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import os
import json
import torch
from PIL import Image
from torch.utils.data import Dataset
from transformers import AutoProcessor
from torch.utils.data import DataLoader
import pickle
import requests
from datasets import Dataset, load_dataset
import pandas as pd
import numpy as np
class ClipDataset(Dataset):
'''ClipDataset class for loading the CLIP dataset'''
def __init__(self, coco_data, model_name, tokenizer):
self.tokenizer = tokenizer
self.processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
self.caption_dataset = coco_data
def __len__(self):
#Return the length of the dataset
return len(self.caption_dataset)
def __getitem__(self, idx):
#Get the image url and caption
img_url = self.caption_dataset[idx]["image_url"]
caption = self.caption_dataset[idx]["caption"]
#Get the image and caption embeddings
image = Image.open(requests.get(img_url,stream=True).raw)
width, height = image.size
new_width = 224
new_height = new_width * height // width
new_height = 224
new_width = new_height * width // height
image = image.resize((new_width, new_height), Image.LANCZOS)
image_processed = self.processor(images=image, return_tensors="pt") ['pixel_values']
image_sqeezed = image_processed.squeeze(0)
tokenized_caption = self.tokenizer(caption, return_tensors="pt", return_attention_mask=False)
tokenized_caption_ids = tokenized_caption['input_ids'].squeeze(0)
return(image_sqeezed , tokenized_caption_ids)
def collate_fn_phase1(batch):
#Unzip the batch
image_embeddings, captions = zip(*batch)
#Stack the image embeddings
image_embeddings_stacked = torch.stack(image_embeddings, dim=0)
#Pad the captions, padded value is the <eos> token
captions_padded = torch.nn.utils.rnn.pad_sequence(captions, batch_first=True, padding_value=50256)
#Return the stacked image embeddings and padded captions
return (image_embeddings_stacked, captions_padded)
def get_data_loaders_phase1(data_dir, clip_model_name, tokenizer, train_batch_size, val_batch_size, num_workers):
# Load the data
with open(os.path.join(data_dir, 'coco_train.pkl'), 'rb') as fp:
train_pkl = pickle.load(fp)
with open(os.path.join(data_dir, "coco_val.pkl"), "rb") as fp:
val_pkl = pickle.load(fp)
# train data loaders
train_dataloader = DataLoader(ClipDataset(train_pkl, clip_model_name, tokenizer), collate_fn=collate_fn_phase1, batch_size=train_batch_size, num_workers = num_workers, shuffle=True, pin_memory=True)
# val data loaders
val_dataloader = DataLoader(ClipDataset(val_pkl, clip_model_name, tokenizer), collate_fn=collate_fn_phase1, batch_size=val_batch_size, num_workers = num_workers, shuffle=False, pin_memory=True)
return train_dataloader, val_dataloader
##################################### Phase 2 #########################################
class ClipDatasetPhase2(Dataset):
'''ClipDataset class for loading the CLIP dataset'''
def __init__(self, data_frame, model_name, tokenizer):
self.tokenizer = tokenizer
self.processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
self.df = data_frame
def __len__(self):
#Return the length of the dataset
return len(self.df)
def __getitem__(self, idx):
#Get the image url and QAs
img_url = self.df.ImageUrl[idx[0]]
que = self.df.Question[idx[0]]
ans = self.df.Answer[idx[0]]
print("img_url", img_url)
print("que", que)
print("ans", ans)
#Get the image and caption embeddings
if img_url is None:
print("img_url is None")
image_sqeezed = None
else:
image = Image.open(requests.get(img_url,stream=True).raw)
width, height = image.size
new_width = 224
new_height = new_width * height // width
new_height = 224
new_width = new_height * width // height
