copy work from repo

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+saved_training/HTSAT_ESC_exp=1_fold=1_acc=0.985.ckpt filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1 @@
+__pycache__

app.py

@@ -0,0 +1,71 @@
+import os
+import gradio as gr
+from scipy.io.wavfile import write
+import config
+
+import torch
+from model.htsat import HTSAT_Swin_Transformer
+from sed_model import SEDWrapper
+import librosa
+import numpy as np
+
+example_path = './examples_audio'
+
+class_mapping = ['dog', 'rooster', 'pig', 'cow', 'frog', 'cat', 'hen', 'insects', 'sheep', 'crow', 'rain', 'sea_waves', 'crackling_fire', 'crickets', 'chirping_birds', 'water_drops', 'wind', 'pouring_water', 'toilet_flush', 'thunderstorm', 'crying_baby', 'sneezing', 'clapping', 'breathing', 'coughing', 'footsteps', 'laughing',
+                 'brushing_teeth', 'snoring', 'drinking_sipping', 'door_wood_knock', 'mouse_click', 'keyboard_typing', 'door_wood_creaks', 'can_opening', 'washing_machine', 'vacuum_cleaner', 'clock_alarm', 'clock_tick', 'glass_breaking', 'helicopter', 'chainsaw', 'siren', 'car_horn', 'engine', 'train', 'church_bells', 'airplane', 'fireworks', 'hand_saw']
+
+sed_model = HTSAT_Swin_Transformer(
+    spec_size=config.htsat_spec_size,
+    patch_size=config.htsat_patch_size,
+    in_chans=1,
+    num_classes=config.classes_num,
+    window_size=config.htsat_window_size,
+    config=config,
+    depths=config.htsat_depth,
+    embed_dim=config.htsat_dim,
+    patch_stride=config.htsat_stride,
+    num_heads=config.htsat_num_head
+)
+
+model = SEDWrapper(
+    sed_model=sed_model,
+    config=config,
+    dataset=None
+)
+
+ckpt = torch.load(config.resume_checkpoint, map_location="cpu")
+model.load_state_dict(ckpt["state_dict"], strict=False)
+
+def inference(audio):
+    sr, y = audio
+    y = y/32767.0 # scipy vs librosa
+    if len(y.shape) != 1: # to mono
+        y = y[:,0]
+    y = librosa.resample(y, orig_sr=sr, target_sr=32000)
+    in_val = np.array([y])
+    result = model.inference(in_val)
+    pred = result['clipwise_output'][0]
+    # pred = np.exp(pred)/np.sum(np.exp(pred)) # softmax
+    return {class_mapping[i]: float(p) for i, p in enumerate(pred)}
+    # win_classes = np.argmax(result['clipwise_output'], axis=1)
+    # win_class_index = win_classes[0]
+    # win_class_name = class_mapping[win_class_index]
+    # return str({win_class_name: result['clipwise_output'][0][win_class_index]})
+
+
+title = "HTS-Audio-Transformer"
+description = "Audio classificatio with ESC-50."
+# article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1911.13254' target='_blank'>Music Source Separation in the Waveform Domain</a> | <a href='https://github.com/facebookresearch/demucs' target='_blank'>Github Repo</a></p>"
+
+examples = [['test.mp3']]
+gr.Interface(
+    inference,
+    gr.inputs.Audio(type="numpy", label="Input"),
+    # gr.outputs.Textbox(),
+    gr.outputs.Label(),
+    title=title,
+    description=description,
+    # article=article,
+    examples=[[os.path.join(example_path, f)]
+              for f in os.listdir(example_path)]
+).launch(enable_queue=True)

config.py

@@ -0,0 +1,132 @@
+# Ke Chen
+# knutchen@ucsd.edu
+# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
+# The configuration for training the model
+
+exp_name = "exp_htsat_pretrain" # the saved ckpt prefix name of the model 
+workspace = "/home/super/nic/HTS-Audio-Transformer" # the folder of your code
+dataset_path = "/home/super/datasets-nas/ESC-50/" # the dataset path
+desed_folder = "/home/super/nic/HTS-Audio-Transformer/DESED" # the desed file
+
+dataset_type = "esc-50" # "audioset" "esc-50" "scv2"
+index_type = "full_train" # only works for audioset
+balanced_data = True # only works for audioset
+
+loss_type = "clip_bce" # 
+# AudioSet & SCV2: "clip_bce" |  ESC-50: "clip_ce" 
+
+# trained from a checkpoint, or evaluate a single model 
+# resume_checkpoint = None 
+# resume_checkpoint = "/home/super/nic/HTS-Audio-Transformer/saved_training/HTSAT_ESC_exp=1_fold=1_acc=0.985.ckpt"
+resume_checkpoint = "/home/super/nic/HTS-Audio-Transformer/saved_training/HTSAT_ESC_exp=1_fold=1_acc=0.985.ckpt"
+# resume_checkpoint = "/home/super/nic/HTS-Audio-Transformer/results/exp_htsat_pretrain/checkpoint_1/lightning_logs/version_9/checkpoints/l-epoch=99-acc=0.490.ckpt"
+
+# "/home/Research/model_backup/AudioSet/HTSAT_AudioSet_Saved_1.ckpt"
+ 
+esc_fold = 0 # just for esc dataset, select the fold you need for evaluation and (+1) validation
+
+
+debug = False
+
+random_seed = 970131 # 19970318 970131 12412 127777 1009 34047
+batch_size = 32 * 1 # batch size per GPU x GPU number , default is 32 x 4 = 128
+learning_rate = 1e-3 # 1e-4 also workable 
+max_epoch = 100
