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from matplotlib import pyplot as plt
import torch
from torch.nn import functional as F
import numpy as np
# %%
def smooth_lbl_loop(lbl, smooth_center, smooth_length, smooth_shape):
lbl_new = lbl.clone().detach().float()
lbl_copy = lbl.clone().detach()
lbl_weight = torch.zeros(smooth_length // 2)
for i in range(lbl_weight.size(0)):
if smooth_shape == 'square':
lbl_weight[i] = 1
elif smooth_shape == 'triangle':
lbl_weight[i] = 1 - (i + 1) / (smooth_length // 2 + 1)
elif smooth_shape == 'hann':
lbl_weight[i] = np.hanning(smooth_length + 2)[(smooth_length + 2) // 2 + 1 + i]
for i in range(1, lbl_weight.size(0) + 1):
if smooth_center:
lbl_new[i:] += lbl_copy[:-i] * lbl_weight[i - 1]
lbl_new[:-i] += lbl_copy[i:] * lbl_weight[i - 1]
else:
lbl_new[i:] += lbl_copy[:-i]
lbl_new[lbl_new > 1] = 1
return lbl_new
# %%
def smooth_lbl_conv(lbl, smooth_center, smooth_length, smooth_shape):
lbl_new = lbl.clone().detach().cpu().float().unsqueeze(0) # [N, C, L]
lbl_weight = torch.zeros(1, 1, smooth_length)
for i in range(lbl_weight.size(2)):
if smooth_shape == 'square':
lbl_weight[:, :, i] = 1
elif smooth_shape == 'triangle':
if i < smooth_length // 2:
lbl_weight[:, :, i] = (i + 1) / (smooth_length // 2 + 1)
else:
lbl_weight[:, :, i] = 1 - (i - smooth_length // 2) / (smooth_length // 2 + 1)
elif smooth_shape == 'hann':
lbl_weight[:, :, i] = np.hanning(smooth_length + 2)[i + 1]
if smooth_center:
lbl_new = F.conv1d(lbl_new, lbl_weight, bias=None, padding=smooth_length // 2).squeeze()
else:
lbl_new = F.conv1d(lbl_new, lbl_weight, bias=None).squeeze()
lbl_new[lbl_new > 1] = 1
return lbl_new
# %%
signal = torch.randint(0, 2, (50,))
signal_smooth_loop = smooth_lbl_loop(signal, True, 3, 'triangle')
signal_smooth_conv = smooth_lbl_conv(signal, True, 3, 'triangle')
plt.plot(signal)
#plt.plot(signal_smooth_loop, 'r')
plt.plot(signal_smooth_conv.squeeze(), 'g', linestyle='--')
# %%
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