models.py

import torchvision.models as torchmodels
from torch import nn
import utils as u
from filterbank import STFT, MelFilter, Log1p, MedFilt


vgg16 = torchmodels.vgg16(weights=torchmodels.VGG16_Weights.DEFAULT)
vgg16 = vgg16.features[:13]
for nm, mod in vgg16.named_modules():
    if isinstance(mod, nn.MaxPool2d):
        setattr(vgg16, nm,  nn.AvgPool2d(2, 2))


frontend = lambda sr, nfft, sampleDur, n_mel : nn.Sequential(
    STFT(nfft, int((sampleDur*sr - nfft)/128)),
    MelFilter(sr, nfft, n_mel, 0, sr//2),
    Log1p(7, trainable=False),
    nn.InstanceNorm2d(1),
    u.Croper2D(n_mel, 128)
  )


frontend_medfilt = lambda sr, nfft, sampleDur, n_mel: nn.Sequential(
  STFT(nfft, int((sampleDur*sr - nfft)/128)),
  MelFilter(sr, nfft, n_mel, sr//nfft, sr//2),
  Log1p(7, trainable=False),
  nn.InstanceNorm2d(1),
  MedFilt(),
  u.Croper2D(n_mel, 128)
)


frontend_crop = lambda: nn.Sequential(
  Log1p(7, trainable=False),
  nn.InstanceNorm2d(1)
)

frontend_crop_duration = lambda sr, nfft, sampleDur, n_mel : nn.Sequential(
    MelFilter(sr, nfft, n_mel, 0, sr//2),
    Log1p(7, trainable=False),
    nn.InstanceNorm2d(1)
)


sparrow_encoder = lambda nfeat, shape : nn.Sequential(
  nn.Conv2d(1, 32, 3, stride=2, bias=False, padding=(1)),
  nn.BatchNorm2d(32),
  nn.ReLU(True),
  nn.Conv2d(32, 64, 3, stride=2, bias=False, padding=1),
  nn.BatchNorm2d(64),
  nn.ReLU(True),
  nn.Conv2d(64, 128, 3, stride=2, bias=False, padding=1),
  nn.BatchNorm2d(128),
  nn.ReLU(True),
  nn.Conv2d(128, 256, 3, stride=2, bias=False, padding=1),
  nn.BatchNorm2d(256),
  nn.ReLU(True),
  nn.Conv2d(256, nfeat, 3, stride=2, padding=1),
  u.Reshape(nfeat * shape[0] * shape[1])
)

sparrow_decoder = lambda nfeat, shape : nn.Sequential(
  u.Reshape(nfeat//(shape[0]*shape[1]), *shape),
  nn.ReLU(True),

  nn.Upsample(scale_factor=2),
  nn.Conv2d(nfeat//(shape[0]*shape[1]), 256, (3, 3), bias=False, padding=1),
  nn.BatchNorm2d(256),
  nn.ReLU(True),
  nn.Conv2d(256, 256, (3, 3), bias=False, padding=1),
  nn.BatchNorm2d(256),
  nn.ReLU(True),

  nn.Upsample(scale_factor=2),
  nn.Conv2d(256, 128, (3, 3), bias=False, padding=1),
  nn.BatchNorm2d(128),
  nn.ReLU(True),
  nn.Conv2d(128, 128, (3, 3), bias=False, padding=1),
  nn.BatchNorm2d(128),
  nn.ReLU(True),

  nn.Upsample(scale_factor=2),
  nn.Conv2d(128, 64, (3, 3), bias=False, padding=1),
  nn.BatchNorm2d(64),
  nn.ReLU(True),
  nn.Conv2d(64, 64, (3, 3), bias=False, padding=1),
  nn.BatchNorm2d(64),
  nn.ReLU(True),

  nn.Upsample(scale_factor=2),
  nn.Conv2d(64, 32, (3, 3), bias=False, padding=1),
  nn.BatchNorm2d(32),
  nn.ReLU(True),
  nn.Conv2d(32, 32, (3, 3), bias=False, padding=1),
  nn.BatchNorm2d(32),
  nn.ReLU(True),

  nn.Upsample(scale_factor=2),
  nn.Conv2d(32, 1, (3, 3), bias=False, padding=1),
  nn.BatchNorm2d(1),
  nn.ReLU(True),
  nn.Conv2d(1, 1, (3, 3), bias=False, padding=1),
)