AudioClassifier/model.py at main · sid995/AudioClassifier · GitHub

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import torch.nn as nn
import torch


class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()
        self.conv1 = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=3,
            stride=stride,
            padding=1,
            bias=False,
        )
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(
            out_channels, out_channels, kernel_size=3, padding=1, bias=False
        )
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.shortcut = nn.Sequential()
        self.use_shortcut = stride != 1 or in_channels != out_channels
        if self.use_shortcut:
            self.shortcut = nn.Sequential(
                nn.Conv2d(
                    in_channels, out_channels, kernel_size=1, stride=stride, bias=False
                ),
                nn.BatchNorm2d(out_channels),
            )

    def forward(self, x, fmap_dict=None, prefix=""):
        out = self.conv1(x)
        out = self.bn1(out)
        out = torch.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = torch.relu(out)
        shortcut = self.shortcut(x) if self.use_shortcut else x
        out_add = out + shortcut

        if fmap_dict is not None:
            fmap_dict[f"{prefix}.conv"] = out_add

        out = torch.relu(out_add)

        if fmap_dict is not None:
            fmap_dict[f"{prefix}.relu"] = out_add

        return out


class AudioCNN(nn.Module):
    def __init__(self, num_classes=50):
        super().__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
        )
        self.layer1 = nn.ModuleList([ResidualBlock(64, 64) for _ in range(3)])
        self.layer2 = nn.ModuleList(
            [
                ResidualBlock(64 if i == 0 else 128, 128, stride=2 if i == 0 else 1)
                for i in range(4)
            ]
        )
        self.layer3 = nn.ModuleList(
            [
                ResidualBlock(128 if i == 0 else 256, 256, stride=2 if i == 0 else 1)
                for i in range(6)
            ]
        )
        self.layer4 = nn.ModuleList(
            [
                ResidualBlock(256 if i == 0 else 512, 512, stride=2 if i == 0 else 1)
                for i in range(3)
            ]
        )

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout(0.5)
        self.fc = nn.Linear(512, num_classes)

    def forward(self, x, return_feature_maps=False):
        if not return_feature_maps:
            x = self.conv1(x)
            for block in self.layer1:
                x = block(x)
            for block in self.layer2:
                x = block(x)
            for block in self.layer3:
                x = block(x)
            for block in self.layer4:
                x = block(x)

            x = self.avgpool(x)
            x = x.view(x.size(0), -1)  # Flatten the tensor
            x = self.dropout(x)
            x = self.fc(x)

            return x
        else:
            feature_maps = {}
            x = self.conv1(x)
            feature_maps["conv1"] = x

            for ind, block in enumerate(self.layer1):
                x = block(x, feature_maps, prefix=f"layer1.block{ind}")
            feature_maps["layer1"] = x

            for ind, block in enumerate(self.layer2):
                x = block(x, feature_maps, prefix=f"layer2.block{ind}")
            feature_maps["layer2"] = x

            for ind, block in enumerate(self.layer3):
                x = block(x, feature_maps, prefix=f"layer3.block{ind}")
            feature_maps["layer3"] = x

            for ind, block in enumerate(self.layer4):
                x = block(x, feature_maps, prefix=f"layer4.block{ind}")
            feature_maps["layer4"] = x

            x = self.avgpool(x)
            x = x.view(x.size(0), -1)  # Flatten the tensor
            x = self.dropout(x)
            x = self.fc(x)
            return x, feature_maps