import torch.nn as nn import torch import torch.nn.functional as F import copy from typing import Optional, Tuple # from megatron.model import LayerNorm import transformers 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 #===================qwen2================================ class CustomQwen2Decoder(nn.Module): """ Qwen2 visual encoder non-causal attention + causal attention token_type_ids ：0=non-causal, 1=causal """ def __init__( self, decoder_layer: int = 24, max_position_embeddings: int = 131072, hidden_dimension: int = 896, num_attention_heads: int = 14, num_key_value_heads: int = 2, intermediate_size: int = 4864, vocab_size: int = 151936, attn_implementation: str = "sdpa", # ⭐ rms_norm_eps: float = 1e-06, rope_theta: float = 1000000.0, attention_dropout: float = 0.0, hidden_act: str = "silu", initializer_range: float = 0.02, ): super().__init__() # attn_implementation check if attn_implementation == "flash_attention_2": raise ValueError( "CustomQwen2Decoder do not support flash_attention_2，" "new attention mask needs 'sdpa' or 'eager'" ) # load Qwen2Model = getattr(transformers.models.qwen2.modeling_qwen2, 'Qwen2Model') Qwen2Config = getattr(transformers, 'Qwen2Config') # config config = Qwen2Config( hidden_size=hidden_dimension, num_hidden_layers=decoder_layer, num_attention_heads=num_attention_heads, num_key_value_heads=num_key_value_heads, intermediate_size=intermediate_size, max_position_embeddings=max_position_embeddings, vocab_size=vocab_size, rms_norm_eps=rms_norm_eps, rope_theta=rope_theta, attention_dropout=attention_dropout, hidden_act=hidden_act, initializer_range=initializer_range, _attn_implementation=attn_implementation, # ⭐ ) # self.model = self._create_custom_model(Qwen2Model, config) del self.model.embed_tokens def _create_custom_model(self, Qwen2Model, config): """ Qwen2Model """ class CustomQwen2ModelInner(Qwen2Model): def forward( self, input_ids=None, attention_mask=None, position_ids=None, past_key_values=None, inputs_embeds=None, token_type_ids=None, # ⭐ use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, cache_position=None, ): # token_type_ids self._current_token_type_ids = token_type_ids outputs = super().forward( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, ) return outputs def _update_causal_mask( self, attention_mask, input_tensor, cache_position, past_key_values, output_attentions, ): dtype, device = input_tensor.dtype, input_tensor.device min_dtype = torch.finfo(dtype).min batch_size, sequence_length = input_tensor.shape[0], input_tensor.shape[1] token_type_ids = self._current_token_type_ids # attention mask causal_mask = self._create_custom_4d_mask( sequence_length=sequence_length, dtype=dtype, device=device, batch_size=batch_size, token_type_ids=token_type_ids, ) # padding mask if attention_mask is not None and attention_mask.dim() == 2: padding_mask = attention_mask[:, None, None, :].to(dtype=dtype) padding_mask = (1.0 - padding_mask) * min_dtype causal_mask = causal_mask + padding_mask return causal_mask def _create_custom_4d_mask( self, sequence_length, dtype, device, batch_size, token_type_ids, ): min_dtype = torch.finfo(dtype).min masks = [] for b in range(batch_size): mask = torch.full( (sequence_length, sequence_length), fill_value=min_dtype, dtype=dtype, device=device ) type_ids = token_type_ids[b] image_positions = (type_ids == 0).nonzero(as_tuple=True)[0] text_positions = (type_ids == 1).nonzero(as_tuple=True)[0] # non-casual if len(image_positions) > 0: mask[image_positions[:, None], image_positions] = 0.0 # causal for i, text_pos in enumerate(text_positions): if len(image_positions) > 0: mask[text_pos, image_positions] = 0.0 mask[text_pos, text_positions[:i+1]] = 0.0 masks.append(mask) mask = torch.stack(masks, dim=0).unsqueeze(1) return mask return CustomQwen2ModelInner(config) def forward( self, inputs_embeds, token_type_ids, attention_mask=None, **kwargs ): """ Args: inputs_embeds: [batch_size, seq_len, hidden_dim] token_type_ids: [batch_size, seq_len], 0=non-causal, 1=causal attention_mask: [batch_size, seq_len], optional """ return self.model( inputs_embeds=inputs_embeds, token_type_ids=token_type_ids, attention_mask=attention_mask, **kwargs ) # batch_size = 2 # inputs_embeds = torch.randn(batch_size, 512, 896).cuda() # inputs_embeds = torch.randn(batch_size, 512, 896).cuda() # token_type_ids = torch.cat([ # torch.zeros(batch_size, 256, dtype=torch.long), # torch.ones(batch_size, 256, dtype=torch.long), # ], dim=1).cuda() # # start = time.time() # with torch.no_grad(): # outputs_sdpa = decoder_sdpa(inputs_embeds, token_type_ids) # print(outputs_sdpa[0].shape) # print(f"SDPA time: {time.time() - start:.4f}s") class Qwen2Decoder2Encoder(nn.Module): """ Decoder based on Multilingual BART Set the initial weights and configuration with a pretrained multilingual BART model, and modify the detailed configurations as a Nougat decoder """ def __init__( self, decoder_layer: int, hidden_dimension: int, num_attention_heads: int, num_key_value_heads: int, intermediate_size: int, max_query: int, ): super().__init__() self.model = CustomQwen2Decoder( decoder_layer=decoder_layer, hidden_dimension=hidden_dimension, num_attention_heads=num_attention_heads, num_key_value_heads=num_key_value_heads, intermediate_size=intermediate_size, attn_implementation="sdpa", ) self.query_768 = nn.Embedding(144, hidden_dimension) self.query_1024 = nn.Embedding(256, hidden_dimension) # self.query_refixation = nn.Embedding(int(math.sqrt(max_query)), hidden_dimension) def forward(self, x: torch.Tensor) -> torch.Tensor: x = x.flatten(2).transpose(1, 2) bs, n_query, _ = x.shape if n_query == 144: param_img = self.query_768.weight elif n_query == 256: param_img = self.query_1024.weight batch_query_imgs = param_img.unsqueeze(0).expand( bs, -1, -1 ) # (batch_size, num_queries, hidden_size) x_combined = torch.cat([x, batch_query_imgs], dim=1) token_type_ids = torch.cat([ torch.zeros(bs, n_query, dtype=torch.long), torch.ones(bs, n_query, dtype=torch.long), ], dim=1) y = self.model(x_combined, token_type_ids)[0] y = y[:, n_query:, :] # causal flow query return y def build_qwen2_decoder_as_encoder( decoder_layer=24, hidden_dimension=896, num_attention_heads=14, num_key_value_heads=2, intermediate_size=4864, max_query = 400, checkpoint=None, ): decoder_as_encoder = Qwen2Decoder2Encoder( decoder_layer=decoder_layer, hidden_dimension = hidden_dimension, num_attention_heads = num_attention_heads, num_key_value_heads = num_key_value_heads, intermediate_size = intermediate_size, max_query = max_query ) if checkpoint is not None: # with open(checkpoint, "rb") as f: state_dict = torch.load(checkpoint) decoder_as_encoder.load_state_dict(state_dict, strict=True) # tob print(checkpoint) return decoder_as_encoder #=========================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, 896, 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