+num_workers = 3
+
+lr_scheduler_epoch = [10,20,30]
+lr_rate = [0.02, 0.05, 0.1]
+
+# these data preparation optimizations do not bring many improvements, so deprecated
+enable_token_label = False # token label
+class_map_path = "esc-50-data.npy"
+class_filter = None 
+retrieval_index = [15382, 9202, 130, 17618, 17157, 17516, 16356, 6165, 13992, 9238, 5550, 5733, 1914, 1600, 3450, 13735, 11108, 3762, 
+    9840, 11318, 8131, 4429, 16748, 4992, 16783, 12691, 4945, 8779, 2805, 9418, 2797, 14357, 5603, 212, 3852, 12666, 1338, 10269, 2388, 8260, 4293, 14454, 7677, 11253, 5060, 14938, 8840, 4542, 2627, 16336, 8992, 15496, 11140, 446, 6126, 10691, 8624, 10127, 9068, 16710, 10155, 14358, 7567, 5695, 2354, 8057, 17635, 133, 16183, 14535, 7248, 4560, 14429, 2463, 10773, 113, 2462, 9223, 4929, 14274, 4716, 17307, 4617, 2132, 11083, 1039, 1403, 9621, 13936, 2229, 2875, 17840, 9359, 13311, 9790, 13288, 4750, 17052, 8260, 14900]
+token_label_range = [0.2,0.6]
+enable_time_shift = False # shift time
+enable_label_enhance = False # enhance hierarchical label
+enable_repeat_mode = False # repeat the spectrogram / reshape the spectrogram
+
+
+
+# for model's design
+enable_tscam = True # enbale the token-semantic layer
+
+# for signal processing
+sample_rate = 32000 # 16000 for scv2, 32000 for audioset and esc-50
+clip_samples = sample_rate * 10 # audio_set 10-sec clip
+window_size = 1024
+hop_size = 320 # 160 for scv2, 320 for audioset and esc-50
+mel_bins = 64
+fmin = 50
+fmax = 14000
+shift_max = int(clip_samples * 0.5)
+
+# for data collection
+classes_num = 50 # esc: 50 | audioset: 527 | scv2: 35
+patch_size = (25, 4) # deprecated
+crop_size = None # int(clip_samples * 0.5) deprecated
+
+# for htsat hyperparamater
+htsat_window_size = 8
+htsat_spec_size =  256
+htsat_patch_size = 4 
+htsat_stride = (4, 4)
+htsat_num_head = [4,8,16,32]
+htsat_dim = 96 
+htsat_depth = [2,2,6,2]
+
+swin_pretrain_path = None
+# "/home/Research/model_backup/pretrain/swin_tiny_c24_patch4_window8_256.pth"
+
+# Some Deprecated Optimization in the model design, check the model code for details
+htsat_attn_heatmap = False
+htsat_hier_output = False 
+htsat_use_max = False
+
+
+# for ensemble test 
+
+ensemble_checkpoints = []
+ensemble_strides = []
+
+
+# weight average folder
+wa_folder = "/home/super/nic/HTS-Audio-Transformer/checkpoints/"
+# weight average output filename
+wa_model_path = "HTSAT_AudioSet_Saved_x.ckpt"
+
+esm_model_pathes = [
+    "/home/super/nic/HTS-Audio-Transformer/HTSAT_AudioSet_Saved_1.ckpt",
+    "/home/super/nic/HTS-Audio-Transformer/HTSAT_AudioSet_Saved_2.ckpt",
+    "/home/super/nic/HTS-Audio-Transformer/HTSAT_AudioSet_Saved_3.ckpt",
+    "/home/super/nic/HTS-Audio-Transformer/HTSAT_AudioSet_Saved_4.ckpt",
+    "/home/super/nic/HTS-Audio-Transformer/HTSAT_AudioSet_Saved_5.ckpt",
+    "/home/super/nic/HTS-Audio-Transformer/HTSAT_AudioSet_Saved_6.ckpt"
+]
+
+# for framewise localization
+heatmap_dir = "//home/super/nic/HTS-Audio-Transformer/heatmap_output"
+test_file = "htsat-test-ensemble"
+fl_local = False # indicate if we need to use this dataset for the framewise detection
+fl_dataset = "/home/Research/desed/desed_eval.npy"  
+fl_class_num = [
+    "Speech", "Frying", "Dishes", "Running_water",
+    "Blender", "Electric_shaver_toothbrush", "Alarm_bell_ringing",
+    "Cat", "Dog", "Vacuum_cleaner"
+]
+
+# map 527 classes into 10 classes
+fl_audioset_mapping = [
+    [0,1,2,3,4,5,6,7],
+    [366, 367, 368],
+    [364],
+    [288, 289, 290, 291, 292, 293, 294, 295, 296, 297],
+    [369],
+    [382],
+    [310, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402],
+    [81, 82, 83, 84, 85],
+    [74, 75, 76, 77, 78, 79],
+    [377]
+]

model/htsat.py

@@ -0,0 +1,836 @@
+# Ke Chen
+# knutchen@ucsd.edu
+# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
+# Model Core
+# 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 logging
+import pdb
+import math
+import random
+from numpy.core.fromnumeric import clip, reshape
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as checkpoint
+
+from torchlibrosa.stft import Spectrogram, LogmelFilterBank
+from torchlibrosa.augmentation import SpecAugmentation
+
+from itertools import repeat
+from typing import List
+from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_, to_2tuple
+from utils import do_mixup, interpolate
+
+
+# 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
+
+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., proj_drop=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=.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., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=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. 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., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=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., qkv_bias=True, qk_scale=None,
