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import random
import numpy as np
import requests
from io import BytesIO
from PIL import Image
from statistics import mean
import copy
import json
from typing import Any, Mapping
import open_clip
import torch
from sentence_transformers.util import (semantic_search,
dot_score,
normalize_embeddings)
def nn_project(curr_embeds, embedding_layer, print_hits=False):
with torch.no_grad():
bsz,seq_len,emb_dim = curr_embeds.shape
curr_embeds = curr_embeds.reshape((-1,emb_dim))
curr_embeds = normalize_embeddings(curr_embeds) # queries
embedding_matrix = embedding_layer.weight
embedding_matrix = normalize_embeddings(embedding_matrix)
hits = semantic_search(curr_embeds, embedding_matrix,
query_chunk_size=curr_embeds.shape[0],
top_k=1,
score_function=dot_score)
if print_hits:
all_hits = []
for hit in hits:
all_hits.append(hit[0]["score"])
print(f"mean hits:{mean(all_hits)}")
nn_indices = torch.tensor([hit[0]["corpus_id"] for hit in hits], device=curr_embeds.device)
nn_indices = nn_indices.reshape((bsz,seq_len))
projected_embeds = embedding_layer(nn_indices)
return projected_embeds, nn_indices
def decode_ids(input_ids, tokenizer, by_token=False):
input_ids = input_ids.detach().cpu().numpy()
texts = []
if by_token:
for input_ids_i in input_ids:
curr_text = []
for tmp in input_ids_i:
curr_text.append(tokenizer.decode([tmp]))
texts.append('|'.join(curr_text))
else:
for input_ids_i in input_ids:
texts.append(tokenizer.decode(input_ids_i))
return texts
def get_target_feature(model, preprocess, tokenizer_funct, device, target_images=None, target_prompts=None):
if target_images is not None:
with torch.no_grad():
curr_images = [preprocess(i).unsqueeze(0) for i in target_images]
curr_images = torch.concatenate(curr_images).to(device)
all_target_features = model.encode_image(curr_images)
else:
texts = tokenizer_funct(target_prompts).to(device)
all_target_features = model.encode_text(texts)
return all_target_features
def encode_text_embedding(model, text_embedding, ids, avg_text=False):
cast_dtype = model.transformer.get_cast_dtype()
x = text_embedding + model.positional_embedding.to(cast_dtype)
x = x.permute(1, 0, 2) # NLD -> LND
x = model.transformer(x, attn_mask=model.attn_mask)
x = x.permute(1, 0, 2) # LND -> NLD
x = model.ln_final(x)
# x.shape = [batch_size, n_ctx, transformer.width]
# take features from the eot embedding (eot_token is the highest number in each sequence)
if avg_text:
x = x[torch.arange(x.shape[0]), :ids.argmax(dim=-1)]
x[:, 1:-1]
x = x.mean(dim=1) @ model.text_projection
else:
x = x[torch.arange(x.shape[0]), ids.argmax(dim=-1)] @ model.text_projection
return x
def forward_text_embedding(model, embeddings, ids, image_features, avg_text=False, return_feature=False):
text_features = encode_text_embedding(model, embeddings, ids, avg_text=avg_text)
if return_feature:
return text_features
image_features = image_features / image_features.norm(dim=1, keepdim=True)
text_features = text_features / text_features.norm(dim=1, keepdim=True)
logits_per_image = image_features @ text_features.t()
logits_per_text = logits_per_image.t()
return logits_per_image, logits_per_text
def initialize_prompt(tokenizer, token_embedding, args, device, original_prompt):
prompt_len = args["prompt_len"]
# randomly optimize prompt embeddings
tokens = tokenizer.encode(original_prompt)
if len(tokens) > prompt_len:
tokens = tokens[:prompt_len]
if len(tokens) < prompt_len:
tokens += [0] * (prompt_len - len(tokens))
prompt_ids = torch.tensor([tokens] * args["prompt_bs"]).to(device)
# prompt_ids = torch.randint(len(tokenizer.encoder), (args.prompt_bs, prompt_len)).to(device)
prompt_embeds = token_embedding(prompt_ids).detach()
prompt_embeds.requires_grad = True
# initialize the template
template_text = "{}"
padded_template_text = template_text.format(" ".join(["<start_of_text>"] * prompt_len))
dummy_ids = tokenizer.encode(padded_template_text)
# -1 for optimized tokens
dummy_ids = [i if i != 49406 else -1 for i in dummy_ids]
