train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5ea0a3e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import timm\n",
    "import logging\n",
    "import time\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import warnings\n",
    "from contextlib import contextmanager\n",
    "from pathlib import Path\n",
    "from typing import List\n",
    "from typing import Optional\n",
    "from sklearn import metrics\n",
    "from tqdm import tqdm\n",
    "import random\n",
    "\n",
    "from collections import defaultdict\n",
    "\n",
    "\n",
    "import scipy\n",
    "from IPython.display import Audio, display\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn import functional as F\n",
    "import torchaudio.functional as taF\n",
    "import librosa\n",
    "import torchaudio as ta\n",
    "\n",
    "from torch_audiomentations import Compose, Gain, PolarityInversion, Shift\n",
    "from audiomentations import Compose as Compose_aud\n",
    "from audiomentations import Shift as Shift_aud\n",
    "from audiomentations import Gain as Gain_aud\n",
    "from audiomentations import Normalize as Normalize_aud\n",
    "from audiomentations import AddGaussianNoise, TimeStretch, PitchShift, OneOf\n",
    "from torchaudio import transforms as T\n",
    "\n",
    "\n",
    "from typing import Callable\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import WeightedRandomSampler\n",
    "from torch.optim import Adam, AdamW\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
    "\n",
    "\n",
    "path = '/home/acoustery/kf/noteb/kaggle/birds/'\n",
    "\n",
    "#import noisereduce as nr\n",
    "from ast import literal_eval as LE\n",
    "from IPython.display import clear_output\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error, roc_auc_score\n",
    "\n",
    "from albumentations.core.transforms_interface import ImageOnlyTransform\n",
    "#from torchlibrosa.stft import LogmelFilterBank, Spectrogram\n",
    "#from torchlibrosa.augmentation import SpecAugmentation\n",
    "\n",
    "#import transformers\n",
    "import gc\n",
    "from colorama import Fore, Back, Style\n",
    "c_  = Fore.GREEN\n",
    "sr_ = Style.RESET_ALL\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "#import transformers\n",
    "from torch.cuda import amp\n",
    "from torch.cuda.amp import autocast, GradScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07327ae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CFG:\n",
    "    ######################\n",
    "    # Globals #\n",
    "    ######################\n",
    "    seed = 42\n",
    "    epochs = 80\n",
    "    folds = [2,3,4] # [0, 1, 2, 3, 4]\n",
    "    N_FOLDS = 15\n",
    "    LR = 0.0008 #1e-3\n",
    "    ETA_MIN = 1e-7\n",
    "    WEIGHT_DECAY = 1e-5\n",
    "    train_bs = 32 # 32\n",
    "    valid_bs = train_bs*2 # 64\n",
    "    base_model_name = \"tf_efficientnet_b0_ns\"\n",
    "    EARLY_STOPPING = True\n",
    "    DEBUG = False # True\n",
    "    apex = True\n",
    "    \n",
    "    dropout = 0.4\n",
    "    pooling = \"max\"\n",
    "    pretrained = True\n",
    "    num_classes = 152\n",
    "    in_channels = 3\n",
    "    target_columns = 'afrsil1 akekee akepa1 akiapo akikik amewig aniani apapan arcter \\\n",
    "                      barpet bcnher belkin1 bkbplo bknsti bkwpet blkfra blknod bongul \\\n",
    "                      brant brnboo brnnod brnowl brtcur bubsan buffle bulpet burpar buwtea \\\n",
    "                      cacgoo1 calqua cangoo canvas caster1 categr chbsan chemun chukar cintea \\\n",
    "                      comgal1 commyn compea comsan comwax coopet crehon dunlin elepai ercfra eurwig \\\n",
    "                      fragul gadwal gamqua glwgul gnwtea golphe grbher3 grefri gresca gryfra gwfgoo \\\n",
    "                      hawama hawcoo hawcre hawgoo hawhaw hawpet1 hoomer houfin houspa hudgod iiwi incter1 \\\n",
    "                      jabwar japqua kalphe kauama laugul layalb lcspet leasan leater1 lessca lesyel lobdow lotjae \\\n",
    "                      madpet magpet1 mallar3 masboo mauala maupar merlin mitpar moudov norcar norhar2 normoc norpin \\\n",
    "                      norsho nutman oahama omao osprey pagplo palila parjae pecsan peflov perfal pibgre pomjae puaioh \\\n",
    "                      reccar redava redjun redpha1 refboo rempar rettro ribgul rinduc rinphe rocpig rorpar rudtur ruff \\\n",
    "                      saffin sander semplo sheowl shtsan skylar snogoo sooshe sooter1 sopsku1 sora spodov sposan \\\n",
    "                      towsol wantat1 warwhe1 wesmea wessan wetshe whfibi whiter whttro wiltur yebcar yefcan zebdov'.split()\n",
    "    \n",
    "    scored = [\"akiapo\", \"aniani\", \"apapan\", \"barpet\", \"crehon\", \"elepai\", \"ercfra\", \"hawama\", \"hawcre\",\n",
    "              \"hawgoo\", \"hawhaw\", \"hawpet1\", \"houfin\", \"iiwi\", \"jabwar\", \"maupar\", \n",
    "              \"omao\", \"puaioh\", \"skylar\", \"warwhe1\", \"yefcan\"]\n",
    "    \n",
    "    rare = ['omao', 'akiapo', 'barpet','hawama', 'elepai', 'aniani', 'hawgoo', 'ercfra', 'maupar',\n",
    "            'hawpet1', 'hawhaw', 'crehon', 'puaioh']\n",
    "    \n",
    "    img_size = 224 # 128\n",
    "    main_metric = \"epoch_f1_at_03\"\n",
    "\n",
    "    period = 5\n",
    "    n_mels = 224 # 128\n",
    "    fmin = 20\n",
    "    fmax = 16000\n",
    "    n_fft = 2048\n",
    "    hop_length = 501\n",
    "    sample_rate = 32000\n",
    "    melspectrogram_parameters = {\n",
    "        \"n_mels\": 224, # 128,\n",
    "        \"fmin\": 20,\n",
    "        \"fmax\": 16000\n",
    "    }\n",
    "    \n",
    "    cuda_num = 7\n",
    "    device = torch.device(f'cuda:{cuda_num}')if torch.cuda.is_available() else torch.device('cpu')\n",
    "    notebook_number = 7\n",
    "    path_to_checks = '/home/acoustery/kf/noteb/kaggle/AST_m/checks/'\n",
    "    \n",
    "    base_dir = '/home/acoustery/kf/noteb/kaggle/AST_m/'\n",
    "    \n",
    "    seed_for_split = 1\n",
    "    \n",
    "    \n",
    "    \n",
    "class AudioParams:\n",
    "    \"\"\"\n",
    "    Parameters used for the audio data\n",
    "    \"\"\"\n",
    "    sr = CFG.sample_rate\n",
    "    duration = CFG.period\n",
    "    # Melspectrogram\n",
    "    n_mels = CFG.n_mels\n",
    "    fmin = CFG.fmin\n",
    "    fmax = CFG.fmax\n",
    "    hop_length = CFG.hop_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "badf7db5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15966, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_label</th>\n",
       "      <th>secondary_labels</th>\n",
       "      <th>filename</th>\n",
       "      <th>kfold_raw</th>\n",
       "      <th>kfold_split</th>\n",
       "      <th>logits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>afrsil1</td>\n",
       "      <td>[]</td>\n",
       "      <td>/home/acoustery/data/kf/birds/all_aud/afrsil1/...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.816120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>afrsil1</td>\n",
       "      <td>[]</td>\n",
       "      <td>/home/acoustery/data/kf/birds/all_aud/afrsil1/...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.834092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>afrsil1</td>\n",
       "      <td>[]</td>\n",
       "      <td>/home/acoustery/data/kf/birds/all_aud/afrsil1/...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.841536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>afrsil1</td>\n",
       "      <td>[]</td>\n",
       "      <td>/home/acoustery/data/kf/birds/all_aud/afrsil1/...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.805498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>afrsil1</td>\n",
       "      <td>[]</td>\n",
       "      <td>/home/acoustery/data/kf/birds/all_aud/afrsil1/...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.815090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  primary_label secondary_labels  \\\n",
       "0       afrsil1               []   \n",
       "1       afrsil1               []   \n",
       "2       afrsil1               []   \n",
       "3       afrsil1               []   \n",
       "4       afrsil1               []   \n",
       "\n",
       "                                            filename  kfold_raw  kfold_split  \\\n",
       "0  /home/acoustery/data/kf/birds/all_aud/afrsil1/...          1            1   \n",
       "1  /home/acoustery/data/kf/birds/all_aud/afrsil1/...          1            0   \n",
       "2  /home/acoustery/data/kf/birds/all_aud/afrsil1/...          1            0   \n",
       "3  /home/acoustery/data/kf/birds/all_aud/afrsil1/...          5            3   \n",
       "4  /home/acoustery/data/kf/birds/all_aud/afrsil1/...          5            4   \n",
       "\n",
       "     logits  \n",
       "0  0.816120  \n",
       "1  0.834092  \n",
       "2  0.841536  \n",
       "3  0.805498  \n",
       "4  0.815090  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv(CFG.base_dir + 'pseudo_df.csv').reset_index(drop=True)\n",
    "train_data = train_data[train_data['logits'] > 0.8].reset_index(drop=True)\n",
    "print(train_data.shape)\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "09053e94",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "OUTPUT_DIR = '/home/acoustery/kf/noteb/kaggle/outputs'\n",
    "if not os.path.exists(OUTPUT_DIR):\n",
    "    os.makedirs(OUTPUT_DIR)\n",
    "   \n",
    "    \n",
    "def set_seed(seed=42):\n",
    "    random.seed(seed)\n",
    "    os.environ[\"PYTHONHASHSEED\"] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    \n",
    "set_seed(CFG.seed)\n",
    "device = CFG.device\n",
    "\n",
    "\n",
    "class AverageMeter(object):\n",
    "    \"\"\"Computes and stores the average and current value\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.reset()\n",
    "\n",
    "    def reset(self):\n",
    "        self.val = 0\n",
    "        self.avg = 0\n",
    "        self.sum = 0\n",
    "        self.count = 0\n",
    "\n",
    "    def update(self, val, n=1):\n",
    "        self.val = val\n",
    "        self.sum += val * n\n",
    "        self.count += n\n",
    "        self.avg = self.sum / self.count\n",
    "        \n",
    "\n",
    "class MetricMeter(object):\n",
    "    def __init__(self):\n",
    "        self.reset()\n",
    "    \n",
    "    def reset(self):\n",
    "        self.y_true = []\n",
    "        self.y_pred = []\n",
    "    \n",
    "    def update(self, y_true, y_pred):\n",
    "        self.y_true.extend(y_true.cpu().detach().numpy().tolist())\n",
    "        self.y_pred.extend(y_pred[\"clipwise_output\"].cpu().detach().numpy().tolist())\n",
    "\n",
    "    @property\n",
    "    def avg(self):\n",
    "        self.f1_005 = metrics.f1_score(np.array(self.y_true) == 1, np.array(self.y_pred) > 0.05, average=\"micro\")\n",
    "        self.f1_01 = metrics.f1_score(np.array(self.y_true) == 1, np.array(self.y_pred) > 0.1, average=\"micro\")\n",
    "        self.f1_015 = metrics.f1_score(np.array(self.y_true) == 1, np.array(self.y_pred) > 0.15, average=\"micro\")\n",
    "        self.f1_02 = metrics.f1_score(np.array(self.y_true) == 1, np.array(self.y_pred) > 0.2, average=\"micro\")\n",
    "        self.f1_025 = metrics.f1_score(np.array(self.y_true) == 1, np.array(self.y_pred) > 0.25, average=\"micro\")\n",
    "        self.f1_03 = metrics.f1_score(np.array(self.y_true) == 1, np.array(self.y_pred) > 0.3, average=\"micro\")\n",
    "        self.f1_035 = metrics.f1_score(np.array(self.y_true) == 1, np.array(self.y_pred) > 0.35, average=\"micro\")\n",
    "        self.f1_04 = metrics.f1_score(np.array(self.y_true) == 1, np.array(self.y_pred) > 0.4, average=\"micro\")\n",
    "        self.f1_045 = metrics.f1_score(np.array(self.y_true) == 1, np.array(self.y_pred) > 0.45, average=\"micro\")\n",
    "        self.f1_05 = metrics.f1_score(np.array(self.y_true) == 1, np.array(self.y_pred) > 0.5, average=\"micro\")\n",
    "        \n",
    "        return {\n",
    "            \"f1_at_005\" : self.f1_005,\n",
    "            \"f1_at_01\" : self.f1_01,\n",
    "            \"f1_at_015\" : self.f1_015,\n",
    "            \"f1_at_02\" : self.f1_02,\n",
    "            \"f1_at_025\": self.f1_025,\n",
    "            \"f1_at_03\": self.f1_03,\n",
    "            \"f1_at_035\": self.f1_035,\n",
    "            \"f1_at_04\": self.f1_04,\n",
    "            \"f1_at_045\": self.f1_045,\n",
    "            \"f1_at_05\": self.f1_05\n",
    "        }\n",
    "    \n",
    "    \n",
    "# https://www.kaggle.com/c/rfcx-species-audio-detection/discussion/213075\n",
    "class BCEFocalLoss(nn.Module):\n",
    "    def __init__(self, alpha=0.25, gamma=2.0):\n",
    "        super().__init__()\n",
    "        self.alpha = alpha\n",
    "        self.gamma = gamma\n",
    "\n",
    "    def forward(self, preds, targets):\n",
    "        bce_loss = nn.BCEWithLogitsLoss(reduction='none')(preds, targets)\n",
    "        probas = torch.sigmoid(preds)\n",
    "        loss = targets * self.alpha * \\\n",
    "            (1. - probas)**self.gamma * bce_loss + \\\n",
    "            (1. - targets) * probas**self.gamma * bce_loss\n",
    "        loss = loss.mean()\n",
    "        return loss\n",
    "\n",
    "\n",
    "class BCEFocal2WayLoss(nn.Module):\n",
    "    def __init__(self, weights=[1, 1], class_weights=None):\n",
    "        super().__init__()\n",
    "\n",