+                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
+                 norm_layer=nn.LayerNorm, 
+                 ape=False, patch_norm=True,
+                 use_checkpoint=False, norm_before_mlp='ln', config = 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
+
+        #  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)
+
+        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=.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)
+
+        # A deprecated optimization for using a hierarchical output from different blocks
+        # if self.config.htsat_hier_output:
+        #     self.norm = nn.ModuleList(
+        #         [self.norm_layer(
+        #             min(
+        #               self.embed_dim * (2 ** (len(self.depths) - 1)),
+        #               self.embed_dim * (2 ** (i + 1)) 
+        #                 )
+        #         ) for i in range(len(self.depths))]   
+        #     )
+        # else:
+
+        self.norm = self.norm_layer(self.num_features)
+        self.avgpool = nn.AdaptiveAvgPool1d(1)
+        self.maxpool = nn.AdaptiveMaxPool1d(1)
+        
+        # A deprecated optimization for using the max value instead of average value
+        # if self.config.htsat_use_max:
+        #     self.a_avgpool = nn.AvgPool1d(kernel_size=3, stride=1, padding=1)
+        #     self.a_maxpool = nn.MaxPool1d(kernel_size=3, stride=1, padding=1)
+
+        if self.config.enable_tscam:
+            # if self.config.htsat_hier_output:
+            #     self.tscam_conv = nn.ModuleList()
+            #     for i in range(len(self.depths)):
+            #         zoom_ratio = 2 ** min(len(self.depths) - 1, i + 1)
+            #         zoom_dim = min(
+            #             self.embed_dim * (2 ** (len(self.depths) - 1)),
+            #             self.embed_dim * (2 ** (i + 1)) 
+            #         )
+            #         SF = self.spec_size // zoom_ratio // self.patch_stride[0] // self.freq_ratio
+            #         self.tscam_conv.append(
+            #             nn.Conv2d(
+            #                 in_channels = zoom_dim,
+            #                 out_channels = self.num_classes,
+            #                 kernel_size = (SF, 3),
+            #                 padding = (0,1)
+            #             )
+            #         )
+            #     self.head = nn.Linear(num_classes * len(self.depths), num_classes)
+            # else:
+
+            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)
+        else:
+            self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.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):
+        # A deprecated optimization for using a hierarchical output from different blocks
+        # if self.config.htsat_hier_output:
+        #     hier_x = []
+        #     hier_attn = []
+
+        frames_num = x.shape[2]        
+        x = self.patch_embed(x)
+        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)
+            # A deprecated optimization for using a hierarchical output from different blocks
+            # if self.config.htsat_hier_output:
+            #     hier_x.append(x)
+            #     if i == len(self.layers) - 1:
+            #         hier_attn.append(attn)
+
+        # A deprecated optimization for using a hierarchical output from different blocks
+        # if self.config.htsat_hier_output:
+        #     hxs = []
+        #     fphxs = []
+        #     for i in range(len(hier_x)):
+        #         hx = hier_x[i]
+        #         hx = self.norm[i](hx)
+        #         B, N, C = hx.shape
+        #         zoom_ratio = 2 ** min(len(self.depths) - 1, i + 1)
+        #         SF = frames_num // zoom_ratio // self.patch_stride[0]
+        #         ST = frames_num // zoom_ratio // self.patch_stride[1]
+        #         hx = hx.permute(0,2,1).contiguous().reshape(B, C, SF, ST)
+        #         B, C, F, T = hx.shape
+        #         c_freq_bin = F // self.freq_ratio
+        #         hx = hx.reshape(B, C, F // c_freq_bin, c_freq_bin, T)
+        #         hx = hx.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1)
+                
+        #         hx = self.tscam_conv[i](hx)
+        #         hx = torch.flatten(hx, 2)
+        #         fphx = interpolate(hx.permute(0,2,1).contiguous(), self.spec_size * self.freq_ratio // hx.shape[2])
+                
+        #         hx = self.avgpool(hx)
+        #         hx = torch.flatten(hx, 1)
+        #         hxs.append(hx)
