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import pandas as pd
import torch
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, DataCollatorWithPadding
from torch.utils.data import Dataset, DataLoader
df = pd.read_feather("data/ccs_synthetic.feather")
# from transformers import MarianTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class CaptionDataset(Dataset):
def __init__(self, df, tokenizer_name):
self.df = df
self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
def __len__(self):
return len(self.df)
def __getitem__(self, index):
sentence1 = df.loc[index, "caption"]
tokens = self.tokenizer(sentence1, return_tensors="pt")
return tokens
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-sv")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-sv")
model.to(device)
model.eval()
def custom_collate_fn(data):
"""
Data collator with padding.
"""
tokens = [sample["input_ids"][0] for sample in data]
attention_masks = [sample["attention_mask"][0] for sample in data]
attention_masks = torch.nn.utils.rnn.pad_sequence(attention_masks, batch_first=True)
padded_tokens = torch.nn.utils.rnn.pad_sequence(tokens, batch_first=True)
batch = {"input_ids": padded_tokens, "attention_mask": attention_masks}
return batch
test_data = CaptionDataset(df, "Helsinki-NLP/opus-mt-en-sv")
test_dataloader = DataLoader(
test_data,
batch_size=64,
shuffle=False,
num_workers=4,
collate_fn=custom_collate_fn,
)
with torch.no_grad():
decoded_tokens = []
for i, batch in enumerate(tqdm(test_dataloader)):
batch = {k: v.to(device) for k, v in batch.items()}
output_tokens = model.generate(**batch)
decoded_tokens += tokenizer.batch_decode(output_tokens.to("cpu"), skip_special_tokens=True)
df["caption_sv"] = decoded_tokens
df.to_feather("data/ccs_synthetic_sv.feather")
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