    "        self.focal = BCEFocalLoss()\n",
    "\n",
    "        self.weights = weights\n",
    "\n",
    "    def forward(self, input, target):\n",
    "        input_ = input[\"logit\"]\n",
    "        target = target.float()\n",
    "\n",
    "        framewise_output = input[\"framewise_logit\"]\n",
    "        clipwise_output_with_max, _ = framewise_output.max(dim=1)\n",
    "\n",
    "        loss = self.focal(input_, target)\n",
    "        aux_loss = self.focal(clipwise_output_with_max, target)\n",
    "\n",
    "        return self.weights[0] * loss + self.weights[1] * aux_loss\n",
    "\n",
    "\n",
    "class AudioTransform:\n",
    "    def __init__(self, always_apply=False, p=0.5):\n",
    "        self.always_apply = always_apply\n",
    "        self.p = p\n",
    "\n",
    "    def __call__(self, y: np.ndarray, sr):\n",
    "        if self.always_apply:\n",
    "            return self.apply(y, sr=sr)\n",
    "        else:\n",
    "            if np.random.rand() < self.p:\n",
    "                return self.apply(y, sr=sr)\n",
    "            else:\n",
    "                return y\n",
    "\n",
    "    def apply(self, y: np.ndarray, **params):\n",
    "        raise NotImplementedError\n",
    "        \n",
    "class NoiseInjection(AudioTransform):\n",
    "    def __init__(self, always_apply=False, p=0.5, max_noise_level=0.5):\n",
    "        super().__init__(always_apply, p)\n",
    "\n",
    "        self.noise_level = (0.0, max_noise_level)\n",
    "\n",
    "    def apply(self, y: np.ndarray, **params):\n",
    "        noise_level = np.random.uniform(*self.noise_level)\n",
    "        noise = np.random.randn(len(y))\n",
    "        augmented = (y + noise * noise_level).astype(y.dtype)\n",
    "        return augmented\n",
    "    \n",
    "\n",
    "def init_layer(layer):\n",
    "    nn.init.xavier_uniform_(layer.weight)\n",
    "\n",
    "    if hasattr(layer, \"bias\"):\n",
    "        if layer.bias is not None:\n",
    "            layer.bias.data.fill_(0.)\n",
    "\n",
    "\n",
    "def init_bn(bn):\n",
    "    bn.bias.data.fill_(0.)\n",
    "    bn.weight.data.fill_(1.0)\n",
    "\n",
    "\n",
    "def init_weights(model):\n",
    "    classname = model.__class__.__name__\n",
    "    if classname.find(\"Conv2d\") != -1:\n",
    "        nn.init.xavier_uniform_(model.weight, gain=np.sqrt(2))\n",
    "        model.bias.data.fill_(0)\n",
    "    elif classname.find(\"BatchNorm\") != -1:\n",
    "        model.weight.data.normal_(1.0, 0.02)\n",
    "        model.bias.data.fill_(0)\n",
    "    elif classname.find(\"GRU\") != -1:\n",
    "        for weight in model.parameters():\n",
    "            if len(weight.size()) > 1:\n",
    "                nn.init.orghogonal_(weight.data)\n",
    "    elif classname.find(\"Linear\") != -1:\n",
    "        model.weight.data.normal_(0, 0.01)\n",
    "        model.bias.data.zero_()\n",
    "\n",
    "\n",
    "def interpolate(x: torch.Tensor, ratio: int):\n",
    "    \"\"\"Interpolate data in time domain. This is used to compensate the\n",
    "    resolution reduction in downsampling of a CNN.\n",
    "    Args:\n",
    "      x: (batch_size, time_steps, classes_num)\n",
    "      ratio: int, ratio to interpolate\n",
    "    Returns:\n",
    "      upsampled: (batch_size, time_steps * ratio, classes_num)\n",
    "    \"\"\"\n",
    "    (batch_size, time_steps, classes_num) = x.shape\n",
    "    upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)\n",
    "    upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)\n",
    "    return upsampled\n",
    "\n",
    "\n",
    "def pad_framewise_output(framewise_output: torch.Tensor, frames_num: int):\n",
    "    \"\"\"Pad framewise_output to the same length as input frames. The pad value\n",
    "    is the same as the value of the last frame.\n",
    "    Args:\n",
    "      framewise_output: (batch_size, frames_num, classes_num)\n",
    "      frames_num: int, number of frames to pad\n",
    "    Outputs:\n",
    "      output: (batch_size, frames_num, classes_num)\n",
    "    \"\"\"\n",
    "    output = F.interpolate(\n",
    "        framewise_output.unsqueeze(1),\n",
    "        size=(frames_num, framewise_output.size(2)),\n",
    "        align_corners=True,\n",
    "        mode=\"bilinear\").squeeze(1)\n",
    "\n",
    "    return output\n",
    "\n",
    "\n",
    "class AttBlockV2(nn.Module):\n",
    "    def __init__(self,\n",
    "                 in_features: int,\n",
    "                 out_features: int,\n",
    "                 activation=\"linear\"):\n",
    "        super().__init__()\n",
    "\n",
    "        self.activation = activation\n",
    "        self.att = nn.Conv1d(\n",
    "            in_channels=in_features,\n",
    "            out_channels=out_features,\n",
    "            kernel_size=1,\n",
    "            stride=1,\n",
    "            padding=0,\n",
    "            bias=True)\n",
    "        self.cla = nn.Conv1d(\n",
    "            in_channels=in_features,\n",
    "            out_channels=out_features,\n",
    "            kernel_size=1,\n",
    "            stride=1,\n",
    "            padding=0,\n",
    "            bias=True)\n",
    "\n",
    "        self.init_weights()\n",
    "\n",
    "    def init_weights(self):\n",
    "        init_layer(self.att)\n",
    "        init_layer(self.cla)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x: (n_samples, n_in, n_time)\n",
    "        norm_att = torch.softmax(torch.tanh(self.att(x)), dim=-1)\n",
    "        cla = self.nonlinear_transform(self.cla(x))\n",
    "        x = torch.sum(norm_att * cla, dim=2)\n",
    "        return x, norm_att, cla\n",
    "\n",
    "    def nonlinear_transform(self, x):\n",
    "        if self.activation == 'linear':\n",
    "            return x\n",
    "        elif self.activation == 'sigmoid':\n",
    "            return torch.sigmoid(x)\n",
    "\n",
    "\n",
    "class Model_image(nn.Module):\n",
    "    def __init__(self, base_model_name: str, pretrained=False, num_classes=24,\n",
    "                 in_channels=1, dropout_rate=0.4):\n",
    "        super().__init__()\n",
    "        \n",
    "        self._dropout_rate = dropout_rate\n",
    "        \n",
    "        self.bn0 = nn.BatchNorm2d(CFG.n_mels)\n",
    "\n",
    "        base_model = timm.create_model(\n",
    "            base_model_name, pretrained=pretrained, in_chans=in_channels)\n",
    "        layers = list(base_model.children())[:-2]\n",
    "        self.encoder = nn.Sequential(*layers)\n",
    "\n",
    "        if hasattr(base_model, \"fc\"):\n",
    "            in_features = base_model.fc.in_features\n",
    "        else:\n",
    "            in_features = base_model.classifier.in_features\n",
    "\n",
    "        self.fc1 = nn.Linear(in_features, in_features, bias=True)\n",
    "        self.att_block = AttBlockV2(\n",
    "            in_features, num_classes, activation=\"sigmoid\")\n",
    "\n",
    "        self.init_weight()\n",
    "\n",
    "    def init_weight(self):\n",
    "        init_bn(self.bn0)\n",
    "        init_layer(self.fc1)\n",
    "        \n",
    "\n",
    "    def forward(self, input_data):\n",
    "        x = input_data # (batch_size, 3, time_steps, mel_bins)\n",
    "\n",
    "        frames_num = x.shape[2]\n",
    "\n",
    "        x = x.transpose(1, 3)\n",
    "        x = self.bn0(x)\n",
    "        x = x.transpose(1, 3)\n",
    "\n",
    "        x = x.transpose(2, 3)\n",
    "\n",
    "        x = self.encoder(x)\n",
    "        \n",
    "        # Aggregate in frequency axis\n",
    "        x = torch.mean(x, dim=3)\n",
    "\n",
    "        x1 = F.max_pool1d(x, kernel_size=3, stride=1, padding=1)\n",
    "        x2 = F.avg_pool1d(x, kernel_size=3, stride=1, padding=1)\n",
    "        x = x1 + x2\n",
    "\n",
    "        x = F.dropout(x, p=self._dropout_rate, training=self.training)\n",
    "        x = x.transpose(1, 2)\n",
    "        x = F.relu_(self.fc1(x))\n",
    "        x = x.transpose(1, 2)\n",
    "        x = F.dropout(x, p=self._dropout_rate, training=self.training)\n",
    "\n",
    "        (clipwise_output, norm_att, segmentwise_output) = self.att_block(x)\n",
    "        logit = torch.sum(norm_att * self.att_block.cla(x), dim=2)\n",
    "        segmentwise_logit = self.att_block.cla(x).transpose(1, 2)\n",
    "        segmentwise_output = segmentwise_output.transpose(1, 2)\n",
    "\n",
    "        interpolate_ratio = frames_num // segmentwise_output.size(1)\n",
    "\n",
    "        # Get framewise output\n",
    "        framewise_output = interpolate(segmentwise_output,\n",
    "                                       interpolate_ratio)\n",
    "        framewise_output = pad_framewise_output(framewise_output, frames_num)\n",
    "\n",
    "        framewise_logit = interpolate(segmentwise_logit, interpolate_ratio)\n",
    "        framewise_logit = pad_framewise_output(framewise_logit, frames_num)\n",
    "\n",
    "        output_dict = {\n",
    "            'framewise_output': framewise_output,\n",
    "            'clipwise_output': clipwise_output,\n",
    "            'logit': logit,\n",
    "            'framewise_logit': framewise_logit,\n",
    "        }\n",
    "\n",
    "        return output_dict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd4134b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_conf = {'num_mel_bins': 224, 'target_length': 512, 'freqm': 24, 'timem': 96, \n",
    "              'mixup': 0.4, 'skip_norm': True, 'mode': 'train', 'dataset': 'Birds custom',\n",
    "             'mean': -6.98279, 'std': 3.05492, 'noise': True}\n",
    "\n",
    "\n",
    "val_audio_conf = {'num_mel_bins': 224, 'target_length': 512, 'freqm': 0, 'timem': 0, \n",
    "              'mixup': -1, 'skip_norm': True, 'mode': 'eval', 'dataset': 'Birds custom',\n",
    "             'mean': -6.98279, 'std': 3.05492, 'noise': False}\n",
    "\n",
    "\n",
    "def crop_or_pad(y, sr=CFG.sample_rate, length=CFG.sample_rate*CFG.period, mode='train'):\n",
    "    leny = y.size(1)\n",
    "    if leny <= length:\n",
    "        y = torch.nn.functional.pad(y, (0,length - leny), \"constant\", 0)\n",
    "    else:\n",
    "        if mode != 'train':\n",
    "            start_ = 0\n",
    "        else:        \n",
    "            start_ = np.random.randint(leny - length)\n",
    "        y = y[:, start_: start_ + length]\n",
    "    return y\n",
    "\n",
    "\n",
    "\n",
    "class AudiosetDataset(Dataset):\n",
    "    def __init__(self, df_in: pd.DataFrame, audio_conf=audio_conf, label_csv=None):\n",
    "        \"\"\"\n",
    "        Dataset that manages audio recordings\n",
    "        :param audio_conf: Dictionary containing the audio loading and preprocessing settings\n",
    "        :param dataset_json_file\n",
    "        \"\"\"\n",
    "        self.data = df_in\n",
    "        \n",
    "        self.audio_conf = audio_conf\n",
    "        \n",
    "        self.mode = self.audio_conf.get('mode')\n",
    "        print('---------------the {:s} dataloader---------------'.format(self.audio_conf.get('mode')))\n",
    "        self.melbins = self.audio_conf.get('num_mel_bins')\n",
    "        self.freqm = self.audio_conf.get('freqm')\n",
    "        self.timem = self.audio_conf.get('timem')\n",
    "        print('now using following mask: {:d} freq, {:d} time'.format(self.audio_conf.get('freqm'), \n",
    "                                                                      self.audio_conf.get('timem')))\n",
    "        self.mixup = self.audio_conf.get('mixup')\n",
    "        print('now using mix-up with rate {:f}'.format(self.mixup))\n",
    "        self.dataset = self.audio_conf.get('dataset')\n",
    "        print('now process ' + self.dataset)\n",
    "        # dataset spectrogram mean and std, used to normalize the input\n",
    "        self.norm_mean = self.audio_conf.get('mean')\n",
    "        self.norm_std = self.audio_conf.get('std')\n",
    "        print('use dataset mean {:.3f} and std {:.3f} to normalize the input'.format(self.norm_mean, self.norm_std))\n",
    "        # if add noise for data augmentation\n",
    "        self.noise = self.audio_conf.get('noise')\n",
    "        if self.noise == True:\n",
    "            print('now use noise augmentation')\n",
    "\n",
    "        self.label_num = len(CFG.target_columns)\n",
    "        print('number of classes is {:d}'.format(self.label_num))\n",
    "        \n",
    "        print(f'Dataset size: {df_in.shape[0]}')\n",
    "        \n",
    "        self.freqm_t = ta.transforms.FrequencyMasking(self.freqm)\n",
    "        self.timem_t = ta.transforms.TimeMasking(self.timem)\n",
    "        \n",
    "        if self.mode == 'train':\n",
    "            self.wave_transforms = Compose_aud([\n",
    "                                OneOf([AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.011, p=1),\n",
    "                                NoiseInjection(p=1, max_noise_level=0.04)], p=0.4),\n",
    "                                \n",
    "                                PitchShift(min_semitones=-4, max_semitones=4, p=0.1),\n",
    "                                Shift_aud(min_fraction=-0.5, max_fraction=0.5, p=0.1),\n",
    "                                Gain_aud(min_gain_in_db=-12, max_gain_in_db=12, p=0.2),    \n",
    "                                Normalize_aud(p=1.)\n",
    "\n",
    "                            ])\n",