+        #         fphxs.append(fphx)
+        #     hxs = torch.cat(hxs, dim=1)
+        #     fphxs = torch.cat(fphxs, dim = 2)
+        #     hxs = self.head(hxs)
+        #     fphxs = self.head(fphxs)
+        #     output_dict = {'framewise_output': torch.sigmoid(fphxs), 
+        #         'clipwise_output': torch.sigmoid(hxs)}
+        #     return output_dict
+
+        if self.config.enable_tscam:
+            # 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
+            latent_output = self.avgpool(torch.flatten(x,2))
+            latent_output = torch.flatten(latent_output, 1)
+
+            # display the attention map, if needed
+            if self.config.htsat_attn_heatmap:
+                # for attn
+                attn = torch.mean(attn, dim = 1)
+                attn = torch.mean(attn, dim = 1)
+                attn = attn.reshape(B, SF, ST)
+                c_freq_bin = SF // self.freq_ratio
+                attn = attn.reshape(B, SF // c_freq_bin, c_freq_bin, ST) 
+                attn = attn.permute(0,2,1,3).contiguous().reshape(B, c_freq_bin, -1)
+                attn = attn.mean(dim = 1)
+                attn_max = torch.max(attn, dim = 1, keepdim = True)[0]
+                attn_min = torch.min(attn, dim = 1, keepdim = True)[0]
+                attn = ((attn * 0.15) + (attn_max * 0.85 - attn_min)) / (attn_max - attn_min)
+                attn = attn.unsqueeze(dim = 2)
+
+            x = self.tscam_conv(x)
+            x = torch.flatten(x, 2) # B, C, T
+            
+            # A deprecated optimization for using the max value instead of average value
+            # if self.config.htsat_use_max:
+            #     x1 = self.a_maxpool(x)
+            #     x2 = self.a_avgpool(x)
+            #     x = x1 + x2
+
+            if self.config.htsat_attn_heatmap:
+                fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous() * attn, 8 * self.patch_stride[1]) 
+            else: 
+                fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) 
+            
+            # A deprecated optimization for using the max value instead of average value
+            # if self.config.htsat_use_max:
+            #     x1 = self.avgpool(x)
+            #     x2 = self.maxpool(x)
+            #     x = x1 + x2
+            # else:
+            x = self.avgpool(x)
+            x = torch.flatten(x, 1)
+
+            if self.config.loss_type == "clip_ce":
+                output_dict = {
+                    'framewise_output': fpx, # already sigmoided
+                    'clipwise_output': x,
+                    'latent_output': latent_output
+                }
+            else:
+                output_dict = {
+                    'framewise_output': fpx, # already sigmoided
+                    'clipwise_output': torch.sigmoid(x),
+                    'latent_output': latent_output
+                }
+           
+        else:
+            x = self.norm(x)  # B N C
+            B, N, C = x.shape
+            
+            fpx = x.permute(0,2,1).contiguous().reshape(B, C, frames_num // (2 ** (len(self.depths) + 1)), frames_num // (2 ** (len(self.depths) + 1)) )
+            B, C, F, T = fpx.shape
+            c_freq_bin = F // self.freq_ratio
+            fpx = fpx.reshape(B, C, F // c_freq_bin, c_freq_bin, T)
+            fpx = fpx.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1)
+            fpx = torch.sum(fpx, dim = 2)
+            fpx = interpolate(fpx.permute(0,2,1).contiguous(), 8 * self.patch_stride[1]) 
+            x = self.avgpool(x.transpose(1, 2))  # B C 1
+            x = torch.flatten(x, 1)
+            if self.num_classes > 0:
+                x = self.head(x)
+                fpx = self.head(fpx)
+            output_dict = {'framewise_output': torch.sigmoid(fpx), 
+                'clipwise_output': torch.sigmoid(x)}
+        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):# out_feat_keys: List[str] = None):
+        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)
+        
+        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)
+        elif self.config.enable_repeat_mode:
+            if self.training:
+                cur_pos = random.randint(0, (self.freq_ratio - 1) * self.spec_size - 1)
+                x = self.repeat_wat2img(x, cur_pos)
+                output_dict = self.forward_features(x)
+            else:
+                output_dicts = []
+                for cur_pos in range(0, (self.freq_ratio - 1) * self.spec_size + 1, self.spec_size):
+                    tx = x.clone()
+                    tx = self.repeat_wat2img(tx, cur_pos)
+                    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:
+            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)
+        return output_dict

model/layers.py

@@ -0,0 +1,195 @@
+# Ke Chen
+# knutchen@ucsd.edu
+# 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
+
+
+# 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., 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. 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):
+        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
+        
+        padding = ((patch_size[0] - patch_stride[0]) // 2, (patch_size[1] - patch_stride[1]) // 2)
+
+        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()
+
+    def forward(self, x):
+        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.):
+        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. + math.erf(x / math.sqrt(2.))) / 2.
+
+    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.))
+        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., std=1., a=-2., b=2.):
+    # 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) / .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')

requirements.txt

@@ -0,0 +1,18 @@
+h5py==3.6.0
+librosa==0.8.1
+matplotlib==3.5.1
+museval==0.4.0
+numpy==1.22.0
+pandas==1.4.0
+pytorch_lightning==1.5.9
+scikit_learn==1.0.2
+scipy==1.7.3
+soundfile==0.10.3.post1
+tensorboard==2.8.0
+torch==1.10.2
+torchaudio==0.10.2
+torchcontrib==0.0.2
+torchlibrosa==0.0.9
+tqdm==4.62.3
+
+gradio

saved_training/HTSAT_ESC_exp=1_fold=1_acc=0.985.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:02478f008fad5c0fa6cff856c729f1f22deb73c8e254c652785371355c27ce0f
+size 339619927

sed_model.py

@@ -0,0 +1,357 @@
+# Ke Chen
+# knutchen@ucsd.edu
+# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
+# The Model Training Wrapper
+import numpy as np
+import librosa
+import os
+import bisect
+from numpy.lib.function_base import average
+
+from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score
+
+from utils import get_loss_func, get_mix_lambda, d_prime
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as cp
+import torch.optim as optim
+from torch.nn.parameter import Parameter
+import torch.distributed as dist
+import pytorch_lightning as pl
+from utils import do_mixup, get_mix_lambda, do_mixup_label
+
+
+class SEDWrapper(pl.LightningModule):
+    def __init__(self, sed_model, config, dataset):
+        super().__init__()
+        self.sed_model = sed_model
+        self.config = config
+        self.dataset = dataset
+        self.loss_func = get_loss_func(config.loss_type)
+
+    def evaluate_metric(self, pred, ans):
+        ap = []
+        if self.config.dataset_type == "audioset":
+            mAP = np.mean(average_precision_score(ans, pred, average = None))
+            mAUC = np.mean(roc_auc_score(ans, pred, average = None))
+            dprime = d_prime(mAUC)
+            return {"mAP": mAP, "mAUC": mAUC, "dprime": dprime}
+        else:
+            acc = accuracy_score(ans, np.argmax(pred, 1))
+            return {"acc": acc}  
+    def forward(self, x, mix_lambda = None):
+        output_dict = self.sed_model(x, mix_lambda)
+        return output_dict["clipwise_output"], output_dict["framewise_output"]
+
+    def inference(self, x):
+        self.device_type = next(self.parameters()).device
+        self.eval()
+        x = torch.from_numpy(x).float().to(self.device_type)
+        print(x.shape)
+        output_dict = self.sed_model(x, None, True)
+        for key in output_dict.keys():
+            output_dict[key] = output_dict[key].detach().cpu().numpy()
+        return output_dict
+
+    def training_step(self, batch, batch_idx):
+        self.device_type = next(self.parameters()).device
+        mix_lambda = torch.from_numpy(get_mix_lambda(0.5, len(batch["waveform"]))).to(self.device_type)
+        # Another Choice: also mixup the target, but AudioSet is not a perfect data
+        # so "adding noise" might be better than purly "mix"
+        # batch["target"] = do_mixup_label(batch["target"])
+        # batch["target"] = do_mixup(batch["target"], mix_lambda)
+        pred, _ = self(batch["waveform"], mix_lambda)
+        loss = self.loss_func(pred, batch["target"])
+        self.log("loss", loss, on_epoch= True, prog_bar=True)
+        return loss
+    def training_epoch_end(self, outputs):
+        # Change: SWA, deprecated
+        # for opt in self.trainer.optimizers:
+        #     if not type(opt) is SWA:
+        #         continue
+        #     opt.swap_swa_sgd()
+        self.dataset.generate_queue()
+
+
+    def validation_step(self, batch, batch_idx):
+        pred, _ = self(batch["waveform"])
+        return [pred.detach(), batch["target"].detach()]
+    
+    def validation_epoch_end(self, validation_step_outputs):
