import torch.nn as nn import torch import torch.nn.functional as F import copy from contextlib import nullcontext import math from typing import Optional, Tuple # from megatron.model import LayerNorm from einops import rearrange from easydict import EasyDict as adict from typing import Optional, Tuple, Type from functools import partial class MlpProjector(nn.Module): def __init__(self, cfg): super().__init__() self.cfg = cfg if cfg.projector_type == "identity": modules = nn.Identity() elif cfg.projector_type == "linear": modules = nn.Linear(cfg.input_dim, cfg.n_embed) elif cfg.projector_type == "mlp_gelu": mlp_depth = cfg.get("depth", 1) modules = [nn.Linear(cfg.input_dim, cfg.n_embed)] for _ in range(1, mlp_depth): modules.append(nn.GELU()) modules.append(nn.Linear(cfg.n_embed, cfg.n_embed)) modules = nn.Sequential(*modules) elif cfg.projector_type == "normlayer_downsample_mlp_gelu": mlp_depth = cfg.get("depth", 1) mlp_ratio = cfg.get("mlp_ratio", 1) modules = [ nn.LayerNorm(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio), nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio) ] for _ in range(1, mlp_depth - 1): modules.append(nn.GELU()) modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio)) modules.append(nn.GELU()) modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed)) modules = nn.Sequential(*modules) elif cfg.projector_type == "downsample_mlp_gelu": mlp_depth = cfg.get("depth", 1) mlp_ratio = cfg.get("mlp_ratio", 1) modules = [nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)] for _ in range(1, mlp_depth - 1): modules.append(nn.GELU()) modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio)) modules.append(nn.GELU()) modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed)) modules = nn.Sequential(*modules) elif cfg.projector_type == "low_high_hybrid_split_mlp_gelu": mlp_depth = cfg.get("depth", 1) self.high_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2) self.low_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2) modules = [] for _ in range(1, mlp_depth): modules.append(nn.GELU()) modules.append(nn.Linear(cfg.n_embed, cfg.n_embed)) modules = nn.Sequential(*modules) elif cfg.projector_type == "hybrid_split_feature_mlp_gelu": mlp_depth = cfg.get("depth", 1) channel_div = cfg.get("channel_div", 0.5) self.high_up_proj = nn.Linear(cfg.input_dim[0], int(cfg.n_embed * channel_div)) self.low_up_proj = nn.Linear(cfg.input_dim[1], cfg.n_embed - int(cfg.n_embed * channel_div)) modules = [] for _ in range(1, mlp_depth): modules.append(nn.GELU()) modules.append(nn.Linear(cfg.n_embed, cfg.n_embed)) modules = nn.Sequential(*modules) elif cfg.projector_type == "low_high_split_mlp_gelu": mlp_depth = cfg.get("depth", 1) modules = [] for _ in range(1, mlp_depth): modules.append(nn.GELU()) modules.append(nn.Linear(cfg.n_embed // 2, cfg.n_embed // 2)) modules = nn.Sequential(*modules) self.high_layers = nn.Sequential(*modules) self.low_layers = copy.deepcopy(modules) else: raise ValueError(f"Unknown projector type: {cfg.projector_type}") if cfg.get("token_pooling", False): self.token_pooling_layer = nn.Linear(cfg.input_dim * 4, cfg.input_dim) if cfg.get("conv_fusion_high_low_features", False): self.fusion_layer = nn.Linear(cfg.input_dim, cfg.input_dim) self.layers = modules def forward(self, x): if self.cfg.get("token_pooling", False): batch_size, wxh, channels = x.shape w = h = int(wxh**0.5) x = x.view(batch_size, w, h, channels) x = x.permute(0, 3, 1, 2) # import ipdb; ipdb.set_trace() patches = x.unfold(2, 2, 2).unfold(3, 2, 2) batch_size, channels, h_patches, w_patches, _, _ = patches.size() # 在通道维度上拼接 patches = patches.contiguous().view(batch_size, channels, h_patches * w_patches, -1) # 通过线性层 patches = patches.permute(0, 2, 1, 3).contiguous() patches = patches.view(batch_size, h_patches * w_patches, channels * 4) x = self.token_pooling_layer(patches) if self.cfg.get("conv_fusion_high_low_features", False): x = self.fusion_layer(x[:, 0]) + x[:, 1] if self.cfg.projector_type == 'low_high_hybrid_split_mlp_gelu': high_x, low_x = x[0], x[1] high_x = self.high_up_proj(high_x) low_x = self.low_up_proj(low_x) x = torch.concat([high_x, low_x], dim=-1) if self.cfg.projector_type == 'hybrid_split_feature_mlp_gelu': high_x = x[...,:self.cfg.input_dim[0]] low_x = x[...,self.cfg.input_dim[0]:] high_x = self.high_up_proj(high_x) low_x = self.low_up_proj(low_x) x = torch.concat([high_x, low_x], dim=-1) if self.cfg.projector_type == 'low_high_split_mlp_gelu': high_x, low_x = x[0], x[1] high_x = self.high_layers(high_x) low_x = self.low_layers(low_x) x = torch.concat([high_x, low_x], dim=-1) return x if self.cfg.projector_type == 'downsample_mlp_gelu' or self.cfg.projector_type == 'normlayer_downsample_mlp_gelu': bs, hw, input_dim = x.shape h = w = int((hw) ** 0.5) """compute padding""" if h % self.cfg.downsample_ratio: pad = self.cfg.downsample_ratio - h % self.cfg.downsample_ratio else: pad = 0 x = x.reshape(bs, h, w, input_dim) if pad > 0: x = F.pad(x, (0, 0, 0, pad, 0, pad), "constant", 0) """4 to 1 concat""" x = x.permute(0, 3, 1, 2) # B, C, H, W x = F.unfold(x, kernel_size=self.cfg.downsample_ratio, stride=self.cfg.downsample_ratio, padding=0) # B, C*4, HW // 4 x = x.permute(0, 2, 1) return self.layers(x) @staticmethod def get_flops_per_sample(cfg): if cfg.projector_type == "linear": fwd = 2 * cfg.input_dim * cfg.n_embed elif "mlp_gelu" in cfg.projector_type : mlp_depth = cfg.get("depth", 1) downsample_ratio = cfg.get("downsample_ratio", 1) input_dim = sum(cfg.input_dim) if isinstance(cfg.input_dim, list) else cfg.input_dim input_dim = input_dim * downsample_ratio * downsample_ratio fwd = 2 * input_dim * cfg.n_embed + (mlp_depth - 1) * 2 * cfg.n_embed * cfg.n_embed else: fwd = 0 return fwd * 3 #===================clip============================================================ class LayerNormfp32(torch.nn.LayerNorm): """Subclass torch's LayerNorm to handle fp16.""" def forward(self, x: torch.Tensor): orig_type = x.dtype ret = super().forward(x.type(torch.float32)) return ret.type(orig_type) def get_abs_pos(abs_pos, tgt_size): # abs_pos: L, C # tgt_size: M # return: M, C # print(tgt_size) # print(abs_pos.shape) # exit() dim = abs_pos.size(-1) # print(dim) abs_pos_new = abs_pos.squeeze(0) cls_token, old_pos_embed = abs_pos_new[:1], abs_pos_new[1:] src_size = int(math.sqrt(abs_pos_new.shape[0] - 1)) tgt_size = int(math.sqrt(tgt_size)) dtype = abs_pos.dtype if src_size != tgt_size: old_pos_embed = old_pos_embed.view(1, src_size, src_size, dim).permute(0, 3, 1, 2).contiguous() old_pos_embed = old_pos_embed.to(torch.float32) new_pos_embed = F.interpolate( old_pos_embed, size=(tgt_size, tgt_size), mode='bicubic', antialias=True, align_corners=False, ).to(dtype) new_pos_embed = new_pos_embed.permute(0, 2, 3, 1) new_pos_embed = new_pos_embed.view(tgt_size * tgt_size, dim) vision_pos_embed = torch.cat([cls_token, new_pos_embed], dim=0) vision_pos_embed = vision_pos_embed.view(1, tgt_size * tgt_size + 1, dim) return vision_pos_embed else: return abs_pos @torch.jit.script def quick_gelu(x): return x * torch.sigmoid(1.702 * x) class CLIPVisionEmbeddings(nn.Module): def __init__(self, hidden_size=1024, image_size=224, patch_size=14, num_channels=3): super().__init__() self.embed_dim = hidden_size self.image_size = image_size self.patch_size = patch_size self.class_embedding = torch.nn.Parameter(torch.randn(self.embed_dim)) self.patch_embedding = torch.nn.Conv2d( in_channels=num_channels, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False, ) self.num_patches = (self.image_size // self.patch_size) ** 2 self.num_positions = self.num_patches + 1 self.position_embedding = torch.nn.Embedding(self.num_positions, self.embed_dim) self.register_buffer( "position_ids", torch.arange(self.num_positions).expand((1, -1)) ) def forward(self, pixel_values, patch_embeds): batch_size = pixel_values.shape[0] # patch_embeds = self.patch_embedding( # pixel_values # ) # shape = [*, width, grid, grid] if patch_embeds is not None: patch_embeds = patch_embeds # print(patch_embeds.shape) else: patch_embeds = self.patch_embedding(pixel_values) # print(111111) # shape = [*, width, grid, grid] # patch_embeds = patch_embeds.flatten(2).transpose(1, 2) patch_embeds = patch_embeds.flatten(2).transpose(1, 2) class_embeds = self.class_embedding.expand(batch_size, 1, -1) embeddings = torch.cat([class_embeds, patch_embeds], dim=1) # x = torch.cat([cls_token, x], dim=1) embeddings = embeddings + get_abs_pos(self.position_embedding(self.position_ids), embeddings.size(1)) # embeddings = embeddings + self.position_embedding(self.position_ids) return embeddings class NoTPFeedForward(nn.Module): def __init__( self, cfg, dim: int, hidden_dim: int, ): super().