1059 lines
37 KiB
Python
1059 lines
37 KiB
Python
import torch.nn as nn
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import torch
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import torch.nn.functional as F
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import copy
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from contextlib import nullcontext
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import math
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from typing import Optional, Tuple
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# from megatron.model import LayerNorm
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from einops import rearrange
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from easydict import EasyDict as adict
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from typing import Optional, Tuple, Type
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from functools import partial
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class MlpProjector(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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self.cfg = cfg
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if cfg.projector_type == "identity":
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modules = nn.Identity()
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elif cfg.projector_type == "linear":
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modules = nn.Linear(cfg.input_dim, cfg.n_embed)
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elif cfg.projector_type == "mlp_gelu":
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mlp_depth = cfg.get("depth", 1)
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modules = [nn.Linear(cfg.input_dim, cfg.n_embed)]
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for _ in range(1, mlp_depth):
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
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modules = nn.Sequential(*modules)
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elif cfg.projector_type == "normlayer_downsample_mlp_gelu":
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mlp_depth = cfg.get("depth", 1)
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mlp_ratio = cfg.get("mlp_ratio", 1)
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modules = [
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nn.LayerNorm(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio),
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nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)
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]
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for _ in range(1, mlp_depth - 1):
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio))
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
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modules = nn.Sequential(*modules)
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elif cfg.projector_type == "downsample_mlp_gelu":
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mlp_depth = cfg.get("depth", 1)
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mlp_ratio = cfg.get("mlp_ratio", 1)
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modules = [nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)]
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for _ in range(1, mlp_depth - 1):
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio))
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
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modules = nn.Sequential(*modules)
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elif cfg.projector_type == "low_high_hybrid_split_mlp_gelu":
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mlp_depth = cfg.get("depth", 1)
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self.high_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
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self.low_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
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modules = []
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for _ in range(1, mlp_depth):
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
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modules = nn.Sequential(*modules)
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elif cfg.projector_type == "hybrid_split_feature_mlp_gelu":
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mlp_depth = cfg.get("depth", 1)
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channel_div = cfg.get("channel_div", 0.5)
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self.high_up_proj = nn.Linear(cfg.input_dim[0], int(cfg.n_embed * channel_div))
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self.low_up_proj = nn.Linear(cfg.input_dim[1], cfg.n_embed - int(cfg.n_embed * channel_div))
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modules = []
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for _ in range(1, mlp_depth):
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
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modules = nn.Sequential(*modules)
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elif cfg.projector_type == "low_high_split_mlp_gelu":
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mlp_depth = cfg.get("depth", 1)
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modules = []
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for _ in range(1, mlp_depth):
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modules.append(nn.GELU())
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modules.append(nn.Linear(cfg.n_embed // 2, cfg.n_embed // 2))
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modules = nn.Sequential(*modules)
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self.high_layers = nn.Sequential(*modules)
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self.low_layers = copy.deepcopy(modules)
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else:
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raise ValueError(f"Unknown projector type: {cfg.projector_type}")
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if cfg.get("token_pooling", False):
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self.token_pooling_layer = nn.Linear(cfg.input_dim * 4, cfg.input_dim)
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if cfg.get("conv_fusion_high_low_features", False):
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self.fusion_layer = nn.Linear(cfg.input_dim, cfg.input_dim)
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self.layers = modules
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def forward(self, x):
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if self.cfg.get("token_pooling", False):
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batch_size, wxh, channels = x.shape
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w = h = int(wxh**0.5)
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x = x.view(batch_size, w, h, channels)
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x = x.permute(0, 3, 1, 2)
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# import ipdb; ipdb.set_trace()
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patches = x.unfold(2, 2, 2).unfold(3, 2, 2)
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batch_size, channels, h_patches, w_patches, _, _ = patches.size()
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# 在通道维度上拼接
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patches = patches.contiguous().view(batch_size, channels, h_patches * w_patches, -1)
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# 通过线性层
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patches = patches.permute(0, 2, 1, 3).contiguous()
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patches = patches.view(batch_size, h_patches * w_patches, channels * 4)
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x = self.token_pooling_layer(patches)
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if self.cfg.get("conv_fusion_high_low_features", False):
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x = self.fusion_layer(x[:, 0]) + x[:, 1]
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if self.cfg.projector_type == 'low_high_hybrid_split_mlp_gelu':
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high_x, low_x = x[0], x[1]
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high_x = self.high_up_proj(high_x)
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low_x = self.low_up_proj(low_x)
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x = torch.concat([high_x, low_x], dim=-1)
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if self.cfg.projector_type == 'hybrid_split_feature_mlp_gelu':
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high_x = x[...,:self.cfg.input_dim[0]]
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low_x = x[...,self.cfg.input_dim[0]:]
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high_x = self.high_up_proj(high_x)
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low_x = self.low_up_proj(low_x)
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x = torch.concat([high_x, low_x], dim=-1)
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if self.cfg.projector_type == 'low_high_split_mlp_gelu':
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high_x, low_x = x[0], x[1]
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high_x = self.high_layers(high_x)
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low_x = self.low_layers(low_x)
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x = torch.concat([high_x, low_x], dim=-1)
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return x
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if self.cfg.projector_type == 'downsample_mlp_gelu' or self.cfg.projector_type == 'normlayer_downsample_mlp_gelu':
