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fineTune.py
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from utils import *
from train import training
from model import PBT
import numpy as np
import random
import torch
def fine_tune(config):
if config['load']:
checkpoint = torch.load(config['load'])
dict_channels = checkpoint['dict_channels']
train_data_set = SeqDataset(dim_token=config['d_input'],
num_tokens_per_channel=config['num_tokens_per_channel'],
reduce_num_chs_to=False,
augmentation=config['augmentation'])
test_data_set = SeqDataset(dim_token=config['d_input'],
num_tokens_per_channel=config['num_tokens_per_channel'],
reduce_num_chs_to=False)
if config['data_set'] == 'BNCI2014001':
data, labels, meta, channels = get_BNCI2014001(subject=list(range(1, 10)), freq_min=config['freq'][0],
freq_max=config['freq'][1])
train_data = data[np.where(meta['session'] == 'session_T')]
train_labels = labels[np.where(meta['session'] == 'session_T')]
train_meta = meta.iloc[np.where(meta['session'] == 'session_T')]
test_data = data[np.where(meta['session'] == 'session_E')]
test_labels = labels[np.where(meta['session'] == 'session_E')]
test_meta = meta.iloc[np.where(meta['session'] == 'session_E')]
elif config['data_set'] == 'BNCI2014004':
data, labels, meta, channels = get_BNCI2014004(subject=list(range(1, 10)), freq_min=config['freq'][0],
freq_max=config['freq'][1])
train_data = data[np.where((meta['session'] == 'session_0') | (meta['session'] == 'session_1') |
(meta['session'] == 'session_2'))]
train_labels = labels[np.where((meta['session'] == 'session_0') | (meta['session'] == 'session_1') |
(meta['session'] == 'session_2'))]
train_meta = meta.iloc[np.where((meta['session'] == 'session_0') | (meta['session'] == 'session_1') |
(meta['session'] == 'session_2'))]
test_data = data[np.where((meta['session'] == 'session_3') | (meta['session'] == 'session_4'))]
test_labels = labels[np.where((meta['session'] == 'session_3') | (meta['session'] == 'session_4'))]
test_meta = meta.iloc[np.where((meta['session'] == 'session_3') | (meta['session'] == 'session_4'))]
else:
raise ValueError('Please choose data_set in {BNCI2014001, BNCI2014004}')
train_data = zero_mean_unit_var(mne_epochs=train_data, meta_data=train_meta)
test_data = zero_mean_unit_var(mne_epochs=test_data, meta_data=test_meta)
train_data_set.append_data_set(data_set=train_data, channel_names=channels, label=train_labels)
test_data_set.append_data_set(data_set=test_data, channel_names=channels, label=test_labels)
if config['load']:
train_data_set.prepare_data_set(dict_channels)
test_data_set.prepare_data_set(dict_channels)
else:
train_data_set.prepare_data_set()
test_data_set.prepare_data_set(train_data_set.dict_channels)
model = PBT(d_input=config['d_input'], n_classes=len(set(test_labels)),
num_embeddings=torch.max(torch.cat(list(train_data_set.dict_channels.values()))).item() + 1,
num_tokens_per_channel=config['num_tokens_per_channel'], d_model=config['d_model'],
n_blocks=config['num_transformer_blocks'], num_heads=config['num_heads'],
dropout=config['dropout'], device=device, learnable_cls=config['learnable_cls'],
bias_transformer=config['bias_transformer'],
bert=True if config['bert_supervised'] or config['pre_train_bert'] else False)
if config['load']:
# delete weights that should not be loaded
# checkpoint['model_state_dict'].pop('pos_embedding.weight')
checkpoint['model_state_dict'].pop('cls_head.weight')
checkpoint['model_state_dict'].pop('cls_head.bias')
checkpoint['model_state_dict'].pop('linear_projection_out.weight')
model.load_state_dict(checkpoint['model_state_dict'], strict=False)
training(parameter=config, model=model, train_data_set=train_data_set, test_data_set=test_data_set,
n_classes=len(set(test_labels)))
if __name__ == '__main__':
config = {
'pre_train_bert': False, # unsupervised pre-training in BERT-style
# Pre - Processing
'freq': [8, 45],
'normalization': 'zscore',
# Model
'd_input': 64,
'd_model': 128, # Input gets expanded in lin. projection
'dim_feedforward': 128 * 4,
'num_tokens_per_channel': 8,
'num_transformer_blocks': 4,
'num_heads': 4, # number attention heads transformer
'bert_supervised': False, # add a reconstruction task (BERT) as regularisation to loss
'learnable_cls': False,
'bias_transformer': True,
# Train Hyper-Parameters
'lr': 3e-4,
'lr_warm_up_iters': 50,
'batch_size': 64,
'num_epochs': 120,
'betas': (0.9, 0.95), # betas AdamW
'clip_gradient': 1.0,
# Regularization & Augmentation
'weight_decay': 0.01, # not applied to LayerNorm, self_att and biases
'weight_decay_pos_embedding': 0.0, # weight decay applied to learnable pos. embedding
'weight_decay_cls_head': 0.0, # cls_head = classification head (linear layer)
# higher for pre-train may improve few-shot adaptation
'dropout': 0.1,
'label_smoothing': 0,
'augmentation': ['time_shifts'], # [] for no aug, else list:
# ['time_shifts', 'DC_shifts', 'amplitude_scaling','noise']
# WandB
'wandb_log': True,
'wandb_name': False,
'wandb_proj': 'Patched Brain Transformer',
'wandb_watch': True,
'save': False, # add path as string where to save
'checkpoints': False,
'load': False,
'seed': 42, # set random seed
'compile_model': False, # compile model with PyTroch to speed up
'data_set': 'BNCI2014001'
}
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
for i in range(1, 4):
config['seed'] = i
torch.manual_seed(config['seed'])
torch.cuda.manual_seed(config['seed'])
np.random.seed(config['seed'])
random.seed(config['seed'])
torch.backends.cudnn.deterministic = True
# torch.use_deterministic_algorithms(True)
fine_tune(config)