def run_exp(data_folder, subject_id, low_cut_hz, model, cuda):
ival = [-500, 4000]
max_epochs = 1600
max_increase_epochs = 160
batch_size = 60
high_cut_hz = 38
factor_new = 1e-3
init_block_size = 1000
valid_set_fraction = 0.2
train_filename = "A{:02d}T.gdf".format(subject_id)
test_filename = "A{:02d}E.gdf".format(subject_id)
train_filepath = os.path.join(data_folder, train_filename)
test_filepath = os.path.join(data_folder, test_filename)
train_label_filepath = train_filepath.replace(".gdf", ".mat")
test_label_filepath = test_filepath.replace(".gdf", ".mat")
train_loader = BCICompetition4Set2A(
train_filepath, labels_filename=train_label_filepath
)
test_loader = BCICompetition4Set2A(
test_filepath, labels_filename=test_label_filepath
)
train_cnt = train_loader.load()
test_cnt = test_loader.load()
# Preprocessing
train_cnt = train_cnt.drop_channels(
["EOG-left", "EOG-central", "EOG-right"]
)
assert len(train_cnt.ch_names) == 22
# lets convert to millvolt for numerical stability of next operations
train_cnt = mne_apply(lambda a: a * 1e6, train_cnt)
train_cnt = mne_apply(
lambda a: bandpass_cnt(
a,
low_cut_hz,
high_cut_hz,
train_cnt.info["sfreq"],
filt_order=3,
axis=1,
),
train_cnt,
)
train_cnt = mne_apply(
lambda a: exponential_running_standardize(
a.T,
factor_new=factor_new,
init_block_size=init_block_size,
eps=1e-4,
).T,
train_cnt,
)
test_cnt = test_cnt.drop_channels(["EOG-left", "EOG-central", "EOG-right"])
assert len(test_cnt.ch_names) == 22
test_cnt = mne_apply(lambda a: a * 1e6, test_cnt)
test_cnt = mne_apply(
lambda a: bandpass_cnt(
a,
low_cut_hz,
high_cut_hz,
test_cnt.info["sfreq"],
filt_order=3,
axis=1,
),
test_cnt,
)
test_cnt = mne_apply(
lambda a: exponential_running_standardize(
a.T,
factor_new=factor_new,
init_block_size=init_block_size,
eps=1e-4,
).T,
test_cnt,
)
marker_def = OrderedDict(
[
("Left Hand", [1]),
("Right Hand", [2]),
("Foot", [3]),
("Tongue", [4]),
]
)
train_set = create_signal_target_from_raw_mne(train_cnt, marker_def, ival)
test_set = create_signal_target_from_raw_mne(test_cnt, marker_def, ival)
train_set, valid_set = split_into_two_sets(
train_set, first_set_fraction=1 - valid_set_fraction
)
set_random_seeds(seed=20190706, cuda=cuda)
n_classes = 4
n_chans = int(train_set.X.shape[1])
input_time_length = train_set.X.shape[2]
if model == "shallow":
model = ShallowFBCSPNet(
n_chans,
n_classes,
input_time_length=input_time_length,
final_conv_length="auto",
).create_network()
elif model == "deep":
model = Deep4Net(
n_chans,
n_classes,
input_time_length=input_time_length,
final_conv_length="auto",
).create_network()
if cuda:
model.cuda()
log.info("Model: \n{:s}".format(str(model)))
optimizer = optim.Adam(model.parameters())
iterator = BalancedBatchSizeIterator(batch_size=batch_size)
stop_criterion = Or(
[
MaxEpochs(max_epochs),
NoDecrease("valid_misclass", max_increase_epochs),
]
)
monitors = [LossMonitor(), MisclassMonitor(), RuntimeMonitor()]
model_constraint = MaxNormDefaultConstraint()
exp = Experiment(
model,
train_set,
valid_set,
test_set,
iterator=iterator,
loss_function=F.nll_loss,
optimizer=optimizer,
model_constraint=model_constraint,
monitors=monitors,
stop_criterion=stop_criterion,
remember_best_column="valid_misclass",
run_after_early_stop=True,
cuda=cuda,
)
exp.run()
return exp
def run_exp(data_folder, subject_id, low_cut_hz, model, cuda):
train_filename = 'A{:02d}T.gdf'.format(subject_id)
test_filename = 'A{:02d}E.gdf'.format(subject_id)
train_filepath = os.path.join(data_folder, train_filename)
test_filepath = os.path.join(data_folder, test_filename)
train_label_filepath = train_filepath.replace('.gdf', '.mat')