+        self.device_type = next(self.parameters()).device
+        pred = torch.cat([d[0] for d in validation_step_outputs], dim = 0)
+        target = torch.cat([d[1] for d in validation_step_outputs], dim = 0)
+        gather_pred = [torch.zeros_like(pred) for _ in range(dist.get_world_size())]
+        gather_target = [torch.zeros_like(target) for _ in range(dist.get_world_size())]
+        dist.barrier()
+        if self.config.dataset_type == "audioset":
+            metric_dict = {
+                "mAP": 0.,
+                "mAUC": 0.,
+                "dprime": 0.
+            }
+        else:
+            metric_dict = {
+                "acc":0.
+            }
+        dist.all_gather(gather_pred, pred)
+        dist.all_gather(gather_target, target)
+        if dist.get_rank() == 0:
+            gather_pred = torch.cat(gather_pred, dim = 0).cpu().numpy()
+            gather_target = torch.cat(gather_target, dim = 0).cpu().numpy()
+            if self.config.dataset_type == "scv2":
+                gather_target = np.argmax(gather_target, 1)
+            metric_dict = self.evaluate_metric(gather_pred, gather_target)
+            print(self.device_type, dist.get_world_size(), metric_dict, flush = True)
+        
+        if self.config.dataset_type == "audioset":
+            self.log("mAP", metric_dict["mAP"] * float(dist.get_world_size()), on_epoch = True, prog_bar=True, sync_dist=True)
+            self.log("mAUC", metric_dict["mAUC"] * float(dist.get_world_size()), on_epoch = True, prog_bar=True, sync_dist=True)
+            self.log("dprime", metric_dict["dprime"] * float(dist.get_world_size()), on_epoch = True, prog_bar=True, sync_dist=True)
+        else:
+            self.log("acc", metric_dict["acc"] * float(dist.get_world_size()), on_epoch = True, prog_bar=True, sync_dist=True)
+        dist.barrier()
+        
+    def time_shifting(self, x, shift_len):
+        shift_len = int(shift_len)
+        new_sample = torch.cat([x[:, shift_len:], x[:, :shift_len]], axis = 1)
+        return new_sample 
+
+    def test_step(self, batch, batch_idx):
+        print(batch['waveform'].shape)
+        exit()
+        self.device_type = next(self.parameters()).device
+        preds = []
+        # time shifting optimization
+        if self.config.fl_local or self.config.dataset_type != "audioset": 
+            shift_num = 1 # framewise localization cannot allow the time shifting
+        else:
+            shift_num = 10 
+        for i in range(shift_num):
+            pred, pred_map = self(batch["waveform"])
+            preds.append(pred.unsqueeze(0))
+            batch["waveform"] = self.time_shifting(batch["waveform"], shift_len = 100 * (i + 1))
+        preds = torch.cat(preds, dim=0)
+        pred = preds.mean(dim = 0)
+        if self.config.fl_local:
+            return [
+                pred.detach().cpu().numpy(), 
+                pred_map.detach().cpu().numpy(),
+                batch["audio_name"],
+                batch["real_len"].cpu().numpy()
+            ]
+        else:
+            return [pred.detach(), batch["target"].detach()]
+
+    def test_epoch_end(self, test_step_outputs):
+        self.device_type = next(self.parameters()).device
+        if self.config.fl_local:
+            pred = np.concatenate([d[0] for d in test_step_outputs], axis = 0)
+            pred_map = np.concatenate([d[1] for d in test_step_outputs], axis = 0)
+            audio_name = np.concatenate([d[2] for d in test_step_outputs], axis = 0)
+            real_len = np.concatenate([d[3] for d in test_step_outputs], axis = 0)
+            heatmap_file = os.path.join(self.config.heatmap_dir, self.config.test_file + "_" + str(self.device_type) + ".npy")
+            save_npy = [
+                {
+                    "audio_name": audio_name[i],
+                    "heatmap": pred_map[i],
+                    "pred": pred[i],
+                    "real_len":real_len[i]
+                }
+                for i in range(len(pred))
+            ]
+            np.save(heatmap_file, save_npy)
+        else:
+            self.device_type = next(self.parameters()).device
+            pred = torch.cat([d[0] for d in test_step_outputs], dim = 0)
+            target = torch.cat([d[1] for d in test_step_outputs], dim = 0)
+            gather_pred = [torch.zeros_like(pred) for _ in range(dist.get_world_size())]
+            gather_target = [torch.zeros_like(target) for _ in range(dist.get_world_size())]
+            dist.barrier()
+            if self.config.dataset_type == "audioset":
+                metric_dict = {
+                "mAP": 0.,
+                "mAUC": 0.,
+                "dprime": 0.