    "        else:\n",
    "            self.wave_transforms = Compose_aud([Normalize_aud(p=1)])\n",
    "            \n",
    "    def normis(self, clip, sr, SR_):\n",
    "        if clip.size(0) != 1:\n",
    "            clip = clip.mean(dim=0, keepdim=True)\n",
    "            \n",
    "        if sr != SR_:\n",
    "            clip = taF.resample(clip, sr, SR_, lowpass_filter_width=64,\n",
    "                                            rolloff=0.9475937167399596, resampling_method=\"kaiser_window\",\n",
    "                                            beta=14.769656459379492)\n",
    "        return clip\n",
    "        \n",
    "    def _wav2fbank(self, filename, filename2=None):\n",
    "            # mixup\n",
    "            SR = 32000\n",
    "            if filename2 == None:\n",
    "                waveform, sr1 = ta.load(filename)\n",
    "                waveform = self.normis(waveform, sr1, SR)\n",
    "                waveform = crop_or_pad(waveform, sr=SR, mode=self.mode)\n",
    "                waveform = waveform - waveform.mean()\n",
    "            # mixup\n",
    "            else:\n",
    "                waveform1, sr1 = ta.load(filename)\n",
    "                waveform1 = self.normis(waveform1, sr1, SR)\n",
    "                waveform1 = crop_or_pad(waveform1, sr=SR, mode=self.mode)\n",
    "                waveform2, sr2 = ta.load(filename2)\n",
    "                waveform2 = self.normis(waveform2, sr2, SR)\n",
    "                waveform2 = crop_or_pad(waveform2, sr=SR, mode=self.mode)\n",
    "\n",
    "                waveform1 = waveform1 - waveform1.mean()\n",
    "                waveform2 = waveform2 - waveform2.mean()\n",
    "\n",
    "                # sample lambda from uniform distribution\n",
    "                #mix_lambda = random.random()\n",
    "                # sample lambda from beta distribtion\n",
    "                mix_lambda = np.random.beta(10, 10)\n",
    "\n",
    "                mix_waveform = mix_lambda * waveform1 + (1 - mix_lambda) * waveform2\n",
    "                waveform = mix_waveform - mix_waveform.mean()\n",
    "            \n",
    "            waveform = torch.tensor(self.wave_transforms(samples=waveform[0].numpy(), sample_rate=SR)).unsqueeze(0)\n",
    "            fbank = ta.compliance.kaldi.fbank(waveform, htk_compat=True, sample_frequency=SR, \n",
    "                                                      use_energy=False, window_type='hanning', \n",
    "                                                      num_mel_bins=self.melbins, dither=0.0, frame_shift=9.7)\n",
    "\n",
    "            target_length = self.audio_conf.get('target_length')\n",
    "            n_frames = fbank.shape[0]\n",
    "\n",
    "            p = target_length - n_frames\n",
    "\n",
    "            # cut and pad\n",
    "            if p > 0:\n",
    "                m = torch.nn.ZeroPad2d((0, 0, 0, p))\n",
    "                fbank = m(fbank)\n",
    "            elif p < 0:\n",
    "                fbank = fbank[0:target_length, :]\n",
    "\n",
    "            if filename2 == None:\n",
    "                return fbank, 0\n",
    "            else:\n",
    "                return fbank, mix_lambda\n",
    "            \n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        \"\"\"\n",
    "        returns: image, audio, nframes\n",
    "        where image is a FloatTensor of size (3, H, W)\n",
    "        audio is a FloatTensor of size (N_freq, N_frames) for spectrogram, or (N_frames) for waveform\n",
    "        nframes is an integer\n",
    "        \"\"\"\n",
    "        # do mix-up for this sample (controlled by the given mixup rate) \n",
    "        \n",
    "        if random.random() < self.mixup:\n",
    "            datum = self.data.loc[index, :]\n",
    "            mix_sample_idx = random.randint(0, len(self.data)-1)\n",
    "            mix_datum = self.data.loc[mix_sample_idx, :]\n",
    "            fbank, mix_lambda = self._wav2fbank(datum['filename'], mix_datum['filename'])\n",
    "            \n",
    "            label_indices = np.zeros(self.label_num)\n",
    "            # add sample 1 labels\n",
    "            for bird_code in LE(datum['secondary_labels']):\n",
    "                label_indices[CFG.target_columns.index(bird_code)] += mix_lambda * 0.4\n",
    "            label_indices[CFG.target_columns.index(datum['primary_label'])] += mix_lambda\n",
    "            # add sample 2 labels\n",
    "            for bird_code in LE(mix_datum['secondary_labels']):\n",
    "                label_indices[CFG.target_columns.index(bird_code)] += (1.0-mix_lambda) * 0.4\n",
    "            label_indices[CFG.target_columns.index(mix_datum['primary_label'])] += 1.0-mix_lambda\n",
    "            \n",
    "            label_indices = torch.FloatTensor(label_indices)\n",
    "        # if not do mixup\n",
    "        else:\n",
    "            datum = self.data.loc[index, :]\n",
    "            label_indices = np.zeros(self.label_num)\n",
    "            fbank, mix_lambda = self._wav2fbank(datum['filename'])\n",
    "            for bird_code in LE(datum['secondary_labels']):\n",
    "                label_indices[CFG.target_columns.index(bird_code)] = 0.4\n",
    "            label_indices[CFG.target_columns.index(datum['primary_label'])] = 1.\n",
    "\n",
    "            label_indices = torch.FloatTensor(label_indices)\n",
    "\n",
    "        # SpecAug, not do for eval set\n",
    "        #freqm = torchaudio.transforms.FrequencyMasking(self.freqm)\n",
    "        #timem = torchaudio.transforms.TimeMasking(self.timem)\n",
    "        fbank = torch.transpose(fbank, 0, 1)\n",
    "        fbank = fbank.unsqueeze(0)\n",
    "        if self.freqm != 0:\n",
    "            fbank = self.freqm_t(fbank)\n",
    "        if self.timem != 0:\n",
    "            fbank = self.timem_t(fbank)\n",
    "        fbank = fbank.squeeze(0)\n",
    "        fbank = torch.transpose(fbank, 0, 1)\n",
    "\n",
    "        # normalize the input\n",
    "        fbank = (fbank - self.norm_mean) / (self.norm_std * 2)\n",
    "\n",
    "        if self.noise == True and False:\n",
    "            fbank = fbank + torch.rand(fbank.shape[0], fbank.shape[1]) * np.random.rand() / 10\n",
    "            fbank = torch.roll(fbank, np.random.randint(-10, 10), 0)\n",
    "\n",
    "        mix_ratio = min(mix_lambda, 1-mix_lambda) / max(mix_lambda, 1-mix_lambda)\n",
    "\n",
    "        # the output fbank shape is [time_frame_num, frequency_bins], e.g., [1024, 128]\n",
    "        fbank = torch.stack([fbank, fbank, fbank])\n",
    "        return {'image':fbank, 'targets':label_indices}\n",
    "    \n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "297b7944",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_weights_arr(dff, cols, max_count=100, max_weight=10, save=False):\n",
    "    d_c = {}\n",
    "    d_w = {}\n",
    "    for b in cols:\n",
    "        d_c[b] = dff[dff.primary_label == b].shape[0]\n",
    "    \n",
    "    for k in d_c:\n",
    "        if d_c[k] == 0:\n",
    "            ite = 1\n",
    "        else:\n",
    "            ite = max_count / d_c[k]\n",
    "        if ite > max_weight:\n",
    "            ite = max_weight\n",
    "        elif ite < 1:\n",
    "            ite = 1\n",
    "        d_w[k] = ite\n",
    "    weights_ar = []\n",
    "    for bir in cols:\n",
    "        weights_ar.append(d_w[bir])\n",
    "    weights_ar = np.array(weights_ar)\n",
    "    if save:\n",
    "        np.save(f'{CFG.base_dir}PSEUDO_WGTS_{max_count}_{max_weight}.npy', weights_ar)\n",
    "    return weights_ar\n",
    "\n",
    "def make500(data, skok=500):\n",
    "    all_idx = []\n",
    "    for brd in CFG.target_columns:\n",
    "        sub_d = list(data[data.primary_label == brd].index)\n",
    "        random.shuffle(sub_d)\n",
    "        all_idx.extend(sub_d[:skok])\n",
    "    return data.loc[all_idx, :].reset_index(drop=True)\n",
    "\n",
    "audio_conf = {'num_mel_bins': 224, 'target_length': 512, 'freqm': 24, 'timem': 96, \n",
    "              'mixup': 0.3, 'skip_norm': True, 'mode': 'train', 'dataset': 'Birds custom',\n",
    "             'mean': -6.98279, 'std': 3.05492, 'noise': True}\n",
    "\n",
    "\n",
    "val_audio_conf = {'num_mel_bins': 224, 'target_length': 512, 'freqm': 0, 'timem': 0, \n",
    "              'mixup': -1, 'skip_norm': True, 'mode': 'eval', 'dataset': 'Birds custom',\n",
    "             'mean': -6.98279, 'std': 3.05492, 'noise': False}\n",
    "\n",
    "\n",
    "def prepare_loaders(df, fold, epoch, fold_type, r_saml=False, use_rare=False, use_500=False):\n",
    "    #fold types: kfold_raw   kfold_split\n",
    "    #if use_rare:\n",
    "    #    train_df = df.query(f\"{fold_type}!=@fold\").reset_index(drop=True)\n",
    "    #    valid_df = df.query(f\"{fold_type}==@fold\").reset_index(drop=True)\n",
    "    #else:\n",
    "    #    train_df = df[(df[fold_type] != fold) | (df.primary_label.isin(CFG.rare))].reset_index(drop=True)\n",
    "    #    valid_df = df[(df[fold_type] == fold) & ~(df.primary_label.isin(CFG.rare))].reset_index(drop=True)\n",
    "        \n",
    "    \n",
    "    train_df = make500(df, 300)\n",
    "    maks_for_weig = 300    \n",
    "    valid_df = make500(df, 10)\n",
    "    \n",
    "    valid_target = make500(df[df.primary_label.isin(CFG.scored)], 40)\n",
    "    \n",
    "    weig = create_weights_arr(train_df, CFG.target_columns, maks_for_weig, 15, True)  \n",
    "    if r_saml:\n",
    "        ww = np.ones(len(CFG.target_columns))\n",
    "        ww[np.isin(CFG.target_columns, CFG.scored).nonzero()[0]] = weig[np.isin(CFG.target_columns, \n",
    "                                                                                CFG.scored).nonzero()[0]]\n",
    "        ww = ww/ww.max()\n",
    "\n",
    "        target = train_df.primary_label.values\n",
    "        samples_weight = np.array([ww[CFG.target_columns.index(t)] for t in target])\n",
    "\n",
    "        samples_weight = torch.from_numpy(samples_weight)\n",
    "        samples_weigth = samples_weight.double()\n",
    "        sampler = WeightedRandomSampler(samples_weight, len(samples_weight), replacement=True)\n",
    "        shuffler = False\n",
    "    else:\n",
    "        sampler = None\n",
    "        shuffler = True\n",
    "        \n",
    "    if epoch < 15:\n",
    "        cur_mixup = 0.4\n",
    "    elif 15 <= epoch < 20:\n",
    "        cur_mixup = 0.07\n",
    "    else:\n",
    "        cur_mixup = 0.\n",
    "        \n",
    "    cur_audio_conf = {'num_mel_bins': 224, 'target_length': 512, 'freqm': 24, 'timem': 96, \n",
    "              'mixup': cur_mixup, 'skip_norm': True, 'mode': 'train', 'dataset': 'Birds custom',\n",
    "             'mean': -6.98279, 'std': 3.05492, 'noise': True}\n",
    "        \n",
    "        \n",
    "    train_dataset = AudiosetDataset(train_df, cur_audio_conf)\n",
    "    valid_dataset = AudiosetDataset(valid_df, val_audio_conf)\n",
    "    target_dataset = AudiosetDataset(valid_target, val_audio_conf)\n",
    "    \n",
    "    print(f'Length train dataset: {len(train_dataset)}')\n",
    "    print(f'Length valid dataset: {len(valid_dataset)}')\n",
    "    print(f'Length target dataset: {len(target_dataset)}')\n",
    "    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=CFG.train_bs, shuffle=shuffler,\n",
    "                                               sampler=sampler, pin_memory=True, drop_last=True)\n",
    "    \n",
    "    valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=CFG.valid_bs, shuffle=False,\n",
    "                                               pin_memory=True)\n",
    "    \n",
    "    target_loader = torch.utils.data.DataLoader(target_dataset, batch_size=CFG.valid_bs, shuffle=False,\n",
    "                                               pin_memory=True)\n",
    "   \n",
    "    \n",
    "    return train_loader, valid_loader, target_loader\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32674f7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    }
   ],
   "source": [
    "train_loader, valid_loader, target_loader = prepare_loaders(train_data, 0, 0, \n",
    "                                                            'kfold_raw', r_saml=False, use_rare=False, use_500=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eda81f77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 3, 512, 224]) torch.Size([32, 152])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "batch = next(iter(train_loader))\n",
    "print(batch['image'].size(), batch['targets'].size())\n",
    "def plot_batch(imgs, msks, size=3):\n",
    "    fig, axs = plt.subplots(nrows=3, sharex=True, figsize=(15, 12))\n",
    "    for idx in range(size):\n",
    "        axs[idx].set_title(CFG.target_columns[torch.argmax(msks[idx]).item()])\n",
    "        img = imgs[idx].transpose(1,2)[0].numpy()\n",
    "        #print(img.shape)\n",
    "        axs[idx].imshow(img)\n",
    "        #img = img.astype('uint8')\n",
    "        #msk = msks[idx,].permute((1, 2, 0)).numpy()*255.0\n",
    "        #plt.legend(CFG.target_columns[torch.argmax(msks[0]).item()])\n",
    "        #plt.imshow(img)\n",
    "    #plt.show()\n",
    "plot_batch(batch['image'], batch['targets'], size=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ec8545cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "416\n"
     ]
    }
   ],
   "source": [
    "len_train_loader = len(train_loader)\n",
