Temporarily disable BGM module

audioldm/__init__.py

@@ -1,3 +0,0 @@
-from .ldm import LatentDiffusion
-from .utils import seed_everything
-from .pipeline import *

audioldm/audio/audio_processing.py

@@ -1,100 +0,0 @@
-import torch
-import numpy as np
-import librosa.util as librosa_util
-from scipy.signal import get_window
-
-
-def window_sumsquare(
-    window,
-    n_frames,
-    hop_length,
-    win_length,
-    n_fft,
-    dtype=np.float32,
-    norm=None,
-):
-    """
-    # from librosa 0.6
-    Compute the sum-square envelope of a window function at a given hop length.
-
-    This is used to estimate modulation effects induced by windowing
-    observations in short-time fourier transforms.
-
-    Parameters
-    ----------
-    window : string, tuple, number, callable, or list-like
-        Window specification, as in `get_window`
-
-    n_frames : int > 0
-        The number of analysis frames
-
-    hop_length : int > 0
-        The number of samples to advance between frames
-
-    win_length : [optional]
-        The length of the window function.  By default, this matches `n_fft`.
-
-    n_fft : int > 0
-        The length of each analysis frame.
-
-    dtype : np.dtype
-        The data type of the output
-
-    Returns
-    -------
-    wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
-        The sum-squared envelope of the window function
-    """
-    if win_length is None:
-        win_length = n_fft
-
-    n = n_fft + hop_length * (n_frames - 1)
-    x = np.zeros(n, dtype=dtype)
-
-    # Compute the squared window at the desired length
-    win_sq = get_window(window, win_length, fftbins=True)
-    win_sq = librosa_util.normalize(win_sq, norm=norm) ** 2
-    win_sq = librosa_util.pad_center(win_sq, n_fft)
-
-    # Fill the envelope
-    for i in range(n_frames):
-        sample = i * hop_length
-        x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))]
-    return x
-
-
-def griffin_lim(magnitudes, stft_fn, n_iters=30):
-    """
-    PARAMS
-    ------
-    magnitudes: spectrogram magnitudes
-    stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods
-    """
-
-    angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size())))
-    angles = angles.astype(np.float32)
-    angles = torch.autograd.Variable(torch.from_numpy(angles))
-    signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
-
-    for i in range(n_iters):
-        _, angles = stft_fn.transform(signal)
-        signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
-    return signal
-
-
-def dynamic_range_compression(x, normalize_fun=torch.log, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return normalize_fun(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C

audioldm/audio/stft.py

@@ -1,180 +0,0 @@
-import torch
-import torch.nn.functional as F
-import numpy as np
-from scipy.signal import get_window
-from librosa.util import pad_center, tiny
-from librosa.filters import mel as librosa_mel_fn
-
-from audioldm.audio.audio_processing import (
-    dynamic_range_compression,
-    dynamic_range_decompression,
-    window_sumsquare,
-)
-
-
-class STFT(torch.nn.Module):
-    """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
-
-    def __init__(self, filter_length, hop_length, win_length, window="hann"):
-        super(STFT, self).__init__()
-        self.filter_length = filter_length
-        self.hop_length = hop_length
-        self.win_length = win_length
-        self.window = window
-        self.forward_transform = None
-        scale = self.filter_length / self.hop_length
-        fourier_basis = np.fft.fft(np.eye(self.filter_length))
-
-        cutoff = int((self.filter_length / 2 + 1))
-        fourier_basis = np.vstack(
-            [np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])]
-        )
-
-        forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
-        inverse_basis = torch.FloatTensor(
-            np.linalg.pinv(scale * fourier_basis).T[:, None, :]
-        )
-
-        if window is not None:
-            assert filter_length >= win_length
-            # get window and zero center pad it to filter_length
-            fft_window = get_window(window, win_length, fftbins=True)
-            fft_window = pad_center(fft_window, filter_length)
-            fft_window = torch.from_numpy(fft_window).float()
-
-            # window the bases
-            forward_basis *= fft_window
-            inverse_basis *= fft_window
-
-        self.register_buffer("forward_basis", forward_basis.float())
-        self.register_buffer("inverse_basis", inverse_basis.float())
-
-    def transform(self, input_data):
-        num_batches = input_data.size(0)
-        num_samples = input_data.size(1)
-
-        self.num_samples = num_samples
-
-        # similar to librosa, reflect-pad the input
-        input_data = input_data.view(num_batches, 1, num_samples)
-        input_data = F.pad(
-            input_data.unsqueeze(1),
-            (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0),
-            mode="reflect",
-        )
-        input_data = input_data.squeeze(1)
-
-        forward_transform = F.conv1d(
-            input_data,
-            torch.autograd.Variable(self.forward_basis, requires_grad=False),
-            stride=self.hop_length,
-            padding=0,
-        ).cpu()
-
-        cutoff = int((self.filter_length / 2) + 1)
-        real_part = forward_transform[:, :cutoff, :]
-        imag_part = forward_transform[:, cutoff:, :]
-
-        magnitude = torch.sqrt(real_part**2 + imag_part**2)
-        phase = torch.autograd.Variable(torch.atan2(imag_part.data, real_part.data))
-
-        return magnitude, phase
-
-    def inverse(self, magnitude, phase):
-        recombine_magnitude_phase = torch.cat(
-            [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1
-        )
-
-        inverse_transform = F.conv_transpose1d(
-            recombine_magnitude_phase,
-            torch.autograd.Variable(self.inverse_basis, requires_grad=False),
-            stride=self.hop_length,
-            padding=0,
-        )
-
-        if self.window is not None:
-            window_sum = window_sumsquare(
-                self.window,
-                magnitude.size(-1),
-                hop_length=self.hop_length,
-                win_length=self.win_length,
-                n_fft=self.filter_length,
-                dtype=np.float32,
-            )
-            # remove modulation effects
-            approx_nonzero_indices = torch.from_numpy(
-                np.where(window_sum > tiny(window_sum))[0]
-            )
-            window_sum = torch.autograd.Variable(
-                torch.from_numpy(window_sum), requires_grad=False
-            )
-            window_sum = window_sum
-            inverse_transform[:, :, approx_nonzero_indices] /= window_sum[
-                approx_nonzero_indices
-            ]
-
-            # scale by hop ratio
-            inverse_transform *= float(self.filter_length) / self.hop_length
-
-        inverse_transform = inverse_transform[:, :, int(self.filter_length / 2) :]
-        inverse_transform = inverse_transform[:, :, : -int(self.filter_length / 2) :]
-
-        return inverse_transform
-
-    def forward(self, input_data):
-        self.magnitude, self.phase = self.transform(input_data)
-        reconstruction = self.inverse(self.magnitude, self.phase)
-        return reconstruction
-
-
-class TacotronSTFT(torch.nn.Module):
-    def __init__(
-        self,
-        filter_length,
-        hop_length,
-        win_length,
-        n_mel_channels,
-        sampling_rate,
-        mel_fmin,
-        mel_fmax,
-    ):
-        super(TacotronSTFT, self).__init__()
-        self.n_mel_channels = n_mel_channels
-        self.sampling_rate = sampling_rate
-        self.stft_fn = STFT(filter_length, hop_length, win_length)
-        mel_basis = librosa_mel_fn(
-            sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax
-        )
-        mel_basis = torch.from_numpy(mel_basis).float()
-        self.register_buffer("mel_basis", mel_basis)
-
-    def spectral_normalize(self, magnitudes, normalize_fun):
-        output = dynamic_range_compression(magnitudes, normalize_fun)
-        return output
-
-    def spectral_de_normalize(self, magnitudes):
-        output = dynamic_range_decompression(magnitudes)
-        return output
-
-    def mel_spectrogram(self, y, normalize_fun=torch.log):
-        """Computes mel-spectrograms from a batch of waves
-        PARAMS
-        ------
-        y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
-
-        RETURNS
-        -------
-        mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
-        """
-        assert torch.min(y.data) >= -1, torch.min(y.data)
-        assert torch.max(y.data) <= 1, torch.max(y.data)
-
-        magnitudes, phases = self.stft_fn.transform(y)
-        magnitudes = magnitudes.data
-        mel_output = torch.matmul(self.mel_basis, magnitudes)
-        mel_output = self.spectral_normalize(mel_output, normalize_fun)
-        energy = torch.norm(magnitudes, dim=1)
-
-        log_magnitudes = self.spectral_normalize(magnitudes, normalize_fun)
-
-        return mel_output, log_magnitudes, energy

audioldm/audio/tools.py

@@ -1,33 +0,0 @@
-import torch
-import numpy as np
-
-
-def get_mel_from_wav(audio, _stft):
-    audio = torch.clip(torch.FloatTensor(audio).unsqueeze(0), -1, 1)
-    audio = torch.autograd.Variable(audio, requires_grad=False)
-    melspec, log_magnitudes_stft, energy = _stft.mel_spectrogram(audio)
-    melspec = torch.squeeze(melspec, 0).numpy().astype(np.float32)
-    log_magnitudes_stft = (
-        torch.squeeze(log_magnitudes_stft, 0).numpy().astype(np.float32)
-    )
-    energy = torch.squeeze(energy, 0).numpy().astype(np.float32)
-    return melspec, log_magnitudes_stft, energy
-
-
-# def inv_mel_spec(mel, out_filename, _stft, griffin_iters=60):
-#     mel = torch.stack([mel])
-#     mel_decompress = _stft.spectral_de_normalize(mel)
-#     mel_decompress = mel_decompress.transpose(1, 2).data.cpu()
-#     spec_from_mel_scaling = 1000
-#     spec_from_mel = torch.mm(mel_decompress[0], _stft.mel_basis)
-#     spec_from_mel = spec_from_mel.transpose(0, 1).unsqueeze(0)
-#     spec_from_mel = spec_from_mel * spec_from_mel_scaling
-
-#     audio = griffin_lim(
-#         torch.autograd.Variable(spec_from_mel[:, :, :-1]), _stft._stft_fn, griffin_iters
-#     )
-
-#     audio = audio.squeeze()
-#     audio = audio.cpu().numpy()
-#     audio_path = out_filename
-#     write(audio_path, _stft.sampling_rate, audio)

audioldm/clap/encoders.py

@@ -1,170 +0,0 @@
-import torch
-import torch.nn as nn
-from audioldm.clap.open_clip import create_model
-from audioldm.clap.training.data import get_audio_features
-import torchaudio
-from transformers import RobertaTokenizer
-import torch.nn.functional as F
-
-
-class CLAPAudioEmbeddingClassifierFreev2(nn.Module):
-    def __init__(
-        self,
-        pretrained_path="",
-        key="class",
-        sampling_rate=16000,
-        embed_mode="audio",
-        amodel = "HTSAT-tiny",
-        unconditional_prob=0.1,
-        random_mute=False,
-        max_random_mute_portion=0.5,
-        training_mode=True,
-    ):
-        super().__init__()
-
-        self.key = key
-        self.device = "cpu"
-        self.precision = "fp32"
-        self.amodel = amodel
-        self.tmodel = "roberta"  # the best text encoder in our training
-        self.enable_fusion = False  # False if you do not want to use the fusion model
-        self.fusion_type = "aff_2d"
-        self.pretrained = pretrained_path
-        self.embed_mode = embed_mode
-        self.embed_mode_orig = embed_mode
-        self.sampling_rate = sampling_rate
-        self.unconditional_prob = unconditional_prob
-        self.random_mute = random_mute
-        self.tokenize = RobertaTokenizer.from_pretrained("roberta-base")
-        self.max_random_mute_portion = max_random_mute_portion
-        self.training_mode = training_mode
-        self.model, self.model_cfg = create_model(
-            self.amodel,
-            self.tmodel,
-            self.pretrained,
-            precision=self.precision,
-            device=self.device,
-            enable_fusion=self.enable_fusion,
-            fusion_type=self.fusion_type,
-        )
-        for p in self.model.parameters():
-            p.requires_grad = False
-
-        self.model.eval()
-
-    def get_unconditional_condition(self, batchsize):
-        self.unconditional_token = self.model.get_text_embedding(
-            self.tokenizer(["", ""])
-        )[0:1]
-        return torch.cat([self.unconditional_token.unsqueeze(0)] * batchsize, dim=0)
-
-    def batch_to_list(self, batch):
-        ret = []
-        for i in range(batch.size(0)):
-            ret.append(batch[i])
-        return ret
-
-    def make_decision(self, probability):
-        if float(torch.rand(1)) < probability:
-            return True
-        else:
-            return False
-
-    def random_uniform(self, start, end):
-        val = torch.rand(1).item()
-        return start + (end - start) * val
-
-    def _random_mute(self, waveform):
-        # waveform: [bs, t-steps]
-        t_steps = waveform.size(-1)
-        for i in range(waveform.size(0)):
-            mute_size = int(
-                self.random_uniform(0, end=int(t_steps * self.max_random_mute_portion))
-            )
-            mute_start = int(self.random_uniform(0, t_steps - mute_size))
-            waveform[i, mute_start : mute_start + mute_size] = 0
-        return waveform
-
-    def cos_similarity(self, waveform, text):
-        # waveform: [bs, t_steps]
-        with torch.no_grad():
-            self.embed_mode = "audio"
-            audio_emb = self(waveform.cuda())
-            self.embed_mode = "text"
-            text_emb = self(text)
-            similarity = F.cosine_similarity(audio_emb, text_emb, dim=2)
-            return similarity.squeeze()
-
-    def forward(self, batch, key=None):
-        # If you want this conditioner to be unconditional, set self.unconditional_prob = 1.0
-        # If you want this conditioner to be fully conditional, set self.unconditional_prob = 0.0
-        if self.model.training == True and not self.training_mode:
-            print(
-                "The pretrained CLAP model should always be in eval mode. Reloading model just in case you change the parameters."
-            )
-            self.model, self.model_cfg = create_model(
-                self.amodel,
-                self.tmodel,
-                self.pretrained,
-                precision=self.precision,
-                device="cuda",
-                enable_fusion=self.enable_fusion,
-                fusion_type=self.fusion_type,
-            )
-            for p in self.model.parameters():
-                p.requires_grad = False
-            self.model.eval()
-
-        # the 'fusion' truncate mode can be changed to 'rand_trunc' if run in unfusion mode
-        if self.embed_mode == "audio":
-            with torch.no_grad():
-                audio_dict_list = []
-                assert (
-                    self.sampling_rate == 16000
-                ), "We only support 16000 sampling rate"
-                if self.random_mute:
-                    batch = self._random_mute(batch)
-                # batch: [bs, 1, t-samples]
-                batch = torchaudio.functional.resample(
-                    batch, orig_freq=self.sampling_rate, new_freq=48000
-                )
-                for waveform in self.batch_to_list(batch):
-                    audio_dict = {}
-                    audio_dict = get_audio_features(
-                        audio_dict,
-                        waveform,
-                        480000,
-                        data_truncating="fusion",
-                        data_filling="repeatpad",
-                        audio_cfg=self.model_cfg["audio_cfg"],
-                    )
-                    audio_dict_list.append(audio_dict)
-                # [bs, 512]
-                embed = self.model.get_audio_embedding(audio_dict_list)
-        elif self.embed_mode == "text":
-            with torch.no_grad():
-                # the 'fusion' truncate mode can be changed to 'rand_trunc' if run in unfusion mode
-                text_data = self.tokenizer(batch)
-                embed = self.model.get_text_embedding(text_data)
-
-        embed = embed.unsqueeze(1)
-        self.unconditional_token = self.model.get_text_embedding(
-            self.tokenizer(["", ""])
-        )[0:1]
-
-        for i in range(embed.size(0)):
-            if self.make_decision(self.unconditional_prob):
-                embed[i] = self.unconditional_token
-
-        # [bs, 1, 512]
-        return embed.detach()
-
-    def tokenizer(self, text):
-        result = self.tokenize(
-            text,
-            padding="max_length",
-            truncation=True,
-            max_length=512,
-            return_tensors="pt",
-        )
-        return {k: v.squeeze(0) for k, v in result.items()}