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bs, hw, input_dim = x.shape
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h = w = int((hw) ** 0.5)
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"""compute padding"""
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if h % self.cfg.downsample_ratio:
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pad = self.cfg.downsample_ratio - h % self.cfg.downsample_ratio
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else:
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pad = 0
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x = x.reshape(bs, h, w, input_dim)
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if pad > 0:
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x = F.pad(x, (0, 0, 0, pad, 0, pad), "constant", 0)
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"""4 to 1 concat"""
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x = x.permute(0, 3, 1, 2) # B, C, H, W
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x = F.unfold(x, kernel_size=self.cfg.downsample_ratio, stride=self.cfg.downsample_ratio, padding=0) # B, C*4, HW // 4
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x = x.permute(0, 2, 1)
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return self.layers(x)
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@staticmethod
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def get_flops_per_sample(cfg):
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if cfg.projector_type == "linear":
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fwd = 2 * cfg.input_dim * cfg.n_embed
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elif "mlp_gelu" in cfg.projector_type :
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mlp_depth = cfg.get("depth", 1)
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downsample_ratio = cfg.get("downsample_ratio", 1)
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input_dim = sum(cfg.input_dim) if isinstance(cfg.input_dim, list) else cfg.input_dim
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input_dim = input_dim * downsample_ratio * downsample_ratio
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fwd = 2 * input_dim * cfg.n_embed + (mlp_depth - 1) * 2 * cfg.n_embed * cfg.n_embed
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else:
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fwd = 0
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return fwd * 3
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#===================clip============================================================
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class LayerNormfp32(torch.nn.LayerNorm):
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"""Subclass torch's LayerNorm to handle fp16."""
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def forward(self, x: torch.Tensor):
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orig_type = x.dtype
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ret = super().forward(x.type(torch.float32))
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return ret.type(orig_type)
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def get_abs_pos(abs_pos, tgt_size):
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# abs_pos: L, C
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# tgt_size: M
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# return: M, C
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# print(tgt_size)
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# print(abs_pos.shape)
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# exit()
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dim = abs_pos.size(-1)
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# print(dim)
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abs_pos_new = abs_pos.squeeze(0)
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cls_token, old_pos_embed = abs_pos_new[:1], abs_pos_new[1:]
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src_size = int(math.sqrt(abs_pos_new.shape[0] - 1))
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tgt_size = int(math.sqrt(tgt_size))
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dtype = abs_pos.dtype
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if src_size != tgt_size:
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old_pos_embed = old_pos_embed.view(1, src_size, src_size, dim).permute(0, 3, 1,
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2).contiguous()
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old_pos_embed = old_pos_embed.to(torch.float32)
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new_pos_embed = F.interpolate(
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old_pos_embed,
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size=(tgt_size, tgt_size),
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mode='bicubic',
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antialias=True,
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align_corners=False,
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).to(dtype)
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new_pos_embed = new_pos_embed.permute(0, 2, 3, 1)
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new_pos_embed = new_pos_embed.view(tgt_size * tgt_size, dim)
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vision_pos_embed = torch.cat([cls_token, new_pos_embed], dim=0)
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vision_pos_embed = vision_pos_embed.view(1, tgt_size * tgt_size + 1, dim)
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return vision_pos_embed
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else:
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return abs_pos
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#@torch.jit.script
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def quick_gelu(x):
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return x * torch.sigmoid(1.702 * x)
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class CLIPVisionEmbeddings(nn.Module):
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def __init__(self, hidden_size=1024, image_size=224, patch_size=14, num_channels=3):
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super().__init__()
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self.embed_dim = hidden_size
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self.image_size = image_size
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self.patch_size = patch_size
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self.class_embedding = torch.nn.Parameter(torch.randn(self.embed_dim))
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self.patch_embedding = torch.nn.Conv2d(
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in_channels=num_channels,
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out_channels=self.embed_dim,
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kernel_size=self.patch_size,
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stride=self.patch_size,
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bias=False,
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)
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self.num_patches = (self.image_size // self.patch_size) ** 2
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self.num_positions = self.num_patches + 1
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self.position_embedding = torch.nn.Embedding(self.num_positions, self.embed_dim)
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self.register_buffer(
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"position_ids", torch.arange(self.num_positions).expand((1, -1))
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)
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def forward(self, pixel_values, patch_embeds):
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batch_size = pixel_values.shape[0]
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# patch_embeds = self.patch_embedding(
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# pixel_values
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# ) # shape = [*, width, grid, grid]
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if patch_embeds is not None:
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patch_embeds = patch_embeds
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# print(patch_embeds.shape)
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else:
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patch_embeds = self.patch_embedding(pixel_values)
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# print(111111)
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# shape = [*, width, grid, grid]
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# patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
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patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
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class_embeds = self.class_embedding.expand(batch_size, 1, -1)
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embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
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# x = torch.cat([cls_token, x], dim=1)
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embeddings = embeddings + get_abs_pos(self.position_embedding(self.position_ids), embeddings.size(1))
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# embeddings = embeddings + self.position_embedding(self.position_ids)
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return embeddings
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class NoTPFeedForward(nn.Module):
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def __init__(
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self,
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cfg,