test_label_filepath = test_filepath.replace('.gdf', '.mat')
train_loader = BCICompetition4Set2A(train_filepath,
labels_filename=train_label_filepath)
test_loader = BCICompetition4Set2A(test_filepath,
labels_filename=test_label_filepath)
train_cnt = train_loader.load()
test_cnt = test_loader.load()
# Preprocessing
train_cnt = train_cnt.drop_channels(
['STI 014', 'EOG-left', 'EOG-central', 'EOG-right'])
assert len(train_cnt.ch_names) == 22
# lets convert to millvolt for numerical stability of next operations
train_cnt = mne_apply(lambda a: a * 1e6, train_cnt)
train_cnt = mne_apply(
lambda a: bandpass_cnt(
a, low_cut_hz, 38, train_cnt.info['sfreq'], filt_order=3, axis=1),
train_cnt)
train_cnt = mne_apply(
lambda a: exponential_running_standardize(
a.T, factor_new=1e-3, init_block_size=1000, eps=1e-4).T, train_cnt)
test_cnt = test_cnt.drop_channels(
['STI 014', 'EOG-left', 'EOG-central', 'EOG-right'])
assert len(test_cnt.ch_names) == 22
test_cnt = mne_apply(lambda a: a * 1e6, test_cnt)
test_cnt = mne_apply(
lambda a: bandpass_cnt(
a, low_cut_hz, 38, test_cnt.info['sfreq'], filt_order=3, axis=1),
test_cnt)
test_cnt = mne_apply(
lambda a: exponential_running_standardize(
a.T, factor_new=1e-3, init_block_size=1000, eps=1e-4).T, test_cnt)
marker_def = OrderedDict([('Left Hand', [1]), (
'Right Hand',
[2],
), ('Foot', [3]), ('Tongue', [4])])
ival = [-500, 4000]
train_set = create_signal_target_from_raw_mne(train_cnt, marker_def, ival)
test_set = create_signal_target_from_raw_mne(test_cnt, marker_def, ival)
train_set, valid_set = split_into_two_sets(train_set,
first_set_fraction=0.8)
set_random_seeds(seed=20190706, cuda=cuda)
n_classes = 4
n_chans = int(train_set.X.shape[1])
input_time_length = 1000
if model == 'shallow':
model = ShallowFBCSPNet(n_chans,
n_classes,
input_time_length=input_time_length,
final_conv_length=30).create_network()
elif model == 'deep':
model = Deep4Net(n_chans,
n_classes,
input_time_length=input_time_length,
final_conv_length=2).create_network()
to_dense_prediction_model(model)
if cuda:
model.cuda()
log.info("Model: \n{:s}".format(str(model)))
dummy_input = np_to_var(train_set.X[:1, :, :, None])
if cuda:
dummy_input = dummy_input.cuda()
out = model(dummy_input)
n_preds_per_input = out.cpu().data.numpy().shape[2]
optimizer = optim.Adam(model.parameters())
iterator = CropsFromTrialsIterator(batch_size=60,
input_time_length=input_time_length,
n_preds_per_input=n_preds_per_input)
stop_criterion = Or([MaxEpochs(800), NoDecrease('valid_misclass', 80)])
monitors = [
LossMonitor(),
MisclassMonitor(col_suffix='sample_misclass'),
CroppedTrialMisclassMonitor(input_time_length=input_time_length),
RuntimeMonitor()
]
model_constraint = MaxNormDefaultConstraint()
loss_function = lambda preds, targets: F.nll_loss(
th.mean(preds, dim=2)[:, :, 0], targets)
exp = Experiment(model,
train_set,
valid_set,
test_set,
iterator=iterator,
loss_function=loss_function,
optimizer=optimizer,
model_constraint=model_constraint,
monitors=monitors,
stop_criterion=stop_criterion,
remember_best_column='valid_misclass',
run_after_early_stop=True,
cuda=cuda)
exp.run()
return exp
def run_exp_on_high_gamma_dataset(train_filename, test_filename, low_cut_hz,
model_name, max_epochs, max_increase_epochs,
np_th_seed, debug):
input_time_length = 1000
batch_size = 60
lr = 1e-3
weight_decay = 0
train_set, valid_set, test_set = load_train_valid_test(
train_filename=train_filename,
test_filename=test_filename,
low_cut_hz=low_cut_hz,
debug=debug)
if debug:
max_epochs = 4
set_random_seeds(np_th_seed, cuda=True)
#torch.backends.cudnn.benchmark = True# sometimes crashes?
