LSTM dnb mono.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM на оригинальном датасете\n",
    "\n",
    "Попытка сделать монофонический выход из сетки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сделаем также пользовательский импорт"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from decode_patterns import data_conversion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Загружаем датасет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dataset\n",
    "drum, bass = data_conversion.make_lstm_dataset(height=16, patterns_file=\"decode_patterns/train.tsv\", mono=True)\n",
    "\n",
    "\n",
    "# define shuffling of dataset\n",
    "def shuffle(A, B, p=0.8):\n",
    "    # take 80% to training, other to testing\n",
    "    L = len(A)\n",
    "    idx = np.arange(L) < p*L\n",
    "    np.random.shuffle(idx)\n",
    "    yield A[idx]\n",
    "    yield B[idx]\n",
    "    yield A[np.logical_not(idx)]\n",
    "    yield B[np.logical_not(idx)]\n",
    "    \n",
    "# we can shuffle train and test set like this:\n",
    "drum_train, bass_train, drum_test, bass_test = shuffle(drum, bass)\n",
    "\n",
    "# selecting a validation set\n",
    "drum_validation, bass_validation = data_conversion.make_lstm_dataset(height=16\n",
    "                                                        , patterns_file=\"decode_patterns/validation.tsv\"\n",
    "                                                        , mono=True)\n",
    "drum_validation = torch.tensor(drum_validation, dtype=torch.float)\n",
    "bass_validation = torch.tensor(bass_validation, dtype=torch.float)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40000, 16, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drum_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Модель определим в самом простом варианте, который только можно себе представить -- как в примере с конечным автоматом"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# попробуем определить модель LSTM как конечный автомат\n",
    "class DrumNBassLSTM(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(DrumNBassLSTM, self).__init__()\n",
    "        # one input neuron, one output neuron, one layer in LSTM block\n",
    "        self.input_size = 14\n",
    "        self.hidden_size = 34\n",
    "        self.layer_count = 1\n",
    "        self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.layer_count)\n",
    "        self.embed_layer = nn.Linear(self.hidden_size, 1)\n",
    "        self.sigm = nn.Sigmoid()\n",
    "    \n",
    "    def forward(self, input):\n",
    "        # пусть в input у нас приходит вектор размерности (64, 32, 14)\n",
    "        # то есть 64 отсчёта, тридцать два примера (минибатч), 14 значение в каждом (барабанная партия)\n",
    "        output, _ = self.lstm(input)\n",
    "        output = self.sigm(self.embed_layer(output))*37\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# часть обучения\n",
    "dnb_lstm = DrumNBassLSTM()\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "# для сравнения моделей необходим reconstruction_loss\n",
    "reconstruction_loss = criterion\n",
    "\n",
    "# оценим также и разнообразие мелодии по её.. дисперсии?)\n",
    "# def melody_variety(melody):\n",
    "#     return 1/(1 + (melody.sum(axis=2) > 1).int())\n",
    "    \n",
    "# criterion = nn.NLLLoss() # -- этот товарищ требует, чтобы LSTM выдавал классы,\n",
    "# criterion = nn.CrossEntropyLoss() # и этот тоже\n",
    "# (числа от 0 до C-1), но как всё-таки его заставить это делать?...\n",
    "# optimizer = optim.SGD(dnb_lstm.parameters(), lr=0.001, momentum=0.9)\n",
    "optimizer = optim.Adam(dnb_lstm.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Найденные баги и их решения:\n",
    "\n",
    "https://stackoverflow.com/questions/56741087/how-to-fix-runtimeerror-expected-object-of-scalar-type-float-but-got-scalar-typ\n",
    "\n",
    "https://stackoverflow.com/questions/49206550/pytorch-error-multi-target-not-supported-in-crossentropyloss/49209628\n",
    "\n",
    "https://stackoverflow.com/questions/56243672/expected-target-size-50-88-got-torch-size50-288-88"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shuffle_every_epoch is on\n",
      "Epoch #0\n",
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      "#1 reconstruction test loss: 53.360755920410156\n",
      "Epoch #1\n",
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      "#9 reconstruction test loss: 56.11188888549805\n",
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      "#10 reconstruction test loss: 54.681617736816406\n",
      "Epoch #10\n",
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      "#23 reconstruction test loss: 56.562225341796875\n",
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      "#63 reconstruction test loss: 62.36033630371094\n",
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      "#64 reconstruction test loss: 63.065032958984375\n",
      "Epoch #64\n",
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      "[72,  1106] train loss: 46.1850942\n",