__init__() self.fc1 = torch.nn.Linear(dim, hidden_dim, bias=True) self.fc2 = torch.nn.Linear(hidden_dim, dim, bias=True) def forward(self, x): output = self.fc2(quick_gelu(self.fc1(x))) return output class NoTPAttention(torch.nn.Module): def __init__(self, cfg): super().__init__() self.num_heads = cfg.num_attention_heads self.n_local_heads = cfg.num_attention_heads self.head_dim = cfg.hidden_size // cfg.num_attention_heads self.max_seq_len = cfg.seq_length self.use_flash_attention = cfg.use_flash_attn self.qkv_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size * 3, bias=True) self.out_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size, bias=True) # self.core_attention = CoreAttention(cfg, AttnType.self_attn) self.attn_drop = cfg.attention_dropout def forward( self, x: torch.Tensor, ): bsz, seqlen, _ = x.shape xqkv = self.qkv_proj(x) xqkv = xqkv.view(bsz, seqlen, 3, self.num_heads, self.head_dim) if self.use_flash_attention: xq, xk, xv = torch.split(xqkv, 1, dim=2) xq = xq.squeeze(2) xk = xk.squeeze(2) xv = xv.squeeze(2) # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...] # （B, num_head, S, head_size) xq = xq.permute(0, 2, 1, 3) xk = xk.permute(0, 2, 1, 3) xv = xv.permute(0, 2, 1, 3) # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False): output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None) output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1) # output = output.permute(0, 2, 1, 3).contiguous().view(bsz, seqlen, -1) else: # print(22222) xq, xk, xv = torch.split(xqkv, 1, dim=2) xq = xq.squeeze(2) xk = xk.squeeze(2) xv = xv.squeeze(2) # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...] # （B, num_head, S, head_size) xq = xq.permute(0, 2, 1, 3) xk = xk.permute(0, 2, 1, 3) xv = xv.permute(0, 2, 1, 3) # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False): output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None) output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1) # output = output.permute(0, 2, 1, 3).contiguous().view(bsz, seqlen, -1) output = self.out_proj(output) return output class NoTPTransformerBlock(nn.Module): def __init__(self, cfg, layer_id: int, multiple_of=256): super().__init__() self.n_heads = cfg.num_attention_heads self.dim = cfg.hidden_size self.head_dim = cfg.hidden_size // cfg.num_attention_heads self.self_attn = NoTPAttention(cfg) self.mlp = NoTPFeedForward( cfg, dim=cfg.hidden_size, hidden_dim=cfg.ffn_hidden_size ) self.layer_id = layer_id self.layer_norm1 = torch.nn.LayerNorm( cfg.hidden_size, eps=cfg.layernorm_epsilon ) self.layer_norm2 = torch.nn.LayerNorm( cfg.hidden_size, eps=cfg.layernorm_epsilon ) def forward(self, x: torch.Tensor): residual = self.self_attn.forward(self.layer_norm1(x)) h = x + residual out = h + self.mlp.forward(self.layer_norm2(h)) return out class NoTPTransformer(nn.Module): def __init__(self, cfg): super().__init__() self.cfg = cfg # self.recompute_list = self.cfg.get("recompute_list", []) self.num_layers = cfg.num_layers # _get_num_layers(cfg) self.layers = torch.nn.ModuleList() for layer_id in range(self.num_layers): self.layers.append( NoTPTransformerBlock( cfg, layer_id + 1, ) ) def forward( self, hidden_states, ): for lid, layer in enumerate(self.layers): # if lid in self.recompute_list: # def custom(layer_id): # def custom_forward(*args, **kwargs): # x_ = self.layers[layer_id](*args, **kwargs) # return x_ # return custom_forward # assert hidden_states.requires_grad == True, logger.warning( # "When using recalculation, the input must have grad fn" # ) # hidden_states = tensor_parallel.checkpoint( # custom(lid), # False, # hidden_states.contiguous() # ) # else: hidden_states = layer(hidden_states) return hidden_states # from megatron.core.tensor_parallel.layers import non_tensor_paralleled, local_dp_reduce, local_dp_scatter class VitModel(nn.Module): def __init__( self, cfg, freeze_embed=False, freeze_pre_norm=False ) -> None: super().