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2025-10-23 17:44:32 +09:00
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model_services/deepseek_ocr/deepencoder/__init__.py Normal file
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model_services/deepseek_ocr/deepencoder/build_linear.py Normal file
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import torch.nn as nn
import torch
import torch.nn.functional as F
import copy
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

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model_services/deepseek_ocr/deepencoder/clip_sdpa.py Normal file
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from contextlib import nullcontext
import math
from typing import Optional, Tuple
# from megatron.model import LayerNorm
from easydict import EasyDict as adict
import torch
from torch.nn import functional as F
from torch import nn
from flash_attn import flash_attn_qkvpacked_func, flash_attn_func
# from optimus import flash_attn_func
# from megatron.core import tensor_parallel
# from megatron.core import parallel_state as mpu
# from megatron.core.utils import make_viewless_tensor, divide
# from megatron.model.fused_rms_norm import RMSNorm
# from megatron.model.transformer import (
# FlashSelfAttention,
# NoopTransformerLayer,
# _cfg_to_kwargs,
# )
# from megatron.model.enums import AttnMaskType, AttnType
# from megatron.model.fused_softmax import FusedScaleMaskSoftmax
# from megatron.model.utils import attention_mask_func
# from megatron.model.module import MegatronModule
# try:
# from einops import rearrange
# except ImportError:
# rearrange = None
# from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func
# try:
# # flash attention 2.x
# from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func
# except ImportError:
# try:
# # flash attention 1.x
# from flash_attn.flash_attn_interface import flash_attn_unpadded_func
# except ImportError:
# flash_attn_unpadded_func = None
# try:
# from flash_attn.flash_attn_interface import flash_attn_unpadded_relative_attention_bias_func
# except ImportError:
# flash_attn_unpadded_relative_attention_bias_func = None
# try:
# from flash_attn.flash_attn_interface import mask_flash_attn_unpadded_func
# except ImportError:
# mask_flash_attn_unpadded_func = None
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
# from optimus.flash_attn_interface import flash_attn_qkvpacked_func
# class NoTPAttention(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:
# output = flash_attn_qkvpacked_func(xqkv)
# output = output.view(bsz, seqlen, -1)
# else:
# 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)
# output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None)
# utput = output.permute(0, 2, 1, 3).view(bsz, seqlen, -1)
# output = self.out_proj(output)
# return output
# from optimus.flash_attn_interface import flash_attn_qkvpacked_func
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:
output = flash_attn_qkvpacked_func(xqkv)
output = output.view(bsz, seqlen, -1)
# 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:
# output = flash_attn_qkvpacked_func(xqkv)
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 = 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,
)
if __name__ == '__main__':
from mmgpt.model.vision_encoder.sam_b import build_sam_vit_b
vit_model_cfg = adict(
num_layers=24,
hidden_size=1024,
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 = []
)
sam_model = build_sam_vit_b()
vision_model = VitModel(
cfg=vit_model_cfg,
freeze_embed=False,
freeze_pre_norm=False,
)
# model = VitModel(1344)
# x = torch.zeros(2, 3, 224, 224)
x = torch.zeros(2, 3, 1024, 1024)
with torch.no_grad():
# y = vision_model(x)
patch_embed = sam_model(x)
print(patch_embed.shape)
y = vision_model(x, patch_embed)
print(y.shape)
image_feature = torch.add(y[:, 1:], patch_embed.flatten(2).permute(0, 2, 1))
print(image_feature.shape)

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model_services/deepseek_ocr/deepencoder/sam_vary_sdpa.py Normal file
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Type
from functools import partial
from flash_attn import flash_attn_qkvpacked_func
# from .common import LayerNorm2d, MLPBlock
# from mmgpt.model.vision_encoder.flash_4 import _attention_rel_h_rel_w
def get_abs_pos(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(self.pos_embed, x.size(1))
for blk in self.blocks:
x = blk(x)
neck_output = self.neck(x.permute(0, 3, 1, 2))
conv2_output = self.net_2(neck_output)
# print(f"conv2_output shape: {conv2_output.shape}")
conv3_output = self.net_3(conv2_output)
return conv3_output
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)
# qkv = torch.stack([q, k, v], dim=1).transpose(1, 3).reshape(B, H * W, 3, self.num_heads, -1)
# x = flash_attn_qkvpacked_func(qkv, dropout_p=0.0, causal=False).transpose(1, 2)
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(
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,
)
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