      "[72,  1111] train loss: 37.3859990\n",
      "[72,  1116] train loss: 42.2036006\n",
      "[72,  1121] train loss: 40.6921991\n",
      "[72,  1126] train loss: 46.5865173\n",
      "[72,  1131] train loss: 35.4422525\n",
      "[72,  1136] train loss: 37.6236674\n",
      "[72,  1141] train loss: 42.1852582\n",
      "[72,  1146] train loss: 42.5208963\n",
      "[72,  1151] train loss: 37.2642028\n",
      "[72,  1156] train loss: 34.9926936\n",
      "[72,  1161] train loss: 41.9926383\n",
      "[72,  1166] train loss: 45.3763803\n",
      "[72,  1171] train loss: 36.9240049\n",
      "[72,  1176] train loss: 40.7133935\n",
      "[72,  1181] train loss: 33.6844323\n",
      "[72,  1186] train loss: 47.1460724\n",
      "[72,  1191] train loss: 40.4119549\n",
      "[72,  1196] train loss: 41.8996073\n",
      "[72,  1201] train loss: 38.2574457\n",
      "[72,  1206] train loss: 45.4025853\n",
      "[72,  1211] train loss: 40.3232581\n",
      "[72,  1216] train loss: 45.7270514\n",
      "[72,  1221] train loss: 43.0842737\n",
      "[72,  1226] train loss: 43.7251034\n",
      "[72,  1231] train loss: 35.8570754\n",
      "[72,  1236] train loss: 52.1793073\n",
      "[72,  1241] train loss: 32.9231942\n",
      "[72,  1246] train loss: 38.0485903\n",
      "[72,  1250] train loss: 40.4238533\n",
      "#72 reconstruction test loss: 64.7191162109375\n",
      "#72 reconstruction validation loss: 65.73664093017578\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "epoch_count = 72\n",
    "batch_size = 32\n",
    "shuffle_every_epoch = True\n",
    "    \n",
    "if shuffle_every_epoch:\n",
    "    print(f\"shuffle_every_epoch is on\")\n",
    "else:\n",
    "    print(f\"shuffle_every_epoch is off\")\n",
    "    # shuffle train and test set:\n",
    "    drum_train, bass_train, drum_test, bass_test = shuffle(drum, bass)\n",
    "    drum_train = torch.tensor(drum_train, dtype=torch.float)\n",
    "    bass_train = torch.tensor(bass_train, dtype=torch.float)\n",
    "    drum_test = torch.tensor(drum_test, dtype=torch.float)\n",
    "    drum_test = torch.tensor(drum_test, dtype=torch.float)\n",
    "        \n",
    "for epoch in range(epoch_count):  # loop over the dataset multiple times\n",
    "    print(f\"Epoch #{epoch}\")\n",
    "    if shuffle_every_epoch:\n",
    "        # shuffle train and test set:\n",
    "        drum_train, bass_train, drum_test, bass_test = shuffle(drum, bass)\n",
    "        drum_train = torch.tensor(drum_train, dtype=torch.float)\n",
    "        bass_train = torch.tensor(bass_train, dtype=torch.float)\n",
    "        drum_test = torch.tensor(drum_test, dtype=torch.float)\n",
    "        bass_test = torch.tensor(bass_test, dtype=torch.float)\n",
    "        \n",
    "    examples_count = drum_train.size()[0]\n",
    "    examples_id = 0\n",
    "    \n",
    "    running_loss = 0.0\n",
    "    runnint_count = 0\n",
    "    batch_id = 0\n",
    "    while examples_id < examples_count:\n",
    "        batch_drum_train = drum_train[examples_id:examples_id + batch_size,:,:].transpose(0,1)\n",
    "        batch_bass_train = bass_train[examples_id:examples_id + batch_size,].transpose(0,1)\n",
    "        # transpose нужен для обмена размерности батча и размерности шагов\n",
    "#         print(f\"batch_drum_train:{batch_drum_train.size()}, batch_bass_train:{batch_bass_train.size()}\")\n",
    "\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        bass_outputs = dnb_lstm(batch_drum_train).squeeze()\n",
    "        \n",
    "        # loss = criterion(bass_outputs, batch_bass_train.long())\n",
    "        loss = criterion(bass_outputs, batch_bass_train)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "        runnint_count += 1\n",
    "        period = 5\n",
    "        if batch_id % period == 0 or examples_id + batch_size >= examples_count:\n",
    "            print('[%d, %5d] train loss: %.7f' %\n",
    "                  (epoch + 1, batch_id + 1, running_loss / runnint_count))\n",
    "            running_loss = 0.0\n",
    "            runnint_count = 1\n",
    "            \n",
    "        # update batch info\n",
    "        examples_id += batch_size\n",
    "        batch_id += 1\n",
    "        \n",
    "    # here we can insert measure error on test set\n",
    "    with torch.no_grad():\n",
    "        bass_outputs = dnb_lstm(drum_test).squeeze()\n",
    "        \n",
    "        test_count = len(drum_test)\n",
    "        test_loss = 0\n",
    "        for k in range(test_count):\n",
    "            test_loss += reconstruction_loss(bass_outputs[k], bass_test[k])\n",
    "        print(f\"#{epoch + 1} reconstruction test loss: {test_loss/test_count}\")\n",
    "    \n",
    "\n",
    "#should check accuracy on validation set\n",
    "with torch.no_grad():\n",
    "    bass_outputs = dnb_lstm(drum_validation).squeeze()\n",
    "\n",
    "    validation_count = len(drum_validation)\n",
    "    validation_loss = 0\n",
