights[0] * self.pre_conv(res) + fuse_weights[1] * x x = self.post_conv(x) return x class FeatureRefinementHead(nn.Module): def __init__(self, in_channels=64, decode_channels=64): super().__init__() self.pre_conv = Conv(in_channels, decode_channels, kernel_size=1) self.weights = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) self.eps = 1e-8 self.post_conv = ConvBNReLU(decode_channels, decode_channels, kernel_size=3) self.pa = nn.Sequential( nn.Conv2d(decode_channels, decode_channels, kernel_size=3, padding=1, groups=decode_channels), nn.Sigmoid()) self.ca = nn.Sequential(nn.AdaptiveAvgPool2d(1), Conv(decode_channels, decode_channels // 16, kernel_size=1), nn.ReLU6(), Conv(decode_channels // 16, decode_channels, kernel_size=1), nn.Sigmoid()) self.shortcut = ConvBN(decode_channels, decode_channels, kernel_size=1) self.proj = SeparableConvBN(decode_channels, decode_channels, kernel_size=3) self.act = nn.ReLU6() def forward(self, x, res): x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False) weights = nn.ReLU()(self.weights) fuse_weights = weights / (torch.sum(weights, dim=0) + self.eps) x = fuse_weights[0] * self.pre_conv(res) + fuse_weights[1] * x x = self.post_conv(x) shortcut = self.shortcut(x) pa = self.pa(x) * x ca = self.ca(x) * x x = pa + ca x = self.proj(x) + shortcut x = self.act(x) return x class AuxHead(nn.Module): def __init__(self, in_channels=64, num_classes=8): super().__init__() self.conv = ConvBNReLU(in_channels, in_channels) self.drop = nn.Dropout(0.1) self.conv_out = Conv(in_channels, num_classes, kernel_size=1) def forward(self, x, h, w): feat = self.conv(x) feat = self.drop(feat) feat = self.conv_out(feat) feat = F.interpolate(feat, size=(h, w), mode='bilinear', align_corners=False) return feat class Decoder(nn.Module): def __init__(self, encoder_channels=(64, 128, 256, 512), decode_channels=64, dropout=0.1, window_size=8, num_classes=6): super(Decoder, self).__init__() self.pre_conv = ConvBN(encoder_channels[-1], decode_channels, kernel_size=1) self.b4 = Block(dim=decode_channels, num_heads=8, window_size=window_size) self.b3 = Block(dim=decode_channels, num_heads=8, window_size=window_size) self.p3 = WF(encoder_channels[-2], decode_channels) self.b2 = Block(dim=decode_channels, num_heads=8, window_size=window_size) self.p2 = WF(encoder_channels[-3], decode_channels) self.p1 = FeatureRefinementHead(encoder_channels[-4], decode_channels) self.segmentation_head = nn.Sequential(ConvBNReLU(decode_channels, decode_channels), nn.Dropout2d(p=dropout, inplace=True), Conv(decode_channels, num_classes, kernel_size=1)) self.init_weight() if self.training: self.up4 = nn.UpsamplingBilinear2d(scale_factor=4) self.up3 = nn.UpsamplingBilinear2d(scale_factor=2) self.aux_head = AuxHead(decode_channels, num_classes) self.p1 = FeatureRefinementHead(encoder_channels[-4], decode_channels) self.segmentation_head = nn.Sequential(ConvBNReLU(decode_channels, decode_channels), nn.Dropout2d(p=dropout, inplace=True), Conv(decode_channels, num_classes, kernel_size=1)) self.init_weight() def forward(self, res1, res2, res3, res4, h, w): x = self.b4(self.pre_conv(res4)) h4 = self.up4(x) x = self.p3(x, res3) x = self.b3(x) h3 = self.up3(x) x = self.p2(x, res2) x = self.b2(x) h2 = x x = self.p1(x, res1) x = self.segmentation_head(x) x = F.interpolate(x, size=(h, w), mode='bilinear', align_corners=False) ah = h4 + h3 + h2 ah = self.aux_head(ah, h, w) return x, ah def init_weight(self): for m in self.children(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, a=1) if m.bias is not None: nn.init.constant_(m.bias, 0) from mmseg.registry import MODELS @MODELS.register_module() class UNetFormer(nn.Module): def __init__(self, decode_channels=64, dropout=0.1, backbone_name='swsl_resnet18', pretrained=False, window_size=8, num_classes=6 ): super().__init__() self.backbone = timm.create_model(backbone_name, features_only=True, output_stride=32, out_indices=(1, 2, 3, 4), pretrained=False) encoder_channels = self.backbone.feature_info.channels() self.decoder = Decoder(encoder_channels, decode_channels, dropout, window_size, num_classes) def forward(self, x): h, w = x.size()[-2:] res1, res2, res3, res4 = self.backbone(x) x,ah = self.decoder(res1, res2, res3, res4, h, w) return x,ah if __name__ == '__main__': import time from thop import profile device = torch.device('cuda:3') model = UNetFormer().to('cuda:3') model.eval() model.to(device) iterations = None input = torch.randn(1, 3, 2448, 2448).to('cuda:3') with torch.no_grad(): for _ in range(10): model(input) if iterations is None: elapsed_time = 0 iterations = 100 while elapsed_time < 1: torch.cuda.synchronize() torch.cuda.synchronize() t_start = time.time() for _ in range(iterations): model(input) torch.cuda.synchronize() torch.cuda.synchronize() elapsed_time = time.time() - t_start iterations *= 2 FPS = iterations / elapsed_time iterations = int(FPS * 6) print('=========Speed Testing=========') torch.cuda.synchronize() torch.cuda.synchronize() t_start = time.time() for _ in range(iterations): model(input) torch.cuda.synchronize() torch.cuda.synchronize() elapsed_time = time.time() - t_start latency = elapsed_time / iterations * 1000 torch.cuda.empty_cache() FPS = 1000 / latency print(FPS)