n_classes = int(np.max(train_set.y) + 1)
n_chans = int(train_set.X.shape[1])
if model_name == 'deep':
model = Deep4Net(n_chans,
n_classes,
input_time_length=input_time_length,
final_conv_length=2).create_network()
elif model_name == 'shallow':
model = ShallowFBCSPNet(n_chans,
n_classes,
input_time_length=input_time_length,
final_conv_length=30).create_network()
to_dense_prediction_model(model)
model.cuda()
model.eval()
out = model(np_to_var(train_set.X[:1, :, :input_time_length, None]).cuda())
n_preds_per_input = out.cpu().data.numpy().shape[2]
optimizer = optim.Adam(model.parameters(),
weight_decay=weight_decay,
lr=lr)
iterator = CropsFromTrialsIterator(batch_size=batch_size,
input_time_length=input_time_length,
n_preds_per_input=n_preds_per_input,
seed=np_th_seed)
monitors = [
LossMonitor(),
MisclassMonitor(col_suffix='sample_misclass'),
CroppedTrialMisclassMonitor(input_time_length=input_time_length),
RuntimeMonitor()
]
model_constraint = MaxNormDefaultConstraint()
loss_function = lambda preds, targets: F.nll_loss(th.mean(preds, dim=2),
targets)
run_after_early_stop = True
do_early_stop = True
remember_best_column = 'valid_misclass'
stop_criterion = Or([
MaxEpochs(max_epochs),
NoDecrease('valid_misclass', max_increase_epochs)
])
exp = Experiment(model,
train_set,
valid_set,
test_set,
iterator=iterator,
loss_function=loss_function,
optimizer=optimizer,
model_constraint=model_constraint,
monitors=monitors,
stop_criterion=stop_criterion,
remember_best_column=remember_best_column,
run_after_early_stop=run_after_early_stop,
cuda=True,
do_early_stop=do_early_stop)
exp.run()
return exp
def network_model(subject_id, model_type, data_type, cropped, cuda, parameters, hyp_params):
best_params = dict() # dictionary to store hyper-parameter values
#####Parameter passed to funciton#####
max_epochs = parameters['max_epochs']
max_increase_epochs = parameters['max_increase_epochs']
batch_size = parameters['batch_size']
#####Constant Parameters#####
best_loss = 100.0 # instatiate starting point for loss
iterator = BalancedBatchSizeIterator(batch_size=batch_size)
stop_criterion = Or([MaxEpochs(max_epochs),
NoDecrease('valid_misclass', max_increase_epochs)])
monitors = [LossMonitor(), MisclassMonitor(), RuntimeMonitor()]
model_constraint = MaxNormDefaultConstraint()
epoch = 4096
#####Collect and format data#####
if data_type == 'words':
data, labels = format_data(data_type, subject_id, epoch)
data = data[:,:,768:1280] # within-trial window selected for classification
elif data_type == 'vowels':
data, labels = format_data(data_type, subject_id, epoch)
data = data[:,:,512:1024]
elif data_type == 'all_classes':
data, labels = format_data(data_type, subject_id, epoch)
data = data[:,:,768:1280]
x = lambda a: a * 1e6 # improves numerical stability
data = x(data)
data = normalize(data)
data, labels = balanced_subsample(data, labels) # downsampling the data to ensure equal classes
data, _, labels, _ = train_test_split(data, labels, test_size=0, random_state=42) # redundant shuffle of data/labels
#####model inputs#####
unique, counts = np.unique(labels, return_counts=True)
n_classes = len(unique)
n_chans = int(data.shape[1])
input_time_length = data.shape[2]
#####k-fold nested corss-validation#####
num_folds = 4
skf = StratifiedKFold(n_splits=num_folds, shuffle=True, random_state=10)
out_fold_num = 0 # outer-fold number
cv_scores = []
#####Outer=Fold#####
for inner_ind, outer_index in skf.split(data, labels):
inner_fold, outer_fold = data[inner_ind], data[outer_index]
inner_labels, outer_labels = labels[inner_ind], labels[outer_index]