audioldm/clap/open_clip/__init__.py

@@ -1,25 +0,0 @@
-from .factory import (
-    list_models,
-    create_model,
-    create_model_and_transforms,
-    add_model_config,
-)
-from .loss import ClipLoss, gather_features, LPLoss, lp_gather_features, LPMetrics
-from .model import (
-    CLAP,
-    CLAPTextCfg,
-    CLAPVisionCfg,
-    CLAPAudioCfp,
-    convert_weights_to_fp16,
-    trace_model,
-)
-from .openai import load_openai_model, list_openai_models
-from .pretrained import (
-    list_pretrained,
-    list_pretrained_tag_models,
-    list_pretrained_model_tags,
-    get_pretrained_url,
-    download_pretrained,
-)
-from .tokenizer import SimpleTokenizer, tokenize
-from .transform import image_transform

audioldm/clap/open_clip/bert.py

@@ -1,40 +0,0 @@
-from transformers import BertTokenizer, BertModel
-
-tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-model = BertModel.from_pretrained("bert-base-uncased")
-text = "Replace me by any text you'd like."
-
-
-def bert_embeddings(text):
-    # text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(text, return_tensors="pt")
-    output = model(**encoded_input)
-    return output
-
-
-from transformers import RobertaTokenizer, RobertaModel
-
-tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
-model = RobertaModel.from_pretrained("roberta-base")
-text = "Replace me by any text you'd like."
-
-
-def Roberta_embeddings(text):
-    # text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(text, return_tensors="pt")
-    output = model(**encoded_input)
-    return output
-
-
-from transformers import BartTokenizer, BartModel
-
-tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
-model = BartModel.from_pretrained("facebook/bart-base")
-text = "Replace me by any text you'd like."
-
-
-def bart_embeddings(text):
-    # text = "Replace me by any text you'd like."
-    encoded_input = tokenizer(text, return_tensors="pt")
-    output = model(**encoded_input)
-    return output

audioldm/clap/open_clip/bpe_simple_vocab_16e6.txt.gz

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
-size 1356917

audioldm/clap/open_clip/factory.py

@@ -1,277 +0,0 @@
-import json
-import logging
-import os
-import pathlib
-import re
-from copy import deepcopy
-from pathlib import Path
-
-import torch
-
-from .model import CLAP, convert_weights_to_fp16
-from .openai import load_openai_model
-from .pretrained import get_pretrained_url, download_pretrained
-from .transform import image_transform
-
-_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
-_MODEL_CONFIGS = {}  # directory (model_name: config) of model architecture configs
-
-
-def _natural_key(string_):
-    return [int(s) if s.isdigit() else s for s in re.split(r"(\d+)", string_.lower())]
-
-
-def _rescan_model_configs():
-    global _MODEL_CONFIGS
-
-    config_ext = (".json",)
-    config_files = []
-    for config_path in _MODEL_CONFIG_PATHS:
-        if config_path.is_file() and config_path.suffix in config_ext:
-            config_files.append(config_path)
-        elif config_path.is_dir():
-            for ext in config_ext:
-                config_files.extend(config_path.glob(f"*{ext}"))
-
-    for cf in config_files:
-        if os.path.basename(cf)[0] == ".":
-            continue  # Ignore hidden files
-
-        with open(cf, "r") as f:
-            model_cfg = json.load(f)
-            if all(a in model_cfg for a in ("embed_dim", "audio_cfg", "text_cfg")):
-                _MODEL_CONFIGS[cf.stem] = model_cfg
-
-    _MODEL_CONFIGS = {
-        k: v
-        for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))
-    }
-
-
-_rescan_model_configs()  # initial populate of model config registry
-
-
-def load_state_dict(checkpoint_path: str, map_location="cpu", skip_params=True):
-    checkpoint = torch.load(checkpoint_path, map_location=map_location)
-    if isinstance(checkpoint, dict) and "state_dict" in checkpoint:
-        state_dict = checkpoint["state_dict"]
-    else:
-        state_dict = checkpoint
-    if skip_params:
-        if next(iter(state_dict.items()))[0].startswith("module"):
-            state_dict = {k[7:]: v for k, v in state_dict.items()}
-    # for k in state_dict:
-    #     if k.startswith('transformer'):
-    #         v = state_dict.pop(k)
-    #         state_dict['text_branch.' + k[12:]] = v
-    return state_dict
-
-
-def create_model(
-    amodel_name: str,
-    tmodel_name: str,
-    pretrained: str = "",
-    precision: str = "fp32",
-    device: torch.device = torch.device("cpu"),
-    jit: bool = False,
-    force_quick_gelu: bool = False,
-    openai_model_cache_dir: str = os.path.expanduser("~/.cache/clip"),
-    skip_params=True,
-    pretrained_audio: str = "",
-    pretrained_text: str = "",
-    enable_fusion: bool = False,
-    fusion_type: str = "None"
-    # pretrained_image: bool = False,
-):
-    amodel_name = amodel_name.replace(
-        "/", "-"
-    )  # for callers using old naming with / in ViT names
-    pretrained_orig = pretrained
-    pretrained = pretrained.lower()
-    if pretrained == "openai":
-        if amodel_name in _MODEL_CONFIGS:
-            logging.info(f"Loading {amodel_name} model config.")
-            model_cfg = deepcopy(_MODEL_CONFIGS[amodel_name])
-        else:
-            logging.error(
-                f"Model config for {amodel_name} not found; available models {list_models()}."
-            )
-            raise RuntimeError(f"Model config for {amodel_name} not found.")
-
-        logging.info(f"Loading pretrained ViT-B-16 text encoder from OpenAI.")
-        # Hard Code in model name
-        model_cfg["text_cfg"]["model_type"] = tmodel_name
-        model = load_openai_model(
-            "ViT-B-16",
-            model_cfg,
-            device=device,
-            jit=jit,
-            cache_dir=openai_model_cache_dir,
-            enable_fusion=enable_fusion,
-            fusion_type=fusion_type,
-        )
-        # See https://discuss.pytorch.org/t/valueerror-attemting-to-unscale-fp16-gradients/81372
-        if precision == "amp" or precision == "fp32":
-            model = model.float()
-    else:
-        if amodel_name in _MODEL_CONFIGS:
-            logging.info(f"Loading {amodel_name} model config.")
-            model_cfg = deepcopy(_MODEL_CONFIGS[amodel_name])
-        else:
-            logging.error(
-                f"Model config for {amodel_name} not found; available models {list_models()}."
-            )
-            raise RuntimeError(f"Model config for {amodel_name} not found.")
-
-        if force_quick_gelu:
-            # override for use of QuickGELU on non-OpenAI transformer models
-            model_cfg["quick_gelu"] = True
-
-        # if pretrained_image:
-        #     if 'timm_amodel_name' in model_cfg.get('vision_cfg', {}):
-        #         # pretrained weight loading for timm models set via vision_cfg
-        #         model_cfg['vision_cfg']['timm_model_pretrained'] = True
-        #     else:
-        #         assert False, 'pretrained image towers currently only supported for timm models'
-        model_cfg["text_cfg"]["model_type"] = tmodel_name
-        model_cfg["enable_fusion"] = enable_fusion
-        model_cfg["fusion_type"] = fusion_type
-        model = CLAP(**model_cfg)
-
-        if pretrained:
-            checkpoint_path = ""
-            url = get_pretrained_url(amodel_name, pretrained)
-            if url:
-                checkpoint_path = download_pretrained(url, root=openai_model_cache_dir)
-            elif os.path.exists(pretrained_orig):
-                checkpoint_path = pretrained_orig
-            if checkpoint_path:
-                logging.info(
-                    f"Loading pretrained {amodel_name}-{tmodel_name} weights ({pretrained})."
-                )
-                ckpt = load_state_dict(checkpoint_path, skip_params=True)
-                model.load_state_dict(ckpt)
-                param_names = [n for n, p in model.named_parameters()]
-                # for n in param_names:
-                #     print(n, "\t", "Loaded" if n in ckpt else "Unloaded")
-            else:
-                logging.warning(
-                    f"Pretrained weights ({pretrained}) not found for model {amodel_name}."
-                )
-                raise RuntimeError(
-                    f"Pretrained weights ({pretrained}) not found for model {amodel_name}."
-                )
-
-        if pretrained_audio:
-            if amodel_name.startswith("PANN"):
-                if "Cnn14_mAP" in pretrained_audio:  # official checkpoint
-                    audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
-                    audio_ckpt = audio_ckpt["model"]
-                    keys = list(audio_ckpt.keys())
-                    for key in keys:
-                        if (
-                            "spectrogram_extractor" not in key
-                            and "logmel_extractor" not in key
-                        ):
-                            v = audio_ckpt.pop(key)
-                            audio_ckpt["audio_branch." + key] = v
-                elif os.path.basename(pretrained_audio).startswith(
-                    "PANN"
-                ):  # checkpoint trained via HTSAT codebase
-                    audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
-                    audio_ckpt = audio_ckpt["state_dict"]
-                    keys = list(audio_ckpt.keys())
-                    for key in keys:
-                        if key.startswith("sed_model"):
-                            v = audio_ckpt.pop(key)
-                            audio_ckpt["audio_branch." + key[10:]] = v
-                elif os.path.basename(pretrained_audio).startswith(
-                    "finetuned"
-                ):  # checkpoint trained via linear probe codebase
-                    audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
-                else:
-                    raise ValueError("Unknown audio checkpoint")
-            elif amodel_name.startswith("HTSAT"):
-                if "HTSAT_AudioSet_Saved" in pretrained_audio:  # official checkpoint
-                    audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
-                    audio_ckpt = audio_ckpt["state_dict"]
-                    keys = list(audio_ckpt.keys())
-                    for key in keys:
-                        if key.startswith("sed_model") and (
-                            "spectrogram_extractor" not in key
-                            and "logmel_extractor" not in key
-                        ):
-                            v = audio_ckpt.pop(key)
-                            audio_ckpt["audio_branch." + key[10:]] = v
-                elif os.path.basename(pretrained_audio).startswith(
-                    "HTSAT"
-                ):  # checkpoint trained via HTSAT codebase
-                    audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
-                    audio_ckpt = audio_ckpt["state_dict"]
-                    keys = list(audio_ckpt.keys())
-                    for key in keys:
-                        if key.startswith("sed_model"):
-                            v = audio_ckpt.pop(key)
-                            audio_ckpt["audio_branch." + key[10:]] = v
-                elif os.path.basename(pretrained_audio).startswith(
-                    "finetuned"
-                ):  # checkpoint trained via linear probe codebase
-                    audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
-                else:
-                    raise ValueError("Unknown audio checkpoint")
-            else:
-                raise f"this audio encoder pretrained checkpoint is not support"
-
-            model.load_state_dict(audio_ckpt, strict=False)
-            logging.info(
-                f"Loading pretrained {amodel_name} weights ({pretrained_audio})."
-            )
-            param_names = [n for n, p in model.named_parameters()]
-            for n in param_names:
-                print(n, "\t", "Loaded" if n in audio_ckpt else "Unloaded")
-
-        model.to(device=device)
-        if precision == "fp16":
-            assert device.type != "cpu"
-            convert_weights_to_fp16(model)
-
-        if jit:
-            model = torch.jit.script(model)
-
-    return model, model_cfg
-
-
-def create_model_and_transforms(
-    model_name: str,
-    pretrained: str = "",
-    precision: str = "fp32",
-    device: torch.device = torch.device("cpu"),
-    jit: bool = False,
-    force_quick_gelu: bool = False,
-    # pretrained_image: bool = False,
-):
-    model = create_model(
-        model_name,
-        pretrained,
-        precision,
-        device,
-        jit,
-        force_quick_gelu=force_quick_gelu,
-        # pretrained_image=pretrained_image
-    )
-    preprocess_train = image_transform(model.visual.image_size, is_train=True)
-    preprocess_val = image_transform(model.visual.image_size, is_train=False)
-    return model, preprocess_train, preprocess_val
-
-
-def list_models():
-    """enumerate available model architectures based on config files"""
-    return list(_MODEL_CONFIGS.keys())
-
-
-def add_model_config(path):
-    """add model config path or file and update registry"""
-    if not isinstance(path, Path):
-        path = Path(path)
-    _MODEL_CONFIG_PATHS.append(path)
-    _rescan_model_configs()

