def overlap_and_add(signal, frame_step):
"""Reconstructs a signal from a framed representation.
Adds potentially overlapping frames of a signal with shape
`[..., frames, frame_length]`, offsetting subsequent frames by `frame_step`.
The resulting tensor has shape `[..., output_size]` where
output_size = (frames - 1) * frame_step + frame_length
Args:
signal: A [..., frames, frame_length] Tensor. All dimensions may be unknown, and rank must be at least 2.
frame_step: An integer denoting overlap offsets. Must be less than or equal to frame_length.
Returns:
A Tensor with shape [..., output_size] containing the overlap-added frames of signal's inner-most two dimensions.
output_size = (frames - 1) * frame_step + frame_length
Based on https://github.com/tensorflow/tensorflow/blob/r1.12/tensorflow/contrib/signal/python/ops/reconstruction_ops.py
"""
outer_dimensions = signal.size()[:-2]
frames, frame_length = signal.size()[-2:]
subframe_length = math.gcd(frame_length, frame_step) # gcd=Greatest Common Divisor
subframe_step = frame_step // subframe_length
subframes_per_frame = frame_length // subframe_length
output_size = frame_step * (frames - 1) + frame_length
output_subframes = output_size // subframe_length
subframe_signal = signal.view(*outer_dimensions, -1, subframe_length)
frame = torch.arange(0, output_subframes).unfold(0, subframes_per_frame, subframe_step)
frame = signal.new_tensor(frame).long() # signal may in GPU or CPU
frame = frame.contiguous().view(-1)
result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)
result.index_add_(-2, frame, subframe_signal)
result = result.view(*outer_dimensions, -1)
return result
def augment(signal, target, increase_size):
"""Function to augment the data
Args:
input (torch.FloatTensor) : Tensor with dimensions as (number of samples x rows x columns)
target (torch.FloatTensor) : Tensor with dimensions as (number of samples x 1)
increase_size (int) : the number of data to be increased
"""
tmp = torch.zeros(increase_size, signal.size(1), signal.size(2))
aug_target = torch.zeros(increase_size)
ind_max = signal.size(0)
ls1 = np.where(target.data.numpy() == 1)[0]
ls2 = np.where(target.data.numpy() == 0)[0]
m = int(increase_size / 2)
aug_target[0:m] = 1
aug_target[m:] = 0
for i in range(m):
a = signal[random.choice(ls1), :, :]
b = signal[random.choice(ls1), :, :]
tmp[i, :, :] = (a + b) / 2
for i in range(m, increase_size):
a = signal[random.choice(ls2), :, :]
b = signal[random.choice(ls2), :, :]
tmp[i, :, :] = (a + b) / 2
return torch.cat((signal, tmp), 0), torch.cat(
(target, Variable(aug_target.long())), 0)
def ctp_s0(signal, config, device):
'''
Calculate the CTP bolus arrival time (bat) and corresponding S0: averaged over signals before bat
return: s0 # (n_slice, n_row, n_column)
'''
sig_avg = torch.zeros([signal.size()[3]],
device=device,
dtype=torch.float,
requires_grad=False)
for t in range(signal.size()[3]):
sig_avg[t] = torch.mean(signal[..., t])
threshold = config.ctp_s0_threshold * (torch.max(sig_avg) -
torch.min(sig_avg))
flag = True
bat = 1
while flag:
if sig_avg[bat] - sig_avg[bat - 1] >= threshold and sig_avg[
bat + 1] > sig_avg[bat]:
flag = False
else:
bat += 1
if bat == signal.size()[3]:
flag = False
print(' Bolus arrival time (start from 0):', bat - 1)
s0 = torch.mean(signal[..., :bat], dim=3) # time dimension == 3
return s0, bat
def mrp_s0(signal, config, device):
'''
Calculate the MRP bolus arrival time (bat) and corresponding S0: averaged over signals before bat
return: s0 # (n_slice, n_row, n_column)
'''
sig_avg = torch.zeros([signal.size()[3]],
device=device,
dtype=torch.float,
requires_grad=False)
for t in range(signal.size()[3]):
sig_avg[t] = torch.mean(signal[..., t])
flag = True
bat = 0
while flag:
s0_avg = torch.mean(sig_avg[:bat + 1])
if torch.abs(s0_avg -
sig_avg[bat + 1]) / s0_avg < config.mrp_s0_threshold:
bat += 1
else:
flag = False
bat -= 1
if bat == signal.size()[3] - 1:
flag = False
bat -= 1
print(' Bolus arrival time (start from 0):', bat)
s0 = torch.mean(signal[..., :bat], dim=3) # time dimension == 3
return s0, bat
def ct2ctc(signal, config, device):
s0, _ = ctp_s0(signal, config, device)
ctc = torch.zeros(signal.size(),
device=device,
dtype=torch.float,
requires_grad=False)
for t in range(signal.size()[3]):
ctc[..., t] = config.k_ct * (signal[..., t] - s0)
# Check computed CTC: should have no NaN value
if not len(torch.nonzero(torch.isnan(ctc))) == 0:
raise ValueError('Computed CTC contains NaN value, check out!')
return ctc
def mr2ctc(signal, config, device):
# TODO: use mask if needed
s0, _ = mrp_s0(signal, config, device)
ctc = torch.zeros(signal.size(),
device=device,
dtype=torch.float,
requires_grad=False)
for t in range(signal.size()[3]):
ctc[..., t] = -config.k_mr / config.TE * torch.log(signal[..., t] / s0)
# Check computed CTC: should have no NaN value
if not len(torch.nonzero(torch.isnan(ctc))) == 0:
raise ValueError('Computed CTC contains NaN value, check out!')
return ctc