out_fold_num += 1
# list for storing cross-validated scores
loss_with_params = dict()# for storing param values and losses
in_fold_num = 0 # inner-fold number
#####Inner-Fold#####
for train_idx, valid_idx in skf.split(inner_fold, inner_labels):
X_Train, X_val = inner_fold[train_idx], inner_fold[valid_idx]
y_train, y_val = inner_labels[train_idx], inner_labels[valid_idx]
in_fold_num += 1
train_set = SignalAndTarget(X_Train, y_train)
valid_set = SignalAndTarget(X_val, y_val)
loss_with_params[f"Fold_{in_fold_num}"] = dict()
####Nested cross-validation#####
for drop_prob in hyp_params['drop_prob']:
for loss_function in hyp_params['loss']:
for i in range(len(hyp_params['lr_adam'])):
model = None # ensure no duplication of models
# model, learning-rate and optimizer setup according to model_type
if model_type == 'shallow':
model = ShallowFBCSPNet(in_chans=n_chans, n_classes=n_classes, input_time_length=input_time_length,
n_filters_time=80, filter_time_length=40, n_filters_spat=80,
pool_time_length=75, pool_time_stride=25, final_conv_length='auto',
conv_nonlin=square, pool_mode='max', pool_nonlin=safe_log,
split_first_layer=True, batch_norm=True, batch_norm_alpha=0.1,
drop_prob=drop_prob).create_network()
lr = hyp_params['lr_ada'][i]
optimizer = optim.Adadelta(model.parameters(), lr=lr, rho=0.9, weight_decay=0.1, eps=1e-8)
elif model_type == 'deep':
model = Deep4Net(in_chans=n_chans, n_classes=n_classes, input_time_length=input_time_length,
final_conv_length='auto', n_filters_time=20, n_filters_spat=20, filter_time_length=10,
pool_time_length=3, pool_time_stride=3, n_filters_2=50, filter_length_2=15,
n_filters_3=100, filter_length_3=15, n_filters_4=400, filter_length_4=10,
first_nonlin=leaky_relu, first_pool_mode='max', first_pool_nonlin=safe_log, later_nonlin=leaky_relu,
later_pool_mode='max', later_pool_nonlin=safe_log, drop_prob=drop_prob,
double_time_convs=False, split_first_layer=False, batch_norm=True, batch_norm_alpha=0.1,
stride_before_pool=False).create_network() #filter_length_4 changed from 15 to 10
lr = hyp_params['lr_ada'][i]
optimizer = optim.Adadelta(model.parameters(), lr=lr, weight_decay=0.1, eps=1e-8)
elif model_type == 'eegnet':
model = EEGNetv4(in_chans=n_chans, n_classes=n_classes, final_conv_length='auto',
input_time_length=input_time_length, pool_mode='mean', F1=16, D=2, F2=32,
kernel_length=64, third_kernel_size=(8,4), drop_prob=drop_prob).create_network()
lr = hyp_params['lr_adam'][i]
optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=0, eps=1e-8, amsgrad=False)
set_random_seeds(seed=20190629, cuda=cuda)
if cuda:
model.cuda()
torch.backends.cudnn.deterministic = True
model = torch.nn.DataParallel(model)
log.info("%s model: ".format(str(model)))
loss_function = loss_function
model_loss_function = None
#####Setup to run the selected model#####
model_test = Experiment(model, train_set, valid_set, test_set=None, iterator=iterator,
loss_function=loss_function, optimizer=optimizer,
model_constraint=model_constraint, monitors=monitors,
stop_criterion=stop_criterion, remember_best_column='valid_misclass',
run_after_early_stop=True, model_loss_function=model_loss_function, cuda=cuda,
data_type=data_type, subject_id=subject_id, model_type=model_type,
cropped=cropped, model_number=str(out_fold_num))
model_test.run()
model_loss = model_test.epochs_df['valid_loss'].astype('float')
current_val_loss = current_loss(model_loss)
loss_with_params[f"Fold_{in_fold_num}"][f"{drop_prob}/{loss_function}/{lr}"] = current_val_loss
####Select and train optimized model#####
df = pd.DataFrame(loss_with_params)
