File size: 4,536 Bytes
3118055 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms.functional import to_tensor, to_pil_image
import torchvision.transforms as transforms
from transformers import AutoModel
from transformers import AutoTokenizer, AutoConfig
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from torch.cuda.amp import autocast, GradScaler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm
import random
import numpy as np
from collections import OrderedDict
from rich import print
import time
import cv2
from glob import glob
import string
from torch.optim import AdamW
from transformers import get_linear_schedule_with_warmup
from models import get_model
from dataset import MyDataset
from utils import save_checkpoint, AverageMeter, ProgressMeter
def test_epoch(model, epoch, dataloader, tokenizer):
print(f"\n\n=> val")
data_time = AverageMeter('- data', ':4.3f')
batch_time = AverageMeter('- batch', ':6.3f')
progress = ProgressMeter(
len(dataloader), data_time, batch_time, prefix=f"Epoch: [{epoch}]")
end = time.time()
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
model.to(device)
model.eval()
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
predictions = []
for batch_index, data_batch in enumerate(tqdm(dataloader)):
context_str_batch = data_batch
# data tokenizer
context_token_batch = tokenizer(context_str_batch, padding=True, truncation=True, max_length=500, return_tensors='pt')
# to gpu
context_token_batch = {k:v.to(device) for k,v in context_token_batch.items()}
# forward
data_input_batch = context_token_batch
output_batch = model(**data_input_batch)
pred_batch = output_batch.softmax(dim=-1)
pred = torch.argmax(pred_batch, dim=-1)
predictions.extend(pred.cpu().numpy())
batch_time.update(time.time() - end)
end = time.time()
if batch_index % 50 == 0:
progress.print(batch_index)
return predictions
def infer20221212():
checkpoint_file = '/home/elaine/Desktop/macbert_code/checkpoints_name/checkpoint_epoch015_acc1.0000.pth.tar'
output_file = r'/home/elaine/Desktop/macbert_code/output.csv'
cache_dir = '/home/elaine/Desktop/macbert_code/code/cache'
ann_file_test = r'/home/elaine/Desktop/macbert_code/dataset/name_test_8000.csv'
model_cfg = {
"pretrained_transformers": "hfl/chinese-macbert-base",
"cache_dir": cache_dir
}
# 模型
model_dict = get_model(model_cfg, mode='base')
model = model_dict['model']
tokenizer = model_dict['tokenizer']
print(model)
data_loader_cfg = {}
test_dataset = MyDataset(ann_file_test, data_loader_cfg, mode='test')
test_loader = DataLoader(test_dataset, batch_size=8, num_workers=4, pin_memory=True)
# resume
assert checkpoint_file is not None and os.path.exists(checkpoint_file)
checkpoint = torch.load(checkpoint_file, map_location='cpu')
# model.load_state_dict(checkpoint['state_dict'])
model.load_state_dict({k.replace('module.', ''): v for k, v in checkpoint['state_dict'].items()})
print(f"=> Resume: loaded checkpoint {checkpoint_file} (epoch {checkpoint['epoch']})")
#model = model.cuda()
pred_res = test_epoch(model, 1, test_loader, tokenizer)
with open(output_file, 'w') as f:
for pred in pred_res:
f.write(f"{pred}\n")
# 讀取val.csv的label
import csv
true_labels = []
with open(ann_file_test, 'r') as f:
reader = csv.reader(f)
next(reader) # skip header
for row in reader:
true_labels.append(int(row[3]))
# 計算confusion matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(true_labels, pred_res)
print('Confusion Matrix:')
print(cm)
# 印出預測錯誤的內容、預測值和正確答案
with open(ann_file_test) as f:
reader = csv.reader(f)
next(reader) # skip header
for idx, row in enumerate(reader):
sms, label = row[1], int(row[3])
pred = pred_res[idx]
if pred != label:
print(f"錯誤: sms='{sms}'，預測={pred}，正確={label}")
if __name__ == '__main__':
infer20221212() |