audioldm/clap/open_clip/feature_fusion.py

@@ -1,192 +0,0 @@
-"""
-Feature Fusion for Varible-Length Data Processing
-AFF/iAFF is referred and modified from https://github.com/YimianDai/open-aff/blob/master/aff_pytorch/aff_net/fusion.py
-According to the paper: Yimian Dai et al, Attentional Feature Fusion, IEEE Winter Conference on Applications of Computer Vision, WACV 2021
-"""
-
-import torch
-import torch.nn as nn
-
-
-class DAF(nn.Module):
-    """
-    直接相加 DirectAddFuse
-    """
-
-    def __init__(self):
-        super(DAF, self).__init__()
-
-    def forward(self, x, residual):
-        return x + residual
-
-
-class iAFF(nn.Module):
-    """
-    多特征融合 iAFF
-    """
-
-    def __init__(self, channels=64, r=4, type="2D"):
-        super(iAFF, self).__init__()
-        inter_channels = int(channels // r)
-
-        if type == "1D":
-            # 本地注意力
-            self.local_att = nn.Sequential(
-                nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm1d(inter_channels),
-                nn.ReLU(inplace=True),
-                nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm1d(channels),
-            )
-
-            # 全局注意力
-            self.global_att = nn.Sequential(
-                nn.AdaptiveAvgPool1d(1),
-                nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm1d(inter_channels),
-                nn.ReLU(inplace=True),
-                nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm1d(channels),
-            )
-
-            # 第二次本地注意力
-            self.local_att2 = nn.Sequential(
-                nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm1d(inter_channels),
-                nn.ReLU(inplace=True),
-                nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm1d(channels),
-            )
-            # 第二次全局注意力
-            self.global_att2 = nn.Sequential(
-                nn.AdaptiveAvgPool1d(1),
-                nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm1d(inter_channels),
-                nn.ReLU(inplace=True),
-                nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm1d(channels),
-            )
-        elif type == "2D":
-            # 本地注意力
-            self.local_att = nn.Sequential(
-                nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm2d(inter_channels),
-                nn.ReLU(inplace=True),
-                nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm2d(channels),
-            )
-
-            # 全局注意力
-            self.global_att = nn.Sequential(
-                nn.AdaptiveAvgPool2d(1),
-                nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm2d(inter_channels),
-                nn.ReLU(inplace=True),
-                nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm2d(channels),
-            )
-
-            # 第二次本地注意力
-            self.local_att2 = nn.Sequential(
-                nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm2d(inter_channels),
-                nn.ReLU(inplace=True),
-                nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm2d(channels),
-            )
-            # 第二次全局注意力
-            self.global_att2 = nn.Sequential(
-                nn.AdaptiveAvgPool2d(1),
-                nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm2d(inter_channels),
-                nn.ReLU(inplace=True),
-                nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm2d(channels),
-            )
-        else:
-            raise f"the type is not supported"
-
-        self.sigmoid = nn.Sigmoid()
-
-    def forward(self, x, residual):
-        flag = False
-        xa = x + residual
-        if xa.size(0) == 1:
-            xa = torch.cat([xa, xa], dim=0)
-            flag = True
-        xl = self.local_att(xa)
-        xg = self.global_att(xa)
-        xlg = xl + xg
-        wei = self.sigmoid(xlg)
-        xi = x * wei + residual * (1 - wei)
-
-        xl2 = self.local_att2(xi)
-        xg2 = self.global_att(xi)
-        xlg2 = xl2 + xg2
-        wei2 = self.sigmoid(xlg2)
-        xo = x * wei2 + residual * (1 - wei2)
-        if flag:
-            xo = xo[0].unsqueeze(0)
-        return xo
-
-
-class AFF(nn.Module):
-    """
-    多特征融合 AFF
-    """
-
-    def __init__(self, channels=64, r=4, type="2D"):
-        super(AFF, self).__init__()
-        inter_channels = int(channels // r)
-
-        if type == "1D":
-            self.local_att = nn.Sequential(
-                nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm1d(inter_channels),
-                nn.ReLU(inplace=True),
-                nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm1d(channels),
-            )
-            self.global_att = nn.Sequential(
-                nn.AdaptiveAvgPool1d(1),
-                nn.Conv1d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm1d(inter_channels),
-                nn.ReLU(inplace=True),
-                nn.Conv1d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm1d(channels),
-            )
-        elif type == "2D":
-            self.local_att = nn.Sequential(
-                nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm2d(inter_channels),
-                nn.ReLU(inplace=True),
-                nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm2d(channels),
-            )
-            self.global_att = nn.Sequential(
-                nn.AdaptiveAvgPool2d(1),
-                nn.Conv2d(channels, inter_channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm2d(inter_channels),
-                nn.ReLU(inplace=True),
-                nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
-                nn.BatchNorm2d(channels),
-            )
-        else:
-            raise f"the type is not supported."
-
-        self.sigmoid = nn.Sigmoid()
-
-    def forward(self, x, residual):
-        flag = False
-        xa = x + residual
-        if xa.size(0) == 1:
-            xa = torch.cat([xa, xa], dim=0)
-            flag = True
-        xl = self.local_att(xa)
-        xg = self.global_att(xa)
-        xlg = xl + xg
-        wei = self.sigmoid(xlg)
-        xo = 2 * x * wei + 2 * residual * (1 - wei)
-        if flag:
-            xo = xo[0].unsqueeze(0)
-        return xo

