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DEAP_time_o32_nb.py
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import sys
import os
# Dynamically add the root directory to sys.path
# Assumes that 'models' and 'utils' are in the same project root directory
project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
sys.path.insert(0, project_root)
import math
from sklearn.metrics import accuracy_score
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torcheeg import transforms
from torch.utils.data import DataLoader
from torcheeg.datasets import DREAMERDataset
from torcheeg.datasets import DEAPDataset
from torcheeg.datasets.constants import DREAMER_CHANNEL_LOCATION_DICT
from torcheeg.datasets.constants import DREAMER_ADJACENCY_MATRIX
from torcheeg.datasets.constants import DEAP_CHANNEL_LOCATION_DICT
from torcheeg.model_selection import KFoldGroupbyTrial
from torcheeg.model_selection import train_test_split_groupby_trial
from tqdm import tqdm
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
# Local Imports
from utils.checkpoint import train_and_save, train_validate_and_save, train_validate_test_and_save, train_validate_test_lrschedule_and_save_
from utils.log import get_logger
from utils.utils import print_var, train_one_epoch, train_one_epoch_lstm, get_num_params, train_one_step_tqdm
from utils.transforms import STFTSpectrogram
from models.cnn import Two_Layer_CNN, Two_Layer_CNN_Pro, Simplified_CNN
from models.rnns import LSTM
from models.cnn_lstm import LSTM_CNN_Model
from models.Tsception import TSCEPTIONModel
from models.YoloV9 import YOLO9_Backbone_Classifier
from models.eegnet import EEGNet_Normal_data
from models.Transformer import VanillaTransformer_time
if __name__ == "__main__":
rng_num = 2024 #122
batch_size = 256
emotion_dim = 'valence' # valence, dominance, or arousal
dataset_name = 'DEAP_time_o32_nb' # Time data which is stored in memory
root_path = './raw_data/DEAP'
io_path = f'./saves/datasets/{dataset_name}' # IO path to store the dataset
dataset = DEAPDataset( root_path=root_path,
io_path=io_path,
offline_transform=transforms.Compose([
transforms.MeanStdNormalize(axis=1, apply_to_baseline=True),# MeanStdNormalize() , MinMaxNormalize()
]),
online_transform=transforms.Compose([
transforms.ToTensor(),
]),
label_transform=transforms.Compose([
transforms.Select(emotion_dim),
transforms.Binary(5.0),
]),
overlap = 96, # 128 - 32
io_mode='lmdb',
num_worker=4,
)
# print(dataset)
# print(dataset[0])
# print(dataset[0][0].shape)
# print(dataset[0][1])
# sys.exit()
# Split train val test
train_dataset, test_dataset = train_test_split_groupby_trial(dataset= dataset, test_size = 0.2, shuffle= True) #, random_state= rng_num)
train_dataset, val_dataset = train_test_split_groupby_trial(dataset= train_dataset, test_size = 0.1, shuffle=True) #, random_state= rng_num)
# Create train/val/test dataloaders
train_loader = DataLoader(train_dataset, batch_size= batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size= batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size= batch_size, shuffle=False)
print("Dataset is ready!")
print(f"Dataset size: {len(dataset)}")
print(f"Train Size: {len(train_dataset)}, Validation Size: {len(val_dataset)} , Test Size: {len(test_dataset)}")
print(f"Input data shape: {dataset[0][0].shape}")
print(f"Output data (one sample): {dataset[0][1]}")
print('*' * 30)
print_var("Number of batches inside train dataloader",len(train_loader))
print_var("Number of batches inside validation dataloader",len(val_loader))
print_var("Number of batches inside test dataloader",len(test_loader))
print('*' * 30)
# ****************** Choose your Model ******************************
# model = Two_Layer_CNN()
# model = Two_Layer_CNN_Pro() ####################w 74.5
# model = Simplified_CNN()
# model = LSTM(128,64,2,1) # IT should be L*F
# model = LSTM(14,256,4,1) # Should take 14 input features not 128 of the length ##############w
# model = LSTM_CNN_Model() ########## 95.5
# model = TSCEPTIONModel() ############
# model = YOLO9_Backbone_Classifier()
# model = EEGNet_Normal_data()
# model = TSCEPTIONModel() #### validation is Ok almost
model = VanillaTransformer_time(num_electrodes=32) ########## GOOD ON THE NEW OVERLAP DATA till 97 96
print(f"Selected model name : {model.__class__.__name__}")
# print(f"Model parameter count: {get_num_params(model,1)}")
print_var("Model is ", model)
print('*' * 30)
# ****************** Choose your Loss Function ******************************
loss_fn = nn.BCEWithLogitsLoss()
# loss_fn = nn.MSELoss()
# ****************** Choose your Optimizer ******************************
optimizer = optim.Adam(model.parameters(), lr=0.01) # lr = 0.0001 0.001
# optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.937)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
num_epochs = 50 # 300 500 600
model_name = "DEAP_" + model.__class__.__name__ + "_o32_nb" # 96 OVERLAP no baseline removal
print(f"Start training for {num_epochs} epoch")
model = model.to(device)
loss_hist, acc_hist , loss_val_hist , acc_val_hist, loss_test, acc_test = train_validate_test_and_save(model,
dataset_name,
model_name,
emotion_dim,
train_loader,
val_loader,
test_loader,
optimizer,
loss_fn,
device,
num_epochs=num_epochs,
is_binary=True)
print("Training process is done!")
print(f"Test: LOSS: {loss_test}, ACC: {acc_test}")
print(f"Model parameter count: {get_num_params(model,1)}")
# # Plot Losses
# plt.figure()
# plt.plot(range(len(loss_hist)), loss_hist)
# plt.plot(range(len(loss_val_hist)), loss_val_hist)
# plt.legend(["Train Loss", "Val Loss"], loc="lower right")
# plt.title('Loss over Epochs')
# plt.xlabel('Epoch')
# plt.ylabel('Loss')
# plt.show()
# # plot Accuracies
# plt.figure()
# plt.plot(range(len(acc_hist)), acc_hist)
# plt.plot(range(len(acc_val_hist)), acc_val_hist)
# plt.legend(["Train Acc", "Val Acc"], loc="lower right")
# plt.title('Acc over Epochs')
# plt.xlabel('Epoch')
# plt.ylabel('Acc')
# plt.show()
# transforms.Concatenate([
# transforms.BandDifferentialEntropy(),
# transforms.BandMeanAbsoluteDeviation()])