Hardcoded 12-leads #52
Description
hello,
Trying to use fewer than 12 leads leads to an issue here. Might I suggest an improved version:
def __call__(self, sample):
if self.p >= np.random.uniform(0,1):
new_sample = sample.new_zeros(sample.size())
num_leads = sample.size(0)
if self.mask_leads_selection == "random":
survivors = np.random.uniform(0, 1, size=num_leads) >= self.mask_leads_prob
new_sample[survivors] = sample[survivors]
elif self.mask_leads_selection == "conditional":
# original implementation assumes 12 leads split into first 6 and last 6.
# We generalize by splitting the leads into two halves.
n_total = num_leads
half = n_total // 2
n1, n2 = self.mask_leads_condition if self.mask_leads_condition is not None else (4,5)
n1 = min(n1, half)
n2 = min(n2, n_total - half)
# leads to mask count = half - n1 and half - n2 in two halves
s1 = np.array(random.sample(list(np.arange(half)), half - n1))
s2 = np.array(random.sample(list(np.arange(half, n_total)), (n_total - half) - n2))
new_sample[s1] = sample[s1]
new_sample[s2] = sample[s2]
else:
# fallback: no masking if unknown selection mode
new_sample = sample.clone()
else:
new_sample = sample.clone()
return new_sample.float()
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