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CC3M_translate_inference.py
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CC3M_translate_inference.py
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import torch
import pandas as pd
from tqdm import tqdm
import time
import numpy as np
from datasets import load_dataset, load_metric, Dataset
from transformers import (
AutoTokenizer,
MarianMTModel,
AutoTokenizer,
AutoModelForSeq2SeqLM,
T5Tokenizer
)
from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments, DataCollatorForSeq2Seq
import multiprocessing
from easydict import EasyDict
import yaml
# Read config.yaml file
with open("config.yaml") as infile:
SAVED_CFG = yaml.load(infile, Loader=yaml.FullLoader)
CFG = EasyDict(SAVED_CFG["CFG"])
device = "cuda:0" if torch.cuda.is_available() else "cpu"
training_args = Seq2SeqTrainingArguments
model_name = CFG.inference_model_name
CFG.dset_name = "conceptual_captions"
train_dataset = load_dataset(CFG.dset_name, split="train")
valid_dataset = load_dataset(CFG.dset_name, split="validation")
print(train_dataset)
print(valid_dataset)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name, use_auth_token=True)
model.to(device)
start = 0
batch_size = 600 # P100:batch_size 250 / A100:batch_size 600
length = len(train_dataset)
cnt = length//batch_size + 1
df = pd.DataFrame(columns = {"english_caption", "korean_caption", "image_url"})
# start train dastasets translate
csv_start = 0
save_start = csv_start
save_count = 0
for i in tqdm(range(start,cnt)):
save_count+=1
check = False
end=csv_start+batch_size
if end>len(train_dataset):
check = True
end = len(train_dataset)
src_sentences = train_dataset['caption'][csv_start:end]
urls = train_dataset['image_url'][csv_start:end]
encoding = tokenizer(
src_sentences, padding=True, return_tensors="pt", max_length=CFG.max_token_length
).to(device)
# https://huggingface.co/docs/transformers/internal/generation_utils
with torch.no_grad():
translated = model.generate(
**encoding,
max_length=CFG.max_token_length,
num_beams=CFG.num_beams,
repetition_penalty=CFG.repetition_penalty,
no_repeat_ngram_size=CFG.no_repeat_ngram_size,
num_return_sequences=CFG.num_return_sequences,
)
del encoding
# https://github.com/huggingface/transformers/issues/10704
generated_texts = tokenizer.batch_decode(translated, skip_special_tokens=True)
del translated
print(generated_texts[0:2])
df1 = pd.DataFrame({"english_caption": src_sentences, "korean_caption": generated_texts, "image_url": urls})
df = df.append(df1, ignore_index = True)
if save_count == 30 or check==True:
save_count=0
df.to_csv(f"./results/train_translated-{save_start}-{end}-sentences.csv", index=False)
csv_start = end
start = 0
batch_size = 600 # P100:batch_size 250 / A100:batch_size 600
length = len(valid_dataset)
cnt = length//batch_size + 1
df = pd.DataFrame(columns = {"english_caption", "korean_caption", "image_url"})
# start validation dastasets translate
csv_start = 0
save_start = csv_start
save_count = 0
for i in tqdm(range(start,cnt)):
save_count+=1
check = False
end=csv_start+batch_size
if end>len(valid_dataset):
check = True
end = len(valid_dataset)
src_sentences = valid_dataset['caption'][csv_start:end]
urls = valid_dataset['image_url'][csv_start:end]
encoding = tokenizer(
src_sentences, padding=True, return_tensors="pt", max_length=CFG.max_token_length
).to(device)
# https://huggingface.co/docs/transformers/internal/generation_utils
with torch.no_grad():
translated = model.generate(
**encoding,
max_length=CFG.max_token_length,
num_beams=CFG.num_beams,
repetition_penalty=CFG.repetition_penalty,
no_repeat_ngram_size=CFG.no_repeat_ngram_size,
num_return_sequences=CFG.num_return_sequences,
)
del encoding
# https://github.com/huggingface/transformers/issues/10704
generated_texts = tokenizer.batch_decode(translated, skip_special_tokens=True)
del translated
print(generated_texts[0:2])
df1 = pd.DataFrame({"english_caption": src_sentences, "korean_caption": generated_texts, "image_url": urls})
df = df.append(df1, ignore_index = True)
if save_count == 30 or check==True:
save_count=0
df.to_csv(f"./results/valid_translated-{save_start}-{end}-sentences.csv", index=False)
csv_start = end
# df = pd.DataFrame({"src": src_sentences, "tgt": tgt_sentences, "gen": generated_texts})
# df.to_csv(f"./results/translated-{CFG.no_inference_sentences}-sentences.csv", index=False)