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from transformers import AutoTokenizer, AutoModel
import clip
import skimage.io as io
import PIL.Image
from transformers import GPT2Tokenizer, GPT2LMHeadModel, AdamW, get_linear_schedule_with_warmup
import os
import pickle
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from torch.nn import functional as F
import pandas as pd
from tqdm import tqdm
from PIL import Image
from typing import Tuple
import numpy as np
import time
import json
import nltk
nltk.download('punkt')
class Adapter(nn.Module):
def forward(self, x):
return self.model(x)
def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh):
super(Adapter, self).__init__()
layers = []
for i in range(len(sizes) -1):
layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias))
if i < len(sizes) - 2:
layers.append(act())
self.model = nn.Sequential(*layers)
class ClipGPT2Model(nn.Module):
def __init__(self, img_feature_length, img_feature_size = 512):
super(ClipGPT2Model, self).__init__()
self.img_feature_length = img_feature_length
self.gpt = GPT2LMHeadModel.from_pretrained('gpt2')
self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1]
self.clip_project = Adapter((img_feature_size,
(self.gpt_embedding_size * img_feature_length) // 2,
self.gpt_embedding_size * img_feature_length))
def get_dummy_token(self,
batch_size: int,
device: torch.device) -> torch.Tensor:
return torch.zeros(batch_size, self.img_feature_length, dtype=torch.int64, device=device)
def forward(self,
tokens: torch.Tensor,
feature: torch.Tensor,
mask = None,
labels = None):
embedding_text = self.gpt.transformer.wte(tokens)
feature_projections = self.clip_project(feature).view(-1, self.img_feature_length, self.gpt_embedding_size)
embedding_cat = torch.cat((feature_projections, embedding_text), dim=1)
if labels is not None:
dummy_token = self.get_dummy_token(tokens.shape[0], tokens.device)
labels = torch.cat((dummy_token, tokens), dim=1)
out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask)
return out
def generate_beam(
model,
tokenizer,
entry_length,
temperature,
embed=None,
beam_size: int = 10,
prompt=None,
stop_token: str = ".",
):
model.eval()
stop_token_index = tokenizer.encode(stop_token)[0]
tokens = None
scores = None
device = next(model.parameters()).device
seq_lengths = torch.ones(beam_size, device=device)
is_stopped = torch.zeros(beam_size, device=device, dtype=torch.bool)
with torch.no_grad():
if embed is not None:
generated = embed
else:
if tokens is None:
tokens = torch.tensor(tokenizer.encode(prompt))
tokens = tokens.unsqueeze(0).to(device)
generated = model.gpt.transformer.wte(tokens)
for i in range(entry_length):
outputs = model.gpt(inputs_embeds=generated)
logits = outputs.logits
logits = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
logits = logits.softmax(-1).log()
if scores is None:
scores, next_tokens = logits.topk(beam_size, -1)
generated = generated.expand(beam_size, *generated.shape[1:])
next_tokens, scores = next_tokens.permute(1, 0), scores.squeeze(0)
if tokens is None:
tokens = next_tokens
else:
tokens = tokens.expand(beam_size, *tokens.shape[1:])
tokens = torch.cat((tokens, next_tokens), dim=1)
else:
logits[is_stopped] = -float(np.inf)
logits[is_stopped, 0] = 0
scores_sum = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
scores_sum_average = scores_sum / seq_lengths[:, None]
scores_sum_average, next_tokens = scores_sum_average.view(-1).topk(
beam_size, -1
)
next_tokens_source = next_tokens // scores_sum.shape[1]
seq_lengths = seq_lengths[next_tokens_source]
next_tokens = next_tokens % scores_sum.shape[1]
next_tokens = next_tokens.unsqueeze(1)
tokens = tokens[next_tokens_source]
tokens = torch.cat((tokens, next_tokens), dim=1)
generated = generated[next_tokens_source]
scores = scores_sum_average * seq_lengths
is_stopped = is_stopped[next_tokens_source]
next_token_embed = model.gpt.transformer.wte(next_tokens.squeeze()).view(
generated.shape[0], 1, -1
)
generated = torch.cat((generated, next_token_embed), dim=1)
is_stopped = is_stopped + next_tokens.eq(stop_token_index).squeeze()
if is_stopped.all():
break
scores = scores / seq_lengths
output_list = tokens.cpu().numpy()
output_texts = [
tokenizer.decode(output[: int(length)])
for output, length in zip(output_list, seq_lengths)
]
order = scores.argsort(descending=True)
output_texts = [output_texts[i] for i in order]
return output_texts
def generate_caption_clipgpt(img, entry_length, temperature):
prefix_length = 10
model = ClipGPT2Model(prefix_length)
model.load_state_dict(torch.load('model/model_train_best_run_clipGPT.pt', map_location=torch.device('cpu')))
model = model.eval()
device=torch.device('cpu')
model = model.to(device)
clip_model, preprocess = clip.load('ViT-B/32', device, jit=False)
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
start_time = time.time()
# pil_image = PIL.Image.fromarray(img)
image = preprocess(img).unsqueeze(0).to(device)
with torch.no_grad():
prefix = clip_model.encode_image(image).to(device, dtype=torch.float32)
prefix_embed = model.clip_project(prefix).reshape(1, prefix_length, -1)
beam_caption = generate_beam(model, tokenizer, entry_length, temperature, embed=prefix_embed)[0]
end_time = time.time()
print("--- Time taken to generate: %s seconds ---" % (end_time - start_time))
return beam_caption
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