def test_len_split():
for len in range(2000):
train, valid, test = len_split(len)
assert (
len == train + valid + test
), "Splitting of the dataset wrong, total len expected: {}, got {}".format(
train + valid + test, len)
def test_train_MEG_swap():
dataset_path = ["Z:\Desktop\sub8\\ball1_sss.fif"]
dataset = MEG_Dataset(dataset_path, duration=1.0, overlap=0.0)
train_len, valid_len, test_len = len_split(len(dataset))
train_dataset, valid_dataset, test_dataset = random_split(
dataset, [train_len, valid_len, test_len]
)
device = "cpu"
trainloader = DataLoader(
train_dataset, batch_size=10, shuffle=False, num_workers=1
)
validloader = DataLoader(
valid_dataset, batch_size=2, shuffle=False, num_workers=1
)
epochs = 1
with torch.no_grad():
x, _, _ = iter(trainloader).next()
n_times = x.shape[-1]
net = models.MNet(n_times)
optimizer = SGD(net.parameters(), lr=0.0001, weight_decay=5e-4)
loss_function = torch.nn.MSELoss()
model, _, _ = train(
net,
trainloader,
validloader,
optimizer,
loss_function,
device,
epochs,
10,
0,
"",
)
print("Test succeeded!")
def test_MEG_dataset_shape_2():
dataset_path = ["Z:\Desktop\sub8\\ball1_sss.fif"]
dataset = MEG_Dataset2(dataset_path, duration=1.0, overlap=0.0)
train_len, valid_len, test_len = len_split(len(dataset))
print(len(dataset))
print("{} {} {}".format(train_len, valid_len, test_len))
train_dataset, valid_test, test_dataset = random_split(
dataset, [train_len, valid_len, test_len])
assert (train_dataset.__len__() == 524
), "Bad split, train set length expected = 524, got {}".format(
train_dataset.__len__())
assert (
valid_test.__len__() == 112
), "Bad split, validation set length expected = 112 , got {}".format(
valid_test.__len__())
assert (test_dataset.__len__() == 113
), "Bad split, test set length expected = 113 , got {}".format(
test_dataset.__len__())
trainloader = DataLoader(train_dataset,
batch_size=50,
shuffle=False,
num_workers=1)
sample_data, sample_target, sample_bp = iter(trainloader).next()
assert sample_data.shape == torch.Size(
[50, 1, 204,
501]), "wrong data shape, data shape expected = {}, got {}".format(
torch.Size([50, 1, 204, 501]), sample_data.shape)
assert sample_target.shape == torch.Size(
[50, 2,
2]), "wrong target shape, data shape expected = {}, got {}".format(
torch.Size([50, 2, 2]), sample_target.shape)
assert sample_bp.shape == torch.Size(
[50, 204,
6]), "wrong target shape, data shape expected = {}, got {}".format(
torch.Size([50, 204, 6]), sample_target.shape)