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from collections import OrderedDict
import math
import requests
from io import BytesIO
from functools import partial
from PIL import Image
from typing import Callable, Optional, Sequence, Tuple, List
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.checkpoint import checkpoint
from torch.nn.init import trunc_normal_
from torchvision import transforms
from torchvision.transforms import InterpolationMode
def get_abs_pos(abs_pos, tgt_size):
# abs_pos: L, C
# tgt_size: M
# return: M, C
src_size = int(math.sqrt(abs_pos.size(0)))
tgt_size = int(math.sqrt(tgt_size))
dtype = abs_pos.dtype
if src_size != tgt_size:
return F.interpolate(
abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2),
size=(tgt_size, tgt_size),
mode="bicubic",
align_corners=False,
).permute(0, 2, 3, 1).flatten(0, 2).to(dtype=dtype)
else:
return abs_pos
class Resampler(nn.Module):
def __init__(
self,
grid_size,
embed_dim,
num_heads,
kv_dim=None,
norm_layer=nn.LayerNorm
):
super().__init__()
self.num_queries = grid_size ** 2
self.embed_dim = embed_dim
self.num_heads = num_heads
self.pos_embed = nn.Parameter(torch.randn(embed_dim, grid_size)).requires_grad_(False)
self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
trunc_normal_(self.query, std=.02)
if kv_dim is not None and kv_dim != embed_dim:
self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False)
else:
self.kv_proj = nn.Identity()
self.attn = nn.MultiheadAttention(embed_dim, num_heads)
self.ln_q = norm_layer(embed_dim)
self.ln_kv = norm_layer(embed_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, x, attn_mask=None):
pos_embed = get_abs_pos(self.pos_embed, x.size(1))
x = self.kv_proj(x)
x = self.ln_kv(x).permute(1, 0, 2)
N = x.shape[1]
q = self.ln_q(self.query)
out = self.attn(
self._repeat(q, N) + self.pos_embed.unsqueeze(1),
x + pos_embed.unsqueeze(1),
x,
attn_mask=attn_mask)[0]
return out.permute(1, 0, 2)
def _repeat(self, query, N: int):
return query.unsqueeze(1).repeat(1, N, 1)
class VisualAttention(nn.Module):
"""self-attention layer class.
Self-attention layer takes input with size [s, b, h]
and returns output of the same size.
"""
def __init__(self, embed_dim, num_heads,
bias=True, kdim=None, vdim=None):
super(VisualAttention, self).__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim
self.num_heads = num_heads
# Per attention head and per partition values.
assert embed_dim % num_heads == 0
self.hidden_size_per_attention_head = embed_dim // num_heads
self.num_attention_heads_per_partition = num_heads
self.hidden_size_per_partition = embed_dim
# Strided linear layer.
assert self._qkv_same_embed_dim, 'Only Support SelfAttention Currently'
self.in_proj = nn.Linear(embed_dim, 3 * embed_dim)
self.out_proj = nn.Linear(embed_dim, embed_dim)
self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
def forward(self, query, key, value, attn_mask = None):
# query/key/value: [sq, b, h]
sq, b, _ = query.size()
assert query is key, 'Only Support Self-Attention Currently'
sk = sq
mixed_x_layer = self.in_proj(query)
# [sq, b, (np * 3 * hn)] --> [sq, b, np, 3 * hn]
new_tensor_shape = mixed_x_layer.size()[:-1] + \
(self.num_attention_heads_per_partition,
3 * self.hidden_size_per_attention_head)
mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
# [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn]
query_layer, key_layer, value_layer = mixed_x_layer.split(
self.hidden_size_per_attention_head, dim=-1)
# [sq, b, np, hn] -> [sq, b * np, hn]
query_layer = query_layer.view(sq,
b * self.num_attention_heads_per_partition,
self.hidden_size_per_attention_head).transpose(0, 1)
# [sk, b, np, hn] -> [sk, b * np, hn]
key_layer = key_layer.view(sk,
b * self.num_attention_heads_per_partition,
self.hidden_size_per_attention_head).transpose(0, 1)
q_scaled = query_layer / self.norm_factor
if attn_mask is not None:
attention_probs = torch.baddbmm(attn_mask, q_scaled, key_layer.transpose(-2, -1))
else:
attention_probs = torch.bmm(q_scaled, key_layer.transpose(-2, -1))
attention_probs = attention_probs.softmax(dim=-1)
value_layer = value_layer.view(sk,
b * self.num_attention_heads_per_partition,
self.hidden_size_per_attention_head).transpose(0, 1)
# matmul: [b * np, sq, hn]
context_layer = torch.bmm(attention_probs, value_layer)
# change view [b, np, sq, hn]
context_layer = context_layer.view(b,
self.num_attention_heads_per_partition,
sq, self.hidden_size_per_attention_head)
# [b, np, sq, hn] --> [sq, b, np, hn]
context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
# [sq, b, np, hn] --> [sq, b, hp]
new_context_layer_shape = context_layer.size()[:-2] + \
(self.hidden_size_per_partition,)
context_layer = context_layer.view(*new_context_layer_shape)
output = self.out_proj(context_layer)
return output
class VisualAttentionBlock(nn.Module):