+                }
+            else:
+                metric_dict = {
+                    "acc":0.
+                }
+            dist.all_gather(gather_pred, pred)
+            dist.all_gather(gather_target, target)
+            if dist.get_rank() == 0:
+                gather_pred = torch.cat(gather_pred, dim = 0).cpu().numpy()
+                gather_target = torch.cat(gather_target, dim = 0).cpu().numpy()
+                if self.config.dataset_type == "scv2":
+                    gather_target = np.argmax(gather_target, 1)
+                metric_dict = self.evaluate_metric(gather_pred, gather_target)
+                print(self.device_type, dist.get_world_size(), metric_dict, flush = True)
+            if self.config.dataset_type == "audioset":
+                self.log("mAP", metric_dict["mAP"] * float(dist.get_world_size()), on_epoch = True, prog_bar=True, sync_dist=True)
+                self.log("mAUC", metric_dict["mAUC"] * float(dist.get_world_size()), on_epoch = True, prog_bar=True, sync_dist=True)
+                self.log("dprime", metric_dict["dprime"] * float(dist.get_world_size()), on_epoch = True, prog_bar=True, sync_dist=True)
+            else:
+                self.log("acc", metric_dict["acc"] * float(dist.get_world_size()), on_epoch = True, prog_bar=True, sync_dist=True)
+            dist.barrier()
+    
+
+    def configure_optimizers(self):
+        optimizer = optim.AdamW(
+            filter(lambda p: p.requires_grad, self.parameters()),
+            lr = self.config.learning_rate, 
+            betas = (0.9, 0.999), eps = 1e-08, weight_decay = 0.05, 
+        )
+        # Change: SWA, deprecated
+        # optimizer = SWA(optimizer, swa_start=10, swa_freq=5)
+        def lr_foo(epoch):       
+            if epoch < 3:
+                # warm up lr
+                lr_scale = self.config.lr_rate[epoch]
+            else:
+                # warmup schedule
+                lr_pos = int(-1 - bisect.bisect_left(self.config.lr_scheduler_epoch, epoch))
+                if lr_pos < -3:
+                    lr_scale = max(self.config.lr_rate[0] * (0.98 ** epoch), 0.03 )
+                else:
+                    lr_scale = self.config.lr_rate[lr_pos]
+            return lr_scale
+        scheduler = optim.lr_scheduler.LambdaLR(
+            optimizer,
+            lr_lambda=lr_foo
+        )
+        
+        return [optimizer], [scheduler]
+
+
+
+class Ensemble_SEDWrapper(pl.LightningModule):
+    def __init__(self, sed_models, config, dataset):
+        super().__init__()
+
+        self.sed_models = nn.ModuleList(sed_models)
+        self.config = config
+        self.dataset = dataset
+
+    def evaluate_metric(self, pred, ans):
+        if self.config.dataset_type == "audioset":
+            mAP = np.mean(average_precision_score(ans, pred, average = None))
+            mAUC = np.mean(roc_auc_score(ans, pred, average = None))
+            dprime = d_prime(mAUC)
+            return {"mAP": mAP, "mAUC": mAUC, "dprime": dprime}
+        else:
+            acc = accuracy_score(ans, np.argmax(pred, 1))
+            return {"acc": acc}
+        
+    def forward(self, x, sed_index, mix_lambda = None):
+        self.sed_models[sed_index].eval()
+        preds = [] 
+        pred_maps = []
+        # time shifting optimization
+        if self.config.fl_local or self.config.dataset_type != "audioset": 
+            shift_num = 1 # framewise localization cannot allow the time shifting
+        else:
+            shift_num = 10
+        for i in range(shift_num):
+            pred, pred_map = self.sed_models[sed_index](x)
+            pred_maps.append(pred_map.unsqueeze(0))
+            preds.append(pred.unsqueeze(0))
+            x = self.time_shifting(x, shift_len = 100 * (i + 1))
+        preds = torch.cat(preds, dim=0)
+        pred_maps = torch.cat(pred_maps, dim = 0)
+        pred = preds.mean(dim = 0)
+        pred_map = pred_maps.mean(dim = 0)
+        return pred, pred_map
+
+
+    def time_shifting(self, x, shift_len):
+        shift_len = int(shift_len)
+        new_sample = torch.cat([x[:, shift_len:], x[:, :shift_len]], axis = 1)
+        return new_sample 
+
+    def test_step(self, batch, batch_idx):
