utils.py

import os
import glob
import torch
import torch.nn as nn
import matplotlib
matplotlib.use("Agg")
import matplotlib.pylab as plt

def plot_spectrogram(spectrogram):
    fig, ax = plt.subplots(figsize=(4, 3))
    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
                   interpolation='none')
    plt.colorbar(im, ax=ax)
    fig.canvas.draw()
    plt.close()

    return fig


def get_padding(kernel_size, dilation=1):
    return int((kernel_size*dilation - dilation)/2)


def get_padding_2d(kernel_size, dilation=(1, 1)):
    return (int((kernel_size[0]*dilation[0] - dilation[0])/2), int((kernel_size[1]*dilation[1] - dilation[1])/2))


class LearnableSigmoid1d(nn.Module):
    def __init__(self, in_features, beta=1):
        super().__init__()
        self.beta = beta
        self.slope = nn.Parameter(torch.ones(in_features))
        self.slope.requiresGrad = True

    def forward(self, x):
        return self.beta * torch.sigmoid(self.slope * x)


class LearnableSigmoid2d(nn.Module):
    def __init__(self, in_features, beta=1):
        super().__init__()
        self.beta = beta
        self.slope = nn.Parameter(torch.ones(in_features, 1))
        self.slope.requiresGrad = True

    def forward(self, x):
        return self.beta * torch.sigmoid(self.slope * x)


class Sigmoid2d(nn.Module):
    def __init__(self, in_features, beta=1):
        super().__init__()
        self.beta = beta
        self.slope = torch.ones(in_features, 1)

    def forward(self, x):
        return self.beta * torch.sigmoid(self.slope * x)
    

class PLSigmoid(nn.Module):
    def __init__(self, in_features):
        super().__init__()
        self.beta = nn.Parameter(torch.ones(in_features, 1) * 2.0)
        self.slope = nn.Parameter(torch.ones(in_features, 1))
        self.beta.requiresGrad = True
        self.slope.requiresGrad = True

    def forward(self, x):
        return self.beta * torch.sigmoid(self.slope * x)
    

def load_checkpoint(filepath, device):
    assert os.path.isfile(filepath)
    print("Loading '{}'".format(filepath))
    checkpoint_dict = torch.load(filepath, map_location=device)
    print("Complete.")
    return checkpoint_dict


def save_checkpoint(filepath, obj):
    print("Saving checkpoint to {}".format(filepath))
    torch.save(obj, filepath)
    print("Complete.")


def scan_checkpoint(cp_dir, prefix):
    pattern = os.path.join(cp_dir, prefix + '????????')
    cp_list = glob.glob(pattern)
    if len(cp_list) == 0:
        return None
    return sorted(cp_list)[-1]

def main():
    plsigmoid = PLSigmoid(201)
    a = torch.randn(4, 201, 100)
    print(plsigmoid(a))

if __name__ == '__main__':
    main()