icefall/egs/audioset/AT/zipformer/model.py

# Copyright    2021-2023  Xiaomi Corp.        (authors: Xiaoyu Yang,
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import random
from typing import List, Optional, Tuple

import k2
import torch
import torch.nn as nn
import torch.nn.functional as F
from encoder_interface import EncoderInterface

from icefall.utils import AttributeDict, make_pad_mask


class AudioTaggingModel(nn.Module):
    def __init__(
        self,
        encoder_embed: nn.Module,
        encoder: EncoderInterface,
        encoder_dim: int = 384,
        num_events: int = 527,
    ):
        """An audio tagging model

        Args:
          encoder_embed:
            It is a Convolutional 2D subsampling module. It converts
            an input of shape (N, T, idim) to an output of of shape
            (N, T', odim), where T' = (T-3)//2-2 = (T-7)//2.
          encoder:
            It is the transcription network in the paper. Its accepts
            two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,).
            It returns two tensors: `logits` of shape (N, T, encoder_dim) and
            `logit_lens` of shape (N,).
          encoder_dim:
            Dimension of the encoder.
          num_event:
            The number of classes.
        """
        super().__init__()

        assert isinstance(encoder, EncoderInterface), type(encoder)

        self.encoder_embed = encoder_embed
        self.encoder = encoder
        self.encoder_dim = encoder_dim

        self.classifier = nn.Sequential(
            nn.Dropout(0.1),
            nn.Linear(encoder_dim, num_events),
        )

        # for multi-class classification
        self.criterion = torch.nn.BCEWithLogitsLoss(reduction="sum")

    def forward_encoder(
        self,
        x: torch.Tensor,
        x_lens: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Compute encoder outputs.
        Args:
          x:
            A 3-D tensor of shape (N, T, C).
          x_lens:
            A 1-D tensor of shape (N,). It contains the number of frames in `x`
            before padding.

        Returns:
          encoder_out:
            Encoder output, of shape (N, T, C).
          encoder_out_lens:
            Encoder output lengths, of shape (N,).
        """
        # logging.info(f"Memory allocated at entry: {torch.cuda.memory_allocated() // 1000000}M")
        x, x_lens = self.encoder_embed(x, x_lens)
        # logging.info(f"Memory allocated after encoder_embed: {torch.cuda.memory_allocated() // 1000000}M")

        src_key_padding_mask = make_pad_mask(x_lens)
        x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)

        encoder_out, encoder_out_lens = self.encoder(x, x_lens, src_key_padding_mask)

        encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
        assert torch.all(encoder_out_lens > 0), (x_lens, encoder_out_lens)

        return encoder_out, encoder_out_lens

    def forward(
        self,
        x: torch.Tensor,
        x_lens: torch.Tensor,
        target: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Args:
          x:
            A 3-D tensor of shape (N, T, C).
          x_lens:
            A 1-D tensor of shape (N,). It contains the number of frames in `x`
            before padding.
          target:
            The ground truth label of audio events, could be many hot
        Returns:
          Return the binary crossentropy loss
        """
        assert x.ndim == 3, x.shape
        assert x_lens.ndim == 1, x_lens.shape

        # Compute encoder outputs
        encoder_out, encoder_out_lens = self.forward_encoder(x, x_lens)

        # Forward the speaker module
        logits = self.forward_audio_tagging(
            encoder_out=encoder_out, encoder_out_lens=encoder_out_lens
        )  # (N, num_classes)

        loss = self.criterion(logits, target)

        return loss

    def forward_audio_tagging(self, encoder_out, encoder_out_lens):
        """
        Args:
          encoder_out:
            A 3-D tensor of shape (N, T, C).
          encoder_out_lens:
            A 1-D tensor of shape (N,). It contains the number of frames in `x`
            before padding.

        Returns:
          A 3-D tensor of shape (N, num_classes).
        """
        logits = self.classifier(encoder_out)  # (N, T, num_classes)
        padding_mask = make_pad_mask(encoder_out_lens)
        logits[padding_mask] = 0
        logits = logits.sum(dim=1)  # mask the padding frames
        logits = logits / (~padding_mask).sum(dim=1).unsqueeze(-1).expand_as(
            logits
        )  # normalize the logits

        return logits