    "print(len_train_loader)\n",
    "del train_loader, valid_loader, target_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e31453a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_one_epoch(model, optimizer, scheduler, dataloader, device, epoch, warmup=False):\n",
    "    model.train()\n",
    "    scaler = amp.GradScaler(enabled=True)\n",
    "    \n",
    "    warmup = False  ##\n",
    "    \n",
    "    losses = AverageMeter()\n",
    "    scores = MetricMeter()\n",
    "    \n",
    "    pbar = tqdm(enumerate(dataloader), total=len(dataloader), desc='Train ')\n",
    "    for step, (data) in pbar:    \n",
    "        \n",
    "        inputs = data['image'].to(device)\n",
    "        targets = data['targets'].to(device)\n",
    "        \n",
    "        B = inputs.size(0) \n",
    "        \n",
    "        with amp.autocast(enabled=True):\n",
    "            outputs = model(inputs)\n",
    "            loss = loss_fn(outputs, targets)\n",
    "            \n",
    "    \n",
    "        optimizer.zero_grad()\n",
    "        scaler.scale(loss).backward()\n",
    "        scaler.step(optimizer)\n",
    "        scaler.update()\n",
    "        #scheduler.step()\n",
    "        \n",
    "        losses.update(loss.item(), B)\n",
    "        scores.update(targets, outputs)    \n",
    "        \n",
    "        mem = torch.cuda.memory_reserved(f'cuda:{CFG.cuda_num}') / 1E9 if torch.cuda.is_available() else 0\n",
    "        current_lr = optimizer.param_groups[0]['lr']\n",
    "        pbar.set_postfix(train_loss=f'{losses.avg:0.6f}',\n",
    "                        lr=f'{current_lr:0.6f}',\n",
    "                        gpu_mem=f'{mem:0.2f} GB')\n",
    "        torch.cuda.empty_cache()\n",
    "        gc.collect()\n",
    "        \n",
    "    scheduler.step()\n",
    "    return scores.avg, losses.avg\n",
    "\n",
    "@torch.no_grad()\n",
    "def valid_one_epoch(model, dataloader, device, epoch):\n",
    "    model.eval()\n",
    "    \n",
    "    losses = AverageMeter()\n",
    "    scores = MetricMeter()\n",
    "    \n",
    "    val_scores = []\n",
    "    \n",
    "    pbar = tqdm(enumerate(dataloader), total=len(dataloader), desc='Valid ')\n",
    "    for step, (data) in pbar:    \n",
    "        \n",
    "        inputs = data['image'].to(device)\n",
    "        targets = data['targets'].to(device)\n",
    "        \n",
    "        B = inputs.size(0) \n",
    "            #print('warm-up learning rate is {:f}'.format(optimizer.param_groups[0]['lr']))\n",
    "        \n",
    "        with amp.autocast(enabled=True):\n",
    "            outputs = model(inputs)\n",
    "            loss = loss_fn(outputs, targets)\n",
    "\n",
    "        \n",
    "        losses.update(loss.item(), B)\n",
    "        scores.update(targets, outputs)\n",
    "        \n",
    "        mem = torch.cuda.memory_reserved(f'cuda:{CFG.cuda_num}') / 1E9 if torch.cuda.is_available() else 0\n",
    "        current_lr = optimizer.param_groups[0]['lr']\n",
    "        pbar.set_postfix(valid_loss=f'{losses.avg:0.6f}',\n",
    "                        lr=f'{current_lr:0.6f}',\n",
    "                        gpu_memory=f'{mem:0.2f} GB')\n",
    "\n",
    "    torch.cuda.empty_cache()\n",
    "    gc.collect()\n",
    "    \n",
    "    return scores.avg, losses.avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "139ee0c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_training(model, optimizer, scheduler, device, num_epochs, fold, warmup=False):\n",
    "    # To automatically log gradients\n",
    "    #wandb.watch(model, log_freq=100)\n",
    "    \n",
    "    if torch.cuda.is_available():\n",
    "        print(\"cuda: {}\\n\".format(torch.cuda.get_device_name(f'cuda:{CFG.cuda_num}')))\n",
    "    \n",
    "    start = time.time()\n",
    "    #best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_score_train   = -np.inf\n",
    "    best_score_valid   = -np.inf\n",
    "    best_score_target  = -np.inf\n",
    "    min_loss       = 10000\n",
    "    best_epoch     = -1\n",
    "    history = defaultdict(list)\n",
    "    \n",
    "    for epoch in range(1, num_epochs + 1): \n",
    "        gc.collect()\n",
    "        print(f'Epoch {epoch}/{num_epochs}')\n",
    "        \n",
    "        start_time = time.time()\n",
    "        \n",
    "        train_loader, valid_loader, target_loader = prepare_loaders(train_data, fold, epoch, 'kfold_raw',\n",
    "                                                                 r_saml=False, use_rare=False, use_500=True)\n",
    "        \n",
    "        train_avg, train_loss = train_one_epoch(model, optimizer, scheduler, \n",
    "                                           dataloader=train_loader, \n",
    "                                           device=CFG.device, epoch=epoch, warmup=warmup)\n",
    "        \n",
    "        valid_avg, valid_loss = valid_one_epoch(model, valid_loader, \n",
    "                                                 device=CFG.device, \n",
    "                                                 epoch=epoch)\n",
    "        \n",
    "        target_avg, target_loss = valid_one_epoch(model, target_loader, \n",
    "                                                 device=CFG.device, \n",
    "                                                 epoch=epoch)\n",
    "\n",
    "        \n",
    "        elapsed = time.time() - start_time\n",
    "        \n",
    "        print('TRAIN SCORES:')\n",
    "        print(f'Epoch {epoch} - avg_train_loss: {train_loss:.5f} time: {elapsed:.0f}s')\n",
    "        print(f\"Epoch {epoch} - valid_f1_at_005:{train_avg['f1_at_005']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_01:{train_avg['f1_at_01']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_015:{train_avg['f1_at_015']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_02:{train_avg['f1_at_02']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_025:{train_avg['f1_at_025']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_03:{train_avg['f1_at_03']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_035:{train_avg['f1_at_035']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_04:{train_avg['f1_at_04']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_045:{train_avg['f1_at_045']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_05:{train_avg['f1_at_05']:0.5f}\")\n",
    "        \n",
    "        print('='*50)\n",
    "        \n",
    "        print('VALIDATION SCORES:')\n",
    "        print(f'Epoch {epoch} - avg_val_loss: {valid_loss:.5f}')\n",
    "        print(f\"Epoch {epoch} - valid_f1_at_005:{valid_avg['f1_at_005']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_01:{valid_avg['f1_at_01']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_015:{valid_avg['f1_at_015']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_02:{valid_avg['f1_at_02']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_025:{valid_avg['f1_at_025']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_03:{valid_avg['f1_at_03']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_035:{valid_avg['f1_at_035']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_04:{valid_avg['f1_at_04']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_045:{valid_avg['f1_at_045']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_05:{valid_avg['f1_at_05']:0.5f}\")\n",
    "        \n",
    "        \n",
    "        print('TARGET SCORES:')\n",
    "        print(f'Epoch {epoch} - avg_train_loss: {target_loss:.5f}  ')\n",
    "        print(f\"Epoch {epoch} - valid_f1_at_005:{target_avg['f1_at_005']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_01:{target_avg['f1_at_01']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_015:{target_avg['f1_at_015']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_02:{target_avg['f1_at_02']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_025:{target_avg['f1_at_025']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_03:{target_avg['f1_at_03']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_035:{target_avg['f1_at_035']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_04:{target_avg['f1_at_04']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_045:{target_avg['f1_at_045']:0.5f}\")\n",
    "        print(f\"Epoch {epoch}x - valid_f1_at_05:{target_avg['f1_at_05']:0.5f}\")\n",
    "        # deep copy the model\n",
    "        if valid_avg['f1_at_03'] >= best_score_valid:\n",
    "            print(f\"{c_}Valid Score 03 Improved ({best_score_valid:0.4f} ---> {valid_avg['f1_at_03']:0.4f})\")\n",
    "            best_score_valid    = valid_avg['f1_at_03']\n",
    "        \n",
    "            best_epoch   = epoch\n",
    "\n",
    "            PATH = f\"{CFG.path_to_checks}PSEUDO_VALID_03BEST.bin\"\n",
    "            torch.save(model.state_dict(), PATH)\n",
    "            print(f\"Model Saved here: {PATH}{sr_}\")\n",
    "        else:\n",
    "            print(f\"Valid Score 03 Not Improved, still: {best_score_valid:0.4f}\")\n",
    "            \n",
    "        if target_avg['f1_at_03'] >= best_score_target:\n",
    "            print(f\"{c_}Target Score 03 Improved ({best_score_target:0.4f} ---> {target_avg['f1_at_03']:0.4f})\")\n",
    "            best_score_target = target_avg['f1_at_03']        \n",
    "            best_epoch    = epoch\n",
    "\n",
    "            PATH = f\"{CFG.path_to_checks}PSEUDO_TARGET_03BEST.bin\"\n",
    "            torch.save(model.state_dict(), PATH)\n",
    "            print(f\"Model Saved here: {PATH}{sr_}\")\n",
    "        else:\n",
    "            print(f\"Target Score 03 Not Improved, still: {best_score_target:0.4f}\")\n",
    "            \n",
    "        if train_avg['f1_at_03'] >= best_score_train:\n",
    "            print(f\"{c_}Train Score 03 Improved ({best_score_train:0.4f} ---> {train_avg['f1_at_03']:0.4f})\")\n",
    "            best_score_train = train_avg['f1_at_03']        \n",
    "            best_epoch    = epoch\n",
    "\n",
    "            PATH = f\"{CFG.path_to_checks}PSEUDO_TRAIN_03BEST.bin\"\n",
    "            torch.save(model.state_dict(), PATH)\n",
    "            print(f\"Model Saved here: {PATH}{sr_}\")\n",
    "        else:\n",
    "            print(f\"Train Score 03 Not Improved, still: {best_score_train:0.4f}\")\n",
    "            \n",
    "        \n",
    "        \n",
    "        PATH = f\"{CFG.path_to_checks}PSEUDO_last_epoch.bin\"\n",
    "        torch.save(model.state_dict(), PATH)\n",
    "            \n",
    "        print(); print()\n",
    "    \n",
    "    end = time.time()\n",
    "    time_elapsed = end - start\n",
    "    print('Training complete in {:.0f}h {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 3600, (time_elapsed % 3600) // 60, (time_elapsed % 3600) % 60))\n",
    "    \n",
    "    # load best model weights\n",
    "    #model.load_state_dict(best_model_wts)\n",
    "    \n",
    "    return model, history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f68f014",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###############\n",
      "### Fold: 999 from [2, 3, 4]\n",
      "###############\n",
      "Total parameter number is : 6.037 million\n",
      "Total trainable parameter number is : 6.037 million\n",
      "cuda: Tesla V100-SXM2-16GB\n",
      "\n",
      "Epoch 1/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train : 100%|███████████████████████████████████████████████████████████████████████████████████| 416/416 [11:14<00:00,  1.62s/it, gpu_mem=3.62 GB, lr=0.001400, train_loss=0.013528]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.64it/s, gpu_memory=3.87 GB, lr=0.001400, valid_loss=0.008282]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.72it/s, gpu_memory=3.80 GB, lr=0.001400, valid_loss=0.008331]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN SCORES:\n",
      "Epoch 1 - avg_train_loss: 0.01353 time: 698s\n",
      "Epoch 1 - valid_f1_at_005:0.01719\n",
      "Epoch 1x - valid_f1_at_01:0.03874\n",
      "Epoch 1x - valid_f1_at_015:0.02196\n",
      "Epoch 1x - valid_f1_at_02:0.00866\n",
      "Epoch 1x - valid_f1_at_025:0.00510\n",
      "Epoch 1x - valid_f1_at_03:0.00405\n",
      "Epoch 1x - valid_f1_at_035:0.00295\n",
      "Epoch 1x - valid_f1_at_04:0.00280\n",
      "Epoch 1x - valid_f1_at_045:0.00204\n",
      "Epoch 1x - valid_f1_at_05:0.00166\n",
      "==================================================\n",
      "VALIDATION SCORES:\n",
      "Epoch 1 - avg_val_loss: 0.00828\n",
      "Epoch 1 - valid_f1_at_005:0.02090\n",
      "Epoch 1x - valid_f1_at_01:0.04730\n",
      "Epoch 1x - valid_f1_at_015:0.08525\n",
      "Epoch 1x - valid_f1_at_02:0.08442\n",
      "Epoch 1x - valid_f1_at_025:0.04993\n",
      "Epoch 1x - valid_f1_at_03:0.02450\n",
      "Epoch 1x - valid_f1_at_035:0.02019\n",
      "Epoch 1x - valid_f1_at_04:0.00783\n",
      "Epoch 1x - valid_f1_at_045:0.00157\n",
      "Epoch 1x - valid_f1_at_05:0.00000\n",
      "TARGET SCORES:\n",
      "Epoch 1 - avg_train_loss: 0.00833  \n",
      "Epoch 1 - valid_f1_at_005:0.02531\n",
      "Epoch 1x - valid_f1_at_01:0.04945\n",
      "Epoch 1x - valid_f1_at_015:0.07582\n",
      "Epoch 1x - valid_f1_at_02:0.08155\n",
      "Epoch 1x - valid_f1_at_025:0.06971\n",
      "Epoch 1x - valid_f1_at_03:0.04657\n",
      "Epoch 1x - valid_f1_at_035:0.02649\n",
      "Epoch 1x - valid_f1_at_04:0.00536\n",
      "Epoch 1x - valid_f1_at_045:0.00000\n",
      "Epoch 1x - valid_f1_at_05:0.00000\n",
