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predict_test_origin_text.py
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predict_test_origin_text.py
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import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
from torchvision import transforms
import os.path
from sklearn.utils import shuffle
import torch
from sklearn.metrics import classification_report
from torch.utils.data import DataLoader, TensorDataset, random_split, SubsetRandomSampler, ConcatDataset
import torch.nn.init
import pandas as pd
from torch import nn
import numpy as np
import torch.optim as optim
import torch.nn.init
from sklearn.model_selection import KFold
from sklearn.metrics import roc_auc_score
import copy
import time
from utils import *
from models import *
from dataloader import *
def get_parameter_number(model):
total_num = sum(p.numel() for p in model.parameters())
trainable_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
return {'Total': total_num, 'Trainable': trainable_num}
def print_model(model):
# 查看网络的结构
print(model)
# 打印模型参数
for param in model.parameters():
print(param)
# 打印模型名称与shape
for name, parameters in model.named_parameters():
print(name, ':', parameters.size())
print("\n\n")
print(get_parameter_number(model))
def predict(model, test_dataloaders, criterion, output_name):
print("\n######## test ########")
model.eval()
predicted_labels = []
predicted_probs = []
predicted_text_ids = []
with torch.no_grad():
for i, (text_ids, input_ids, attention_masks) in enumerate(test_dataloaders):
input_ids = input_ids.to(device)
attention_masks = attention_masks.to(device)
logits = model(input_ids, attention_masks)
outputs = torch.sigmoid(logits)
preds = outputs.reshape(-1).round()
predicted_text_ids += list(text_ids)
predicted_labels += preds.detach().cpu().tolist()
predicted_probs += outputs.reshape(-1).detach().cpu().tolist()
predict_df = pd.DataFrame(
{"tweet_id": predicted_text_ids, "predicted_labels": predicted_labels,
"probabilities": predicted_probs})
predict_df.to_csv(f"./output/{output_name}", index=False)
# model.eval()
# running_loss = 0.0
# running_corrects = 0
# predicted_labels = []
# predicted_probs = []
# predicted_text_ids = []
# gold_labels = []
#
# with torch.no_grad():
# for i, (text_ids, input_ids, attention_masks, labels) in enumerate(test_dataloaders):
# input_ids = input_ids.to(device)
# attention_masks = attention_masks.to(device)
# labels = labels.to(device)
#
# logits = model(input_ids, attention_masks)
# loss = criterion(logits, labels)
# outputs = torch.sigmoid(logits)
#
# preds = outputs.reshape(-1).round()
#
# running_loss += loss.item() * input_ids.size(0)
# running_corrects += torch.sum(preds == labels.reshape(-1))
#
# predicted_text_ids += list(text_ids)
# predicted_labels += preds.detach().cpu().tolist()
# predicted_probs += outputs.reshape(-1).detach().cpu().tolist()
# gold_labels += labels.reshape(-1).detach().cpu().tolist()
#
# epoch_loss = running_loss / len(test_dataset)
# # epoch_acc = running_corrects.double() / len(val_dataset)
#
# epoch_metrics = classification_report(gold_labels, predicted_labels, output_dict=True, digits=4)
# epoch_f1 = epoch_metrics["1.0"]['f1-score']
# epoch_precision = epoch_metrics["1.0"]['precision']
# epoch_recall = epoch_metrics["1.0"]['recall']
# epoch_acc = epoch_metrics["accuracy"]
#
# macro_f1 = (epoch_metrics["1.0"]['f1-score'] + epoch_metrics["0.0"]['f1-score']) / 2
# auc_score = roc_auc_score(gold_labels, predicted_labels)
#
# predict_df = pd.DataFrame(
# {"tweet_id": predicted_text_ids, "gold_labels": gold_labels, "predicted_labels": predicted_labels,
# "probabilities": predicted_probs})
# predict_df.to_csv(f"./output/{output_name}", index=False)
#
# print(
# 'test loss: {:.4f}, acc: {:.4f}, f1: {:.4f}, precision: {:.4f}, recall: {:.4f}, macro_f1: {:.4f}, auc_score: {:.4f}'.format(
# epoch_loss, epoch_acc,
# epoch_f1,
# epoch_precision,
# epoch_recall, macro_f1, auc_score))
#
# print(classification_report(gold_labels, predicted_labels, digits=4))
if __name__ == '__main__':
dataset_name = 'abortion'
model_name = 'stance_alltrain_text_resnet50_deberta-v3-large_lr1e-05_bs8_augmentation_wordnet0_pooler0'
output_name = 'stance_alltrain_text_resnet50_deberta-v3-large_lr1e-05_bs8_augmentation_wordnet0_pooler0_abortion.csv'
args = get_argparser().parse_args()
df_test = pd.read_csv(f"./data/{dataset_name}_test.csv", index_col=0)
# df_test = pd.read_csv(f"./data/{dataset_name}_dev.csv", index_col=0)
test_annotation = df_test.reset_index()
test_dataset = TextTestDataset(args, annotation=test_annotation, root_dir=os.path.join(args.data_dir, f'images/{dataset_name}'))
# test_dataset = TextDataset(args, annotation=test_annotation, root_dir=os.path.join(args.data_dir, f'images/{dataset_name}'))
test_dataloaders = DataLoader(test_dataset, collate_fn=collate_fn, batch_size=args.batch_size)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.manual_seed(args.seed)
model1 = TextModel(text_model_name=args.text_model_name, out_dim=1, freeze_model=args.freeze_model)
criterion = nn.BCEWithLogitsLoss()
checkpoint1 = torch.load(os.path.join(f"/home/data/zwanggy/2023/image_arg_experiments/{model_name}/{dataset_name}", f'model_best.pth.tar'))
# checkpoint1 = torch.load(os.path.join(f"./experiments/{model_name}/{dataset_name}", f'model_best.pth.tar'))
model1.load_state_dict(checkpoint1['state_dict'])
model1.to(device)
# print_model(model1)
predict(model1, test_dataloaders, criterion, output_name)