__init__() self.embeddings = CLIPVisionEmbeddings(hidden_size=cfg.hidden_size, image_size=cfg.image_size, patch_size=cfg.patch_size) if freeze_embed: for name, param in self.embeddings.named_parameters(): param.requires_grad = False self.transformer = NoTPTransformer(cfg=cfg) if cfg.get("fp32norm", False): logger.info("Load fp32 layernorm for ViT.") self.pre_layrnorm = LayerNormfp32( cfg.hidden_size, eps=cfg.get("pre_layernorm_epsilon", 1e-5), ) else: self.pre_layrnorm = torch.nn.LayerNorm( cfg.hidden_size, eps=cfg.get("pre_layernorm_epsilon", 1e-5), ) # self.pre_layrnorm = RMSNorm( # cfg.hidden_size, # eps=cfg.get("pre_layernorm_epsilon", 1e-5), # sequence_parallel=False, # use_fp32=True, # use_optimus=True, # ) if freeze_pre_norm: for name, param in self.pre_layrnorm.named_parameters(): param.requires_grad = False for p in self.parameters(): p.micro_dp = True def set_input_tensor(self, input_tensor): if not isinstance(input_tensor, list): input_tensor = [input_tensor] self.transformer.set_input_tensor(input_tensor[0]) def __str__(self) -> str: return "open_clip" def forward( self, x, patch_embeds ): x = self.embeddings(x, patch_embeds) hidden_states = self.pre_layrnorm(x) # hidden_states, dis = local_dp_scatter(hidden_states) output = self.transformer(hidden_states) # output = local_dp_reduce(output, dis) return output vit_model_cfg = adict( num_layers=24, hidden_size=1024, num_heads = 16, num_attention_heads=16, ffn_hidden_size=4096, seq_length=256, max_position_embeddings=256, use_flash_attn=False, understand_projector_stride=2, hidden_dropout = 0.0, attention_dropout = 0.0, no_persist_layer_norm = False, layernorm_epsilon = 1e-5, pre_layernorm_epsilon = 1e-5, image_size = 224, patch_size = 14, recompute_list = [] ) def build_clip_l(): return VitModel( cfg=vit_model_cfg, freeze_embed=False, freeze_pre_norm=False, ) #=========================Sam-Vary================================= def get_abs_pos_sam(abs_pos, tgt_size): dtype = abs_pos.dtype src_size = abs_pos.size(1) if src_size != tgt_size: old_pos_embed = abs_pos.permute(0, 3, 1, 2) old_pos_embed = old_pos_embed.to(torch.float32) new_pos_embed = F.interpolate( old_pos_embed, size=(tgt_size, tgt_size), mode='bicubic', antialias=True, align_corners=False, ).to(dtype) new_pos_embed = new_pos_embed.permute(0, 2, 3, 1) return new_pos_embed else: return abs_pos class MLPBlock(nn.Module): def __init__( self, embedding_dim: int, mlp_dim: int, act: Type[nn.Module] = nn.GELU, ) -> None: super().__init__() self.lin1 = nn.Linear(embedding_dim, mlp_dim) self.lin2 = nn.Linear(mlp_dim, embedding_dim) self.act = act() def forward(self, x: torch.Tensor) -> torch.Tensor: return self.lin2(self.act(self.lin1(x))) # From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa # Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa class LayerNorm2d(nn.Module): def __init__(self, num_channels: int, eps: float = 1e-6) -> None: super().__init__() self.weight = nn.Parameter(torch.ones(num_channels)) self.bias = nn.Parameter(torch.zeros(num_channels)) self.eps = eps def forward(self, x: torch.Tensor) -> torch.Tensor: u = x.mean(1, keepdim=True) s = (x - u).pow(2).mean(1, keepdim=True) x = (x - u) / torch.sqrt(s + self.eps) x = self.weight[:, None, None] * x + self.bias[:, None, None] return x # This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa class ImageEncoderViT(nn.Module): def __init__( self, img_size: int = 1024, patch_size: int = 16, in_chans: int = 3, embed_dim: int = 768, depth: int = 12, num_heads: int = 12, mlp_ratio: float = 4.0, out_chans: int = 256, qkv_bias: bool = True, norm_layer: Type[nn.Module] = nn.LayerNorm, act_layer: Type[nn.Module] = nn.GELU, use_abs_pos: bool = True, use_rel_pos: bool = False, rel_pos_zero_init: bool = True, window_size: int = 0, global_attn_indexes: Tuple[int, ...] = (), ) -> None: """ Args: img_size (int): Input image size. patch_size (int): Patch size. in_chans (int): Number of input image channels. embed_dim (int): Patch embedding dimension. depth (int): Depth of ViT. num_heads (int): Number of attention heads in each ViT block. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool): If True, add a learnable bias to query, key, value. norm_layer (nn.Module): Normalization layer. act_layer (nn.Module): Activation layer. use_abs_pos (bool): If True, use absolute positional embeddings. use_rel_pos (bool): If True, add relative positional embeddings to the attention map. rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. window_size (int): Window size for window attention blocks. global_attn_indexes (list): Indexes for blocks using global attention. """ super().__init__() self.img_size = img_size self.patch_embed = PatchEmbed( kernel_size=(patch_size, patch_size), stride=(patch_size, patch_size), in_chans=in_chans, embed_dim=embed_dim, ) self.pos_embed: Optional[nn.Parameter] = None if use_abs_pos: # Initialize absolute positional embedding with pretrain image size. self.pos_embed = nn.Parameter( torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim) ) self.blocks = nn.ModuleList() for i in range(depth): block = Block( dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, norm_layer=norm_layer, act_layer=act_layer, use_rel_pos=use_rel_pos, rel_pos_zero_init=rel_pos_zero_init, window_size=window_size if i not in global_attn_indexes else 0, input_size=(img_size // patch_size, img_size // patch_size), ) self.blocks.append(block) self.neck = nn.Sequential( nn.Conv2d( embed_dim, out_chans, kernel_size=1, bias=False, ), LayerNorm2d(out_chans), nn.Conv2d( out_chans, out_chans, kernel_size=3, padding=1, bias=False, ), LayerNorm2d(out_chans), ) self.net_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False) self.net_3 = nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1, bias=False) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.patch_embed(x) if self.pos_embed is not None: # x = x + self.pos_embed x = x + get_abs_pos_sam(self.pos_embed, x.size(1)) for blk in self.blocks: x = blk(x) x = self.neck(x.permute(0, 3, 1, 2)) x2 = self.net_2(x) x3 = self.net_3(x2.clone()) return x3 class Block(nn.Module): """Transformer blocks with support of window attention and residual propagation blocks""" def __init__( self, dim: int, num_heads: int, mlp_ratio: float = 4.0, qkv_bias: bool = True, norm_layer: Type[nn.Module] = nn.LayerNorm, act_layer: Type[nn.Module] = nn.GELU, use_rel_pos: bool = False, rel_pos_zero_init: bool = True, window_size: int = 0, input_size: Optional[Tuple[int, int]] = None, ) -> None: """ Args: dim (int): Number of input channels. num_heads (int): Number of attention heads in each ViT block. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool): If True, add a learnable bias to query, key, value. norm_layer (nn.Module): Normalization layer. act_layer (nn.Module): Activation layer. use_rel_pos (bool): If True, add relative positional embeddings to the attention map. rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. window_size (int): Window size for window attention blocks. If it equals 0, then use global attention. input_size (tuple(int, int) or None): Input resolution for calculating the relative positional parameter size. """ super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, use_rel_pos=use_rel_pos, rel_pos_zero_init=rel_pos_zero_init, input_size=input_size if window_size == 0 else (window_size, window_size), ) self.norm2 = norm_layer(dim) self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer) self.window_size = window_size def forward(self, x: torch.Tensor) -> torch.Tensor: shortcut = x x = self.norm1(x) # Window partition if