df['mean'] = df.mean(axis=1) # compute mean loss across k-folds
writer_df = f"results_folder\\results\\S{subject_id}\\{model_type}_parameters.xlsx"
df.to_excel(writer_df)
best_dp, best_loss, best_lr = df.loc[df['mean'].idxmin()].__dict__['_name'].split("/") # extract best param values
if str(best_loss[10:13]) == 'nll':
best_loss = F.nll_loss
elif str(best_loss[10:13]) == 'cro':
best_loss = F.cross_entropy
print(f"Best parameters: dropout: {best_dp}, loss: {str(best_loss)[10:13]}, lr: {best_lr}")
#####Train model on entire inner fold set#####
torch.backends.cudnn.deterministic = True
model = None
#####Create outer-fold validation and test sets#####
X_valid, X_test, y_valid, y_test = train_test_split(outer_fold, outer_labels, test_size=0.5, random_state=42, stratify=outer_labels)
train_set = SignalAndTarget(inner_fold, inner_labels)
valid_set = SignalAndTarget(X_valid, y_valid)
test_set = SignalAndTarget(X_test, y_test)
if model_type == 'shallow':
model = ShallowFBCSPNet(in_chans=n_chans, n_classes=n_classes, input_time_length=input_time_length,
n_filters_time=60, filter_time_length=5, n_filters_spat=40,
pool_time_length=50, pool_time_stride=15, final_conv_length='auto',
conv_nonlin=relu6, pool_mode='mean', pool_nonlin=safe_log,
split_first_layer=True, batch_norm=True, batch_norm_alpha=0.1,
drop_prob=0.1).create_network() #50 works better than 75
optimizer = optim.Adadelta(model.parameters(), lr=2.0, rho=0.9, weight_decay=0.1, eps=1e-8)
elif model_type == 'deep':
model = Deep4Net(in_chans=n_chans, n_classes=n_classes, input_time_length=input_time_length,
final_conv_length='auto', n_filters_time=20, n_filters_spat=20, filter_time_length=5,
pool_time_length=3, pool_time_stride=3, n_filters_2=20, filter_length_2=5,
n_filters_3=40, filter_length_3=5, n_filters_4=1500, filter_length_4=10,
first_nonlin=leaky_relu, first_pool_mode='mean', first_pool_nonlin=safe_log, later_nonlin=leaky_relu,
later_pool_mode='mean', later_pool_nonlin=safe_log, drop_prob=0.1,
double_time_convs=False, split_first_layer=True, batch_norm=True, batch_norm_alpha=0.1,
stride_before_pool=False).create_network()
optimizer = AdamW(model.parameters(), lr=0.1, weight_decay=0)
elif model_type == 'eegnet':
model = EEGNetv4(in_chans=n_chans, n_classes=n_classes, final_conv_length='auto',
input_time_length=input_time_length, pool_mode='mean', F1=16, D=2, F2=32,
kernel_length=64, third_kernel_size=(8,4), drop_prob=0.1).create_network()
optimizer = optim.Adam(model.parameters(), lr=0.1, weight_decay=0, eps=1e-8, amsgrad=False)
if cuda:
model.cuda()
torch.backends.cudnn.deterministic = True
#model = torch.nn.DataParallel(model)
log.info("Optimized model")
model_loss_function=None
#####Setup to run the optimized model#####
optimized_model = op_exp(model, train_set, valid_set, test_set=test_set, iterator=iterator,
loss_function=best_loss, optimizer=optimizer,
model_constraint=model_constraint, monitors=monitors,
stop_criterion=stop_criterion, remember_best_column='valid_misclass',
run_after_early_stop=True, model_loss_function=model_loss_function, cuda=cuda,
data_type=data_type, subject_id=subject_id, model_type=model_type,
cropped=cropped, model_number=str(out_fold_num))
optimized_model.run()
log.info("Last 5 epochs")
log.info("\n" + str(optimized_model.epochs_df.iloc[-5:]))
writer = f"results_folder\\results\\S{subject_id}\\{data_type}_{model_type}_{str(out_fold_num)}.xlsx"
optimized_model.epochs_df.iloc[-30:].to_excel(writer)
accuracy = 1 - np.min(np.array(optimized_model.class_acc))
cv_scores.append(accuracy) # k accuracy scores for this param set.
