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inferv1_img_bce_val.py
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#!/usr/bin/env python
# coding: utf-8
import polars as pl
import numpy as np
from tqdm.auto import tqdm
import torch
from data import load_parquets_from_zip, merge_article_with_imgs
from data.dataset import EkstraDataset, ekstra_inference_collate
from model import to_device, load_checkpoint
from model.model_v1 import EsktraSort, interpret_inference
import random
import pickle
print('Infering v1 imgs bin')
random.seed(42)
torch.manual_seed(42)
np.random.seed(42)
debug = False
print('Loading dataset...')
behaviors = pl.read_parquet('preprocess/test_behaviors.parquet')
history = pl.read_parquet('preprocess/test_history.parquet')
article = pl.read_parquet('preprocess/article.parquet')
images_embeddings = pl.read_parquet('preprocess/image_embs.parquet')
categories = pl.read_parquet('preprocess/categories_embs.parquet')
article_embeddings = load_parquets_from_zip('dataset/FacebookAI_xlm_roberta_base.zip')['FacebookAI_xlm_roberta_base/xlm_roberta_base']
art_img_embeddings = merge_article_with_imgs(article_embeddings, images_embeddings, col='embeddings')
filtered_behaviors = behaviors.filter(~pl.col('is_beyond_accuracy'))
if debug:
filtered_behaviors = filtered_behaviors[:1_000]
ds = EkstraDataset(filtered_behaviors, history, article, art_img_embeddings, categories, labels=False)
del filtered_behaviors
dl = torch.utils.data.DataLoader(ds, batch_size=10, collate_fn=ekstra_inference_collate, shuffle=False)
model = EsktraSort()
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
path = 'checkpoint_v1_img_bce_val'
print('Loading model')
epoch = load_checkpoint(path, model)
print(epoch)
model = model.to(device)
model.eval()
res = []
i = 0
with torch.no_grad():
for idx, (in_view_len, behavior), history in tqdm(dl):
torch.cuda.empty_cache()
behavior = to_device(behavior, device)
history = to_device(history, device)
pred = model(behavior, history)
res.extend(interpret_inference(idx, pred.cpu().numpy(), in_view_len))
i += 1
if debug and i == 20:
break
del ds
del dl
behaviors = pl.read_parquet('preprocess/test_behaviors.parquet')
history = pl.read_parquet('preprocess/test_history.parquet')
article = pl.read_parquet('preprocess/article.parquet')
images_embeddings = pl.read_parquet('preprocess/image_embs.parquet')
categories = pl.read_parquet('preprocess/categories_embs.parquet')
article_embeddings = load_parquets_from_zip('dataset/FacebookAI_xlm_roberta_base.zip')['FacebookAI_xlm_roberta_base/xlm_roberta_base']
art_img_embeddings = merge_article_with_imgs(article_embeddings, images_embeddings, col='embeddings')
filtered_behaviors = behaviors.filter(pl.col('is_beyond_accuracy'))
if debug:
filtered_behaviors = filtered_behaviors[:1_000]
ds = EkstraDataset(filtered_behaviors, history, article, art_img_embeddings, categories, labels=False)
dl = torch.utils.data.DataLoader(ds, batch_size=10, collate_fn=ekstra_inference_collate, shuffle=False)
i = 0
with torch.no_grad():
for idx, (in_view_len, behavior), history in tqdm(dl):
torch.cuda.empty_cache()
behavior = to_device(behavior, device)
history = to_device(history, device)
pred = model(behavior, history)
res.extend(interpret_inference(idx, pred.cpu().numpy(), in_view_len))
i += 1
if debug and i == 20:
break
with open(f'inference_v1_img_bce_val_epoch_{epoch}.pickle', 'wb') as f:
pickle.dump(res, f)
print('Done.')