    "    for k in range(validation_count):\n",
    "        validation_loss += reconstruction_loss(bass_outputs[k], bass_validation[k])\n",
    "    print(f\"#{epoch + 1} reconstruction validation loss: {validation_loss/validation_count}\")\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сохраним веса модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(dnb_lstm.state_dict(), \"models/lstm_dnb_mono_72iter\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Аналогично происходит загрузка модели:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnb_lstm.load_state_dict(torch.load(\"models/lstm_dnb_mono_72iter\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_drum_train = drum_train[:,:,:].transpose(0,1)\n",
    "batch_bass_train = bass_train[:,:].transpose(0,1)\n",
    "with torch.no_grad():\n",
    "    bass_outputs = dnb_lstm(batch_drum_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 6,  8,  7,  ...,  4,  6,  6],\n",
       "        [ 6,  5,  8,  ...,  6,  7,  6],\n",
       "        [ 6,  8,  9,  ...,  4,  7,  8],\n",
       "        ...,\n",
       "        [ 6,  9, 18,  ...,  8,  5,  5],\n",
       "        [ 5,  9, 11,  ...,  3,  3,  6],\n",
       "        [ 6, 12, 11,  ...,  4,  3,  5]], dtype=torch.int32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = bass_outputs.squeeze().int()\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Попробуем сохранить результаты работы сети. На anaconda нет mido, поэтому сохраняем результаты работы просто в массивчик npy... Однако, как альтернатива, его можно поставить чере pip в conda:\n",
    "https://github.com/mido/mido/issues/198"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import mido\n",
    "from decode_patterns.data_conversion import build_track, DrumMelodyPair, NumpyImage, Converter\n",
    "\n",
    "\n",
    "# вспомогательная функция для генерации midi\n",
    "# переводит посл-ть нот в \"картинку\"\n",
    "def seq_to_img(bass_seq):\n",
    "    bass_output = []\n",
    "    for bass_note in bass_seq:\n",
    "        bass_row = np.eye(1, 36, bass_note - 1)[0]\n",
    "        bass_output.append(bass_row)\n",
    "    bass_output = torch.tensor(bass_output).int().squeeze()\n",
    "    return bass_output\n",
    "\n",
    "converter = Converter((16,50))\n",
    "\n",
    "# batch_drum = torch.cat((drum_train, drum_test, torch.tensor(drum_validation))).transpose(0,1)\n",
    "# batch_bass = torch.cat((bass_train.int(), bass_test.int(), torch.tensor(bass_validation).int())).transpose(0,1)\n",
    "def output_midi(batch_drum, batch_bass, folder, output_original=False):\n",
    "    batch_drum = batch_drum.transpose(0,1)\n",
    "    batch_bass = batch_bass.int().transpose(0,1)\n",
    "    with torch.no_grad():\n",
    "        bass_outputs = dnb_lstm(batch_drum)\n",
    "        bass_outputs = bass_outputs.squeeze().int()\n",
    "\n",
    "        for i in range(bass_outputs.size()[1]):\n",
    "            def output_seq(bass_seq, prefix=\"sample\"):\n",
    "                bass_output = seq_to_img(bass_seq)\n",
    "\n",
    "                img_dnb = torch.cat((batch_drum[:,i,:].int(),bass_output), axis=1)\n",
    "                numpy_pair = NumpyImage(np.array(img_dnb), 120, 1, 1, 36)\n",
    "                pair = converter.convert_numpy_image_to_pair(numpy_pair)\n",
    "                mid = build_track(pair, tempo=pair.tempo)\n",
    "                mid.save(f\"{folder}/{prefix}{i+1}.mid\")\n",
    "                \n",
    "            output_seq(bass_outputs[:,i])\n",
    "            \n",
    "            # TODO неплохо вынести генерацию оригинальной музыки в отдельный .py файл... т.е. нужно убрать этот костыль...\n",
    "            if output_original:\n",
    "                output_seq(batch_bass[:,i], \"orig/original\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводим обучающую и валидационную выборку"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # если очень надо послушать тренировчную -- лучше её перезагрузить, потому что она перемешивается\n",
    "# drum, bass = data_conversion.make_lstm_dataset(height=16, patterns_file=\"decode_patterns/train.tsv\", mono=True)\n",
    "# drum = torch.tensor(drum, dtype=torch.float)\n",
    "# bass = torch.tensor(bass, dtype=torch.float)\n",
    "# output_midi(drum, bass, \"midi/lstm_mono/train\")\n",
    "output_midi(drum_validation, bass_validation, \"midi/lstm_mono/validation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По вкусу, выводим тот же результат для кожанных мешков на ассесмент. На самом деле ничем от валидационной выборки не отличается :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "drum_hum, bass_hum = data_conversion.make_lstm_dataset(height=16, patterns_file=\"decode_patterns/human.tsv\", mono=True)\n",
    "drum_hum = torch.tensor(drum_hum, dtype=torch.float)\n",
    "bass_hum = torch.tensor(bass_hum, dtype=torch.float)\n",
    "output_midi(drum_hum, bass_hum, \"midi/lstm_mono/human\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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