      "\u001b[32mValid Score 03 Improved (-inf ---> 0.0245)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_VALID_03BEST.bin\u001b[0m\n",
      "\u001b[32mTarget Score 03 Improved (-inf ---> 0.0466)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TARGET_03BEST.bin\u001b[0m\n",
      "\u001b[32mTrain Score 03 Improved (-inf ---> 0.0040)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TRAIN_03BEST.bin\u001b[0m\n",
      "\n",
      "\n",
      "Epoch 2/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train : 100%|███████████████████████████████████████████████████████████████████████████████████| 416/416 [10:57<00:00,  1.58s/it, gpu_mem=3.62 GB, lr=0.001400, train_loss=0.006197]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.66it/s, gpu_memory=3.87 GB, lr=0.001398, valid_loss=0.007057]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.76it/s, gpu_memory=3.80 GB, lr=0.001398, valid_loss=0.006883]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN SCORES:\n",
      "Epoch 2 - avg_train_loss: 0.00620 time: 681s\n",
      "Epoch 2 - valid_f1_at_005:0.02317\n",
      "Epoch 2x - valid_f1_at_01:0.09095\n",
      "Epoch 2x - valid_f1_at_015:0.18413\n",
      "Epoch 2x - valid_f1_at_02:0.15401\n",
      "Epoch 2x - valid_f1_at_025:0.09447\n",
      "Epoch 2x - valid_f1_at_03:0.05718\n",
      "Epoch 2x - valid_f1_at_035:0.03294\n",
      "Epoch 2x - valid_f1_at_04:0.02107\n",
      "Epoch 2x - valid_f1_at_045:0.01331\n",
      "Epoch 2x - valid_f1_at_05:0.00939\n",
      "==================================================\n",
      "VALIDATION SCORES:\n",
      "Epoch 2 - avg_val_loss: 0.00706\n",
      "Epoch 2 - valid_f1_at_005:0.03659\n",
      "Epoch 2x - valid_f1_at_01:0.09186\n",
      "Epoch 2x - valid_f1_at_015:0.17978\n",
      "Epoch 2x - valid_f1_at_02:0.24556\n",
      "Epoch 2x - valid_f1_at_025:0.21915\n",
      "Epoch 2x - valid_f1_at_03:0.18134\n",
      "Epoch 2x - valid_f1_at_035:0.13296\n",
      "Epoch 2x - valid_f1_at_04:0.09212\n",
      "Epoch 2x - valid_f1_at_045:0.07132\n",
      "Epoch 2x - valid_f1_at_05:0.05215\n",
      "TARGET SCORES:\n",
      "Epoch 2 - avg_train_loss: 0.00688  \n",
      "Epoch 2 - valid_f1_at_005:0.04532\n",
      "Epoch 2x - valid_f1_at_01:0.11653\n",
      "Epoch 2x - valid_f1_at_015:0.20392\n",
      "Epoch 2x - valid_f1_at_02:0.24447\n",
      "Epoch 2x - valid_f1_at_025:0.20791\n",
      "Epoch 2x - valid_f1_at_03:0.16740\n",
      "Epoch 2x - valid_f1_at_035:0.11737\n",
      "Epoch 2x - valid_f1_at_04:0.08030\n",
      "Epoch 2x - valid_f1_at_045:0.06701\n",
      "Epoch 2x - valid_f1_at_05:0.06744\n",
      "\u001b[32mValid Score 03 Improved (0.0245 ---> 0.1813)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_VALID_03BEST.bin\u001b[0m\n",
      "\u001b[32mTarget Score 03 Improved (0.0466 ---> 0.1674)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TARGET_03BEST.bin\u001b[0m\n",
      "\u001b[32mTrain Score 03 Improved (0.0040 ---> 0.0572)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TRAIN_03BEST.bin\u001b[0m\n",
      "\n",
      "\n",
      "Epoch 3/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train : 100%|███████████████████████████████████████████████████████████████████████████████████| 416/416 [11:09<00:00,  1.61s/it, gpu_mem=3.62 GB, lr=0.001398, train_loss=0.005347]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.66it/s, gpu_memory=3.87 GB, lr=0.001396, valid_loss=0.005345]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.74it/s, gpu_memory=3.80 GB, lr=0.001396, valid_loss=0.005091]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN SCORES:\n",
      "Epoch 3 - avg_train_loss: 0.00535 time: 693s\n",
      "Epoch 3 - valid_f1_at_005:0.03283\n",
      "Epoch 3x - valid_f1_at_01:0.13116\n",
      "Epoch 3x - valid_f1_at_015:0.27769\n",
      "Epoch 3x - valid_f1_at_02:0.32141\n",
      "Epoch 3x - valid_f1_at_025:0.27466\n",
      "Epoch 3x - valid_f1_at_03:0.20888\n",
      "Epoch 3x - valid_f1_at_035:0.15308\n",
      "Epoch 3x - valid_f1_at_04:0.10617\n",
      "Epoch 3x - valid_f1_at_045:0.07091\n",
      "Epoch 3x - valid_f1_at_05:0.04457\n",
      "==================================================\n",
      "VALIDATION SCORES:\n",
      "Epoch 3 - avg_val_loss: 0.00534\n",
      "Epoch 3 - valid_f1_at_005:0.05477\n",
      "Epoch 3x - valid_f1_at_01:0.17789\n",
      "Epoch 3x - valid_f1_at_015:0.32731\n",
      "Epoch 3x - valid_f1_at_02:0.39434\n",
      "Epoch 3x - valid_f1_at_025:0.39559\n",
      "Epoch 3x - valid_f1_at_03:0.34615\n",
      "Epoch 3x - valid_f1_at_035:0.28590\n",
      "Epoch 3x - valid_f1_at_04:0.23378\n",
      "Epoch 3x - valid_f1_at_045:0.17784\n",
      "Epoch 3x - valid_f1_at_05:0.13567\n",
      "TARGET SCORES:\n",
      "Epoch 3 - avg_train_loss: 0.00509  \n",
      "Epoch 3 - valid_f1_at_005:0.06584\n",
      "Epoch 3x - valid_f1_at_01:0.21251\n",
      "Epoch 3x - valid_f1_at_015:0.35442\n",
      "Epoch 3x - valid_f1_at_02:0.39841\n",
      "Epoch 3x - valid_f1_at_025:0.39733\n",
      "Epoch 3x - valid_f1_at_03:0.33996\n",
      "Epoch 3x - valid_f1_at_035:0.28131\n",
      "Epoch 3x - valid_f1_at_04:0.19451\n",
      "Epoch 3x - valid_f1_at_045:0.15459\n",
      "Epoch 3x - valid_f1_at_05:0.12281\n",
      "\u001b[32mValid Score 03 Improved (0.1813 ---> 0.3462)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_VALID_03BEST.bin\u001b[0m\n",
      "\u001b[32mTarget Score 03 Improved (0.1674 ---> 0.3400)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TARGET_03BEST.bin\u001b[0m\n",
      "\u001b[32mTrain Score 03 Improved (0.0572 ---> 0.2089)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TRAIN_03BEST.bin\u001b[0m\n",
      "\n",
      "\n",
      "Epoch 4/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train : 100%|███████████████████████████████████████████████████████████████████████████████████| 416/416 [11:19<00:00,  1.63s/it, gpu_mem=3.62 GB, lr=0.001396, train_loss=0.004752]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:11<00:00,  1.67it/s, gpu_memory=3.87 GB, lr=0.001394, valid_loss=0.004470]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.73it/s, gpu_memory=3.80 GB, lr=0.001394, valid_loss=0.004206]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN SCORES:\n",
      "Epoch 4 - avg_train_loss: 0.00475 time: 703s\n",
      "Epoch 4 - valid_f1_at_005:0.04038\n",
      "Epoch 4x - valid_f1_at_01:0.15665\n",
      "Epoch 4x - valid_f1_at_015:0.32084\n",
      "Epoch 4x - valid_f1_at_02:0.40117\n",
      "Epoch 4x - valid_f1_at_025:0.39164\n",
      "Epoch 4x - valid_f1_at_03:0.34371\n",
      "Epoch 4x - valid_f1_at_035:0.28703\n",
      "Epoch 4x - valid_f1_at_04:0.23449\n",
      "Epoch 4x - valid_f1_at_045:0.17449\n",
      "Epoch 4x - valid_f1_at_05:0.12008\n",
      "==================================================\n",
      "VALIDATION SCORES:\n",
      "Epoch 4 - avg_val_loss: 0.00447\n",
      "Epoch 4 - valid_f1_at_005:0.06349\n",
      "Epoch 4x - valid_f1_at_01:0.20787\n",
      "Epoch 4x - valid_f1_at_015:0.39089\n",
      "Epoch 4x - valid_f1_at_02:0.47930\n",
      "Epoch 4x - valid_f1_at_025:0.50448\n",
      "Epoch 4x - valid_f1_at_03:0.48564\n",
      "Epoch 4x - valid_f1_at_035:0.44891\n",
      "Epoch 4x - valid_f1_at_04:0.37273\n",
      "Epoch 4x - valid_f1_at_045:0.30810\n",
      "Epoch 4x - valid_f1_at_05:0.25736\n",
      "TARGET SCORES:\n",
      "Epoch 4 - avg_train_loss: 0.00421  \n",
      "Epoch 4 - valid_f1_at_005:0.07270\n",
      "Epoch 4x - valid_f1_at_01:0.23113\n",
      "Epoch 4x - valid_f1_at_015:0.40350\n",
      "Epoch 4x - valid_f1_at_02:0.51867\n",
      "Epoch 4x - valid_f1_at_025:0.54031\n",
      "Epoch 4x - valid_f1_at_03:0.52738\n",
      "Epoch 4x - valid_f1_at_035:0.46980\n",
      "Epoch 4x - valid_f1_at_04:0.39158\n",
      "Epoch 4x - valid_f1_at_045:0.31496\n",
      "Epoch 4x - valid_f1_at_05:0.22010\n",
      "\u001b[32mValid Score 03 Improved (0.3462 ---> 0.4856)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_VALID_03BEST.bin\u001b[0m\n",
      "\u001b[32mTarget Score 03 Improved (0.3400 ---> 0.5274)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TARGET_03BEST.bin\u001b[0m\n",
      "\u001b[32mTrain Score 03 Improved (0.2089 ---> 0.3437)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TRAIN_03BEST.bin\u001b[0m\n",
      "\n",
      "\n",
      "Epoch 5/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train : 100%|███████████████████████████████████████████████████████████████████████████████████| 416/416 [10:47<00:00,  1.56s/it, gpu_mem=3.62 GB, lr=0.001394, train_loss=0.004382]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.65it/s, gpu_memory=3.87 GB, lr=0.001390, valid_loss=0.003846]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.73it/s, gpu_memory=3.80 GB, lr=0.001390, valid_loss=0.003427]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN SCORES:\n",
      "Epoch 5 - avg_train_loss: 0.00438 time: 671s\n",
      "Epoch 5 - valid_f1_at_005:0.04591\n",
      "Epoch 5x - valid_f1_at_01:0.17750\n",
      "Epoch 5x - valid_f1_at_015:0.35170\n",
      "Epoch 5x - valid_f1_at_02:0.44978\n",
      "Epoch 5x - valid_f1_at_025:0.46122\n",
      "Epoch 5x - valid_f1_at_03:0.42490\n",
      "Epoch 5x - valid_f1_at_035:0.36900\n",
      "Epoch 5x - valid_f1_at_04:0.30493\n",
      "Epoch 5x - valid_f1_at_045:0.24268\n",
      "Epoch 5x - valid_f1_at_05:0.17990\n",
      "==================================================\n",
      "VALIDATION SCORES:\n",
      "Epoch 5 - avg_val_loss: 0.00385\n",
      "Epoch 5 - valid_f1_at_005:0.08725\n",
      "Epoch 5x - valid_f1_at_01:0.27964\n",
      "Epoch 5x - valid_f1_at_015:0.47791\n",
      "Epoch 5x - valid_f1_at_02:0.57466\n",
      "Epoch 5x - valid_f1_at_025:0.60552\n",
      "Epoch 5x - valid_f1_at_03:0.56460\n",
      "Epoch 5x - valid_f1_at_035:0.51942\n",
      "Epoch 5x - valid_f1_at_04:0.46686\n",
      "Epoch 5x - valid_f1_at_045:0.41350\n",
      "Epoch 5x - valid_f1_at_05:0.34345\n",
      "TARGET SCORES:\n",
      "Epoch 5 - avg_train_loss: 0.00343  \n",
      "Epoch 5 - valid_f1_at_005:0.10121\n",
      "Epoch 5x - valid_f1_at_01:0.33140\n",
      "Epoch 5x - valid_f1_at_015:0.56576\n",
      "Epoch 5x - valid_f1_at_02:0.67629\n",
      "Epoch 5x - valid_f1_at_025:0.70173\n",
      "Epoch 5x - valid_f1_at_03:0.62435\n",
      "Epoch 5x - valid_f1_at_035:0.53116\n",
      "Epoch 5x - valid_f1_at_04:0.47214\n",
      "Epoch 5x - valid_f1_at_045:0.38619\n",
      "Epoch 5x - valid_f1_at_05:0.28078\n",
      "\u001b[32mValid Score 03 Improved (0.4856 ---> 0.5646)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_VALID_03BEST.bin\u001b[0m\n",
      "\u001b[32mTarget Score 03 Improved (0.5274 ---> 0.6243)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TARGET_03BEST.bin\u001b[0m\n",
      "\u001b[32mTrain Score 03 Improved (0.3437 ---> 0.4249)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TRAIN_03BEST.bin\u001b[0m\n",
      "\n",
      "\n",
      "Epoch 6/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train : 100%|███████████████████████████████████████████████████████████████████████████████████| 416/416 [11:10<00:00,  1.61s/it, gpu_mem=3.62 GB, lr=0.001390, train_loss=0.004153]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.62it/s, gpu_memory=3.87 GB, lr=0.001386, valid_loss=0.003333]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.75it/s, gpu_memory=3.80 GB, lr=0.001386, valid_loss=0.002813]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN SCORES:\n",
      "Epoch 6 - avg_train_loss: 0.00415 time: 695s\n",
      "Epoch 6 - valid_f1_at_005:0.04869\n",
      "Epoch 6x - valid_f1_at_01:0.18683\n",
      "Epoch 6x - valid_f1_at_015:0.37036\n",
      "Epoch 6x - valid_f1_at_02:0.47776\n",
      "Epoch 6x - valid_f1_at_025:0.49891\n",
      "Epoch 6x - valid_f1_at_03:0.47356\n",
      "Epoch 6x - valid_f1_at_035:0.42554\n",
      "Epoch 6x - valid_f1_at_04:0.36612\n",
      "Epoch 6x - valid_f1_at_045:0.29847\n",
      "Epoch 6x - valid_f1_at_05:0.23361\n",
      "==================================================\n",
      "VALIDATION SCORES:\n",
      "Epoch 6 - avg_val_loss: 0.00333\n",
      "Epoch 6 - valid_f1_at_005:0.10805\n",
      "Epoch 6x - valid_f1_at_01:0.33148\n",
      "Epoch 6x - valid_f1_at_015:0.52832\n",
      "Epoch 6x - valid_f1_at_02:0.63696\n",
      "Epoch 6x - valid_f1_at_025:0.65423\n",
      "Epoch 6x - valid_f1_at_03:0.64031\n",
      "Epoch 6x - valid_f1_at_035:0.60429\n",
      "Epoch 6x - valid_f1_at_04:0.55284\n",
      "Epoch 6x - valid_f1_at_045:0.49354\n",
      "Epoch 6x - valid_f1_at_05:0.42327\n",
      "TARGET SCORES:\n",
      "Epoch 6 - avg_train_loss: 0.00281  \n",
      "Epoch 6 - valid_f1_at_005:0.12614\n",
      "Epoch 6x - valid_f1_at_01:0.36661\n",
      "Epoch 6x - valid_f1_at_015:0.59357\n",
      "Epoch 6x - valid_f1_at_02:0.71656\n",
      "Epoch 6x - valid_f1_at_025:0.73287\n",
      "Epoch 6x - valid_f1_at_03:0.71621\n",
      "Epoch 6x - valid_f1_at_035:0.66893\n",
      "Epoch 6x - valid_f1_at_04:0.62319\n",
      "Epoch 6x - valid_f1_at_045:0.54475\n",
      "Epoch 6x - valid_f1_at_05:0.45708\n",
      "\u001b[32mValid Score 03 Improved (0.5646 ---> 0.6403)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_VALID_03BEST.bin\u001b[0m\n",
      "\u001b[32mTarget Score 03 Improved (0.6243 ---> 0.7162)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TARGET_03BEST.bin\u001b[0m\n",
      "\u001b[32mTrain Score 03 Improved (0.4249 ---> 0.4736)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TRAIN_03BEST.bin\u001b[0m\n",