audioldm/clap/open_clip/htsat.py

@@ -1,1308 +0,0 @@
-# Ke Chen
-# [email protected]
-# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
-# Some layers designed on the model
-# below codes are based and referred from https://github.com/microsoft/Swin-Transformer
-# Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14030.pdf
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from itertools import repeat
-import collections.abc
-import math
-import warnings
-
-from torch.nn.init import _calculate_fan_in_and_fan_out
-import torch.utils.checkpoint as checkpoint
-
-import random
-
-from torchlibrosa.stft import Spectrogram, LogmelFilterBank
-from torchlibrosa.augmentation import SpecAugmentation
-
-from itertools import repeat
-from .utils import do_mixup, interpolate
-
-from .feature_fusion import iAFF, AFF, DAF
-
-# from PyTorch internals
-def _ntuple(n):
-    def parse(x):
-        if isinstance(x, collections.abc.Iterable):
-            return x
-        return tuple(repeat(x, n))
-
-    return parse
-
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
-
-
-def drop_path(x, drop_prob: float = 0.0, training: bool = False):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
-    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
-    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
-    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
-    'survival rate' as the argument.
-    """
-    if drop_prob == 0.0 or not training:
-        return x
-    keep_prob = 1 - drop_prob
-    shape = (x.shape[0],) + (1,) * (
-        x.ndim - 1
-    )  # work with diff dim tensors, not just 2D ConvNets
-    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
-    random_tensor.floor_()  # binarize
-    output = x.div(keep_prob) * random_tensor
-    return output
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""
-
-    def __init__(self, drop_prob=None):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training)
-
-
-class PatchEmbed(nn.Module):
-    """2D Image to Patch Embedding"""
-
-    def __init__(
-        self,
-        img_size=224,
-        patch_size=16,
-        in_chans=3,
-        embed_dim=768,
-        norm_layer=None,
-        flatten=True,
-        patch_stride=16,
-        enable_fusion=False,
-        fusion_type="None",
-    ):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patch_stride = to_2tuple(patch_stride)
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patch_stride = patch_stride
-        self.grid_size = (
-            img_size[0] // patch_stride[0],
-            img_size[1] // patch_stride[1],
-        )
-        self.num_patches = self.grid_size[0] * self.grid_size[1]
-        self.flatten = flatten
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-
-        padding = (
-            (patch_size[0] - patch_stride[0]) // 2,
-            (patch_size[1] - patch_stride[1]) // 2,
-        )
-
-        if (self.enable_fusion) and (self.fusion_type == "channel_map"):
-            self.proj = nn.Conv2d(
-                in_chans * 4,
-                embed_dim,
-                kernel_size=patch_size,
-                stride=patch_stride,
-                padding=padding,
-            )
-        else:
-            self.proj = nn.Conv2d(
-                in_chans,
-                embed_dim,
-                kernel_size=patch_size,
-                stride=patch_stride,
-                padding=padding,
-            )
-        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
-
-        if (self.enable_fusion) and (
-            self.fusion_type in ["daf_2d", "aff_2d", "iaff_2d"]
-        ):
-            self.mel_conv2d = nn.Conv2d(
-                in_chans,
-                embed_dim,
-                kernel_size=(patch_size[0], patch_size[1] * 3),
-                stride=(patch_stride[0], patch_stride[1] * 3),
-                padding=padding,
-            )
-            if self.fusion_type == "daf_2d":
-                self.fusion_model = DAF()
-            elif self.fusion_type == "aff_2d":
-                self.fusion_model = AFF(channels=embed_dim, type="2D")
-            elif self.fusion_type == "iaff_2d":
-                self.fusion_model = iAFF(channels=embed_dim, type="2D")
-
-    def forward(self, x, longer_idx=None):
-        if (self.enable_fusion) and (
-            self.fusion_type in ["daf_2d", "aff_2d", "iaff_2d"]
-        ):
-            global_x = x[:, 0:1, :, :]
-
-            # global processing
-            B, C, H, W = global_x.shape
-            assert (
-                H == self.img_size[0] and W == self.img_size[1]
-            ), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-            global_x = self.proj(global_x)
-            TW = global_x.size(-1)
-            if len(longer_idx) > 0:
-                # local processing
-                local_x = x[longer_idx, 1:, :, :].contiguous()
-                B, C, H, W = local_x.shape
-                local_x = local_x.view(B * C, 1, H, W)
-                local_x = self.mel_conv2d(local_x)
-                local_x = local_x.view(
-                    B, C, local_x.size(1), local_x.size(2), local_x.size(3)
-                )
-                local_x = local_x.permute((0, 2, 3, 1, 4)).contiguous().flatten(3)
-                TB, TC, TH, _ = local_x.size()
-                if local_x.size(-1) < TW:
-                    local_x = torch.cat(
-                        [
-                            local_x,
-                            torch.zeros(
-                                (TB, TC, TH, TW - local_x.size(-1)),
-                                device=global_x.device,
-                            ),
-                        ],
-                        dim=-1,
-                    )
-                else:
-                    local_x = local_x[:, :, :, :TW]
-
-                global_x[longer_idx] = self.fusion_model(global_x[longer_idx], local_x)
-            x = global_x
-        else:
-            B, C, H, W = x.shape
-            assert (
-                H == self.img_size[0] and W == self.img_size[1]
-            ), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-            x = self.proj(x)
-
-        if self.flatten:
-            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
-        x = self.norm(x)
-        return x
-
-
-class Mlp(nn.Module):
-    """MLP as used in Vision Transformer, MLP-Mixer and related networks"""
-
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        act_layer=nn.GELU,
-        drop=0.0,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def _no_grad_trunc_normal_(tensor, mean, std, a, b):
-    # Cut & paste from PyTorch official master until it's in a few official releases - RW
-    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
-    def norm_cdf(x):
-        # Computes standard normal cumulative distribution function
-        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        warnings.warn(
-            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
-            "The distribution of values may be incorrect.",
-            stacklevel=2,
-        )
-
-    with torch.no_grad():
-        # Values are generated by using a truncated uniform distribution and
-        # then using the inverse CDF for the normal distribution.
-        # Get upper and lower cdf values
-        l = norm_cdf((a - mean) / std)
-        u = norm_cdf((b - mean) / std)
-
-        # Uniformly fill tensor with values from [l, u], then translate to
-        # [2l-1, 2u-1].
-        tensor.uniform_(2 * l - 1, 2 * u - 1)
-
-        # Use inverse cdf transform for normal distribution to get truncated
-        # standard normal
-        tensor.erfinv_()
-
-        # Transform to proper mean, std
-        tensor.mul_(std * math.sqrt(2.0))
-        tensor.add_(mean)
-
-        # Clamp to ensure it's in the proper range
-        tensor.clamp_(min=a, max=b)
-        return tensor
-
-
-def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
-    # type: (Tensor, float, float, float, float) -> Tensor
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution. The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
-    Args:
-        tensor: an n-dimensional `torch.Tensor`
-        mean: the mean of the normal distribution
-        std: the standard deviation of the normal distribution
-        a: the minimum cutoff value
-        b: the maximum cutoff value
-    Examples:
-        >>> w = torch.empty(3, 5)
-        >>> nn.init.trunc_normal_(w)
-    """
-    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
-
-
-def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
-    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
-    if mode == "fan_in":
-        denom = fan_in
-    elif mode == "fan_out":
-        denom = fan_out
-    elif mode == "fan_avg":
-        denom = (fan_in + fan_out) / 2
-
-    variance = scale / denom
-
-    if distribution == "truncated_normal":
-        # constant is stddev of standard normal truncated to (-2, 2)
-        trunc_normal_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
-    elif distribution == "normal":
-        tensor.normal_(std=math.sqrt(variance))
-    elif distribution == "uniform":
-        bound = math.sqrt(3 * variance)
-        tensor.uniform_(-bound, bound)
-    else:
-        raise ValueError(f"invalid distribution {distribution}")
-
-
-def lecun_normal_(tensor):
-    variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = (
-        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    )
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(
-        B, H // window_size, W // window_size, window_size, window_size, -1
-    )
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r"""Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(
-        self,
-        dim,
-        window_size,
-        num_heads,
-        qkv_bias=True,
-        qk_scale=None,
-        attn_drop=0.0,
-        proj_drop=0.0,
-    ):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
-        )  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = (
-            coords_flatten[:, :, None] - coords_flatten[:, None, :]
-        )  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(
-            1, 2, 0
-        ).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=0.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = (
-            self.qkv(x)
-            .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
-            .permute(2, 0, 3, 1, 4)
-        )
-        q, k, v = (
-            qkv[0],
-            qkv[1],
-            qkv[2],
-        )  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = q @ k.transpose(-2, -1)
-
-        relative_position_bias = self.relative_position_bias_table[
-            self.relative_position_index.view(-1)
-        ].view(
-            self.window_size[0] * self.window_size[1],
-            self.window_size[0] * self.window_size[1],
-            -1,
-        )  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(
-            2, 0, 1
-        ).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
-                1
-            ).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x, attn
-
-    def extra_repr(self):
-        return f"dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}"
-
-
-# We use the model based on Swintransformer Block, therefore we can use the swin-transformer pretrained model
-class SwinTransformerBlock(nn.Module):
-    r"""Swin Transformer Block.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        num_heads,
-        window_size=7,
-        shift_size=0,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-        norm_before_mlp="ln",
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        self.norm_before_mlp = norm_before_mlp
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert (
-            0 <= self.shift_size < self.window_size
-        ), "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim,
-            window_size=to_2tuple(self.window_size),
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            attn_drop=attn_drop,
-            proj_drop=drop,
-        )
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        if self.norm_before_mlp == "ln":
-            self.norm2 = nn.LayerNorm(dim)
-        elif self.norm_before_mlp == "bn":
-            self.norm2 = lambda x: nn.BatchNorm1d(dim)(x.transpose(1, 2)).transpose(
-                1, 2
-            )
-        else:
-            raise NotImplementedError
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim,
-            hidden_features=mlp_hidden_dim,
-            act_layer=act_layer,
-            drop=drop,
-        )
-
-        if self.shift_size > 0:
-            # calculate attention mask for SW-MSA
-            H, W = self.input_resolution
-            img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-            h_slices = (
-                slice(0, -self.window_size),
-                slice(-self.window_size, -self.shift_size),
-                slice(-self.shift_size, None),
-            )
-            w_slices = (
-                slice(0, -self.window_size),
-                slice(-self.window_size, -self.shift_size),
-                slice(-self.shift_size, None),
-            )
-            cnt = 0
-            for h in h_slices:
-                for w in w_slices:
-                    img_mask[:, h, w, :] = cnt
-                    cnt += 1
-
-            mask_windows = window_partition(
-                img_mask, self.window_size
-            )  # nW, window_size, window_size, 1
-            mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-            attn_mask = attn_mask.masked_fill(
-                attn_mask != 0, float(-100.0)
-            ).masked_fill(attn_mask == 0, float(0.0))
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def forward(self, x):
-        # pdb.set_trace()
-        H, W = self.input_resolution
-        # print("H: ", H)
-        # print("W: ", W)
-        # pdb.set_trace()
-        B, L, C = x.shape
-        # assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(
-                x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
-            )
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(
-            shifted_x, self.window_size
-        )  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(
-            -1, self.window_size * self.window_size, C
-        )  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows, attn = self.attn(
-            x_windows, mask=self.attn_mask
-        )  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(
-                shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
-            )
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x, attn
-
-    def extra_repr(self):
-        return (
-            f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
-            f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-        )
-
-
-class PatchMerging(nn.Module):
-    r"""Patch Merging Layer.
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self):
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-
-class BasicLayer(nn.Module):
-    """A basic Swin Transformer layer for one stage.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        depth,
-        num_heads,
-        window_size,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-        norm_before_mlp="ln",
-    ):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList(
-            [
-                SwinTransformerBlock(
-                    dim=dim,
-                    input_resolution=input_resolution,
-                    num_heads=num_heads,
-                    window_size=window_size,
-                    shift_size=0 if (i % 2 == 0) else window_size // 2,
-                    mlp_ratio=mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    qk_scale=qk_scale,
-                    drop=drop,
-                    attn_drop=attn_drop,
-                    drop_path=drop_path[i]
-                    if isinstance(drop_path, list)
-                    else drop_path,
-                    norm_layer=norm_layer,
-                    norm_before_mlp=norm_before_mlp,
-                )
-                for i in range(depth)
-            ]
-        )
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(
-                input_resolution, dim=dim, norm_layer=norm_layer
-            )
-        else:
-            self.downsample = None
-
-    def forward(self, x):
-        attns = []
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x)
-            else:
-                x, attn = blk(x)
-                if not self.training:
-                    attns.append(attn.unsqueeze(0))
-        if self.downsample is not None:
-            x = self.downsample(x)
-        if not self.training:
-            attn = torch.cat(attns, dim=0)
-            attn = torch.mean(attn, dim=0)
-        return x, attn
-
-    def extra_repr(self):
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-
-# The Core of HTSAT
-class HTSAT_Swin_Transformer(nn.Module):
-    r"""HTSAT based on the Swin Transformer
-    Args:
-        spec_size (int | tuple(int)): Input Spectrogram size. Default 256
-        patch_size (int | tuple(int)): Patch size. Default: 4
-        path_stride (iot | tuple(int)): Patch Stride for Frequency and Time Axis. Default: 4
-        in_chans (int): Number of input image channels. Default: 1 (mono)
-        num_classes (int): Number of classes for classification head. Default: 527
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each HTSAT-Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 8
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        config (module): The configuration Module from config.py
-    """
-
-    def __init__(
-        self,
-        spec_size=256,
-        patch_size=4,
-        patch_stride=(4, 4),
-        in_chans=1,
-        num_classes=527,
-        embed_dim=96,
-        depths=[2, 2, 6, 2],
-        num_heads=[4, 8, 16, 32],
-        window_size=8,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop_rate=0.0,
-        attn_drop_rate=0.0,
-        drop_path_rate=0.1,
-        norm_layer=nn.LayerNorm,
-        ape=False,
-        patch_norm=True,
-        use_checkpoint=False,
-        norm_before_mlp="ln",
-        config=None,
-        enable_fusion=False,
-        fusion_type="None",
-        **kwargs,
-    ):
-        super(HTSAT_Swin_Transformer, self).__init__()
-
-        self.config = config
-        self.spec_size = spec_size
-        self.patch_stride = patch_stride
-        self.patch_size = patch_size
-        self.window_size = window_size
-        self.embed_dim = embed_dim
-        self.depths = depths
-        self.ape = ape
-        self.in_chans = in_chans
-        self.num_classes = num_classes
-        self.num_heads = num_heads
-        self.num_layers = len(self.depths)
-        self.num_features = int(self.embed_dim * 2 ** (self.num_layers - 1))
-
-        self.drop_rate = drop_rate
-        self.attn_drop_rate = attn_drop_rate
-        self.drop_path_rate = drop_path_rate
-
-        self.qkv_bias = qkv_bias
-        self.qk_scale = None
-
-        self.patch_norm = patch_norm
-        self.norm_layer = norm_layer if self.patch_norm else None
-        self.norm_before_mlp = norm_before_mlp
-        self.mlp_ratio = mlp_ratio
-
-        self.use_checkpoint = use_checkpoint
-
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-
-        #  process mel-spec ; used only once
-        self.freq_ratio = self.spec_size // self.config.mel_bins
-        window = "hann"
-        center = True
-        pad_mode = "reflect"
-        ref = 1.0
-        amin = 1e-10
-        top_db = None
-        self.interpolate_ratio = 32  # Downsampled ratio
-        # Spectrogram extractor
-        self.spectrogram_extractor = Spectrogram(
-            n_fft=config.window_size,
-            hop_length=config.hop_size,
-            win_length=config.window_size,
-            window=window,
-            center=center,
-            pad_mode=pad_mode,
-            freeze_parameters=True,
-        )
-        # Logmel feature extractor
-        self.logmel_extractor = LogmelFilterBank(
-            sr=config.sample_rate,
-            n_fft=config.window_size,
-            n_mels=config.mel_bins,
-            fmin=config.fmin,
-            fmax=config.fmax,
-            ref=ref,
-            amin=amin,
-            top_db=top_db,
-            freeze_parameters=True,
-        )
-        # Spec augmenter
-        self.spec_augmenter = SpecAugmentation(
-            time_drop_width=64,
-            time_stripes_num=2,
-            freq_drop_width=8,
-            freq_stripes_num=2,
-        )  # 2 2
-        self.bn0 = nn.BatchNorm2d(self.config.mel_bins)
-
-        # split spctrogram into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=self.spec_size,
-            patch_size=self.patch_size,
-            in_chans=self.in_chans,
-            embed_dim=self.embed_dim,
-            norm_layer=self.norm_layer,
-            patch_stride=patch_stride,
-            enable_fusion=self.enable_fusion,
-            fusion_type=self.fusion_type,
-        )
-
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.grid_size
-        self.patches_resolution = patches_resolution
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(
-                torch.zeros(1, num_patches, self.embed_dim)
-            )
-            trunc_normal_(self.absolute_pos_embed, std=0.02)
-
-        self.pos_drop = nn.Dropout(p=self.drop_rate)
-
-        # stochastic depth
-        dpr = [
-            x.item() for x in torch.linspace(0, self.drop_path_rate, sum(self.depths))
-        ]  # stochastic depth decay rule
-
-        # build layers
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = BasicLayer(
-                dim=int(self.embed_dim * 2**i_layer),
-                input_resolution=(
-                    patches_resolution[0] // (2**i_layer),
-                    patches_resolution[1] // (2**i_layer),
-                ),
-                depth=self.depths[i_layer],
-                num_heads=self.num_heads[i_layer],
-                window_size=self.window_size,
-                mlp_ratio=self.mlp_ratio,
-                qkv_bias=self.qkv_bias,
-                qk_scale=self.qk_scale,
-                drop=self.drop_rate,
-                attn_drop=self.attn_drop_rate,
-                drop_path=dpr[
-                    sum(self.depths[:i_layer]) : sum(self.depths[: i_layer + 1])
-                ],
-                norm_layer=self.norm_layer,
-                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
-                use_checkpoint=use_checkpoint,
-                norm_before_mlp=self.norm_before_mlp,
-            )
-            self.layers.append(layer)
-
-        self.norm = self.norm_layer(self.num_features)
-        self.avgpool = nn.AdaptiveAvgPool1d(1)
-        self.maxpool = nn.AdaptiveMaxPool1d(1)
-
-        SF = (
-            self.spec_size
-            // (2 ** (len(self.depths) - 1))
-            // self.patch_stride[0]
-            // self.freq_ratio
-        )
-        self.tscam_conv = nn.Conv2d(
-            in_channels=self.num_features,
-            out_channels=self.num_classes,
-            kernel_size=(SF, 3),
-            padding=(0, 1),
-        )
-        self.head = nn.Linear(num_classes, num_classes)
-
-        if (self.enable_fusion) and (
-            self.fusion_type in ["daf_1d", "aff_1d", "iaff_1d"]
-        ):
-            self.mel_conv1d = nn.Sequential(
-                nn.Conv1d(64, 64, kernel_size=5, stride=3, padding=2),
-                nn.BatchNorm1d(64),
-            )
-            if self.fusion_type == "daf_1d":
-                self.fusion_model = DAF()
-            elif self.fusion_type == "aff_1d":
-                self.fusion_model = AFF(channels=64, type="1D")
-            elif self.fusion_type == "iaff_1d":
-                self.fusion_model = iAFF(channels=64, type="1D")
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {"absolute_pos_embed"}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {"relative_position_bias_table"}
-
-    def forward_features(self, x, longer_idx=None):
-        # A deprecated optimization for using a hierarchical output from different blocks
-
-        frames_num = x.shape[2]
-        x = self.patch_embed(x, longer_idx=longer_idx)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-        for i, layer in enumerate(self.layers):
-            x, attn = layer(x)
-        # for x
-        x = self.norm(x)
-        B, N, C = x.shape
-        SF = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[0]
-        ST = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[1]
-        x = x.permute(0, 2, 1).contiguous().reshape(B, C, SF, ST)
-        B, C, F, T = x.shape
-        # group 2D CNN
-        c_freq_bin = F // self.freq_ratio
-        x = x.reshape(B, C, F // c_freq_bin, c_freq_bin, T)
-        x = x.permute(0, 1, 3, 2, 4).contiguous().reshape(B, C, c_freq_bin, -1)
-        # get latent_output
-        fine_grained_latent_output = torch.mean(x, dim=2)
-        fine_grained_latent_output = interpolate(
-            fine_grained_latent_output.permute(0, 2, 1).contiguous(),
-            8 * self.patch_stride[1],
-        )
-
-        latent_output = self.avgpool(torch.flatten(x, 2))
-        latent_output = torch.flatten(latent_output, 1)
-
-        # display the attention map, if needed
-
-        x = self.tscam_conv(x)
-        x = torch.flatten(x, 2)  # B, C, T
-
-        fpx = interpolate(
-            torch.sigmoid(x).permute(0, 2, 1).contiguous(), 8 * self.patch_stride[1]
-        )
-
-        x = self.avgpool(x)
-        x = torch.flatten(x, 1)
-
-        output_dict = {
-            "framewise_output": fpx,  # already sigmoided
-            "clipwise_output": torch.sigmoid(x),
-            "fine_grained_embedding": fine_grained_latent_output,
-            "embedding": latent_output,
-        }
-
-        return output_dict
-
-    def crop_wav(self, x, crop_size, spe_pos=None):
-        time_steps = x.shape[2]
-        tx = torch.zeros(x.shape[0], x.shape[1], crop_size, x.shape[3]).to(x.device)
-        for i in range(len(x)):
-            if spe_pos is None:
-                crop_pos = random.randint(0, time_steps - crop_size - 1)
-            else:
-                crop_pos = spe_pos
-            tx[i][0] = x[i, 0, crop_pos : crop_pos + crop_size, :]
-        return tx
-
-    # Reshape the wavform to a img size, if you want to use the pretrained swin transformer model
-    def reshape_wav2img(self, x):
-        B, C, T, F = x.shape
-        target_T = int(self.spec_size * self.freq_ratio)
-        target_F = self.spec_size // self.freq_ratio
-        assert (
-            T <= target_T and F <= target_F
-        ), "the wav size should less than or equal to the swin input size"
-        # to avoid bicubic zero error
-        if T < target_T:
-            x = nn.functional.interpolate(
-                x, (target_T, x.shape[3]), mode="bicubic", align_corners=True
-            )
-        if F < target_F:
-            x = nn.functional.interpolate(
-                x, (x.shape[2], target_F), mode="bicubic", align_corners=True
-            )
-        x = x.permute(0, 1, 3, 2).contiguous()
-        x = x.reshape(
-            x.shape[0],
-            x.shape[1],
-            x.shape[2],
-            self.freq_ratio,
-            x.shape[3] // self.freq_ratio,
-        )
-        # print(x.shape)
-        x = x.permute(0, 1, 3, 2, 4).contiguous()
-        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3], x.shape[4])
-        return x
-
-    # Repeat the wavform to a img size, if you want to use the pretrained swin transformer model
-    def repeat_wat2img(self, x, cur_pos):
-        B, C, T, F = x.shape
-        target_T = int(self.spec_size * self.freq_ratio)
-        target_F = self.spec_size // self.freq_ratio
-        assert (
-            T <= target_T and F <= target_F
-        ), "the wav size should less than or equal to the swin input size"
-        # to avoid bicubic zero error
-        if T < target_T:
-            x = nn.functional.interpolate(
-                x, (target_T, x.shape[3]), mode="bicubic", align_corners=True
-            )
-        if F < target_F:
-            x = nn.functional.interpolate(
-                x, (x.shape[2], target_F), mode="bicubic", align_corners=True
-            )
-        x = x.permute(0, 1, 3, 2).contiguous()  # B C F T
-        x = x[:, :, :, cur_pos : cur_pos + self.spec_size]
-        x = x.repeat(repeats=(1, 1, 4, 1))
-        return x
-
-    def forward(
-        self, x: torch.Tensor, mixup_lambda=None, infer_mode=False, device=None
-    ):  # out_feat_keys: List[str] = None):
-
-        if self.enable_fusion and x["longer"].sum() == 0:
-            # if no audio is longer than 10s, then randomly select one audio to be longer
-            x["longer"][torch.randint(0, x["longer"].shape[0], (1,))] = True
-
-        if not self.enable_fusion:
-            x = x["waveform"].to(device=device, non_blocking=True)
-            x = self.spectrogram_extractor(x)  # (batch_size, 1, time_steps, freq_bins)
-            x = self.logmel_extractor(x)  # (batch_size, 1, time_steps, mel_bins)
-            x = x.transpose(1, 3)
-            x = self.bn0(x)
-            x = x.transpose(1, 3)
-            if self.training:
-                x = self.spec_augmenter(x)
-
-            if self.training and mixup_lambda is not None:
-                x = do_mixup(x, mixup_lambda)
-
-            x = self.reshape_wav2img(x)
-            output_dict = self.forward_features(x)
-        else:
-            longer_list = x["longer"].to(device=device, non_blocking=True)
-            x = x["mel_fusion"].to(device=device, non_blocking=True)
-            x = x.transpose(1, 3)
-            x = self.bn0(x)
-            x = x.transpose(1, 3)
-            longer_list_idx = torch.where(longer_list)[0]
-            if self.fusion_type in ["daf_1d", "aff_1d", "iaff_1d"]:
-                new_x = x[:, 0:1, :, :].clone().contiguous()
-                if len(longer_list_idx) > 0:
-                    # local processing
-                    fusion_x_local = x[longer_list_idx, 1:, :, :].clone().contiguous()
-                    FB, FC, FT, FF = fusion_x_local.size()
-                    fusion_x_local = fusion_x_local.view(FB * FC, FT, FF)
-                    fusion_x_local = torch.permute(
-                        fusion_x_local, (0, 2, 1)
-                    ).contiguous()
-                    fusion_x_local = self.mel_conv1d(fusion_x_local)
-                    fusion_x_local = fusion_x_local.view(
-                        FB, FC, FF, fusion_x_local.size(-1)
-                    )
-                    fusion_x_local = (
-                        torch.permute(fusion_x_local, (0, 2, 1, 3))
-                        .contiguous()
-                        .flatten(2)
-                    )
-                    if fusion_x_local.size(-1) < FT:
-                        fusion_x_local = torch.cat(
-                            [
-                                fusion_x_local,
-                                torch.zeros(
-                                    (FB, FF, FT - fusion_x_local.size(-1)),
-                                    device=device,
-                                ),
-                            ],
-                            dim=-1,
-                        )
-                    else:
-                        fusion_x_local = fusion_x_local[:, :, :FT]
-                    # 1D fusion
-                    new_x = new_x.squeeze(1).permute((0, 2, 1)).contiguous()
-                    new_x[longer_list_idx] = self.fusion_model(
-                        new_x[longer_list_idx], fusion_x_local
-                    )
-                    x = new_x.permute((0, 2, 1)).contiguous()[:, None, :, :]
-                else:
-                    x = new_x
-
-            elif self.fusion_type in ["daf_2d", "aff_2d", "iaff_2d", "channel_map"]:
-                x = x  # no change
-
-            if self.training:
-                x = self.spec_augmenter(x)
-            if self.training and mixup_lambda is not None:
-                x = do_mixup(x, mixup_lambda)
-
-            x = self.reshape_wav2img(x)
-            output_dict = self.forward_features(x, longer_idx=longer_list_idx)
-
-        # if infer_mode:
-        #     # in infer mode. we need to handle different length audio input
-        #     frame_num = x.shape[2]
-        #     target_T = int(self.spec_size * self.freq_ratio)
-        #     repeat_ratio = math.floor(target_T / frame_num)
-        #     x = x.repeat(repeats=(1,1,repeat_ratio,1))
-        #     x = self.reshape_wav2img(x)
-        #     output_dict = self.forward_features(x)
-        # else:
-        #     if x.shape[2] > self.freq_ratio * self.spec_size:
-        #         if self.training:
-        #             x = self.crop_wav(x, crop_size=self.freq_ratio * self.spec_size)
-        #             x = self.reshape_wav2img(x)
-        #             output_dict = self.forward_features(x)
-        #         else:
-        #             # Change: Hard code here
-        #             overlap_size = (x.shape[2] - 1) // 4
-        #             output_dicts = []
-        #             crop_size = (x.shape[2] - 1) // 2
-        #             for cur_pos in range(0, x.shape[2] - crop_size - 1, overlap_size):
-        #                 tx = self.crop_wav(x, crop_size = crop_size, spe_pos = cur_pos)
-        #                 tx = self.reshape_wav2img(tx)
-        #                 output_dicts.append(self.forward_features(tx))
-        #             clipwise_output = torch.zeros_like(output_dicts[0]["clipwise_output"]).float().to(x.device)
-        #             framewise_output = torch.zeros_like(output_dicts[0]["framewise_output"]).float().to(x.device)
-        #             for d in output_dicts:
-        #                 clipwise_output += d["clipwise_output"]
-        #                 framewise_output += d["framewise_output"]
-        #             clipwise_output  = clipwise_output / len(output_dicts)
-        #             framewise_output = framewise_output / len(output_dicts)
-        #             output_dict = {
-        #                 'framewise_output': framewise_output,
-        #                 'clipwise_output': clipwise_output
-        #             }
-        #     else: # this part is typically used, and most easy one
-        #         x = self.reshape_wav2img(x)
-        #         output_dict = self.forward_features(x)
-        # x = self.head(x)
-
-        # We process the data in the dataloader part, in that here we only consider the input_T < fixed_T
-
-        return output_dict
-
-
-def create_htsat_model(audio_cfg, enable_fusion=False, fusion_type="None"):
-    try:
-
-        assert audio_cfg.model_name in [
-            "tiny",
-            "base",
-            "large",
-        ], "model name for HTS-AT is wrong!"
-        if audio_cfg.model_name == "tiny":
-            model = HTSAT_Swin_Transformer(
-                spec_size=256,
-                patch_size=4,
-                patch_stride=(4, 4),
-                num_classes=audio_cfg.class_num,
-                embed_dim=96,
-                depths=[2, 2, 6, 2],
-                num_heads=[4, 8, 16, 32],
-                window_size=8,
-                config=audio_cfg,
-                enable_fusion=enable_fusion,
-                fusion_type=fusion_type,
-            )
-        elif audio_cfg.model_name == "base":
-            model = HTSAT_Swin_Transformer(
-                spec_size=256,
-                patch_size=4,
-                patch_stride=(4, 4),
-                num_classes=audio_cfg.class_num,
-                embed_dim=128,
-                depths=[2, 2, 12, 2],
-                num_heads=[4, 8, 16, 32],
-                window_size=8,
-                config=audio_cfg,
-                enable_fusion=enable_fusion,
-                fusion_type=fusion_type,
-            )
-        elif audio_cfg.model_name == "large":
-            model = HTSAT_Swin_Transformer(
-                spec_size=256,
-                patch_size=4,
-                patch_stride=(4, 4),
-                num_classes=audio_cfg.class_num,
-                embed_dim=256,
-                depths=[2, 2, 12, 2],
-                num_heads=[4, 8, 16, 32],
-                window_size=8,
-                config=audio_cfg,
-                enable_fusion=enable_fusion,
-                fusion_type=fusion_type,
-            )
-
-        return model
-    except:
-        raise RuntimeError(
-            f"Import Model for {audio_cfg.model_name} not found, or the audio cfg parameters are not enough."
-        )

