Full_t.py

from tqdm import tqdm
import torch
import torch.nn as nn
from torch.autograd import no_grad
import numpy as np

def full_t(epoch, model, prefix, writer=None):   
    print(prefix+' start...')
    model.eval()

    with no_grad():
        precision, recall, ndcg_score = model.accuracy()
        print('---------------------------------{0}-th Precition:{1:.4f} Recall:{2:.4f} NDCG:{3:.4f}---------------------------------'.format(
            epoch, precision, recall, ndcg_score))
        if writer is not None:
            writer.add_scalar(prefix+'_Precition', precision, epoch)
            writer.add_scalar(prefix+'_Recall', recall, epoch)
            writer.add_scalar(prefix+'_NDCG', ndcg_score, epoch)

            writer.add_histogram(prefix+'_visual_distribution', model.v_rep, epoch)
            writer.add_histogram(prefix+'_acoustic_distribution', model.a_rep, epoch)
            writer.add_histogram(prefix+'_textual_distribution', model.t_rep, epoch)
            
            writer.add_histogram(prefix+'_user_visual_distribution', model.user_preferences[:,:44], epoch)
            writer.add_histogram(prefix+'_user_acoustic_distribution', model.user_preferences[:, 44:-44], epoch)
            writer.add_histogram(prefix+'_user_textual_distribution', model.user_preferences[:, -44:], epoch)

            writer.add_embedding(model.v_rep)
            #writer.add_embedding(model.a_rep)
            #writer.add_embedding(model.t_rep)
            
            #writer.add_embedding(model.user_preferences[:,:44])
            #writer.add_embedding(model.user_preferences[:, 44:-44])
            #writer.add_embedding(model.user_preferences[:, -44:])
        return precision, recall, ndcg_score