      "\n",
      "\n",
      "Epoch 7/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train : 100%|███████████████████████████████████████████████████████████████████████████████████| 416/416 [11:03<00:00,  1.59s/it, gpu_mem=3.62 GB, lr=0.001386, train_loss=0.004002]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.64it/s, gpu_memory=3.87 GB, lr=0.001381, valid_loss=0.003451]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.76it/s, gpu_memory=3.80 GB, lr=0.001381, valid_loss=0.003024]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN SCORES:\n",
      "Epoch 7 - avg_train_loss: 0.00400 time: 687s\n",
      "Epoch 7 - valid_f1_at_005:0.05132\n",
      "Epoch 7x - valid_f1_at_01:0.19635\n",
      "Epoch 7x - valid_f1_at_015:0.38486\n",
      "Epoch 7x - valid_f1_at_02:0.49963\n",
      "Epoch 7x - valid_f1_at_025:0.52986\n",
      "Epoch 7x - valid_f1_at_03:0.50828\n",
      "Epoch 7x - valid_f1_at_035:0.46737\n",
      "Epoch 7x - valid_f1_at_04:0.40311\n",
      "Epoch 7x - valid_f1_at_045:0.33517\n",
      "Epoch 7x - valid_f1_at_05:0.25430\n",
      "==================================================\n",
      "VALIDATION SCORES:\n",
      "Epoch 7 - avg_val_loss: 0.00345\n",
      "Epoch 7 - valid_f1_at_005:0.10856\n",
      "Epoch 7x - valid_f1_at_01:0.33344\n",
      "Epoch 7x - valid_f1_at_015:0.52143\n",
      "Epoch 7x - valid_f1_at_02:0.62653\n",
      "Epoch 7x - valid_f1_at_025:0.64673\n",
      "Epoch 7x - valid_f1_at_03:0.63330\n",
      "Epoch 7x - valid_f1_at_035:0.60073\n",
      "Epoch 7x - valid_f1_at_04:0.55263\n",
      "Epoch 7x - valid_f1_at_045:0.49391\n",
      "Epoch 7x - valid_f1_at_05:0.42796\n",
      "TARGET SCORES:\n",
      "Epoch 7 - avg_train_loss: 0.00302  \n",
      "Epoch 7 - valid_f1_at_005:0.11872\n",
      "Epoch 7x - valid_f1_at_01:0.34168\n",
      "Epoch 7x - valid_f1_at_015:0.53187\n",
      "Epoch 7x - valid_f1_at_02:0.65338\n",
      "Epoch 7x - valid_f1_at_025:0.71594\n",
      "Epoch 7x - valid_f1_at_03:0.72753\n",
      "Epoch 7x - valid_f1_at_035:0.70588\n",
      "Epoch 7x - valid_f1_at_04:0.65925\n",
      "Epoch 7x - valid_f1_at_045:0.60326\n",
      "Epoch 7x - valid_f1_at_05:0.50545\n",
      "Valid Score 03 Not Improved, still: 0.6403\n",
      "\u001b[32mTarget Score 03 Improved (0.7162 ---> 0.7275)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TARGET_03BEST.bin\u001b[0m\n",
      "\u001b[32mTrain Score 03 Improved (0.4736 ---> 0.5083)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TRAIN_03BEST.bin\u001b[0m\n",
      "\n",
      "\n",
      "Epoch 8/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train : 100%|███████████████████████████████████████████████████████████████████████████████████| 416/416 [10:50<00:00,  1.56s/it, gpu_mem=3.62 GB, lr=0.001381, train_loss=0.003881]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.65it/s, gpu_memory=3.87 GB, lr=0.001375, valid_loss=0.002822]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.72it/s, gpu_memory=3.80 GB, lr=0.001375, valid_loss=0.002581]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN SCORES:\n",
      "Epoch 8 - avg_train_loss: 0.00388 time: 674s\n",
      "Epoch 8 - valid_f1_at_005:0.05270\n",
      "Epoch 8x - valid_f1_at_01:0.20227\n",
      "Epoch 8x - valid_f1_at_015:0.39643\n",
      "Epoch 8x - valid_f1_at_02:0.51197\n",
      "Epoch 8x - valid_f1_at_025:0.54210\n",
      "Epoch 8x - valid_f1_at_03:0.52673\n",
      "Epoch 8x - valid_f1_at_035:0.48912\n",
      "Epoch 8x - valid_f1_at_04:0.43069\n",
      "Epoch 8x - valid_f1_at_045:0.36364\n",
      "Epoch 8x - valid_f1_at_05:0.29023\n",
      "==================================================\n",
      "VALIDATION SCORES:\n",
      "Epoch 8 - avg_val_loss: 0.00282\n",
      "Epoch 8 - valid_f1_at_005:0.12028\n",
      "Epoch 8x - valid_f1_at_01:0.36521\n",
      "Epoch 8x - valid_f1_at_015:0.56713\n",
      "Epoch 8x - valid_f1_at_02:0.68953\n",
      "Epoch 8x - valid_f1_at_025:0.72720\n",
      "Epoch 8x - valid_f1_at_03:0.71270\n",
      "Epoch 8x - valid_f1_at_035:0.68883\n",
      "Epoch 8x - valid_f1_at_04:0.64758\n",
      "Epoch 8x - valid_f1_at_045:0.59166\n",
      "Epoch 8x - valid_f1_at_05:0.51425\n",
      "TARGET SCORES:\n",
      "Epoch 8 - avg_train_loss: 0.00258  \n",
      "Epoch 8 - valid_f1_at_005:0.13401\n",
      "Epoch 8x - valid_f1_at_01:0.39298\n",
      "Epoch 8x - valid_f1_at_015:0.61806\n",
      "Epoch 8x - valid_f1_at_02:0.75286\n",
      "Epoch 8x - valid_f1_at_025:0.78123\n",
      "Epoch 8x - valid_f1_at_03:0.77217\n",
      "Epoch 8x - valid_f1_at_035:0.73909\n",
      "Epoch 8x - valid_f1_at_04:0.68233\n",
      "Epoch 8x - valid_f1_at_045:0.62716\n",
      "Epoch 8x - valid_f1_at_05:0.54261\n",
      "\u001b[32mValid Score 03 Improved (0.6403 ---> 0.7127)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_VALID_03BEST.bin\u001b[0m\n",
      "\u001b[32mTarget Score 03 Improved (0.7275 ---> 0.7722)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TARGET_03BEST.bin\u001b[0m\n",
      "\u001b[32mTrain Score 03 Improved (0.5083 ---> 0.5267)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TRAIN_03BEST.bin\u001b[0m\n",
      "\n",
      "\n",
      "Epoch 9/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train : 100%|███████████████████████████████████████████████████████████████████████████████████| 416/416 [10:46<00:00,  1.55s/it, gpu_mem=3.62 GB, lr=0.001375, train_loss=0.003763]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.64it/s, gpu_memory=3.87 GB, lr=0.001368, valid_loss=0.003009]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.72it/s, gpu_memory=3.80 GB, lr=0.001368, valid_loss=0.002321]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN SCORES:\n",
      "Epoch 9 - avg_train_loss: 0.00376 time: 670s\n",
      "Epoch 9 - valid_f1_at_005:0.05308\n",
      "Epoch 9x - valid_f1_at_01:0.20984\n",
      "Epoch 9x - valid_f1_at_015:0.40959\n",
      "Epoch 9x - valid_f1_at_02:0.51890\n",
      "Epoch 9x - valid_f1_at_025:0.55393\n",
      "Epoch 9x - valid_f1_at_03:0.53979\n",
      "Epoch 9x - valid_f1_at_035:0.50183\n",
      "Epoch 9x - valid_f1_at_04:0.45444\n",
      "Epoch 9x - valid_f1_at_045:0.38940\n",
      "Epoch 9x - valid_f1_at_05:0.31509\n",
      "==================================================\n",
      "VALIDATION SCORES:\n",
      "Epoch 9 - avg_val_loss: 0.00301\n",
      "Epoch 9 - valid_f1_at_005:0.10952\n",
      "Epoch 9x - valid_f1_at_01:0.35876\n",
      "Epoch 9x - valid_f1_at_015:0.57062\n",
      "Epoch 9x - valid_f1_at_02:0.66838\n",
      "Epoch 9x - valid_f1_at_025:0.69475\n",
      "Epoch 9x - valid_f1_at_03:0.68087\n",
      "Epoch 9x - valid_f1_at_035:0.64870\n",
      "Epoch 9x - valid_f1_at_04:0.60791\n",
      "Epoch 9x - valid_f1_at_045:0.53622\n",
      "Epoch 9x - valid_f1_at_05:0.46763\n",
      "TARGET SCORES:\n",
      "Epoch 9 - avg_train_loss: 0.00232  \n",
      "Epoch 9 - valid_f1_at_005:0.12954\n",
      "Epoch 9x - valid_f1_at_01:0.39126\n",
      "Epoch 9x - valid_f1_at_015:0.63192\n",
      "Epoch 9x - valid_f1_at_02:0.74899\n",
      "Epoch 9x - valid_f1_at_025:0.79230\n",
      "Epoch 9x - valid_f1_at_03:0.79478\n",
      "Epoch 9x - valid_f1_at_035:0.78003\n",
      "Epoch 9x - valid_f1_at_04:0.74092\n",
      "Epoch 9x - valid_f1_at_045:0.67728\n",
      "Epoch 9x - valid_f1_at_05:0.62292\n",
      "Valid Score 03 Not Improved, still: 0.7127\n",
      "\u001b[32mTarget Score 03 Improved (0.7722 ---> 0.7948)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TARGET_03BEST.bin\u001b[0m\n",
      "\u001b[32mTrain Score 03 Improved (0.5267 ---> 0.5398)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TRAIN_03BEST.bin\u001b[0m\n",
      "\n",
      "\n",
      "Epoch 10/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train : 100%|███████████████████████████████████████████████████████████████████████████████████| 416/416 [10:51<00:00,  1.57s/it, gpu_mem=3.62 GB, lr=0.001368, train_loss=0.003720]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.65it/s, gpu_memory=3.87 GB, lr=0.001361, valid_loss=0.002660]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.76it/s, gpu_memory=3.80 GB, lr=0.001361, valid_loss=0.002384]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN SCORES:\n",
      "Epoch 10 - avg_train_loss: 0.00372 time: 675s\n",
      "Epoch 10 - valid_f1_at_005:0.05330\n",
      "Epoch 10x - valid_f1_at_01:0.21170\n",
      "Epoch 10x - valid_f1_at_015:0.41386\n",
      "Epoch 10x - valid_f1_at_02:0.53474\n",
      "Epoch 10x - valid_f1_at_025:0.56927\n",
      "Epoch 10x - valid_f1_at_03:0.55693\n",
      "Epoch 10x - valid_f1_at_035:0.52360\n",
      "Epoch 10x - valid_f1_at_04:0.47258\n",
      "Epoch 10x - valid_f1_at_045:0.40144\n",
      "Epoch 10x - valid_f1_at_05:0.32592\n",
      "==================================================\n",
      "VALIDATION SCORES:\n",
      "Epoch 10 - avg_val_loss: 0.00266\n",
      "Epoch 10 - valid_f1_at_005:0.13843\n",
      "Epoch 10x - valid_f1_at_01:0.40178\n",
      "Epoch 10x - valid_f1_at_015:0.57958\n",
      "Epoch 10x - valid_f1_at_02:0.67983\n",
      "Epoch 10x - valid_f1_at_025:0.72019\n",
      "Epoch 10x - valid_f1_at_03:0.72339\n",
      "Epoch 10x - valid_f1_at_035:0.71846\n",
      "Epoch 10x - valid_f1_at_04:0.69269\n",
      "Epoch 10x - valid_f1_at_045:0.64859\n",
      "Epoch 10x - valid_f1_at_05:0.61388\n",
      "TARGET SCORES:\n",
      "Epoch 10 - avg_train_loss: 0.00238  \n",
      "Epoch 10 - valid_f1_at_005:0.17754\n",
      "Epoch 10x - valid_f1_at_01:0.46204\n",
      "Epoch 10x - valid_f1_at_015:0.63706\n",
      "Epoch 10x - valid_f1_at_02:0.73734\n",
      "Epoch 10x - valid_f1_at_025:0.77437\n",
      "Epoch 10x - valid_f1_at_03:0.78779\n",
      "Epoch 10x - valid_f1_at_035:0.77529\n",
      "Epoch 10x - valid_f1_at_04:0.72847\n",
      "Epoch 10x - valid_f1_at_045:0.68059\n",
      "Epoch 10x - valid_f1_at_05:0.65712\n",
      "\u001b[32mValid Score 03 Improved (0.7127 ---> 0.7234)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_VALID_03BEST.bin\u001b[0m\n",
      "Target Score 03 Not Improved, still: 0.7948\n",
      "\u001b[32mTrain Score 03 Improved (0.5398 ---> 0.5569)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TRAIN_03BEST.bin\u001b[0m\n",
      "\n",
      "\n",
      "Epoch 11/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train : 100%|███████████████████████████████████████████████████████████████████████████████████| 416/416 [10:52<00:00,  1.57s/it, gpu_mem=3.62 GB, lr=0.001361, train_loss=0.003683]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.63it/s, gpu_memory=3.87 GB, lr=0.001353, valid_loss=0.002602]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.75it/s, gpu_memory=3.80 GB, lr=0.001353, valid_loss=0.002476]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN SCORES:\n",
      "Epoch 11 - avg_train_loss: 0.00368 time: 676s\n",
      "Epoch 11 - valid_f1_at_005:0.05327\n",
      "Epoch 11x - valid_f1_at_01:0.21177\n",
      "Epoch 11x - valid_f1_at_015:0.41390\n",
      "Epoch 11x - valid_f1_at_02:0.53401\n",
      "Epoch 11x - valid_f1_at_025:0.56631\n",
      "Epoch 11x - valid_f1_at_03:0.55994\n",
      "Epoch 11x - valid_f1_at_035:0.52604\n",
      "Epoch 11x - valid_f1_at_04:0.47785\n",
      "Epoch 11x - valid_f1_at_045:0.41409\n",
      "Epoch 11x - valid_f1_at_05:0.34549\n",
      "==================================================\n",
      "VALIDATION SCORES:\n",
      "Epoch 11 - avg_val_loss: 0.00260\n",
      "Epoch 11 - valid_f1_at_005:0.11011\n",
      "Epoch 11x - valid_f1_at_01:0.38037\n",
      "Epoch 11x - valid_f1_at_015:0.61072\n",
      "Epoch 11x - valid_f1_at_02:0.72808\n",
      "Epoch 11x - valid_f1_at_025:0.74614\n",
      "Epoch 11x - valid_f1_at_03:0.72147\n",
      "Epoch 11x - valid_f1_at_035:0.67839\n",
      "Epoch 11x - valid_f1_at_04:0.63219\n",
      "Epoch 11x - valid_f1_at_045:0.58324\n",
      "Epoch 11x - valid_f1_at_05:0.51830\n",
      "TARGET SCORES:\n",
      "Epoch 11 - avg_train_loss: 0.00248  \n",
      "Epoch 11 - valid_f1_at_005:0.11196\n",
      "Epoch 11x - valid_f1_at_01:0.38290\n",
      "Epoch 11x - valid_f1_at_015:0.61991\n",
      "Epoch 11x - valid_f1_at_02:0.74339\n",
      "Epoch 11x - valid_f1_at_025:0.78987\n",
      "Epoch 11x - valid_f1_at_03:0.77693\n",
      "Epoch 11x - valid_f1_at_035:0.72818\n",
      "Epoch 11x - valid_f1_at_04:0.65536\n",
      "Epoch 11x - valid_f1_at_045:0.59475\n",
      "Epoch 11x - valid_f1_at_05:0.55319\n",
      "Valid Score 03 Not Improved, still: 0.7234\n",
      "Target Score 03 Not Improved, still: 0.7948\n",
      "\u001b[32mTrain Score 03 Improved (0.5569 ---> 0.5599)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TRAIN_03BEST.bin\u001b[0m\n",
      "\n",
      "\n",
      "Epoch 12/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train : 100%|███████████████████████████████████████████████████████████████████████████████████| 416/416 [11:03<00:00,  1.59s/it, gpu_mem=3.62 GB, lr=0.001353, train_loss=0.003652]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.62it/s, gpu_memory=3.87 GB, lr=0.001344, valid_loss=0.002887]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.77it/s, gpu_memory=3.80 GB, lr=0.001344, valid_loss=0.002615]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN SCORES:\n",
      "Epoch 12 - avg_train_loss: 0.00365 time: 687s\n",
      "Epoch 12 - valid_f1_at_005:0.05290\n",
      "Epoch 12x - valid_f1_at_01:0.21340\n",