def __init__(
self,
d_model: int,
n_head: int,
mlp_ratio: float = 4.0,
act_layer: Callable = nn.GELU,
norm_layer: Callable = nn.LayerNorm,
is_cross_attention: bool = False,
):
super().__init__()
self.ln_1 = norm_layer(d_model)
if is_cross_attention:
self.ln_1_kv = norm_layer(d_model)
self.ln_2 = norm_layer(d_model)
mlp_width = int(d_model * mlp_ratio)
self.attn = VisualAttention(d_model, n_head)
self.mlp = nn.Sequential(OrderedDict([
("c_fc", nn.Linear(d_model, mlp_width)),
("gelu", act_layer()),
("c_proj", nn.Linear(mlp_width, d_model))
]))
def attention(
self,
q_x: torch.Tensor,
k_x: Optional[torch.Tensor] = None,
v_x: Optional[torch.Tensor] = None,
attn_mask: Optional[torch.Tensor] = None,
):
k_x = k_x if k_x is not None else q_x
v_x = v_x if v_x is not None else q_x
attn_mask = attn_mask.to(q_x.dtype) if attn_mask is not None else None
return self.attn(q_x, k_x, v_x, attn_mask=attn_mask)
def forward(
self,
q_x: torch.Tensor,
k_x: Optional[torch.Tensor] = None,
v_x: Optional[torch.Tensor] = None,
attn_mask: Optional[torch.Tensor] = None,
):
k_x = self.ln_1_kv(k_x) if hasattr(self, "ln_1_kv") and k_x is not None else None
v_x = self.ln_1_kv(v_x) if hasattr(self, "ln_1_kv") and v_x is not None else None
x = q_x + self.attention(q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask)
x = x + self.mlp(self.ln_2(x))
return x
class Transformer(nn.Module):
def __init__(
self,
width: int,
layers: int,
heads: int,
mlp_ratio: float = 4.0,
act_layer: Callable = nn.GELU,
norm_layer: Callable = nn.LayerNorm,
):
super().__init__()
self.width = width
self.layers = layers
self.grad_checkpointing = False
self.resblocks = nn.ModuleList([
VisualAttentionBlock(
width, heads, mlp_ratio, act_layer=act_layer, norm_layer=norm_layer)
for _ in range(layers)
])
def get_cast_dtype(self) -> torch.dtype:
return self.resblocks[0].mlp.c_fc.weight.dtype
def get_cast_device(self) -> torch.device:
return self.resblocks[0].mlp.c_fc.weight.device
def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
for r in self.resblocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
# TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372
x = checkpoint(r, x, None, None, attn_mask)
else:
x = r(x, attn_mask=attn_mask)
return x
class VisionTransformer(nn.Module):
def __init__(
self,
image_size: int,
patch_size: int,
width: int,
layers: int,
heads: int,
mlp_ratio: float,
n_queries: int = 256,
output_dim: int = 512,
**kwargs
):
super().__init__()
image_height, image_width = self.image_size = (image_size, image_size)
patch_height, patch_width = self.patch_size = (patch_size, patch_size)
self.grid_size = (image_height // patch_height, image_width // patch_width)
self.output_dim = output_dim
mean = (0.48145466, 0.4578275, 0.40821073)
std = (0.26862954, 0.26130258, 0.27577711)
self.image_transform = transforms.Compose([
transforms.Resize(
(image_size, image_size),
interpolation=InterpolationMode.BICUBIC
),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std),
])
self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
# class embeddings and positional embeddings
scale = width ** -0.5
self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1], width))
norm_layer = partial(nn.LayerNorm, eps=1e-6)
act_layer = nn.GELU
self.ln_pre = norm_layer(width)
self.transformer = Transformer(
width,
layers,
heads,
mlp_ratio,
act_layer=act_layer,
norm_layer=norm_layer,
)
self.attn_pool = Resampler(
grid_size=int(math.sqrt(n_queries)),
embed_dim=output_dim,
num_heads=output_dim // 128,
kv_dim=width,
norm_layer=norm_layer,
)
self.ln_post = norm_layer(output_dim)
self.proj = nn.Parameter((output_dim** -0.5) * torch.randn(output_dim, output_dim))
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.transformer.grad_checkpointing = enable
def forward(self, x: torch.Tensor):
x = x.to(
dtype=self.transformer.get_cast_dtype(),
device=self.transformer.get_cast_device(),
)
# to patches
x = self.conv1(x) # shape = [*, width, grid, grid]
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
x = x + get_abs_pos(self.positional_embedding, x.size(1))
x = self.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
x = self.transformer(x)
x = x.permute(1, 0, 2) # LND -> NLD
if self.attn_pool:
x = self.attn_pool(x)
x = self.ln_post(x)
x = x @ self.proj
return x
def encode(self, image_paths: List[str]):
images = []
for image_path in image_paths:
if image_path.startswith("http://") or image_path.startswith("https://"):
image = Image.open(requests.get(image_path, stream=True).raw)
elif image_path.startswith("oss://"):
raise NotImplementedError
else:
image = Image.open(image_path)
image = image.convert("RGB")
images.append(self.image_transform(image))
images = torch.stack(images, dim=0)
return self(images)
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