+        self.device_type = next(self.parameters()).device
+        if self.config.fl_local:
+            pred = torch.zeros(len(batch["waveform"]), self.config.classes_num).float().to(self.device_type)
+            pred_map = torch.zeros(len(batch["waveform"]), 1024, self.config.classes_num).float().to(self.device_type)
+            for j in range(len(self.sed_models)):
+                temp_pred, temp_pred_map = self(batch["waveform"], j)
+                pred = pred + temp_pred
+                pred_map = pred_map + temp_pred_map 
+            pred = pred / len(self.sed_models)
+            pred_map = pred_map / len(self.sed_models)
+            return [
+                pred.detach().cpu().numpy(), 
+                pred_map.detach().cpu().numpy(),
+                batch["audio_name"],
+                batch["real_len"].cpu().numpy()
+            ]
+        else:
+            pred = torch.zeros(len(batch["waveform"]), self.config.classes_num).float().to(self.device_type)
+            for j in range(len(self.sed_models)):
+                temp_pred, _ = self(batch["waveform"], j)
+                pred = pred + temp_pred
+            pred = pred / len(self.sed_models)
+            return [
+                pred.detach(), 
+                batch["target"].detach(), 
+            ]
+
+    def test_epoch_end(self, test_step_outputs):
+        self.device_type = next(self.parameters()).device
+        if self.config.fl_local:
+            pred = np.concatenate([d[0] for d in test_step_outputs], axis = 0)
+            pred_map = np.concatenate([d[1] for d in test_step_outputs], axis = 0)
+            audio_name = np.concatenate([d[2] for d in test_step_outputs], axis = 0)
+            real_len = np.concatenate([d[3] for d in test_step_outputs], axis = 0)
+            heatmap_file = os.path.join(self.config.heatmap_dir, self.config.test_file + "_" + str(self.device_type) + ".npy")
+            print(pred.shape)
+            print(pred_map.shape)
+            print(real_len.shape)
+            save_npy = [
+                {
+                    "audio_name": audio_name[i],
+                    "heatmap": pred_map[i],
+                    "pred": pred[i],
+                    "real_len":real_len[i]
+                }
+                for i in range(len(pred))
+            ]
+            np.save(heatmap_file, save_npy)
+        else:
+            pred = torch.cat([d[0] for d in test_step_outputs], dim = 0)
+            target = torch.cat([d[1] for d in test_step_outputs], dim = 0)
+            gather_pred = [torch.zeros_like(pred) for _ in range(dist.get_world_size())]
+            gather_target = [torch.zeros_like(target) for _ in range(dist.get_world_size())]
+
+            dist.barrier()
+            if self.config.dataset_type == "audioset":
+                metric_dict = {
+                "mAP": 0.,
+                "mAUC": 0.,
+                "dprime": 0.
+                }
+            else:
+                metric_dict = {
+                    "acc":0.
+                }
+            dist.all_gather(gather_pred, pred)
+            dist.all_gather(gather_target, target)
+            if dist.get_rank() == 0:
+                gather_pred = torch.cat(gather_pred, dim = 0).cpu().numpy()
+                gather_target = torch.cat(gather_target, dim = 0).cpu().numpy()
+                if self.config.dataset_type == "scv2":
+                    gather_target = np.argmax(gather_target, 1)
+                metric_dict = self.evaluate_metric(gather_pred, gather_target)
+                print(self.device_type, dist.get_world_size(), metric_dict, flush = True)
+            if self.config.dataset_type == "audioset":
+                self.log("mAP", metric_dict["mAP"] * float(dist.get_world_size()), on_epoch = True, prog_bar=True, sync_dist=True)
+                self.log("mAUC", metric_dict["mAUC"] * float(dist.get_world_size()), on_epoch = True, prog_bar=True, sync_dist=True)
+                self.log("dprime", metric_dict["dprime"] * float(dist.get_world_size()), on_epoch = True, prog_bar=True, sync_dist=True)
+            else:
+                self.log("acc", metric_dict["acc"] * float(dist.get_world_size()), on_epoch = True, prog_bar=True, sync_dist=True)
+            dist.barrier()
+
+
+