audioldm/clap/open_clip/linear_probe.py

@@ -1,66 +0,0 @@
-import numpy as np
-import torch.nn.functional as F
-from torch import nn
-from .model import MLPLayers
-
-
-class LinearProbe(nn.Module):
-    def __init__(self, model, mlp, freeze, in_ch, out_ch, act=None):
-        """
-        Args:
-            model: nn.Module
-            mlp: bool, if True, then use the MLP layer as the linear probe module
-            freeze: bool, if Ture, then freeze all the CLAP model's layers when training the linear probe
-            in_ch: int, the output channel from CLAP model
-            out_ch: int, the output channel from linear probe (class_num)
-            act: torch.nn.functional, the activation function before the loss function
-        """
-        super().__init__()
-        in_ch = 512
-        self.clap_model = model
-        self.clap_model.text_branch = None  # to save memory
-        self.freeze = freeze
-        if mlp:
-            self.lp_layer = MLPLayers(units=[in_ch, in_ch * 2, out_ch])
-        else:
-            self.lp_layer = nn.Linear(in_ch, out_ch)
-
-        if self.freeze:
-            for param in self.clap_model.parameters():
-                param.requires_grad = False
-
-        if act == "None":
-            self.act = None
-        elif act == "relu":
-            self.act = nn.ReLU()
-        elif act == "elu":
-            self.act = nn.ELU()
-        elif act == "prelu":
-            self.act = nn.PReLU(num_parameters=in_ch)
-        elif act == "softmax":
-            self.act = nn.Softmax(dim=-1)
-        elif act == "sigmoid":
-            self.act = nn.Sigmoid()
-
-    def forward(self, x, mix_lambda=None, device=None):
-        """
-        Args:
-            x: waveform, torch.tensor [batch, t_samples] / batch of mel_spec and longer list
-            mix_lambda: torch.tensor [batch], the mixup lambda
-        Returns:
-            class_prob: torch.tensor [batch, class_num]
-
-        """
-        # batchnorm cancel grandient
-        if self.freeze:
-            self.clap_model.eval()
-
-        x = self.clap_model.audio_projection(
-            self.clap_model.audio_branch(x, mixup_lambda=mix_lambda, device=device)[
-                "embedding"
-            ]
-        )
-        out = self.lp_layer(x)
-        if self.act is not None:
-            out = self.act(out)
-        return out

