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DEAP_T1_O0.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.constants import DREAMER_CHANNEL_LOCATION_DICT
from torcheeg.datasets.constants import DEAP_CHANNEL_LOCATION_DICT
from torcheeg.datasets import DREAMERDataset , DEAPDataset
from torcheeg.model_selection import KFoldGroupbyTrial
from torcheeg.model_selection import train_test_split_groupby_trial, train_test_split
from torcheeg.models import ArjunViT
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, tvt_save_acc_loss_f1
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 TORCHEEGBaselineCorrection
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 , VisionTransformerEEG
from models.tcn_based import *
from models.models import NovModel
from models.cnn_based import TSceptionATN, UNET_VIT
if __name__ == "__main__":
rng_num = 122
batch_size = 256
dataset_name= 'DEAP_T1_O0'
emotion_dim= 'valence' #valence arousal dominance
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.To2d(apply_to_baseline=True),
transforms.ToTensor(),
]),
label_transform=transforms.Compose([
transforms.Select(emotion_dim),
transforms.Binary(5.0),
]),
chunk_size=128, # 1 Seconds
overlap = 0, # 0 seconds
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
split_type = 'group_by_trial'
if split_type == 'group_by_trial':
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 = 0.2, shuffle=True) #, random_state= rng_num)
elif split_type == 'simple':
train_dataset, test_dataset = train_test_split(dataset= dataset, test_size = 0.2, shuffle= True) #, random_state= rng_num)
train_dataset, val_dataset = train_test_split(dataset= train_dataset, test_size = 0.2, 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()
# model = EEGTCNet(n_classes=2)
# model = TCNet_Fusion(input_size= dataset[0][0].shape, n_classes= 2, channels= dataset[0][0].shape[1], sampling_rate= 128)
# model = ATCNet(dataset[0][0].shape, dataset[0][0].shape[1] , n_classes=2, n_windows=8,
# eegn_F1=24, eegn_D=2, eegn_kernelSize=50, eegn_poolSize=1, eegn_dropout=0.3, num_heads=8,
# tcn_depth=4, tcn_kernelSize=16, tcn_filters=32, tcn_dropout=0.3, fuse='average',activation='elu')
# model = DGCNN(in_channels= 5,
# num_electrodes= 32,
# num_layers= 2,
# hid_channels= 32,
# num_classes= 2)
# model = NovModel(F1= 14, layers_tcn=4, filt_tcn= 14, kernel_tcn=16, dropout_tcn= 0.5, activation_tcn= 'relu',
# temporal_size=512, num_electrodes=14, layers_cheby=10, hid_channels_cheby=64, num_classes=2)# EXPERIMENTAL!
# model = TSceptionATN(num_classes=2, input_size= dataset[0][0].shape, sampling_rate=128, num_T=32, num_S=32, hidden=64, dropout_rate=0.4)
# model = VisionTransformerEEG(img_size= dataset[0][0].shape[1], # data[128,9,9]
# patch_size=3,
# in_chans=dataset[0][0].shape[0],
# n_classes=2,
# embed_dim=768,
# depth=5, #12
# n_heads=6, # 12
# mlp_ratio=4.,
# qkv_bias=True,
# p=0.5,
# attn_p=0.5,)
# model = UNET_VIT(
# in_channels=dataset[0][0].shape[0], unet_out_channels=3,
# img_size=dataset[0][0].shape[1], patch_size=3,
# n_classes=2, embed_dim=768, depth=5, n_heads=6,
# mlp_ratio=4., qkv_bias=True, p=0.5, attn_p=0.5
# )
model = ArjunViT( #Does not accept To2d data, must keep in mind
chunk_size=128,
t_patch_size=16,
num_electrodes=32,
num_classes=2)
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()
loss_fn = nn.CrossEntropyLoss()
# ****************** Choose your Optimizer ******************************
optimizer = optim.Adam(model.parameters(), lr=0.001) # lr = 0.0001 0.001
# optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.937)
# ********************** Set The Device ***************************************
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
num_epochs = 800 # 300 500 600
model_name = dataset_name + "_" + model.__class__.__name__ + "" #
print(f"Start training for {num_epochs} epoch")
# model = model.to(device)
# train_validate_and_save(model, dataset_name, model_name, emotion_dim, train_loader, val_loader, optimizer, loss_fn, device,num_epochs=num_epochs)
# 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)
model = model.to(device)
loss_hist, acc_hist , loss_val_hist , \
acc_val_hist, loss_test, acc_test ,\
(f1_hist, f1_val_hist, f1_test) = tvt_save_acc_loss_f1(model,
dataset_name,
model_name,
emotion_dim,
train_loader,
val_loader,
test_loader,
optimizer,
loss_fn,
device,
num_epochs=num_epochs,
is_binary= False,
num_classes= 2)
print("Training process is done!")
print(f"Test: LOSS: {loss_test}, ACC: {acc_test}, F1: {f1_test}")
print(f"Model parameter count: {get_num_params(model,1)}")