self.window_size > 0: H, W = x.shape[1], x.shape[2] x, pad_hw = window_partition(x, self.window_size) x = self.attn(x) # Reverse window partition if self.window_size > 0: x = window_unpartition(x, self.window_size, pad_hw, (H, W)) x = shortcut + x x = x + self.mlp(self.norm2(x)) return x class Attention(nn.Module): """Multi-head Attention block with relative position embeddings.""" def __init__( self, dim: int, num_heads: int = 8, qkv_bias: bool = True, use_rel_pos: bool = False, rel_pos_zero_init: bool = True, input_size: Optional[Tuple[int, int]] = None, ) -> None: """ Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. qkv_bias (bool): If True, add a learnable bias to query, key, value. rel_pos (bool): If True, add relative positional embeddings to the attention map. rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. input_size (tuple(int, int) or None): Input resolution for calculating the relative positional parameter size. """ super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.scale = head_dim**-0.5 self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.proj = nn.Linear(dim, dim) self.use_rel_pos = use_rel_pos if self.use_rel_pos: assert ( input_size is not None ), "Input size must be provided if using relative positional encoding." # initialize relative positional embeddings self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) def forward(self, x: torch.Tensor) -> torch.Tensor: B, H, W, _ = x.shape # qkv with shape (3, B, nHead, H * W, C) qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # q, k, v with shape (B * nHead, H * W, C) q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0) rel_h, rel_w = None, None if self.use_rel_pos: rel_h, rel_w = add_decomposed_rel_pos(q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)) q = q.view(B, self.num_heads, H * W, -1) k = k.view(B, self.num_heads, H * W, -1) v = v.view(B, self.num_heads, H * W, -1) if self.use_rel_pos: rel_h = rel_h.view(B, self.num_heads, rel_h.size(1), rel_h.size(2), rel_h.size(3)) rel_w = rel_w.view(B, self.num_heads, rel_w.size(1), rel_w.size(2), rel_w.size(3)) attn_bias = (rel_h + rel_w).view(B, self.num_heads, rel_h.size(2), rel_h.size(3) * rel_w.size(4)) x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias) # x = _attention_rel_h_rel_w(q, k, v, rel_h, rel_w) else: x = torch.nn.functional.scaled_dot_product_attention(q, k, v) x = x.view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1) x = self.proj(x) return x def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]: """ Partition into non-overlapping windows with padding if needed. Args: x (tensor): input tokens with [B, H, W, C]. window_size (int): window size. Returns: windows: windows after partition with [B * num_windows, window_size, window_size, C]. (Hp, Wp): padded height and width before partition """ B, H, W, C = x.shape pad_h = (window_size - H % window_size) % window_size pad_w = (window_size - W % window_size) % window_size if pad_h > 0 or pad_w > 0: x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) Hp, Wp = H + pad_h, W + pad_w x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) return windows, (Hp, Wp) def window_unpartition( windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int] ) -> torch.Tensor: """ Window unpartition into original sequences and removing padding. Args: windows (tensor): input tokens with [B * num_windows, window_size, window_size, C]. window_size (int): window size. pad_hw (Tuple): padded height and width (Hp, Wp). hw (Tuple): original height and width (H, W) before padding. Returns: x: unpartitioned sequences with [B, H, W, C]. """ Hp, Wp = pad_hw H, W = hw B = windows.shape[0] // (Hp * Wp // window_size // window_size) x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) if Hp > H or Wp > W: x = x[:, :H, :W, :].contiguous() return x def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor: """ Get relative positional embeddings according to the relative positions of query and key sizes. Args: q_size (int): size of query q. k_size (int): size of key