audioldm/clap/open_clip/loss.py

@@ -1,398 +0,0 @@
-from multiprocessing.sharedctypes import Value
-import torch
-import torch.distributed.nn
-from torch import distributed as dist, nn as nn
-from torch.nn import functional as F
-import numpy as np
-from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score
-
-try:
-    import horovod.torch as hvd
-except ImportError:
-    hvd = None
-
-
-def gather_features(
-    audio_features,
-    text_features,
-    audio_features_mlp=None,
-    text_features_mlp=None,
-    local_loss=False,
-    gather_with_grad=False,
-    rank=0,
-    world_size=1,
-    use_horovod=False,
-    mlp_loss=False,
-):
-    if use_horovod:
-        assert hvd is not None, "Please install horovod"
-        if gather_with_grad:
-            all_audio_features = hvd.allgather(audio_features)
-            all_text_features = hvd.allgather(text_features)
-            if mlp_loss:
-                all_audio_features_mlp = hvd.allgather(audio_features_mlp)
-                all_text_features_mlp = hvd.allgather(text_features_mlp)
-        else:
-            with torch.no_grad():
-                all_audio_features = hvd.allgather(audio_features)
-                all_text_features = hvd.allgather(text_features)
-                if mlp_loss:
-                    all_audio_features_mlp = hvd.allgather(audio_features_mlp)
-                    all_text_features_mlp = hvd.allgather(text_features_mlp)
-            if not local_loss:
-                # ensure grads for local rank when all_* features don't have a gradient
-                gathered_audio_features = list(
-                    all_audio_features.chunk(world_size, dim=0)
-                )
-                gathered_text_features = list(
-                    all_text_features.chunk(world_size, dim=0)
-                )
-                gathered_audio_features[rank] = audio_features
-                gathered_text_features[rank] = text_features
-                all_audio_features = torch.cat(gathered_audio_features, dim=0)
-                all_text_features = torch.cat(gathered_text_features, dim=0)
-                if mlp_loss:
-                    gathered_audio_features_mlp = list(
-                        all_audio_features_mlp.chunk(world_size, dim=0)
-                    )
-                    gathered_text_features_mlp = list(
-                        all_text_features_mlp.chunk(world_size, dim=0)
-                    )
-                    gathered_audio_features_mlp[rank] = audio_features_mlp
-                    gathered_text_features_mlp[rank] = text_features_mlp
-                    all_audio_features_mlp = torch.cat(
-                        gathered_audio_features_mlp, dim=0
-                    )
-                    all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0)
-    else:
-        # We gather tensors from all gpus
-        if gather_with_grad:
-            all_audio_features = torch.cat(
-                torch.distributed.nn.all_gather(audio_features), dim=0
-            )
-            all_text_features = torch.cat(
-                torch.distributed.nn.all_gather(text_features), dim=0
-            )
-            if mlp_loss:
-                all_audio_features_mlp = torch.cat(
-                    torch.distributed.nn.all_gather(audio_features_mlp), dim=0
-                )
-                all_text_features_mlp = torch.cat(
-                    torch.distributed.nn.all_gather(text_features_mlp), dim=0
-                )
-        else:
-            gathered_audio_features = [
-                torch.zeros_like(audio_features) for _ in range(world_size)
-            ]
-            gathered_text_features = [
-                torch.zeros_like(text_features) for _ in range(world_size)
-            ]
-            dist.all_gather(gathered_audio_features, audio_features)
-            dist.all_gather(gathered_text_features, text_features)
-            if mlp_loss:
-                gathered_audio_features_mlp = [
-                    torch.zeros_like(audio_features_mlp) for _ in range(world_size)
-                ]
-                gathered_text_features_mlp = [
-                    torch.zeros_like(text_features_mlp) for _ in range(world_size)
-                ]
-                dist.all_gather(gathered_audio_features_mlp, audio_features_mlp)
-                dist.all_gather(gathered_text_features_mlp, text_features_mlp)
-            if not local_loss:
-                # ensure grads for local rank when all_* features don't have a gradient
-                gathered_audio_features[rank] = audio_features
-                gathered_text_features[rank] = text_features
-                if mlp_loss:
-                    gathered_audio_features_mlp[rank] = audio_features_mlp
-                    gathered_text_features_mlp[rank] = text_features_mlp
-
-            all_audio_features = torch.cat(gathered_audio_features, dim=0)
-            all_text_features = torch.cat(gathered_text_features, dim=0)
-            if mlp_loss:
-                all_audio_features_mlp = torch.cat(gathered_audio_features_mlp, dim=0)
-                all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0)
-    if mlp_loss:
-        return (
-            all_audio_features,
-            all_text_features,
-            all_audio_features_mlp,
-            all_text_features_mlp,
-        )
-    else:
-        return all_audio_features, all_text_features
-
-
-class ClipLoss(nn.Module):
-    def __init__(
-        self,
-        local_loss=False,
-        gather_with_grad=False,
-        cache_labels=False,
-        rank=0,
-        world_size=1,
-        use_horovod=False,
-        mlp_loss=False,
-        weight_loss_kappa=0,
-    ):
-        super().__init__()
-        self.local_loss = local_loss
-        self.gather_with_grad = gather_with_grad
-        self.cache_labels = cache_labels
-        self.rank = rank
-        self.world_size = world_size
-        self.use_horovod = use_horovod
-        self.mlp_loss = mlp_loss
-        self.weighted_loss = bool(weight_loss_kappa != 0)
-        self.weight_loss_kappa = weight_loss_kappa
-        # cache state
-        self.prev_num_logits = 0
-        self.labels = {}
-
-    def forward(
-        self,
-        audio_features,
-        text_features,
-        logit_scale_a,
-        logit_scale_t=None,
-        audio_features_mlp=None,
-        text_features_mlp=None,
-    ):
-        device = audio_features.device
-        if self.mlp_loss:
-            if self.world_size > 1:
-                (
-                    all_audio_features,
-                    all_text_features,
-                    all_audio_features_mlp,
-                    all_text_features_mlp,
-                ) = gather_features(
-                    audio_features=audio_features,
-                    text_features=text_features,
-                    audio_features_mlp=audio_features_mlp,
-                    text_features_mlp=text_features_mlp,
-                    local_loss=self.local_loss,
-                    gather_with_grad=self.gather_with_grad,
-                    rank=self.rank,
-                    world_size=self.world_size,
-                    use_horovod=self.use_horovod,
-                    mlp_loss=self.mlp_loss,
-                )
-                if self.local_loss:
-                    a_logits_per_audio = (
-                        logit_scale_a * audio_features @ all_text_features_mlp.T
-                    )
-                    a_logits_per_text = (
-                        logit_scale_a * text_features_mlp @ all_audio_features.T
-                    )
-                    t_logits_per_audio = (
-                        logit_scale_t * audio_features_mlp @ all_text_features.T
-                    )
-                    t_logits_per_text = (
-                        logit_scale_t * text_features @ all_audio_features_mlp.T
-                    )
-                else:
-                    a_logits_per_audio = (
-                        logit_scale_a * all_audio_features @ all_text_features_mlp.T
-                    )
-                    a_logits_per_text = a_logits_per_audio.T
-                    t_logits_per_audio = (
-                        logit_scale_t * all_audio_features_mlp @ all_text_features.T
-                    )
-                    t_logits_per_text = t_logits_per_audio.T
-            else:
-                a_logits_per_audio = (
-                    logit_scale_a * audio_features @ text_features_mlp.T
-                )
-                a_logits_per_text = logit_scale_a * text_features_mlp @ audio_features.T
-                t_logits_per_audio = (
-                    logit_scale_t * audio_features_mlp @ text_features.T
-                )
-                t_logits_per_text = logit_scale_t * text_features @ audio_features_mlp.T
-
-            # calculated ground-truth and cache if enabled
-            num_logits = a_logits_per_audio.shape[0]
-            if self.prev_num_logits != num_logits or device not in self.labels:
-                labels = torch.arange(num_logits, device=device, dtype=torch.long)
-                if self.world_size > 1 and self.local_loss:
-                    labels = labels + num_logits * self.rank
-                if self.cache_labels:
-                    self.labels[device] = labels
-                    self.prev_num_logits = num_logits
-            else:
-                labels = self.labels[device]
-
-            if not self.weighted_loss:
-                total_loss = (
-                    F.cross_entropy(a_logits_per_audio, labels)
-                    + F.cross_entropy(a_logits_per_text, labels)
-                    + F.cross_entropy(t_logits_per_audio, labels)
-                    + F.cross_entropy(t_logits_per_text, labels)
-                ) / 4
-            else:
-                audio_weight = (audio_features @ audio_features.T).detach()
-                audio_weight = (
-                    torch.exp(
-                        torch.sum(audio_weight, axis=1)
-                        / (self.weight_loss_kappa * len(audio_weight))
-                    )
-                ).detach()
-                text_weight = (text_features @ text_features.T).detach()
-                text_weight = (
-                    torch.exp(
-                        torch.sum(text_weight, axis=1)
-                        / (self.weight_loss_kappa * len(text_features))
-                    )
-                ).detach()
-                total_loss = (
-                    F.cross_entropy(a_logits_per_audio, labels, weight=audio_weight)
-                    + F.cross_entropy(a_logits_per_text, labels, weight=audio_weight)
-                    + F.cross_entropy(t_logits_per_audio, labels, weight=text_weight)
-                    + F.cross_entropy(t_logits_per_text, labels, weight=text_weight)
-                ) / 4
-        else:
-            if self.world_size > 1:
-                all_audio_features, all_text_features = gather_features(
-                    audio_features=audio_features,
-                    text_features=text_features,
-                    local_loss=self.local_loss,
-                    gather_with_grad=self.gather_with_grad,
-                    rank=self.rank,
-                    world_size=self.world_size,
-                    use_horovod=self.use_horovod,
-                    mlp_loss=self.mlp_loss,
-                )
-
-                if self.local_loss:
-                    logits_per_audio = (
-                        logit_scale_a * audio_features @ all_text_features.T
-                    )
-                    logits_per_text = (
-                        logit_scale_a * text_features @ all_audio_features.T
-                    )
-                else:
-                    logits_per_audio = (
-                        logit_scale_a * all_audio_features @ all_text_features.T
-                    )
-                    logits_per_text = logits_per_audio.T
-            else:
-                logits_per_audio = logit_scale_a * audio_features @ text_features.T
-                logits_per_text = logit_scale_a * text_features @ audio_features.T
-
-            # calculated ground-truth and cache if enabled
-            num_logits = logits_per_audio.shape[0]
-            if self.prev_num_logits != num_logits or device not in self.labels:
-                labels = torch.arange(num_logits, device=device, dtype=torch.long)
-                if self.world_size > 1 and self.local_loss:
-                    labels = labels + num_logits * self.rank
-                if self.cache_labels:
-                    self.labels[device] = labels
-                    self.prev_num_logits = num_logits
-            else:
-                labels = self.labels[device]
-            if not self.weighted_loss:
-                total_loss = (
-                    F.cross_entropy(logits_per_audio, labels)
-                    + F.cross_entropy(logits_per_text, labels)
-                ) / 2
-            else:
-                audio_weight = (all_audio_features @ all_audio_features.T).detach()
-                audio_weight = (
-                    torch.exp(
-                        torch.sum(audio_weight, axis=1)
-                        / (self.weight_loss_kappa * len(all_audio_features))
-                    )
-                ).detach()
-                text_weight = (all_text_features @ all_text_features.T).detach()
-                text_weight = (
-                    torch.exp(
-                        torch.sum(text_weight, axis=1)
-                        / (self.weight_loss_kappa * len(all_text_features))
-                    )
-                ).detach()
-                total_loss = (
-                    F.cross_entropy(logits_per_audio, labels, weight=text_weight)
-                    + F.cross_entropy(logits_per_text, labels, weight=audio_weight)
-                ) / 2
-        return total_loss
-
-
-def lp_gather_features(pred, target, world_size=1, use_horovod=False):
-    if use_horovod:
-        assert hvd is not None, "Please install horovod"
-        with torch.no_grad():
-            all_preds = hvd.allgather(pred)
-            all_targets = hvd.allgath(target)
-    else:
-        gathered_preds = [torch.zeros_like(pred) for _ in range(world_size)]
-        gathered_targets = [torch.zeros_like(target) for _ in range(world_size)]
-
-        dist.all_gather(gathered_preds, pred)
-        dist.all_gather(gathered_targets, target)
-        all_preds = torch.cat(gathered_preds, dim=0)
-        all_targets = torch.cat(gathered_targets, dim=0)
-
-    return all_preds, all_targets
-
-
-def get_map(pred, target):
-    pred = torch.sigmoid(pred).numpy()
-    target = target.numpy()
-    return np.mean(average_precision_score(target, pred, average=None))
-
-
-def get_acc(pred, target):
-    pred = torch.argmax(pred, 1).numpy()
-    target = torch.argmax(target, 1).numpy()
-    return accuracy_score(target, pred)
-
-
-def get_mauc(pred, target):
-    pred = torch.sigmoid(pred).numpy()
-    target = target.numpy()
-    return np.mean(roc_auc_score(target, pred, average=None))
-
-
-class LPMetrics(object):
-    def __init__(self, metric_names=["map", "acc", "mauc"]):
-        self.metrics = []
-        for name in metric_names:
-            self.metrics.append(self.get_metric(name))
-        self.metric_names = metric_names
-
-    def get_metric(self, name):
-        if name == "map":
-            return get_map
-        elif name == "acc":
-            return get_acc
-        elif name == "mauc":
-            return get_mauc
-        else:
-            raise ValueError(f"the metric should be at least one of [map, acc, mauc]")
-
-    def evaluate_mertics(self, pred, target):
-        metric_dict = {}
-        for i in range(len(self.metric_names)):
-            metric_dict[self.metric_names[i]] = self.metrics[i](pred, target)
-        return metric_dict
-
-
-def calc_celoss(pred, target):
-    target = torch.argmax(target, 1).long()
-    return nn.CrossEntropyLoss()(pred, target)
-
-
-class LPLoss(nn.Module):
-    def __init__(self, loss_name):
-        super().__init__()
-        if loss_name == "bce":
-            self.loss_func = nn.BCEWithLogitsLoss()
-        elif loss_name == "ce":
-            self.loss_func = calc_celoss
-        elif loss_name == "mse":
-            self.loss_func = nn.MSELoss()
-        else:
-            raise ValueError(f"the loss func should be at least one of [bce, ce, mse]")
-
-    def forward(self, pred, target):
-        loss = self.loss_func(pred, target)
-        return loss

