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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import paddle.nn as nn
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import paddle.nn.functional as F
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from ..utils.download import load_state_dict_from_url
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from ..utils.env import MODEL_HOME
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__all__ = ['CNN14', 'CNN10', 'CNN6', 'cnn14', 'cnn10', 'cnn6']
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pretrained_model_urls = {
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'cnn14': 'https://bj.bcebos.com/paddleaudio/models/panns_cnn14.pdparams',
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'cnn10': 'https://bj.bcebos.com/paddleaudio/models/panns_cnn10.pdparams',
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'cnn6': 'https://bj.bcebos.com/paddleaudio/models/panns_cnn6.pdparams',
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}
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class ConvBlock(nn.Layer):
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def __init__(self, in_channels, out_channels):
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super(ConvBlock, self).__init__()
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self.conv1 = nn.Conv2D(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=(1, 1),
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padding=(1, 1),
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bias_attr=False)
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self.conv2 = nn.Conv2D(
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in_channels=out_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=(1, 1),
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padding=(1, 1),
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bias_attr=False)
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self.bn1 = nn.BatchNorm2D(out_channels)
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self.bn2 = nn.BatchNorm2D(out_channels)
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def forward(self, x, pool_size=(2, 2), pool_type='avg'):
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x = self.conv1(x)
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x = self.bn1(x)
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x = F.relu(x)
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x = self.conv2(x)
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x = self.bn2(x)
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x = F.relu(x)
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if pool_type == 'max':
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x = F.max_pool2d(x, kernel_size=pool_size)
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elif pool_type == 'avg':
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x = F.avg_pool2d(x, kernel_size=pool_size)
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elif pool_type == 'avg+max':
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x = F.avg_pool2d(
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x, kernel_size=pool_size) + F.max_pool2d(
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x, kernel_size=pool_size)
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else:
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raise Exception(
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f'Pooling type of {pool_type} is not supported. It must be one of "max", "avg" and "avg+max".'
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)
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return x
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class ConvBlock5x5(nn.Layer):
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def __init__(self, in_channels, out_channels):
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super(ConvBlock5x5, self).__init__()
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self.conv1 = nn.Conv2D(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=(5, 5),
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stride=(1, 1),
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padding=(2, 2),
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bias_attr=False)
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self.bn1 = nn.BatchNorm2D(out_channels)
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def forward(self, x, pool_size=(2, 2), pool_type='avg'):
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x = self.conv1(x)
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x = self.bn1(x)
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x = F.relu(x)
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if pool_type == 'max':
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x = F.max_pool2d(x, kernel_size=pool_size)
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elif pool_type == 'avg':
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x = F.avg_pool2d(x, kernel_size=pool_size)
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elif pool_type == 'avg+max':
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x = F.avg_pool2d(
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x, kernel_size=pool_size) + F.max_pool2d(
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x, kernel_size=pool_size)
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else:
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raise Exception(
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f'Pooling type of {pool_type} is not supported. It must be one of "max", "avg" and "avg+max".'
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)
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return x
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class CNN14(nn.Layer):
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"""
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The CNN14(14-layer CNNs) mainly consist of 6 convolutional blocks while each convolutional
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block consists of 2 convolutional layers with a kernel size of 3 × 3.
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Reference:
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PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
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https://arxiv.org/pdf/1912.10211.pdf
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"""
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emb_size = 2048
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def __init__(self, extract_embedding: bool=True):
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super(CNN14, self).__init__()
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self.bn0 = nn.BatchNorm2D(64)
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self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
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self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
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self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
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self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
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self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
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self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
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self.fc1 = nn.Linear(2048, self.emb_size)
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self.fc_audioset = nn.Linear(self.emb_size, 527)
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self.extract_embedding = extract_embedding
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def forward(self, x):
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x.stop_gradient = False
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x = x.transpose([0, 3, 2, 1])
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x = self.bn0(x)
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x = x.transpose([0, 3, 2, 1])
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x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = x.mean(axis=3)
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x = x.max(axis=2) + x.mean(axis=2)
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x = F.dropout(x, p=0.5, training=self.training)
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x = F.relu(self.fc1(x))
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if self.extract_embedding:
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output = F.dropout(x, p=0.5, training=self.training)
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else:
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output = F.sigmoid(self.fc_audioset(x))
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return output
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class CNN10(nn.Layer):
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"""
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The CNN10(14-layer CNNs) mainly consist of 4 convolutional blocks while each convolutional
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block consists of 2 convolutional layers with a kernel size of 3 × 3.
