108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
#! /usr/bin/env python3
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from transformers import BartForConditionalGeneration, BartTokenizer, AdamW
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import torch
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from tqdm import tqdm
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import os
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from torch.utils.data import Dataset
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import pandas as pd
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class DL(Dataset):
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def __init__(self, path, max_length, buffer_size):
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self.data = pd.read_csv(path, delimiter="\t", header=None)
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self.max_length = max_length
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self.buffer_size = buffer_size
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def __len__(self):
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return len(self.data)
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def __getitem__(self, idx):
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ix, text = self.data.iloc[idx]
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return text
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class FT:
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def __init__(self):
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# Enable cudnn optimizations
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torch.backends.cudnn.benchmark = True
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# load tokenizer and model
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self.tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
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self.model = BartForConditionalGeneration.from_pretrained("facebook/bart-base")
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self.model.to(self.device)
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# set up optimizer
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self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=1e-5)
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self.load_checkpoint()
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try:
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from torch.cuda.amp import GradScaler, autocast
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self.scaler = GradScaler()
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except ImportError:
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class autocast:
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def __enter__(self):
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pass
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def __exit__(self, *args):
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pass
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self.scaler = None # We won't use a scaler if we don't have Amp
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def test_model(self, dataloader):
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self.model.eval()
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predictions = [] # List to store the generated text outputs
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print("Testing model...")
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for batch in tqdm(dataloader):
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with torch.no_grad():
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inputs = self.tokenizer(
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batch,
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return_tensors="pt",
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padding=True,
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truncation=True,
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max_length=512,
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)
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inputs.to(self.device)
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outputs = self.model.generate(
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input_ids=inputs["input_ids"],
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attention_mask=inputs["attention_mask"],
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max_length=200, # Set the maximum length of the generated output
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num_beams=4, # Number of beams for beam search (optional)
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early_stopping=True, # Stop generation when all beams are finished (optional)
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)
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generated_text = self.tokenizer.decode(
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outputs[0], skip_special_tokens=True
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)
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predictions.append(generated_text)
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return predictions
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def load_checkpoint(self):
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checkpoint_path = "./checkpoints/ft_14.pt"
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if os.path.exists(checkpoint_path):
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checkpoint = torch.load(checkpoint_path)
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self.current_epoch = checkpoint["epoch"]
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self.model.load_state_dict(checkpoint["model_state_dict"])
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self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
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print(f"Loaded checkpoint from epoch {self.current_epoch}")
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def validate(self):
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dataloader = torch.utils.data.DataLoader(
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DL(
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path="./dataset/gt.txt",
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max_length=300,
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buffer_size=100,
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),
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batch_size=1,
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)
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outputs = self.test_model(dataloader)
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print(outputs)
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if __name__ == "__main__":
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validator = FT()
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validator.validate()
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