audioldm/clap/open_clip/model.py

@@ -1,936 +0,0 @@
-""" CLAP Model
-
-Adapted from CLIP: https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
-Adapted to the Audio Task.
-"""
-
-from collections import OrderedDict
-from dataclasses import dataclass
-from email.mime import audio
-from typing import Tuple, Union, Callable, Optional
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from .timm_model import TimmModel
-import logging
-from .utils import freeze_batch_norm_2d
-
-from .pann_model import create_pann_model
-from .htsat import create_htsat_model
-from transformers import BertModel, RobertaModel, BartModel
-from transformers.tokenization_utils_base import BatchEncoding
-
-
-class MLPLayers(nn.Module):
-    def __init__(self, units=[512, 512, 512], nonlin=nn.ReLU(), dropout=0.1):
-        super(MLPLayers, self).__init__()
-        self.nonlin = nonlin
-        self.dropout = dropout
-
-        sequence = []
-        for u0, u1 in zip(units[:-1], units[1:]):
-            sequence.append(nn.Linear(u0, u1))
-            sequence.append(self.nonlin)
-            sequence.append(nn.Dropout(self.dropout))
-        sequence = sequence[:-2]
-
-        self.sequential = nn.Sequential(*sequence)
-
-    def forward(self, X):
-        X = self.sequential(X)
-        return X
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1):
-        super().__init__()
-
-        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
-        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-
-        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-
-        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = None
-        self.stride = stride
-
-        if stride > 1 or inplanes != planes * Bottleneck.expansion:
-            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
-            self.downsample = nn.Sequential(
-                OrderedDict(
-                    [
-                        ("-1", nn.AvgPool2d(stride)),
-                        (
-                            "0",
-                            nn.Conv2d(
-                                inplanes,
-                                planes * self.expansion,
-                                1,
-                                stride=1,
-                                bias=False,
-                            ),
-                        ),
-                        ("1", nn.BatchNorm2d(planes * self.expansion)),
-                    ]
-                )
-            )
-
-    def forward(self, x: torch.Tensor):
-        identity = x
-
-        out = self.relu(self.bn1(self.conv1(x)))
-        out = self.relu(self.bn2(self.conv2(out)))
-        out = self.avgpool(out)
-        out = self.bn3(self.conv3(out))
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-        return out
-
-
-class AttentionPool2d(nn.Module):
-    def __init__(
-        self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None
-    ):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(
-            torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5
-        )
-        self.k_proj = nn.Linear(embed_dim, embed_dim)
-        self.q_proj = nn.Linear(embed_dim, embed_dim)
-        self.v_proj = nn.Linear(embed_dim, embed_dim)
-        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
-        self.num_heads = num_heads
-
-    def forward(self, x):
-        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(
-            2, 0, 1
-        )  # NCHW -> (HW)NC
-        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
-        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
-        x, _ = F.multi_head_attention_forward(
-            query=x,
-            key=x,
-            value=x,
-            embed_dim_to_check=x.shape[-1],
-            num_heads=self.num_heads,
-            q_proj_weight=self.q_proj.weight,
-            k_proj_weight=self.k_proj.weight,
-            v_proj_weight=self.v_proj.weight,
-            in_proj_weight=None,
-            in_proj_bias=torch.cat(
-                [self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]
-            ),
-            bias_k=None,
-            bias_v=None,
-            add_zero_attn=False,
-            dropout_p=0,
-            out_proj_weight=self.c_proj.weight,
-            out_proj_bias=self.c_proj.bias,
-            use_separate_proj_weight=True,
-            training=self.training,
-            need_weights=False,
-        )
-
-        return x[0]
-
-
-class ModifiedResNet(nn.Module):
-    """
-    A ResNet class that is similar to torchvision's but contains the following changes:
-    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
-    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
-    - The final pooling layer is a QKV attention instead of an average pool
-    """
-
-    def __init__(self, layers, output_dim, heads, image_size=224, width=64):
-        super().__init__()
-        self.output_dim = output_dim
-        self.image_size = image_size
-
-        # the 3-layer stem
-        self.conv1 = nn.Conv2d(
-            3, width // 2, kernel_size=3, stride=2, padding=1, bias=False
-        )
-        self.bn1 = nn.BatchNorm2d(width // 2)
-        self.conv2 = nn.Conv2d(
-            width // 2, width // 2, kernel_size=3, padding=1, bias=False
-        )
-        self.bn2 = nn.BatchNorm2d(width // 2)
-        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(width)
-        self.avgpool = nn.AvgPool2d(2)
-        self.relu = nn.ReLU(inplace=True)
-
-        # residual layers
-        self._inplanes = width  # this is a *mutable* variable used during construction
-        self.layer1 = self._make_layer(width, layers[0])
-        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
-        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
-        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-
-        embed_dim = width * 32  # the ResNet feature dimension
-        self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)
-
-        self.init_parameters()
-
-    def _make_layer(self, planes, blocks, stride=1):
-        layers = [Bottleneck(self._inplanes, planes, stride)]
-
-        self._inplanes = planes * Bottleneck.expansion
-        for _ in range(1, blocks):
-            layers.append(Bottleneck(self._inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def init_parameters(self):
-        if self.attnpool is not None:
-            std = self.attnpool.c_proj.in_features**-0.5
-            nn.init.normal_(self.attnpool.q_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.k_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.v_proj.weight, std=std)
-            nn.init.normal_(self.attnpool.c_proj.weight, std=std)
-
-        for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:
-            for name, param in resnet_block.named_parameters():
-                if name.endswith("bn3.weight"):
-                    nn.init.zeros_(param)
-
-    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
-        assert (
-            unlocked_groups == 0
-        ), "partial locking not currently supported for this model"
-        for param in self.parameters():
-            param.requires_grad = False
-        if freeze_bn_stats:
-            freeze_batch_norm_2d(self)
-
-    def stem(self, x):
-        for conv, bn in [
-            (self.conv1, self.bn1),
-            (self.conv2, self.bn2),
-            (self.conv3, self.bn3),
-        ]:
-            x = self.relu(bn(conv(x)))
-        x = self.avgpool(x)
-        return x
-
-    def forward(self, x):
-        x = self.stem(x)
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.attnpool(x)
-
-        return x
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
-        return x.to(orig_type)
-
-
-class QuickGELU(nn.Module):
-    # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(self, d_model: int, n_head: int, act_layer: Callable = nn.GELU):
-        super().__init__()
-
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(
-            OrderedDict(
-                [
-                    ("c_fc", nn.Linear(d_model, d_model * 4)),
-                    ("gelu", act_layer()),
-                    ("c_proj", nn.Linear(d_model * 4, d_model)),
-                ]
-            )
-        )
-        self.ln_2 = LayerNorm(d_model)
-
-    def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0]
-
-    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        x = x + self.attention(self.ln_1(x), attn_mask=attn_mask)
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(
-        self, width: int, layers: int, heads: int, act_layer: Callable = nn.GELU
-    ):
-        super().__init__()
-        self.width = width
-        self.layers = layers
-        self.resblocks = nn.ModuleList(
-            [
-                ResidualAttentionBlock(width, heads, act_layer=act_layer)
-                for _ in range(layers)
-            ]
-        )
-
-    def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
-        for r in self.resblocks:
-            x = r(x, attn_mask=attn_mask)
-        return x
-
-
-class VisualTransformer(nn.Module):
-    def __init__(
-        self,
-        image_size: int,
-        patch_size: int,
-        width: int,
-        layers: int,
-        heads: int,
-        output_dim: int,
-        act_layer: Callable = nn.GELU,
-    ):
-        super().__init__()
-        self.image_size = image_size
-        self.output_dim = output_dim
-        self.conv1 = nn.Conv2d(
-            in_channels=3,
-            out_channels=width,
-            kernel_size=patch_size,
-            stride=patch_size,
-            bias=False,
-        )
-
-        scale = width**-0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(
-            scale * torch.randn((image_size // patch_size) ** 2 + 1, width)
-        )
-        self.ln_pre = LayerNorm(width)
-
-        self.text_branch = Transformer(width, layers, heads, act_layer=act_layer)
-
-        self.ln_post = LayerNorm(width)
-        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
-    def lock(self, unlocked_groups=0, freeze_bn_stats=False):
-        assert (
-            unlocked_groups == 0
-        ), "partial locking not currently supported for this model"
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, x: torch.Tensor):
-        x = self.conv1(x)  # shape = [*, width, grid, grid]
-        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-        x = torch.cat(
-            [
-                self.class_embedding.to(x.dtype)
-                + torch.zeros(
-                    x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device
-                ),
-                x,
-            ],
-            dim=1,
-        )  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.text_branch(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        x = self.ln_post(x[:, 0, :])
-
-        if self.proj is not None:
-            x = x @ self.proj
-
-        return x
-
-
-@dataclass
-class CLAPVisionCfg:
-    layers: Union[Tuple[int, int, int, int], int] = 12
-    width: int = 768
-    patch_size: int = 16
-    image_size: Union[Tuple[int, int], int] = 224
-    timm_model_name: str = (
-        None  # a valid model name overrides layers, width, patch_size
-    )
-    timm_model_pretrained: bool = (
-        False  # use (imagenet) pretrained weights for named model
-    )
-    timm_pool: str = (
-        "avg"  # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
-    )
-    timm_proj: str = (
-        "linear"  # linear projection for timm model output ('linear', 'mlp', '')
-    )
-
-
-# Audio Config Class
-@dataclass
-class CLAPAudioCfp:
-    model_type: str = "PANN"
-    model_name: str = "Cnn14"
-    sample_rate: int = 48000
-    # Param
-    audio_length: int = 1024
-    window_size: int = 1024
-    hop_size: int = 1024
-    fmin: int = 50
-    fmax: int = 14000
-    class_num: int = 527
-    mel_bins: int = 64
-    clip_samples: int = 480000
-
-
-@dataclass
-class CLAPTextCfg:
-    context_length: int
-    vocab_size: int
-    width: int
-    heads: int
-    layers: int
-    model_type: str
-
-
-class CLAP(nn.Module):
-    def __init__(
-        self,
-        embed_dim: int,
-        audio_cfg: CLAPAudioCfp,
-        text_cfg: CLAPTextCfg,
-        quick_gelu: bool = False,
-        enable_fusion: bool = False,
-        fusion_type: str = "None",
-        joint_embed_shape: int = 512,
-        mlp_act: str = "relu",
-    ):
-        super().__init__()
-        if isinstance(audio_cfg, dict):
-            audio_cfg = CLAPAudioCfp(**audio_cfg)
-        if isinstance(text_cfg, dict):
-            text_cfg = CLAPTextCfg(**text_cfg)
-
-        self.audio_cfg = audio_cfg
-        self.text_cfg = text_cfg
-        self.enable_fusion = enable_fusion
-        self.fusion_type = fusion_type
-        self.joint_embed_shape = joint_embed_shape
-        self.mlp_act = mlp_act
-
-        self.context_length = text_cfg.context_length
-
-        # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more
-        # memory efficient in recent PyTorch releases (>= 1.10).
-        # NOTE: timm models always use native GELU regardless of quick_gelu flag.
-        act_layer = QuickGELU if quick_gelu else nn.GELU
-
-        if mlp_act == "relu":
-            mlp_act_layer = nn.ReLU()
-        elif mlp_act == "gelu":
-            mlp_act_layer = nn.GELU()
-        else:
-            raise NotImplementedError
-
-        # audio branch
-        # audio branch parameters
-        if audio_cfg.model_type == "PANN":
-            self.audio_branch = create_pann_model(audio_cfg, enable_fusion, fusion_type)
-        elif audio_cfg.model_type == "HTSAT":
-            self.audio_branch = create_htsat_model(
-                audio_cfg, enable_fusion, fusion_type
-            )
-        else:
-            logging.error(f"Model config for {audio_cfg.model_type} not found")
-            raise RuntimeError(f"Model config for {audio_cfg.model_type} not found.")
-
-        # text branch
-        # text branch parameters
-        if text_cfg.model_type == "transformer":
-            self.text_branch = Transformer(
-                width=text_cfg.width,
-                layers=text_cfg.layers,
-                heads=text_cfg.heads,
-                act_layer=act_layer,
-            )
-            self.vocab_size = text_cfg.vocab_size
-            self.token_embedding = nn.Embedding(text_cfg.vocab_size, text_cfg.width)
-            self.positional_embedding = nn.Parameter(
-                torch.empty(self.context_length, text_cfg.width)
-            )
-            self.ln_final = LayerNorm(text_cfg.width)
-            self.text_transform = MLPLayers(
-                units=[
-                    self.joint_embed_shape,
-                    self.joint_embed_shape,
-                    self.joint_embed_shape,
-                ],
-                dropout=0.1,
-            )
-            self.text_projection = nn.Sequential(
-                nn.Linear(text_cfg.width, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
-            )
-        elif text_cfg.model_type == "bert":
-            self.text_branch = BertModel.from_pretrained("bert-base-uncased")
-            self.text_transform = MLPLayers(
-                units=[
-                    self.joint_embed_shape,
-                    self.joint_embed_shape,
-                    self.joint_embed_shape,
-                ],
-                dropout=0.1,
-            )
-            self.text_projection = nn.Sequential(
-                nn.Linear(768, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
-            )
-        elif text_cfg.model_type == "roberta":
-            self.text_branch = RobertaModel.from_pretrained("roberta-base")
-            self.text_transform = MLPLayers(
-                units=[
-                    self.joint_embed_shape,
-                    self.joint_embed_shape,
-                    self.joint_embed_shape,
-                ],
-                dropout=0.1,
-            )
-            self.text_projection = nn.Sequential(
-                nn.Linear(768, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
-            )
-        elif text_cfg.model_type == "bart":
-            self.text_branch = BartModel.from_pretrained("facebook/bart-base")
-            self.text_transform = MLPLayers(
-                units=[
-                    self.joint_embed_shape,
-                    self.joint_embed_shape,
-                    self.joint_embed_shape,
-                ],
-                dropout=0.1,
-            )
-            self.text_projection = nn.Sequential(
-                nn.Linear(768, self.joint_embed_shape),
-                mlp_act_layer,
-                nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
-            )
-        else:
-            logging.error(f"Model config for {text_cfg.model_type} not found")
-            raise RuntimeError(f"Model config for {text_cfg.model_type} not found.")
-        self.text_branch_type = text_cfg.model_type
-        # text branch parameters
-
-        # audio branch parameters
-        self.audio_transform = MLPLayers(
-            units=[
-                self.joint_embed_shape,
-                self.joint_embed_shape,
-                self.joint_embed_shape,
-            ],
-            dropout=0.1,
-        )
-
-        # below here is text branch parameters
-
-        # ============================================================================================================
-        self.audio_projection = nn.Sequential(
-            nn.Linear(embed_dim, self.joint_embed_shape),
-            mlp_act_layer,
-            nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
-        )
-
-        self.logit_scale_a = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-        self.logit_scale_t = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-        self.register_buffer("attn_mask", self.build_attention_mask(), persistent=False)
-
-        self.init_text_branch_parameters()
-
-    def init_text_branch_parameters(self):
-        if self.text_branch_type == "transformer":
-            nn.init.normal_(self.token_embedding.weight, std=0.02)
-            nn.init.normal_(self.positional_embedding, std=0.01)
-            proj_std = (self.text_branch.width**-0.5) * (
-                (2 * self.text_branch.layers) ** -0.5
-            )
-            attn_std = self.text_branch.width**-0.5
-            fc_std = (2 * self.text_branch.width) ** -0.5
-            for block in self.text_branch.resblocks:
-                nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-                nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-                nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-                nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-        if self.text_branch_type == "bert" or self.text_branch_type == "roberta":
-            width = self.text_branch.embeddings.word_embeddings.weight.shape[-1]
-        elif self.text_branch_type == "bart":
-            width = self.text_branch.shared.weight.shape[-1]
-        else:
-            width = self.text_branch.width
-        nn.init.constant_(self.logit_scale_a, np.log(1 / 0.07))
-        nn.init.constant_(self.logit_scale_t, np.log(1 / 0.07))
-
-        # deprecated
-        # if hasattr(self.visual, 'init_parameters'):
-        # self.visual.init_parameters()
-
-        # if self.text_projection is not None:
-        #     nn.init.normal_(self.text_projection, std=width**-0.5)
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    def encode_audio(self, audio, device):
-        return self.audio_branch(
-            audio, mixup_lambda=None, device=device
-        )  # mix lambda needs to add
-
-    # def list_of_dict_of_tensor2dict_of_tensor(self, x, device):
-    #     tmp = {}
-    #     for k in x[0].keys():
-    #         tmp[k] = []
-    #         for i in range(len(x)):
-    #             tmp[k].append(x[i][k][:77])
-    #     for k in x[0].keys():
-    #         tmp[k] = torch.tensor(tmp[k]).to(device=device, non_blocking=True)
-    #     return tmp
-
-    def encode_text(self, text, device):
-        if self.text_branch_type == "transformer":
-            text = text.to(device=device, non_blocking=True)
-            x = self.token_embedding(text)  # [batch_size, n_ctx, d_model]
-
-            x = x + self.positional_embedding
-            x = x.permute(1, 0, 2)  # NLD -> LND
-            x = self.text_branch(x, attn_mask=self.attn_mask)
-            x = x.permute(1, 0, 2)  # LND -> NLD
-            x = self.ln_final(x)
-
-            # x.shape = [batch_size, n_ctx, transformer.width]
-            # take features from the eot embedding (eot_token is the highest number in each sequence)
-            x = self.text_projection(x[torch.arange(x.shape[0]), text.argmax(dim=-1)])
-        elif self.text_branch_type == "bert":
-            # text = self.list_of_dict_of_tensor2dict_of_tensor(text, device)
-            # text = BatchEncoding(text)
-            x = self.text_branch(
-                input_ids=text["input_ids"].to(device=device, non_blocking=True),
-                attention_mask=text["attention_mask"].to(
-                    device=device, non_blocking=True
-                ),
-                token_type_ids=text["token_type_ids"].to(
-                    device=device, non_blocking=True
-                ),
-            )["pooler_output"]
-            x = self.text_projection(x)
-        elif self.text_branch_type == "roberta":
-            x = self.text_branch(
-                input_ids=text["input_ids"].to(device=device, non_blocking=True),
-                attention_mask=text["attention_mask"].to(
-                    device=device, non_blocking=True
-                ),
-            )["pooler_output"]
-            x = self.text_projection(x)
-        elif self.text_branch_type == "bart":
-            x = torch.mean(
-                self.text_branch(
-                    input_ids=text["input_ids"].to(device=device, non_blocking=True),
-                    attention_mask=text["attention_mask"].to(
-                        device=device, non_blocking=True
-                    ),
-                )["encoder_last_hidden_state"],
-                axis=1,
-            )
-            x = self.text_projection(x)
-        else:
-            logging.error(f"Model type {self.text_branch_type} not found")
-            raise RuntimeError(f"Model type {self.text_branch_type} not found.")
-        return x
-
-    def forward(self, audio, text, device=None):
-        """Forward audio and text into the CLAP
-
-        Parameters
-        ----------
-        audio: torch.Tensor (batch_size, audio_length)
-            the time-domain audio input / the batch of mel_spec and longer list.
-        text: torch.Tensor () // need to add
-            the text token input
-        """
-        if device is None:
-            if audio is not None:
-                device = audio.device
-            elif text is not None:
-                device = text.device
-        if audio is None and text is None:
-            # a hack to get the logit scale
-            return self.logit_scale_a.exp(), self.logit_scale_t.exp()
-        elif audio is None:
-            return self.encode_text(text, device=device)
-        elif text is None:
-            return self.audio_projection(
-                self.encode_audio(audio, device=device)["embedding"]
-            )
-        audio_features = self.audio_projection(
-            self.encode_audio(audio, device=device)["embedding"]
-        )
-        audio_features = F.normalize(audio_features, dim=-1)
-
-        text_features = self.encode_text(text, device=device)
-        # print("text_features", text_features)
-        # print("text_features.shape", text_features.shape)
-        # print("text_features.type", type(text_features))
-        text_features = F.normalize(text_features, dim=-1)
-
-        audio_features_mlp = self.audio_transform(audio_features)
-        text_features_mlp = self.text_transform(text_features)
-        # Four outputs: audio features (basic & MLP), text features (basic & MLP)
-        return (
-            audio_features,
-            text_features,
-            audio_features_mlp,
-            text_features_mlp,
-            self.logit_scale_a.exp(),
-            self.logit_scale_t.exp(),
-        )
-
-    def get_logit_scale(self):
-        return self.logit_scale_a.exp(), self.logit_scale_t.exp()
-
-    def get_text_embedding(self, data):
-        """Get the text embedding from the model
-
-        Parameters
-        ----------
-        data: torch.Tensor
-            a tensor of text embedding
-
-        Returns
-        ----------
-        text_embed: torch.Tensor
-            a tensor of text_embeds (N, D)
-
-        """
-        device = next(self.parameters()).device
-        for k in data:
-            data[k] = data[k].to(device)
-            if(len(data[k].size()) < 2):
-                data[k] = data[k].unsqueeze(0)
-        text_embeds = self.encode_text(data, device=device)
-        text_embeds = F.normalize(text_embeds, dim=-1)
-
-        return text_embeds
-
-    def get_audio_embedding(self, data):
-        """Get the audio embedding from the model
-
-        Parameters
-        ----------
-        data: a list of dict
-            the audio input dict list from 'get_audio_feature' method
-
-        Returns
-        ----------
-        audio_embed: torch.Tensor
-            a tensor of audio_embeds (N, D)
-
-        """
-        device = next(self.parameters()).device
-        input_dict = {}
-        keys = data[0].keys()
-        for k in keys:
-            input_dict[k] = torch.cat([d[k].unsqueeze(0) for d in data], dim=0).to(
-                device
-            )
-
-        audio_embeds = self.audio_projection(
-            self.encode_audio(input_dict, device=device)["embedding"]
-        )
-        audio_embeds = F.normalize(audio_embeds, dim=-1)
-
-        return audio_embeds
-
-    def audio_infer(self, audio, hopsize=None, device=None):
-        """Forward one audio and produce the audio embedding
-
-        Parameters
-        ----------
-        audio:  (audio_length)
-            the time-domain audio input, notice that it must be only one input
-        hopsize: int
-            the overlap hopsize as the sliding window
-
-        Returns
-        ----------
-        output_dict: {
-            key: [n, (embedding_shape)] if "HTS-AT"
-            or
-            key: [(embedding_shape)] if "PANN"
-        }
-            the list of key values of the audio branch
-
-        """
-
-        assert not self.training, "the inference mode must be run at eval stage"
-        output_dict = {}
-        # PANN
-        if self.audio_cfg.model_type == "PANN":
-            audio_input = audio.unsqueeze(dim=0)
-            output_dict[key] = self.encode_audio(audio_input, device=device)[
-                key
-            ].squeeze(dim=0)
-        elif self.audio_cfg.model_type == "HTSAT":
-            # repeat
-            audio_len = len(audio)
-            k = self.audio_cfg.clip_samples // audio_len
-            if k > 1:
-                audio = audio.repeat(k)
-                audio_len = len(audio)
-
-            if hopsize is None:
-                hopsize = min(hopsize, audio_len)
-
-            if audio_len > self.audio_cfg.clip_samples:
-                audio_input = [
-                    audio[pos : pos + self.audio_cfg.clip_samples].clone()
-                    for pos in range(
-                        0, audio_len - self.audio_cfg.clip_samples, hopsize
-                    )
-                ]
-                audio_input.append(audio[-self.audio_cfg.clip_samples :].clone())
-                audio_input = torch.stack(audio_input)
-                output_dict[key] = self.encode_audio(audio_input, device=device)[key]
-            else:
-                audio_input = audio.unsqueeze(dim=0)
-                output_dict[key] = self.encode_audio(audio_input, device=device)[
-                    key
-                ].squeeze(dim=0)
-
-        return output_dict
-
-
-def convert_weights_to_fp16(model: nn.Module):
-    """Convert applicable model parameters to fp16"""
-
-    def _convert_weights_to_fp16(l):
-        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
-            l.weight.data = l.weight.data.half()
-            if l.bias is not None:
-                l.bias.data = l.bias.data.half()
-
-        if isinstance(l, nn.MultiheadAttention):
-            for attr in [
-                *[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]],
-                "in_proj_bias",
-                "bias_k",
-                "bias_v",
-            ]:
-                tensor = getattr(l, attr)
-                if tensor is not None:
-                    tensor.data = tensor.data.half()
-
-        for name in ["text_projection", "proj"]:
-            if hasattr(l, name):
-                attr = getattr(l, name)
-                if attr is not None:
-                    attr.data = attr.data.half()
-
-    model.apply(_convert_weights_to_fp16)
-
-
-# Ignore the state dict of the vision part
-def build_model_from_openai_state_dict(
-    state_dict: dict, model_cfg, enable_fusion: bool = False, fusion_type: str = "None"
-):
-
-    embed_dim = model_cfg["embed_dim"]
-    audio_cfg = model_cfg["audio_cfg"]
-    text_cfg = model_cfg["text_cfg"]
-    context_length = state_dict["positional_embedding"].shape[0]
-    vocab_size = state_dict["token_embedding.weight"].shape[0]
-    transformer_width = state_dict["ln_final.weight"].shape[0]
-    transformer_heads = transformer_width // 64
-    transformer_layers = len(
-        set(
-            k.split(".")[2]
-            for k in state_dict
-            if k.startswith(f"transformer.resblocks")
-        )
-    )
-
-    audio_cfg = CLAPAudioCfp(**audio_cfg)
-    text_cfg = CLAPTextCfg(**text_cfg)
-
-    model = CLAP(
-        embed_dim,
-        audio_cfg=audio_cfg,
-        text_cfg=text_cfg,
-        quick_gelu=True,  # OpenAI models were trained with QuickGELU
-        enable_fusion=enable_fusion,
-        fusion_type=fusion_type,
-    )
-    state_dict["logit_scale_a"] = state_dict["logit_scale"]
-    state_dict["logit_scale_t"] = state_dict["logit_scale"]
-    pop_keys = list(state_dict.keys())[::]
-    # pop the visual branch saved weights
-    for key in pop_keys:
-        if key.startswith("visual."):
-            state_dict.pop(key, None)
-
-    for key in ["logit_scale", "input_resolution", "context_length", "vocab_size"]:
-        state_dict.pop(key, None)
-
-    # not use fp16
-    # convert_weights_to_fp16(model)
-    model.load_state_dict(state_dict, strict=False)
-    return model.eval()
-
-
-def trace_model(model, batch_size=256, device=torch.device("cpu")):
-    model.eval()
-    audio_length = model.audio_cfg.audio_length
-    example_audio = torch.ones((batch_size, audio_length), device=device)
-    example_text = torch.zeros(
-        (batch_size, model.context_length), dtype=torch.int, device=device
-    )
-    model = torch.jit.trace_module(
-        model,
-        inputs=dict(
-            forward=(example_audio, example_text),
-            encode_text=(example_text,),
-            encode_image=(example_audio,),
-        ),
-    )
-    model.audio_cfg.audio_length = audio_length  # Question: what does this do?
-    return model

