def inference_augment_t_filter(images, size):
# augment on temporal domain
split_dim = 2 # split the temporal domain and rearrange them to repeat time domain
input_image_list = split_eeg.split_eeg_signal_axes(images, split_dim=split_dim) # 2 represents temporal domain
input_image_length = len(input_image_list)
augment, _ = concat_eeg.conv_eeg_signal_time(input_image_list, np.arange(0, input_image_length), size, 2)
_print_tensor_size(augment)
return augment
def inference_augment_t_filter(images, size):
# augment on temporal domain
split_dim = 2 # split the temporal domain and rearrange them to repeat time domain
input_image_list = split_eeg.split_eeg_signal_axes(
images, split_dim=split_dim) # 2 represents temporal domain
input_image_length = len(input_image_list)
augment, _ = concat_eeg.conv_eeg_signal_time(
input_image_list, np.arange(0, input_image_length), size, 2)
_print_tensor_size(augment)
return augment
def inference_augment_s_rep_t_filter(images):
# augment on spatial domain
augment = deconv_augment_s_filter(images)
# augment on temporal domain
split_dim = 2 # split the temporal domain and rearrange them to repeat time domain
input_image_list = split_eeg.split_eeg_signal_axes(augment, split_dim=split_dim) # 2 represents temporal domain
input_image_length = len(input_image_list)
augment, _ = concat_eeg.conv_eeg_signal_time(
input_image_list, np.arange(0, input_image_length), KERNEL_SIZE, 2, is_rep=True
)
_print_tensor_size(augment)
return augment
def inference_augment_s_rep_t_filter(images):
# augment on spatial domain
augment = inference_augment_s_filter(images)
# augment on temporal domain
split_dim = 2 # split the temporal domain and rearrange them to repeat time domain
input_image_list = split_eeg.split_eeg_signal_axes(augment,
split_dim=split_dim) # 2 represents temporal domain
input_image_length = len(input_image_list)
augment, _ = concat_eeg.conv_eeg_signal_time(input_image_list,
np.arange(0, input_image_length),
KERNEL_SIZE, 2, is_rep=True)
_print_tensor_size(augment)
return augment
