Spaces:
Runtime error
Runtime error
File size: 4,357 Bytes
7d6f241 |
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 |
import torch
import torchaudio
import torch.nn as nn
import torch.nn.functional as F
from utils import init_bn, init_layer
# adapted from https://github.com/qiuqiangkong/audioset_tagging_cnn/blob/master/pytorch/models.py
class Cnn14(nn.Module):
def __init__(
self,
num_classes: int,
sample_rate: float,
n_fft: int = 2048,
hop_length: int = 512,
n_mels: int = 128,
):
super().__init__()
self.num_classes = num_classes
self.n_fft = n_fft
self.hop_length = hop_length
window = torch.hann_window(n_fft)
self.register_buffer("window", window)
self.melspec = torchaudio.transforms.MelSpectrogram(
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.fc_audioset = nn.Linear(2048, num_classes, bias=True)
self.init_weight()
def init_weight(self):
init_bn(self.bn0)
init_layer(self.fc1)
init_layer(self.fc_audioset)
def forward(self, x: torch.Tensor):
"""
Input: (batch_size, data_length)"""
x = self.melspec(x)
x = x.permute(0, 2, 1, 3)
x = self.bn0(x)
x = x.permute(0, 2, 1, 3)
if self.training:
pass
# x = self.spec_augmenter(x)
x = self.conv_block1(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block2(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block3(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block4(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block5(x, pool_size=(2, 2), pool_type="avg")
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block6(x, pool_size=(1, 1), pool_type="avg")
x = F.dropout(x, p=0.2, training=self.training)
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=self.training)
x = F.relu_(self.fc1(x))
clipwise_output = self.fc_audioset(x)
return clipwise_output
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
|