赞
踩
预训练方案:将实例注释重新定义为区域-文本对,通过大规模检测、定位和图像-文本数据进行预训练。
模型架构:YOLO-World由YOLO检测器、文本编码器和RepVL-PAN组成,利用跨模态融合增强文本和图像表示
RepVL-PAN由多尺度图像特征{C3, C4, C5}形成,利用了自顶向下和自底向上的路径来加强图像特征和文本特征之间的交互。
又快又好!V100上达到了52FPS!!!
class RepConvMaxSigmoidAttnBlock(BaseModule): """Max Sigmoid attention block.""" def __init__(self, in_channels: int, out_channels: int, embed_channels: int, guide_channels: int, kernel_size: int = 3, padding: int = 1, num_heads: int = 1, use_depthwise: bool = False, with_scale: bool = False, conv_cfg: OptConfigType = None, norm_cfg: ConfigType = dict(type='BN', momentum=0.03, eps=0.001), init_cfg: OptMultiConfig = None, use_einsum: bool = True) -> None: super().__init__(init_cfg=init_cfg) conv = DepthwiseSeparableConvModule if use_depthwise else ConvModule assert (out_channels % num_heads == 0 and embed_channels % num_heads == 0), \ 'out_channels and embed_channels should be divisible by num_heads.' self.num_heads = num_heads self.head_channels = out_channels // num_heads self.use_einsum = use_einsum self.embed_conv = ConvModule( in_channels, embed_channels, 1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=None) if embed_channels != in_channels else None self.bias = nn.Parameter(torch.zeros(num_heads)) self.num_heads = num_heads self.split_channels = embed_channels // num_heads self.guide_convs = nn.ModuleList( nn.Conv2d(self.split_channels, guide_channels, 1, bias=False) for _ in range(num_heads)) self.project_conv = conv(in_channels, out_channels, kernel_size, stride=1, padding=padding, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=None) def forward(self, x: Tensor, txt_feats: Tensor = None) -> Tensor: """Forward process.""" B, C, H, W = x.shape embed = self.embed_conv(x) if self.embed_conv is not None else x embed = list(embed.split(self.split_channels, 1)) # Bx(MxN)xHxW (H*c=C, H: heads) attn_weight = torch.cat( [conv(x) for conv, x in zip(self.guide_convs, embed)], dim=1) # BxMxNxHxW attn_weight = attn_weight.view(B, self.num_heads, -1, H, W) # attn_weight = torch.stack( # [conv(x) for conv, x in zip(self.guide_convs, embed)]) # BxMxNxHxW -> BxMxHxW attn_weight = attn_weight.max(dim=2)[0] / (self.head_channels**0.5) attn_weight = (attn_weight + self.bias.view(1, -1, 1, 1)).sigmoid() # .transpose(0, 1) # BxMx1xHxW attn_weight = attn_weight[:, :, None] x = self.project_conv(x) # BxHxCxHxW x = x.view(B, self.num_heads, -1, H, W) x = x * attn_weight x = x.view(B, -1, H, W) return x
class ImagePoolingAttentionModule(nn.Module): def __init__(self, image_channels: List[int], text_channels: int, embed_channels: int, with_scale: bool = False, num_feats: int = 3, num_heads: int = 8, pool_size: int = 3, use_einsum: bool = True): super().__init__() self.text_channels = text_channels self.embed_channels = embed_channels self.num_heads = num_heads self.num_feats = num_feats self.head_channels = embed_channels // num_heads self.pool_size = pool_size self.use_einsum = use_einsum if with_scale: self.scale = nn.Parameter(torch.tensor([0.]), requires_grad=True) else: self.scale = 1.0 self.projections = nn.ModuleList([ ConvModule(in_channels, embed_channels, 1, act_cfg=None) for in_channels in image_channels ]) self.query = nn.Sequential(nn.LayerNorm(text_channels), Linear(text_channels, embed_channels)) self.key = nn.Sequential(nn.LayerNorm(embed_channels), Linear(embed_channels, embed_channels)) self.value = nn.Sequential(nn.LayerNorm(embed_channels), Linear(embed_channels, embed_channels)) self.proj = Linear(embed_channels, text_channels) self.image_pools = nn.ModuleList([ nn.AdaptiveMaxPool2d((pool_size, pool_size)) for _ in range(num_feats) ]) def forward(self, text_features, image_features): B = image_features[0].shape[0] assert len(image_features) == self.num_feats num_patches = self.pool_size**2 mlvl_image_features = [ pool(proj(x)).view(B, -1, num_patches) for (x, proj, pool ) in zip(image_features, self.projections, self.image_pools) ] mlvl_image_features = torch.cat(mlvl_image_features, dim=-1).transpose(1, 2) q = self.query(text_features) k = self.key(mlvl_image_features) v = self.value(mlvl_image_features) q = q.reshape(B, -1, self.num_heads, self.head_channels) k = k.reshape(B, -1, self.num_heads, self.head_channels) v = v.reshape(B, -1, self.num_heads, self.head_channels) if self.use_einsum: attn_weight = torch.einsum('bnmc,bkmc->bmnk', q, k) else: q = q.permute(0, 2, 1, 3) k = k.permute(0, 2, 3, 1) attn_weight = torch.matmul(q, k) attn_weight = attn_weight / (self.head_channels**0.5) attn_weight = F.softmax(attn_weight, dim=-1) if self.use_einsum: x = torch.einsum('bmnk,bkmc->bnmc', attn_weight, v) else: v = v.permute(0, 2, 1, 3) x = torch.matmul(attn_weight, v) x = x.permute(0, 2, 1, 3) x = self.proj(x.reshape(B, -1, self.embed_channels)) return x * self.scale + text_features
参考:https://github.com/AILab-CVC/YOLO-World/blob/master/yolo_world/models/layers/yolo_bricks.py
Copyright © 2003-2013 www.wpsshop.cn 版权所有,并保留所有权利。