#####Print and store fold accuracies and mean accuracy#####
print(f"Class Accuracy: {np.mean(np.array(cv_scores))}")
results_df = pd.DataFrame(dict(cv_scores=cv_scores,
cv_mean=np.mean(np.array(cv_scores))))
writer2 = f"results_folder\\results\\S{subject_id}\\{data_type}_{model_type}_cvscores.xlsx"
results_df.to_excel(writer2)
return optimized_model, np.mean(np.array(cv_scores))
def run_exp(epoches, batch_size, subject_num, model_type, cuda, single_subject,
single_subject_num):
# ival = [-500, 4000]
max_increase_epochs = 160
# Preprocessing
X, y = loadSubjects(subject_num, single_subject, single_subject_num)
X = X.astype(np.float32)
y = y.astype(np.int64)
X, y = shuffle(X, y)
trial_length = X.shape[2]
print("trial_length " + str(trial_length))
print("trying to run with {} sec trials ".format((trial_length - 1) / 256))
print("y")
print(y)
trainingSampleSize = int(len(X) * 0.6)
valudationSampleSize = int(len(X) * 0.2)
testSampleSize = int(len(X) * 0.2)
print("INFO : Training sample size: {}".format(trainingSampleSize))
print("INFO : Validation sample size: {}".format(valudationSampleSize))
print("INFO : Test sample size: {}".format(testSampleSize))
train_set = SignalAndTarget(X[:trainingSampleSize],
y=y[:trainingSampleSize])
valid_set = SignalAndTarget(
X[trainingSampleSize:(trainingSampleSize + valudationSampleSize)],
y=y[trainingSampleSize:(trainingSampleSize + valudationSampleSize)])
test_set = SignalAndTarget(X[(trainingSampleSize + valudationSampleSize):],
y=y[(trainingSampleSize +
valudationSampleSize):])
set_random_seeds(seed=20190706, cuda=cuda)
n_classes = 3
n_chans = int(train_set.X.shape[1])
input_time_length = train_set.X.shape[2]
if model_type == 'shallow':
model = ShallowFBCSPNet(n_chans,
n_classes,
input_time_length=input_time_length,
final_conv_length='auto').create_network()
elif model_type == 'deep':
model = Deep4Net(n_chans,
n_classes,
input_time_length=input_time_length,
final_conv_length='auto').create_network()
elif model_type == 'eegnet':
model = EEGNetv4(n_chans,
n_classes,
input_time_length=input_time_length,
final_conv_length='auto').create_network()
if cuda:
model.cuda()
log.info("Model: \n{:s}".format(str(model)))
optimizer = optim.Adam(model.parameters())
iterator = BalancedBatchSizeIterator(batch_size=batch_size)
stop_criterion = Or([
MaxEpochs(max_epochs),
NoDecrease('valid_misclass', max_increase_epochs)
])
monitors = [LossMonitor(), MisclassMonitor(), RuntimeMonitor()]
model_constraint = MaxNormDefaultConstraint()
exp = Experiment(model,
train_set,
valid_set,
test_set,
iterator=iterator,
loss_function=F.nll_loss,
optimizer=optimizer,
model_constraint=model_constraint,
monitors=monitors,
stop_criterion=stop_criterion,
remember_best_column='valid_misclass',
run_after_early_stop=True,
cuda=cuda)
exp.run()
th.save(
model,
"models\{}-cropped-{}subjects-{}sec-{}epoches-torch_model".format(
model_type, subject_num, ((trial_length - 1) / 256), epoches))
return exp
def run_experiment(train_set, valid_set, test_set, model_name, optimizer_name,
init_lr, scheduler_name, use_norm_constraint, weight_decay,
schedule_weight_decay, restarts, max_epochs,
max_increase_epochs, np_th_seed):
set_random_seeds(np_th_seed, cuda=True)
#torch.backends.cudnn.benchmark = True# sometimes crashes?
