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import io
import math
import re
from functools import partial
from typing import List
import albumentations as A
import cv2
import numpy as np
import pyarrow as pa
import requests
import torch
import transformers
from PIL import Image
from albumentations.pytorch import ToTensorV2
from einops import rearrange
from torch import nn
from torch.nn import functional as F
from torch.nn.init import trunc_normal_
from torchvision import transforms
from torchvision.transforms import InterpolationMode
from transformers import AutoModel, AutoProcessor
from transformers.activations import ACT2FN
assert transformers.__version__ == "4.40.0", "Please install a specific HF transformers version: pip install transformers==4.40.0"
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
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token:
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float32)
omega /= embed_dim / 2.
omega = 1. / 10000 ** omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
class Resampler(nn.Module):
"""
A 2D perceiver-resampler network with one cross attention layers by
(grid_size**2) learnable queries and 2d sincos pos_emb
Outputs:
A tensor with the shape of (grid_size**2, embed_dim)
"""
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.from_numpy(get_2d_sincos_pos_embed(embed_dim, grid_size)).float()
).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 CLIPModel(nn.Module):
def __init__(
self,
image_size: int,
n_queries: int = 256,
output_dim: int = 512,
vision_model_name_or_path: str = "StanfordAIMI/XraySigLIP__vit-l-16-siglip-384__webli",
**kwargs
):
super().__init__()
# load model and processor
self.model = AutoModel.from_pretrained(vision_model_name_or_path).vision_model
self.processor = AutoProcessor.from_pretrained(vision_model_name_or_path).image_processor
# set constants
self.image_height, self.image_width = self.image_size = (image_size, image_size)
width = self.model.config.hidden_size
patch_height, patch_width = self.model.embeddings.patch_embedding.kernel_size
self.grid_size = (self.image_height // patch_height, self.image_width // patch_width)
self.output_dim = output_dim
# Transforms
self.mean = self.processor.image_mean
self.std = self.processor.image_std
self.image_transform = transforms.Compose([
transforms.Resize(
(image_size, image_size),
interpolation=InterpolationMode.BICUBIC
),
transforms.ToTensor(),
transforms.Normalize(mean=self.mean, std=self.std),
])
# MLP
self.pos_embed = nn.Parameter(
torch.from_numpy(get_2d_sincos_pos_embed(width, self.grid_size[0])).float()
).requires_grad_(False)
self.attn_pool = nn.Sequential(
nn.Linear(width, output_dim * 4, bias=True),
ACT2FN["gelu"],
nn.Linear(output_dim * 4, output_dim, bias=True)
)
norm_layer = partial(nn.LayerNorm, eps=1e-6)
self.ln_post = norm_layer(output_dim)
self.proj = nn.Parameter((output_dim ** -0.5) * torch.randn(output_dim, output_dim), requires_grad=True)
def forward_resampler(self, x):
pos_embed = get_abs_pos(self.pos_embed, x.size(1))
x = x + pos_embed.unsqueeze(0)
x = self.attn_pool(x)
x = self.ln_post(x)
x = x @ self.proj
return x
def forward(self, x: torch.Tensor):
# get feature
x = self.model(x, output_hidden_states=True).hidden_states[-1]
# resampler
x = self.forward_resampler(x)
return x
def load_image(self, image_path, training):
if image_path.startswith("http://") or image_path.startswith("https://"):
image = Image.open(requests.get(image_path, stream=True).raw)
else:
image = Image.open(image_path)
image = image.convert("RGB")
image_tensor = self.image_transform(image)
return image_tensor
def encode(self, image_paths: List[str], training):
images = []
for image_path in image_paths:
image = self.load_image(image_path, training)
images.append(image)
images = torch.stack(images, dim=0)
images = images.to(dtype=next(self.parameters()).dtype, device=next(self.parameters()).device)
outputs = self.forward(images)
return outputs
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