image = image.resize((new_width, new_height), Image.LANCZOS)
image_processed = self.processor(images=image, return_tensors="pt") ['pixel_values']
image_sqeezed = image_processed.squeeze(0)
que_ids = self.tokenizer(que, return_tensors="pt", return_attention_mask=False)['input_ids'].squeeze(0)
ans_ids = self.tokenizer(ans, return_tensors="pt", return_attention_mask=False)['input_ids'].squeeze(0)
return(image_sqeezed , que_ids, ans_ids)
def collate_fn_phase2(batch):
#Unzip the batch
image_embeddings, ques, ans = zip(*batch)
#Stack the image embeddings
if image_embeddings[0] is None:
image_embeddings_stacked = None
else:
image_embeddings_stacked = torch.stack(image_embeddings, dim=0)
#Pad the QAs, padded value is the <eos> token
ques_padded = torch.nn.utils.rnn.pad_sequence(ques, batch_first=True, padding_value=50256)
ans_padded = torch.nn.utils.rnn.pad_sequence(ans, batch_first=True, padding_value=50256)
#Return the stacked image embeddings and padded QAs
return (image_embeddings_stacked, ques_padded, ans_padded)
def prep_data(df):
df_assistant = df[(df.role == "assistant") & (df["rank"] == 0.0)].copy()
df_prompter = df[(df.role == "prompter")].copy()
df_prompter = df_prompter.set_index("message_id")
df_assistant["Answer"] = df_assistant["text"].values
inputs = []
for _, row in df_assistant.iterrows():
input = df_prompter.loc[row.parent_id]
inputs.append(input.text)
df_assistant["Question"] = inputs
df_assistant["ImageUrl"] = None
df_assistant = df_assistant[df_assistant.lang == "en"]
df_assistant = df_assistant[
["ImageUrl","Question", "Answer", "message_id"]
].rename(columns={"message_id": "Ids"})
return df_assistant
def get_i150_df(config):
with open(config.get("i150k_json"), "r") as fp:
i150k_json_read = json.load(fp)
max_tokens = 100
image_urls = []
ques_list = []
ans_list = []
id_list = []
for idx, data in enumerate(i150k_json_read):
image = data['image']
image_url = 'http://images.cocodataset.org/train2017/' + image
id_ = data["id"]
iterator = iter(data['conversations'])
for i in iterator:
ques = i
ans = next(iterator)
if (len(ques["value"])>100 or len(ans["value"])>max_tokens):
continue
if ques["from"] == "human" and ans["from"] == "gpt":
image_urls.append(image_url)
ques_list.append(ques["value"].replace("<image>\n","").replace("<image>",""))
ans_list.append(ans["value"])
id_list.append(id_)
df_i150k = pd.DataFrame(list(zip(image_urls, ques_list, ans_list, id_list)),
columns =["ImageUrl", "Question", "Answer", "Ids"])
msk = np.random.rand(len(df_i150k)) < 0.96
train_df = df_i150k[msk]
test_df = df_i150k[~msk]
return train_df, test_df
def get_oas_df(config):
train_ds, val_ds = load_dataset(config.get("QA_datasetName"), split=["train", "validation"])
train_df = prep_data(train_ds.to_pandas())
test_df = prep_data(val_ds.to_pandas())
return train_df, test_df
def get_data_loaders_phase2(tokenizer, config):
train_i150k, test_i150k = get_i150_df(config)
train_oas, test_oas = get_oas_df(config)
train_df = pd.concat([train_i150k, train_oas]).reset_index(drop=True)
val_df = pd.concat([test_i150k, test_oas]).reset_index(drop=True)
# train data loaders
train_dataloader = DataLoader(ClipDatasetPhase2(train_df, config.get("clip_model_name"), tokenizer), collate_fn=collate_fn_phase2, batch_size=config.get("train_batch_size"), num_workers = config.get("num_workers"), shuffle=True, pin_memory=True)
# val data loaders
val_dataloader = DataLoader(ClipDatasetPhase2(val_df, config.get("clip_model_name"), tokenizer), collate_fn=collate_fn_phase2, batch_size=config.get("val_batch_size"), num_workers = config.get("num_workers"), shuffle=False, pin_memory=True)
return train_dataloader, val_dataloader |