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inference.py
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inference.py
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import os
import argparse
import numpy as np
import pandas as pd
import os.path as osp
from tqdm import tqdm
from sklearn.metrics import accuracy_score, f1_score, classification_report
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoFeatureExtractor
from dataset import create_data_loader_test
from model import TourClassifier, TourClassifier_Separate
from utils import set_seeds, load_config
PATH_BASE = './'
PATH_DATA = osp.join(PATH_BASE, 'data')
PATH_SUBMIT = osp.join(PATH_BASE, 'ensemble')
os.makedirs(PATH_SUBMIT, exist_ok=True)
df = pd.read_csv(osp.join(PATH_DATA, 'train.csv'))
cat1_labels = sorted(list(set(df['cat1'].values.tolist())))
cat2_labels = sorted(list(set(df['cat2'].values.tolist())))
cat3_labels = sorted(list(set(df['cat3'].values.tolist())))
cat1_to_cat2_dict = dict()
for label in cat1_labels:
tmp = list(set(df[df['cat1'] == label]['cat2'].values))
cat1_to_cat2_dict[cat1_labels.index(label)] = sorted([cat2_labels.index(i) for i in tmp])
cat1_to_cat3_dict = dict()
for label in cat1_labels:
tmp = list(set(df[df['cat1'] == label]['cat3'].values))
cat1_to_cat3_dict[cat1_labels.index(label)] = sorted([cat3_labels.index(i) for i in tmp])
cat2_to_cat1_dict = dict()
for label in cat2_labels:
tmp = list(set(df[df['cat2'] == label]['cat1'].values))
cat2_to_cat1_dict[cat2_labels.index(label)] = sorted([cat1_labels.index(i) for i in tmp])
cat2_to_cat3_dict = dict()
for label in cat2_labels:
tmp = list(set(df[df['cat2'] == label]['cat3'].values))
cat2_to_cat3_dict[cat2_labels.index(label)] = sorted([cat3_labels.index(i) for i in tmp])
cat3_to_cat1_dict = dict()
for label in cat3_labels:
tmp = list(set(df[df['cat3'] == label]['cat1'].values))
cat3_to_cat1_dict[cat3_labels.index(label)] = sorted([cat1_labels.index(i) for i in tmp])
cat3_to_cat2_dict = dict()
for label in cat3_labels:
tmp = list(set(df[df['cat3'] == label]['cat2'].values))
cat3_to_cat2_dict[cat3_labels.index(label)] = sorted([cat2_labels.index(i) for i in tmp])
def consider_multi_label(outputs, outputs2, outputs3, cat, dict1, dict2):
softmax = nn.Softmax(dim=1)
outputs, outputs2, outputs3 = softmax(outputs), softmax(outputs2), softmax(outputs3)
if cat == 'cat1':
main, sub1, sub2 = outputs, outputs2, outputs3
elif cat == 'cat2':
sub1, main, sub2 = outputs, outputs2, outputs3
elif cat == 'cat3':
sub1, sub2, main = outputs, outputs2, outputs3
tmp1 = torch.zeros_like(main)
for i in range(len(dict1)):
tmp1[:, i] = sub1[:, dict1[i]].sum(dim=1)
tmp2 = torch.zeros_like(main)
for i in range(len(dict2)):
tmp2[:, i] = sub2[:, dict2[i]].sum(dim=1)
return torch.stack((main, tmp1, tmp2), dim=-1).sum(dim=-1)
def mask_with_before_pred(before_pred, logit, dict):
output = torch.full(logit.size(), torch.min(logit))
for i in range(len(before_pred)):
label = int(before_pred[i])
for j in dict[label]:
output[i, j] = logit[i, j]
return output
def inference(model, data_loader, device, multi_label, mask_label, mode):
model = model.eval()
preds_arr, preds_arr2, preds_arr3 = [], [], []
for d in tqdm(data_loader):
with torch.no_grad():
input_ids = d["input_ids"].to(device)
attention_mask = d["attention_mask"].to(device)
pixel_values = d['pixel_values'].to(device)
outputs, outputs2, outputs3 = model(
input_ids=input_ids, attention_mask=attention_mask, pixel_values=pixel_values
)
if multi_label:
outputs = consider_multi_label(outputs, outputs2, outputs3, 'cat1', cat1_to_cat2_dict, cat1_to_cat3_dict)
outputs2 = consider_multi_label(outputs, outputs2, outputs3, 'cat2', cat2_to_cat1_dict, cat2_to_cat3_dict)
outputs3 = consider_multi_label(outputs, outputs2, outputs3, 'cat3', cat3_to_cat1_dict, cat3_to_cat2_dict)
if mode == 'soft':
preds_arr.append(np.squeeze(outputs.cpu().numpy()))
preds_arr2.append(np.squeeze(outputs2.cpu().numpy()))
preds_arr3.append(np.squeeze(outputs3.cpu().numpy()))
else:
_, preds = torch.max(outputs, dim=1)
if mask_label: outputs2 = mask_with_before_pred(preds, outputs2, cat1_to_cat2_dict)
_, preds2 = torch.max(outputs2, dim=1)