dummy_ids = [49406] + dummy_ids + [49407]
dummy_ids += [0] * (77 - len(dummy_ids))
dummy_ids = torch.tensor([dummy_ids] * args["prompt_bs"]).to(device)
# for getting dummy embeds; -1 won't work for token_embedding
tmp_dummy_ids = copy.deepcopy(dummy_ids)
tmp_dummy_ids[tmp_dummy_ids == -1] = 0
dummy_embeds = token_embedding(tmp_dummy_ids).detach()
dummy_embeds.requires_grad = False
return prompt_embeds, dummy_embeds, dummy_ids
def optimize_prompt_loop(model, tokenizer, token_embedding, all_target_features, args, device, original_prompt):
opt_iters = args["iter"]
lr = args["lr"]
weight_decay = args["weight_decay"]
print_step = args["print_step"]
batch_size = args["batch_size"]
print_new_best = True
# initialize prompt
prompt_embeds, dummy_embeds, dummy_ids = initialize_prompt(tokenizer, token_embedding, args, device, original_prompt)
p_bs, p_len, p_dim = prompt_embeds.shape
# get optimizer
input_optimizer = torch.optim.AdamW([prompt_embeds], lr=lr, weight_decay=weight_decay)
best_sim = -1000 * args["loss_weight"]
best_text = ""
for step in range(opt_iters):
# randomly sample sample images and get features
if batch_size is None:
target_features = all_target_features
else:
curr_indx = torch.randperm(len(all_target_features))
target_features = all_target_features[curr_indx][0:batch_size]
universal_target_features = all_target_features
# forward projection
projected_embeds, nn_indices = nn_project(prompt_embeds, token_embedding, print_hits=False)
# get cosine similarity score with all target features
with torch.no_grad():
# padded_embeds = copy.deepcopy(dummy_embeds)
padded_embeds = dummy_embeds.detach().clone()
padded_embeds[dummy_ids == -1] = projected_embeds.reshape(-1, p_dim)
logits_per_image, _ = forward_text_embedding(model, padded_embeds, dummy_ids, universal_target_features)
scores_per_prompt = logits_per_image.mean(dim=0)
universal_cosim_score = scores_per_prompt.max().item()
best_indx = scores_per_prompt.argmax().item()
# tmp_embeds = copy.deepcopy(prompt_embeds)
tmp_embeds = prompt_embeds.detach().clone()
tmp_embeds.data = projected_embeds.data
tmp_embeds.requires_grad = True
# padding
# padded_embeds = copy.deepcopy(dummy_embeds)
padded_embeds = dummy_embeds.detach().clone()
padded_embeds[dummy_ids == -1] = tmp_embeds.reshape(-1, p_dim)
logits_per_image, _ = forward_text_embedding(model, padded_embeds, dummy_ids, target_features)
cosim_scores = logits_per_image
loss = 1 - cosim_scores.mean()
loss = loss * args["loss_weight"]
prompt_embeds.grad, = torch.autograd.grad(loss, [tmp_embeds])
input_optimizer.step()
input_optimizer.zero_grad()
curr_lr = input_optimizer.param_groups[0]["lr"]
cosim_scores = cosim_scores.mean().item()
decoded_text = decode_ids(nn_indices, tokenizer)[best_indx]
if print_step is not None and (step % print_step == 0 or step == opt_iters-1):
per_step_message = f"step: {step}, lr: {curr_lr}"
# if not print_new_best:
# per_step_message = f"\n{per_step_message}, cosim: {universal_cosim_score:.3f}, text: {decoded_text}"
# print(per_step_message)
if best_sim * args["loss_weight"] < universal_cosim_score * args["loss_weight"]:
best_sim = universal_cosim_score
best_text = decoded_text
if print_new_best:
print(f"step: {step}, new best cosine sim: {best_sim}, new best prompt: {best_text}")
if print_step is not None:
print(f"best cosine sim: {best_sim}, best prompt: {best_text}")
return best_text
def optimize_prompt(model, preprocess, args, device, target_images=None, target_prompts=None):
token_embedding = model.token_embedding
tokenizer = open_clip.tokenizer._tokenizer
tokenizer_funct = open_clip.get_tokenizer(args["clip_model"])
all_target_features = get_target_feature(model, preprocess, tokenizer_funct, device, target_images=target_images)
learned_prompt = optimize_prompt_loop(model, tokenizer, token_embedding, all_target_features, args, device, target_prompts)
return learned_prompt
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