      "Epoch 12x - valid_f1_at_015:0.41809\n",
      "Epoch 12x - valid_f1_at_02:0.53360\n",
      "Epoch 12x - valid_f1_at_025:0.57223\n",
      "Epoch 12x - valid_f1_at_03:0.56696\n",
      "Epoch 12x - valid_f1_at_035:0.53020\n",
      "Epoch 12x - valid_f1_at_04:0.47758\n",
      "Epoch 12x - valid_f1_at_045:0.41691\n",
      "Epoch 12x - valid_f1_at_05:0.34864\n",
      "==================================================\n",
      "VALIDATION SCORES:\n",
      "Epoch 12 - avg_val_loss: 0.00289\n",
      "Epoch 12 - valid_f1_at_005:0.13761\n",
      "Epoch 12x - valid_f1_at_01:0.38385\n",
      "Epoch 12x - valid_f1_at_015:0.58011\n",
      "Epoch 12x - valid_f1_at_02:0.67810\n",
      "Epoch 12x - valid_f1_at_025:0.72211\n",
      "Epoch 12x - valid_f1_at_03:0.71353\n",
      "Epoch 12x - valid_f1_at_035:0.68346\n",
      "Epoch 12x - valid_f1_at_04:0.65895\n",
      "Epoch 12x - valid_f1_at_045:0.61367\n",
      "Epoch 12x - valid_f1_at_05:0.57048\n",
      "TARGET SCORES:\n",
      "Epoch 12 - avg_train_loss: 0.00262  \n",
      "Epoch 12 - valid_f1_at_005:0.15129\n",
      "Epoch 12x - valid_f1_at_01:0.37308\n",
      "Epoch 12x - valid_f1_at_015:0.56351\n",
      "Epoch 12x - valid_f1_at_02:0.68550\n",
      "Epoch 12x - valid_f1_at_025:0.73610\n",
      "Epoch 12x - valid_f1_at_03:0.74335\n",
      "Epoch 12x - valid_f1_at_035:0.73865\n",
      "Epoch 12x - valid_f1_at_04:0.72020\n",
      "Epoch 12x - valid_f1_at_045:0.67365\n",
      "Epoch 12x - valid_f1_at_05:0.64260\n",
      "Valid Score 03 Not Improved, still: 0.7234\n",
      "Target Score 03 Not Improved, still: 0.7948\n",
      "\u001b[32mTrain Score 03 Improved (0.5599 ---> 0.5670)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TRAIN_03BEST.bin\u001b[0m\n",
      "\n",
      "\n",
      "Epoch 13/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train : 100%|███████████████████████████████████████████████████████████████████████████████████| 416/416 [10:57<00:00,  1.58s/it, gpu_mem=3.62 GB, lr=0.001344, train_loss=0.003634]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:12<00:00,  1.61it/s, gpu_memory=3.87 GB, lr=0.001335, valid_loss=0.002676]\n",
      "Valid : 100%|██████████████████████████████████████████████████████████████████████████████████| 12/12 [00:06<00:00,  1.75it/s, gpu_memory=3.80 GB, lr=0.001335, valid_loss=0.002786]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN SCORES:\n",
      "Epoch 13 - avg_train_loss: 0.00363 time: 681s\n",
      "Epoch 13 - valid_f1_at_005:0.05218\n",
      "Epoch 13x - valid_f1_at_01:0.20838\n",
      "Epoch 13x - valid_f1_at_015:0.41486\n",
      "Epoch 13x - valid_f1_at_02:0.53966\n",
      "Epoch 13x - valid_f1_at_025:0.58127\n",
      "Epoch 13x - valid_f1_at_03:0.57174\n",
      "Epoch 13x - valid_f1_at_035:0.53726\n",
      "Epoch 13x - valid_f1_at_04:0.49187\n",
      "Epoch 13x - valid_f1_at_045:0.43540\n",
      "Epoch 13x - valid_f1_at_05:0.36372\n",
      "==================================================\n",
      "VALIDATION SCORES:\n",
      "Epoch 13 - avg_val_loss: 0.00268\n",
      "Epoch 13 - valid_f1_at_005:0.14492\n",
      "Epoch 13x - valid_f1_at_01:0.41124\n",
      "Epoch 13x - valid_f1_at_015:0.61547\n",
      "Epoch 13x - valid_f1_at_02:0.70965\n",
      "Epoch 13x - valid_f1_at_025:0.72796\n",
      "Epoch 13x - valid_f1_at_03:0.72570\n",
      "Epoch 13x - valid_f1_at_035:0.70588\n",
      "Epoch 13x - valid_f1_at_04:0.66701\n",
      "Epoch 13x - valid_f1_at_045:0.61141\n",
      "Epoch 13x - valid_f1_at_05:0.55040\n",
      "TARGET SCORES:\n",
      "Epoch 13 - avg_train_loss: 0.00279  \n",
      "Epoch 13 - valid_f1_at_005:0.16905\n",
      "Epoch 13x - valid_f1_at_01:0.44901\n",
      "Epoch 13x - valid_f1_at_015:0.64720\n",
      "Epoch 13x - valid_f1_at_02:0.74938\n",
      "Epoch 13x - valid_f1_at_025:0.74738\n",
      "Epoch 13x - valid_f1_at_03:0.72811\n",
      "Epoch 13x - valid_f1_at_035:0.68920\n",
      "Epoch 13x - valid_f1_at_04:0.65455\n",
      "Epoch 13x - valid_f1_at_045:0.61608\n",
      "Epoch 13x - valid_f1_at_05:0.56038\n",
      "\u001b[32mValid Score 03 Improved (0.7234 ---> 0.7257)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_VALID_03BEST.bin\u001b[0m\n",
      "Target Score 03 Not Improved, still: 0.7948\n",
      "\u001b[32mTrain Score 03 Improved (0.5670 ---> 0.5717)\n",
      "Model Saved here: /home/acoustery/kf/noteb/kaggle/AST_m/checks/PSEUDO_TRAIN_03BEST.bin\u001b[0m\n",
      "\n",
      "\n",
      "Epoch 14/85\n",
      "---------------the train dataloader---------------\n",
      "now using following mask: 24 freq, 96 time\n",
      "now using mix-up with rate 0.400000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "now use noise augmentation\n",
      "number of classes is 152\n",
      "Dataset size: 13340\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 1268\n",
      "---------------the eval dataloader---------------\n",
      "now using following mask: 0 freq, 0 time\n",
      "now using mix-up with rate -1.000000\n",
      "now process Birds custom\n",
      "use dataset mean -6.983 and std 3.055 to normalize the input\n",
      "number of classes is 152\n",
      "Dataset size: 743\n",
      "Length train dataset: 13340\n",
      "Length valid dataset: 1268\n",
      "Length target dataset: 743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train :  56%|██████████████████████████████████████████████                                     | 231/416 [05:51<04:41,  1.52s/it, gpu_mem=3.66 GB, lr=0.001335, train_loss=0.003570]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [16]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;66;03m#scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [10, 15, 20, 25, 30], gamma=0.6, last_epoch=-1)\u001b[39;00m\n\u001b[1;32m     46\u001b[0m scheduler \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39moptim\u001b[38;5;241m.\u001b[39mlr_scheduler\u001b[38;5;241m.\u001b[39mCosineAnnealingLR(optimizer, T_max\u001b[38;5;241m=\u001b[39mepchs, last_epoch\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, eta_min\u001b[38;5;241m=\u001b[39mmin_LR)\n\u001b[0;32m---> 47\u001b[0m model, history \u001b[38;5;241m=\u001b[39m \u001b[43mrun_training\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheduler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     48\u001b[0m \u001b[43m                              \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     49\u001b[0m \u001b[43m                              \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepchs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwarmup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwarmup\u001b[49m\u001b[43m)\u001b[49m\n",
      "Input \u001b[0;32mIn [15]\u001b[0m, in \u001b[0;36mrun_training\u001b[0;34m(model, optimizer, scheduler, device, num_epochs, fold, warmup)\u001b[0m\n\u001b[1;32m     21\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m     23\u001b[0m train_loader, valid_loader, target_loader \u001b[38;5;241m=\u001b[39m prepare_loaders(train_data, fold, epoch, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkfold_raw\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m     24\u001b[0m                                                          r_saml\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, use_rare\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, use_500\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m---> 26\u001b[0m train_avg, train_loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_one_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscheduler\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     27\u001b[0m \u001b[43m                                   \u001b[49m\u001b[43mdataloader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     28\u001b[0m \u001b[43m                                   \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCFG\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwarmup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwarmup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     30\u001b[0m valid_avg, valid_loss \u001b[38;5;241m=\u001b[39m valid_one_epoch(model, valid_loader, \n\u001b[1;32m     31\u001b[0m                                          device\u001b[38;5;241m=\u001b[39mCFG\u001b[38;5;241m.\u001b[39mdevice, \n\u001b[1;32m     32\u001b[0m                                          epoch\u001b[38;5;241m=\u001b[39mepoch)\n\u001b[1;32m     34\u001b[0m target_avg, target_loss \u001b[38;5;241m=\u001b[39m valid_one_epoch(model, target_loader, \n\u001b[1;32m     35\u001b[0m                                          device\u001b[38;5;241m=\u001b[39mCFG\u001b[38;5;241m.\u001b[39mdevice, \n\u001b[1;32m     36\u001b[0m                                          epoch\u001b[38;5;241m=\u001b[39mepoch)\n",
      "Input \u001b[0;32mIn [13]\u001b[0m, in \u001b[0;36mtrain_one_epoch\u001b[0;34m(model, optimizer, scheduler, dataloader, device, epoch, warmup)\u001b[0m\n\u001b[1;32m      8\u001b[0m scores \u001b[38;5;241m=\u001b[39m MetricMeter()\n\u001b[1;32m     10\u001b[0m pbar \u001b[38;5;241m=\u001b[39m tqdm(\u001b[38;5;28menumerate\u001b[39m(dataloader), total\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(dataloader), desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTrain \u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, (data) \u001b[38;5;129;01min\u001b[39;00m pbar:    \n\u001b[1;32m     13\u001b[0m     inputs \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimage\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     14\u001b[0m     targets \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtargets\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mto(device)\n",
      "File \u001b[0;32m~/miniconda3/envs/kf/lib/python3.9/site-packages/tqdm/std.py:1195\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1192\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m   1194\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1195\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m   1196\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m   1197\u001b[0m         \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1198\u001b[0m         \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/kf/lib/python3.9/site-packages/torch/utils/data/dataloader.py:530\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    528\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    529\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()\n\u001b[0;32m--> 530\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    531\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    532\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    533\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    534\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[0;32m~/miniconda3/envs/kf/lib/python3.9/site-packages/torch/utils/data/dataloader.py:570\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    568\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    569\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 570\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m    571\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m    572\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data)\n",
      "File \u001b[0;32m~/miniconda3/envs/kf/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py:49\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfetch\u001b[39m(\u001b[38;5;28mself\u001b[39m, possibly_batched_index):\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_collation:\n\u001b[0;32m---> 49\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m     50\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
      "File \u001b[0;32m~/miniconda3/envs/kf/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py:49\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfetch\u001b[39m(\u001b[38;5;28mself\u001b[39m, possibly_batched_index):\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_collation:\n\u001b[0;32m---> 49\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m     50\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[possibly_batched_index]\n",
      "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36mAudiosetDataset.__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m    167\u001b[0m datum \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mloc[index, :]\n\u001b[1;32m    168\u001b[0m label_indices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel_num)\n\u001b[0;32m--> 169\u001b[0m fbank, mix_lambda \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wav2fbank\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdatum\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfilename\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    170\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m bird_code \u001b[38;5;129;01min\u001b[39;00m LE(datum[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msecondary_labels\u001b[39m\u001b[38;5;124m'\u001b[39m]):\n\u001b[1;32m    171\u001b[0m     label_indices[CFG\u001b[38;5;241m.\u001b[39mtarget_columns\u001b[38;5;241m.\u001b[39mindex(bird_code)] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.4\u001b[39m\n",