if valid_set is not None:
assert max_increase_epochs is not None
assert (max_epochs is None) != (restarts is None)
if max_epochs is None:
max_epochs = np.sum(restarts)
n_classes = int(np.max(train_set.y) + 1)
n_chans = int(train_set.X.shape[1])
input_time_length = 1000
if model_name == 'deep':
model = Deep4Net(n_chans,
n_classes,
input_time_length=input_time_length,
final_conv_length=2).create_network()
elif model_name == 'shallow':
model = ShallowFBCSPNet(n_chans,
n_classes,
input_time_length=input_time_length,
final_conv_length=30).create_network()
elif model_name in [
'resnet-he-uniform', 'resnet-he-normal', 'resnet-xavier-normal',
'resnet-xavier-uniform'
]:
init_name = model_name.lstrip('resnet-')
from torch.nn import init
init_fn = {
'he-uniform': lambda w: init.kaiming_uniform(w, a=0),
'he-normal': lambda w: init.kaiming_normal(w, a=0),
'xavier-uniform': lambda w: init.xavier_uniform(w, gain=1),
'xavier-normal': lambda w: init.xavier_normal(w, gain=1)
}[init_name]
model = EEGResNet(in_chans=n_chans,
n_classes=n_classes,
input_time_length=input_time_length,
final_pool_length=10,
n_first_filters=48,
conv_weight_init_fn=init_fn).create_network()
else:
raise ValueError("Unknown model name {:s}".format(model_name))
if 'resnet' not in model_name:
to_dense_prediction_model(model)
model.cuda()
model.eval()
out = model(np_to_var(train_set.X[:1, :, :input_time_length, None]).cuda())
n_preds_per_input = out.cpu().data.numpy().shape[2]
if optimizer_name == 'adam':
optimizer = optim.Adam(model.parameters(),
weight_decay=weight_decay,
lr=init_lr)
elif optimizer_name == 'adamw':
optimizer = AdamW(model.parameters(),
weight_decay=weight_decay,
lr=init_lr)
iterator = CropsFromTrialsIterator(batch_size=60,
input_time_length=input_time_length,
n_preds_per_input=n_preds_per_input,
seed=np_th_seed)
if scheduler_name is not None:
assert schedule_weight_decay == (optimizer_name == 'adamw')
if scheduler_name == 'cosine':
n_updates_per_epoch = sum(
[1 for _ in iterator.get_batches(train_set, shuffle=True)])
if restarts is None:
n_updates_per_period = n_updates_per_epoch * max_epochs
else:
n_updates_per_period = np.array(restarts) * n_updates_per_epoch
scheduler = CosineAnnealing(n_updates_per_period)
optimizer = ScheduledOptimizer(
scheduler,
optimizer,
schedule_weight_decay=schedule_weight_decay)
elif scheduler_name == 'cut_cosine':
# TODO: integrate with if clause before, now just separate
# to avoid messing with code
n_updates_per_epoch = sum(
[1 for _ in iterator.get_batches(train_set, shuffle=True)])
if restarts is None:
n_updates_per_period = n_updates_per_epoch * max_epochs
else:
n_updates_per_period = np.array(restarts) * n_updates_per_epoch
scheduler = CutCosineAnnealing(n_updates_per_period)
optimizer = ScheduledOptimizer(
scheduler,
optimizer,
schedule_weight_decay=schedule_weight_decay)
else:
raise ValueError("Unknown scheduler")
monitors = [
LossMonitor(),
MisclassMonitor(col_suffix='sample_misclass'),
CroppedTrialMisclassMonitor(input_time_length=input_time_length),
RuntimeMonitor()
]
if use_norm_constraint:
model_constraint = MaxNormDefaultConstraint()
else:
model_constraint = None
# change here this cell
loss_function = lambda preds, targets: F.nll_loss(th.mean(preds, dim=2),