audioldm/clap/open_clip/model_configs/HTSAT-base.json

@@ -1,23 +0,0 @@
-{
-    "embed_dim": 1024,
-    "audio_cfg": {
-        "audio_length": 1024,
-        "clip_samples": 480000,
-        "mel_bins": 64,
-        "sample_rate": 48000,
-        "window_size": 1024,
-        "hop_size": 480,
-        "fmin": 50,
-        "fmax": 14000,
-        "class_num": 527,
-        "model_type": "HTSAT",
-        "model_name": "base"
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}

audioldm/clap/open_clip/model_configs/HTSAT-large.json

@@ -1,23 +0,0 @@
-{
-    "embed_dim": 2048,
-    "audio_cfg": {
-        "audio_length": 1024,
-        "clip_samples": 480000,
-        "mel_bins": 64,
-        "sample_rate": 48000,
-        "window_size": 1024,
-        "hop_size": 480,
-        "fmin": 50,
-        "fmax": 14000,
-        "class_num": 527,
-        "model_type": "HTSAT",
-        "model_name": "large"
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}

audioldm/clap/open_clip/model_configs/HTSAT-tiny-win-1536.json

@@ -1,23 +0,0 @@
-{
-    "embed_dim": 768,
-    "audio_cfg": {
-        "audio_length": 1024,
-        "clip_samples": 480000,
-        "mel_bins": 64,
-        "sample_rate": 48000,
-        "window_size": 1536,
-        "hop_size": 480,
-        "fmin": 50,
-        "fmax": 14000,
-        "class_num": 527,
-        "model_type": "HTSAT",
-        "model_name": "tiny"
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}

audioldm/clap/open_clip/model_configs/HTSAT-tiny.json

@@ -1,23 +0,0 @@
-{
-    "embed_dim": 768,
-    "audio_cfg": {
-        "audio_length": 1024,
-        "clip_samples": 480000,
-        "mel_bins": 64,
-        "sample_rate": 48000,
-        "window_size": 1024,
-        "hop_size": 480,
-        "fmin": 50,
-        "fmax": 14000,
-        "class_num": 527,
-        "model_type": "HTSAT",
-        "model_name": "tiny"
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}

audioldm/clap/open_clip/model_configs/PANN-10.json

@@ -1,23 +0,0 @@
-{
-    "embed_dim": 1024,
-    "audio_cfg": {
-        "audio_length": 1024,
-        "clip_samples": 480000,
-        "mel_bins": 64,
-        "sample_rate": 48000,
-        "window_size": 1024,
-        "hop_size": 480,
-        "fmin": 50,
-        "fmax": 14000,
-        "class_num": 527,
-        "model_type": "PANN",
-        "model_name": "Cnn10"
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}

audioldm/clap/open_clip/model_configs/PANN-14-fmax-18k.json

@@ -1,23 +0,0 @@
-{
-    "embed_dim": 2048,
-    "audio_cfg": {
-        "audio_length": 1024,
-        "clip_samples": 480000,
-        "mel_bins": 64,
-        "sample_rate": 48000,
-        "window_size": 1024,
-        "hop_size": 480,
-        "fmin": 50,
-        "fmax": 18000,
-        "class_num": 527,
-        "model_type": "PANN",
-        "model_name": "Cnn14"
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}

audioldm/clap/open_clip/model_configs/PANN-14-fmax-8k-20s.json

@@ -1,23 +0,0 @@
-{
-    "embed_dim": 2048,
-    "audio_cfg": {
-        "audio_length": 1024,
-        "clip_samples": 960000,
-        "mel_bins": 64,
-        "sample_rate": 48000,
-        "window_size": 1024,
-        "hop_size": 360,
-        "fmin": 50,
-        "fmax": 8000,
-        "class_num": 527,
-        "model_type": "PANN",
-        "model_name": "Cnn14"
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}

audioldm/clap/open_clip/model_configs/PANN-14-tiny-transformer.json

@@ -1,23 +0,0 @@
-{
-    "embed_dim": 2048,
-    "audio_cfg": {
-        "audio_length": 1024,
-        "clip_samples": 480000,
-        "mel_bins": 64,
-        "sample_rate": 48000,
-        "window_size": 1024,
-        "hop_size": 480,
-        "fmin": 50,
-        "fmax": 14000,
-        "class_num": 527,
-        "model_type": "PANN",
-        "model_name": "Cnn14"
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 4
-    }
-}

audioldm/clap/open_clip/model_configs/PANN-14-win-1536.json

@@ -1,23 +0,0 @@
-{
-    "embed_dim": 2048,
-    "audio_cfg": {
-        "audio_length": 1024,
-        "clip_samples": 480000,
-        "mel_bins": 64,
-        "sample_rate": 48000,
-        "window_size": 1536,
-        "hop_size": 480,
-        "fmin": 50,
-        "fmax": 14000,
-        "class_num": 527,
-        "model_type": "PANN",
-        "model_name": "Cnn14"
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}

audioldm/clap/open_clip/model_configs/PANN-14.json

@@ -1,23 +0,0 @@
-{
-    "embed_dim": 2048,
-    "audio_cfg": {
-        "audio_length": 1024,
-        "clip_samples": 480000,
-        "mel_bins": 64,
-        "sample_rate": 48000,
-        "window_size": 1024,
-        "hop_size": 480,
-        "fmin": 50,
-        "fmax": 14000,
-        "class_num": 527,
-        "model_type": "PANN",
-        "model_name": "Cnn14"
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}

audioldm/clap/open_clip/model_configs/PANN-6.json

@@ -1,23 +0,0 @@
-{
-    "embed_dim": 512,
-    "audio_cfg": {
-        "audio_length": 1024,
-        "clip_samples": 480000,
-        "mel_bins": 64,
-        "sample_rate": 48000,
-        "window_size": 1024,
-        "hop_size": 480,
-        "fmin": 50,
-        "fmax": 14000,
-        "class_num": 527,
-        "model_type": "PANN",
-        "model_name": "Cnn6"
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}

audioldm/clap/open_clip/model_configs/RN101-quickgelu.json

@@ -1,22 +0,0 @@
-{
-    "embed_dim": 512,
-    "quick_gelu": true,
-    "vision_cfg": {
-        "image_size": 224,
-        "layers": [
-            3,
-            4,
-            23,
-            3
-        ],
-        "width": 64,
-        "patch_size": null
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}

audioldm/clap/open_clip/model_configs/RN101.json

@@ -1,21 +0,0 @@
-{
-    "embed_dim": 512,
-    "vision_cfg": {
-        "image_size": 224,
-        "layers": [
-            3,
-            4,
-            23,
-            3
-        ],
-        "width": 64,
-        "patch_size": null
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}

audioldm/clap/open_clip/model_configs/RN50-quickgelu.json

@@ -1,22 +0,0 @@
-{
-    "embed_dim": 1024,
-    "quick_gelu": true,
-    "vision_cfg": {
-        "image_size": 224,
-        "layers": [
-            3,
-            4,
-            6,
-            3
-        ],
-        "width": 64,
-        "patch_size": null
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}

audioldm/clap/open_clip/model_configs/RN50.json

@@ -1,21 +0,0 @@
-{
-    "embed_dim": 1024,
-    "vision_cfg": {
-        "image_size": 224,
-        "layers": [
-            3,
-            4,
-            6,
-            3
-        ],
-        "width": 64,
-        "patch_size": null
-    },
-    "text_cfg": {
-        "context_length": 77,
-        "vocab_size": 49408,
-        "width": 512,
-        "heads": 8,
-        "layers": 12
-    }
-}