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Reference:
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PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
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https://arxiv.org/pdf/1912.10211.pdf
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"""
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emb_size = 512
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def __init__(self, extract_embedding: bool=True):
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super(CNN10, self).__init__()
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self.bn0 = nn.BatchNorm2D(64)
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self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
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self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
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self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
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self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
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self.fc1 = nn.Linear(512, self.emb_size)
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self.fc_audioset = nn.Linear(self.emb_size, 527)
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self.extract_embedding = extract_embedding
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def forward(self, x):
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x.stop_gradient = False
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x = x.transpose([0, 3, 2, 1])
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x = self.bn0(x)
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x = x.transpose([0, 3, 2, 1])
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x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = x.mean(axis=3)
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x = x.max(axis=2) + x.mean(axis=2)
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x = F.dropout(x, p=0.5, training=self.training)
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x = F.relu(self.fc1(x))
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if self.extract_embedding:
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output = F.dropout(x, p=0.5, training=self.training)
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else:
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output = F.sigmoid(self.fc_audioset(x))
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return output
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class CNN6(nn.Layer):
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"""
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The CNN14(14-layer CNNs) mainly consist of 4 convolutional blocks while each convolutional
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block consists of 1 convolutional layers with a kernel size of 5 × 5.
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Reference:
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PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
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https://arxiv.org/pdf/1912.10211.pdf
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"""
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emb_size = 512
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def __init__(self, extract_embedding: bool=True):
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super(CNN6, self).__init__()
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self.bn0 = nn.BatchNorm2D(64)
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self.conv_block1 = ConvBlock5x5(in_channels=1, out_channels=64)
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self.conv_block2 = ConvBlock5x5(in_channels=64, out_channels=128)
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self.conv_block3 = ConvBlock5x5(in_channels=128, out_channels=256)
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self.conv_block4 = ConvBlock5x5(in_channels=256, out_channels=512)
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self.fc1 = nn.Linear(512, self.emb_size)
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self.fc_audioset = nn.Linear(self.emb_size, 527)
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self.extract_embedding = extract_embedding
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def forward(self, x):
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x.stop_gradient = False
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x = x.transpose([0, 3, 2, 1])
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x = self.bn0(x)
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x = x.transpose([0, 3, 2, 1])
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x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
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x = F.dropout(x, p=0.2, training=self.training)
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x = x.mean(axis=3)
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x = x.max(axis=2) + x.mean(axis=2)
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x = F.dropout(x, p=0.5, training=self.training)
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x = F.relu(self.fc1(x))
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if self.extract_embedding:
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output = F.dropout(x, p=0.5, training=self.training)
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else:
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output = F.sigmoid(self.fc_audioset(x))
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return output
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def cnn14(pretrained: bool=False, extract_embedding: bool=True) -> CNN14:
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model = CNN14(extract_embedding=extract_embedding)
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if pretrained:
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state_dict = load_state_dict_from_url(
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url=pretrained_model_urls['cnn14'],
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path=os.path.join(MODEL_HOME, 'panns'))
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model.set_state_dict(state_dict)
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return model
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def cnn10(pretrained: bool=False, extract_embedding: bool=True) -> CNN10:
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model = CNN10(extract_embedding=extract_embedding)
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if pretrained:
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state_dict = load_state_dict_from_url(
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url=pretrained_model_urls['cnn10'],
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path=os.path.join(MODEL_HOME, 'panns'))
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model.set_state_dict(state_dict)
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return model
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def cnn6(pretrained: bool=False, extract_embedding: bool=True) -> CNN6:
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model = CNN6(extract_embedding=extract_embedding)
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if pretrained:
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state_dict = load_state_dict_from_url(
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url=pretrained_model_urls['cnn6'],
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path=os.path.join(MODEL_HOME, 'panns'))
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model.set_state_dict(state_dict)
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return model
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