if mask_label: outputs3 = mask_with_before_pred(preds2, outputs3, cat2_to_cat3_dict)
_, preds3 = torch.max(outputs3, dim=1)
preds_arr.append(np.squeeze(preds.cpu().numpy()))
preds_arr2.append(np.squeeze(preds2.cpu().numpy()))
preds_arr3.append(np.squeeze(preds3.cpu().numpy()))
return preds_arr, preds_arr2, preds_arr3
def save(pred_arr, save_path):
sample_submission = pd.read_csv(osp.join(PATH_DATA, 'sample_submission.csv'))
for i in range(len(pred_arr)):
sample_submission.loc[i, 'cat3'] = cat3_labels[pred_arr[i]]
sample_submission.to_csv(save_path, index=False)
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--mode', type=str, default='test') # valid test soft hard
parser.add_argument('--path', nargs='+') # work_dirs/exp0/best.pt
parser.add_argument('--multi_label', action='store_true')
parser.add_argument('--mask_label', action='store_true')
parser.add_argument('--hflip', action='store_true') # only for 'soft' and 'hard' mode
parser.add_argument('--seed', type=int, default=42)
args = parser.parse_args()
return args
def main(args, train_config, hflip=False):
device = torch.device("cuda:0")
if args.mode == 'valid':
if train_config.df_ver == 1:
df = 'train_5fold.csv'
elif train_config.df_ver == 2:
df = 'train_5fold_ver2.csv'
df = pd.read_csv(osp.join(PATH_DATA, df))
df = df[df["kfold"] == train_config.fold].reset_index(drop=True)
else:
df = pd.read_csv(osp.join(PATH_DATA, 'test.csv'))
tokenizer = AutoTokenizer.from_pretrained(train_config.text_model)
feature_extractor = AutoFeatureExtractor.from_pretrained(train_config.image_model)
eval_data_loader = create_data_loader_test(df, tokenizer, feature_extractor, train_config.max_len, hflip=hflip)
if train_config.separate:
model = TourClassifier_Separate(
n_classes1=6, n_classes2=18, n_classes3=128,
text_model_name=train_config.text_model, image_model_name=train_config.image_model, device=device,
dropout=train_config.dropout, alpha=train_config.separate_alpha,
).to(device)
else:
model = TourClassifier(
n_classes1=6, n_classes2=18, n_classes3=128,
text_model_name=train_config.text_model, image_model_name=train_config.image_model, device=device,
dropout=train_config.dropout,
).to(device)
model.load_state_dict(torch.load(osp.join(args.work_dir_exp, args.ckpt)))
preds_arr, preds_arr2, preds_arr3 = inference(model, eval_data_loader, device, args.multi_label, args.mask_label, args.mode)
if args.mode == 'valid':
for col, arr in zip(['cat1','cat2','cat3'], [preds_arr, preds_arr2, preds_arr3]):
pred = np.array(arr)
gt = df[col].values
acc = accuracy_score(gt, pred)
f1 = f1_score(gt, pred, average='weighted')
print(f"[{col}] acc: {round(acc,4)}, f1_acc: {round(f1,4)}")
# print(df[pred != gt]["id"].to_numpy())
# print(classification_report(gt, pred))
elif args.mode == 'test':
save_path = f'submit_{args.exp}_' + args.ckpt.split('.')[0]
if args.multi_label: save_path += '_multi'
if args.mask_label: save_path += '_mask'
save(preds_arr3, osp.join(args.work_dir_exp, save_path+'.csv'))
elif args.mode in ['soft', 'hard']:
return preds_arr3
if __name__ == '__main__':
args = get_parser()
ensemble = []
ensemble_file_name = args.mode
if args.hflip:
ensemble_file_name += '_hflip'
if args.multi_label:
ensemble_file_name += '_multi'
if args.mask_label:
ensemble_file_name += '_mask'
for i in range(len(args.path)):
path = args.path[i] # work_dirs/exp0/best.pt
args.exp = path.split('/')[-2] # exp0
args.ckpt = path.split('/')[-1] # best.pt
args.work_dir_exp = path[:-len(args.ckpt)-1] # work_dirs/exp0
set_seeds(args.seed)
train_config = load_config(osp.join(args.work_dir_exp, 'config.yaml'))
if args.mode in ['soft', 'hard']:
ensemble_file_name += '_' + args.exp + args.ckpt.split('.')[0]
for hflip in range(args.hflip + 1):
out = main(args, train_config, hflip=hflip)
ensemble.append(out)
else:
main(args, train_config)
ensemble_save_path = osp.join(PATH_SUBMIT, f'{ensemble_file_name}.csv')
if args.mode == 'soft':
preds_arr3 = np.argmax(np.sum(ensemble, axis=0), axis=1)
save(preds_arr3, ensemble_save_path)
elif args.mode == 'hard':
ensemble = np.array(ensemble)
preds_arr3 = [np.argmax(np.bincount(ensemble[:,i])) for i in range(ensemble.shape[1])]
save(preds_arr3, ensemble_save_path)