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2e92c2b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | import torch
import torchaudio
import torch.nn as nn
import torch.nn.functional as F
from remfx.utils import init_bn, init_layer
## Deleted a bunch of code that's not needed for inference
class Cnn14(nn.Module):
def __init__(
self,
num_classes: int,
sample_rate: float,
model_sample_rate: float,
n_fft: int = 1024,
hop_length: int = 256,
n_mels: int = 128,
specaugment: bool = False,
):
super().__init__()
self.num_classes = num_classes
self.n_fft = n_fft
self.hop_length = hop_length
self.sample_rate = sample_rate
self.model_sample_rate = model_sample_rate
self.specaugment = specaugment
window = torch.hann_window(n_fft)
self.register_buffer("window", window)
self.melspec = torchaudio.transforms.MelSpectrogram(
model_sample_rate,
n_fft,
hop_length=hop_length,
n_mels=n_mels,
)
self.bn0 = nn.BatchNorm2d(n_mels)
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
self.fc1 = nn.Linear(2048, 2048, bias=True)
self.heads = torch.nn.ModuleList()
for _ in range(num_classes):
self.heads.append(nn.Linear(2048, 1, bias=True))
self.init_weight()
if sample_rate != model_sample_rate:
self.resample = torchaudio.transforms.Resample(
orig_freq=sample_rate, new_freq=model_sample_rate
)
if self.specaugment:
self.freq_mask = torchaudio.transforms.FrequencyMasking(64, True)
self.time_mask = torchaudio.transforms.TimeMasking(128, True)
def init_weight(self):
init_bn(self.bn0)
init_layer(self.fc1)
def forward(self, x: torch.Tensor, train: bool = False):
"""
Input: (batch_size, data_length)"""
if self.sample_rate != self.model_sample_rate:
x = self.resample(x)
x = self.melspec(x)
if self.specaugment and train:
x = self.freq_mask(x)
x = self.time_mask(x)
# apply standardization
x = (x - x.mean(dim=(2, 3), keepdim=True)) / x.std(dim=(2, 3), keepdim=True)
x = self.conv_block1(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=train)
x = self.conv_block2(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=train)
x = self.conv_block3(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=train)
x = self.conv_block4(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=train)
x = self.conv_block5(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=train)
x = self.conv_block6(x, pool_size=(1, 1), pool_type="avg")
x = F.dropout(x, p=0.2, training=train)
x = torch.mean(x, dim=3)
(x1, _) = torch.max(x, dim=2)
x2 = torch.mean(x, dim=2)
x = x1 + x2
x = F.dropout(x, p=0.5, training=train)
x = F.relu_(self.fc1(x))
outputs = []
for head in self.heads:
outputs.append(torch.sigmoid(head(x)))
return outputs
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ConvBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
)
self.conv2 = nn.Conv2d(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
)
self.bn1 = nn.BatchNorm2d(out_channels)
self.bn2 = nn.BatchNorm2d(out_channels)
self.init_weight()
def init_weight(self):
init_layer(self.conv1)
init_layer(self.conv2)
init_bn(self.bn1)
init_bn(self.bn2)
def forward(self, input, pool_size=(2, 2), pool_type="avg"):
x = input
x = F.relu_(self.bn1(self.conv1(x)))
x = F.relu_(self.bn2(self.conv2(x)))
if pool_type == "max":
x = F.max_pool2d(x, kernel_size=pool_size)
elif pool_type == "avg":
x = F.avg_pool2d(x, kernel_size=pool_size)
elif pool_type == "avg+max":
x1 = F.avg_pool2d(x, kernel_size=pool_size)
x2 = F.max_pool2d(x, kernel_size=pool_size)
x = x1 + x2
else:
raise Exception("Incorrect argument!")
return x
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