k. rel_pos (Tensor): relative position embeddings (L, C). Returns: Extracted positional embeddings according to relative positions. """ max_rel_dist = int(2 * max(q_size, k_size) - 1) # Interpolate rel pos if needed. if rel_pos.shape[0] != max_rel_dist: # Interpolate rel pos. dtype = rel_pos.dtype rel_pos = rel_pos.to(torch.float32) rel_pos_resized = F.interpolate( rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), size=max_rel_dist, mode="linear", ).to(dtype) rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) else: rel_pos_resized = rel_pos # Scale the coords with short length if shapes for q and k are different. q_coords = torch.arange(q_size, device=rel_pos.device)[:, None] * max(k_size / q_size, 1.0) k_coords = torch.arange(k_size, device=rel_pos.device)[None, :] * max(q_size / k_size, 1.0) relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) return rel_pos_resized[relative_coords.long()] def add_decomposed_rel_pos( q: torch.Tensor, rel_pos_h: torch.Tensor, rel_pos_w: torch.Tensor, q_size: Tuple[int, int], k_size: Tuple[int, int], ) -> torch.Tensor: """ Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 Args: q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. q_size (Tuple): spatial sequence size of query q with (q_h, q_w). k_size (Tuple): spatial sequence size of key k with (k_h, k_w). Returns: attn (Tensor): attention map with added relative positional embeddings. """ q_h, q_w = q_size k_h, k_w = k_size Rh = get_rel_pos(q_h, k_h, rel_pos_h) Rw = get_rel_pos(q_w, k_w, rel_pos_w) B, _, dim = q.shape r_q = q.reshape(B, q_h, q_w, dim) rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) rel_h = rel_h.unsqueeze(-1) rel_w = rel_w.unsqueeze(-2) rel_h = rel_h.reshape(B, q_h * q_w, k_h, 1) rel_w = rel_w.reshape(B, q_h * q_w, 1, k_w) return rel_h, rel_w class PatchEmbed(nn.Module): """ Image to Patch Embedding. """ def __init__( self, kernel_size: Tuple[int, int] = (16, 16), stride: Tuple[int, int] = (16, 16), padding: Tuple[int, int] = (0, 0), in_chans: int = 3, embed_dim: int = 768, ) -> None: """ Args: kernel_size (Tuple): kernel size of the projection layer. stride (Tuple): stride of the projection layer. padding (Tuple): padding size of the projection layer. in_chans (int): Number of input image channels. embed_dim (int): Patch embedding dimension. """ super().__init__() self.proj = nn.Conv2d( in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.proj(x) # B C H W -> B H W C x = x.permute(0, 2, 3, 1) return x def build_sam_vit_b(checkpoint=None): return _build_sam( encoder_embed_dim=768, encoder_depth=12, encoder_num_heads=12, encoder_global_attn_indexes=[2, 5, 8, 11], checkpoint=checkpoint, ) def build_sam_fast_vit_b(checkpoint=None, compile_mode='max-autotune', dtype=torch.bfloat16): image_encoder = build_sam_vit_b(checkpoint).eval().to(dtype) # sam = _apply_eval_dtype_sam(sam, dtype) image_encoder = torch.compile(image_encoder, mode=compile_mode) return image_encoder def _build_sam( encoder_embed_dim, encoder_depth, encoder_num_heads, encoder_global_attn_indexes, checkpoint=None, ): prompt_embed_dim = 256 image_size = 1024 vit_patch_size = 16 image_embedding_size = image_size // vit_patch_size image_encoder=ImageEncoderViT( depth=encoder_depth, embed_dim=encoder_embed_dim, img_size=image_size, mlp_ratio=4, norm_layer=partial(torch.nn.LayerNorm, eps=1e-6), num_heads=encoder_num_heads, patch_size=vit_patch_size, qkv_bias=True, use_rel_pos=True, global_attn_indexes=encoder_global_attn_indexes, window_size=14, out_chans=prompt_embed_dim, ) image_encoder.eval() if checkpoint is not None: # with open(checkpoint, "rb") as f: state_dict = torch.load(checkpoint) # print(state_dict.keys()) # for key in state_dict: # image_encoder.load_state_dict({k[14:]: v for k, v in state_dict.items() if 'image_encoder' in k}, strict=False) # ocr-anyting # image_encoder.load_state_dict(state_dict, strict=True) # tob image_encoder.load_state_dict({k[30:]: v for k, v in state_dict.items() if 'vision_tower_high' in k}, strict=True) print(checkpoint) return image_encoder