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dim: int,
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hidden_dim: int,
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):
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super().__init__()
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self.fc1 = torch.nn.Linear(dim, hidden_dim, bias=True)
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self.fc2 = torch.nn.Linear(hidden_dim, dim, bias=True)
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def forward(self, x):
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output = self.fc2(quick_gelu(self.fc1(x)))
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return output
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class NoTPAttention(torch.nn.Module):
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def __init__(self, cfg):
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super().__init__()
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self.num_heads = cfg.num_attention_heads
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self.n_local_heads = cfg.num_attention_heads
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self.head_dim = cfg.hidden_size // cfg.num_attention_heads
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self.max_seq_len = cfg.seq_length
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self.use_flash_attention = cfg.use_flash_attn
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self.qkv_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size * 3, bias=True)
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self.out_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size, bias=True)
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# self.core_attention = CoreAttention(cfg, AttnType.self_attn)
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self.attn_drop = cfg.attention_dropout
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def forward(
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self,
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x: torch.Tensor,
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):
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bsz, seqlen, _ = x.shape
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xqkv = self.qkv_proj(x)
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xqkv = xqkv.view(bsz, seqlen, 3, self.num_heads, self.head_dim)
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if self.use_flash_attention:
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xq, xk, xv = torch.split(xqkv, 1, dim=2)
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xq = xq.squeeze(2)
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xk = xk.squeeze(2)
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xv = xv.squeeze(2)
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# xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...]
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# （B, num_head, S, head_size)
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xq = xq.permute(0, 2, 1, 3)
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xk = xk.permute(0, 2, 1, 3)
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xv = xv.permute(0, 2, 1, 3)
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# with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
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output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None)
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output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1)
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# output = output.permute(0, 2, 1, 3).contiguous().view(bsz, seqlen, -1)
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else:
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# print(22222)
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xq, xk, xv = torch.split(xqkv, 1, dim=2)
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xq = xq.squeeze(2)
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xk = xk.squeeze(2)
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xv = xv.squeeze(2)
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# xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...]
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# （B, num_head, S, head_size)
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xq = xq.permute(0, 2, 1, 3)
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xk = xk.permute(0, 2, 1, 3)
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xv = xv.permute(0, 2, 1, 3)
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# with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
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output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None)
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output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1)
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# output = output.permute(0, 2, 1, 3).contiguous().view(bsz, seqlen, -1)
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output = self.out_proj(output)
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return output
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class NoTPTransformerBlock(nn.Module):
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def __init__(self, cfg, layer_id: int, multiple_of=256):
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super().__init__()
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self.n_heads = cfg.num_attention_heads
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self.dim = cfg.hidden_size
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self.head_dim = cfg.hidden_size // cfg.num_attention_heads
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self.self_attn = NoTPAttention(cfg)
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self.mlp = NoTPFeedForward(
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cfg, dim=cfg.hidden_size, hidden_dim=cfg.ffn_hidden_size
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)
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self.layer_id = layer_id
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self.layer_norm1 = torch.nn.LayerNorm(
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cfg.hidden_size, eps=cfg.layernorm_epsilon
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)
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self.layer_norm2 = torch.nn.LayerNorm(
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cfg.hidden_size, eps=cfg.layernorm_epsilon
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)
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def forward(self, x: torch.Tensor):
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residual = self.self_attn.forward(self.layer_norm1(x))
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h = x + residual
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out = h + self.mlp.forward(self.layer_norm2(h))
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return out
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class NoTPTransformer(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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self.cfg = cfg
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# self.recompute_list = self.cfg.get("recompute_list", [])
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self.num_layers = cfg.num_layers # _get_num_layers(cfg)
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self.layers = torch.nn.ModuleList()
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for layer_id in range(self.num_layers):
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self.layers.append(
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NoTPTransformerBlock(
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cfg,
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layer_id + 1,
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)
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)
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def forward(
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self,
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hidden_states,
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):
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for lid, layer in enumerate(self.layers):
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# if lid in self.recompute_list:
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# def custom(layer_id):
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# def custom_forward(*args, **kwargs):
|
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# x_ = self.layers[layer_id](*args, **kwargs)
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# return x_
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# return custom_forward
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||
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# assert hidden_states.requires_grad == True, logger.warning(
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# "When using recalculation, the input must have grad fn"
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# )
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# hidden_states = tensor_parallel.checkpoint(
|
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# custom(lid),
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# False,
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# hidden_states.contiguous()
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# )
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||
# else:
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||
hidden_states = layer(hidden_states)
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return hidden_states
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||
|
||
# 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
|