      "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36mAudiosetDataset._wav2fbank\u001b[0;34m(self, filename, filename2)\u001b[0m\n\u001b[1;32m    113\u001b[0m     mix_waveform \u001b[38;5;241m=\u001b[39m mix_lambda \u001b[38;5;241m*\u001b[39m waveform1 \u001b[38;5;241m+\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m mix_lambda) \u001b[38;5;241m*\u001b[39m waveform2\n\u001b[1;32m    114\u001b[0m     waveform \u001b[38;5;241m=\u001b[39m mix_waveform \u001b[38;5;241m-\u001b[39m mix_waveform\u001b[38;5;241m.\u001b[39mmean()\n\u001b[0;32m--> 116\u001b[0m waveform \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwave_transforms\u001b[49m\u001b[43m(\u001b[49m\u001b[43msamples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwaveform\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnumpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mSR\u001b[49m\u001b[43m)\u001b[49m)\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    117\u001b[0m fbank \u001b[38;5;241m=\u001b[39m ta\u001b[38;5;241m.\u001b[39mcompliance\u001b[38;5;241m.\u001b[39mkaldi\u001b[38;5;241m.\u001b[39mfbank(waveform, htk_compat\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, sample_frequency\u001b[38;5;241m=\u001b[39mSR, \n\u001b[1;32m    118\u001b[0m                                           use_energy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, window_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhanning\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[1;32m    119\u001b[0m                                           num_mel_bins\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmelbins, dither\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.0\u001b[39m, frame_shift\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m9.7\u001b[39m)\n\u001b[1;32m    121\u001b[0m target_length \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maudio_conf\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtarget_length\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/kf/lib/python3.9/site-packages/audiomentations/core/composition.py:88\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[0;34m(self, samples, sample_rate)\u001b[0m\n\u001b[1;32m     86\u001b[0m         random\u001b[38;5;241m.\u001b[39mshuffle(transforms)\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m transform \u001b[38;5;129;01min\u001b[39;00m transforms:\n\u001b[0;32m---> 88\u001b[0m         samples \u001b[38;5;241m=\u001b[39m \u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43msamples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_rate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m samples\n",
      "File \u001b[0;32m~/miniconda3/envs/kf/lib/python3.9/site-packages/audiomentations/core/transforms_interface.py:84\u001b[0m, in \u001b[0;36mBaseWaveformTransform.__call__\u001b[0;34m(self, samples, sample_rate)\u001b[0m\n\u001b[1;32m     78\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msupports_mono:\n\u001b[1;32m     79\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m MonoAudioNotSupportedException(\n\u001b[1;32m     80\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m only supports multichannel audio, not mono audio\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m     81\u001b[0m                 \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\n\u001b[1;32m     82\u001b[0m             )\n\u001b[1;32m     83\u001b[0m         )\n\u001b[0;32m---> 84\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43msamples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_rate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m samples\n",
      "File \u001b[0;32m~/miniconda3/envs/kf/lib/python3.9/site-packages/audiomentations/augmentations/pitch_shift.py:32\u001b[0m, in \u001b[0;36mPitchShift.apply\u001b[0;34m(self, samples, sample_rate)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, samples, sample_rate):\n\u001b[1;32m     31\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 32\u001b[0m         pitch_shifted_samples \u001b[38;5;241m=\u001b[39m \u001b[43mlibrosa\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meffects\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpitch_shift\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     33\u001b[0m \u001b[43m            \u001b[49m\u001b[43msamples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_rate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnum_semitones\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m     34\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     35\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m librosa\u001b[38;5;241m.\u001b[39mutil\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mParameterError:\n\u001b[1;32m     36\u001b[0m         warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m     37\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWarning: You are probably using an old version of librosa. Upgrade\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     38\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m librosa to 0.9.0 or later for better performance when applying\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     39\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m PitchShift to stereo audio.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     40\u001b[0m         )\n",
      "File \u001b[0;32m~/miniconda3/envs/kf/lib/python3.9/site-packages/librosa/util/decorators.py:88\u001b[0m, in \u001b[0;36mdeprecate_positional_args.<locals>._inner_deprecate_positional_args.<locals>.inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m extra_args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mlen\u001b[39m(all_args)\n\u001b[1;32m     87\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m extra_args \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 88\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     90\u001b[0m \u001b[38;5;66;03m# extra_args > 0\u001b[39;00m\n\u001b[1;32m     91\u001b[0m args_msg \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     92\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(name, arg)\n\u001b[1;32m     93\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m name, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(kwonly_args[:extra_args], args[\u001b[38;5;241m-\u001b[39mextra_args:])\n\u001b[1;32m     94\u001b[0m ]\n",
      "File \u001b[0;32m~/miniconda3/envs/kf/lib/python3.9/site-packages/librosa/effects.py:327\u001b[0m, in \u001b[0;36mpitch_shift\u001b[0;34m(y, sr, n_steps, bins_per_octave, res_type, **kwargs)\u001b[0m\n\u001b[1;32m    324\u001b[0m rate \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2.0\u001b[39m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m (\u001b[38;5;241m-\u001b[39m\u001b[38;5;28mfloat\u001b[39m(n_steps) \u001b[38;5;241m/\u001b[39m bins_per_octave)\n\u001b[1;32m    326\u001b[0m \u001b[38;5;66;03m# Stretch in time, then resample\u001b[39;00m\n\u001b[0;32m--> 327\u001b[0m y_shift \u001b[38;5;241m=\u001b[39m \u001b[43mcore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresample\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    328\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtime_stretch\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    329\u001b[0m \u001b[43m    \u001b[49m\u001b[43morig_sr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msr\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mrate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    330\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget_sr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    331\u001b[0m \u001b[43m    \u001b[49m\u001b[43mres_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mres_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    332\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    334\u001b[0m \u001b[38;5;66;03m# Crop to the same dimension as the input\u001b[39;00m\n\u001b[1;32m    335\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m util\u001b[38;5;241m.\u001b[39mfix_length(y_shift, size\u001b[38;5;241m=\u001b[39my\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\n",
      "File \u001b[0;32m~/miniconda3/envs/kf/lib/python3.9/site-packages/librosa/util/decorators.py:88\u001b[0m, in \u001b[0;36mdeprecate_positional_args.<locals>._inner_deprecate_positional_args.<locals>.inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     86\u001b[0m extra_args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mlen\u001b[39m(all_args)\n\u001b[1;32m     87\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m extra_args \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 88\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     90\u001b[0m \u001b[38;5;66;03m# extra_args > 0\u001b[39;00m\n\u001b[1;32m     91\u001b[0m args_msg \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m     92\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(name, arg)\n\u001b[1;32m     93\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m name, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(kwonly_args[:extra_args], args[\u001b[38;5;241m-\u001b[39mextra_args:])\n\u001b[1;32m     94\u001b[0m ]\n",
      "File \u001b[0;32m~/miniconda3/envs/kf/lib/python3.9/site-packages/librosa/core/audio.py:617\u001b[0m, in \u001b[0;36mresample\u001b[0;34m(y, orig_sr, target_sr, res_type, fix, scale, **kwargs)\u001b[0m\n\u001b[1;32m    615\u001b[0m     y_hat \u001b[38;5;241m=\u001b[39m soxr\u001b[38;5;241m.\u001b[39mresample(y\u001b[38;5;241m.\u001b[39mT, orig_sr, target_sr, quality\u001b[38;5;241m=\u001b[39mres_type)\u001b[38;5;241m.\u001b[39mT\n\u001b[1;32m    616\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 617\u001b[0m     y_hat \u001b[38;5;241m=\u001b[39m \u001b[43mresampy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresample\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morig_sr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_sr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mfilter\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mres_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    619\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fix:\n\u001b[1;32m    620\u001b[0m     y_hat \u001b[38;5;241m=\u001b[39m util\u001b[38;5;241m.\u001b[39mfix_length(y_hat, size\u001b[38;5;241m=\u001b[39mn_samples, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/miniconda3/envs/kf/lib/python3.9/site-packages/resampy/core.py:120\u001b[0m, in \u001b[0;36mresample\u001b[0;34m(x, sr_orig, sr_new, axis, filter, **kwargs)\u001b[0m\n\u001b[1;32m    118\u001b[0m x_2d \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mswapaxes(\u001b[38;5;241m0\u001b[39m, axis)\u001b[38;5;241m.\u001b[39mreshape((x\u001b[38;5;241m.\u001b[39mshape[axis], \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m    119\u001b[0m y_2d \u001b[38;5;241m=\u001b[39m y\u001b[38;5;241m.\u001b[39mswapaxes(\u001b[38;5;241m0\u001b[39m, axis)\u001b[38;5;241m.\u001b[39mreshape((y\u001b[38;5;241m.\u001b[39mshape[axis], \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m--> 120\u001b[0m \u001b[43mresample_f\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_2d\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_2d\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_ratio\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterp_win\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterp_delta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprecision\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "trans = False\n",
    "\n",
    "for fold in CFG.folds:\n",
    "    if fold == 2:\n",
    "        trans = True\n",
    "        LR = 0.00055\n",
    "        min_LR = CFG.ETA_MIN\n",
    "        epchs = 30\n",
    "        warmup = False\n",
    "    else:\n",
    "        trans = False\n",
    "        LR = CFG.LR \n",
    "        min_LR = CFG.ETA_MIN\n",
    "        epchs = CFG.epochs\n",
    "        warmup = False\n",
    "        \n",
    "    #T_MAX_ = len_train_loader * epchs + 50    \n",
    "    torch.cuda.empty_cache()\n",
    "    print(f'#'*15)\n",
    "    print(f'### Fold: {fold} from {CFG.folds}')\n",
    "    print(f'#'*15)\n",
    "    \n",
    "    model = Model_image(\n",
    "                    base_model_name=CFG.base_model_name,\n",
    "                    pretrained=CFG.pretrained,\n",
    "                    num_classes=CFG.num_classes,\n",
    "                    in_channels=CFG.in_channels,\n",
    "                    dropout_rate=CFG.dropout)\n",
    "    model.to(CFG.device)\n",
    "    if trans:\n",
    "        warmup = False\n",
    "        print('You loading checkpoint, set the lower LR\\n')\n",
    "        model.load_state_dict(torch.load(f'{CFG.path_to_checks}SEDMIX_check.bin'))\n",
    "        \n",
    "    trainables = [p for p in model.parameters() if p.requires_grad]\n",
    "    print('Total parameter number is : {:.3f} million'.format(sum(p.numel() for p in model.parameters()) / 1e6))\n",
    "    print('Total trainable parameter number is : {:.3f} million'.format(sum(p.numel() for p in trainables) / 1e6))\n",
    "    optimizer = torch.optim.Adam(trainables, lr=LR, weight_decay=5e-7, betas=(0.95, 0.999))\n",
    "    \n",
    "    \n",
    "    #scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [10, 15, 20, 25, 30], gamma=0.6, last_epoch=-1)\n",
    "    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epchs, last_epoch=-1, eta_min=min_LR)\n",
    "    model, history = run_training(model, optimizer, scheduler,\n",
    "                                  device=CFG.device,\n",
    "                                  num_epochs=epchs, fold=fold, warmup=warmup)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f61e21d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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