targets)
if valid_set is not None:
run_after_early_stop = True
do_early_stop = True
remember_best_column = 'valid_misclass'
stop_criterion = Or([
MaxEpochs(max_epochs),
NoDecrease('valid_misclass', max_increase_epochs)
])
else:
run_after_early_stop = False
do_early_stop = False
remember_best_column = None
stop_criterion = MaxEpochs(max_epochs)
exp = Experiment(model,
train_set,
valid_set,
test_set,
iterator=iterator,
loss_function=loss_function,
optimizer=optimizer,
model_constraint=model_constraint,
monitors=monitors,
stop_criterion=stop_criterion,
remember_best_column=remember_best_column,
run_after_early_stop=run_after_early_stop,
cuda=True,
do_early_stop=do_early_stop)
exp.run()
return exp
def train_model(self, train_set_1, val_set_1, test_set_1, train_set_2,
val_set_2, test_set_2, save_model):
"""
:param train_set_1: (np.array) n_trials*n_channels*n_samples
:param val_set_1: (np.array) n_trials*n_channels*n_samples
:param test_set_1: (np.array) n_trials*n_channels*n_samples - can be None when training on inner-fold
:param train_set_2: (np.array) n_trials*n_channels*n_samples
:param val_set_2: (np.array) n_trials*n_channels*n_samples
:param test_set_2: (np.array) n_trials*n_channels*n_samples - can be None when training on inner-fold
:param save_model: (Bool) True if trained model is to be saved
:return: Accuracy and loss scores for the model trained with a given set of hyper-parameters
"""
model = self.call_model()
predictions = None
set_random_seeds(seed=20190629, cuda=self.cuda)
if self.cuda:
model.cuda()
torch.backends.cudnn.deterministic = True
model = torch.nn.DataParallel(model)
log.info(f"Cuda in use")
log.info("%s model: ".format(str(model)))
optimizer = optim.Adam(model.parameters(),
lr=self.learning_rate,
weight_decay=0.01,
eps=1e-8,
amsgrad=False)
stop_criterion = Or([
MaxEpochs(self.epochs),
NoDecrease('valid_loss', self.max_increase_epochs)
])
model_loss_function = None
#####Setup to run the selected model#####
model_test = Experiment(model,
train_set_1,
val_set_1,
train_set_2,
val_set_2,
test_set_1=test_set_1,
test_set_2=test_set_2,
iterator=self.iterator,
loss_function=self.loss,
optimizer=optimizer,
lr_scheduler=self.lr_scheduler(
optimizer,
step_size=self.lr_step,
gamma=self.lr_gamma),
model_constraint=self.model_constraint,
monitors=self.monitors,
stop_criterion=stop_criterion,
remember_best_column='valid_misclass',
run_after_early_stop=True,
model_loss_function=model_loss_function,
cuda=self.cuda,
save_file=self.model_save_path,
tag=self.tag,
save_model=save_model)
model_test.run()
model_acc = model_test.epochs_df['valid_misclass'].astype('float')
model_loss = model_test.epochs_df['valid_loss'].astype('float')
current_val_acc = 1 - current_acc(model_acc)
current_val_loss = current_loss(model_loss)
test_accuracy = None
if train_set_1 is not None and test_set_2 is not None:
val_metric_index = self.get_model_index(model_test.epochs_df)
test_accuracy = round(
(1 -
model_test.epochs_df['test_misclass'].iloc[val_metric_index])
* 100, 3)
predictions = model_test.model_predictions
probabilities = model_test.model_probabilities
return current_val_acc, current_val_loss, test_accuracy, model_test, predictions, probabilities