Wrong average accuracy report.

[ltkhang]

Hi,

I found the wrong code about validating average accuracy in your rafdb.py #231 and #239

Mathematically, your calculation is not correct. I was writing a new one and your average accuracy is only 83.76.

#!/usr/bin/env python
# coding: utf-8

# In[12]:


import os

from PIL import Image

import torch
from torchvision import transforms

from networks.dan import DAN
import torch.utils.data as data
import numpy as np
import pandas as pd


# In[2]:


device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")


# In[5]:


model = DAN(num_head=4, num_class=7, pretrained=False)
checkpoint = torch.load('rafdb_epoch21_acc0.897_bacc0.8532.pth', map_location=device)
model.load_state_dict(checkpoint['model_state_dict'],strict=True)
model.to(device)
model.eval()


# In[10]:


class RafDataSet(data.Dataset):
    def __init__(self, raf_path, phase, transform = None):
        self.phase = phase
        self.transform = transform
        self.raf_path = raf_path

        df = pd.read_csv(os.path.join(self.raf_path, 'EmoLabel/list_patition_label.txt'), sep=' ', header=None,names=['name','label'])

        if phase == 'train':
            self.data = df[df['name'].str.startswith('train')]
        else:
            self.data = df[df['name'].str.startswith('test')]

        file_names = self.data.loc[:, 'name'].values
        self.label = self.data.loc[:, 'label'].values - 1 # 0:Surprise, 1:Fear, 2:Disgust, 3:Happiness, 4:Sadness, 5:Anger, 6:Neutral

        _, self.sample_counts = np.unique(self.label, return_counts=True)
        # print(f' distribution of {phase} samples: {self.sample_counts}')

        self.file_paths = []
        for f in file_names:
            f = f.split(".")[0]
            f = f +"_aligned.jpg"
            path = os.path.join(self.raf_path, 'Image/aligned', f)
            self.file_paths.append(path)

    def __len__(self):
        return len(self.file_paths)

    def __getitem__(self, idx):
        path = self.file_paths[idx]
        image = Image.open(path).convert('RGB')
        label = self.label[idx]

        if self.transform is not None:
            image = self.transform(image)
        
        return image, label


# In[13]:


data_transforms_val = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])])   

val_dataset = RafDataSet('datasets/', phase = 'test', transform = data_transforms_val)  


# In[14]:


val_loader = torch.utils.data.DataLoader(val_dataset,
                                           batch_size = 64,
                                           num_workers = 1,
                                           shuffle = False,  
                                           pin_memory = True)


# In[16]:


y_true = []
y_pred = []
with torch.no_grad():

    model.eval()
    for (imgs, targets) in val_loader:
        imgs = imgs.to(device)
        targets = targets.to(device)

        out,feat,heads = model(imgs)

        _, predicts = torch.max(out, 1)
        y_true.append(predicts.cpu().numpy())
        y_pred.append(targets.cpu().numpy())


# In[20]:


y_true = np.concatenate(y_true)
y_pred = np.concatenate(y_pred)


# In[21]:


from sklearn.metrics import confusion_matrix, accuracy_score, ConfusionMatrixDisplay, balanced_accuracy_score


# In[23]:


print('Acc', accuracy_score(y_true, y_pred))
print('Mean Acc', balanced_accuracy_score(y_true, y_pred))

Acc 0.8970013037809648
Mean Acc 0.8376120557760152

[yaoing]

Thanks for your reminder, I will check it~

[yaoing]

It my mistake, the result will be updated,
Thanks again!